NIH.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fcef6dee",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Test of simple scraper and entity extractor.\n",
    "import requests\n",
    "import datetime\n",
    "# import packages\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import requests\n",
    "from pandas.io.json import json_normalize #special package in pandas\n",
    "import json\n",
    "from matplotlib.pyplot import *\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fdebf0b6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "{\"meta\":{\"search_id\":\"ewua676-KkC-UWp3BTyI7Q\",\"total\":4818,\"offset\":0,\"limit\":50,\"sort_field\":null,\"sort_order\":\"ASC\",\"sorted_by_relevance\":true,\"properties\":{\"URL\":\"https:/reporter.nih.gov/search/ewua676-KkC-UWp3BTyI7Q/projects\"}},\"results\":[{\"appl_id\":10542758,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01HL157323-02\",\"project_serial_num\":\"HL157323\",\"organization\":{\"org_name\":\"EMORY UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"ATLANTA\",\"org_country\":\"UNITED STATES\",\"org_state\":\"GA\",\"org_state_name\":null,\"dept_type\":\"SURGERY\",\"fips_country_code\":null,\"org_duns\":[\"066469933\"],\"org_ueis\":[\"S352L5PJLMP8\"],\"primary_duns\":\"066469933\",\"primary_uei\":\"S352L5PJLMP8\",\"org_fips\":\"US\",\"org_ipf_code\":\"2384501\",\"org_zipcode\":\"303221007\",\"external_org_id\":2384501},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":677235,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"HL\",\"serial_num\":\"157323\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9239369,\"first_name\":\"Nader\",\"middle_name\":\"Nabile\",\"last_name\":\"Massarweh\",\"is_contact_pi\":true,\"full_name\":\"Nader Nabile Massarweh\",\"title\":\"ASSOCIATE PROFESSOR OF SURGERY\"},{\"profile_id\":8613963,\"first_name\":\"Alex\",\"middle_name\":\"\",\"last_name\":\"Sox-Harris\",\"is_contact_pi\":false,\"full_name\":\"Alex  Sox-Harris\",\"title\":\"\"}],\"contact_pi_name\":\"MASSARWEH, NADER NABILE\",\"program_officers\":[{\"first_name\":\"Michael\",\"middle_name\":\"\",\"last_name\":\"Wolz\",\"full_name\":\"Michael  Wolz\"}],\"agency_ic_admin\":{\"code\":\"HL\",\"abbreviation\":\"NHLBI\",\"name\":\"National Heart Lung and Blood Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"HL\",\"name\":\"National Heart Lung and Blood Institute\",\"abbreviation\":\"NHLBI\",\"total_cost\":677235.0}],\"cong_dist\":\"GA-05\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"CDMA\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Clinical Data Management and Analysis Study Section[CDMA]\"},\"award_notice_date\":\"2022-12-01T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01HL157323\",\"terms\":\"<Automation><Awareness><Data Analyses><Data Analysis><data interpretation><Data Collection><Feedback><Future><Goals><Gold><Hand><Cardiac Surgery procedures><Cardiac Surgery><Cardiac Surgical Procedures><Heart Surgical Procedures><heart surgery><Hospitals><Industry><Interview><Manuals><Methods><Methodology><Modernization><mortality><Natural Language Processing><natural language understanding><Patients><Private Sector><Registries><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Risk><Safety><Societies><Statistical Computing><statistical process><statistical reasoning><Time><United States Department of Veterans Affairs><United States Veterans Administration><Veterans Administration><Veterans Affairs><Work><Excess Mortality><Caring><Perioperative Care><base><improved><Clinical><Link><Evaluation><Data Bases><data base><Databases><Measurement><Thoracic Surgeon><Chest Surgeon><Research Priority><programs><Operative Surgical Procedures><Operative Procedures><Surgical><Surgical Interventions><Surgical Procedure><surgery><interest><experience><Performance><Structure><novel><Reporting><Modeling><response><Institution><Address><qualitative reasoning><Qualitative Methods><Data><Data Element><Sum><American College of Surgeons><Collection><Observation research><Observation study><Observational research><Observational Study><Update><Process><Modification><Cardiac><electronic health care record><electronic healthcare record><Electronic Health Record><NHLBI><National Heart, Lung, and Blood Institute><Outcome><innovate><innovative><innovation><Data Science><Data Analytics><implementation strategy><strategies for implementation><data warehouse><Infrastructure><hospital performance><advanced analytics>\",\"pref_terms\":\"Address;American College of Surgeons;Automation;Awareness;Cardiac;Cardiac Surgery procedures;Caring;Clinical;Collection;Data;Data Analyses;Data Analytics;Data Collection;Data Element;Data Science;Databases;Electronic Health Record;Evaluation;Excess Mortality;Feedback;Future;Goals;Gold;Hand;Hospitals;Industry;Infrastructure;Institution;Interview;Link;Manuals;Measurement;Methodology;Methods;Modeling;Modernization;Modification;National Heart, Lung, and Blood Institute;Natural Language Processing;Observational Study;Operative Surgical Procedures;Outcome;Patients;Performance;Perioperative Care;Private Sector;Process;Qualitative Methods;Registries;Reporting;Research;Research Personnel;Research Priority;Resources;Risk;Safety;Societies;Statistical Computing;Structure;Sum;Thoracic Surgeon;Time;United States Department of Veterans Affairs;Update;Work;advanced analytics;base;data warehouse;experience;hospital performance;implementation strategy;improved;innovation;interest;mortality;novel;programs;response\",\"abstract_text\":\"Within existing national surgical quality improvement (QI) programs, there are numerous opportunities to\\nimprove the efficiency of data flow from the point of collection to the time at which performance-based\\nfeedback is provided to stakeholders. Current limitations of the QI data cycle include: (a) reliance on hand\\nabstraction for data collection; (b) a retrospective and episodic (e.g.: quarterly, bi-annually, etc.) approach to\\nanalysis and feedback which creates a time lag from when the hospital’s performance is declining and when it is\\nmade aware; (c) small clusters of clinically meaningful poor performance may go of undetected using current\\nepisodic analytic structures. To address the first limitation, modern data science methods (MDSMs) could be\\nused to automate the collection of some, or all, of the variables within surgical QI registries. Full or partial\\nautomation of data collection could allow the substantial resources currently committed to manual data\\nabstraction to be repurposed to support more continuous, proactive engagement in local QI activities. To\\naddress the limitations associated with episodic performance evaluation, alternative approaches for analyzing\\ndata in more real-time could be applied to provide an early warning of declining performance. The Veterans\\nAffairs (VA) Surgical Quality Improvement Program (VASQIP) is one of the most successful and longest-\\nstanding national clinical registries used for surgical QI and has been the template for a number of similar\\nprograms in the private sector. As such, VASQIP represents an excellent model for evaluating alternative\\napproaches to data collection and analysis that could allow for more efficient data flow through the quality\\nimprovement cycle and enhance national surgical QI efforts. The overall goal of this proposal is to evaluate\\nalternative, potentially more efficient strategies that can be readily implemented within the existing\\ninfrastructure of contemporary surgical QI programs and aid in the more efficient flow of data. The specific\\naims are to: (1) develop and validate MDSMs to use structured and unstructured electronic health record data\\nto automate cardiac VASQIP data collection; (2) compare the risk-adjusted CUSUM (a statistical process\\ncontrol methodology borrowed from industry) to quarterly observed-to-expected ratios (i.e.: VASQIP’s current\\napproach to assessing performance) for evaluating VA hospital cardiac surgical performance; (3) conduct semi-\\nstructured interviews with diverse stakeholder groups to set a national research agenda for expansion and\\nimprovement of surgical QI programs. This mixed-methods proposal will involve observational studies using\\nVASQIP and VA Corporate Data Warehouse data for patients who underwent cardiac surgery at a VA hospital\\nbetween 2016 and 2020 as well as qualitative interviews with stakeholders who can help to inform future\\nchanges that can improve the data available within VASQIP. This project is important and novel because it will\\nprovide real-world, generalizable data that can be used to inform national surgical and non-surgical QI\\ninitiatives within VA and the private sector.\",\"project_title\":\"Using Modern Data Science Methods and Advanced Analytics to Improve the Efficiency, Reliability, and Timeliness of Cardiac Surgical Quality Data\",\"phr_text\":\"Existing national surgical quality improvement programs have not significantly updated their processes for\\ndata collection, data analysis, or providing feedback to stakeholders in decades. Alternative strategies that\\ncould be implemented within quality improvement programs’ current infrastructure could make these\\nprograms more timely, efficient, and effective. This mixed-methods proposal will evaluate the extent to which\\nsuch alternative approaches could be used to improve existing national surgical quality improvement\\nprograms.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"837\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":561066,\"indirect_cost_amt\":116169,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10542758\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10544322,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01CA270454-02\",\"project_serial_num\":\"CA270454\",\"organization\":{\"org_name\":\"UNIVERSITY OF CALIFORNIA AT DAVIS\",\"city\":null,\"country\":null,\"org_city\":\"DAVIS\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":\"BIOSTATISTICS & OTHER MATH SCI\",\"fips_country_code\":null,\"org_duns\":[\"047120084\"],\"org_ueis\":[\"TX2DAGQPENZ5\"],\"primary_duns\":\"047120084\",\"primary_uei\":\"TX2DAGQPENZ5\",\"org_fips\":\"US\",\"org_ipf_code\":\"577503\",\"org_zipcode\":\"956186153\",\"external_org_id\":577503},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":245468,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"CA\",\"serial_num\":\"270454\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":10051972,\"first_name\":\"Kwan-Liu\",\"middle_name\":\"\",\"last_name\":\"Ma\",\"is_contact_pi\":true,\"full_name\":\"Kwan-Liu  Ma\",\"title\":\"\"},{\"profile_id\":6158233,\"first_name\":\"SHIN-PING\",\"middle_name\":\"\",\"last_name\":\"TU\",\"is_contact_pi\":false,\"full_name\":\"SHIN-PING  TU\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"MA, KWAN-LIU \",\"program_officers\":[{\"first_name\":\"SALLIE\",\"middle_name\":\"JAYNE\",\"last_name\":\"Weaver\",\"full_name\":\"SALLIE JAYNE Weaver\"}],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":245468.0}],\"cong_dist\":\"CA-03\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"BIOMED ENGR/COL ENGR/ENGR STA\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-21-004\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"VH\",\"sra_flex_code\":\"N\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-VH-N(50)R]\"},\"award_notice_date\":\"2022-12-12T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01CA270454\",\"terms\":\"<Academy><Breast><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Non-Small-Cell Lung Carcinoma><NSCLC><NSCLC - Non-Small Cell Lung Cancer><Non-Small Cell Lung Cancer><Nonsmall Cell Lung Carcinoma><nonsmall cell lung cancer><Cause of Death><Communication><Communications Media><Forms of Communication><Cessation of life><Death><health care delivery><Healthcare Delivery><health delivery systems><health services delivery><Future><Growth><Generalized Growth><Tissue Growth><ontogeny><Libraries><Medicine><Mentors><Methods><mortality><Persons><Patients><Problem Solving><Research><Role><social role><Computer software><Software><Systems Theory><Technology><Time><Work><Healthcare><health care><Medical Care Team><Health Care Team><Healthcare Team><Mediating><Caring><analytical method><base><improved><Clinical><Visual><Discipline><Individual><Research Activity><Collaborations><Machine Learning><machine learned><programs><Complex><System><Location><Colorectal><Colon or Rectum><colo-rectal><colorectum><Performance><interdisciplinary collaboration><transdisciplinary collaboration><Structure><novel><Reporting><Network Analysis><Pathway Analysis><Cancer Treatment><Malignant Neoplasm Therapy><Malignant Neoplasm Treatment><anti-cancer therapy><anticancer therapy><cancer-directed therapy><cancer therapy><cancer care><cancer diagnosis><Effectiveness><Address><Data><NCI Organization><National Cancer Institute><Cancer Patient><Clinical Sciences><Communication Research><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Validation><Process><electronic health care record><electronic healthcare record><Electronic Health Record><Health Care Professional><Healthcare professional><Health Professional><digital><designing><design><Coupling><analytical tool><translational research center><translational sciences center><Center for Translational Science Activities><open source><Network-based><information processing><evidence base><disease diagnosis><flexible><flexibility><undergrad><undergraduate><undergraduate student><undergraduate research><BigData><Big Data><pedagogy><support tools><Data Science><Data Analytics><minority student><health care settings><healthcare settings><Visualization><machine learning method><machine learning based method><machine learning methodologies><virtual healthcare><virtual care><virtual health care>\",\"pref_terms\":\"Academy;Address;Big Data;Breast;Cancer Patient;Caring;Cause of Death;Center for Translational Science Activities;Cessation of life;Clinical;Clinical Sciences;Collaborations;Colorectal;Communication;Communication Research;Communications Media;Complex;Computer software;Coupling;Data;Data Analytics;Data Science;Discipline;Effectiveness;Electronic Health Record;Future;Growth;Health Professional;Healthcare;Individual;Libraries;Location;Machine Learning;Malignant Neoplasms;Mediating;Medical Care Team;Medicine;Mentors;Methods;National Cancer Institute;Network-based;Non-Small-Cell Lung Carcinoma;Pathway Analysis;Patient-Focused Outcomes;Patients;Performance;Persons;Problem Solving;Process;Reporting;Research;Research Activity;Role;Structure;System;Systems Theory;Technology;Time;Validation;Visual;Visualization;Work;analytical method;analytical tool;base;cancer care;cancer diagnosis;cancer therapy;design;digital;disease diagnosis;evidence base;flexibility;health care delivery;health care settings;improved;information processing;interdisciplinary collaboration;machine learning method;minority student;mortality;novel;open source;pedagogy;programs;support tools;undergraduate research;undergraduate student;virtual healthcare\",\"abstract_text\":\"Cancer continues to rank as the second leading cause of mortality in the US, with 1.8 million projected new cancer cases in 2020, and 606,520 cancer deaths. The National Academy of Medicine report underscored that 18 or more different clinical disciplines or roles may be involved in patients' comprehensive cancer care. Accordingly, the National Cancer Institute prioritized the need to improve team-based cancer care. The multi-team system (MTS) perspective offers a theoretical framework to examine interdependent work among multiple teams of healthcare professionals (HCPs). To achieve better system performance, MTS theory submits that the system must develop effective communication and coordination. Electronic health records (EHRs) serve a central role in connecting virtual care teams, i.e., groups of HCPS who provide care to the same patients, work at different times and locations, and support their work with technology-mediated communication. Through EH Rs, virtual care teams develop a distributed communication system for encoding, storing, and retrieving patient information, which can be examined as a communication network with individual HCPs representing information agents and communication linkages among them indicating information flow. Understanding this network form of communication and factors influencing its effectiveness will be crucial for the practice of virtual care teams. However, with the explosive growth of EHR data, these \\\"digital traces\\\" of complex teamwork interactions provide data of unprecedented size and complexity to study MTSs in their natural healthcare settings. We propose to develop methods for ML-based network analysis of EHR data and visual interpretation of analysis results for understanding and promoting more effective communication and teamwork in cancer care team systems with a focus on studying cancer care MTSs of breast, colorectal, and non-small cell lung cancer patients. Our project uniquely addresses the Transformative Data Science research theme. The interdisciplinary collaboration in our project will offer a diverse basis for creative problem solving and validation. We expect this project will make fundamental contributions to the advancements of visual analytics technology and explainable Al and machine learning for optimizing healthcare processes, specifically making impact to future team-based cancer care.\",\"project_title\":\"SCH: Smart EHR Data Analytics to Enhance Cancer Care Multiteam Systems\",\"phr_text\":\"Cancer continues to rank as the second leading cause of death in the US. We propose to apply novel machine learning and visual analytics methods to study how teamwork, specifically communication and information sharing among large numbers of healthcare professionals, impact their patients' outcome.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"393\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":165133,\"indirect_cost_amt\":80335,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10544322\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10595672,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5I01HX003151-03\",\"project_serial_num\":\"HX003151\",\"organization\":{\"org_name\":\"VETERANS ADMIN PALO ALTO HEALTH CARE SYS\",\"city\":null,\"country\":null,\"org_city\":\"PALO ALTO\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"046017455\"],\"org_ueis\":[\"ZN2TPL7TCAY7\"],\"primary_duns\":\"046017455\",\"primary_uei\":\"ZN2TPL7TCAY7\",\"org_fips\":\"US\",\"org_ipf_code\":\"481014\",\"org_zipcode\":\"943041207\",\"external_org_id\":481014},\"award_type\":\"5\",\"activity_code\":\"I01\",\"award_amount\":null,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"I01\",\"ic_code\":\"HX\",\"serial_num\":\"003151\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9860649,\"first_name\":\"Daniel\",\"middle_name\":\"Michael\",\"last_name\":\"Blonigen\",\"is_contact_pi\":true,\"full_name\":\"Daniel Michael Blonigen\",\"title\":\"\"}],\"contact_pi_name\":\"BLONIGEN, DANIEL MICHAEL\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"VA\",\"abbreviation\":\"VA\",\"name\":\"Veterans Affairs\"},\"agency_ic_fundings\":null,\"cong_dist\":\"CA-18\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"HX-21-001\",\"full_study_section\":{\"srg_code\":\"HSR2\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"HSR-2 Behavioral, Social, and Cultural Determinants of Health[HSR2]\"},\"award_notice_date\":\"2023-01-05T12:01:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"I01HX003151\",\"terms\":\"<Academy><Algorithms><Ambulatory Care><Outpatient Care><outpatient treatment><Appointment><Mental disorders><Mental health disorders><Psychiatric Disease><Psychiatric Disorder><mental illness><psychiatric illness><psychological disorder><Communities><comorbidity><co-morbid><co-morbidity><Emergency medical service><emergency service><Fees><Future><Goals><Health><Healthcare Systems><Health Care Systems><Hospitalization><Hospital Admission><Housing><Interview><Medicine><Mental Health><Mental Hygiene><Psychological Health><Methodology><Patients><Personal Satisfaction><well-being><wellbeing><Pilot Projects><pilot study><Primary Health Care><Primary Care><Primary Healthcare><Recommendation><Registries><Research><Resources><Research Resources><Risk><Role><social role><Testing><Time><Traction><Use Effectiveness><Veterans><Work><Health Care Costs><Health Costs><Healthcare Costs><Healthcare><health care><Mediating><Specialist><Caring><base><Suicide prevention><Suicide precaution><prevent suicidality><prevent suicide><suicidality prevention><suicide intervention><Site><Chronic><Clinical><Medical><Link><Training><homeless><unhoused><Homelessness><psychosocial rehabilitation><Premature Mortality><Patient-Centered Care><Randomized Controlled Trials><randomized control trial><Research Priority><Disease Management><Disorder Management><programs><Complex><physical conditioning><physical health><Services><suicidal risk><suicide risk><experience><Informatics><Structure><peer><Reporting><Modeling><QOC><Quality of Care><stigma><social stigma><Intervention Strategies><interventional strategy><Intervention><Data><randomisation><randomization><randomly assigned><Randomized><Process><Text><developmental><Development><informant><implementation trial><cost><inpatient care><inpatient service><care services><care systems><Outcome><innovate><innovative><innovation><patient centered><patient oriented><high risk><primary outcome><usual care><treatment as usual><data format><Data Analytics><prediction algorithm><predictive algorithm><predictor algorithm><dashboard><recruit><Text Messaging><short message service><texting><primary care services><care costs><health goals><Financial Hardship><financial burden><financial distress><financial strain><financial stress><social determinants><sociodeterminant><acute care><effectiveness implementation study><effectiveness implementation hybrid><budget impact><Consolidated Framework for Implementation Research><Consolidated Framework for Implementation Science><Consolidated Framework for Implementing Change><hybrid type 1 design><hybrid type 1 factorial design><hybrid type I design><substance use><substance using><process evaluation>\",\"pref_terms\":\"Academy;Algorithms;Ambulatory Care;Appointment;Caring;Chronic;Clinical;Communities;Complex;Consolidated Framework for Implementation Research;Data;Data Analytics;Development;Disease Management;Emergency medical service;Fees;Financial Hardship;Future;Goals;Health;Health Care Costs;Healthcare;Healthcare Systems;Homelessness;Hospitalization;Housing;Informatics;Intervention;Interview;Link;Mediating;Medical;Medicine;Mental Health;Mental disorders;Methodology;Modeling;Outcome;Patient-Centered Care;Patients;Personal Satisfaction;Pilot Projects;Premature Mortality;Primary Health Care;Process;Quality of Care;Randomized;Randomized Controlled Trials;Recommendation;Registries;Reporting;Research;Research Priority;Resources;Risk;Role;Services;Site;Specialist;Structure;Suicide prevention;Testing;Text;Text Messaging;Time;Traction;Training;Use Effectiveness;Veterans;Work;acute care;base;budget impact;care costs;care systems;comorbidity;cost;dashboard;data format;effectiveness implementation study;experience;health goals;high risk;hybrid type 1 design;implementation trial;informant;innovation;inpatient service;patient oriented;peer;physical conditioning;prediction algorithm;primary care services;primary outcome;process evaluation;programs;psychosocial rehabilitation;recruit;social determinants;social stigma;substance use;suicidal risk;treatment as usual\",\"abstract_text\":\"Background: Ten percent of patients account for up to 70% of acute care costs. Among these “super-utilizer”\\npatients, homelessness is a robust social determinant of acute care utilization. Through a field-based\\ndashboard and clinical aids, the Hot Spotter Analytic program assists Patient Aligned Care Teams (PACT) with\\ntargeting and tailoring care for the highest-need homeless Veterans. However, many Veterans identified by the\\nAnalytics do not engage in supportive services that reduce risk for acute care utilization. Peer Specialists (PS)\\nare a high-value workforce that can facilitate Veterans’ engagement in care. Yet, there is a need to enhance\\nthe PS role with a structured approach that can capitalize on known facilitators of care engagement among\\nhomeless Veterans. Whole Health Coaching (WHC) is one such approach. By focusing on patients’ values and\\ngoals rather than treatment of specific conditions, WHC reduces patients’ stigma regarding their care needs\\nand increases patient activation and well-being, which can increase engagement in supportive services.\\nSignificance: By training a high-value workforce in a patient-centered approach to care that facilitates\\nengagement in supportive services, our proposed research can reduce homeless Veterans’ reliance on acute\\ncare services, thereby minimizing the financial burden these patients exert on the care system. This proposal\\nresponds to several VA HSR&D Research Priorities including Mental Health, Healthcare Value, Primary Care\\nPractice, Healthcare Informatics, and Whole Health, as well as VA-related Legislative Priorities (MISSION Act).\\nInnovation and Impact: A critical innovation of this research is use of data-driven processes (Hot Spotter\\nAnalytics) to better target and tailor care for high-need, homeless Veterans in VHA. Our proposed research is\\nalso innovative in that it seeks to integrate the Analytics with a workforce (PS) and approach to care (WHC)\\nthat are rapidly expanding in primary care services VA-wide. These features of our target intervention are\\nconsistent with the National Academy of Medicine’s recommendations for high-quality care for high-need\\npatients. Finally, by focusing on the development of personal health goals that are aligned with patients’\\npriorities and values, WHC is a key innovation to be added to existing VHA services for homeless Veterans.\\nSpecific Aims: The goal of this project is to integrate use of Hot Spotter Analytics with Peer Specialists trained\\nin Whole Health Coaching (PS-WHC) and evaluate whether this approach reduces homeless Veterans’\\nfrequent use of acute care. Aim 1: Conduct an RCT to test whether receipt of PS-WHC (vs. Enhanced Usual\\nCare; EUC) predicts (1a) lower acute care utilization, (1b) better health-related outcomes, and whether (1c) the\\neffects of PS-WHC on 1a and 1b are mediated by increased (i) patient activation and well-being, and (ii)\\naccess to supportive services. Aim 2: Conduct a process evaluation to inform VA's potential widespread\\nimplementation of Hot Spotter Analytics + PS-WHC on PACTs. Aim 3: Conduct a Budget Impact Analysis (BIA)\\nto determine the impact on total costs of VA care due to implementing PS-WHC.\\nMethodology: Using a Hybrid Type 1 design at the Palo Alto and Bedford VAs, 220 Veterans on PACT panels\\nwho are (i) on the VA Homeless Registry, and (ii) persistent super-utilizers of acute care will complete a\\nbaseline interview, be randomized to either EUC (usual PACT care + Hot Spotter Analytics and text reminders\\nof appointments) or EUC plus 12 sessions of PS-WHC over 12 weeks, and be re-interviewed at 3, 6, and 9\\nmonths. For Aim 2, the CFIR framework will guide key informant interviews with 7 PACT staff/leaders and 12\\npatients from each site. For the BIA, we will include only VA costs from VA, Fee Basis care, and Choice care.\\nCosts will be estimated per patient for all treatment beginning with randomization and continuing for 9 months.\\nNext Steps/Implementation: Depending on the results, we will work with our VACO partners in the National\\nCenter for Homelessness Among Veterans, the Office of Patient Centered Care & Cultural Transformation, and\\nthe Office of Mental Health & Suicide Prevention to conduct a large multisite implementation trial.\",\"project_title\":\"Using Data Analytics and Targeted Whole Health Coaching to Reduce Frequent Utilization of Acute Care among Homeless Veterans\",\"phr_text\":\"Acute care utilization contributes substantially to the cost of healthcare for Veterans. Homelessness is a robust\\nsocial determinant of super utilization of acute care. Data analytics offer a means of targeting and tailoring care\\nfor the highest-need homeless Veterans; however, analytics alone are insufficient for facilitating these\\nVeterans’ engagement in supportive services that can reduce acute care utilization. Peer Specialists trained in\\nWhole Health Coaching (PS-WHC) is a high-value, patient-centered approach to care that can enhance\\nhomeless Veterans’ engagement in supportive services and, in turn, reduce their frequent use of acute care.\\nBy integrating data analytics with PS-WHC, our proposed research will test whether this innovative model of\\ncare reduces homeless Veterans’ reliance on acute care services. If successful, these efforts can better target\\nand tailor care to the homeless Veterans at highest risk of poor health outcomes and premature mortality, as\\nwell as minimize the financial burden these patients exert on the care system.\",\"spending_categories_desc\":null,\"agency_code\":\"VA\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"999\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":null,\"indirect_cost_amt\":null,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10595672\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10706750,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"3OT2TR003443-01S3\",\"project_serial_num\":\"TR003443\",\"organization\":{\"org_name\":\"INSTITUTE FOR SYSTEMS BIOLOGY\",\"city\":null,\"country\":null,\"org_city\":\"SEATTLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"135646524\"],\"org_ueis\":[\"SMK9PCMKXED6\"],\"primary_duns\":\"135646524\",\"primary_uei\":\"SMK9PCMKXED6\",\"org_fips\":\"US\",\"org_ipf_code\":\"4106301\",\"org_zipcode\":\"981095263\",\"external_org_id\":4106301},\"award_type\":\"3\",\"activity_code\":\"OT2\",\"award_amount\":609144,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"3\",\"activity_code\":\"OT2\",\"ic_code\":\"TR\",\"serial_num\":\"003443\",\"support_year\":\"01\",\"full_support_year\":\"01S3\",\"suffix_code\":\"S3\"},\"principal_investigators\":[{\"profile_id\":9841070,\"first_name\":\"Gustavo\",\"middle_name\":\"\",\"last_name\":\"Glusman\",\"is_contact_pi\":true,\"full_name\":\"Gustavo  Glusman\",\"title\":\"PRINCIPAL SCIENTIST\"},{\"profile_id\":15443354,\"first_name\":\"Jennifer\",\"middle_name\":\"\",\"last_name\":\"Hadlock\",\"is_contact_pi\":false,\"full_name\":\"Jennifer  Hadlock\",\"title\":\"PRINCIPAL MEDICAL INFORMATICIST\"},{\"profile_id\":8300690,\"first_name\":\"ILYA\",\"middle_name\":\"\",\"last_name\":\"SHMULEVICH\",\"is_contact_pi\":false,\"full_name\":\"ILYA  SHMULEVICH\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"GLUSMAN, GUSTAVO \",\"program_officers\":[{\"first_name\":\"TYLER\",\"middle_name\":\"\",\"last_name\":\"BECK\",\"full_name\":\"TYLER  BECK\"}],\"agency_ic_admin\":{\"code\":\"TR\",\"abbreviation\":\"NCATS\",\"name\":\"National Center for Advancing Translational Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"TR\",\"name\":\"National Center for Advancing Translational Sciences\",\"abbreviation\":\"NCATS\",\"total_cost\":609144.0}],\"cong_dist\":\"WA-07\",\"spending_categories\":null,\"project_start_date\":\"2020-01-24T12:01:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"OTA-19-009\",\"full_study_section\":{\"srg_code\":\"ZTR1\",\"srg_flex\":\"\",\"sra_designator_code\":\"SRC\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"ZTR1-SRC(99)\"},\"award_notice_date\":\"2022-12-10T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OT\",\"core_project_num\":\"OT2TR003443\",\"terms\":\"<Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Cell Line><CellLine><Strains Cell Lines><cultured cell line><Communities><Data Analyses><Data Analysis><data interpretation><Disease><Disorder><Pharmaceutical Preparations><Drugs><Medication><Pharmaceutic Preparations><drug/agent><Ethics><ethical><indexing><Manuals><Methods><Proteins><Records><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Standardization><Technology><Time><Work><Data Set><Dataset><Clinical><Specific qualifier value><Specified><Ingestion><Individual><Measurement><Collaborations><Letters><tool><Machine Learning><machine learned><Knowledge><Complex><Location><interest><experience><Gene Mutation><Gene Alteration><success><novel><Provider><Address><Data><Collection><Ontology><Translational Science><translation research><Translational Research><Process><electronic health care record><electronic healthcare record><Electronic Health Record><pathway><Pathway interactions><microbiome><Knowledge Extraction><Outcome><resistant><Resistance><interoperability><tumor><BigData><Big Data><Data Analytics><FAIR principles><FAIR data><FAIR guiding principles><Findable, Accessible, Interoperable and Re-usable><Findable, Accessible, Interoperable, and Reusable><knowledge integration><large datasets><large data sets><knowledge graph><graph knowledge base><graph knowledgebase>\",\"pref_terms\":\"Address;Big Data;Cell Line;Clinical;Collaborations;Collection;Communities;Complex;Data;Data Analyses;Data Analytics;Data Set;Disease;Electronic Health Record;Ethics;FAIR principles;Gene Mutation;Individual;Ingestion;Knowledge;Knowledge Extraction;Letters;Location;Machine Learning;Malignant Neoplasms;Manuals;Measurement;Methods;Ontology;Outcome;Pathway interactions;Pharmaceutical Preparations;Process;Proteins;Provider;Records;Research;Research Personnel;Resistance;Resources;Specific qualifier value;Standardization;Technology;Time;Translational Research;Work;experience;indexing;interest;interoperability;knowledge graph;knowledge integration;large datasets;microbiome;novel;success;tool;tumor\",\"abstract_text\":\"Component type: This Knowledge Provider project will continue and significantly extend work\\ndone by the Translator Consortium Blue Team, focusing on deriving knowledge from real-world\\ndata through complex analytic workflows, integrated to the Translator Knowledge Graph, and\\nserved via tools like Big GIM and the Translator Standard API.\\nThe problem: We aim to solve the “first mile” problem of translational research: how to\\nintegrate the multitude of dynamic small-to-large data sets that have been produced by the\\nresearch and clinical communities, but that are in different locations, processed in different\\nways, and in a variety of formats that may not be mutually interoperable. Integrating these data\\nsets requires significant manual work downloading, reformatting, parsing, indexing and\\nanalyzing each data set in turn. The technical and ethical challenges of accessing diverse\\ncollections of big data, efficiently selecting information relevant to different users’ interests, and\\nextracting the underlying knowledge are problems that remain unsolved. Here, we propose to\\nleverage lessons distilled from our previous and ongoing big data analysis projects to develop a\\nhighly automated tool for removing these bottlenecks, enabling researchers to analyze and\\nintegrate many valuable data sets with ease and efficiency, and making the data FAIR [1].\\nPlan: (AIM 1) We will analyze and extract knowledge from rich real-world biomedical data sets\\n(listed in the Resources page) in the domains of wellness, cancer, and large-scale clinical\\nrecords. (AIM 2) We will formalize methods from Aim 1 to develop DOCKET, a novel tool for\\nonboarding and integrating data from multiple domains. (AIM 3) We will work with other teams\\nto adapt DOCKET to additional knowledge domains. ■ The DOCKET tool will offer 3 modules:\\n(1) DOCKET Overview: Analysis of, and knowledge extraction from, an individual data set. (2)\\nDOCKET Compare: Comparing versions of the same data set to compute confidence values,\\nand comparing different data sets to find commonalities. (3) DOCKET Integrate: Deriving\\nknowledge through integrating different data sets. ■ Researchers will be able to parameterize\\nthese functions, resolve inconsistencies, and derive knowledge through the command line,\\nJupyter notebooks, or other interfaces as specified by Translator Standards. ■ The outcome will\\nbe a collection of nodes and edges, richly annotated with context, provenance and confidence\\nlevels, ready for incorporation into the Translator Knowledge Graph (TKG). ■ All analyses and\\nderived knowledge will be stored in standardized formats, enabling querying through the\\nReasoner Std API and ingestion into downstream AI assisted machine learning. ■ Example\\nquestions this will allow us to address include: (Wellness) Which clinical analytes, metabolites,\\nproteins, microbiome taxa, etc. are significantly correlated, and which changing analytes predict\\ntransition to which disease? [2,3] (Cancer) Which gene mutations in any of X pathways are\\nassociated with sensitivity or resistance to any of Y drugs, in cell lines from Z tumor types? (All\\ndata sets) Which data set entities are similar to this one? Are there significant clusters? What\\ndistinguishes between the clusters? What significant correlations of attributes can be observed?\\nHow can this set of entities be expanded by adding similar ones? How do these N versions of\\nthis data set differ, and how stable is each knowledge edge as the data set changes over time?\\nCollaboration strengths: Our team has extensive experience with biomedical and domainagnostic\\ndata analytics, integrating multiple relevant data types: omics, clinical measurements\\nand electronic health records (EHRs). We have participated in large collaborative consortia and\\nhave subject matter experts willing to advise on proper data interpretation. Our application\\nsynergizes with those of other Translator teams (see Letters of Collaboration).\\nChallenges: Data can come in a bewildering diversity of formats. Our solution will be modular,\\nwill address the most common formats first, and will leverage established technologies like\\nDataFrames and importers (like pandas.io) where possible. Mapping nodes and edge types\\nonto standard ontologies is crucial for knowledge integration; we will collaborate with the\\nStandards component to maximize success.\",\"project_title\":\"DOCKET: accelerating knowledge extraction from biomedical data sets\",\"phr_text\":\"\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"350\",\"funding_mechanism\":\"Other\",\"direct_cost_amt\":324013,\"indirect_cost_amt\":285131,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10706750\",\"date_added\":\"2022-12-17T04:12:51Z\"},{\"appl_id\":10706762,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"3OT2TR003450-01S3\",\"project_serial_num\":\"TR003450\",\"organization\":{\"org_name\":\"INSTITUTE FOR SYSTEMS BIOLOGY\",\"city\":null,\"country\":null,\"org_city\":\"SEATTLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"135646524\"],\"org_ueis\":[\"SMK9PCMKXED6\"],\"primary_duns\":\"135646524\",\"primary_uei\":\"SMK9PCMKXED6\",\"org_fips\":\"US\",\"org_ipf_code\":\"4106301\",\"org_zipcode\":\"981095263\",\"external_org_id\":4106301},\"award_type\":\"3\",\"activity_code\":\"OT2\",\"award_amount\":532891,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"3\",\"activity_code\":\"OT2\",\"ic_code\":\"TR\",\"serial_num\":\"003450\",\"support_year\":\"01\",\"full_support_year\":\"01S3\",\"suffix_code\":\"S3\"},\"principal_investigators\":[{\"profile_id\":2089750,\"first_name\":\"SERGIO\",\"middle_name\":\"E\",\"last_name\":\"BARANZINI\",\"is_contact_pi\":false,\"full_name\":\"SERGIO E BARANZINI\",\"title\":\"\"},{\"profile_id\":9454292,\"first_name\":\"Sui\",\"middle_name\":\"\",\"last_name\":\"Huang\",\"is_contact_pi\":true,\"full_name\":\"Sui  Huang\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"HUANG, SUI \",\"program_officers\":[{\"first_name\":\"TYLER\",\"middle_name\":\"\",\"last_name\":\"BECK\",\"full_name\":\"TYLER  BECK\"}],\"agency_ic_admin\":{\"code\":\"TR\",\"abbreviation\":\"NCATS\",\"name\":\"National Center for Advancing Translational Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"TR\",\"name\":\"National Center for Advancing Translational Sciences\",\"abbreviation\":\"NCATS\",\"total_cost\":532891.0}],\"cong_dist\":\"WA-07\",\"spending_categories\":null,\"project_start_date\":\"2020-01-24T12:01:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"OTA-19-009\",\"full_study_section\":{\"srg_code\":\"ZTR1\",\"srg_flex\":\"\",\"sra_designator_code\":\"SRC\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"ZTR1-SRC(99)\"},\"award_notice_date\":\"2022-12-13T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OT\",\"core_project_num\":\"OT2TR003450\",\"terms\":\"<Algorithms><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Chickens><Gallus domesticus><Gallus gallus><Gallus gallus domesticus><Conflict (Psychology)><Conflict><Disease><Disorder><Epidemiology><epidemiologic><epidemiological><Recording of previous events><History><Learning><Names><Quality Control><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Role><social role><Science><Signal Transduction><Cell Communication and Signaling><Cell Signaling><Intracellular Communication and Signaling><Signal Transduction Systems><Signaling><biological signal transduction><medical specialties><Specialty><stem cells><Progenitor Cells><Testing><Vision><Sight><visual function><Vitamin K><Weight><Work><Technical Expertise><technical skills><Walking><Glycine decarboxylase><Glycine cleavage system P-protein><P-protein><glycine decarboxylase P-protein><Data Set><Dataset><base><improved><Medical><Data Bases><data base><Databases><Measurement><tool><Epistemology><Knowledge><programs><Source><System><cancer risk><experience><cohort><disorder risk><disease risk><Provider><Address><Data><Molecular><developmental><Development><pathway><Pathway interactions><implementation research><egg><interoperability><biorepository><biobank><BigData><Big Data><Data Analytics><multiomics><multiple omics><Multiomic Data><multiple omic data><knowledge graph><graph knowledge base><graph knowledgebase><National Center for Advancing Translational Sciences><NCATS><algorithmic bias><antagonist>\",\"pref_terms\":\"Address;Algorithms;Big Data;Chickens;Conflict (Psychology);Data;Data Analytics;Data Set;Databases;Development;Disease;Epidemiology;Epistemology;Glycine decarboxylase;Knowledge;Learning;Malignant Neoplasms;Measurement;Medical;Molecular;Multiomic Data;Names;National Center for Advancing Translational Sciences;Pathway interactions;Provider;Quality Control;Recording of previous events;Research;Research Personnel;Resources;Role;Science;Signal Transduction;Source;System;Technical Expertise;Testing;Vision;Vitamin K;Walking;Weight;Work;algorithmic bias;antagonist;base;biobank;cancer risk;cohort;disorder risk;egg;experience;implementation research;improved;interoperability;knowledge graph;medical specialties;multiple omics;programs;stem cells;tool\",\"abstract_text\":\"1) Component: Autonomous Relay Agent.\\nWe will develop an ARA named EVIDARA to\\nevaluate returns from queries in knowledge\\nsources (KS) using a new epistemology: The\\n“reasoning” is based on checking against empirical\\nevidence available in raw data (measurements)\\ninstead of deductive reasoning\\n(FIG.►). EVIDARA will assist the Autonomous\\nRelay System (ARS) to identify paths in returned\\nknowledge graphs (KG) that may\\nconflict with real-word evidence and to relay queries to appropriate specialty KS or database.\\n(2) Problem addressed: EHR and multi-omics raw data from large cohorts, if properly preprocessed\\n[e.g., by Knowledge Providers, such as the DOCKET, see application by Dr. Glusman],\\noffers a new opportunity for ad hoc systematic extraction of empirical knowledge on relationships\\n(“Protein P level correlates with risk for disease D”) instead of relying on specific epidemiological\\nanalyses. The problem in harnessing raw data for empirical support in lieu of deductive reasoning\\nis that the KGs to be evaluated are extracted from knowledge sources of distinct types and that\\nthe relevance of paths depends on the query context Q. Also the ARA algorithm should be scalable\\nto digest the emerging multi-omics data from projects like All-of-Us, the UK Biobank.\\n(3) Plan for implementation: Research will be conducted to evaluate a new epistemic realm:\\nmake empirical evidence central to “reasoning”. We have assembled a set of functioning tools to\\novercome the chicken-egg problem of getting a project started and jumpstart development and\\ntesting of EVIDARA: (i) SPOKE, one of the largest biomedical knowledge network (KN) has integrated\\n25 diverse of KS into a single (neo4j) network database of 2 million nodes and will serve\\nas testing ground for research well before we can use KGs produced by the Knowledge Providers.\\n(ii) Algorithms that use raw data from EHR and multi-omics studies to evaluate the returned KGs.\\nFor instance, we compute weights of all nodes in the entire KN through a random-walk algorithm\\nbiased by their role for a given condition Q observed in the raw data. (iii) Raw data beyond EHR:\\nmulti-omics profiles from a study at ISB with >10k variables which vastly exceeds coverage of\\nobservable nodes in KNs offered by EHRs. Example query: “Vitamin K stimulates stem-cell signaling,\\nthus could promote cancer. What is the molecular pathway? Mechanisms returned as KG\\nwill be pruned by EVIDARA and checked against correlative evidence in the raw data: Is there\\nevidence that taking Vit. K or its antagonist reduces cancer risk?”. Importantly, since EVIDARA\\nlearns on a network of many types of KS, it will provide information to the ARS about which type\\nof KS/Knowledge Provider to invoke next (in iterative queries) to improve the knowledge graph.\\n(4) Expertise & resources: The MPIs, Drs. S. Baranzini (UCSF) and S. Huang (ISB) are researchers\\nwith long history of working with medical big data, thus offering technical expertise and the\\ncritical SME perspective. SB’s team has created and maintains SPOKE. The uniquely self-contained\\nSPOKE network will allow NCATS staff to test other ARAs. SH brings decades of experience\\nin research of disease mechanisms and medical epistemology. His team will provide multi-omics\\ndatasets and data analytics expertise. With his prior work in the NCATS Translator program, he is\\nwell poised to maximize team science efficiency and help convert its vision into tangible results.\\n(5) Potential challenges. (i) Quality of evidential support depends on quality of raw data. A quality\\ncontrol is beyond the scope of EVIDARA but could be provided by Knowledge Providers focusing\\non new multi-omics data sets (e.g. DOCKET). (ii) Testing EVIDARA on other KS from Knowledge\\nProviders) may be slowed down by interoperability issues (e.g. incompatible identifiers). Such\\nissues will be addressed early in Year 1 with help of the Standard and Reference group.\",\"project_title\":\"EVIDARA: Automated Evidential Support from Raw Data for relay agents in Biomedical KG Queries\",\"phr_text\":\"\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"350\",\"funding_mechanism\":\"Other\",\"direct_cost_amt\":370515,\"indirect_cost_amt\":162376,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10706762\",\"date_added\":\"2022-12-17T04:12:51Z\"},{\"appl_id\":10526409,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01NS119825-03\",\"project_serial_num\":\"NS119825\",\"organization\":{\"org_name\":\"STANFORD UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"STANFORD\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":\"NEUROLOGY\",\"fips_country_code\":null,\"org_duns\":[\"009214214\"],\"org_ueis\":[\"HJD6G4D6TJY5\"],\"primary_duns\":\"009214214\",\"primary_uei\":\"HJD6G4D6TJY5\",\"org_fips\":\"US\",\"org_ipf_code\":\"8046501\",\"org_zipcode\":\"943052004\",\"external_org_id\":8046501},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":1113712,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"NS\",\"serial_num\":\"119825\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":10790241,\"first_name\":\"Jonathan\",\"middle_name\":\"\",\"last_name\":\"Elmer\",\"is_contact_pi\":false,\"full_name\":\"Jonathan  Elmer\",\"title\":\"\"},{\"profile_id\":6270502,\"first_name\":\"KAREN\",\"middle_name\":\"G\",\"last_name\":\"HIRSCH\",\"is_contact_pi\":true,\"full_name\":\"KAREN G HIRSCH\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"HIRSCH, KAREN G\",\"program_officers\":[{\"first_name\":\"MARIA\",\"middle_name\":\"CAROLINA\",\"last_name\":\"Mendoza-Puccini\",\"full_name\":\"MARIA CAROLINA Mendoza-Puccini\"}],\"agency_ic_admin\":{\"code\":\"NS\",\"abbreviation\":\"NINDS\",\"name\":\"National Institute of Neurological Disorders and Stroke\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"NS\",\"name\":\"National Institute of Neurological Disorders and Stroke\",\"abbreviation\":\"NINDS\",\"total_cost\":1113712.0}],\"cong_dist\":\"CA-18\",\"spending_categories\":null,\"project_start_date\":\"2020-12-15T12:12:00Z\",\"project_end_date\":\"2025-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-19-056\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BDCN\",\"sra_flex_code\":\"L\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BDCN-L(90)S]\"},\"award_notice_date\":\"2022-12-01T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01NS119825\",\"terms\":\"<Acute Disease><acute disease/disorder><acute disorder><Affect><Brain><Brain Nervous System><Encephalon><Brain Death><Brain Dead><Coma Depasse><cerebral death><Cardiopulmonary Resuscitation><cardiac resuscitation><heart resuscitation><Clinical Research><Clinical Study><Critical Care><Critical Illness><Critically Ill><Cessation of life><Death><Disease><Disorder><Electroencephalography><EEG><Electrophysiology (science)><Electrophysiology><Neurophysiology / Electrophysiology><electrophysiological><Foundations><Goals><Head><Heart Arrest><Asystole><Cardiac Arrest><Heterogeneity><natural hypothermia><Hypothermia><Lead><Pb element><heavy metal Pb><heavy metal lead><Methods><Morbidity - disease rate><Morbidity><mortality><United States National Institutes of Health><NIH><National Institutes of Health><Neurologic Examination><Neurological Examination><Neurosciences><Patients><Patient Rights><Physiology><Public Health><Research><Research Personnel><Investigators><Researchers><Role><social role><Savings><Specificity><Standardization><Temperature><Testing><Time><Translating><Work><Measures><Data Set><Dataset><Caring><Titrations><Injury><injuries><base><Organ><improved><Acute><Clinical><Physiological><Physiologic><Neurologic><Neurological><Rewarming><Survivors><prognostic><disability><Individual><Recovery><Brain Injuries><Acquired brain injury><brain damage><brain-injured><Functional disorder><Dysfunction><Physiopathology><pathophysiology><tool><Diagnostic><Knowledge><Life><Pupil light reflex><pupillary light reflex><pupillary reflex><Severities><Source><Pattern><Severity of illness><disease severity><Benchmarking><Best Practice Analysis><Cardiopulmonary><Performance><Recovery of Function><functional recovery><novel><Modality><Positioning Attribute><Position><Nervous System Trauma><Nervous System Injuries><Nervous System damage><Neurological Damage><Neurological Injury><Neurological trauma><neurotrauma><Sampling><response><Intervention Strategies><interventional strategy><Intervention><Organ failure><Address><Dose><Data><Measurable><Resolution><Subgroup><Cardiopulmonary Physiology><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Validation><Monitor><Molecular><developmental><Development><imaging><Image><computer based prediction><prediction model><predictive modeling><designing><design><Outcome><prospective><innovate><innovative><innovation><treatment effect><multidisciplinary><multi-modality><multimodality><clinical care><response to therapy><response to treatment><therapeutic response><therapy response><treatment response><candidate biomarker><candidate marker><new marker><novel biomarker><novel marker><bio-markers><biologic marker><biomarker><Biological Markers><arm><precision-based medicine><precision medicine><personalization of treatment><personalized therapy><personalized treatment><personalized medicine><clinical predictors><predictive biomarkers><predictive molecular biomarker><predictive marker><Precision care><individualized care><individualized patient care><personalized patient care><personalized care><prognostic ability><prognostic power><prognostic utility><prognostic value><Data Science><Data Analytics><individual patient><outcome prediction><predictive outcomes><predictors of outcomes><improved outcome><Hypoxic-Ischemic Brain Injury><patient subsets><patient subgroups><patient subpopulations><patient subtypes><radiological imaging><Radiography><Roentgenography><radiologic imaging><treatment arm><intervention arm><unsupervised learning><unsupervised machine learning><random forest><supervised learning><supervised machine learning><multimodal data><multi-modal data><multi-modal datasets><multimodal datasets><advanced analytics><biomarker signature>\",\"pref_terms\":\"Acute;Acute Disease;Address;Affect;Benchmarking;Biological Markers;Brain;Brain Death;Brain Injuries;Cardiopulmonary;Cardiopulmonary Physiology;Cardiopulmonary Resuscitation;Caring;Cessation of life;Clinical;Clinical Research;Critical Care;Critical Illness;Data;Data Analytics;Data Science;Data Set;Development;Diagnostic;Disease;Dose;Electroencephalography;Electrophysiology (science);Foundations;Functional disorder;Goals;Head;Heart Arrest;Heterogeneity;Hypoxic-Ischemic Brain Injury;Image;Individual;Injury;Intervention;Knowledge;Lead;Life;Measurable;Measures;Methods;Modality;Molecular;Monitor;Morbidity - disease rate;Nervous System Trauma;Neurologic;Neurologic Examination;Neurosciences;Organ;Organ failure;Outcome;Patient Rights;Patient-Focused Outcomes;Patients;Pattern;Performance;Physiological;Physiology;Positioning Attribute;Public Health;Pupil light reflex;Recovery;Recovery of Function;Research;Research Personnel;Resolution;Rewarming;Role;Sampling;Savings;Severities;Severity of illness;Source;Specificity;Standardization;Subgroup;Survivors;Temperature;Testing;Time;Titrations;Translating;United States National Institutes of Health;Validation;Work;advanced analytics;arm;base;biomarker signature;candidate marker;clinical care;clinical predictors;design;disability;improved;improved outcome;individual patient;innovation;mortality;multidisciplinary;multimodal data;multimodality;natural hypothermia;novel;novel marker;outcome prediction;patient subsets;personalized care;personalized medicine;precision medicine;predictive marker;predictive modeling;prognostic;prognostic value;prospective;radiological imaging;random forest;response;supervised learning;tool;treatment arm;treatment effect;treatment response;unsupervised learning\",\"abstract_text\":\"Project Summary\\nThe objective of PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP) is to discover novel biomarker\\nsignatures of post cardiac arrest brain and extracerebral organ failure that predict treatment responsiveness\\nand long-term recovery. We will achieve this by partnering with the ICECAP trial, a response-adaptive dose\\nfinding clinical research trial that seeks to determine the optimal duration of post-arrest hypothermia. Cardiac\\narrest is a major public health problem with high morbidity and mortality. Four in five patients hospitalized\\nafter cardiac arrest have significant brain injury, and death from neurologic damage is common. Improving\\nsurvival and functional recovery is a critical public health objective and will require innovative approaches.\\nThe current clinical situation is unprecedented. Currently post-CA brain injury is an acute, sudden critical\\nillness with major knowledge gaps about how best to characterize severity of injury and to identify which\\nindividual patients are likely to benefit from specific neuroprotective strategies. Thus, development of high-\\nperforming biomarker signatures is a critical need and would translate in to immediate changes in care. We\\nhypothesize that not all patients are identical (i.e. there will be a heterogeneity of treatment effect) and that\\nthrough our innovative, multi-parametric data driven approach, we will be able to identify novel signatures that\\ndefine subgroups of patients. Advanced data science and analytical approaches will allow the identification of\\nthese subgroups that was previously not possible. These subgroups will be clinically important insofar as they\\nwill indicate differential responses to treatment and different trajectories of functional recovery. This project\\nwill acquire high resolution multi-modal data early in the disease course that will allow us to address these\\ncurrent knowledge gaps and improve our understanding of the disease in the early acute setting when\\ninterventions can improve outcome. The knowledge learned here will be applied to develop personalized\\ntreatments for cardiac arrest survivors, addressing the NIH's and our goals of lengthening life and reducing\\ndisability.\",\"project_title\":\"PREcision Care In Cardiac ArrEst - ICECAP (PRECICECAP)\",\"phr_text\":\"Project Narrative\\nPost-cardiac arrest injury affects the brain and other critical organs and is a major public health problem\\ncausing significant death and disability. This project will use novel data-driven approaches to collect high\\nresolution monitoring data from patients in the critical care unit and will use that multi-parametric data to\\ndevelop newly defined patient sub-groups with differing clinical trajectories and responses to therapies. This\\nproject will have a major impact on public health by allowing treatment of the right patient at the right time\\nwith the right therapy, thereby reducing disability and saving lives.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"853\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":915609,\"indirect_cost_amt\":198103,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10526409\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10579825,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01HL141286-05\",\"project_serial_num\":\"HL141286\",\"organization\":{\"org_name\":\"CINCINNATI CHILDRENS HOSP MED CTR\",\"city\":null,\"country\":null,\"org_city\":\"CINCINNATI\",\"org_country\":\"UNITED STATES\",\"org_state\":\"OH\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"071284913\"],\"org_ueis\":[\"JZD1HLM2ZU83\"],\"primary_duns\":\"071284913\",\"primary_uei\":\"JZD1HLM2ZU83\",\"org_fips\":\"US\",\"org_ipf_code\":\"615001\",\"org_zipcode\":\"452293039\",\"external_org_id\":615001},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":406637,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"HL\",\"serial_num\":\"141286\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9574233,\"first_name\":\"Rhonda\",\"middle_name\":\"\",\"last_name\":\"Szczesniak\",\"is_contact_pi\":true,\"full_name\":\"Rhonda  Szczesniak\",\"title\":\"\"}],\"contact_pi_name\":\"SZCZESNIAK, RHONDA \",\"program_officers\":[{\"first_name\":\"Marrah\",\"middle_name\":\"Elizabeth\",\"last_name\":\"Lachowicz-Scroggins\",\"full_name\":\"Marrah Elizabeth Lachowicz-Scroggins\"}],\"agency_ic_admin\":{\"code\":\"HL\",\"abbreviation\":\"NHLBI\",\"name\":\"National Heart Lung and Blood Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"HL\",\"name\":\"National Heart Lung and Blood Institute\",\"abbreviation\":\"NHLBI\",\"total_cost\":406637.0}],\"cong_dist\":\"OH-01\",\"spending_categories\":null,\"project_start_date\":\"2019-01-18T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"PA-18-484\",\"full_study_section\":{\"srg_code\":\"IRAP\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Infectious Diseases, Reproductive Health, Asthma and Pulmonary Conditions Study Section[IRAP]\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01HL141286\",\"terms\":\"<Accounting><Adolescence><12-20 years old><adolescence (12-20)><Age><ages><Air><Algorithms><Antibiotics><Antibiotic Agents><Antibiotic Drugs><Miscellaneous Antibiotic><Cause of Death><Communities><Cystic Fibrosis><Mucoviscidosis><Data Analyses><Data Analysis><data interpretation><Data Sources><Decision Making><Disease><Disorder><Environment><Environmental Exposure><Foundations><Genes><Geography><Joints><Lead><Pb element><heavy metal Pb><heavy metal lead><Light><Photoradiation><Lung><Lung Respiratory System><pulmonary><Lung diseases><Pulmonary Diseases><Pulmonary Disorder><disease of the lung><disorder of the lung><lung disorder><male><Maps><Medicine><Mission><mortality><United States National Institutes of Health><NIH><National Institutes of Health><Neighborhoods><Patients><Phenotype><Prostate><Prostate Gland><Prostatic Gland><Prothrombin><Blood Coagulation Factor II><Coagulation Factor II><Differentiation Reversal Factor><Factor II><Public Health><Recurrence><Recurrent><Registries><Research><Risk><Science><Temperature><Time><Translating><Translations><Transplantation><transplant><Work><Cystic Fibrosis Transmembrane Conductance Regulator><CFTR><CFTR Protein><cystic fibrosis transmembrane regulator><Measures><Socioeconomic Status><Socio-economic status><socio-economic position><socioeconomic position><Caring><base><improved><Acute><Clinical><Variant><Variation><Medical><Link><prognostic><lung function><pulmonary function><Disease Progression><Early Intervention><lung injury><Lung damage><pulmonary damage><pulmonary injury><pulmonary tissue damage><pulmonary tissue injury><Functional disorder><Dysfunction><Physiopathology><pathophysiology><tool><Pulmonary Cystic Fibrosis><CF lung disease><cystic fibrosis lung><cystic fibrosis lung disease><Frequencies><Complex><Event><System><cancer risk><Early Diagnosis><early detection><early experience><novel><Predictive Factor><Environmental Risk Factor><Environmental Factor><environmental risk><Prevention><Positioning Attribute><Position><Statistical Methods><FEV1><FEV1%VC><Forced Expiratory Volume 1 Test><Forced Expiratory Volume in 1 Second><PFT/FEV1><Pulmonary Function Test/Forced Expiratory Volume 1><Modeling><Intervention Strategies><interventional strategy><Intervention><cancer recurrence><patient registry><Drops><Provider><preventing><prevent><Data><Detection><Clinical Management><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Monitor><Characteristics><socio-economic><socio-economically><socioeconomically><socioeconomics><point of care><lung function decline><pulmonary function decline><epidemiological model><computer based prediction><prediction model><predictive modeling><designing><design><Clinical assessments><Clinical effectiveness><Outcome><CF patients><individuals with CF><individuals with cystic fibrosis><patients with CF><patients with cystic fibrosis><cystic fibrosis patients><prospective><Coupled><innovate><innovative><innovation><man's><man><environmental intervention><spatiotemporal><clinical care><treatment strategy><nongenetic><non-genetic><early adulthood><emerging adult><clinical practice><flexible><flexibility><clinical decision-making><precision-based medicine><precision medicine><Precision care><individualized care><individualized patient care><personalized patient care><personalized care><support tools><Predictive Analytics><individual patient><personalized decision><individualized clinical decision><individualized decision><personalized clinical decision><personalized data-driven decision><high dimensionality><personalized predictions><Individualized Predictors><individualized predictions><personalized predictors><real time monitoring><realtime monitoring><decision making algorithm><Big Data Methods><Big Data Analytics><Big Data Tools><clinical encounter><Prognosis>\",\"pref_terms\":\"Accounting;Acute;Adolescence;Age;Air;Algorithms;Antibiotics;Big Data Methods;Caring;Cause of Death;Characteristics;Clinical;Clinical Management;Clinical assessments;Clinical effectiveness;Communities;Complex;Coupled;Cystic Fibrosis;Cystic Fibrosis Transmembrane Conductance Regulator;Data;Data Analyses;Data Sources;Decision Making;Detection;Disease;Disease Progression;Drops;Early Diagnosis;Early Intervention;Environment;Environmental Exposure;Environmental Risk Factor;Event;Foundations;Frequencies;Functional disorder;Genes;Geography;Intervention;Joints;Lead;Light;Link;Lung;Lung diseases;Maps;Measures;Medical;Medicine;Mission;Modeling;Monitor;Neighborhoods;Outcome;Patient-Focused Outcomes;Patients;Phenotype;Positioning Attribute;Predictive Analytics;Predictive Factor;Prevention;Prognosis;Prostate;Prothrombin;Provider;Public Health;Pulmonary Cystic Fibrosis;Pulmonary Function Test/Forced Expiratory Volume 1;Recurrence;Registries;Research;Risk;Science;Socioeconomic Status;Statistical Methods;System;Temperature;Time;Translating;Translations;Transplantation;United States National Institutes of Health;Variant;Work;base;cancer recurrence;cancer risk;clinical care;clinical decision-making;clinical encounter;clinical practice;cystic fibrosis patients;decision making algorithm;design;early experience;emerging adult;environmental intervention;epidemiological model;flexibility;high dimensionality;improved;individual patient;innovation;lung injury;male;man;mortality;non-genetic;novel;patient registry;personalized care;personalized decision;personalized predictions;point of care;precision medicine;predictive modeling;prevent;prognostic;prospective;pulmonary function;pulmonary function decline;real time monitoring;socioeconomics;spatiotemporal;support tools;tool;treatment strategy\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nProgressive lung disease is the leading cause of death in individuals with cystic fibrosis (CF). Rapid decline,\\ncharacterized by accelerated loss of lung function, is common for CF patients, and cannot be explained or\\npredicted by CFTR/gene dysfunction alone. Mapping the environmental exposures and community\\ncharacteristics (geomarkers) that predict patient-specific rapid decline and providing tools for earlier detection\\nand monitoring at the center level are essential to transforming the precision of CF clinical care, and offer an\\nopportunity to adjust interventions to prevent irreversible lung damage. The translation of these tools into\\npractice is further hindered by continued use of antiquated statistical methods that ignore the interplay between\\nnonlinear lung function and recurrent pulmonary exacerbations in the clinical course of CF, disregard known\\nmortality biases that can lead to inaccurate projections of rapid decline, and do not leverage extant geographic\\ndata on geomarkers, such as air quality or neighborhood socioeconomic conditions, to improve prediction of\\nrapid decline. In this proposal, we will utilize comprehensive geocoding algorithms, novel statistical methods\\nand powerful computational medicine tools for integration into clinical algorithms for the detection to drive early\\nintervention of rapid lung disease progression. The overall objective of this research is to leverage a rich CF\\nregistry, extant national and local environmental data sources and prospectively collected study data to\\naccurately forecast the onset of rapid decline in individual patients, and to develop a feasible medical-\\nmonitoring tool that positively impacts CF point-of-care decision-making. Our overarching hypothesis is that\\ninteractive computational medicine tools for dynamic prediction and clinical surveillance of rapid pulmonary\\ndecline in CF will enhance local disease monitoring. This will be accomplished by incorporating both\\nestablished and novel environmental exposures and community characteristics of CF patients. Our specific\\naims are to 1) phenotype patients who experience early, rapid pulmonary decline informed by environmental\\nexposures; 2) transform dynamic prediction of rapid lung-function decline and exacerbations in CF patients\\nthrough high-dimensional, multi-level joint model mapping with environmental factors; 3) design and implement\\ndecision support capabilities that monitor real-time lung-function decline and risk of exacerbations for\\npersonalized, center-specific CF patient management. Once systems to accurately and precisely predict rapid\\ndecline are in place, better prospective treatment decisions will become possible, resulting in better patient\\noutcomes and improved precision medicine/care.\",\"project_title\":\"R01- Mapping environmental contributions to rapid lung disease progression in cystic fibrosis\",\"phr_text\":\"PROJECT NARRATIVE\\nThe proposed research is relevant to public health because having clinical tools for early detection and timely\\ntreatment of rapid pulmonary decline in cystic fibrosis will improve the real-world effectiveness of clinical\\ninterventions. Accounting for environmental influences is ultimately expected to optimize these tools and to\\nappropriately assign treatment and prevention of rapid decline, thereby helping individuals with cystic fibrosis\\nlive healthier, longer lives. Thus, the proposed research is relevant to multiple parts of NIH’s mission that\\npertain to i) enabling rapid translation of discoveries in quantitative biomedicine into clinical practice; ii)\\nconsidering the environment of each patient to facilitate precision medicine activities.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"838\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":266928,\"indirect_cost_amt\":139709,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10579825\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10545172,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R37MH059105-21\",\"project_serial_num\":\"MH059105\",\"organization\":{\"org_name\":\"OREGON HEALTH & SCIENCE UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"PORTLAND\",\"org_country\":\"UNITED STATES\",\"org_state\":\"OR\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"096997515\"],\"org_ueis\":[\"NPSNT86JKN51\"],\"primary_duns\":\"096997515\",\"primary_uei\":\"NPSNT86JKN51\",\"org_fips\":\"US\",\"org_ipf_code\":\"6297007\",\"org_zipcode\":\"972393098\",\"external_org_id\":6297007},\"award_type\":\"5\",\"activity_code\":\"R37\",\"award_amount\":682943,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R37\",\"ic_code\":\"MH\",\"serial_num\":\"059105\",\"support_year\":\"21\",\"full_support_year\":\"21\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":1903458,\"first_name\":\"JOEL\",\"middle_name\":\"T\",\"last_name\":\"NIGG\",\"is_contact_pi\":true,\"full_name\":\"JOEL T NIGG\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"NIGG, JOEL T\",\"program_officers\":[{\"first_name\":\"Jenni\",\"middle_name\":\"\",\"last_name\":\"Pacheco\",\"full_name\":\"Jenni  Pacheco\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":682943.0}],\"cong_dist\":\"OR-03\",\"spending_categories\":null,\"project_start_date\":\"1999-09-20T12:09:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PA-11-260\",\"full_study_section\":{\"srg_code\":\"NSS\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"NSS\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R37MH059105\",\"terms\":\"<Affect><Algorithms><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Family><Heterogeneity><Longitudinal Studies><long-term study><longitudinal outcome studies><longterm study><Public Health><Quality of life><QOL><Risk><Schools><Informal Social Control><Self Regulation><Syndrome><Translating><Measures><Caring><improved><Clinical><Knowledge><Emotional><Intervention Strategies><interventional strategy><Intervention><AD/HD><ADHD><Predominantly Hyperactive-Impulsive Type Attention-Deficit Disorder><Predominantly Hyperactive-Impulsive Type Hyperactivity Disorder><attention deficit hyperactive disorder><Attention deficit hyperactivity disorder><Data><Long-term Follow-up><long-term followup><longterm followup><Longterm Follow-up><Characteristics><Active Follow-up><active followup><follow up><followed up><followup><follow-up><developmental><Development><Instruction><computational tools><computerized tools><next generation><Outcome><Population><19 years of age><age 19 years><nineteen year old><nineteen years old><19 year old><Impairment><cognitive control><emotional regulation><emotion regulation><clinical care><clinical practice><clinical decision-making><clinical predictors><Developmental Course><clinical translation><advanced analytics>\",\"pref_terms\":\"19 year old;Affect;Algorithms;Attention deficit hyperactivity disorder;Caring;Characteristics;Child;Clinical;Data;Development;Developmental Course;Emotional;Family;Heterogeneity;Impairment;Informal Social Control;Instruction;Intervention;Knowledge;Longitudinal Studies;Longterm Follow-up;Measures;Outcome;Population;Public Health;Quality of life;Risk;Schools;Syndrome;Translating;advanced analytics;clinical care;clinical decision-making;clinical practice;clinical predictors;clinical translation;cognitive control;computerized tools;emotion regulation;follow-up;improved;next generation\",\"abstract_text\":\"ADHD remains a serious public health concern particularly in relation to long-term outcome, with recent\\nfollow up data from other projects, such as the MTA study, suggesting that current assessment and\\nsustained treatment practices do not much alter clinical course. In addition, societal and clinical concerns\\nremain about mis-identification of children with ADHD who need care, because some children seem to\\nimprove developmentally, while others have poor outcomes. While a great deal is known about the\\ncorrelates of developmental course, little of this knowledge has translated to clinical practice, and in\\nparticular to clinical prediction—essentially, deciding whether a child, presenting with ADHD, requires\\nintervention or for whom a clinician can afford to wait and observed. We have previously made progress\\nhere by re-conceptualizing ADHD as a problem in self-regulation involving cognitive control as well as\\nemotion regulation and emotionality. This has helped us find new and valid sub-profiles that are clinically\\npredictive. We follow that up here and add new effort to use advanced computational tools to predict\\nclinical course over a developmental period from 7-19 years of age, using a range of measures at different\\nlevels of analysis, and thus to develop algorithms that could point the way toward next-generation clinical\\ntranslation from longitudinal studies.\\nRELEVANCE (See instructions):\\nADHD continues to be a serious and impairing syndrome for millions of children, affecting families, schools,\\nand life quality. While a great deal is known about the correlates of ADHD, this information has not\\ntranslated sufficiently into more efficient clinical decision-making that recognizes the variety of children\\nwithin this population. This project seeks to use advanced analytics and long-term follow up data to identify\\nbetter ways to identify groups of children with ADHD with characteristic profiles to improve clinical care.\",\"project_title\":\"ADHD heterogeneity, mechanisms, and risk profile\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":444396,\"indirect_cost_amt\":238547,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10545172\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10548840,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K99MD016895-02\",\"project_serial_num\":\"MD016895\",\"organization\":{\"org_name\":\"HARVARD MEDICAL SCHOOL\",\"city\":null,\"country\":null,\"org_city\":\"BOSTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":\"DENTISTRY\",\"fips_country_code\":null,\"org_duns\":[\"047006379\"],\"org_ueis\":[\"JDLVAVGYJQ21\"],\"primary_duns\":\"047006379\",\"primary_uei\":\"JDLVAVGYJQ21\",\"org_fips\":\"US\",\"org_ipf_code\":\"3212902\",\"org_zipcode\":\"021201616\",\"external_org_id\":3212902},\"award_type\":\"5\",\"activity_code\":\"K99\",\"award_amount\":114113,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K99\",\"ic_code\":\"MD\",\"serial_num\":\"016895\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14315038,\"first_name\":\"Sung Eun\",\"middle_name\":\"EUN\",\"last_name\":\"Choi\",\"is_contact_pi\":true,\"full_name\":\"Sung Eun EUN Choi\",\"title\":\"INSTRUCTOR\"}],\"contact_pi_name\":\"CHOI, SUNG EUN \",\"program_officers\":[{\"first_name\":\"Rada\",\"middle_name\":\"K\",\"last_name\":\"Dagher\",\"full_name\":\"Rada K Dagher\"}],\"agency_ic_admin\":{\"code\":\"MD\",\"abbreviation\":\"NIMHD\",\"name\":\"National Institute on Minority Health and Health Disparities\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MD\",\"name\":\"National Institute on Minority Health and Health Disparities\",\"abbreviation\":\"NIMHD\",\"total_cost\":114113.0}],\"cong_dist\":\"MA-07\",\"spending_categories\":null,\"project_start_date\":\"2022-01-10T12:01:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF DENTISTRY/ORAL HYGN\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-188\",\"full_study_section\":{\"srg_code\":\"ZMD1\",\"srg_flex\":\"\",\"sra_designator_code\":\"DRI\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"ZMD1-DRI(O1)R\"},\"award_notice_date\":\"2022-11-12T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K99MD016895\",\"terms\":\"<Affect><Age><ages><Alabama><Arkansas><Behavior><Black race><Black><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Chronic Disease><Chronic Illness><chronic disorder><Colorado><Communities><Complement><Complement Proteins><Connecticut><Cost Analysis><Cost Analyses><assess cost><cost assessment><cost effectiveness><Decision Making><Dental Care><Dental Procedure><dental service><Dental caries><Caries><Dental Decay><tooth decay><Dentists><Goals><Health><Health behavior><health related behavior><Health Policy><health care policy><healthcare policy><Heterogeneity><Hispanic Americans><Recording of previous events><History><Incidence><Dental Insurance><Dental Surgery Insurance><Oral Surgery Insurance><Orthodontia insurance><Orthodontic insurance><Periodontic insurance><dental health insurance><Health Insurance><health insurance plan><Learning><Medicaid><Mentorship><Minority Groups><Minority People><Minority Population><Minority individual><Statistical Models><Probabilistic Models><Probability Models><statistical linear mixed models><statistical linear models><Mouth Diseases><Oral Cavity Disease><Oral Cavity Disorder><Oral Disease><Oral Disorder><mouth disorder><North Carolina><Oklahoma><Oral health><dental health><Dental Hygiene><Oral Hygiene><Quality of life><QOL><Race><Racial Group><Racial Stocks><Research><Risk><Risk Factors><Schools><Science><Survival Analysis><Survival Analyses><Testing><Training Programs><Work><County><Gender><Measures><Health Benefit><Healthcare><health care><Hispanic><Process Measure><Resource Allocation><Data Set><Dataset><Caring><Cost of Illness><Disease Costs><Sickness Cost><analytical method><base><career><improved><Area><Adolescent><Adolescent Youth><juvenile><juvenile human><Training><Dental><pediatric><Childhood><Individual><Policies><Interdisciplinary Research><Multidisciplinary Collaboration><Multidisciplinary Research><Interdisciplinary Study><Development Plans><Disease Progression><Ethnicity><Ethnic Origin><Machine Learning><machine learned><programs><Techniques><System><Quality Indicator><contextual factors><Application Context><experience><skills><Disease Outcome><models and simulation><model-based simulation><Modeling><career development><QOC><Quality of Care><response><theories><Math Models><mathematic model><mathematical modeling><mathematical model><Intervention Strategies><interventional strategy><Intervention><vulnerable group><vulnerable people><Vulnerable Populations><Provider><disparity in health><health disparity><Address><Data><Oral Health Epidemiology><oral epidemiology><Dental Epidemiology><Improve Access><Research Training><trend><Characteristics><beneficiary><electronic health care record><electronic healthcare record><Electronic Health Record><cost><neglect><under served group><under served people><under served population><underserved group><underserved people><Underserved Population><new approaches><novel approaches><novel strategy><novel strategies><Outcome><cost effective><malleable risk><modifiable risk><treatment effect><ethnoracial><racial and ethnic><stem><ethnoracial disparities><racial and ethnic disparities><high risk><evidence base><public health intervention><Predictive Analytics><disparity reduction><reduce disparity><public health insurance><public insurance><cost outcomes><Child Health><risk prediction model><advanced analytics><multiple data sources><dietary><Health Disparities Research><Health disparities related research><Black American>\",\"pref_terms\":\"Address;Adolescent;Affect;Age;Alabama;Area;Arkansas;Behavior;Black American;Black race;Caring;Characteristics;Child;Child Health;Childhood;Chronic Disease;Colorado;Communities;Complement;Connecticut;Cost Analysis;Cost of Illness;County;Data;Data Set;Decision Making;Dental;Dental Care;Dental Epidemiology;Dental Hygiene;Dental Insurance;Dental caries;Dentists;Development Plans;Disease Outcome;Disease Progression;Electronic Health Record;Ethnic Origin;Gender;Goals;Health;Health Benefit;Health Disparities Research;Health Insurance;Health Policy;Health behavior;Healthcare;Heterogeneity;Hispanic;Hispanic Americans;Improve Access;Incidence;Individual;Interdisciplinary Study;Intervention;Learning;Machine Learning;Measures;Medicaid;Mentorship;Minority Groups;Modeling;Mouth Diseases;North Carolina;Oklahoma;Oral health;Outcome;Policies;Predictive Analytics;Process Measure;Provider;Quality Indicator;Quality of Care;Quality of life;Race;Recording of previous events;Research;Research Training;Resource Allocation;Risk;Risk Factors;Schools;Science;Statistical Models;Survival Analysis;System;Techniques;Testing;Training;Training Programs;Underserved Population;Vulnerable Populations;Work;advanced analytics;analytical method;base;beneficiary;career;career development;contextual factors;cost;cost effective;cost effectiveness;cost outcomes;dietary;disparity reduction;evidence base;experience;health disparity;high risk;improved;mathematical model;models and simulation;modifiable risk;multiple data sources;neglect;novel strategies;programs;public health insurance;public health intervention;racial and ethnic;racial and ethnic disparities;response;risk prediction model;skills;stem;theories;treatment effect;trend\",\"abstract_text\":\"Project Summary\\nTooth decay is the most common chronic disease among U.S. children.2,3 Despite efforts to increase utilization\\namong minority populations by improving coverage for dental insurance via Medicaid and Children’s Health\\nInsurance Program (CHIP), large oral health disparities remain with Black and Hispanic children having the\\npoorest oral health of any racial groups in the US.5-8 In addition to lack of access to recommended care, individual\\nhealth behaviors (e.g., poor dietary and oral hygiene) and community- and provider-related structural factors\\nalso contribute to the high risk of severe dental caries in minority populations.6,9 State Medicaid and CHIP dental\\nprograms are encouraged to consider strategies to reduce oral health disparities in their delivery system\\nimprovement efforts,6 however, they are challenged with improving quality and reducing quality disparities among\\nhigh-need beneficiaries in a cost-effective way.6 Steps toward successful program improvements include the\\nability to validly measure the value of care (defined by both health outcomes and costs) delivered to their\\nbeneficiaries, as well as incorporating racial/ethnic disparities in the assessment. There is a critical gap in our\\nunderstanding of the influence of race/ethnicity and its interaction with multilevel risk factors on disparities in\\nquality and oral health outcomes. The scientific objective of this research plan is to study multilevel determinants\\nof oral health and disparities in quality of dental care and assess the value of improving care and eliminating\\nracial/ethnic disparities in quality. In Aim 1, we will develop a risk prediction model of severe dental caries by\\napplying machine-learning based survival analysis4,10 on electronic health record (EHR) data to understand the\\ninfluence of race/ethnicity on progression of severe caries and explore heterogenous treatment effects of dental\\ncare. In Aim 2, we will analyze individual-level claims in Medicaid Analytic eXtract (MAX) data combined with\\nmultiple data sources to comprehensively measure racial/ethnic disparities in overall quality of dental care, using\\nevidence-based quality indicators, and identify modifiable structural risk factors creating disparities. In Aim 3,\\nincorporating results from Aims 1 and 2, a microsimulation model of severe caries, integrating individual-level\\ndata with data on key contextual factors, will be developed and used to assess the cost-effectiveness and value\\nof improvements in care stemming from dental quality measures and eliminating racial/ethnic disparities in\\nquality. Findings from this study will support decision-making by policymakers and stakeholders, and will form\\nthe basis of an R01 application to study novel strategies that target underserved and vulnerable populations.\\nThis research plan is complemented by a career development plan that builds on the applicant’s background in\\nhealth policy and decision science. Specifically, this career development plan outlines new training in three\\nareas: (1) oral health epidemiology, (2) health disparities research, and (3) advanced analytics methods. The\\ncombined research and training plan will prepare the applicant for a successful independent research career\\nidentifying, evaluating, and implementing multilevel interventions to reduce racial/ethnic disparities in oral health.\",\"project_title\":\"Reducing oral health disparities in children using predictive analytics and mathematical modeling\",\"phr_text\":\"Project Narrative\\nDespite considerable efforts to reduce oral health disparities, poor oral health continues to disproportionately\\naffect Black and Hispanic children. The proposed research will investigate the influence of race/ethnicity on\\nprogression of dental caries and identify modifiable structural risk factors of disparities in quality of dental care\\nto assess the cost-effectiveness and value of dental care, incorporating racial/ethnic disparities in the\\nassessment of value. The results of this project are expected to inform multilevel interventions and support\\nevidence-based policymaking by identifying strategies resulting in high-value care that also address equity.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"307\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":105660,\"indirect_cost_amt\":8453,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10548840\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10586147,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01MH129493-02\",\"project_serial_num\":\"MH129493\",\"organization\":{\"org_name\":\"HARVARD UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"CAMBRIDGE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":\"PSYCHOLOGY\",\"fips_country_code\":null,\"org_duns\":[\"082359691\"],\"org_ueis\":[\"LN53LCFJFL45\"],\"primary_duns\":\"082359691\",\"primary_uei\":\"LN53LCFJFL45\",\"org_fips\":\"US\",\"org_ipf_code\":\"3212901\",\"org_zipcode\":\"021385369\",\"external_org_id\":3212901},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":726975,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"MH\",\"serial_num\":\"129493\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":1885436,\"first_name\":\"Deanna\",\"middle_name\":\"\",\"last_name\":\"Barch\",\"is_contact_pi\":false,\"full_name\":\"Deanna  Barch\",\"title\":\"\"},{\"profile_id\":9763495,\"first_name\":\"Leah\",\"middle_name\":\"Helene\",\"last_name\":\"Somerville\",\"is_contact_pi\":true,\"full_name\":\"Leah Helene Somerville\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"SOMERVILLE, LEAH HELENE\",\"program_officers\":[{\"first_name\":\"Ashley\",\"middle_name\":\"\",\"last_name\":\"Smith\",\"full_name\":\"Ashley  Smith\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":726975.0}],\"cong_dist\":\"MA-05\",\"spending_categories\":null,\"project_start_date\":\"2022-03-07T12:03:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF ARTS AND SCIENCES\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"CPDD\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Child Psychopathology and Developmental Disabilities Study Section[CPDD]\"},\"award_notice_date\":\"2022-12-19T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01MH129493\",\"terms\":\"<Acceleration><Adolescence><12-20 years old><adolescence (12-20)><Age><ages><Anxiety><Attention><Mental disorders><Mental health disorders><Psychiatric Disease><Psychiatric Disorder><mental illness><psychiatric illness><psychological disorder><Biological Assay><Assay><Bioassay><Biologic Assays><Brain><Brain Nervous System><Encephalon><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Mental Depression><depression><Foundations><Goals><Hormones><Endocrine Gland Secretion><Therapeutic Hormone><Human><Modern Man><Literature><Longitudinal Studies><long-term study><longitudinal outcome studies><longterm study><Mental Health><Mental Hygiene><Psychological Health><Methods><United States National Institutes of Health><NIH><National Institutes of Health><Psychopathology><abnormal psychology><Puberty><Race><Racial Group><Racial Stocks><Research><Risk><Testing><Time><Work><Measures><Mediating><Socioeconomic Status><Socio-economic status><socio-economic position><socioeconomic position><Youth><Youth 10-21><Data Set><Dataset><base><Brain imaging><brain visualization><Clinical><Variant><Variation><Adolescent><Adolescent Youth><juvenile><juvenile human><Link><Ensure><Evaluation><pediatric><Childhood><Individual><Funding><Ethnicity><Ethnic Origin><Investigation><System><Hormonal><human data><cohort><neurodevelopment><Neural Development><Structure><novel><Patient Self-Report><Self-Report><Modality><brain pathway><Modeling><Sampling><boys><girls><Address><Symptoms><Data><Process><developmental><Development><informant><Behavioral><pathway><Pathway interactions><Coupling><multi-modality><multimodality><early adulthood><emerging adult><pubertal timing><cognitive development><Data Analytics><connectome><Longitudinal cohort><Long-term cohort><Longterm cohort><puberty transition><pubertal transition><transition out of puberty><data analysis pipeline><data processing pipeline>\",\"pref_terms\":\"Acceleration;Address;Adolescence;Adolescent;Age;Anxiety;Attention;Behavioral;Biological Assay;Brain;Brain imaging;Child;Childhood;Clinical;Coupling;Data;Data Analytics;Data Set;Development;Ensure;Ethnic Origin;Evaluation;Foundations;Funding;Goals;Hormonal;Hormones;Human;Individual;Investigation;Link;Literature;Longitudinal Studies;Longitudinal cohort;Measures;Mediating;Mental Depression;Mental Health;Mental disorders;Methods;Modality;Modeling;Pathway interactions;Patient Self-Report;Process;Psychopathology;Puberty;Race;Research;Risk;Sampling;Socioeconomic Status;Structure;Symptoms;System;Testing;Time;United States National Institutes of Health;Variant;Work;Youth;base;boys;brain pathway;cognitive development;cohort;connectome;data analysis pipeline;emerging adult;girls;human data;informant;multimodality;neurodevelopment;novel;pubertal timing;puberty transition\",\"abstract_text\":\"PROJECT SUMMARY\\nBuilding comprehensive accounts of human brain development from childhood to early adulthood is crucial to\\nour understanding of both healthy neurodevelopment and the mechanisms underlying threats to youths’ mental\\nhealth. Brain development unfolds on multiple levels, but the field lacks a comprehensive understanding of\\nwhether the trajectories of development are fundamentally similar or different across modalities (e.g., structure\\nand function), and how they reflect developmental mechanisms associated with puberty or age (or both). The\\nproposed research aims to generate a systematic and comprehensive multimodal account of typical\\nneurodevelopment from ages 5 to 21, with a particular focus on a) identifying age versus pubertal- and\\nhormonal-based mechanisms that undergird development in childhood and adolescence; b) systematic\\nanalyses across brain structure, function, and connectivity using state-of-the-art acquisition and analysis\\napproaches; and c) evaluation of maturational variation in multimodal coupling across brain systems/\\nmodalities as a function of development. Once established, this multimodal model of typical neurodevelopment\\nwill be used to test a conceptual model proposing that early pubertal timing leads to intensification of\\ninternalizing symptoms due to disruptions in brain development, including alterations in multimodal coupling.\\nSpecifically, models of the impact of early pubertal timing predict either further enhanced coupling with early\\npuberty (neurodevelopmental acceleration) or disruptions in coupling due to neurodevelopmental delays. To\\ncompare these competing models and advance our understanding of the neurodevelopmental pathways by\\nwhich early pubertal timing contributes to the rise of internalizing symptoms during adolescence, the research\\nwill use both hypothesis-driven methods and novel validated analysis pipelines for data-driven exploration of\\ndevelopmental changes in coupling, with careful attention to robustness and replication. The primary dataset\\nwill be the Human Connectome Project in Development (HCPD), a large, NIH funded, multimodal brain\\nimaging dataset that includes a comprehensive assessment of brain structure, function, and connectivity paired\\nwith pubertal and hormonal measures and extensive behavioral and clinical measures in both a cross-sectional\\ncohort of N=1300+ youth ages 5 to 21, and a longitudinal cohort (N=252) spanning ages 9 to 17 capturing the\\npubertal transition. This sample is purposefully strong diversity in race, ethnicity, and socioeconomic status.\\nKey findings will be replicated to ensure robustness and generalizability in the Adolescent Brain and Cognitive\\nDevelopment (ABCD) longitudinal study. Our Specific Aims are to: A1) Establish a comprehensive, systematic\\naccount of age-and pubertal-linked pathways of brain development across multiple modalities of brain structure\\nand function; A2) Test hypotheses about the relations of age and pubertal development to coupling across\\nbrain modalities; and A3) Test hypotheses about the neurodevelopmental mechanisms contributing to the rise\\nin internalizing symptoms during the pubertal transition.\",\"project_title\":\"Characterizing pubertal and age mechanisms of neurodevelopment and association with rising internalizing symptoms\",\"phr_text\":\"PROJECT NARRATIVE\\nThe proposed research will use two of the largest datasets on child and adolescent neurodevelopment in\\nexistence to inform how puberty influences the development of the brain and emerging risk for mental illness.\\nUsing measures of puberty and hormones, paired with multiple metrics of brain development, we will\\ndisentangle the relationship between age, puberty, and hormone concentrations and brain development, and\\nestablish how different measures of brain development interrelate across puberty. In addition, we will examine\\nthe neurodevelopmental mechanisms that help explain why early puberty increases an individual’s risk for\\nmental health problems including depression and anxiety.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":576129,\"indirect_cost_amt\":150846,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10586147\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10556384,\"subproject_id\":\"8661\",\"fiscal_year\":2023,\"project_num\":\"5P41GM135019-04\",\"project_serial_num\":\"GM135019\",\"organization\":{\"org_name\":\"BROAD INSTITUTE, INC.\",\"city\":null,\"country\":null,\"org_city\":\"CAMBRIDGE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"623544785\"],\"org_ueis\":[\"H5G9NWEFHXN4\"],\"primary_duns\":\"623544785\",\"primary_uei\":\"H5G9NWEFHXN4\",\"org_fips\":\"US\",\"org_ipf_code\":\"10021177\",\"org_zipcode\":\"021421027\",\"external_org_id\":10021177},\"award_type\":\"5\",\"activity_code\":\"P41\",\"award_amount\":302439,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P41\",\"ic_code\":\"GM\",\"serial_num\":\"135019\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":8876992,\"first_name\":\"Anne\",\"middle_name\":\"E.\",\"last_name\":\"Carpenter\",\"is_contact_pi\":true,\"full_name\":\"Anne E. Carpenter\",\"title\":\"DIRECTOR, IMAGING PLATFORM\"}],\"contact_pi_name\":\"CARPENTER, ANNE E.\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":302439.0}],\"cong_dist\":\"MA-07\",\"spending_categories\":null,\"project_start_date\":\"2019-12-01T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"PAR-17-316\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BST\",\"sra_flex_code\":\"T\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BST-T]\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P41GM135019\",\"terms\":\"<Algorithms><Automobile Driving><driving><Biology><Biomedical Research><Cells><Cell Body><Communities><Computers><Data Analyses><Data Analysis><data interpretation><Disease><Disorder><Feedback><Institutes><Laboratories><Libraries><Manuals><Microscopy><Mission><Biological Models><Biologic Models><Model System><Organism><living system><Research><research and development><Development and Research><R & D><R&D><Research Personnel><Investigators><Researchers><Resources><Research Resources><Role><social role><Computer software><Software><Software Engineering><Computer Software Development><Computer Software Engineering><Technology><Time><Tissues><Body Tissues><Translating><Universities><Wisconsin><Work><Measures><catalyst><base><image processing><improved><Image Analysis><Image Analyses><image evaluation><image interpretation><Area><Biological><biologic><Java><Pythons><Ensure><Training><wasting><Biological Process><Biological Function><Letters><tool><Cellular biology><cell biology><Scientist><light microscopy><Dimensions><Complex><Event><Side><Source><Route><System><user friendly software><user friendly computer software><skills><Modality><Modeling><bio-imaging><bioimaging><image-based method><imaging method><imaging modality><Address><International><Reproducibility><Resource Sharing><Computational Technique><Update><developmental><Development><imaging><Image><next generation><innovate><innovative><innovation><biological research><user-friendly><open source><biological systems><Algorithmic Analyses><Analyses of Algorithms><Analysis of Algorithms><Algorithmic Analysis><quantitative imaging><learning activity><learning method><learning strategy><microscopic imaging><microscope imaging><microscopy imaging><experimental study><experiment><experimental research><technology research and development><technological research and development><deep learning><Infrastructure><advanced analytics><software infrastructure><deep learning model><deep learning based model>\",\"pref_terms\":\"Address;Algorithmic Analysis;Algorithms;Area;Automobile Driving;Biological;Biological Models;Biological Process;Biology;Biomedical Research;Cells;Cellular biology;Communities;Complex;Computational Technique;Computer software;Computers;Data Analyses;Development;Dimensions;Disease;Ensure;Event;Feedback;Image;Image Analysis;Infrastructure;Institutes;International;Java;Laboratories;Letters;Libraries;Manuals;Measures;Microscopy;Mission;Modality;Modeling;Organism;Pythons;Reproducibility;Research;Research Personnel;Resource Sharing;Resources;Role;Route;Scientist;Side;Software Engineering;Source;System;Technology;Time;Tissues;Training;Translating;Universities;Update;Wisconsin;Work;advanced analytics;base;bioimaging;biological research;biological systems;catalyst;deep learning;deep learning model;experimental study;image processing;imaging modality;improved;innovation;learning strategy;light microscopy;microscopic imaging;next generation;open source;quantitative imaging;research and development;skills;software infrastructure;technology research and development;tool;user friendly software;user-friendly;wasting\",\"abstract_text\":\"Project Summary\\n The Center for Open Bioimage Analysis will serve the cell biology community’s growing need for\\nsophisticated software for light microscopy image analysis. Quantitative image analysis has become an\\nindispensable tool for biologists using microscopy throughout basic biological and biomedical research.\\n Quantifying images is now a critical, widespread need as imaging experiments continue to grow in scale,\\nsize, dimensionality, scope, modality, and complexity. Many biologists are missing out on the quantitative\\nbioimaging revolution due to lack of effective algorithms and/or usable software for their needs, or lack of\\naccess to training. The Center brings together the Carpenter laboratory at the Broad Institute and the Eliceiri\\nlaboratory at the University of Wisconsin­Madison, and in doing so brings together the two most popular open\\nsource bioimage analysis projects, ImageJ (including ImageJ2 and FIJI) and CellProfiler. Through the\\ncollaborative development and dissemination of open source image analysis software, as well as training\\nevents and resources, the Center will empower thousands of researchers to apply advanced analytics in\\ninnovative ways to address new experimental areas.\\n Building on the team’s expertise developing algorithms and user­friendly software for use in biology under\\nreal­world conditions, the Center will focus on two Technology Research and Development (TR&D) projects:\\ndeep learning­based image processing, and accessibility of image­processing algorithms for biologists. This\\nwork will not occur in isolation at the Center; rather, the Center will nucleate a larger community working on\\nthese two areas and serve as a catalyst and organizing force to create software and resources shared by all.\\n The Driving Biological Projects (DBPs) will serve a major role in driving the TR&D work: our teams are\\naccustomed to working deeply and iteratively on problems side by side and with frequent feedback from\\nbiologists. This will ensure that important cell biological problems drive the work of the Center. The DBPs\\nreflect tremendous variety in terms of biological questions, model systems, imaging modalities, and researcher\\nexpertise and will ensure robustness of our tools for the widest possible impact on the community. Continuing\\nthe teams’ track record with ImageJ and CellProfiler, two mature open source bioimage analysis software\\nprojects critical to the work of biologists worldwide, the Center will also assist and train biologists in applying\\nthe latest computational techniques to important biological problems involving images.\\n In short, the need for robust, accurate, and readily usable software is more urgent than ever. The Center for\\nOpen Bioimage Analysis will serve as a hub for pioneering new computational strategies for diverse biological\\nproblems, translating them into user­friendly software, further developing ImageJ and CellProfiler, and training\\nthe biological community to apply advanced software to important and diverse problems in cell biology.\",\"project_title\":\"Center for Open Bioimage Analysis\",\"phr_text\":\"Project Narrative\\nBiologists studying a huge variety of diseases and basic biological processes need software to measure cells,\\ntissues, and organisms in microscopy images. We will create the Center for Open Bioimage Analysis which will\\ncatalyze the scientific community, creating resources, free software, and training that allow biologists to\\nanalyze images using deep learning and other new image processing algorithms, offering improved accuracy,\\nconvenience, and reproducibility.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":266313,\"indirect_cost_amt\":36126,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10556384\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10551263,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R00HG010493-04\",\"project_serial_num\":\"HG010493\",\"organization\":{\"org_name\":\"VANDERBILT UNIVERSITY MEDICAL CENTER\",\"city\":null,\"country\":null,\"org_city\":\"NASHVILLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"TN\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"079917897\"],\"org_ueis\":[\"GYLUH9UXHDX5\"],\"primary_duns\":\"079917897\",\"primary_uei\":\"GYLUH9UXHDX5\",\"org_fips\":\"US\",\"org_ipf_code\":\"10040927\",\"org_zipcode\":\"372320011\",\"external_org_id\":10040927},\"award_type\":\"5\",\"activity_code\":\"R00\",\"award_amount\":223219,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R00\",\"ic_code\":\"HG\",\"serial_num\":\"010493\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14416846,\"first_name\":\"Luca\",\"middle_name\":\"\",\"last_name\":\"Bonomi\",\"is_contact_pi\":true,\"full_name\":\"Luca  Bonomi\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"BONOMI, LUCA \",\"program_officers\":[{\"first_name\":\"Heidi\",\"middle_name\":\"J\",\"last_name\":\"Sofia\",\"full_name\":\"Heidi J Sofia\"}],\"agency_ic_admin\":{\"code\":\"HG\",\"abbreviation\":\"NHGRI\",\"name\":\"National Human Genome Research Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"HG\",\"name\":\"National Human Genome Research Institute\",\"abbreviation\":\"NHGRI\",\"total_cost\":223219.0}],\"cong_dist\":\"TN-05\",\"spending_categories\":null,\"project_start_date\":\"2022-01-14T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"PA-18-398\",\"full_study_section\":{\"srg_code\":\"NSS\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"NSS\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R00HG010493\",\"terms\":\"<Appointment><Biomedical Research><Complement><Complement Proteins><Data Correlations><Data Collection><Data Reporting><data representation><Disclosure><Information Disclosure><Equilibrium><balance><balance function><Eye><Eyeball><Faculty><Genome><Genotype><Goals><Hair Color><Health><Health Promotion><Salutogenesis><promoting health><Human><Modern Man><Lead><Pb element><heavy metal Pb><heavy metal lead><Methods><Mission><Noise><Phenotype><Privatization><Publications><Scientific Publication><Publishing><Records><Research><Research Personnel><Investigators><Researchers><Risk><statistics><Technology><Training Activity><training module><Universities><Work><Gender><Medical Research><Privacy><Uncertainty><doubt><Custom><base><career><improved><Phase><Variant><Variation><Ensure><Individual><Trust><Data Bases><data base><Databases><Policies><Development Plans><Collaborations><Computational Biology><computer biology><Knowledge><programs><Techniques><experience><computer science><skills><Participant><Excision><Abscission><Extirpation><Removal><Surgical Removal><resection><Modeling><Sampling><career development><Genomics><Institution><preventing><prevent><Address><Data><Reproducibility><NHGRI><National Center for Human Genome Research><National Human Genome Research Institute><GWA study><GWAS><genome wide association><genome wide association scan><genome wide association studies><genomewide association scan><genomewide association studies><genomewide association study><whole genome association analysis><whole genome association studies><whole genome association study><genome wide association study><burden of disease><disease burden><years of life lost to disability><years of life lost to disease><burden of illness><biomed informatics><biomedical informatics><Gene variant><allele variant><allelic variant><genomic variant><genetic variant><designing><design><Outcome><cryptography><usability><data sharing><flexible><flexibility><precision-based medicine><precision medicine><personalization of treatment><personalized therapy><personalized treatment><personalized medicine><privacy protection><Data Analytics><Genomic medicine><data access><genomic data><genomic data-set><genomic dataset><data anonymization><anonymous data><data preservation><privacy preservation><data privacy><data de-identification><data deidentification><de-identified data><deidentified data>\",\"pref_terms\":\"Address;Appointment;Biomedical Research;Collaborations;Complement;Computational Biology;Custom;Data;Data Analytics;Data Collection;Data Correlations;Data Reporting;Databases;Development Plans;Disclosure;Ensure;Equilibrium;Excision;Eye;Faculty;Gender;Genome;Genomic medicine;Genomics;Genotype;Goals;Hair Color;Health;Health Promotion;Human;Individual;Institution;Knowledge;Lead;Medical Research;Methods;Mission;Modeling;National Human Genome Research Institute;Noise;Outcome;Participant;Phase;Phenotype;Policies;Privacy;Privatization;Publications;Publishing;Records;Reproducibility;Research;Research Personnel;Risk;Sampling;Techniques;Technology;Training Activity;Trust;Uncertainty;Universities;Variant;Work;base;biomedical informatics;burden of illness;career;career development;computer science;cryptography;data access;data anonymization;data de-identification;data preservation;data privacy;data sharing;design;experience;flexibility;genetic variant;genome wide association study;genomic data;improved;personalized medicine;precision medicine;prevent;privacy preservation;privacy protection;programs;skills;statistics;usability\",\"abstract_text\":\"Project Summary\\nGenomic data are vital for advancing medical research and achieving breakthroughs. However, disclosure of\\ngenomic data has serious privacy implications that can lead to a loss of trust from data contributors and restricting\\nresearchers’ access to data. To facilitate data-driven genomic research, it is crucial to address the privacy risks\\nin data sharing and to develop privacy-preserving solutions to protect study participants. This project will study\\nthe privacy risks in realistic attack models and develop privacy methods that balance individual privacy and the\\nutility of shared data. Overall, the proposed solutions will enable institutions to share high utility data while\\nproviding strong privacy assurance to data contributors, facilitating data collection and improving data usability.\\nIn the first aim, a privacy-preserving data publication framework will be developed to “safely anonymize” genomic\\ndata and optimize the released data toward application needs. The framework will protect individuals from re-\\nidentification and also prevent inference attacks that may be conducted using publicly available phenotypes (e.g.,\\neye/hair color). In the second aim, customizable privacy solutions will be developed against realistic adversarial\\nmodels when data statistics are released. Building on recent privacy models, the proposed solutions will account\\nfor the adversary's external knowledge and customizable sensitive information to effectively strike a balance\\nbetween privacy and utility, improving data usability compared to standard differential privacy models. This\\nproject will advance current solutions for genomic data anonymization and improve the usability of differential\\nprivacy and its variants, with the goal of facilitating highly usable and privacy-preserving data sharing. This work\\nwill widen the access to genomic data, promote transparency, and facilitate reproducibility for genomic\\napplications. This project is in line with the mission of the National Human Genome Research Institute (NHGRI),\\nas the proposed techniques enhance data sharing and promote collaborative genomic research.\\nThe applicant’s career goal is to become an independent investigator with a primary appointment in a biomedical\\ninformatics program, with a focus on genome privacy technologies, at a major US research university. His long-\\nterm objective is to develop new privacy-preserving technologies for data sharing and data analytics, in order to\\nfacilitate collaborative research efforts in genomics and precision medicine. The applicant proposes a carefully\\ndesigned career development plan, which includes a variety of training activities to complement his computer\\nscience skills with additional biomedical knowledge and smooth his transition into an independent researcher.\\nThe UCSD Health Department of Biomedical Informatics will serve as an exceptional platform for his career\\ndevelopment, given the experience of several faculty in privacy technologies, computational biology, genomic\\nmedicine, and close collaboration with other institutions worldwide.\",\"project_title\":\"SAFEGENOMES: Strong privacy Assurance For Effective GENOME Sharing\",\"phr_text\":\"Project Narrative\\nWhile sharing genomic data holds great promise for advancing biomedical research and personalized medicine,\\nthe uniqueness of the genomic data and the rich information they carry can put participants’ privacy at risk. In\\nthis project, we will develop practical solutions for genomic data sharing that provide data contributors with strong\\nprivacy assurance while enabling the release of high quality data. The outcome of the proposed research will\\nencourage data contributors to participate in studies, widen the access to genomic data, and, in the long-term,\\nfacilitate genomic data-driven medical research to promote health and ease the burden of disease.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"172\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":130793,\"indirect_cost_amt\":92426,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10551263\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10524021,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R00DA046564-05\",\"project_serial_num\":\"DA046564\",\"organization\":{\"org_name\":\"UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON\",\"city\":null,\"country\":null,\"org_city\":\"HOUSTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"TX\",\"org_state_name\":null,\"dept_type\":\"PSYCHOLOGY\",\"fips_country_code\":null,\"org_duns\":[\"800771594\"],\"org_ueis\":[\"ZUFBNVZ587D4\"],\"primary_duns\":\"800771594\",\"primary_uei\":\"ZUFBNVZ587D4\",\"org_fips\":\"US\",\"org_ipf_code\":\"578417\",\"org_zipcode\":\"770305400\",\"external_org_id\":578417},\"award_type\":\"5\",\"activity_code\":\"R00\",\"award_amount\":248993,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R00\",\"ic_code\":\"DA\",\"serial_num\":\"046564\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":13110137,\"first_name\":\"Emily\",\"middle_name\":\"Taylor\",\"last_name\":\"Hebert\",\"is_contact_pi\":true,\"full_name\":\"Emily Taylor Hebert\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"HEBERT, EMILY TAYLOR\",\"program_officers\":[{\"first_name\":\"Guifang\",\"middle_name\":\"\",\"last_name\":\"Lao\",\"full_name\":\"Guifang  Lao\"}],\"agency_ic_admin\":{\"code\":\"DA\",\"abbreviation\":\"NIDA\",\"name\":\"National Institute on Drug Abuse\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"DA\",\"name\":\"National Institute on Drug Abuse\",\"abbreviation\":\"NIDA\",\"total_cost\":248993.0}],\"cong_dist\":\"TX-09\",\"spending_categories\":null,\"project_start_date\":\"2019-03-01T12:03:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF PUBLIC HEALTH\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-16-193\",\"full_study_section\":{\"srg_code\":\"ZDA1\",\"srg_flex\":\"\",\"sra_designator_code\":\"SXM\",\"sra_flex_code\":\"M\",\"group_code\":\"\",\"name\":\"ZDA1-SXM-M(04)S\"},\"award_notice_date\":\"2022-11-01T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R00DA046564\",\"terms\":\"<Affect><Algorithms><Behavior><Classification><Systematics><Control Groups><Cues><Data Collection><Demographic Factors><Diet><diets><Health behavior><health related behavior><Heart Rate><Cardiac Chronotropism><Methods><Methodology><Moods><United States National Institutes of Health><NIH><National Institutes of Health><Oklahoma><Research><Risk><Sensitivity and Specificity><Smoking><Social Environment><social climate><social context><socioenvironment><socioenvironmental><Substance Use Disorder><Technology><Telephone><Phone><Time><Universities><Wrist><Measures><Data Set><Dataset><base><sensor><improved><Physiological><Physiologic><psychologic><psychological><Training><Physical activity><Individual><Funding><Machine Learning><machine learned><Knowledge><Life><Frequencies><Pattern><System><Location><experience><Participant><Patient Self-Report><Self-Report><Devices><Reporting><Positioning Attribute><Position><Modeling><craving><Intervention Strategies><interventional strategy><Intervention><Mobile Phones><Car Phone><smoking cessation treatment><Smoking Cessation Intervention><Effectiveness><preventing><prevent><Address><Data><Health Sciences><randomisation><randomization><randomly assigned><Randomized><Research Training><Cancer Center><Smoking Behavior><Monitor><trend><developmental><Development><computer based prediction><prediction model><predictive modeling><innovate><innovative><innovation><standard treatment><standard care><mobile app><mobile device application><mobile application><precision interventions><personalized intervention><Ecological momentary assessment><mobile platform><mobile technology><mobile computing><Data Analytics><Accelerometer><accelerometry><activity monitor><activity tracker><secondary analysis><wearable device><wearable electronics><wearable technology><machine learning algorithm><machine learned algorithm><machine learning based algorithm><supervised learning><supervised machine learning><adaptive intervention><large datasets><large data sets><automated algorithm><automatic algorithm><machine learning method><machine learning based method><machine learning methodologies><machine learning prediction><machine learning based prediction model><machine learning based predictive model><machine learning prediction model><machine learning predictive model>\",\"pref_terms\":\"Accelerometer;Address;Affect;Algorithms;Behavior;Cancer Center;Car Phone;Classification;Control Groups;Cues;Data;Data Analytics;Data Collection;Data Set;Demographic Factors;Development;Devices;Diet;Ecological momentary assessment;Effectiveness;Frequencies;Funding;Health Sciences;Health behavior;Heart Rate;Individual;Intervention;Knowledge;Life;Location;Machine Learning;Measures;Methodology;Methods;Modeling;Monitor;Moods;Oklahoma;Participant;Patient Self-Report;Pattern;Physical activity;Physiological;Positioning Attribute;Randomized;Reporting;Research;Research Training;Risk;Sensitivity and Specificity;Smoking;Smoking Behavior;Smoking Cessation Intervention;Social Environment;Substance Use Disorder;System;Technology;Telephone;Time;Training;United States National Institutes of Health;Universities;Wrist;adaptive intervention;automated algorithm;base;craving;experience;improved;innovation;large datasets;machine learning algorithm;machine learning method;machine learning prediction;mobile application;mobile computing;personalized intervention;predictive modeling;prevent;psychologic;secondary analysis;sensor;standard care;supervised learning;trend;wearable device\",\"abstract_text\":\"PROJECT SUMMARY\\nMobile technology has enormous potential for delivering highly innovative, dynamic smoking cessation\\ninterventions. Phone sensors, wearable technology, and real time data collection methods such as ecological\\nmomentary assessment (EMA) have made it possible to collect a wealth of environmental and physiological\\ndata such as location, heart rate, and mood. Environmental and situational cues such as craving and proximity\\nto others smoking are highly predictive of lapse among those trying to quit, suggesting that lapse risk is\\ncharacterized by immediate, dynamic influences. Emerging strategies such as just-in-time adaptive\\ninterventions (JITAI), aim to prevent smoking lapse using tailored support delivered via mobile technology in\\nthe moments when it is most needed. Although research has identified antecedents of smoking lapse based on\\nobservations from EMA data, studies have been unable to utilize the full spectrum of contextual and\\nenvironmental data available with current technology. Given the importance of dynamic influences on lapse\\nrisk, there is a critical need for strategies that accurately identify moments of highest lapse risk to improve\\ncessation interventions. Recent research has demonstrated the utility of machine learning to predict individual\\nbehavior. Machine learning is a robust data analytic strategy that can produce highly accurate predictive\\nmodels from large datasets and can automatically adapt to new data in real time. The overall objective of this\\napplication is to use supervised machine learning methods to develop an automated algorithm to quantify\\nsmoking lapse risk at the individual level. Specifically, we aim: 1) to apply supervised machine learning\\nmethods to quantify personalized risk of smoking lapse, and 2) to evaluate the feasibility and preliminary\\neffectiveness of delivering a personalized, just-in-time adaptive intervention driven by machine learning\\nprediction of smoking lapse risk in real time. The proposed research and training plan will take place at The\\nUniversity of Oklahoma Health Sciences Center (OUHSC) and the Stephenson Cancer Center (SCC). Training\\nwill focus on increasing knowledge of machine learning methodology, and the conduct and analysis of JITAIs,\\nwhich will facilitate completion of the proposed project. Results of the proposed research have the potential to\\nreduce the amount and frequency of data needed from participants and sensors, enabling the development of\\nless burdensome interventions. It is expected that completion of these aims will yield preliminary data to inform\\nan automated, dynamic intervention that fully utilizes the strengths of mobile technology for measuring\\nindividual behavior and environmental context in real time.\",\"project_title\":\"Using Machine Learning to Develop Just-in-Time Adaptive Interventions for Smoking Cessation\",\"phr_text\":\"PROJECT NARRATIVE\\nThe proposed project will be among the first to use machine learning methods to predict risk of individual\\nsmoking lapse in real time. The resulting algorithm will provide a personalized model for each participant that\\nadapts to new data and triggers the delivery of a tailored, precision intervention when it is most needed. If\\nsuccessful, this project and its methodology could be adapted to target other health behaviors such as diet,\\nphysical activity, or other substance use disorders.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"279\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":159611,\"indirect_cost_amt\":89382,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10524021\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10556378,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5P41GM135019-04\",\"project_serial_num\":\"GM135019\",\"organization\":{\"org_name\":\"BROAD INSTITUTE, INC.\",\"city\":null,\"country\":null,\"org_city\":\"CAMBRIDGE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"623544785\"],\"org_ueis\":[\"H5G9NWEFHXN4\"],\"primary_duns\":\"623544785\",\"primary_uei\":\"H5G9NWEFHXN4\",\"org_fips\":\"US\",\"org_ipf_code\":\"10021177\",\"org_zipcode\":\"021421027\",\"external_org_id\":10021177},\"award_type\":\"5\",\"activity_code\":\"P41\",\"award_amount\":1136551,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P41\",\"ic_code\":\"GM\",\"serial_num\":\"135019\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9680244,\"first_name\":\"BETH\",\"middle_name\":\"\",\"last_name\":\"CIMINI\",\"is_contact_pi\":true,\"full_name\":\"BETH  CIMINI\",\"title\":\"COMPUTATIONAL BIOLOGIST\"},{\"profile_id\":8876992,\"first_name\":\"Anne\",\"middle_name\":\"E.\",\"last_name\":\"Carpenter\",\"is_contact_pi\":false,\"full_name\":\"Anne E. Carpenter\",\"title\":\"DIRECTOR, IMAGING PLATFORM\"},{\"profile_id\":7908263,\"first_name\":\"Kevin\",\"middle_name\":\"William\",\"last_name\":\"Eliceiri\",\"is_contact_pi\":false,\"full_name\":\"Kevin William Eliceiri\",\"title\":\"\"}],\"contact_pi_name\":\"CIMINI, BETH \",\"program_officers\":[{\"first_name\":\"MARY ANN\",\"middle_name\":\"ANN\",\"last_name\":\"WU\",\"full_name\":\"MARY ANN ANN WU\"}],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":1136551.0}],\"cong_dist\":\"MA-07\",\"spending_categories\":null,\"project_start_date\":\"2019-12-01T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"PAR-17-316\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BST\",\"sra_flex_code\":\"T\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BST-T(40)P]\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P41GM135019\",\"terms\":\"<Algorithms><Automobile Driving><driving><Biology><Biomedical Research><Cells><Cell Body><Communities><Computer Vision Systems><computer vision><Computers><Data Analyses><Data Analysis><data interpretation><Disease><Disorder><Feedback><Hand><Institutes><Laboratories><Microscopy><Mission><Biological Models><Biologic Models><Model System><Modernization><Organism><living system><Organoids><Research><research and development><Development and Research><R & D><R&D><Research Personnel><Investigators><Researchers><Resources><Research Resources><Role><social role><Savings><Computer software><Software><Software Engineering><Computer Software Development><Computer Software Engineering><Technology><Time><Tissues><Body Tissues><Translating><Universities><Wisconsin><Work><Measures><symposium><conference><convention><summit><symposia><catalyst><base><image processing><improved><Image Analysis><Image Analyses><image evaluation><image interpretation><Area><Biological><biologic><Ensure><Training><Workshop><Educational workshop><Cell Components><Cell Structure><Cellular Structures><Biological Process><Biological Function><Collaborations><tool><Cellular biology><cell biology><Scientist><light microscopy><Dimensions><Complex><Event><Side><System><Benchmarking><Best Practice Analysis><user friendly software><user friendly computer software><skills><novel><Modality><Modeling><bio-imaging><bioimaging><image-based method><imaging method><imaging modality><Address><Data><International><Reproducibility><Resource Sharing><Computational Technique><Characteristics><developmental><Development><imaging><Image><next generation><innovate><innovative><innovation><biological research><user-friendly><computer algorithm><Computational algorithm><open source><biological systems><algorithmic methods><algorithmic methodologies><Algorithmic Tools><Software Algorithm><Algorithmic Software><quantitative imaging><Data Science><hackathon><hack day><hackfest><microscopic imaging><microscope imaging><microscopy imaging><experimental study><experiment><experimental research><technology research and development><technological research and development><deep learning><Infrastructure><advanced analytics><community engagement>\",\"pref_terms\":\"Address;Algorithmic Software;Algorithms;Area;Automobile Driving;Benchmarking;Biological;Biological Models;Biological Process;Biology;Biomedical Research;Cells;Cellular Structures;Cellular biology;Characteristics;Collaborations;Communities;Complex;Computational Technique;Computational algorithm;Computer Vision Systems;Computer software;Computers;Data;Data Analyses;Data Science;Development;Dimensions;Disease;Educational workshop;Ensure;Event;Feedback;Hand;Image;Image Analysis;Infrastructure;Institutes;International;Laboratories;Measures;Microscopy;Mission;Modality;Modeling;Modernization;Organism;Organoids;Reproducibility;Research;Research Personnel;Resource Sharing;Resources;Role;Savings;Scientist;Side;Software Engineering;System;Technology;Time;Tissues;Training;Translating;Universities;Wisconsin;Work;advanced analytics;algorithmic methodologies;base;bioimaging;biological research;biological systems;catalyst;community engagement;deep learning;experimental study;hackathon;image processing;imaging modality;improved;innovation;light microscopy;microscopic imaging;next generation;novel;open source;quantitative imaging;research and development;skills;symposium;technology research and development;tool;user friendly software;user-friendly\",\"abstract_text\":\"Project Summary\\n The Center for Open Bioimage Analysis will serve the cell biology community’s growing need for\\nsophisticated software for light microscopy image analysis. Quantitative image analysis has become an\\nindispensable tool for biologists using microscopy throughout basic biological and biomedical research.\\n Quantifying images is now a critical, widespread need as imaging experiments continue to grow in scale,\\nsize, dimensionality, scope, modality, and complexity. Many biologists are missing out on the quantitative\\nbioimaging revolution due to lack of effective algorithms and/or usable software for their needs, or lack of\\naccess to training. The Center brings together the Carpenter laboratory at the Broad Institute and the Eliceiri\\nlaboratory at the University of Wisconsin­Madison, and in doing so brings together the two most popular open\\nsource bioimage analysis projects, ImageJ (including ImageJ2 and FIJI) and CellProfiler. Through the\\ncollaborative development and dissemination of open source image analysis software, as well as training\\nevents and resources, the Center will empower thousands of researchers to apply advanced analytics in\\ninnovative ways to address new experimental areas.\\n Building on the team’s expertise developing algorithms and user­friendly software for use in biology under\\nreal­world conditions, the Center will focus on two Technology Research and Development (TR&D) projects:\\ndeep learning­based image processing, and accessibility of image­processing algorithms for biologists. This\\nwork will not occur in isolation at the Center; rather, the Center will nucleate a larger community working on\\nthese two areas and serve as a catalyst and organizing force to create software and resources shared by all.\\n The Driving Biological Projects (DBPs) will serve a major role in driving the TR&D work: our teams are\\naccustomed to working deeply and iteratively on problems side by side and with frequent feedback from\\nbiologists. This will ensure that important cell biological problems drive the work of the Center. The DBPs\\nreflect tremendous variety in terms of biological questions, model systems, imaging modalities, and researcher\\nexpertise and will ensure robustness of our tools for the widest possible impact on the community. Continuing\\nthe teams’ track record with ImageJ and CellProfiler, two mature open source bioimage analysis software\\nprojects critical to the work of biologists worldwide, the Center will also assist and train biologists in applying\\nthe latest computational techniques to important biological problems involving images.\\n In short, the need for robust, accurate, and readily usable software is more urgent than ever. The Center for\\nOpen Bioimage Analysis will serve as a hub for pioneering new computational strategies for diverse biological\\nproblems, translating them into user­friendly software, further developing ImageJ and CellProfiler, and training\\nthe biological community to apply advanced software to important and diverse problems in cell biology.\",\"project_title\":\"Center for Open Bioimage Analysis\",\"phr_text\":\"Project Narrative\\nBiologists studying a huge variety of diseases and basic biological processes need software to measure cells,\\ntissues, and organisms in microscopy images. We will create the Center for Open Bioimage Analysis which will\\ncatalyze the scientific community, creating resources, free software, and training that allow biologists to\\nanalyze images using deep learning and other new image processing algorithms, offering improved accuracy,\\nconvenience, and reproducibility.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"859\",\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":907149,\"indirect_cost_amt\":229402,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10556378\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10598542,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5U24CA224370-06\",\"project_serial_num\":\"CA224370\",\"organization\":{\"org_name\":\"UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR\",\"city\":null,\"country\":null,\"org_city\":\"ALBUQUERQUE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NM\",\"org_state_name\":null,\"dept_type\":\"INTERNAL MEDICINE/MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"829868723\"],\"org_ueis\":[\"G389MFAYJNG9\"],\"primary_duns\":\"829868723\",\"primary_uei\":\"G389MFAYJNG9\",\"org_fips\":\"US\",\"org_ipf_code\":\"10021612\",\"org_zipcode\":\"871310001\",\"external_org_id\":10021612},\"award_type\":\"5\",\"activity_code\":\"U24\",\"award_amount\":940050,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"U24\",\"ic_code\":\"CA\",\"serial_num\":\"224370\",\"support_year\":\"06\",\"full_support_year\":\"06\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":1881486,\"first_name\":\"Jeremy\",\"middle_name\":\"S\",\"last_name\":\"Edwards\",\"is_contact_pi\":true,\"full_name\":\"Jeremy S Edwards\",\"title\":\"\"}],\"contact_pi_name\":\"EDWARDS, JEREMY S\",\"program_officers\":[{\"first_name\":\"Jerry\",\"middle_name\":\"\",\"last_name\":\"Li\",\"full_name\":\"Jerry  Li\"}],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"OD\",\"name\":\"NIH Office of the Director\",\"abbreviation\":\"OD\",\"total_cost\":940050.0}],\"cong_dist\":\"NM-01\",\"spending_categories\":null,\"project_start_date\":\"2018-01-03T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"RFA-RM-16-024\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"GGG\",\"sra_flex_code\":\"D\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-GGG-D(51)R]\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"U24CA224370\",\"terms\":\"<Adoption><Alleles><Allelomorphs><Archives><Biological Sciences><Biologic Sciences><Bioscience><Life Sciences><Biology><Biomedical Research><Chemistry><Communities><Disease><Disorder><Family><Feedback><Future><Genes><Genome><Genotype><Goals><Grant><Human><Modern Man><Manuals><Medicine><Methods><Mutation><Genetic Alteration><Genetic Change><Genetic defect><genome mutation><United States National Institutes of Health><NIH><National Institutes of Health><Phenotype><Proteins><Publications><Scientific Publication><Reagent><Resources><Research Resources><Computer software><Software><Trees><Work><Generations><Healthcare><health care><Data Set><Dataset><Public Domains><analytical method><improved><Phase><Series><Link><Data Bases><data base><Databases><Funding><Community Outreach><Internet><WWW><web><world wide web><tool><Machine Learning><machine learned><Knowledge><Life><programs><Scientist><Dependence><Source><System><TRD@ gene cluster><T-Cell Receptor Delta Locus><T-Cell Receptor delta Genes><T-Cell Receptor delta-Chain Genes><TCRD><TcR delta Genes><interest><Services><experience><Performance><Protein Isoforms><Isoforms><computer science><knowledge base><Informatics><Structure><novel><member><Basic Science><Basic Research><disease classification><disorder classification><nosology><outreach><Gene Proteins><Protein Gene Products><Proteome><Modeling><depository><repository><native protein drug><pharmaceutical protein><protein drug agent><protein-based drug><therapeutic protein><Documentation><Genome Components><Nonhuman Protein><Non-Human Protein><Address><Data><Data Element><International><Ontology><Translational Science><translation research><Translational Research><Update><Process><pathway><Pathway interactions><meta data><Metadata><website><web site><designing><design><Coupled><health data><on-line community><virtual community><online community><Data Analytics><gender difference><gender-associated difference><FAIR principles><FAIR data><FAIR guiding principles><Findable, Accessible, Interoperable and Re-usable><Findable, Accessible, Interoperable, and Reusable><data resource><data ecosystem><data sharing ecosystem><public repository><publicly accessible repository><publicly available repository><Knowledge Portal><Knowledge base Portal><Knowledgebase Portal><knowledge graph><graph knowledge base><graph knowledgebase><genome resource><Knowledge Management><knowledgebase>\",\"pref_terms\":\"Address;Adoption;Alleles;Archives;Basic Science;Biological Sciences;Biology;Biomedical Research;Chemistry;Communities;Community Outreach;Computer software;Coupled;Data;Data Analytics;Data Element;Data Set;Databases;Dependence;Disease;Documentation;FAIR principles;Family;Feedback;Funding;Future;Gene Proteins;Generations;Genes;Genome;Genome Components;Genotype;Goals;Grant;Healthcare;Human;Informatics;International;Internet;Knowledge;Knowledge Management;Knowledge Portal;Life;Link;Machine Learning;Manuals;Medicine;Metadata;Methods;Modeling;Mutation;Non-Human Protein;Ontology;Pathway interactions;Performance;Phase;Phenotype;Process;Protein Isoforms;Proteins;Proteome;Public Domains;Publications;Reagent;Resources;Scientist;Series;Services;Source;Structure;System;TRD@ gene cluster;Translational Research;Trees;United States National Institutes of Health;Update;Work;analytical method;computer science;data ecosystem;data resource;design;disease classification;experience;gender difference;genome resource;health data;improved;interest;knowledge base;knowledge graph;knowledgebase;member;novel;online community;outreach;programs;public repository;repository;therapeutic protein;tool;web site\",\"abstract_text\":\"The main goal of the Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG)\\nprogram is to aggregate, update and articulate protein-centric data, information and knowledge for the entire\\nhuman proteome with emphasis on understudied proteins from the 3 families that are the focus of the IDG\\n(“IDG List”). The long-term objective of the KMC is to encourage and support biomedical research aimed at\\nunderstudied proteins by providing an extensive resource of data, information, knowledge, methods and\\nreagents for the entire human proteome, and to support the growing online community focused on\\nunderstudied proteins. With focus on the IDG List and human proteins, the KMC will enable support for\\nexpanded coverage for non-human proteins of therapeutic interest and other associated human health data, in\\norder to catalyze novel biomedical discoveries. To support the overall IDG objective, and to maintain, update\\nand improve these integrated resources, the KMC draws upon expertise from multiple knowledge domains,\\nspecifically biology, chemistry and medicine, as well as computer science, graphic design and web\\nprogramming. Specifically, for the Phase 2 of the IDG KMC we propose 4 Aims:1. Create an automated\\nworkflow that captures relevant public data for the entire proteome and manual annotations for the IDG list.\\nThe KMC knowledge management system will be built around knowledge graphs, focused on five major\\nbranches of the target knowledge tree, tkt: Genotype, Phenotype, Expression, Structure & Function, and\\nInteractions & Pathways, respectively. Aim 2: Design, develop and implement a protein knowledgebase with\\nData Analytics support. Our protein-centric biomedical knowledge base, TCKB (Target Central\\nKnowledgebase) will be comprised of the data, knowledge and information container, together with its\\ncodebase and software pipelines. TCKB will be the repository for experimental, processed and computed data\\nand reagents originating from the IDG DRGCs (Data and Resource Generation Centers). We will provide\\ninformatics and modeling support for DRGC activities. Aim 3: We will expand, improve and maintain Pharos.\\nParticularly “knowledge packages,” support automated data summaries for Protein Dossiers, and actively seek\\nfeedback from our community. Aim 4. Outreach to scientific community. We will support a series of activities\\nthat will leverage TCKB, Pharos and other IDG resources to increase adoption of IDG work, while observing\\nFAIR (findable, accessible, interoperable, reusable) principles for our knowledgebase, portal and pipelines.\\nThe KMC will engage in community outreach by leading tutorials and feedback sessions and dissemination of\\nthe Pharos system. To meet its goals, the KMC will coordinate all core activities in close coordination with the\\nIDG Steering Committee and IDG Project Scientists (PS), and include members of the IDG Consortium (IDG-\\nC), other NIH Common Fund programs, NIH Commons, as well as other initiatives.\",\"project_title\":\"Knowledge Management Center for Illuminating the Druggable Genome\",\"phr_text\":\"The Knowledge Management Center (KMC) for the Illuminating the Druggable Genome (IDG) program plans\\nto aggregate, update and articulate protein-centric data, information and knowledge for the entire human\\nproteome with emphasis on understudied proteins from the 3 families that are the focus of the IDG. The KMC\\nlong-term objective is to encourage and support biomedical research aimed at understudied proteins by\\nproviding an extensive resource of data, information, knowledge, methods and reagents for the entire human\\nproteome, and to support the growing online community focused on understudied proteins.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"310\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":721771,\"indirect_cost_amt\":278229,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10598542\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10580718,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01GM144351-02\",\"project_serial_num\":\"GM144351\",\"organization\":{\"org_name\":\"MAYO CLINIC ROCHESTER\",\"city\":null,\"country\":null,\"org_city\":\"ROCHESTER\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MN\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"006471700\"],\"org_ueis\":[\"Y2K4F9RPRRG7\"],\"primary_duns\":\"006471700\",\"primary_uei\":\"Y2K4F9RPRRG7\",\"org_fips\":\"US\",\"org_ipf_code\":\"4976101\",\"org_zipcode\":\"559050001\",\"external_org_id\":4976101},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":289745,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"GM\",\"serial_num\":\"144351\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":11821008,\"first_name\":\"Jun\",\"middle_name\":\"\",\"last_name\":\"Chen\",\"is_contact_pi\":true,\"full_name\":\"Jun  Chen\",\"title\":\"\"},{\"profile_id\":15636208,\"first_name\":\"Xianyang\",\"middle_name\":\"\",\"last_name\":\"Zhang\",\"is_contact_pi\":false,\"full_name\":\"Xianyang  Zhang\",\"title\":\"\"}],\"contact_pi_name\":\"CHEN, JUN \",\"program_officers\":[{\"first_name\":\"Jean\",\"middle_name\":\"\",\"last_name\":\"Yuan\",\"full_name\":\"Jean  Yuan\"}],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":289745.0}],\"cong_dist\":\"MN-01\",\"spending_categories\":null,\"project_start_date\":\"2022-03-01T12:03:00Z\",\"project_end_date\":\"2026-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"NP\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"BMRD\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Biostatistical Methods and Research Design Study Section[BMRD]\"},\"award_notice_date\":\"2022-12-01T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01GM144351\",\"terms\":\"<Accounting><Attention><Biometry><Biometrics><Biostatistics><Charge><Communities><Data Analyses><Data Analysis><data interpretation><Statistical Data Interpretation><Statistical Data Analyses><Statistical Data Analysis><statistical analysis><Disease><Disorder><Experimental Designs><Genome><Goals><Grant><Health><Human><Modern Man><Lead><Pb element><heavy metal Pb><heavy metal lead><Linear Models><Medicine><Methods><Methodology><Phylogeny><Regression Analysis><Regression Analyses><Regression Diagnostics><Statistical Regression><Research Design><Study Type><study design><Research Personnel><Investigators><Researchers><Role><social role><Science><Signal Transduction><Cell Communication and Signaling><Cell Signaling><Intracellular Communication and Signaling><Signal Transduction Systems><Signaling><biological signal transduction><Computer software><Software><statistics><Testing><Trees><Data Set><Dataset><analytical method><base><improved><Procedures><Medical><Sample Size><Collaborations><tool><Nature><Consensus><programs><Scientist><Complex><Clinic><interest><user friendly software><user friendly computer software><microbial><Manuscripts><Structure><simulation><novel><Proteome><Statistical Methods><Modeling><Sampling><theories><Genomics><Address><Data><Phylogenetics><Phylogenetic Analysis><scientific accomplishments><scientific advances><Scientific Advances and Accomplishments><Characteristics><developmental><Development><microbiome><human-associated microbiome><Human Microbiome><designing><design><analytical tool><Functional Metagenomics><Metagenomics><user-friendly><2-dimensional><two-dimensional><open source><bio-markers><biologic marker><biomarker><Biological Markers><disease diagnosis><flexible><flexibility><methylome><customized therapy><customized treatment><individualized patient treatment><individualized therapeutic strategy><individualized therapy><individualized treatment><patient specific therapies><patient specific treatment><tailored medical treatment><tailored therapy><tailored treatment><unique treatment><individualized medicine><targeted drug therapy><targeted drug treatments><targeted therapeutic><targeted therapeutic agents><targeted therapy><targeted treatment><Data Analytics><multiomics><multiple omics><global gene expression><global transcription profile><transcriptome><biomarker discovery><metabolome><metabonome><inter-individual variation><inter-individual variability><interindividual variability><interindividual variation><microbiome research><Microbiomics><microbiome science><microbiome studies><microbiome composition><microbiome community composition><microbiome species composition><microbiome analysis><analyze microbiome><multidimensional data><high dimensional data><multidimensional datasets><Prognosis>\",\"pref_terms\":\"Accounting;Address;Attention;Biological Markers;Biometry;Characteristics;Charge;Clinic;Collaborations;Communities;Complex;Computer software;Consensus;Data;Data Analyses;Data Analytics;Data Set;Development;Disease;Experimental Designs;Genome;Genomics;Goals;Grant;Health;Human;Human Microbiome;Lead;Linear Models;Manuscripts;Medical;Medicine;Metagenomics;Methodology;Methods;Modeling;Nature;Phylogenetic Analysis;Phylogeny;Procedures;Prognosis;Proteome;Regression Analysis;Research Design;Research Personnel;Role;Sample Size;Sampling;Science;Scientific Advances and Accomplishments;Scientist;Signal Transduction;Statistical Data Interpretation;Statistical Methods;Structure;Testing;Trees;analytical method;analytical tool;base;biomarker discovery;design;disease diagnosis;flexibility;improved;individualized medicine;inter-individual variation;interest;metabolome;methylome;microbial;microbiome;microbiome analysis;microbiome composition;microbiome research;multidimensional data;multiple omics;novel;open source;programs;simulation;statistics;targeted treatment;theories;tool;transcriptome;two-dimensional;user friendly software;user-friendly\",\"abstract_text\":\"Project Summary\\nThe broad and long-term objective of this project concerns the development of novel quantitative methods and\\nbiostatistical tools for microbiome data analytics to aid in microbiome-based discovery sciences. The\\nmicrobiome, also called the second genome of the human, has received much attention in the past few years.\\nDue to its critical roles in human health and disease, the human microbiome has now been recognized as an\\nintegral part of the individualized medicine approach because it not only accounts for inter-individual variability\\nin all aspects of a disease but also represents a potentially modifiable factor that is amenable to targeting by\\ntherapeutics. Despite those fruitful and promising findings from microbiome studies, there is no consensus in\\nthe current field as how to appropriately analyze the data, let alone the optimality and efficiency issues that\\nhave yet to be addressed. Several challenges amount to this predicament, including complex experimental\\ndesigns of microbiome studies, an unknown interplay between microbiome and host, extremely sparsity and\\nhigh dimensionality of the data, phylogenetic relatedness of the microbial taxa, and compositional structure of\\nmicrobiome. As a result, although quite a few analytical methods and tools have been developed for\\nmicrobiome data analysis, several specific gaps exist in the methodological toolbox, hindering the advance of\\nmicrobiome-based biomedical sciences. To fill these gaps, this proposal aims to develop robust and powerful\\nquantitative methods and tools for microbiome data analysis. Specifically, Aim 1 focuses on developing robust\\nand powerful methods for differential abundance analysis in complex study designs. It will develop new\\nmethods to address zero-inflation, compositional effects and correlations in microbiome data. Aim 2 focuses on\\nstrategies to increase the power of microbiome-wide multiple testing. It proposes two new multiple testing\\nprocedures, which address confounders and phylogenetic relatedness, respectively. Aim 3 proposes to\\ndevelop compositional canonical correlation analysis methods for integrating microbiome data with other omics\\ndata. Specifically, it will develop an efficient and flexible framework for integrating heterogeneous omics data\\nwith microbiome data, accounting for compositional effects and phylogenetic relatedness. Aim 4 will develop\\nuser-friendly and efficient software packages so the community can benefit maximally from methodological and\\nscientific advances resulting from this application. The proposed methods will be evaluated using simulations,\\nand more importantly, applications to several ongoing microbiome studies in the Center of Individualized\\nMedicine at Mayo Clinic. The proposed quantitative methods and open-source software packages will\\ncontribute to microbiome biomarker discovery and microbiome-based mechanistic studies. All methods and\\ntools developed under this grant will be made available free of charge to interested researchers and the public.\",\"project_title\":\"Methods for microbiome compositional data\",\"phr_text\":\"Project Narrative\\nThe human microbiome provides additional perspective into health and disease. This\\nproject aims to develop analytical methods for microbiome data analysis. The proposed\\napproaches will facilitate microbiome-based discovery sciences.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"859\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":233051,\"indirect_cost_amt\":56694,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10580718\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10531222,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K01AI137317-05\",\"project_serial_num\":\"AI137317\",\"organization\":{\"org_name\":\"UNIVERSITY OF PENNSYLVANIA\",\"city\":null,\"country\":null,\"org_city\":\"PHILADELPHIA\",\"org_country\":\"UNITED STATES\",\"org_state\":\"PA\",\"org_state_name\":null,\"dept_type\":\"INTERNAL MEDICINE/MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"042250712\"],\"org_ueis\":[\"GM1XX56LEP58\"],\"primary_duns\":\"042250712\",\"primary_uei\":\"GM1XX56LEP58\",\"org_fips\":\"US\",\"org_ipf_code\":\"6463801\",\"org_zipcode\":\"191046205\",\"external_org_id\":6463801},\"award_type\":\"5\",\"activity_code\":\"K01\",\"award_amount\":133677,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K01\",\"ic_code\":\"AI\",\"serial_num\":\"137317\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14108757,\"first_name\":\"Judith\",\"middle_name\":\"\",\"last_name\":\"Anesi\",\"is_contact_pi\":true,\"full_name\":\"Judith  Anesi\",\"title\":\"\"}],\"contact_pi_name\":\"ANESI, JUDITH \",\"program_officers\":[{\"first_name\":\"Timothy\",\"middle_name\":\"A.\",\"last_name\":\"Gondre-Lewis\",\"full_name\":\"Timothy A. Gondre-Lewis\"}],\"agency_ic_admin\":{\"code\":\"AI\",\"abbreviation\":\"NIAID\",\"name\":\"National Institute of Allergy and Infectious Diseases\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"AI\",\"name\":\"National Institute of Allergy and Infectious Diseases\",\"abbreviation\":\"NIAID\",\"total_cost\":133677.0}],\"cong_dist\":\"PA-03\",\"spending_categories\":null,\"project_start_date\":\"2018-12-17T12:12:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-18-369\",\"full_study_section\":{\"srg_code\":\"MID\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Microbiology and Infectious Diseases B Research Study Section[MID-B]\"},\"award_notice_date\":\"2022-11-10T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K01AI137317\",\"terms\":\"<Abdomen><Abdominal><Antibiotics><Antibiotic Agents><Antibiotic Drugs><Miscellaneous Antibiotic><Bacteria><Bacterial Infections><bacteria infection><bacterial disease><Biometry><Biometrics><Biostatistics><Blood Vessels><vascular><Clinical Skills><Statistical Data Interpretation><Statistical Data Analyses><Statistical Data Analysis><statistical analysis><Cessation of life><Death><Diagnosis><Education><Educational aspects><Epidemiology><epidemiologic><epidemiological><Exercise><Foundations><Future><Genome><University Hospitals><Incidence><Infection><Mentors><Methods><Organ Donor><Organ Procurements><Organ Transplantation><Grafting Procedure><Organ Transplants><organ allograft><organ graft><organ xenograft><Organism><living system><Pennsylvania><Phenotype><Public Health><Research><Research Personnel><Investigators><Researchers><Risk Factors><Testing><Time><Transplantation><transplant><Treatment Protocols><Treatment Regimen><Treatment Schedule><Wrestling><Case Study><case report><symposium><conference><convention><summit><symposia><Case Series><Advisory Committees><Task Forces><advisory team><Guidelines><career><Organ><Solid><Clinical><Link><Susceptibility><Predisposition><Training><Failure><Data Bases><data base><Databases><Collaborations><Knowledge><programs><Event><Techniques><Allografting><Benchmarking><Best Practice Analysis><interest><experience><cohort><Anastomosis - action><Anastomosis><novel><Predictive Factor><Reporting><Multiple Anti-bacterial Drug Resistance><Multiple Anti-bacterial Drug Resistant><Multiple Antibacterial Drug Resistance><Multiple Antibacterial Drug Resistant><Resistance to Multiple Anti-bacterial Drug><Resistance to Multiple Antibacterial Drug><Resistant to Multiple Anti-bacterial Drug><Resistant to Multiple Antibacterial Drug><multi-drug resistant bacteria><multidrug resistant bacteria><Multiple Bacterial Drug Resistance><preventing><prevent><genome sequencing><Address><Data><International><Research Infrastructure><Clinical Data><Transmission><transmission process><Molecular><developmental><Development><Advanced Development><designing><design><blood infection><bloodstream infection><Sepsis><Outcome><end-stage organ failure><innovate><innovative><innovation><graft failure><improved outcome><whole genome><entire genome><full genome><transplant centers><advanced analytics><organ transplant recipient>\",\"pref_terms\":\"Abdomen;Address;Advanced Development;Advisory Committees;Allografting;Anastomosis - action;Antibiotics;Bacteria;Bacterial Infections;Benchmarking;Biometry;Blood Vessels;Case Series;Case Study;Cessation of life;Clinical;Clinical Data;Clinical Skills;Collaborations;Data;Databases;Development;Diagnosis;Education;Epidemiology;Event;Exercise;Failure;Foundations;Future;Genome;Guidelines;Incidence;Infection;International;Knowledge;Link;Mentors;Methods;Molecular;Multiple Bacterial Drug Resistance;Organ;Organ Donor;Organ Procurements;Organ Transplantation;Organism;Outcome;Pennsylvania;Phenotype;Predictive Factor;Predisposition;Public Health;Reporting;Research;Research Infrastructure;Research Personnel;Risk Factors;Sepsis;Solid;Statistical Data Interpretation;Techniques;Testing;Time;Training;Transplantation;Treatment Protocols;University Hospitals;Wrestling;advanced analytics;career;cohort;design;end-stage organ failure;experience;genome sequencing;graft failure;improved outcome;innovation;interest;novel;organ transplant recipient;prevent;programs;symposium;transmission process;transplant centers;whole genome\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\n The principal treatment for end-stage organ failure is solid organ transplantation (SOT). One of the most\\nsignificant challenges facing organ transplantation is the limited supply of donor organs. As a result, there is\\nsubstantial clinical and public health interest in expanding the donor pool to include those organs that have\\nhistorically been discarded. Specifically, donors with positive bacterial cultures have been inconsistently\\nutilized, because there have been several reports of donors transmitting bacteria to their organ recipients via\\nthe allograft, causing donor-derived bacterial infections (DDBIs). DDBIs have been linked in limited data to\\npoor outcomes including vascular anastomosis dehiscence, overwhelming infection, and death. In some cases,\\nthe poor outcomes were attributed to the fact that the positive donor culture was not identified before transplant\\nand there was a delay in recipient diagnosis and treatment. Conversely, there have also been case series\\ndescribing safe transplantation of organs from donors with positive bacterial cultures. Thus, the true impact of\\ndonor bacterial infection or colonization on SOT recipients remains unclear.\\n In this study, we plan to address this knowledge gap by studying a large, multicenter cohort of SOT\\nrecipients and their donors. This cohort will be built upon an existing multicenter research infrastructure of\\ntransplant centers, as well as our unique research collaboration with the local organ procurement organization,\\nwhich coordinates transplantations in our region. In addition, we will be able to collect bacterial isolates from\\ndonors and recipients at our large transplant center in order to perform molecular testing. Specifically, we plan\\nto: (AIM 1) Determine the risk factors associated with acquiring a DDBI in SOT recipients; (Secondary AIM 1)\\nCompare whole genome sequencing (WGS) versus phenotypic characterization to define DDBI in a\\nsubpopulation of SOT recipients; (AIM 2) Determine the association between positive donor bacterial cultures\\nand time to (a) first bacterial infection and (b) graft failure or death; (AIM 3) Determine the predictors of positive\\ndonor cultures identified after organ transplantation.\\n The results of this study will be important for the field of transplantation as it wrestles with how to safely\\nexpand the donor pool. The aims are combined with a robust training plan that includes formal education in\\nbiostatistics and epidemiology, the development of advanced analytic skills for clinical and genome data,\\nformal benchmarks for progress including presentation at seminars and international conferences, and\\nextensive research experience under the guidance of an expert mentoring and advisory team. This proposal\\nwill form a strong foundation for my continued development toward a career as an independent investigator\\nwith a program of research focused on improving outcomes among SOT recipients.\",\"project_title\":\"Impact of donor bacterial infections on solid organ transplant recipient outcomes\",\"phr_text\":\"PROJECT NARRATIVE\\nThe proposed research will determine the risk factors for donor-derived bacterial infections (DDBIs) in solid\\norgan transplant (SOT) recipients, evaluate new molecular methods for diagnosing DDBIs, assess the impact\\nof positive donor bacterial cultures on SOT recipient outcomes, and determine the predictors of positive donor\\ncultures identified after transplantation. These studies will answer fundamental questions about whether the\\nuse of donors with positive bacterial cultures is associated with negative clinical outcomes in the recipients.\\nGiven the scarcity of donor organs and profound need to expand access to solid organs, this research will\\nhave significant public health implications.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"855\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":123775,\"indirect_cost_amt\":9902,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10531222\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10573145,\"subproject_id\":\"6634\",\"fiscal_year\":2023,\"project_num\":\"5P20GM121327-07\",\"project_serial_num\":\"GM121327\",\"organization\":{\"org_name\":\"UNIVERSITY OF KENTUCKY\",\"city\":null,\"country\":null,\"org_city\":\"LEXINGTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"KY\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"939017877\"],\"org_ueis\":[\"H1HYA8Z1NTM5\"],\"primary_duns\":\"939017877\",\"primary_uei\":\"H1HYA8Z1NTM5\",\"org_fips\":\"US\",\"org_ipf_code\":\"2793601\",\"org_zipcode\":\"405260001\",\"external_org_id\":2793601},\"award_type\":\"5\",\"activity_code\":\"P20\",\"award_amount\":182841,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P20\",\"ic_code\":\"GM\",\"serial_num\":\"121327\",\"support_year\":\"07\",\"full_support_year\":\"07\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":7042565,\"first_name\":\"ANDREW\",\"middle_name\":\"N.\",\"last_name\":\"LANE\",\"is_contact_pi\":true,\"full_name\":\"ANDREW N. LANE\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"LANE, ANDREW N.\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":182841.0}],\"cong_dist\":\"KY-06\",\"spending_categories\":null,\"project_start_date\":\"2017-03-01T12:03:00Z\",\"project_end_date\":\"2026-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PAR-19-312\",\"full_study_section\":{\"srg_code\":\"ZGM1\",\"srg_flex\":\"\",\"sra_designator_code\":\"RCB\",\"sra_flex_code\":\"3\",\"group_code\":\"\",\"name\":\"ZGM1-RCB-3\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P20GM121327\",\"terms\":\"<Algorithms><Behavior><Biochemistry><Biological Chemistry><Biology><Biomass><Biometry><Biometrics><Biostatistics><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Carbon><cell growth><Cellular Expansion><Cellular Growth><Cells><Cell Body><Chromatography><Liquid Chromatography><Classification><Systematics><Culture Media><growth media><Data Analyses><Data Analysis><data interpretation><Statistical Data Interpretation><Statistical Data Analyses><Statistical Data Analysis><statistical analysis><Dinucleoside Phosphates><dinucleotide><Disease><Disorder><Electrophoresis><Electrophoretic Fractionation><Experimental Designs><Goals><Isotope Labeling><Isotopes><Kentucky><Literature><Metabolism><Intermediary Metabolism><Metabolic Processes><Methods><Mus><Mice><Mice Mammals><Murine><Nuclear Magnetic Resonance><Organism><living system><Oxidation-Reduction><Redox><oxidation reduction reaction><Proteins><Research Personnel><Investigators><Researchers><medical specialties><Specialty><Mass Spectrum Analysis><Mass Photometry/Spectrum Analysis><Mass Spectrometry><Mass Spectroscopy><Mass Spectrum><Mass Spectrum Analyses><Standardization><Technology><Time><Tissues><Body Tissues><Universities><Schedule><analytical method><base><Procedures><Tissue Harvesting><Phase><Biological><biologic><Biochemical><Logistic Regressions><Data Bases><data base><Databases><metabolic abnormality assessment><Abnormal Assessment of Metabolism><Metabolic Studies><Metabolism Studies><Collaborations><Fourier Transform><stable isotope><Metabolic><Source><Techniques><System><Operative Surgical Procedures><Operative Procedures><Surgical><Surgical Interventions><Surgical Procedure><surgery><Consult><Services><Infusion procedures><Infusion><Informatics><Human Resources><Manpower><personnel><Network Analysis><Pathway Analysis><Sampling><high throughput analysis><Genomics><Protein Array><Metabolic Pathway><metabolism measurement><metabonomics><metabolomics><Address><bio-informatics shared resource><Bioinformatics Shared Resource><Data><Protein Analysis><Resolution><Retrieval><Cancer Center><COBRE><Center of Biomedical Research Excellence><Centers of Research Excellence><Update><Validation><Preparation><Principal Investigator><Process><Tracer><developmental><Development><Output><website><web site><designing><design><data acquisition><ultra high resolution><data reduction><cancer cell metabolism><cancer metabolism><tumor cell metabolism><tumor metabolism><ICP-MS><Inductively Coupled Plasma Mass Spectrometry><metabolome><metabonome><experimental study><experiment><experimental research><advanced analytics>\",\"pref_terms\":\"Address;Algorithms;Behavior;Biochemical;Biochemistry;Bioinformatics Shared Resource;Biological;Biology;Biomass;Biometry;Cancer Center;Carbon;Cells;Centers of Research Excellence;Chromatography;Classification;Collaborations;Consult;Culture Media;Data;Data Analyses;Databases;Development;Dinucleoside Phosphates;Disease;Electrophoresis;Experimental Designs;Fourier Transform;Genomics;Goals;Human Resources;Inductively Coupled Plasma Mass Spectrometry;Informatics;Infusion procedures;Isotope Labeling;Isotopes;Kentucky;Liquid Chromatography;Literature;Logistic Regressions;Malignant Neoplasms;Mass Spectrum Analysis;Metabolic;Metabolic Pathway;Metabolism;Methods;Mus;Nuclear Magnetic Resonance;Operative Surgical Procedures;Organism;Output;Oxidation-Reduction;Pathway Analysis;Phase;Preparation;Principal Investigator;Procedures;Process;Protein Analysis;Protein Array;Proteins;Research Personnel;Resolution;Retrieval;Sampling;Schedule;Services;Source;Standardization;Statistical Data Interpretation;System;Techniques;Technology;Time;Tissue Harvesting;Tissues;Tracer;Universities;Update;Validation;advanced analytics;analytical method;base;cell growth;data acquisition;data reduction;design;experimental study;high throughput analysis;medical specialties;metabolic abnormality assessment;metabolome;metabolomics;stable isotope;tumor metabolism;ultra high resolution;web site\",\"abstract_text\":\"PROJECT SUMMARY\\n“Metabolomics” largely consists of metabolic profiling, which is the process of identification and quantification of\\na large number of metabolites of many different biochemical classes, from diverse biological samples, including\\ncells, culture media, tissue and biofluids. However, to understand the metabolic underpinning of diseases such\\nas cancer, biochemical mechanism(s) must be elucidated, in which case it is necessary to introduce appropriate\\nstable isotope tracers. Although this greatly increases the number of analytes to be quantified, it also enables\\nthe retrieval of much more biologically relevant information. This Stable Isotope Resolved Metabolomics (SIRM)\\napproach is a unique specialty of the Metabolism Core, led by PIs with over 40 years of combined expertise in\\nSIRM. The goals of the Metabolism Core are to provide services for a wide coverage of the metabolome with\\nisotopomer and isotopologue analysis, and high throughput protein analysis. To achieve this, we make use of\\nfour major analytical platforms, namely (1) high resolution nuclear magnetic resonance (NMR), (2) ultra-high\\nresolution Fourier-transform mass spectrometry (UHR-FTMS), with or without chromatography, (3) ultra-trace\\nelemental analyses by inductively-coupled plasma MS (ICPMS), and (4) Reverse Phase Protein Arrays (RPPA).\\nThe Metabolism Core personnel will advise on analytical methods and SIRM-based experimental design with\\ndevelopment of additional techniques to address specific issues of identification and quantification and biological\\ninterpretation relevant to the COBRE Projects. Because experimental design and sample preparation are integral\\nto the data acquisition and interpretation, the Metabolism Core provides expertise in these and informatics\\nrelevant to the SIRM approach. These goals will be achieved according to the following Specific Aims. Specific\\nAim 1. To provide access and expertise across a wide range of NMR, MS and RPPA technologies to\\ninvestigators. Specific Aim 2. To provide experimental design, sample processing, and support to the\\ninvestigators for data reduction, metabolic pathway, and biochemical mechanism analyses. Specific Aim\\n3. To develop and implement new methods to enhance metabolic capabilities relevant to the COBRE\\nProjects. This Core will support the young investigators by providing advanced analytical technologies and\\nexpertise in the metabolic requirements for cancer development and behavior.\",\"project_title\":\"Metabolism Core\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":119504,\"indirect_cost_amt\":63337,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10573145\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10545164,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01CA224537-06\",\"project_serial_num\":\"CA224537\",\"organization\":{\"org_name\":\"UNIVERSITY OF UTAH\",\"city\":null,\"country\":null,\"org_city\":\"SALT LAKE CITY\",\"org_country\":\"UNITED STATES\",\"org_state\":\"UT\",\"org_state_name\":null,\"dept_type\":\"PUBLIC HEALTH & PREV MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"009095365\"],\"org_ueis\":[\"LL8GLEVH6MG3\"],\"primary_duns\":\"009095365\",\"primary_uei\":\"LL8GLEVH6MG3\",\"org_fips\":\"US\",\"org_ipf_code\":\"514002\",\"org_zipcode\":\"841128906\",\"external_org_id\":514002},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":551120,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"CA\",\"serial_num\":\"224537\",\"support_year\":\"06\",\"full_support_year\":\"06\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9122911,\"first_name\":\"Cho\",\"middle_name\":\"Yan\",\"last_name\":\"Lam\",\"is_contact_pi\":true,\"full_name\":\"Cho Yan Lam\",\"title\":\"RESEARCH ASSOCIATE PROFESSOR\"}],\"contact_pi_name\":\"LAM, CHO YAN\",\"program_officers\":[{\"first_name\":\"Rebecca\",\"middle_name\":\"\",\"last_name\":\"Ferrer\",\"full_name\":\"Rebecca  Ferrer\"}],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":551120.0}],\"cong_dist\":\"UT-02\",\"spending_categories\":null,\"project_start_date\":\"2018-01-19T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-16-380\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"RPHB\",\"sra_flex_code\":\"Z\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-RPHB-Z(56)R]\"},\"award_notice_date\":\"2023-01-04T12:01:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01CA224537\",\"terms\":\"<Affect><Algorithms><Anger><angers><angry><Cause of Death><Cognition><Complement><Complement Proteins><Emotions><Environment><Foundations><Geography><Play><Public Health><Real-Time Systems><realtime systems><Research><Risk><Role><social role><Science><Smoking><Informal Social Control><Self Regulation><Stress><Testing><Time><Tobacco><Volition><smoking cessation><cease smoking><quit smoking><stop smoking><Mediating><Risk Behaviors><Risky Behavior><at risk behavior><base><role model><Physiological><Physiologic><Link><disability><Failure><Measurement><cancer prevention><Smoker><Depressed mood><depressed><sadness><Machine Learning><machine learned><Knowledge><Frequencies><Complex><System><Tobacco use><Tobacco Consumption><tobacco product use><cancer risk><experience><success><knowledge base><Participant><Patient Self-Report><Self-Report><Reporting><Positioning Attribute><Position><geospatial information system><Geographic Information Systems><Emotional><Modeling><Intervention Strategies><interventional strategy><Intervention><Cell Phone><Cellular Telephone><iPhone><smart phone><smartphone><Cellular Phone><Risk Reduction><smoking cessation treatment><Smoking Cessation Intervention><Mediator><Mediator of Activation><Mediator of activation protein><Address><Data><Characteristics><Process><Behavioral><Outcome><cancer type><Affective><addictive disorder><addiction><effective intervention><primary outcome><heart rate variability><Ecological momentary assessment><mobile platform><mobile technology><mobile computing><negative affect><negative affectivity><Longitudinal cohort study><Long-term cohort study><Longterm cohort study><risk prediction model><adaptive intervention><advanced analytics><Home><wireless>\",\"pref_terms\":\"Address;Affect;Affective;Algorithms;Anger;Behavioral;Cause of Death;Cellular Phone;Characteristics;Cognition;Complement;Complex;Data;Depressed mood;Ecological momentary assessment;Emotional;Emotions;Environment;Failure;Foundations;Frequencies;Geographic Information Systems;Geography;Home;Informal Social Control;Intervention;Knowledge;Link;Longitudinal cohort study;Machine Learning;Measurement;Mediating;Mediator of activation protein;Modeling;Outcome;Participant;Patient Self-Report;Physiological;Play;Positioning Attribute;Process;Public Health;Real-Time Systems;Reporting;Research;Risk;Risk Behaviors;Risk Reduction;Role;Science;Smoker;Smoking;Smoking Cessation Intervention;Stress;System;Testing;Time;Tobacco;Tobacco use;Volition;adaptive intervention;addiction;advanced analytics;base;cancer prevention;cancer risk;cancer type;disability;effective intervention;experience;heart rate variability;knowledge base;mobile computing;negative affect;primary outcome;risk prediction model;role model;smoking cessation;success;wireless\",\"abstract_text\":\"Tobacco use plays a causal role in almost 20 different types of cancer, and although smoking\\ncessation is a cornerstone of cancer risk reduction, the vast majority of smoking quit attempts\\nfail. Numerous conceptual models, as well as a large body of empirical evidence, underscore that\\naffect is a potent determinant of smoking lapse. Unfortunately, very little is known about how\\nthe constellation and temporal dynamics of distinct emotions and other factors play out in real\\ntime in the real world to influence lapse risk. This lack of knowledge severely hampers both our\\nconceptual models and our ability to optimally intervene. Thus, the overarching objectives of\\nthis research are to create a more detailed and comprehensive conceptual model of the role of\\ndistinct emotions in self-regulation, as well as the technical, empirical, and analytic foundation\\nnecessary to develop effective interventions for smoking cessation and other cancer risk\\nbehaviors that can target real time, real world mechanisms. The proposed research directly\\naddresses several objectives from the PAR including the influence of distinct emotions and\\ntheir time course on cancer risk behaviors, whether the role of distinct emotions is altered by\\nthe presence of other emotions (e.g., “blended” emotional states), and how the influence of\\naffective experience is modified by context. The proposed longitudinal cohort study among 300\\nsmokers attempting to quit is guided by a conceptual framework grounded in affective science\\nand conceptual models of self-regulation and addiction. Participants will be followed from 1\\nweek prior to their quit date through 6 months post-quit date. They will be assessed from 1 week\\npre-quit date through 2 weeks post-quit date using AutoSense, geographic positioning system\\n(GPS), and ecological momentary assessment (EMA). AutoSense, GPS, and EMA collect real\\ntime data in natural environments, communicate wirelessly with each other, and data are\\nprocessed in real time on a smartphone. AutoSense detects specific behavioral and physiologic\\n“signatures” of smoking (the primary outcome) and self regulatory capacity (an intermediate\\noutcome; assessed using high frequency heart rate variability) in real time. GPS real time spatial\\ntracking will be linked with spatially and temporally relevant characteristics of the environment\\nusing geographic information system (GIS) data. EMAs assess self-reported emotions,\\ncognition, and context. Analyses utilize advanced dynamic risk prediction models and machine\\nlearning approaches to model the dynamics of real time, real world associations among distinct\\nemotions, SRC, and lapse.\",\"project_title\":\"Affective science and smoking cessation: Real time real world assessment\",\"phr_text\":\"Tobacco use is the leading preventable cause of death and disability in the U.S. The proposed study will yield a\\nmore detailed and comprehensive conceptual model of the role of distinct emotions in self-regulation, as well\\nas the technical, empirical, and analytic foundation necessary to develop more effective interventions for smoking cessation and other cancer risk behaviors that can target real time, real world mechanisms. This knowledge can be utilized to reduce the public health burden of tobacco use.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"393\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":433648,\"indirect_cost_amt\":117472,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10545164\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10556386,\"subproject_id\":\"8662\",\"fiscal_year\":2023,\"project_num\":\"5P41GM135019-04\",\"project_serial_num\":\"GM135019\",\"organization\":{\"org_name\":\"BROAD INSTITUTE, INC.\",\"city\":null,\"country\":null,\"org_city\":\"CAMBRIDGE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"623544785\"],\"org_ueis\":[\"H5G9NWEFHXN4\"],\"primary_duns\":\"623544785\",\"primary_uei\":\"H5G9NWEFHXN4\",\"org_fips\":\"US\",\"org_ipf_code\":\"10021177\",\"org_zipcode\":\"021421027\",\"external_org_id\":10021177},\"award_type\":\"5\",\"activity_code\":\"P41\",\"award_amount\":194290,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P41\",\"ic_code\":\"GM\",\"serial_num\":\"135019\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":8876992,\"first_name\":\"Anne\",\"middle_name\":\"E.\",\"last_name\":\"Carpenter\",\"is_contact_pi\":true,\"full_name\":\"Anne E. Carpenter\",\"title\":\"DIRECTOR, IMAGING PLATFORM\"}],\"contact_pi_name\":\"CARPENTER, ANNE E.\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":194290.0}],\"cong_dist\":\"MA-07\",\"spending_categories\":null,\"project_start_date\":\"2019-12-01T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"PAR-17-316\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BST\",\"sra_flex_code\":\"T\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BST-T]\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P41GM135019\",\"terms\":\"<Algorithms><Automobile Driving><driving><Biology><Biomedical Research><Cells><Cell Body><Cultured Cells><Communities><Computers><Data Analyses><Data Analysis><data interpretation><Disease><Disorder><Face><faces><facial><Feedback><Geographic Locations><Geographic Area><Geographic Region><Geographical Location><geographic site><Goals><Hand><Institutes><Laboratories><Microscopy><Fluorescence Microscopy><Fluorescence Light Microscopy><Mission><Biological Models><Biologic Models><Model System><Modernization><Organism><living system><Organoids><Paper><Publishing><Research><research and development><Development and Research><R & D><R&D><Research Personnel><Investigators><Researchers><Resources><Research Resources><Role><social role><Computer software><Software><Software Engineering><Computer Software Development><Computer Software Engineering><Educational process of instructing><Teaching><Technology><Tissues><Body Tissues><Translating><Universities><Wisconsin><Work><Writing><Measures><catalyst><base><image processing><improved><Image Analysis><Image Analyses><image evaluation><image interpretation><Area><Biological><biologic><Ensure><Training><Individual><Cell Components><Cell Structure><Cellular Structures><Biological Process><Biological Function><Collaborations><tool><Cellular biology><cell biology><Scientist><light microscopy><Dimensions><human tissue><Event><Side><college><collegiate><user friendly software><user friendly computer software><skills><novel><Modality><bio-imaging><bioimaging><develop software><developing computer software><software development><Institution><image-based method><imaging method><imaging modality><Address><Brightfield Analysis><Brightfield Microscopy><Educational Materials><International><Reproducibility><Resource Sharing><Chromatin Structure><Computational Technique><developmental><Development><imaging><Image><next generation><innovate><innovative><innovation><biological research><user-friendly><open source><algorithmic methods><algorithmic methodologies><quantitative imaging><microscopic imaging><microscope imaging><microscopy imaging><experimental study><experiment><experimental research><technology research and development><technological research and development><deep learning><advanced analytics>\",\"pref_terms\":\"Address;Algorithms;Area;Automobile Driving;Biological;Biological Models;Biological Process;Biology;Biomedical Research;Brightfield Microscopy;Cells;Cellular Structures;Cellular biology;Chromatin Structure;Collaborations;Communities;Computational Technique;Computer software;Computers;Cultured Cells;Data Analyses;Development;Dimensions;Disease;Educational Materials;Educational process of instructing;Ensure;Event;Face;Feedback;Fluorescence Microscopy;Geographic Locations;Goals;Hand;Image;Image Analysis;Individual;Institutes;Institution;International;Laboratories;Measures;Microscopy;Mission;Modality;Modernization;Organism;Organoids;Paper;Publishing;Reproducibility;Research;Research Personnel;Resource Sharing;Resources;Role;Scientist;Side;Software Engineering;Technology;Tissues;Training;Translating;Universities;Wisconsin;Work;Writing;advanced analytics;algorithmic methodologies;base;bioimaging;biological research;catalyst;college;deep learning;experimental study;human tissue;image processing;imaging modality;improved;innovation;light microscopy;microscopic imaging;next generation;novel;open source;quantitative imaging;research and development;skills;software development;technology research and development;tool;user friendly software;user-friendly\",\"abstract_text\":\"Project Summary\\n The Center for Open Bioimage Analysis will serve the cell biology community’s growing need for\\nsophisticated software for light microscopy image analysis. Quantitative image analysis has become an\\nindispensable tool for biologists using microscopy throughout basic biological and biomedical research.\\n Quantifying images is now a critical, widespread need as imaging experiments continue to grow in scale,\\nsize, dimensionality, scope, modality, and complexity. Many biologists are missing out on the quantitative\\nbioimaging revolution due to lack of effective algorithms and/or usable software for their needs, or lack of\\naccess to training. The Center brings together the Carpenter laboratory at the Broad Institute and the Eliceiri\\nlaboratory at the University of Wisconsin­Madison, and in doing so brings together the two most popular open\\nsource bioimage analysis projects, ImageJ (including ImageJ2 and FIJI) and CellProfiler. Through the\\ncollaborative development and dissemination of open source image analysis software, as well as training\\nevents and resources, the Center will empower thousands of researchers to apply advanced analytics in\\ninnovative ways to address new experimental areas.\\n Building on the team’s expertise developing algorithms and user­friendly software for use in biology under\\nreal­world conditions, the Center will focus on two Technology Research and Development (TR&D) projects:\\ndeep learning­based image processing, and accessibility of image­processing algorithms for biologists. This\\nwork will not occur in isolation at the Center; rather, the Center will nucleate a larger community working on\\nthese two areas and serve as a catalyst and organizing force to create software and resources shared by all.\\n The Driving Biological Projects (DBPs) will serve a major role in driving the TR&D work: our teams are\\naccustomed to working deeply and iteratively on problems side by side and with frequent feedback from\\nbiologists. This will ensure that important cell biological problems drive the work of the Center. The DBPs\\nreflect tremendous variety in terms of biological questions, model systems, imaging modalities, and researcher\\nexpertise and will ensure robustness of our tools for the widest possible impact on the community. Continuing\\nthe teams’ track record with ImageJ and CellProfiler, two mature open source bioimage analysis software\\nprojects critical to the work of biologists worldwide, the Center will also assist and train biologists in applying\\nthe latest computational techniques to important biological problems involving images.\\n In short, the need for robust, accurate, and readily usable software is more urgent than ever. The Center for\\nOpen Bioimage Analysis will serve as a hub for pioneering new computational strategies for diverse biological\\nproblems, translating them into user­friendly software, further developing ImageJ and CellProfiler, and training\\nthe biological community to apply advanced software to important and diverse problems in cell biology.\",\"project_title\":\"Center for Open Bioimage Analysis\",\"phr_text\":\"Project Narrative\\nBiologists studying a huge variety of diseases and basic biological processes need software to measure cells,\\ntissues, and organisms in microscopy images. We will create the Center for Open Bioimage Analysis which will\\ncatalyze the scientific community, creating resources, free software, and training that allow biologists to\\nanalyze images using deep learning and other new image processing algorithms, offering improved accuracy,\\nconvenience, and reproducibility.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":153891,\"indirect_cost_amt\":40399,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10556386\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10533736,\"subproject_id\":\"7665\",\"fiscal_year\":2023,\"project_num\":\"5P01HD104436-03\",\"project_serial_num\":\"HD104436\",\"organization\":{\"org_name\":\"UNIVERSITY OF CALIFORNIA, SAN DIEGO\",\"city\":null,\"country\":null,\"org_city\":\"LA JOLLA\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"804355790\"],\"org_ueis\":[\"UYTTZT6G9DT1\"],\"primary_duns\":\"804355790\",\"primary_uei\":\"UYTTZT6G9DT1\",\"org_fips\":\"US\",\"org_ipf_code\":\"577507\",\"org_zipcode\":\"920930621\",\"external_org_id\":577507},\"award_type\":\"5\",\"activity_code\":\"P01\",\"award_amount\":152806,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P01\",\"ic_code\":\"HD\",\"serial_num\":\"104436\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":6624857,\"first_name\":\"JOSEPH\",\"middle_name\":\"G\",\"last_name\":\"GLEESON\",\"is_contact_pi\":true,\"full_name\":\"JOSEPH G GLEESON\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"GLEESON, JOSEPH G\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"HD\",\"abbreviation\":\"NICHD\",\"name\":\"Eunice Kennedy Shriver National Institute of Child Health and Human Development\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"HD\",\"name\":\"Eunice Kennedy Shriver National Institute of Child Health and Human Development\",\"abbreviation\":\"NICHD\",\"total_cost\":152806.0}],\"cong_dist\":\"CA-52\",\"spending_categories\":null,\"project_start_date\":\"2020-12-01T12:12:00Z\",\"project_end_date\":\"2025-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"RFA-HD-21-013\",\"full_study_section\":{\"srg_code\":\"ZHD1\",\"srg_flex\":\"\",\"sra_designator_code\":\"DSR\",\"sra_flex_code\":\"N\",\"group_code\":\"\",\"name\":\"ZHD1-DSR-N\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"P01HD104436\",\"terms\":\"<Animals><Budgets><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Colorado><Diagnosis><Faculty><Genetic Screening><Goals><Grant><Group Meetings><Human><Modern Man><Human Genome Project><Meningomyelocele><Meningomyelocoele><Myelomeningocele><Mus><Mice><Mice Mammals><Murine><Neural Tube Defects><Neural Tube Developmental Defects><Rana><Frog><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Technology><Time><Translations><Travel><Universities><Work><Administrator><Businesses><symposium><conference><convention><summit><symposia><improved><Clinical><Training><insight><Individual><Collaborations><Genetic><Nature><Knowledge><programs><Scientist><Hour><Techniques><meetings><gene environment interaction><Gene x Environment Interaction><GxE interaction><environment effect on gene><Animal Model><Animal Models and Related Studies><model of animal><model organism><member><technological innovation><Prevention><Modeling><Sampling><response><functional genomics><Genomics><Bio-Informatics><Bioinformatics><Institution><genome sequencing><video conferencing><Videoconferencing><Grant Proposals><Applications Grants><Molecular Diagnosis><Epigenetic><Epigenetic Change><Epigenetic Mechanism><Epigenetic Process><developmental><Development><epigenomics><designing><design><innovate><innovative><innovation><multidisciplinary><Structural Birth Defect><congenital structural malformation><Structural Congenital Anomalies><Core Grant><genetic strategy><genetic approach><Structural malformation><structural abnormalities><structural anomalies><Structural defect><single cell sequencing><reverse genetics><classroom environment><college atmosphere><collegial atmosphere><collegiate atmosphere><education atmosphere><educational environment><intellectual atmosphere><learning atmosphere><learning environment><school atmosphere><school climate><training atmosphere><university atmosphere><educational atmosphere><Data Analytics><whole genome><entire genome><full genome>\",\"pref_terms\":\"Administrator;Animal Model;Animals;Applications Grants;Bioinformatics;Budgets;Businesses;Child;Clinical;Collaborations;Colorado;Core Grant;Data Analytics;Development;Diagnosis;Epigenetic Process;Faculty;Genetic;Genetic Screening;Genomics;Goals;Grant;Group Meetings;Hour;Human;Human Genome Project;Individual;Institution;Knowledge;Meningomyelocele;Modeling;Molecular Diagnosis;Mus;Nature;Neural Tube Defects;Prevention;Rana;Research;Research Personnel;Resources;Sampling;Scientist;Structural Congenital Anomalies;Structural defect;Techniques;Technology;Time;Training;Translations;Travel;Universities;Videoconferencing;Work;design;educational atmosphere;epigenomics;functional genomics;gene environment interaction;genetic approach;genome sequencing;improved;innovation;insight;meetings;member;multidisciplinary;programs;response;reverse genetics;single cell sequencing;symposium;technological innovation;whole genome\",\"abstract_text\":\"PROJECT SUMMARY – Core A: Administrative Core\\nThis Administrative Core (Core A) will have two major functions. First, it will provide administrative support\\nand intellectual enrichment for the investigators in this Program. Due to the fact that the Program includes\\nfive senior investigators from three institutions (UCSD, Salk, U Colorado), each in a different department,\\nCore A will be essential to integrate the disparate administrative hierarchies. Additionally three different\\nvertebrate species will be used for this work (human, mouse, frog), with the need to move animals between\\ninstitutions. Because of this, and because of the multi-disciplinary nature of the Program, a central\\nAdministrative Core is essential. The Core will work closely with the administration of the three partner\\ninstitutions, as well as administrators, business managers, and scientists in each department involved. The\\nAdministrative Core will: 1] Provide and encourage intellectual collaboration between members of the\\nProgram Project, Internal and External Advisory Panels, university faculty who are not members of the\\nProject, and outside consultants. 2] Assist individual PIs in budgeting as well as coordinating travel,\\npurchasing, meetings, and seminars. The Core will achieve these objectives in several ways: 1] We will\\nsupport a Slack instant messaging/video portal for routine programmatic issues. 2] We will have monthly\\ntwo-hour seminars for all Program members in this program via video conference. 3] We will have\\nsemiannual review by the Internal Advisory Group, and biennial Retreats by the External Advisory Group,\\nwith presentation by each PI. We will also have the Directors of Core B and C present new data, analytical\\ntechniques, and technological innovations, especially in the rapidly moving fields of genomics,\\nepigenomics, and bioinformatics. Seminars will average 10-12 per year, with each Project or Core\\npresenting 2-3 times per year. Internal and External Advisors are selected to advise each project\\nspecifically and will work directly with his/her PI regarding their progress, to suggest possible new directions\\nfor both the individual Projects, and potentially the overall Program. In addition, we propose to have two\\nwhole day symposia during Year 2 and Year 4, overlapping with the External Advisory Group meetings, in\\nwhich will invite scientists whose work is on the same aspect of the research of each project. During these\\nsymposia, each of the PI’s will present their progress over the preceding two years in the outside experts\\nwill be asked to critically evaluate progress of each Project.\",\"project_title\":\"Core A - Administrative  Core\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":117934,\"indirect_cost_amt\":34872,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10533736\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10554268,\"subproject_id\":\"8474\",\"fiscal_year\":2023,\"project_num\":\"5P30CA023108-44\",\"project_serial_num\":\"CA023108\",\"organization\":{\"org_name\":\"DARTMOUTH COLLEGE\",\"city\":null,\"country\":null,\"org_city\":\"HANOVER\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NH\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"041027822\"],\"org_ueis\":[\"EB8ASJBCFER9\"],\"primary_duns\":\"041027822\",\"primary_uei\":\"EB8ASJBCFER9\",\"org_fips\":\"US\",\"org_ipf_code\":\"2021601\",\"org_zipcode\":\"037551421\",\"external_org_id\":2021601},\"award_type\":\"5\",\"activity_code\":\"P30\",\"award_amount\":21074,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P30\",\"ic_code\":\"CA\",\"serial_num\":\"023108\",\"support_year\":\"44\",\"full_support_year\":\"44\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9814873,\"first_name\":\"Brian\",\"middle_name\":\"P\",\"last_name\":\"Jackson\",\"is_contact_pi\":true,\"full_name\":\"Brian P Jackson\",\"title\":\"RESEARCH ASSOCIATE PROFESSOR\"}],\"contact_pi_name\":\"JACKSON, BRIAN P\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":21074.0}],\"cong_dist\":\"NH-02\",\"spending_categories\":null,\"project_start_date\":\"1997-08-04T12:08:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PAR-17-095\",\"full_study_section\":{\"srg_code\":\"NCI\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Cancer Centers Study Section (A)[NCI-A]\"},\"award_notice_date\":\"2022-12-01T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P30CA023108\",\"terms\":\"<Animal Organ><Arsenic><Beverages><Blood><Blood Reticuloendothelial System><Budgets><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Carcinogens><Cancer Causing Agents><Oncogens><oncogenic agent><Cells><Cell Body><Centers for Disease Control and Prevention (U.S.)><CDC><Centers for Disease Control><Centers for Disease Control and Prevention><United States Centers for Disease Control><United States Centers for Disease Control and Prevention><Cereals><Inorganic Chemistry><Chromatography><Elements><Fishes><Hair><Health Policy><health care policy><healthcare policy><Human><Modern Man><Infant><instrumentation><Invertebrates><Invertebrata><Methods><Placenta><Cells Placenta-Tissue><Normal Placentoma><Placenta Embryonic Tissue><Placentome><Platinum><Platinum Black><Pt element><Play><Public Health><Quality Control><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Role><social role><Running><Selenium><Se element><Soil><Technology><Testing><Tissues><Body Tissues><Trace Elements><Biometals><Trace Mineral><Urine><Urine Urinary System><Water><Hydrogen Oxide><Work><granite><Measures><analytical method><base><quality assurance><Biological><biologic><Link><Ensure><Chemicals><Toe Nail><Toenail><Structure of nail of toe><animal tissue><Individual><Funding><Iron><Fe element><Exposure to><instrument><programs><Protocols documentation><Protocol><Services><Nutrient><member><Sampling><bio-imaging><bioimaging><Therapeutic Uses><Institution><Data><International><Center for Cancer Research><NCI Center for Cancer Research><Population Sciences><Resource Sharing><Cancer Center><CCSG><Cancer Center Support Grant><Catchment Area><Norris Cotton Cancer Center at the Dartmouth-Htchcock Medical Center><Norris Cotton Cancer Center><Monitor><Preparation><Therapeutic Effect><nano particle><nano-sized particle><nanosized particle><nanoparticle><Coupled><early life exposure><case-by-case basis><ICP-MS><Inductively Coupled Plasma Mass Spectrometry><Data Analytics>\",\"pref_terms\":\"Animal Organ;Arsenic;Beverages;Biological;Blood;Budgets;Cancer Center;Cancer Center Support Grant;Carcinogens;Catchment Area;Cells;Centers for Disease Control and Prevention (U.S.);Cereals;Chemicals;Chromatography;Coupled;Data;Data Analytics;Elements;Ensure;Exposure to;Fishes;Funding;Hair;Health Policy;Human;Individual;Inductively Coupled Plasma Mass Spectrometry;Infant;Inorganic Chemistry;Institution;International;Invertebrates;Iron;Link;Malignant Neoplasms;Measures;Methods;Monitor;NCI Center for Cancer Research;Norris Cotton Cancer Center;Nutrient;Placenta;Platinum;Play;Population Sciences;Preparation;Protocols documentation;Public Health;Quality Control;Research;Research Personnel;Resource Sharing;Resources;Role;Running;Sampling;Selenium;Services;Soil;Structure of nail of toe;Technology;Testing;Therapeutic Effect;Therapeutic Uses;Tissues;Trace Elements;Urine;Water;Work;analytical method;animal tissue;base;bioimaging;case-by-case basis;early life exposure;granite;instrument;instrumentation;member;nanoparticle;programs;quality assurance\",\"abstract_text\":\"Project Summary/Abstract: Trace Elements\\nThe chemistry of inorganic elements is linked inextricably with the study of cancer. This is especially true for\\nour catchment area, given the high level of inorganic element exposures typical of “the Granite State”. Whether\\nstudying known carcinogens, such as arsenic, the potential antagonistic effects of elements, such as selenium,\\nor the therapeutic uses of platinum or iron nanoparticles, there is a clear need for both routine and advanced\\ntrace element analytical methods to support NCCC research. The Trace Elements (TE) Shared Resource\\nprovides access to low-level inorganic elemental analysis to all four research programs within the Cancer\\nCenter. TE uses state-of-the-art ICP-MS instruments to quantify multiple elements and, when coupled with\\nchromatography, multiple contaminant compounds (species) in a variety of biological and environmental\\nmedia. Cutting-edge ICP-MS instrumentation (e.g., triple-quadrupole ICP-QQQ) provides rapid, interference-\\nfree analysis of established and emerging contaminants and macro- and micro-nutrient elements. The breadth\\nof sample types handled by TE spans water, soil, sediment, invertebrates, fish and animal tissues and organs,\\ncell lysates, human hair, toenail, blood, urine, placenta, foodstuffs, and beverages. The ability to handle such a\\nbroad array of sample types is a unique feature of TE. In some cases, the sample mass available for analysis\\nis <10 mg, and methods often are adapted to account for small sample masses on a case-by-case basis.\\nAnalytical data must be collected according to accepted protocols for sample preparation, analysis, and quality\\ncontrol. TE has established protocols and a Quality Assurance program, uses methods based on those of the\\nCDC, FDA, EPA, and USGS; and participates in national and international proficiency testing programs to\\nensure accurate, precise, and defensible data. TE has very strong links to the Cancer Population Sciences\\n(CPS) Program. TE is an internationally-recognized center for low-level analysis of chemical elements and\\ntheir compounds in biological tissues. TE has provided services for 27 other US academic institutions over the\\npast funding cycle and fulfills a unique niche in providing state-of-the-art analytical services in trace elemental\\nanalysis. Over the last funding cycle, TE has run 6,721 samples for 12 NCCC Members from all 4 NCCC\\nPrograms (CPS [1], CBT [1], ICI [2], and TEC [8]). Funded NCCC Members have represented 15% (10) of\\nTotal Users, and we are requesting only 3% of the Total TE Budget from CCSG support. One focus of the CPS\\nProgram is early life exposure to arsenic, and TE is integral to the determination and speciation of arsenic for\\nthis project. This work has directly informed state and national public health policy. For example, the US FDA\\nannounced a proposed limit for inorganic arsenic in infant cereal of 100 ppb in 2016. For the TEC Program, TE\\nprovided a method for measuring iron nanoparticles in animal tissue. TE has contributed to CBT investigators\\nmonitoring the therapeutic effects of platinum. TE is demonstrably an essential, efficient and high-impact\\nresource that continues to play a pivotal role in NCCC research.\",\"project_title\":\"Trace Elements (TE)\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":12850,\"indirect_cost_amt\":8224,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10554268\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10513818,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R24ES030893-04\",\"project_serial_num\":\"ES030893\",\"organization\":{\"org_name\":\"JOHNS HOPKINS UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"BALTIMORE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MD\",\"org_state_name\":null,\"dept_type\":\"OTHER HEALTH PROFESSIONS\",\"fips_country_code\":null,\"org_duns\":[\"001910777\"],\"org_ueis\":[\"FTMTDMBR29C7\"],\"primary_duns\":\"001910777\",\"primary_uei\":\"FTMTDMBR29C7\",\"org_fips\":\"US\",\"org_ipf_code\":\"4134401\",\"org_zipcode\":\"212182680\",\"external_org_id\":4134401},\"award_type\":\"5\",\"activity_code\":\"R24\",\"award_amount\":381953,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R24\",\"ic_code\":\"ES\",\"serial_num\":\"030893\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":8085044,\"first_name\":\"HEATHER\",\"middle_name\":\"E\",\"last_name\":\"VOLK\",\"is_contact_pi\":true,\"full_name\":\"HEATHER E VOLK\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"VOLK, HEATHER E\",\"program_officers\":[{\"first_name\":\"Melissa\",\"middle_name\":\"M\",\"last_name\":\"Smarr\",\"full_name\":\"Melissa M Smarr\"}],\"agency_ic_admin\":{\"code\":\"ES\",\"abbreviation\":\"NIEHS\",\"name\":\"National Institute of Environmental Health Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"ES\",\"name\":\"National Institute of Environmental Health Sciences\",\"abbreviation\":\"NIEHS\",\"total_cost\":381953.0}],\"cong_dist\":\"MD-07\",\"spending_categories\":null,\"project_start_date\":\"2020-01-24T12:01:00Z\",\"project_end_date\":\"2024-10-31T12:10:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF PUBLIC HEALTH\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"RFA-ES-18-009\",\"full_study_section\":{\"srg_code\":\"ZES1\",\"srg_flex\":\"\",\"sra_designator_code\":\"LKB\",\"sra_flex_code\":\"K\",\"group_code\":\"\",\"name\":\"ZES1-LKB-K(R)\"},\"award_notice_date\":\"2022-10-28T12:10:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"R24ES030893\",\"terms\":\"<Affect><Biological Assay><Assay><Bioassay><Biologic Assays><Blood><Blood Reticuloendothelial System><California><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Child Development><Infant and Child Development><Communities><comorbidity><co-morbid><co-morbidity><Data Collection><Environment><Environmental Exposure><Environmental Health><Environmental Health Science><Genes><Goals><Hair><Information Systems><Data Systems><IT Systems><Information Technology Systems><Lead><Pb element><heavy metal Pb><heavy metal lead><Life Cycle Stages><Life Cycle><life course><Maintenance><Medical Records><Methods><Phenotype><Placenta><Cells Placenta-Tissue><Normal Placentoma><Placenta Embryonic Tissue><Placentome><Play><Pregnancy><Gestation><Pregnant Women><expectant mother><expecting mother><pregnant mothers><Primary Prevention><Prospective Studies><Recurrence><Recurrent><Research><Research Design><Study Type><study design><Resources><Research Resources><Risk><Risk Factors><Role><social role><Seminal fluid><Semen><Siblings><Social Interaction><Surveys><Survey Instrument><Time><Tissues><Body Tissues><Tooth structure><Tooth><teeth><deciduous tooth><Primary Tooth><Temporary Tooth><Universities><Urine><Urine Urinary System><Measures><Family member><Umbilical Cord Blood><Cord Blood><fetal cord blood><autistic children><children with ASD><children with autism><children with autism spectrum disorder><method development><Biological><biologic><Medical><Molecular Epidemiology><Biological Specimen Banks><Biological Substance Banks><biological specimen repository><biosample repository><biospecimen bank><biospecimen repository><specimen bank><specimen repository><disability><Data Bases><data base><Databases><school age><School-Age Population><Measurement><Sample Size><Collaborations><Heavy Metals><Life><Investigation><Frequencies><Event><Home environment><Protocols documentation><Protocol><Pattern><Visit><data management><early childhood><cohort><trait><prenatal><unborn><novel><disorder prevention><disease prevention><Secondary Prevention><Population Study><population-based study><population-level study><studies of populations><study of the population><General Population><General Public><Environmental Risk Factor><Environmental Factor><environmental risk><Prevention><Coding System><Code><Sampling><exposed in utero><fetal exposure><in utero exposure><intra-uterine environmental exposure><intrauterine environmental exposure><prenatally exposed><prenatal exposure><Causality><causation><disease causation><Etiology><Address><Symptoms><Autism><Autistic Disorder><Early Infantile Autism><Infantile Autism><Kanner's Syndrome><autistic spectrum disorder><autism spectrum disorder><Aliquot><Data><Reproducibility><Retrieval><Collection><Environmental Epidemiology><Molecular><developmental><Development><Neurological Development Disorder><Neurodevelopmental Disorder><pathway><Pathway interactions><cost><designing><design><Neurodevelopmental Disability><Outcome><cost effective><Prevalence><prospective><social communication><innovate><innovative><innovation><toxic exposure><Toxicant exposure><malleable risk><modifiable risk><data sharing><BigData><Big Data><Data Science><Data Analytics><phenotypic data><recruit><Child Health><Infrastructure><data harmonization><harmonized data>\",\"pref_terms\":\"Address;Affect;Aliquot;Big Data;Biological;Biological Assay;Biological Specimen Banks;Blood;California;Child;Child Development;Child Health;Code;Collaborations;Collection;Communities;Data;Data Analytics;Data Collection;Data Science;Databases;Development;Environment;Environmental Epidemiology;Environmental Exposure;Environmental Health;Environmental Risk Factor;Etiology;Event;Family member;Frequencies;General Population;Genes;Goals;Hair;Heavy Metals;Home environment;Information Systems;Infrastructure;Investigation;Lead;Life;Life Cycle Stages;Maintenance;Measurement;Measures;Medical;Medical Records;Methods;Molecular;Molecular Epidemiology;Neurodevelopmental Disability;Neurodevelopmental Disorder;Outcome;Pathway interactions;Pattern;Phenotype;Placenta;Play;Population Study;Pregnancy;Pregnant Women;Prevalence;Prevention;Primary Prevention;Prospective Studies;Protocols documentation;Recurrence;Reproducibility;Research;Research Design;Resources;Retrieval;Risk;Risk Factors;Role;Sample Size;Sampling;School-Age Population;Secondary Prevention;Seminal fluid;Siblings;Social Interaction;Surveys;Symptoms;Time;Tissues;Tooth structure;Toxicant exposure;Umbilical Cord Blood;Universities;Urine;Visit;autism spectrum disorder;autistic children;cohort;comorbidity;cost;cost effective;data harmonization;data management;data sharing;deciduous tooth;design;disability;disorder prevention;early childhood;innovation;method development;modifiable risk;novel;phenotypic data;prenatal;prenatal exposure;prospective;recruit;social communication;trait\",\"abstract_text\":\"Neurodevelopmental disorders (NDDs), including ASD, affect over 15 percent of US children –\\nAutism Spectrum Disorder (ASD) alone affects approximately 1.5% of US children. Identification\\nof underlying causes can lead to primary and secondary prevention efforts. Substantial evidence\\nsupports contribution of both genes and environment, particularly prenatal exposures.\\nProspective studies, with exposure information collected during pregnancy, prior to symptom\\nonset, are sorely needed although prospective studies in the general population are not feasible,\\ncost effective, or efficient for ASD. The enriched-familial-risk design, which capitalizes on the\\nsubstantial recurrence risk of both ASD and NDD among siblings of children with ASD, has been\\nemployed to achieve prospective data collection with reasonable outcome events. The Early\\nAutism Risk Longitudinal Investigation (EARLI) study has helped to trailblaze this enriched risk\\ncohort approach. EARLI recruited and followed over 260 pregnant women who already had a\\nchild with ASD with multiple study visits, child assessments, biosampling, and home environment\\nsurveys over pregnancy and the first 3 years of life. While the total sample size was constrained;\\nthe value of EARLI is the depth of longitudinal data and molecular measurements across multiple\\ntissue types and early developmental windows. To date, over 9.4 billion data points have been\\ngenerated across multiple phenotypes, tissues, exposures, and -omic measures. The current\\nproposal seeks to maintain and enrich this valuable study to fully realize its impact on\\nenvironmental health. The goals are to: 1. Maintain and extend the EARLI Biosample Repository\\nthrough support of the storage and retrieval efforts and addition and storage of shed baby teeth\\nthat will enable longitudinal prenatal exposure measures across a growing array of toxicant\\nexposures. 2. Measure frequency and patterns of emerging co-occurring conditions at school age\\nto better characterize phenotypes across co-occurring NDDs and quantitative traits. 3.\\nCharacterize variability of heavy metals measurement across timing, family members, and\\nmatrices including maternal blood and urine and shed baby teeth. 4. Develop and apply methods\\nfor phenotype and for exposure harmonization using latent constructs, enabling within and cross-\\ncohort investigations; 5. Migrate, enhance, and increase the reproducibility of EARLI research\\ndata to enable data sharing. The EARLI study is a valuable asset to early life environmental\\nepidemiology of neurodevelopmental outcomes. Conducting these aims will allow us to maximize\\nthe contribution of this novel study to the understanding of ASD and NDD etiology.\",\"project_title\":\"Expanding the Value of the EARLI study: Small Cohort with Big Data\",\"phr_text\":\"A better understanding of what causes autism and other neurodevelopmental disabilities can lead\\nto better treatment and ultimately prevention of onset or disabilities. Although we know genes\\nand prenatal and early life environments play a role, we do not yet understand the specific factors\\nand need rigorous studies that follow pregnancies and early childhood factors as they occur, prior\\nto symptom onset. The EARLI study is designed in this way, with many types of data about\\nenvironments, medical conditions, early child development, and biological samples like blood,\\nurine, hair, semen and placenta that can contribute to the child environmental health research\\ncommunity.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-11-01T12:11:00Z\",\"budget_end\":\"2023-10-31T12:10:00Z\",\"cfda_code\":\"113\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":267695,\"indirect_cost_amt\":114258,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10513818\",\"date_added\":\"2022-11-05T04:11:40Z\"},{\"appl_id\":10543494,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K24DC012801-10\",\"project_serial_num\":\"DC012801\",\"organization\":{\"org_name\":\"NORTHWESTERN UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"Chicago\",\"org_country\":\"UNITED STATES\",\"org_state\":\"IL\",\"org_state_name\":null,\"dept_type\":\"OTHER HEALTH PROFESSIONS\",\"fips_country_code\":null,\"org_duns\":[\"160079455\"],\"org_ueis\":[\"EXZVPWZBLUE8\"],\"primary_duns\":\"160079455\",\"primary_uei\":\"EXZVPWZBLUE8\",\"org_fips\":\"US\",\"org_ipf_code\":\"6144601\",\"org_zipcode\":\"606114579\",\"external_org_id\":6144601},\"award_type\":\"5\",\"activity_code\":\"K24\",\"award_amount\":183209,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K24\",\"ic_code\":\"DC\",\"serial_num\":\"012801\",\"support_year\":\"10\",\"full_support_year\":\"10\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":2409380,\"first_name\":\"BONNIE\",\"middle_name\":\"J\",\"last_name\":\"MARTIN-HARRIS\",\"is_contact_pi\":true,\"full_name\":\"BONNIE J MARTIN-HARRIS\",\"title\":\"\"}],\"contact_pi_name\":\"MARTIN-HARRIS, BONNIE J\",\"program_officers\":[{\"first_name\":\"Alberto\",\"middle_name\":\"L.\",\"last_name\":\"Rivera-Rentas\",\"full_name\":\"Alberto L. Rivera-Rentas\"}],\"agency_ic_admin\":{\"code\":\"DC\",\"abbreviation\":\"NIDCD\",\"name\":\"National Institute on Deafness and Other Communication Disorders\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"DC\",\"name\":\"National Institute on Deafness and Other Communication Disorders\",\"abbreviation\":\"NIDCD\",\"total_cost\":183209.0}],\"cong_dist\":\"IL-07\",\"spending_categories\":null,\"project_start_date\":\"2013-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"GRADUATE SCHOOLS\",\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PA-19-122\",\"full_study_section\":{\"srg_code\":\"CDRC\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Communication Disorders Review Committee[CDRC]\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K24DC012801\",\"terms\":\"<computer vision><Computers><Data Analyses><Data Analysis><data interpretation><Deglutition><Swallowing><Deglutition Disorders><Dysphagia><Swallowing Disorders><Diagnosis><Disease><Disorder><Economics><Engineering><Explosion><Patient Care><Patient Care Delivery><Geography><Goals><Grant><Health><Hearing><Laboratories><Medical Records><Mentors><Mentorship><Methods><Mission><Statistical Models><Probabilistic Models><Probability Models><statistical linear mixed models><statistical linear models><Modernization><United States National Institutes of Health><NIH><National Institutes of Health><Parkinson Disease><Paralysis Agitans><Parkinson><Parkinson's disease><Parkinsons disease><Primary Parkinsonism><Patient Care Planning><Patients><Phenotype><Productivity><Publishing><Registries><Research><research and development><Development and Research><R & D><R&D><Research Personnel><Investigators><Researchers><Science><Speech><Standardization><Stroke><Apoplexy><Brain Vascular Accident><Cerebral Stroke><Cerebrovascular Apoplexy><Cerebrovascular Stroke><brain attack><cerebral vascular accident><cerebrovascular accident><Supervision><Testing><Work><Computerized Medical Record><Electronic Medical Record><Data Set><Dataset><base><career><improved><Barium swallow><Area><Clinical><Biological><biologic><Physiological><Physiologic><Medical><Training><Individual><Sample Size><Funding><uptake><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Head and Neck Cancer><Malignant Head and Neck Neoplasm><head/neck cancer><malignant head and neck tumor><Machine Learning><machine learned><programs><Scientist><Severities><Pattern><Techniques><Dementia><Amentia><impression><computer science><skills><novel><career development><theories><Intervention Strategies><interventional strategy><Intervention><Bio-Informatics><Bioinformatics><Data><K23 Award><K23 Mechanism><K23 Program><Mentored Patient-Oriented Research Career Development Award (K23)><Mentored Patient-Oriented Research Career Development Award><K24 Award><Mid-Career Clinical Scientist Award (K24)><Reproducibility><Strategic Planning><nutritious><Nutritional><developmental><Development><electronic health care record><electronic healthcare record><Electronic Health Record><NIDCD><National Institute on Deafness and Other Communication Disorders><Instruction><digital><computerized><Outcome><Impairment><clinical relevance><clinically relevant><patient centered><patient oriented><high risk><clinical practice><Secure><data registry><BigData><Big Data><Scientific Inquiry><Data Science><improved outcome><experimental study><experiment><experimental research><data resource><human error><health assessment><data warehouse><Data Scientist><Big Data Methods><Big Data Analytics><Big Data Tools><learning algorithm><statistical learning><Chronic Obstructive Pulmonary Disease><COPD><Chronic Obstruction Pulmonary Disease><Chronic Obstructive Lung Disease><Algorithms><Award><Biomedical Research><Biometry><Biometrics><Biostatistics><Classification><Systematics><Communication><Communication impairment><Communication Disorders><Communicative Disorders><Computer Vision Systems>\",\"pref_terms\":\"Algorithms;Area;Award;Barium swallow;Big Data;Big Data Methods;Bioinformatics;Biological;Biomedical Research;Biometry;Chronic Obstructive Pulmonary Disease;Classification;Clinical;Communication;Communication impairment;Computer Vision Systems;Computerized Medical Record;Computers;Data;Data Analyses;Data Science;Data Scientist;Data Set;Deglutition;Deglutition Disorders;Dementia;Development;Diagnosis;Disease;Economics;Electronic Health Record;Engineering;Explosion;Funding;Geography;Goals;Grant;Head and Neck Cancer;Health;Hearing;Impairment;Individual;Instruction;Intervention;Laboratories;Machine Learning;Medical;Medical Records;Mentored Patient-Oriented Research Career Development Award;Mentors;Mentorship;Methods;Mid-Career Clinical Scientist Award (K24);Mission;Modernization;National Institute on Deafness and Other Communication Disorders;Nutritional;Outcome;Parkinson Disease;Patient Care;Patient Care Planning;Patients;Pattern;Phenotype;Physiological;Productivity;Publishing;Registries;Reproducibility;Research;Research Personnel;Research Project Grants;Sample Size;Science;Scientific Inquiry;Scientist;Secure;Severities;Speech;Standardization;Statistical Models;Strategic Planning;Stroke;Supervision;Techniques;Testing;Training;United States National Institutes of Health;Work;base;career;career development;clinical practice;clinically relevant;computer science;computerized;data registry;data resource;data warehouse;digital;experimental study;health assessment;high risk;human error;impression;improved;improved outcome;learning algorithm;novel;patient oriented;programs;research and development;skills;statistical learning;theories;uptake\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nThe emergence of electronic medical records, large data registries and readily accessible, protected servers have\\nresulted in an explosion of digital information with potentially high clinical impact for improving patient\\nmanagement and outcomes. Big data warehouses that capture standardized information within the scope of\\nclinical practices allow trained scientists to not only engage in traditional hypothesis testing, but to also\\nuncover new hypotheses, refine existing theories and apply new discoveries to health assessments and\\ninterventions. Despite the accessibility and potential impact of these data platforms, clinician scientists have\\ntraditionally directed experiments that incorporate relatively small sample sizes and data from individual\\nlaboratories, and have not been trained in big data analytics or in engaging appropriate team scientists who\\nwork in this space, such as computer scientists, biostatisticians and engineers. The overarching goal of this\\nproposal is to mentor early patient oriented communication and swallowing scientists in big data analytics and\\nto mentor and involve early data science scholars in communication and swallowing research. The PI proposes\\nfour primary mentorship and research goals in this K24 renewal proposal: 1. Train a cadre of early stage\\ncommunication and swallowing scientists in data science methods, including machine learning, by an expert,\\ninterdisciplinary, collaborative data science team, 2. Engage and introduce early career data scientists from\\nfields of biostatistics, computer science and engineering to communication and swallowing sciences, and\\nrespective data sets, toward facilitating interdisciplinary data science teams and research productivity, 3. Apply\\nnovel data science methods to identify phenotypes of swallowing impairment and severity classifications in\\npatient groups known to be at high risk for nutritional and health complications related to dysphagia, and 4.\\nDevelop a new area of research in machine learning applications toward improving reliability of physiologic\\nswallowing assessment. The data science theme of the career development and research plan directly align with\\nNIDCD's Strategic Plan for Data Science which lists as its mission: Storing, managing, standardizing and\\npublishing the vast amounts of data produced by biomedical research. NIDCD recognizes that accessible,\\nwell-organized, secure and efficiently operated data resources are critical to modern scientific inquiry…and\\nby maximizing the value of data generated through NIH-funded efforts, the pace of biomedical discoveries\\nand medical breakthroughs for better health outcomes can be accelerated.\",\"project_title\":\"Data Science Applications in Communication andSwallowing Disorders\",\"phr_text\":\"PROJECT NARRATIVE\\nThe emergence of electronic health records exposes clinicians to massive amounts of information about the\\nmillions of patients who suffer from communication and swallowing disorders, yet most clinical scientists do\\nnot have the training or skill to apply meaning to the data toward improving patient care. The overarching\\nmentorship goal of this proposal is to train early, patient-oriented communication and swallowing scientists in\\nbig data analyses, including computer machine learning approaches. The research project will uncover distinct\\npatterns and severity of swallowing impairments in large groups of patients with high risk medical diagnoses,\\nwhich will have high impact on patient care planning and identification of treatments that directly target these\\nimpairments for improved outcomes.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"173\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":169638,\"indirect_cost_amt\":13571,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10543494\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10570160,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01CA249538-04\",\"project_serial_num\":\"CA249538\",\"organization\":{\"org_name\":\"UNIVERSITY OF PENNSYLVANIA\",\"city\":null,\"country\":null,\"org_city\":\"PHILADELPHIA\",\"org_country\":\"UNITED STATES\",\"org_state\":\"PA\",\"org_state_name\":null,\"dept_type\":\"RADIATION-DIAGNOSTIC/ONCOLOGY\",\"fips_country_code\":null,\"org_duns\":[\"042250712\"],\"org_ueis\":[\"GM1XX56LEP58\"],\"primary_duns\":\"042250712\",\"primary_uei\":\"GM1XX56LEP58\",\"org_fips\":\"US\",\"org_ipf_code\":\"6463801\",\"org_zipcode\":\"191046205\",\"external_org_id\":6463801},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":459797,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"CA\",\"serial_num\":\"249538\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14318598,\"first_name\":\"Jianan\",\"middle_name\":\"Grace\",\"last_name\":\"Gang\",\"is_contact_pi\":true,\"full_name\":\"Jianan Grace Gang\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"GANG, JIANAN GRACE\",\"program_officers\":[{\"first_name\":\"Pushpa\",\"middle_name\":\"\",\"last_name\":\"Tandon\",\"full_name\":\"Pushpa  Tandon\"}],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":459797.0}],\"cong_dist\":\"PA-03\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-19-056\",\"full_study_section\":{\"srg_code\":\"ITD\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Imaging Technology Development Study Section[ITD]\"},\"award_notice_date\":\"2022-12-19T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01CA249538\",\"terms\":\"<Adoption><Algorithms><Anatomy><Anatomic><Anatomic Sites><Anatomic structures><Anatomical Sciences><Beauty><Behavior><Dictionary><Genes><Lead><Pb element><heavy metal Pb><heavy metal lead><Libraries><Lung><Lung Respiratory System><pulmonary><Medical Imaging><Methods><Biological Models><Biologic Models><Model System><Statistical Models><Probabilistic Models><Probability Models><statistical linear mixed models><statistical linear models><Nodule><Noise><Patients><Play><Lung nodule><pulmonary nodule><Research><Role><social role><Signal Transduction><Cell Communication and Signaling><Cell Signaling><Intracellular Communication and Signaling><Signal Transduction Systems><Signaling><biological signal transduction><Technology><X-Ray Computed Tomography><CAT scan><CT X Ray><CT Xray><CT imaging><CT scan><Computed Tomography><Tomodensitometry><X-Ray CAT Scan><X-Ray Computerized Tomography><Xray CAT scan><Xray Computed Tomography><Xray computerized tomography><catscan><computed axial tomography><computer tomography><computerized axial tomography><computerized tomography><non-contrast CT><noncontrast CT><noncontrast computed tomography><Work><Imaging Techniques><Imaging Procedures><Imaging Technics><Measures><base><improved><Image Analysis><Image Analyses><image evaluation><image interpretation><Clinical><Non-linear Models><Nonlinear Models><Ensure><Evaluation><Training><Lesion><insight><Data Bases><data base><Databases><lung cancer screening><lung cancer early detection><Shapes><Diagnostic><Machine Learning><machine learned><exhaustion><Dimensions><Complex><Dependence><Scanning><Clinic><Protocols documentation><Protocol><Source><Texture><Techniques><System><Transcend><interest><Radiation Dose Unit><Radiation Dose><Performance><success><simulation><novel><Appearance><Modeling><Sampling><Property><response><Address><Dose><Electronic library><Digital Libraries><Data><Scheme><Derivation><Derivation procedure><developmental><Development><imaging><Image><Output><reconstruction><shape description><shape analysis><designing><design><Outcome><clinical relevance><clinically relevant><clinical applicability><clinical application><flexible><flexibility><screening><3-D print><3-D printer><3D printer><3D printing><three dimensional printing><3D Print><CT Lung Screening><Lung CT><Lung CT scan><computerized tomography lung screening><lung computerized axial tomography scan><lung computerized tomography><lung computerized tomography scan><Lung CAT Scan><quantitative imaging><imaging system><targeted imaging><Predictive Analytics><prediction of response><predictive response><predictor of response><response prediction><predicting response><radiomics><clinical translation><deep neural network><deep learning based neural network><deep learning neural network><deep neural net><deep learning><neural network><deep learning algorithm><machine learning method><machine learning based method><machine learning methodologies><low dose computed tomography><low dose computerized tomography><low-dose CT><lung lesion><pulmonary lesion><data-driven model><public database><public data base><publicly accessible data base><publicly accessible database><publicly available data base><publicly available database>\",\"pref_terms\":\"3D Print;Address;Adoption;Algorithms;Anatomy;Appearance;Beauty;Behavior;Biological Models;Clinic;Clinical;Complex;Data;Databases;Dependence;Derivation procedure;Development;Diagnostic;Dictionary;Digital Libraries;Dimensions;Dose;Ensure;Evaluation;Genes;Image;Image Analysis;Imaging Techniques;Lead;Lesion;Libraries;Lung;Lung CAT Scan;Lung nodule;Machine Learning;Measures;Medical Imaging;Methods;Modeling;Nodule;Noise;Non-linear Models;Outcome;Output;Patients;Performance;Play;Predictive Analytics;Property;Protocols documentation;Radiation Dose Unit;Research;Role;Sampling;Scanning;Scheme;Shapes;Signal Transduction;Source;Statistical Models;System;Techniques;Technology;Texture;Training;Transcend;Work;X-Ray Computed Tomography;base;clinical application;clinical translation;clinically relevant;data-driven model;deep learning;deep learning algorithm;deep neural network;design;exhaustion;flexibility;imaging system;improved;insight;interest;low dose computed tomography;lung cancer screening;lung lesion;machine learning method;neural network;novel;predicting response;public database;quantitative imaging;radiomics;reconstruction;response;screening;shape analysis;simulation;success;targeted imaging\",\"abstract_text\":\"1\\nPROJECT SUMMARY / ABSTRACT\\n 2 Nonlinear algorithms such as model-based reconstruction (MBR) and deep learning (DL) reconstruction have\\n 3 sparked tremendous research interest in recent years. Compared to traditional linear approaches, the nonline-\\n 4 arity of these algorithm transcends traditional signal-to-noise requirement and offer flexibility to draw information\\n 5 from a variety of sources (e.g., statistical model, prior image, dictionary, training data). MBR has enabled numer-\\n 6 ous advancements including low-dose CT and advanced scanning protocols. Deep learning algorithms are rap-\\n 7 idly emerging and have demonstrated superior dose vs. image quality tradeoffs in research settings. However,\\n 8 widespread clinical adoption of nonlinear algorithms has been impeded by the lack of a lack of systematic, quan-\\n 9 titative methods for performance analysis. Nonlinear methods come with numerous dependencies on the imag-\\n10 ing techniques, the imaging target, and the prior information, and the data itself. The relationship between these\\n11 dependencies and image quality is often opaque. Furthermore, improper selection of algorithmic parameters can\\n12 lead to erroneous features (e.g., smaller lesions, texture) in the reconstruction. Therefore, methods to quantify\\n13 and predict performance permit efficient and quantifiable performance evaluation to provide the robust control\\n14 and understanding of imaging output necessary for reliable clinical application and regulatory oversight.\\n15 We propose to establish a robust, predictive framework for performance assessment and optimization that can\\n16 be generalized to any reconstruction method. We quantify performance in turns of the perturbation response and\\n17 covariance as a function of imaging techniques, system configurations, patient anatomy, and, importantly, the\\n18 perturbation itself. The perturbation response quantifies the appearance (e.g., biases, blurs, distortions), and,\\n19 together with the covariance, allows the computation of more complex metrics such as task-based performance\\n20 and radiomic measures including size, shape, and texture information. We illustrate utility of the approach in lung\\n21 imaging with the following specific aims: Aim 1: Develop a lesion library and generate perturbations encom-\\n22 passing clinically relevant features. We will extract lesions from public databases and develop methods lesion\\n23 emulation in for realistic CT simulation and physical data via 3D printing technology. Aim 2: Develop a gener-\\n24 alized prediction framework for perturbation response and covariance. Using analytical and neural network\\n25 modeling, we will establish a framework that predicts perturbation response and covariance across imaging\\n26 scenarios for classes of algorithms with increasing data-dependence including MBR with a Huber penalty, MBR\\n27 with dictionary regularization, and a deep learning reconstructor. Aim 3: Develop assessment and optimiza-\\n28 tion strategies to drive robust, low dose lung screening CT methods. We will optimize and adapt nonlinear\\n29 algorithms and protocols for lung cancer screening to achieve faithful representations of clinical features. This\\n30 work has the potential to drive much-needed quantitative assessment standards that directly relate image quality\\n31 to diagnostic performance and optimal strategies for robust, reliable clinical deployment of nonlinear algorithms.\\n32\",\"project_title\":\"Nonlinear performance analysis and prediction for robust low dose lung CT\",\"phr_text\":\"PROJECT NARRATIVE\\nMajor research efforts have been devoted to the development of nonlinear reconstruction algorithms – from\\nmodel-based reconstruction to deep learning, these algorithms have demonstrated many advantages such as\\nimproved image quality, reduced radiation dose, and additional diagnostic information that are not achievable\\nwith traditional linear reconstructions. However, only a disproportionately small number has reach the clinic\\ndue to the lack of a predictive image quality analysis framework to quantify diagnostic performance, control\\nalgorithm behavior, and ensure consistent performance for robust clinical deployment. The propose effort use\\na combination of analytic and machine learning approaches to drive much-needed quantitative assessment\\nstandards that directly relate image quality to diagnostic performance and establish optimal strategies for\\nrobust, reliable clinical deployment of nonlinear algorithms.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"394\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":383956,\"indirect_cost_amt\":75841,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10570160\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10515332,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R13EY032326-03\",\"project_serial_num\":\"EY032326\",\"organization\":{\"org_name\":\"UNIVERSITY OF MIAMI SCHOOL OF MEDICINE\",\"city\":null,\"country\":null,\"org_city\":\"CORAL GABLES\",\"org_country\":\"UNITED STATES\",\"org_state\":\"FL\",\"org_state_name\":null,\"dept_type\":\"OPHTHALMOLOGY\",\"fips_country_code\":null,\"org_duns\":[\"052780918\"],\"org_ueis\":[\"F8THLJQSAF93\"],\"primary_duns\":\"052780918\",\"primary_uei\":\"F8THLJQSAF93\",\"org_fips\":\"US\",\"org_ipf_code\":\"5221250\",\"org_zipcode\":\"331462926\",\"external_org_id\":5221250},\"award_type\":\"5\",\"activity_code\":\"R13\",\"award_amount\":29000,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R13\",\"ic_code\":\"EY\",\"serial_num\":\"032326\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":6841150,\"first_name\":\"Sanjoy\",\"middle_name\":\"K\",\"last_name\":\"Bhattacharya\",\"is_contact_pi\":true,\"full_name\":\"Sanjoy K Bhattacharya\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"BHATTACHARYA, SANJOY K\",\"program_officers\":[{\"first_name\":\"HOUMAM\",\"middle_name\":\"H\",\"last_name\":\"ARAJ\",\"full_name\":\"HOUMAM H ARAJ\"}],\"agency_ic_admin\":{\"code\":\"EY\",\"abbreviation\":\"NEI\",\"name\":\"National Eye Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"EY\",\"name\":\"National Eye Institute\",\"abbreviation\":\"NEI\",\"total_cost\":29000.0}],\"cong_dist\":\"FL-27\",\"spending_categories\":null,\"project_start_date\":\"2020-12-01T12:12:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-18-648\",\"full_study_section\":{\"srg_code\":\"ZEY1\",\"srg_flex\":\"\",\"sra_designator_code\":\"VSN\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"ZEY1-VSN(06)\"},\"award_notice_date\":\"2022-11-18T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"R13EY032326\",\"terms\":\"<Age><ages><Award><Biotechnology><Biotech><Budgets><Clinical Research><Clinical Study><Creativeness><creativity><Diagnosis><Disease><Disorder><Environment><Ethnic group><Ethnic People><Ethnic Population><Ethnic individual><Ethnicity People><Ethnicity Population><ethnicity group><Eye><Eyeball><Eye diseases><eye disorder><ophthalmopathy><Feedback><Fellowship><Florida><Foundations><Future><Goals><Grant><Industry><Leadership><Learning><Medicine><Metabolism><Intermediary Metabolism><Metabolic Processes><Mission><Persons><Occupations><Jobs><Professional Positions><Peer Review><Drug Kinetics><Pharmacokinetics><Pharmacology><Pharmacy (field)><Pharmaceutics><Private Practice><Publications><Scientific Publication><Natural regeneration><Regeneration><regenerate><Research><Research Personnel><Investigators><Researchers><Rest><Retina><Safety><medical schools><medical college><school of medicine><Societies><Students><Technology><Translations><Travel><Vision><Sight><visual function><Woman><Drug Delivery Systems><Drug Delivery><symposium><conference><convention><summit><symposia><Schedule><Caring><Journals><Magazine><career><Anterior><Clinical><insight><Discipline><Individual><Fostering><Logistics><Funding><Collaborations><editorial><Therapeutic><Nature><Knowledge><posters><lectures><Scientist><Oral><Clinic><interest><meetings><Posterior eyeball segment structure><Posterior Eye Segment><optic nerve regeneration><neuroprotection><Stretching><novel><Participant><member><Basic Science><Basic Research><Devices><social><Positioning Attribute><Position><Intervention Strategies><interventional strategy><Intervention><unpublished works><Pharmaceutical Agent><Pharmaceuticals><Pharmacological Substance><Pharmacologic Substance><Regenerative Medicine><Institution><Academia><International><NIH RFA><Request for Applications><eye morphogenesis><ocular development><Eye Development><Translational Science><translation research><Translational Research><Underrepresented Ethnic Minority><under-representation of minorities><under-represented minority><underrepresentation of minorities><Underrepresented Minority><developmental><Development><Health Care Professional><Healthcare professional><Health Professional><preclinical><pre-clinical><website><web site><Mind><Vision research><new drug treatments><new drugs><new therapeutics><new therapy><next generation therapeutics><novel drug treatments><novel drugs><novel therapy><novel therapeutics><new therapeutic approach><new therapeutic intervention><new therapeutic strategies><new therapy approaches><novel therapeutic approach><novel therapeutic strategies><novel therapy approach><novel therapeutic intervention><ethnic diversity><ethnically diverse><retinal ganglion cell regeneration><Big Data Methods><Big Data Analytics><Big Data Tools><sight restoration><restore sight><restore vision><vision restoration><regenerative approach><regenerative strategy><regenerative technique><Minority Women><Minority Female>\",\"pref_terms\":\"Academia;Age;Anterior;Award;Basic Science;Big Data Methods;Biotechnology;Budgets;Caring;Clinic;Clinical;Clinical Research;Collaborations;Creativeness;Development;Devices;Diagnosis;Discipline;Disease;Drug Delivery Systems;Drug Kinetics;Environment;Ethnic group;Eye;Eye Development;Eye diseases;Feedback;Fellowship;Florida;Fostering;Foundations;Funding;Future;Goals;Grant;Health Professional;Individual;Industry;Institution;International;Intervention;Journals;Knowledge;Leadership;Learning;Logistics;Medicine;Metabolism;Mind;Minority Women;Mission;Natural regeneration;Nature;Occupations;Oral;Participant;Peer Review;Persons;Pharmacologic Substance;Pharmacology;Pharmacy (field);Positioning Attribute;Posterior eyeball segment structure;Private Practice;Publications;Regenerative Medicine;Request for Applications;Research;Research Personnel;Rest;Retina;Safety;Schedule;Scientist;Societies;Stretching;Students;Technology;Therapeutic;Translational Research;Translations;Travel;Underrepresented Minority;Vision;Vision research;Woman;career;editorial;ethnic diversity;insight;interest;lectures;medical schools;meetings;member;neuroprotection;novel;novel therapeutic intervention;novel therapeutics;optic nerve regeneration;posters;pre-clinical;regenerative approach;retinal ganglion cell regeneration;sight restoration;social;symposium;unpublished works;web site\",\"abstract_text\":\"Project Summary\\nThe present application requests funds to support travel for young scientists to attend the XV Biennial Meeting\\nof the Association for Ocular Pharmacology and Therapeutics (AOPT) in Miami from March 4th-7th, 2021. This\\nis a conference that brings together scientists who are developing new ways to treat eye diseases. AOPT is a\\nglobal not-for-profit 501(c)3 organization for scientists and individuals from all disciplines related to ocular\\npharmacology and its therapeutic applications to eye disease. AOPT has a diverse, multi-national membership\\ncomposed of preclinical and clinical scientists, students, and healthcare professionals. Members are from\\nacademic institutions, pharma and biotech industries, device companies, clinics and private practice. To\\nachieve its mission, the society organizes biennial conferences and provides editorial leadership for the peer-\\nreviewed Journal of Ocular Pharmacology and Therapeutics. The mission of AOPT is threefold (from AOPT\\nwebsite: www.aopt.org): (1) To serve as a global forum and network for the publication, dissemination and\\nexchange of information and knowledge on treatments of eye diseases, from basic and clinical ocular\\npharmacology and therapeutics to related disciplines such as pharmacokinetics and dynamics, metabolism,\\ntranslational research, safety, drug delivery, and pharmaceutics; (2)To promote the advancement of research\\nand scientific progress in these fields; (3) To assist researchers and eye-care professionals in furthering their\\nunderstanding of the treatment of ocular disorders. In line with the AOPT mission, the specific Aims of the R13\\napplication is: (I) To enhance the emerging careers of 20 young scientists (including women and\\nunderrepresented minorities) in ocular therapeutics by providing travel awards; (II) To provide a forum for\\ndissemination of the most recent advances in the state of knowledge on novel therapeutic approaches to\\nocular disorders and disease; (III) To create an environment that will facilitate the exchange of novel ideas\\namong basic and clinician scientists and generate the opportunity for vision scientists of all ethnic groups and\\nsocial backgrounds to meet and establish research collaborations. The theme of AOPT-2021 is “Restoring\\nvision through Regeneration” and a number of sessions are scheduled which are focused on regenerative\\nmedicine related to anterior as well as posterior eye segments.\",\"project_title\":\"XV Association for Ocular Pharmacology and Therapeutics Meeting (AOPT 2021)\",\"phr_text\":\"Project Narrative\\nThe Association for Ocular Pharmacology and Therapeutics (AOPT) is a global not-for-profit 501(c)3\\norganization for scientists and individuals from all disciplines related to ocular pharmacology and its therapeutic\\napplications to eye disease. The present application requests funds to support travel for 20 young scientists to\\nattend the XV Biennial AOPT Meeting, a conference that brings together scientists who are developing new\\nways to treat eye disease, in Miami from March 4th-7th, 2021.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"867\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":29000,\"indirect_cost_amt\":0,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10515332\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10636774,\"subproject_id\":\"7834\",\"fiscal_year\":2023,\"project_num\":\"5P30CA015704-48\",\"project_serial_num\":\"CA015704\",\"organization\":{\"org_name\":\"FRED HUTCHINSON CANCER CENTER\",\"city\":null,\"country\":null,\"org_city\":\"Seattle\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"806433145\"],\"org_ueis\":[\"TJFZLPP6NYL6\"],\"primary_duns\":\"806433145\",\"primary_uei\":\"TJFZLPP6NYL6\",\"org_fips\":\"US\",\"org_ipf_code\":\"10068583\",\"org_zipcode\":\"98109\",\"external_org_id\":10068583},\"award_type\":\"5\",\"activity_code\":\"P30\",\"award_amount\":374776,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P30\",\"ic_code\":\"CA\",\"serial_num\":\"015704\",\"support_year\":\"48\",\"full_support_year\":\"48\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":10082895,\"first_name\":\"Philip\",\"middle_name\":\"R\",\"last_name\":\"Gafken\",\"is_contact_pi\":true,\"full_name\":\"Philip R Gafken\",\"title\":\"\"}],\"contact_pi_name\":\"GAFKEN, PHILIP R\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":374776.0}],\"cong_dist\":\"WA-07\",\"spending_categories\":null,\"project_start_date\":\"1997-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"NP\",\"is_other\":false},\"full_foa\":\"PAR-17-095\",\"full_study_section\":{\"srg_code\":\"NCI\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Cancer Centers Study Section (A)[NCI-A]\"},\"award_notice_date\":\"2022-12-21T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P30CA015704\",\"terms\":\"<metabolomics><small molecule><Educational Materials><Proteomics Shared Resource><Research Training><Resource Sharing><Analytical Biochemistry><Cancer Biology><CCSG><Cancer Center Support Grant><Cancer Patient><Slice><Preparation><Molecular><Process><Modification><Tracer><developmental><Development><designing><design><cost effective><effective treatment><effective therapy><operation><metabolomics resource><experimental study><experiment><experimental research><Infrastructure><education resources><educational resources><advanced analytics><complex data><lipidomics><Awareness><Bile Acids><Biological Assay><Assay><Bioassay><Biologic Assays><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Analytical Chemistry><Analytic Chemistry><Chromatography><Gas Chromatography><vapor phase chromatography><High Pressure Liquid Chromatography><HPLC><High Performance Liquid Chromatography><High Speed Liquid Chromatography><Coenzymes><Enzyme Cofactors><coenzyme analog><Consultations><Data Analyses><Data Analysis><data interpretation><Statistical Data Interpretation><Statistical Data Analyses><Statistical Data Analysis><statistical analysis><Digestion><Enzymes><Enzyme Gene><Equipment><Experimental Designs><Volatile Fatty Acids><Short-Chain Fatty Acids><Feedback><Fractionation><Chemical Fractionation><FRACN><Fractionation Radiotherapy><Future><Gel><Grant><Isotopes><Lipids><Major Histocompatibility Complex><Histocompatibility Complex><Histocompatibility Complices><Major Histocompatibility Complices><Methods><Methodology><Mission><Modernization><Nuclear Magnetic Resonance><Peptides><Productivity><Post-Translational Protein Processing><Post-Translational Modification Protein/Amino Acid Biochemistry><Post-Translational Modifications><Post-Translational Protein Modification><Posttranslational Modifications><Posttranslational Protein Processing><Protein Modification><Proteins><Research><Research Personnel><Investigators><Researchers><Research Support><Resources><Research Resources><Mass Spectrum Analysis><Mass Photometry/Spectrum Analysis><Mass Spectrometry><Mass Spectroscopy><Mass Spectrum><Mass Spectrum Analyses><statistics><Technology><Testing><Time><Tissues><Body Tissues><Translating><Universities><Washington><ranpirnase><P-30 Protein><P30><P30 Protein><Measures><symposium><conference><convention><summit><symposia><base><improved><Area><Clinical><Biological><biologic><Training><Individual><Workshop><Educational workshop><Policies><cancer immunotherapy><anti-cancer immunotherapy><anticancer immunotherapy><immune-based cancer therapies><immunotherapy for cancer><immunotherapy of cancer><metabolic abnormality assessment><Abnormal Assessment of Metabolism><Metabolic Studies><Metabolism Studies><cancer prevention><Liquid substance><fluid><liquid><stable isotope><Metabolic><tool><instrument><programs><interest><Consult><Services><Training and Education><Education and Training><experience><knowledge base><Informatics><member><new technology><novel technologies><General Population><General Public><Human Resources><Manpower><personnel><Network Analysis><Pathway Analysis><Sampling><response><Proteomics><LC/MS><liquid chromatography mass spectrometry><NMR Spectrometer><nuclear magnetic resonance spectroscopy><NMR Spectroscopy><cancer immunology><neoplasm immunology><tumor immunology><cancer care><Bio-Informatics><Bioinformatics><metabolism measurement><metabonomics>\",\"pref_terms\":\"Analytical Biochemistry;Analytical Chemistry;Area;Awareness;Bile Acids;Bioinformatics;Biological;Biological Assay;Cancer Biology;Cancer Center Support Grant;Cancer Patient;Chromatography;Clinical;Coenzymes;Consult;Consultations;Data Analyses;Development;Digestion;Educational Materials;Educational workshop;Enzymes;Equipment;Experimental Designs;Feedback;Fractionation;Future;Gas Chromatography;Gel;General Population;Grant;High Pressure Liquid Chromatography;Human Resources;Individual;Informatics;Infrastructure;Isotopes;Lipids;Liquid substance;Major Histocompatibility Complex;Malignant Neoplasms;Mass Spectrum Analysis;Measures;Metabolic;Methodology;Methods;Mission;Modernization;Modification;Molecular;NMR Spectroscopy;Nuclear Magnetic Resonance;Pathway Analysis;Peptides;Policies;Post-Translational Protein Processing;Preparation;Process;Productivity;Proteins;Proteomics;Proteomics Shared Resource;Research;Research Personnel;Research Support;Research Training;Resource Sharing;Resources;Sampling;Services;Slice;Statistical Data Interpretation;Technology;Testing;Time;Tissues;Tracer;Training;Training and Education;Translating;Universities;Volatile Fatty Acids;Washington;advanced analytics;base;cancer care;cancer immunotherapy;cancer prevention;complex data;cost effective;design;education resources;effective therapy;experience;experimental study;improved;instrument;interest;knowledge base;lipidomics;liquid chromatography mass spectrometry;member;metabolic abnormality assessment;metabolomics;metabolomics resource;new technology;operation;programs;ranpirnase;response;small molecule;stable isotope;statistics;symposium;tool;tumor immunology\",\"abstract_text\":\"PROJECT SUMMARY: PROTEOMICS & METABOLOMICS SHARED RESOURCE (P&MSR)\\nThe Proteomics and Metabolomics Shared Resource (P&MSR) consists of the Fred Hutch (FH)-based\\nProteomics Facility and the University of Washington South Lake Union (UW-SLU) Metabolomics Facility. The\\nP&MSR Proteomics Facility was formed in 2002 and has been under the direction of Dr. Phil Gafken since its\\ninception. The P&MSR Metabolomics Facility was formed in 2013 and has been under the direction of Dr.\\nDaniel Raftery. The mission of the P&MSR is (1) to provide high-quality, cost-effective, and reliable service in a\\ntimely manner for proteomics and metabolomics experiments; (2) to develop and implement new omics\\nexperiments, assays, and tools to uncover the molecular details influencing cancer biology; and (3) to support\\nthe development of translatable and clinical tests. The P&MSR operates and maintains chromatography, mass\\nspectrometry, and nuclear magnetic resonance instruments and offers services such as experimental design\\nand consultation, sample preparation and chromatography, protein or metabolite identification and\\nquantification, protein modification characterization, targeted protein or metabolite quantification, metabolic flux\\nanalysis, and data analysis and informatics support. New technologies and services are continually being\\nevaluated based on feedback from Consortium members. Over the next granting period, areas of interest that\\nwill be investigated include the analysis of major histocompatibility complex peptides to support cancer\\nimmunology and immunotherapy research. For metabolomics we will add to our over 20 current assays by\\nexpanding our capabilities to measure metabolic flux, provide the capability to investigate real-time metabolism\\nstudies using stable isotope tracers, and develop higher throughput global profiling assays to support research\\nin cancer biology. With well-established operation policies, Consortium users have cost-effective, reliable, and\\nimmediate access to the P&MSR. The research supported by the P&MSR demonstrates that it is an important\\npart of the critical Shared Resources infrastructure needed to support the Fred Hutch/University of Washington\\nCancer Consortium.\",\"project_title\":\"Shared Resource: Proteomics & Metabolomics\",\"phr_text\":\"Not applicable to P30 CCSG as stated in the FOA PAR-17-095.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":259880,\"indirect_cost_amt\":114896,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10636774\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10543557,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01NS119622-02\",\"project_serial_num\":\"NS119622\",\"organization\":{\"org_name\":\"UNIVERSITY OF CALIFORNIA AT DAVIS\",\"city\":null,\"country\":null,\"org_city\":\"DAVIS\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":\"INTERNAL MEDICINE/MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"047120084\"],\"org_ueis\":[\"TX2DAGQPENZ5\"],\"primary_duns\":\"047120084\",\"primary_uei\":\"TX2DAGQPENZ5\",\"org_fips\":\"US\",\"org_ipf_code\":\"577503\",\"org_zipcode\":\"956186153\",\"external_org_id\":577503},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":499998,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"NS\",\"serial_num\":\"119622\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":1929878,\"first_name\":\"JONATHAN\",\"middle_name\":\"M\",\"last_name\":\"KIMMELMAN\",\"is_contact_pi\":false,\"full_name\":\"JONATHAN M KIMMELMAN\",\"title\":\"\"},{\"profile_id\":6968966,\"first_name\":\"Mark\",\"middle_name\":\"\",\"last_name\":\"Yarborough\",\"is_contact_pi\":true,\"full_name\":\"Mark  Yarborough\",\"title\":\"DEAN'S PROFESSOR OF BIOETHICS\"}],\"contact_pi_name\":\"YARBOROUGH, MARK \",\"program_officers\":[{\"first_name\":\"ADAM\",\"middle_name\":\"L.\",\"last_name\":\"HARTMAN\",\"full_name\":\"ADAM L. HARTMAN\"}],\"agency_ic_admin\":{\"code\":\"NS\",\"abbreviation\":\"NINDS\",\"name\":\"National Institute of Neurological Disorders and Stroke\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"NS\",\"name\":\"National Institute of Neurological Disorders and Stroke\",\"abbreviation\":\"NINDS\",\"total_cost\":499998.0}],\"cong_dist\":\"CA-03\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"SEIR\",\"sra_flex_code\":\"A\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-SEIR-A(80)S]\"},\"award_notice_date\":\"2022-12-13T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01NS119622\",\"terms\":\"<Brain><Brain Nervous System><Encephalon><Clinical Research><Clinical Study><Disclosure><Information Disclosure><Disease><Disorder><Investigational Drugs><Investigational New Drugs><Ethics><ethical><Future><Health><Human><Modern Man><Informed Consent><Interview><Judgment><Learning><Morals><Persons><nervous system disorder><Nervous System Diseases><Neurologic Disorders><Neurological Disorders><neurological disease><Occupations><Jobs><Professional Positions><Online Systems><On-Line Systems><online computer><web based><Organoids><Research><Research Personnel><Investigators><Researchers><Research Technics><Research Techniques><Resources><Research Resources><Risk><Safety><Computer software><Software><Specificity><Surveys><Survey Instrument><Technology><Testing><Translations><United States Dept. of Health and Human Services><Department of Health and Human Services><United States Department of Health and Human Services><United States Food and Drug Administration><Food and Drug Administration><USFDA><falls><Decision Aid><Research Ethics Committees><Uncertainty><doubt><improved><Clinical><Phase><tool><Life><Risk-Benefit Assessment><Structure><novel><Participant><member><Benefits and Risks><Modality><Modeling><Qualitative Research><Legal><Data><International><enroll><Enrollment><Characteristics><Process><Authorization><Permission><Authorization documentation><Text><preclinical><pre-clinical><pre-clinical study><preclinical study><Instruction><designing><design><clinical efficacy><therapeutic agent development><therapeutic development><effective treatment><effective therapy><pre-clinical efficacy><preclinical efficacy><pre-clinical safety><preclinical safety><safety testing><common rule><Phase I Study><phase 1 study><IRB><IRBs><Institutional Review Boards><phase 1 trial><phase I trial><Data Analytics><CRISPR/Cas technology><CRISPR approach><CRISPR based approach><CRISPR method><CRISPR methodology><CRISPR technique><CRISPR technology><CRISPR tools><CRISPR-CAS-9><CRISPR-based method><CRISPR-based technique><CRISPR-based technology><CRISPR-based tool><CRISPR/CAS approach><CRISPR/Cas method><CRISPR/Cas9><CRISPR/Cas9 technology><Cas nuclease technology><Clustered Regularly Interspaced Short Palindromic Repeats approach><Clustered Regularly Interspaced Short Palindromic Repeats method><Clustered Regularly Interspaced Short Palindromic Repeats methodology><Clustered Regularly Interspaced Short Palindromic Repeats technique><Clustered Regularly Interspaced Short Palindromic Repeats technology><efficacy study><clinical translation><early phase trial><pilot test>\",\"pref_terms\":\"Authorization documentation;Benefits and Risks;Brain;CRISPR/Cas technology;Characteristics;Clinical;Clinical Research;Computer software;Data;Data Analytics;Decision Aid;Disclosure;Disease;Enrollment;Ethics;Future;Health;Human;Informed Consent;Institutional Review Boards;Instruction;International;Interview;Investigational Drugs;Judgment;Learning;Legal;Life;Modality;Modeling;Morals;Occupations;Online Systems;Organoids;Participant;Persons;Phase;Process;Qualitative Research;Research;Research Ethics Committees;Research Personnel;Research Technics;Resources;Risk;Risk-Benefit Assessment;Safety;Specificity;Structure;Surveys;Technology;Testing;Text;Translations;Uncertainty;United States Dept. of Health and Human Services;United States Food and Drug Administration;clinical efficacy;clinical translation;common rule;design;early phase trial;effective therapy;efficacy study;falls;improved;member;nervous system disorder;novel;phase 1 study;phase I trial;pilot test;pre-clinical;preclinical efficacy;preclinical safety;preclinical study;safety testing;therapeutic development;tool\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nThe ethical conduct of Phase 1 trials is conditioned on both there being a reasonable ratio between potential\\nrisks and benefits and research candidates being provided with key information about that potential during the\\ninformed consent process. IRBs must carefully assess the scientific information supporting the assessment of\\npotential risks and benefits in order to meet these conditions. Several hurdles hinder such assessment. While\\nthe FDA carefully vets preclinical safety data that relate to Phase 1 trials, the same is not true for the preclinical\\nefficacy data needed to support the potential for benefit. Further, IRBs receive no specific guidance regarding\\nwhat to look for in preclinical efficacy data or how they should vet scientific claims made about Phase 1\\nmodalities. This lack of guidance is disconcerting given extensive research documenting characteristics of\\nmany preclinical efficacy studies that make it difficult to make reliable inferences about whether a new\\ntreatment modality might eventually be capable of demonstrating clinical efficacy. Such inferences are\\nespecially challenging when it comes to preclinical efficacy studies that employ novel research techniques like\\nCRISPR-Cas9 and brain organoids. Knowing what inferences about potential efficacy can reliably be made\\nabout new treatment modalities employing novel preclinical tools will be the primary focus of our project. These\\nare the very inferences that are required if IRBs are to be able to determine that there is a reasonable ratio\\nbetween benefits and risks in Phase 1 trials employing new promissory technologies. In this challenging\\nsetting, IRBs could benefit from a structured approach to risk/benefit assessments. This project is designed to\\ndevelop and pilot such an approach. To do this, we will conduct quantitative and qualitative research to learn\\nabout current strengths and weaknesses in IRB review of Phase 1 studies. We will complete a national survey\\nof IRB chairs. Then we will conduct 3 sets of semi-structured interviews with: (a) past Phase 1 trial\\ninvestigators, (b) IRB members who review Phase 1 trial applications, and (c) past participants or their legal\\nrepresentatives of Phase 1 trials. We will use these findings to refine an existing conceptual framework for\\nassessing the quality and reliability of efficacy data in preclinical studies. From this refined conceptual\\nframework, we will develop IRB and investigator checklists designed to (a) promote highly-structured weighing\\nof potential benefits and risks by IRBs in Phase 1 trials and (b) assist Phase 1 investigators to submit more\\nethically robust Phase 1 trial applications. We will also develop a novel data analytics and decision-support\\ninteractive software platform to help Phase 1 investigators and IRB members evaluate the evidentiary context\\nsurrounding a given Phase 1 trial. These decision aids will also be used to identify critical disclosures about\\npotential risks and benefits to Phase 1 trial research candidates. To see if our decision aids might produce\\nthese desired benefits, in the final year of our project we will pilot them with Phase 1 investigators and IRB\\nmembers.\",\"project_title\":\"The RATIOS Study: Risk/benefit AssessmenT by IRB review of Phase One Studies\",\"phr_text\":\"PROJECT NARRATIVE\\nPhase 1 trials are a critical first step in the search for new treatments and cures for serious neurological\\ndiseases. In order to conduct these studies ethically, research ethics committees must determine both that the\\nrisks of the trials are reasonable in relation to their potential benefits and that research candidates are given\\nthe key information they need in order to understand the potential risks and benefits in such a trial. These\\ncommittees currently are not provided with instructions from the Department of Health and Human Services for\\ndetermining whether there is a reasonable ratio between risks and benefits or what counts as the key\\ninformation about them that need to be disclosed to research candidates. This project will develop and test\\nnew strategies and materials that can assist research ethics committees to make these critical determinations.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"853\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":428120,\"indirect_cost_amt\":71878,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10543557\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10541870,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R21GM144947-02\",\"project_serial_num\":\"GM144947\",\"organization\":{\"org_name\":\"WAKE FOREST UNIVERSITY HEALTH SCIENCES\",\"city\":null,\"country\":null,\"org_city\":\"WINSTON-SALEM\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NC\",\"org_state_name\":null,\"dept_type\":\"INTERNAL MEDICINE/MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"937727907\"],\"org_ueis\":[\"SN7KD2UK7GC5\"],\"primary_duns\":\"937727907\",\"primary_uei\":\"SN7KD2UK7GC5\",\"org_fips\":\"US\",\"org_ipf_code\":\"9021205\",\"org_zipcode\":\"271570001\",\"external_org_id\":9021205},\"award_type\":\"5\",\"activity_code\":\"R21\",\"award_amount\":204659,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R21\",\"ic_code\":\"GM\",\"serial_num\":\"144947\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9547421,\"first_name\":\"Daniel\",\"middle_name\":\"Clark\",\"last_name\":\"Files\",\"is_contact_pi\":false,\"full_name\":\"Daniel Clark Files\",\"title\":\"ASSISTANT PROFESSOR\"},{\"profile_id\":8835750,\"first_name\":\"Cristina\",\"middle_name\":\"Maria\",\"last_name\":\"Furdui\",\"is_contact_pi\":true,\"full_name\":\"Cristina Maria Furdui\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"FURDUI, CRISTINA MARIA\",\"program_officers\":[{\"first_name\":\"XIAOLI\",\"middle_name\":\"\",\"last_name\":\"Zhao\",\"full_name\":\"XIAOLI  Zhao\"}],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":204659.0}],\"cong_dist\":\"NC-06\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-21-077\",\"full_study_section\":{\"srg_code\":\"SAT\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Surgery, Anesthesiology and Trauma Study Section[SAT]\"},\"award_notice_date\":\"2022-12-19T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R21GM144947\",\"terms\":\"<Affect><Age><ages><Biochemistry><Biological Chemistry><Biology><Blood><Blood Reticuloendothelial System><Blood Preservation><Blood Specimen Collection><Cell Membrane Permeability><membrane permeability><Cells><Cell Body><Clinical Research><Clinical Study><Clinical Trials><Critical Care><Critical Illness><Critically Ill><Cryopreservation><Cryofixation><cold preservation><cold storage><Cysteine><Half-Cystine><L-Cysteine><Cessation of life><Death><Disease><Disorder><Electrons><Negative Beta Particle><Negatrons><Freezing><Future><Gold><Human><Modern Man><Laboratories><Medicine><Metabolism><Intermediary Metabolism><Metabolic Processes><Methods><oxidation><Oxidation-Reduction><Redox><oxidation reduction reaction><Legal patent><Patents><Patients><Phenotype><Pilot Projects><pilot study><Plasma><Blood Plasma><Plasma Serum><Reticuloendothelial System, Serum, Plasma><Plasma Cells><Blood Plasma Cell><Plasmacytes><plasmocyte><Proteins><Reagent><Research><Research Personnel><Investigators><Researchers><Mass Spectrum Analysis><Mass Photometry/Spectrum Analysis><Mass Spectrometry><Mass Spectroscopy><Mass Spectrum><Mass Spectrum Analyses><Technology><Testing><Time><Work><Gender><forest><Data Set><Dataset><Caring><Reactive Oxygen Species><Active Oxygen><Oxygen Radicals><Pro-Oxidants><Injury><injuries><base><Blood specimen><Blood Sample><improved><Procedures><sample collection><specimen collection><Site><Clinical><Phase><Physiological><Physiologic><Link><Chemicals><Individual><Recovery><Measurement><Disease Progression><Ethnicity><Ethnic Origin><clinical diagnosis><Staging><tool><Specimen><Research Specimen><Dependence><Reaction><interest><blood fractionation><Blood Fractioning><Performance><cohort><Peripheral Blood Mononuclear Cell><PBMC><Prevention><Modeling><Sampling><response><Organ failure><Bio-Informatics><Bioinformatics><Institution><preventing><prevent><small molecule><Mediator><Mediator of Activation><Mediator of activation protein><Address><Data><Clinical Data><Collection><enroll><Enrollment><Molecular><Process><sex><Modification><developmental><Development><clinical phenotype><designing><design><blood infection><bloodstream infection><Sepsis><Outcome><scale up><prospective><innovate><innovative><innovation><therapeutic agent development><therapeutic development><new drug target><new druggable target><new pharmacotherapy target><new therapy target><novel drug target><novel druggable target><novel pharmacotherapy target><novel therapeutic target><novel therapy target><new therapeutic target><clinical care><standard of care><effective treatment><effective therapy><molecular biomarker><molecular marker><bio-markers><biologic marker><biomarker><Biological Markers><trial design><Data Analytics><Formulation><single cell technology><septic patients><sepsis patients><preservation><single-cell RNA sequencing><scRNA-seq><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell transcriptomic profiling><data warehouse><Multiomic Data><multiple omic data><acute care>\",\"pref_terms\":\"Address;Affect;Age;Biochemistry;Bioinformatics;Biological Markers;Biology;Blood;Blood Preservation;Blood Specimen Collection;Blood specimen;Caring;Cell Membrane Permeability;Cells;Cessation of life;Chemicals;Clinical;Clinical Data;Clinical Research;Clinical Trials;Collection;Critical Care;Critical Illness;Cryopreservation;Cysteine;Data;Data Analytics;Data Set;Dependence;Development;Disease;Disease Progression;Electrons;Enrollment;Ethnic Origin;Formulation;Freezing;Future;Gender;Gold;Human;Individual;Injury;Institution;Laboratories;Legal patent;Link;Mass Spectrum Analysis;Measurement;Mediator of activation protein;Medicine;Metabolism;Methods;Modeling;Modification;Molecular;Multiomic Data;Organ failure;Outcome;Oxidation-Reduction;Patients;Performance;Peripheral Blood Mononuclear Cell;Phase;Phenotype;Physiological;Pilot Projects;Plasma;Plasma Cells;Prevention;Procedures;Process;Proteins;Reaction;Reactive Oxygen Species;Reagent;Recovery;Research;Research Personnel;Sampling;Sepsis;Site;Specimen;Staging;Technology;Testing;Time;Work;acute care;base;blood fractionation;clinical care;clinical diagnosis;clinical phenotype;cohort;data warehouse;design;effective therapy;forest;improved;innovation;interest;molecular marker;new therapeutic target;oxidation;preservation;prevent;prospective;response;sample collection;scale up;septic patients;sex;single cell technology;single-cell RNA sequencing;small molecule;standard of care;therapeutic development;tool;trial design\",\"abstract_text\":\"ABSTRACT\\nWe aim to advance sepsis research and clinical diagnosis by introducing a new redox trapping formulation for\\npreservation of blood specimens. The new formulation is anticipated: 1) to prevent artifactual oxidation and loss\\nof specimen integrity during storage, 2) to enable studies of redox metabolism which underlines the dysregulated\\nimmunometabolic response in sepsis, and 3) to improve analysis of differentiating redox molecular markers\\nassociated with demographic features (age, gender and racial ethnicity). Our combined expertise of redox\\nbiochemistry and innovation in patented redox chemical reagents, sepsis mechanisms, trial design and clinical\\nexpertise will be applied first to validate the new redox trapping formulation (R21 Phase) and then to scale-up\\nby deploying this for collection of blood specimens at sites participating in the EMbedded Precision in Acute Care\\nTrials (EMPACT) Network (R33 Phase). This is a newly formed network of premier critical care clinical trials sites\\ndesigned to conduct precision clinical trials and discovery research in sepsis. Successful accomplishment of our\\naims will improve staging of septic patients and clinical precision research for discovery of new therapeutic\\ntargets for treatment of sepsis. More specifically, during the R21 Phase we will investigate the compatibility of\\nthe new redox formulation with high-end mass spectrometry and single cell technologies to evaluate its\\nperformance for measurement of redox and other biomarkers. The new formulation will be compared with current\\nstandard of care procedures for blood collection for clinical and research purposes (gold standard). To further\\nvalidate this formula in sepsis patients, we will then evaluate its effects on standard clinical lab tests in a pilot\\nstudy using prospectively collected paired single timepoint samples from critically ill patients with sepsis. We will\\nfurther determine the advantage of redox trapping formulation by linking the measurements to physiologic and\\noutcome data collected passively through the Wake Forest Critical Care Data Analytic Platform. Upon\\naccomplishment of key metrics at the completion of R21 Phase, we will work with the EMPACT Network in the\\nR33 Phase to collect blood specimens from 150 patients at 3 timepoints across the continuum of their disease\\nprogression using the standard methods and the new redox formulation. Analysis using high-end omics\\ntechnologies will produce information-rich and redox-specific datasets, which will be integrated using\\nbioinformatics approaches and linked to clinical data captured passively via the EMPACT data warehouse to\\ngenerate new hypotheses for sepsis research. Lastly, we include a plan to share cryopreserved specimens,\\nclinical and molecular data with investigators at the institution to support discovery or hypothesis-driven research\\nthrough pilot mechanisms under the Center for Redox Biology and Medicine and Critical Illness, Injury and\\nResearch Recovery Research Center directed by MPIs Furdui and Files, respectively, and with outside\\ninvestigators via EMPACT.\",\"project_title\":\"Redox Trapping for Biospecimen Preservation and Innovation in Sepsis Care\",\"phr_text\":\"PROJECT NARRATIVE\\nSepsis is estimated to cause 20% of deaths worldwide, though we have a poor understanding of sepsis and no\\neffective treatments. Our understanding of sepsis is limited by both improper collection of blood samples and\\nambiguous clinical phenotyping. This project addresses both of these problems by 1) improving the quality of\\nblood collected from sepsis patients that prevents artifactual oxidation during specimen collection and storage,\\nand 2) linking molecular data including those associated with redox immunometabolism to clinical sepsis\\nphenotypes. These blood samples, molecular and clinical data will provide researchers with new tools to find\\neffective treatments for sepsis.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"859\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":132038,\"indirect_cost_amt\":72621,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10541870\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10556382,\"subproject_id\":\"8660\",\"fiscal_year\":2023,\"project_num\":\"5P41GM135019-04\",\"project_serial_num\":\"GM135019\",\"organization\":{\"org_name\":\"BROAD INSTITUTE, INC.\",\"city\":null,\"country\":null,\"org_city\":\"CAMBRIDGE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"623544785\"],\"org_ueis\":[\"H5G9NWEFHXN4\"],\"primary_duns\":\"623544785\",\"primary_uei\":\"H5G9NWEFHXN4\",\"org_fips\":\"US\",\"org_ipf_code\":\"10021177\",\"org_zipcode\":\"021421027\",\"external_org_id\":10021177},\"award_type\":\"5\",\"activity_code\":\"P41\",\"award_amount\":322548,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P41\",\"ic_code\":\"GM\",\"serial_num\":\"135019\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":8876992,\"first_name\":\"Anne\",\"middle_name\":\"E.\",\"last_name\":\"Carpenter\",\"is_contact_pi\":true,\"full_name\":\"Anne E. Carpenter\",\"title\":\"DIRECTOR, IMAGING PLATFORM\"}],\"contact_pi_name\":\"CARPENTER, ANNE E.\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":322548.0}],\"cong_dist\":\"MA-07\",\"spending_categories\":null,\"project_start_date\":\"2019-12-01T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"PAR-17-316\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BST\",\"sra_flex_code\":\"T\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BST-T]\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P41GM135019\",\"terms\":\"<Algorithms><Automobile Driving><driving><Biology><Biomedical Research><Cell Nucleus><Nucleus><Cells><Cell Body><Communities><Complement><Complement Proteins><Computer Vision Systems><computer vision><Computers><Disease><Disorder><Face><faces><facial><Feedback><Future><Institutes><Laboratories><Methods><Microscopy><Mission><Biological Models><Biologic Models><Model System><Online Systems><On-Line Systems><online computer><web based><Organism><living system><Phenotype><Research><research and development><Development and Research><R & D><R&D><Research Personnel><Investigators><Researchers><Resources><Research Resources><Risk><Role><social role><Computer software><Software><Software Engineering><Computer Software Development><Computer Software Engineering><Software Tools><Computer Software Tools><software toolkit><Technology><Time><Tissues><Body Tissues><Translating><Universities><Wisconsin><Work><Measures><Data Set><Dataset><catalyst><base><image processing><improved><Image Analysis><Image Analyses><image evaluation><image interpretation><Area><Variant><Variation><Biological><biologic><Ensure><Training><Cooperative Learning><Experiential Learning><Active Learning><Cell Components><Cell Structure><Cellular Structures><Biological Process><Biological Function><Letters><tool><Machine Learning><machine learned><Cellular biology><cell biology><Scientist><light microscopy><Dimensions><Complex><Event><Side><Techniques><Benchmarking><Best Practice Analysis><interest><experience><user friendly software><user friendly computer software><Structure><skills><Modality><Modeling><bio-imaging><bioimaging><develop software><developing computer software><software development><image-based method><imaging method><imaging modality><Address><Data><International><Reproducibility><Resource Sharing><Computational Technique><Validation><Process><developmental><Development><imaging><Image><next generation><scale up><innovate><innovative><innovation><biological research><user-friendly><open source><Graphical interface><graphic user interface><software user interface><graphical user interface><quantitative imaging><learning activity><learning method><learning strategy><microscopic imaging><microscope imaging><microscopy imaging><experimental study><experiment><experimental research><technology research and development><technological research and development><deep neural network><deep learning based neural network><deep learning neural network><deep neural net><deep learning><neural network><advanced analytics><machine learning method><machine learning based method><machine learning methodologies><deep learning model><deep learning based model><diagnostic tool>\",\"pref_terms\":\"Active Learning;Address;Algorithms;Area;Automobile Driving;Benchmarking;Biological;Biological Models;Biological Process;Biology;Biomedical Research;Cell Nucleus;Cells;Cellular Structures;Cellular biology;Communities;Complement;Complex;Computational Technique;Computer Vision Systems;Computer software;Computers;Data;Data Set;Development;Dimensions;Disease;Ensure;Event;Face;Feedback;Future;Image;Image Analysis;Institutes;International;Laboratories;Letters;Machine Learning;Measures;Methods;Microscopy;Mission;Modality;Modeling;Online Systems;Organism;Phenotype;Process;Reproducibility;Research;Research Personnel;Resource Sharing;Resources;Risk;Role;Scientist;Side;Software Engineering;Software Tools;Structure;Techniques;Technology;Time;Tissues;Training;Translating;Universities;Validation;Variant;Wisconsin;Work;advanced analytics;base;bioimaging;biological research;catalyst;deep learning;deep learning model;deep neural network;diagnostic tool;experience;experimental study;graphical user interface;image processing;imaging modality;improved;innovation;interest;learning strategy;light microscopy;machine learning method;microscopic imaging;neural network;next generation;open source;quantitative imaging;research and development;scale up;skills;software development;technology research and development;tool;user friendly software;user-friendly\",\"abstract_text\":\"Project Summary\\n The Center for Open Bioimage Analysis will serve the cell biology community’s growing need for\\nsophisticated software for light microscopy image analysis. Quantitative image analysis has become an\\nindispensable tool for biologists using microscopy throughout basic biological and biomedical research.\\n Quantifying images is now a critical, widespread need as imaging experiments continue to grow in scale,\\nsize, dimensionality, scope, modality, and complexity. Many biologists are missing out on the quantitative\\nbioimaging revolution due to lack of effective algorithms and/or usable software for their needs, or lack of\\naccess to training. The Center brings together the Carpenter laboratory at the Broad Institute and the Eliceiri\\nlaboratory at the University of Wisconsin­Madison, and in doing so brings together the two most popular open\\nsource bioimage analysis projects, ImageJ (including ImageJ2 and FIJI) and CellProfiler. Through the\\ncollaborative development and dissemination of open source image analysis software, as well as training\\nevents and resources, the Center will empower thousands of researchers to apply advanced analytics in\\ninnovative ways to address new experimental areas.\\n Building on the team’s expertise developing algorithms and user­friendly software for use in biology under\\nreal­world conditions, the Center will focus on two Technology Research and Development (TR&D) projects:\\ndeep learning­based image processing, and accessibility of image­processing algorithms for biologists. This\\nwork will not occur in isolation at the Center; rather, the Center will nucleate a larger community working on\\nthese two areas and serve as a catalyst and organizing force to create software and resources shared by all.\\n The Driving Biological Projects (DBPs) will serve a major role in driving the TR&D work: our teams are\\naccustomed to working deeply and iteratively on problems side by side and with frequent feedback from\\nbiologists. This will ensure that important cell biological problems drive the work of the Center. The DBPs\\nreflect tremendous variety in terms of biological questions, model systems, imaging modalities, and researcher\\nexpertise and will ensure robustness of our tools for the widest possible impact on the community. Continuing\\nthe teams’ track record with ImageJ and CellProfiler, two mature open source bioimage analysis software\\nprojects critical to the work of biologists worldwide, the Center will also assist and train biologists in applying\\nthe latest computational techniques to important biological problems involving images.\\n In short, the need for robust, accurate, and readily usable software is more urgent than ever. The Center for\\nOpen Bioimage Analysis will serve as a hub for pioneering new computational strategies for diverse biological\\nproblems, translating them into user­friendly software, further developing ImageJ and CellProfiler, and training\\nthe biological community to apply advanced software to important and diverse problems in cell biology.\",\"project_title\":\"Center for Open Bioimage Analysis\",\"phr_text\":\"Project Narrative\\nBiologists studying a huge variety of diseases and basic biological processes need software to measure cells,\\ntissues, and organisms in microscopy images. We will create the Center for Open Bioimage Analysis which will\\ncatalyze the scientific community, creating resources, free software, and training that allow biologists to\\nanalyze images using deep learning and other new image processing algorithms, offering improved accuracy,\\nconvenience, and reproducibility.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":239457,\"indirect_cost_amt\":83091,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10556382\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10533289,\"subproject_id\":\"8101\",\"fiscal_year\":2023,\"project_num\":\"5P01DA047233-05\",\"project_serial_num\":\"DA047233\",\"organization\":{\"org_name\":\"ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI\",\"city\":null,\"country\":null,\"org_city\":\"NEW YORK\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NY\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"078861598\"],\"org_ueis\":[\"C8H9CNG1VBD9\"],\"primary_duns\":\"078861598\",\"primary_uei\":\"C8H9CNG1VBD9\",\"org_fips\":\"US\",\"org_ipf_code\":\"3839801\",\"org_zipcode\":\"100296574\",\"external_org_id\":3839801},\"award_type\":\"5\",\"activity_code\":\"P01\",\"award_amount\":322926,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P01\",\"ic_code\":\"DA\",\"serial_num\":\"047233\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":10583250,\"first_name\":\"Li\",\"middle_name\":\"\",\"last_name\":\"Shen\",\"is_contact_pi\":true,\"full_name\":\"Li  Shen\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"SHEN, LI \",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"DA\",\"abbreviation\":\"NIDA\",\"name\":\"National Institute on Drug Abuse\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"DA\",\"name\":\"National Institute on Drug Abuse\",\"abbreviation\":\"NIDA\",\"total_cost\":322926.0}],\"cong_dist\":\"NY-13\",\"spending_categories\":null,\"project_start_date\":\"2019-02-15T12:02:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PAR-18-425\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"IFCN\",\"sra_flex_code\":\"L\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-IFCN-L]\"},\"award_notice_date\":\"2022-11-23T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"P01DA047233\",\"terms\":\"<Autopsy><necropsy><postmortem><Biological Assay><Assay><Bioassay><Biologic Assays><Brain><Brain Nervous System><Encephalon><Cells><Cell Body><Chromatin><Communities><Faculty><Foundations><Gene Expression><Genes><Human Genome><human whole genome><Goals><Human><Modern Man><Laboratories><Lead><Pb element><heavy metal Pb><heavy metal lead><Leadership><Methods><Neuroglia><Glia><Glial Cells><Kolliker's reticulum><Neuroglial Cells><Non-neuronal cell><Nonneuronal cell><nerve cement><Neurons><Nerve Cells><Nerve Unit><Neural Cell><Neurocyte><neuronal><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Rodent><Rodentia><Rodents Mammals><Running><Substance Use Disorder><Technology><Genetic Transcription><Gene Transcription><RNA Expression><Transcription><Work><Measures><Data Set><Dataset><Advisory Committees><Task Forces><advisory team><improved><Ensure><Individual><Logistics><Collaborations><tool><DNA Methylation><Complex><Protocols documentation><Protocol><cell type><brain tissue><Opiate Addiction><Opiate Dependence><opioid addiction><opioid dependence><opioid dependent><interest><stimulant dependence><addiction to psychostimulants><addiction to stimulants><psychostimulant addiction><psychostimulant dependence><stimulant addiction><meetings><brain cell><success><Animal Model><Animal Models and Related Studies><model of animal><model organism><novel><member><Human Resources><Manpower><personnel><Gene Expression Profiling><Gene Expression Monitoring><Gene Expression Pattern Analysis><Transcript Expression Analyses><Transcript Expression Analysis><gene expression analysis><gene expression assay><transcriptional profiling><Regulation><Modeling><Bio-Informatics><Bioinformatics><chromatin modification><Brain region><Chemical Dependence><Drug Dependence><Drug Dependency><Drug Addiction><Data><P01 Mechanism><P01 Program><Program Project Grant><Research Program Projects><Program Research Project Grants><Resource Sharing><in vivo><Molecular><sex><developmental><Development><reward circuitry><designing><design><analytical tool><rodent genome><addictive disorder><addiction><genome wide analysis><genome wide studies><genome-wide identification><genome-wide analysis><genome scale><genomewide><genome-wide><data sharing><ChIP Sequencing><chromatin immunoprecipitation-sequencing><ChIP-seq><RNA Seq><RNA sequencing><RNAseq><transcriptome sequencing><NGS Method><NGS system><next gen sequencing><nextgen sequencing><next generation sequencing><epigenome><BS-seq><Bisulfite-based sequencing><bisulfite-seq><bisulfite sequencing><ATACseq><ATAC-seq><global gene expression><global transcription profile><transcriptome><chromosome conformation capture><3C-based approach><3C-based assay><3C-based method><3C-based strategy><3C-based technique><3C-based technology><chromatin conformation capture><chromosome capture><whole genome><entire genome><full genome><experimental study><experiment><experimental research><brain reward regions><bioinformatics tool><bio-informatics tool><epigenome editing><advanced analytics><large datasets><large data sets><bioinformatics infrastructure><bio-informatics  infrastructure><sharing platform>\",\"pref_terms\":\"ATAC-seq;Advisory Committees;Animal Model;Autopsy;Bioinformatics;Biological Assay;Brain;Brain region;Cells;ChIP-seq;Chromatin;Collaborations;Communities;Complex;DNA Methylation;Data;Data Set;Development;Drug Addiction;Ensure;Faculty;Foundations;Gene Expression;Gene Expression Profiling;Genes;Genetic Transcription;Goals;Human;Human Genome;Human Resources;Individual;Laboratories;Lead;Leadership;Logistics;Measures;Methods;Modeling;Molecular;Neuroglia;Neurons;Opiate Addiction;Program Research Project Grants;Protocols documentation;Regulation;Research;Research Personnel;Resource Sharing;Resources;Rodent;Running;Substance Use Disorder;Technology;Work;addiction;advanced analytics;analytical tool;bioinformatics infrastructure;bioinformatics tool;bisulfite sequencing;brain cell;brain reward regions;brain tissue;cell type;chromatin modification;chromosome conformation capture;data sharing;design;epigenome;epigenome editing;experimental study;genome-wide;genome-wide analysis;improved;in vivo;interest;large datasets;meetings;member;next generation sequencing;novel;reward circuitry;rodent genome;sex;sharing platform;stimulant dependence;success;tool;transcriptome;transcriptome sequencing;whole genome\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT– GENE AND CHROMATIN ANALYSIS CORE\\n The Gene and Chromatin Analysis Core provides the technical and bioinformatics infrastructure to\\noptimally mine the large amount of genome-wide gene expression and chromatin data that are generated from\\nthe PPG’s work. PPG investigators lead the field in several aspects of genome-wide analyses, including\\npioneering these approaches to specific cell types in brain, which offers several technical challenges. We have\\noptimized methods for several next generation sequencing approaches, including RNA-seq, ChIP-seq, ATAC-\\nseq, HiC, and whole genome bisulfite sequencing, among others, for rodent and human brain, the latter\\noffering an additional set of unique technical challenges. These approaches are being used increasingly to\\nstudy individual cell types within a given brain reward region, in both rodent and human. The Core has\\nestablished expertise in analyzing the highly complex datasets obtained, and will work on further improving the\\ntools available. All of the genome-wide data generated by our PPG are analyzed by this Core. In parallel, the\\nCore runs routine genome-wide assays on defined animal models, with the Animal Models Core, and thereby\\nprovides a foundation for the more specific and sophisticated measures in the individual Projects. Indeed, each\\nProject focuses on genes that are among the most robustly regulated across rodent addiction models and\\nhuman substance use disorders. As well, the Core pilots novel experimental technologies; an example is our\\nability to target single chromatin modifications to a single gene within a single brain region and cell type in vivo,\\nthus providing an unparalleled level of proof to establish transcriptional mechanisms of addiction. By\\nconsolidating the analytical work and routine genome-wide analyses within a centralized Core, we ensure\\nrigorous control over the data and facilitate comparisons of experimental findings across the four individual\\nProjects. This consolidation also makes financial sense, since we concentrate and maximize efficient use of\\nour expertise. Finally, the Core is responsible, with the Administrative Core, in maintaining a platform for\\nsharing our genome-wide datasets and analytical tools across the PPG’s laboratories as well as with the\\nscientific community and lay public at large.\",\"project_title\":\"Gene and Chromatin Analysis Core\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":190398,\"indirect_cost_amt\":132528,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10533289\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10603832,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1R43NS130849-01\",\"project_serial_num\":\"NS130849\",\"organization\":{\"org_name\":\"CYTODEL, INC.\",\"city\":null,\"country\":null,\"org_city\":\"NEW YORK\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NY\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"078736623\"],\"org_ueis\":[\"C1AUXSRGH7W5\"],\"primary_duns\":\"078736623\",\"primary_uei\":\"C1AUXSRGH7W5\",\"org_fips\":\"US\",\"org_ipf_code\":\"10033704\",\"org_zipcode\":\"100272561\",\"external_org_id\":10033704},\"award_type\":\"1\",\"activity_code\":\"R43\",\"award_amount\":295365,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"R43\",\"ic_code\":\"NS\",\"serial_num\":\"130849\",\"support_year\":\"01\",\"full_support_year\":\"01\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":11197513,\"first_name\":\"PHILIP\",\"middle_name\":\"Arthur\",\"last_name\":\"BAND\",\"is_contact_pi\":true,\"full_name\":\"PHILIP Arthur BAND\",\"title\":\"CHEIF EXECUTIVE OFFICER\"}],\"contact_pi_name\":\"BAND, PHILIP ARTHUR\",\"program_officers\":[{\"first_name\":\"DAVID\",\"middle_name\":\"Alan\",\"last_name\":\"JETT\",\"full_name\":\"DAVID Alan JETT\"}],\"agency_ic_admin\":{\"code\":\"NS\",\"abbreviation\":\"NINDS\",\"name\":\"National Institute of Neurological Disorders and Stroke\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"NS\",\"name\":\"National Institute of Neurological Disorders and Stroke\",\"abbreviation\":\"NINDS\",\"total_cost\":295365.0}],\"cong_dist\":\"NY-13\",\"spending_categories\":null,\"project_start_date\":\"2023-01-01T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"FP\",\"is_other\":false},\"full_foa\":\"PA-21-259\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"ETTN\",\"sra_flex_code\":\"S\",\"group_code\":\"14\",\"name\":\"Special Emphasis Panel[ZRG1 ETTN-S (14)]\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"SB\",\"core_project_num\":\"R43NS130849\",\"terms\":\"<Animals><Antibodies><Antidotes><Theriacs><antitoxin><anti-toxin><Biological Assay><Assay><Bioassay><Biologic Assays><Black race><Black><Botulinum Antitoxin><botulinum anti-toxin><Botulism><Toxico-Infectious Botulism><Cells><Cell Body><Chemistry><Affinity Chromatography><affinity purification><Communication><Cytoplasm><Disulfides><Orphan Drugs><Engineering><Histidine><Equus caballus><Domestic Horse><Equine><Equine Species><Equus przewalskii><Horses><Laboratories><Lead><Pb element><heavy metal Pb><heavy metal lead><Libraries><Light><Photoradiation><Methodology><Mus><Mice><Mice Mammals><Murine><Muscle><Muscle Tissue><muscular><Neurons><Nerve Cells><Nerve Unit><Neural Cell><Neurocyte><neuronal><Overdose><Patients><Peptide Hydrolases><Esteroproteases><Peptidases><Protease Gene><Proteases><Proteinases><Proteolytic Enzymes><Production><Proteins><Publishing><Plant Resins><resin><Safety><Science><Serotyping><Specificity><Testing><Thrombin><Thrombase><fibrinogenase><Time><Toxin><Measures><Chimeric Proteins><Chimera Protein><Fusion Protein><base><Label><Mechanical ventilation><mechanical respiratory assist><mechanically ventilated><Site><Clinical><Phase><Biological><biologic><Link><non-human primate><nonhuman primate><Development Plans><gastrocnemius><Gastrocnemius Muscle><Therapeutic><weapons><tool><Spastic Cerebral Palsy><Supportive care><Supportive Therapy><TEV protease><tobacco etch virus protease><Severities><Intramuscular><Viral><Paralysed><Palsy><Plegia><paralysis><paralytic><Amino Acid Substitution><meetings><success><trafficking><Sterility><sterile><Reporting><Intoxication><Excision><Abscission><Extirpation><Removal><Surgical Removal><resection><Maximum Tolerated Dose><Maximal Tolerated Dose><Maximally Tolerated Dose><Pharmacodynamics><Modeling><response><Adverse Experience><Adverse event><Spasmodic torticollis><Cervical Dystonia><Pharmaceutical Agent><Pharmaceuticals><Pharmacological Substance><Pharmacologic Substance><Effectiveness><botulinum neurotoxin><Bontoxilysin><Dose><Symptoms><Affinity><Data><iatrogenic><iatrogenically><iatrogenicity><Iatrogenesis><Recombinants><Regulatory Pathway><Severe Adverse Event><serious adverse experience><serious adverse reaction><Serious Adverse Event><SBIR><Small Business Innovation Research><Small Business Innovation Research Grant><Validation><Molecular><Process><developmental><Development><pre-clinical study><preclinical study><after stroke><poststroke><post stroke><digital><post-market><NH2-terminal><N-terminal><C-terminal><drug detection><drug testing><effectiveness measure><FDA approved><stability testing><good laboratory practice><nanobody><sdAb><single domain antibodies><nanobodies><Data Analytics><Formulation><tobacco etch virus>\",\"pref_terms\":\"Adverse event;Affinity;Affinity Chromatography;Amino Acid Substitution;Animals;Antibodies;Antidotes;Biological;Biological Assay;Black race;Bontoxilysin;Botulinum Antitoxin;Botulism;C-terminal;Cells;Cervical Dystonia;Chemistry;Chimeric Proteins;Clinical;Communication;Cytoplasm;Data;Data Analytics;Development;Development Plans;Disulfides;Dose;Effectiveness;Engineering;Equus caballus;Excision;FDA approved;Formulation;Gastrocnemius Muscle;Histidine;Iatrogenesis;Intoxication;Intramuscular;Label;Laboratories;Lead;Libraries;Light;Link;Maximum Tolerated Dose;Measures;Mechanical ventilation;Methodology;Modeling;Molecular;Mus;Muscle;N-terminal;Neurons;Orphan Drugs;Overdose;Paralysed;Patients;Peptide Hydrolases;Pharmacodynamics;Pharmacologic Substance;Phase;Plant Resins;Process;Production;Proteins;Publishing;Recombinants;Regulatory Pathway;Reporting;Safety;Science;Serious Adverse Event;Serotyping;Severities;Site;Small Business Innovation Research Grant;Spastic Cerebral Palsy;Specificity;Sterility;Supportive care;Symptoms;TEV protease;Testing;Therapeutic;Thrombin;Time;Toxin;Validation;Viral;antitoxin;base;digital;drug testing;effectiveness measure;good laboratory practice;meetings;nanobodies;nonhuman primate;post stroke;post-market;preclinical study;response;stability testing;success;tobacco etch virus;tool;trafficking;weapons\",\"abstract_text\":\"Abstract\\nBotulinum neurotoxin serotype A1 (BoNT/A1) has become an important therapeutic tool for multiple indications,\\ndespite being the most toxic protein known to science. Although serious Adverse Events (AEs) are rare, their\\nimportance is recognized by the Black Box warning included in the labeling for all FDA-approved BoNT/A1\\nproducts. During a recent 3-year period FDA received 13,087 AE reports specifically described as “overdose”,\\nprimarily associated with therapeutic indications. involving the treatment of large muscle groups (eg post-stroke\\nspasticity, cerebral palsy and cervical dystonia). No treatment is available for BoNT/A1-associated iatrogenic\\nAEs other than supportive care. The only available botulism therapeutic is an equine-derived antitoxin (BAT,\\nEmergent Biosolutions) that cannot access the intraneuronal toxin protease responsible for iatrogenic symptoms,\\nand is therefore of little use in treating AEs associated with BoNT/A1 overdose and off-target actions. We have\\ndeveloped a post-symptomatic antidote to BoNT/A1 intoxication with an intraneuronal mechanism of action,\\ndesignated B8C1ad. B8C1ad is produced by genetically fusing a single domain antibody (sdAb, B8) to a\\nrecombinant atoxic derivative of BoNT/C1 (C1ad for atoxic derivative) that acts as a molecular vehicle to deliver\\nthe B8 antibody to the neuronal cytoplasm where the BoNT/A1 toxic protease resides. The B8 antibody was\\nselected from a camelid VHH library for its potent inhibition of the BoNT/A1 protease. B8C1ad has been\\ndemonstrated to effectively rescue animals with systemic BoNT/A1 intoxication at times post-intoxication when\\nconventional antibodies are ineffective, because they cannot access the intra-neuronal BoNT/A1 LC protease.\\nThe safety and effectiveness of B8C1ad to reverse BoNT/A1 intoxication symptoms and rescue animals has\\nbeen published in three species, including non-human primates. We here propose establishing a model for\\nBoNT/A1 overdose via intramuscular administration of supratherapeutic doses, and developing B8C1ad as an\\nOrphan Drug to treat off-target iatrogenic AEs associated with the clinical use of BoNT/A1 pharmaceutical\\nproducts. Successful completion of the proposed studies will support assembly of a target product profile for\\nB8C1ad, which will be shared with FDA to request a Type C meeting for guidance on the regulatory pathway for\\nB8C1ad approval as an Orphan Drug to treat iatrogenic overdose associated with BoNT/A1 pharmaceuticals.\",\"project_title\":\"Development of B8C1ad as an Orphan Drug for Iatrogenic Botulism\",\"phr_text\":\"Project Narrative\\nBotulinum neurotoxin serotype A1 (BoNT/A1) has become an important therapeutic tool for multiple indications,\\ndespite being the most toxic protein known to science. During a recent 3-year period FDA received 13,087 AE\\nreports specifically described as “overdose”, for which no treatment is available other than supportive care. The\\nB8C1ad antidote to be developed in this SBIR is the first botulism therapeutic with an intraneuronal mechanism\\nof action, and has been shown to effectively rescue animals systemically intoxicated with BoNT/A1 in three\\nspecies.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"853\",\"funding_mechanism\":\"SBIR/STTR\",\"direct_cost_amt\":197173,\"indirect_cost_amt\":78869,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10603832\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10547787,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5IK2HX002567-04\",\"project_serial_num\":\"HX002567\",\"organization\":{\"org_name\":\"VA EASTERN COLORADO HEALTH CARE SYSTEM\",\"city\":null,\"country\":null,\"org_city\":\"Aurora\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CO\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"003252830\"],\"org_ueis\":[\"HK7TZB6FX583\"],\"primary_duns\":\"003252830\",\"primary_uei\":\"HK7TZB6FX583\",\"org_fips\":\"US\",\"org_ipf_code\":\"481018\",\"org_zipcode\":\"800457211\",\"external_org_id\":481018},\"award_type\":\"5\",\"activity_code\":\"IK2\",\"award_amount\":null,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"IK2\",\"ic_code\":\"HX\",\"serial_num\":\"002567\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":12602197,\"first_name\":\"Heather\",\"middle_name\":\"M.\",\"last_name\":\"Gilmartin\",\"is_contact_pi\":true,\"full_name\":\"Heather M. Gilmartin\",\"title\":\"POST-DOCTORAL NURSE FELLOW\"}],\"contact_pi_name\":\"GILMARTIN, HEATHER M.\",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"VA\",\"abbreviation\":\"VA\",\"name\":\"Veterans Affairs\"},\"agency_ic_fundings\":null,\"cong_dist\":\"CO-06\",\"spending_categories\":null,\"project_start_date\":\"2020-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"HX-19-009\",\"full_study_section\":{\"srg_code\":\"MRA0\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Mentored Research[MRA0]\"},\"award_notice_date\":\"2023-01-03T12:01:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OT\",\"core_project_num\":\"IK2HX002567\",\"terms\":\"<patient safety><Health system><Evidence based practice><Data><Ph.D.><PhD><Doctor of Philosophy><Health Sciences><Collection><Observation research><Observation study><Observational research><Observational Study><trend><Coronary><Cardiac><developmental><Development><post intervention><implementation research><designing><design><Outcome><scale up><Population><innovate><innovative><innovation><multidisciplinary><implementation science><intervention design><treatment design><therapy design><clinical care><evidence base><mindfulness><clinical practice><flexible><flexibility><classroom environment><college atmosphere><collegial atmosphere><collegiate atmosphere><education atmosphere><educational environment><intellectual atmosphere><learning atmosphere><learning environment><school atmosphere><school climate><training atmosphere><university atmosphere><educational atmosphere><Data Analytics><Employee Engagement><health care settings><healthcare settings><supportive environment><enthusiastic atmosphere><enthusiastic environment><supportive atmosphere><Veterans Health Administration><Veterans Health Affairs><feasibility testing><pilot test><Adoption><Catheterization><Cathetergram><Climate><Meteorological Climate><climatic><Communication><Elements><Environment><Feasibility Studies><Future><Goals><Health><Healthcare Systems><Health Care Systems><Cardiac Catheterization Procedures><Cardiac Catheterization><Heart Catheterization><Heart Catheterization Procedure><Insertion of catheter into heart chamber><Institute of Medicine (U.S.)><Institute of Medicine><NAS/IOM><Lead><Pb element><heavy metal Pb><heavy metal lead><Leadership><Learning><Maintenance><Mentorship><Methods><Methodology><mortality><Nurse Practitioners><Patients><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Safety><Savings><Science><Standardization><Surveys><Survey Instrument><Educational process of instructing><Teaching><Testing><Time><Training Programs><Translating><Veterans><Work><Case Study><case report><Healthcare><health care><Medical Care Team><Health Care Team><Healthcare Team><Caring><Custom><Workplace><Job Location><Job Place><Job Setting><Job Site><Work Location><Work Place><Work-Site><Worksite><work setting><Educational Models><Instructional Models><educational methods><educational practices><educational principles><base><career><Procedures><Clinical><Variant><Variation><psychologic><psychological><Ensure><Evaluation><Training><insight><Trust><Fostering><tool><Consensus><Knowledge><Life><programs><Adopted><Investigation><Source><Techniques><Operative Surgical Procedures><Operative Procedures><Surgical><Surgical Interventions><Surgical Procedure><surgery><experience><Employee><skills><Reporting><Modeling><Sampling>\",\"pref_terms\":\"Adopted;Adoption;Cardiac;Cardiac Catheterization Procedures;Caring;Case Study;Catheterization;Climate;Clinical;Collection;Communication;Consensus;Coronary;Custom;Data;Data Analytics;Development;Doctor of Philosophy;Educational Models;Educational process of instructing;Elements;Employee;Employee Engagement;Ensure;Environment;Evaluation;Evidence based practice;Feasibility Studies;Fostering;Future;Goals;Health;Health Sciences;Health system;Healthcare;Healthcare Systems;Institute of Medicine (U.S.);Investigation;Knowledge;Lead;Leadership;Learning;Life;Maintenance;Medical Care Team;Mentorship;Methodology;Methods;Modeling;Nurse Practitioners;Observational Study;Operative Surgical Procedures;Outcome;Patients;Population;Procedures;Reporting;Research;Research Personnel;Resources;Safety;Sampling;Savings;Science;Source;Standardization;Surveys;Techniques;Testing;Time;Training;Training Programs;Translating;Trust;Variant;Veterans;Veterans Health Administration;Work;Workplace;base;career;clinical care;clinical practice;design;educational atmosphere;evidence base;experience;feasibility testing;flexibility;health care settings;implementation research;implementation science;innovation;insight;mindfulness;mortality;multidisciplinary;patient safety;pilot test;post intervention;programs;psychologic;scale up;skills;supportive environment;therapy design;tool;trend\",\"abstract_text\":\"Background: The Veterans Health Administration (VA) has set a goal to become a learning health system to\\nensure the consistent delivery of high-quality, safe care to Veterans. A learning health system, per the Institute\\nof Medicine, harnesses data and analytics to learn from every patient and feed the knowledge to clinicians to\\nimplement with high-reliability. Transforming the VA into a learning health system is vital to Veterans health,\\ngiven the wide variation in use of evidence-based practices and Veteran outcomes. The learning environments\\nin which clinical care is provided can impact these variations. Learning environments are the educational ap-\\nproaches (e.g., on-line v. unit-based trainings), cultural context, and settings in which teaching, and learning\\nhappen. Supportive learning environments, which are the ideal learning environment, engage employees\\nthrough team-based trainings, such as Crew Resource Management, that include communication and team-\\nwork skills and employ highly-reliable work practices (e.g., checklists). Healthcare teams need flexible training\\ntools that can be easily implemented using available resources to create supportive learning environments.\\nUntil this need is met, one-size fits all approaches will be the dominant educational method in healthcare.\\nSignificance/Impact: The proposed research is significant for its potential to provide teams tailored Crew Re-\\nsource Management tools to create supportive learning environments, resulting in enhanced employee en-\\ngagement, learning, and Veteran care and its contribution to implementation and learning health science.\\nInnovation: The proposed research is innovative in that it attempts to shift the current paradigm for learning\\nfrom standardized procedurally-focused educational methods that lack communication and teamwork content\\nto one that provides teams Crew Resource Management tools to change their culture toward learning to en-\\nhance Veteran care. We will partner with VA cardiac cath labs as a model learning health system for this work.\\nSpecific Aims: Aim 1: Assess how and why VA cath lab learning environments differ. Administer learn-\\ning environment surveys to the 81 VA cath labs, assess for changes over time and if learning environments\\nthat are highly supportive are associated with higher employee engagement and safety climate scores. Ob-\\nserve two consistently supportive and two non-supportive learning environments to understand the basis for\\ndiverse contexts. Aim 2: Adapt Crew Resource Management to the culture, work flow, and learning envi-\\nronments of cath labs. Engage advisory panel and stakeholders using nominal, small group technique to\\nachieve consensus on program design and toolkit components. Assess feasibility, appropriateness, acceptabil-\\nity of toolkit. Aim 3: Pilot test the Crew Resource Management-based toolkit at four cath labs over 1\\nyear. Test the feasibility of implementation and collection of evaluation (e.g., adoption, implementation, mainte-\\nnance) and outcome data (e.g., learning environment, teamwork, and error reporting). Using pre-post interven-\\ntion data, test the hypothesis that as learning environments become more supportive, there will be a positive\\ntrend in employee engagement, safety climate, and a negative trend in cath lab errors.\\nMethodology: We will use a mixed methods sequential explanatory design and partner with staff and leader-\\nship from the 81 VA cath labs for the proposed research.\\nNext Steps: The study findings will be the basis for future work to spread and scale up Crew Resource Man-\\nagement-based toolkits in diverse settings and populations and to study the impact on Veteran outcomes.\",\"project_title\":\"Building Supportive Learning Environments: Implementation Research in VA Cardiac Cath Labs\",\"phr_text\":\"This project is relevant to all aspects of VA healthcare for it will develop and evaluate a training toolkit to assist\\nhealthcare teams to change their culture toward learning to enhance Veteran care. This work will support the\\nVA transformation to a learning health system, so clinicians can see every patient encounter as a way to make\\nthe next one better. This will be achieved through studying unit-level learning environments, identifying high-\\nreliability practices that support a learning culture and using this knowledge to adapt a team training program to\\nthe culture and workflow of clinical units. We will develop and test a training toolkit to empower teams to de-\\nvelop their own culture of learning and a rigorous mixed methods evaluation that leads to valid, generalizable\\nlessons applicable across healthcare settings. We expect this study to result in positive changes in learning\\nenvironments, teamwork, and highly-reliable use of evidence-based practices. The findings will inform future\\nstudies to determine the impact of learning environments on Veteran outcomes.\",\"spending_categories_desc\":null,\"agency_code\":\"VA\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"999\",\"funding_mechanism\":\"Other\",\"direct_cost_amt\":null,\"indirect_cost_amt\":null,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10547787\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10717670,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"3R01NS114259-03S1\",\"project_serial_num\":\"NS114259\",\"organization\":{\"org_name\":\"UNIV OF NORTH CAROLINA CHAPEL HILL\",\"city\":null,\"country\":null,\"org_city\":\"CHAPEL HILL\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NC\",\"org_state_name\":null,\"dept_type\":\"PHYSIOLOGY\",\"fips_country_code\":null,\"org_duns\":[\"608195277\"],\"org_ueis\":[\"D3LHU66KBLD5\"],\"primary_duns\":\"608195277\",\"primary_uei\":\"D3LHU66KBLD5\",\"org_fips\":\"US\",\"org_ipf_code\":\"578206\",\"org_zipcode\":\"275995023\",\"external_org_id\":578206},\"award_type\":\"3\",\"activity_code\":\"R01\",\"award_amount\":39687,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"3\",\"activity_code\":\"R01\",\"ic_code\":\"NS\",\"serial_num\":\"114259\",\"support_year\":\"03\",\"full_support_year\":\"03S1\",\"suffix_code\":\"S1\"},\"principal_investigators\":[{\"profile_id\":8587924,\"first_name\":\"Mark\",\"middle_name\":\"J.\",\"last_name\":\"Zylka\",\"is_contact_pi\":true,\"full_name\":\"Mark J. Zylka\",\"title\":\"\"}],\"contact_pi_name\":\"ZYLKA, MARK J.\",\"program_officers\":[{\"first_name\":\"DURGA\",\"middle_name\":\"PRASANNA\",\"last_name\":\"Mohapatra\",\"full_name\":\"DURGA PRASANNA Mohapatra\"}],\"agency_ic_admin\":{\"code\":\"NS\",\"abbreviation\":\"NINDS\",\"name\":\"National Institute of Neurological Disorders and Stroke\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"NS\",\"name\":\"National Institute of Neurological Disorders and Stroke\",\"abbreviation\":\"NINDS\",\"total_cost\":39687.0}],\"cong_dist\":\"NC-04\",\"spending_categories\":null,\"project_start_date\":\"2020-02-15T12:02:00Z\",\"project_end_date\":\"2025-01-31T12:01:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-272\",\"full_study_section\":null,\"award_notice_date\":\"2022-12-07T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01NS114259\",\"terms\":\"<Acetic Acids><Adoption><Analgesics><Analgesic Agents><Analgesic Drugs><Analgesic Preparation><Anodynes><Antinociceptive Agents><Antinociceptive Drugs><pain killer><pain medication><pain reliever><painkiller><Opioid Analgesics><opiate analgesia><opiate analgesic><opiate pain medication><opiate pain reliever><opioid analgesia><opioid anesthetic><opioid pain medication><opioid pain reliever><opioid painkiller><Non-Steroidal Anti-Inflammatory Agents><NSAIDs><Non Steroidal Antiinflammatory Agents><Nonsteroidal Anti-Inflammatory Agents><Nonsteroidal Antiinflammatory Agents><Nonsteroidal Antiinflammatory Drug><non-steroidal anti-inflammatory drugs><non-steroidal antiinflammatory drugs><nonsteroidal anti-inflammatory drugs><Biological Assay><Assay><Bioassay><Biologic Assays><Body Regions><Classification><Systematics><Color><Computers><Database Management Systems><relational database management systems><Data Base Management><Data Base Management Systems><database management><database systems><Pharmaceutical Preparations><Drugs><Medication><Pharmaceutic Preparations><drug/agent><Face><faces><facial><Facial Expression><face expression><Facial Pain><Face Pain><Future><Genes><Goals><Human><Modern Man><Laparotomy><Manuals><Maps><Medicine><Methods><Motion><Mus><Mice><Mice Mammals><Murine><Online Systems><On-Line Systems><online computer><web based><Pain><Painful><Pain Measurement><Analgesia Tests><Nociception Tests><Pain Assessment><pain assay><Postoperative Pain><Post-operative Pain><pain after surgery><post-surgical pain><postsurgical pain><Publishing><Reflex action><Reflex><Research Personnel><Investigators><Researchers><Resources><Research Resources><Rodent><Rodentia><Rodents Mammals><Science><Sleep><Computer software><Software><Specificity><Supervision><Testing><Time><Measures><chronic pain><Custom><base><improved><screening tools><Screening procedure><Training><Failure><inflammatory pain><tool><Adopted><Dependence><System><Operative Surgical Procedures><Operative Procedures><Surgical><Surgical Interventions><Surgical Procedure><surgery><interest><success><Animal Model><Animal Models and Related Studies><model of animal><model organism><novel><Modality><Human Resources><Manpower><personnel><neural circuitry><neurocircuitry><synaptic circuit><synaptic circuitry><neural circuit><Modeling><Connectionist Models><Neural Network Models><Perceptrons><Neural Network Simulation><Cell Phone><Cellular Telephone><iPhone><smart phone><smartphone><Cellular Phone><Administrative Supplement><Data><Preclinical Models><Pre-Clinical Model><Reproducibility><Characteristics><developmental><Development><imaging><Image><Output><preclinical><pre-clinical><web-based service><web services><neuropathic pain><painful neuropathy><spontaneous pain><efficacy analysis><efficacy assessment><efficacy examination><evaluate efficacy><examine efficacy><efficacy evaluation><blind><analytical tool><user-friendly><open source><mobile device><handheld mobile device><addictive disorder><addiction><multi-modality><multimodality><overdose fatalities><overdose death><Pain intensity><mobile app><mobile device application><mobile application><cloud based><Data Analytics><persistent symptom><chronic symptom><associated symptom><co-morbid symptom><co-occuring symptom><comorbid symptom><concurrent symptom><cooccuring symptom><symptom association><symptom comorbidity><opioid epidemic><opiate crisis><opioid crisis><smartphone Application><Android App><Android Application><Cell Phone Application><Cell phone App><Cellular Phone App><Cellular Phone Application><Smart Phone App><Smart Phone Application><Smartphone App><iOS app><iOS application><iPhone App><iPhone Application><chronic pain relief><pain relief><relieve pain><recurrent neural network><deep neural network><deep learning based neural network><deep learning neural network><deep neural net><convolutional neural network><ConvNet><convolutional network><convolutional neural nets><machine learning algorithm><machine learned algorithm><machine learning based algorithm><side effect><data repository><Data Banks><Databanks><data depository><data set repository><dataset repository><machine learning model><machine learning based model><transfer learning>\",\"pref_terms\":\"Acetic Acids;Administrative Supplement;Adopted;Adoption;Analgesics;Animal Model;Biological Assay;Body Regions;Cellular Phone;Characteristics;Classification;Color;Computer software;Computers;Custom;Data;Data Analytics;Database Management Systems;Dependence;Development;Face;Facial Expression;Facial Pain;Failure;Future;Genes;Goals;Human;Human Resources;Image;Laparotomy;Manuals;Maps;Measures;Medicine;Methods;Modality;Modeling;Motion;Mus;Neural Network Simulation;Non-Steroidal Anti-Inflammatory Agents;Online Systems;Operative Surgical Procedures;Opioid Analgesics;Output;Pain;Pain Measurement;Pain intensity;Pharmaceutical Preparations;Postoperative Pain;Pre-Clinical Model;Publishing;Reflex action;Reproducibility;Research Personnel;Resources;Rodent;Science;Screening procedure;Sleep;Specificity;Supervision;System;Testing;Time;Training;addiction;analytical tool;associated symptom;base;blind;chronic pain;chronic pain relief;cloud based;convolutional neural network;data repository;deep neural network;efficacy evaluation;handheld mobile device;improved;inflammatory pain;interest;machine learning algorithm;machine learning model;mobile application;multimodality;neural circuit;novel;open source;opioid epidemic;overdose death;pain relief;painful neuropathy;persistent symptom;pre-clinical;recurrent neural network;side effect;smartphone Application;spontaneous pain;success;tool;transfer learning;user-friendly;web services\",\"abstract_text\":\"ORIGINAL APPLICATION PROJECT SUMMARY\\nOpioid analgesics are commonly used to treat pain but have serious side effects, including addiction,\\ndependence, and death from overdose. While there is a significant need for new non-addictive analgesics,\\nefforts to develop new pain medicines have met with limited success. In part, this failure is due to an\\noverreliance on evoked pain measures in preclinical models. Indeed, most preclinical models do not measure\\nspontaneous pain—the main symptom of chronic pain in humans. To increase translational relevance, the\\nMouse Grimace Scale (MGS) was developed to quantify characteristic facial expressions associated with\\nspontaneous pain. The MGS is reproducible across labs and was used to evaluate the efficacy of analgesics.\\nHowever, the MGS has not been widely adopted due to its high resource demands and low throughput. To\\novercome this limitation, we adapted a machine learning model to classify the presence or absence of pain\\nfrom mouse facial expressions. We called this model the automated Mouse Grimace Scale (aMGS). After\\ntraining, this model identified mice in pain with 94% accuracy, comparable to a highly-trained human. However,\\nour original “aMGS 1.0” is limited in several respects. It is only accurate at detecting facial grimacing in white-\\ncoated mice, and produces a binary assessment (“pain” vs. “no pain”) instead of a graded score. Moreover,\\naMGS 1.0 cannot dynamically determine pain status from full-motion videos. Additionally, we relied on an older\\npiece of software that does not consistently extract high-quality images of the mouse face. The aMGS 1.0 also\\nhas difficulty distinguishing between images of sleeping and grimacing mice. Finally, aMGS 1.0 suffers from a\\n“black box” problem inherent to most machine learning algorithms, in that we do not know what facial details it\\nuses to produce a pain assessment. Here we propose to overcome all of these limitations by developing\\na more sophisticated version of our automated pain classifier (aMGS 2.0). To achieve this goal we will: 1)\\nDevelop and validate a new open-source platform to classify (frame-by-frame) spontaneous pain intensity from\\nmouse facial expressions, using albino (white) mice and motion information. 2) Enhance the generality of\\naMGS 2.0 for use with black mice. And, 3) Develop a user-friendly web-based platform that operates on\\ncomputer-based and mobile devices. We will validate the utility of aMGS with three pain assays that produce\\ngrimaces in rodents—inflammatory pain, post-surgical (laparotomy) pain, and neuropathic pain. To increase\\nrigor and reproducibility, two pain assays will be performed and scored with aMGS 2.0 in an independent lab.\\nNumerous investigators in the pain field have expressed interest in using our proposed model. The platform\\nwill include a cloud-based data repository and analytic tools to facilitate curation of public data, continuous\\nimprovement of the model over time, and integration of new analytic tools. One analytic tool that we plan to\\ndevelop will identify mouse features that most influence pain classification.\",\"project_title\":\"Development of a deep neural network to measure spontaneous pain from mouse facial expressions\",\"phr_text\":\"ORIGINAL APPLICATION NARRATIVE\\nAs the opioid epidemic has made clear, there is significant need to develop new ways to study chronic pain\\nand relief of pain in preclinical models. Development of an accurate and broadly useful machine learning model\\nand web-based platform will make it possible for researchers to objectively quantify and study spontaneous\\npain in mice, and hence provide a way to more rapidly identify and validate new analgesic drugs.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-10-20T12:10:00Z\",\"budget_end\":\"2023-01-31T12:01:00Z\",\"cfda_code\":\"853\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":39687,\"indirect_cost_amt\":0,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10717670\",\"date_added\":\"2022-12-10T04:12:37Z\"},{\"appl_id\":10740597,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"7R01LM013712-06\",\"project_serial_num\":\"LM013712\",\"organization\":{\"org_name\":\"YALE UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"NEW HAVEN\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CT\",\"org_state_name\":null,\"dept_type\":\"ENGINEERING (ALL TYPES)\",\"fips_country_code\":null,\"org_duns\":[\"043207562\"],\"org_ueis\":[\"FL6GV84CKN57\"],\"primary_duns\":\"043207562\",\"primary_uei\":\"FL6GV84CKN57\",\"org_fips\":\"US\",\"org_ipf_code\":\"9420201\",\"org_zipcode\":\"065208327\",\"external_org_id\":9420201},\"award_type\":\"7\",\"activity_code\":\"R01\",\"award_amount\":613743,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"7\",\"activity_code\":\"R01\",\"ic_code\":\"LM\",\"serial_num\":\"013712\",\"support_year\":\"06\",\"full_support_year\":\"06\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":10960996,\"first_name\":\"Xiaoqian\",\"middle_name\":\"\",\"last_name\":\"Jiang\",\"is_contact_pi\":false,\"full_name\":\"Xiaoqian  Jiang\",\"title\":\"\"},{\"profile_id\":1900244,\"first_name\":\"LUCILA\",\"middle_name\":\"\",\"last_name\":\"OHNO-MACHADO\",\"is_contact_pi\":true,\"full_name\":\"LUCILA  OHNO-MACHADO\",\"title\":\"PROFESSOR OF BIOMEDICAL INFORMATICS\"}],\"contact_pi_name\":\"OHNO-MACHADO, LUCILA \",\"program_officers\":[{\"first_name\":\"LYNDA\",\"middle_name\":\"R\",\"last_name\":\"HARDY\",\"full_name\":\"LYNDA R HARDY\"}],\"agency_ic_admin\":{\"code\":\"LM\",\"abbreviation\":\"NLM\",\"name\":\"National Library of Medicine\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"LM\",\"name\":\"National Library of Medicine\",\"abbreviation\":\"NLM\",\"total_cost\":613743.0}],\"cong_dist\":\"CT-03\",\"spending_categories\":null,\"project_start_date\":\"2023-01-01T12:01:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"BIOMED ENGR/COL ENGR/ENGR STA\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-21-268\",\"full_study_section\":{\"srg_code\":\"CDMA\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Clinical Data Management and Analysis Study Section[CDMA]\"},\"award_notice_date\":\"2023-01-05T12:01:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01LM013712\",\"terms\":\"<Algorithms><Artificial Intelligence><AI system><Computer Reasoning><Machine Intelligence><California><Chronic Disease><Chronic Illness><chronic disorder><Communication><Data Analyses><Data Analysis><data interpretation><Cessation of life><Death><Decentralization><Disease><Disorder><Disease Outbreaks><Outbreaks><Equilibrium><balance><balance function><Future><Genome><Geography><Goals><Health><Heterogeneity><Hospitalization><Hospital Admission><Incidence><Laboratories><Learning><Linear Models><Literature><Methods><Methodology><Morbidity - disease rate><Morbidity><mortality><Persons><Parents><Patients><Phenotype><Privatization><Publishing><Records><Registries><Research><Research Personnel><Investigators><Researchers><Resources><Research Resources><Running><Supervision><Testing><Texas><Time><Universities><County><Privacy><Healthcare><health care><Resource Allocation><base><Site><Acute><Chronic><Clinical><Phase><Link><Evaluation><Training><Recovery><Record Linkage Study><Data Linkages><Sample Size><Knowledge><Event><Reaction><Source><Pattern><Techniques><Country><Visit><Medical center><Performance><hospital readmission><hospital re-admission><re-admission><re-hospitalization><readmission><rehospitalization><Health Insurance Portability and Accountability Act><HIPAA><Kennedy Kassebaum Act><PL 104-191><PL104-191><Public Law 104-191><United States Health Insurance Portability and Accountability Act><Structure><novel><Modality><Modeling><depository><repository><develop software><developing computer software><software development><Intervention Strategies><interventional strategy><Intervention><Institution><Address><Consent><Data><Health Sciences><Resolution><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Security><Underrepresented Ethnic Minority><under-representation of minorities><under-represented minority><underrepresentation of minorities><Underrepresented Minority><developmental><Development><pandemic><pandemic disease><imaging><Image><cost><virtual><computer based prediction><prediction model><predictive modeling><data integration><distributed data><designing><design><patient privacy><new approaches><novel approaches><novel strategy><novel strategies><Outcome><Population><analytical tool><combinatorial><combat><multi-modality><multimodality><data sharing><clinical decision-making><federated data><federated database><federated computing><privacy protection><Predictive Analytics><Data Discovery><individual patient><outcome prediction><predictive outcomes><predictors of outcomes><profiles in patients><patient profile><clinical decision support><unsupervised learning><unsupervised machine learning><supervised learning><supervised machine learning><statistical learning><COVID-19><COVID19><CV-19><CV19><corona virus disease 2019><coronavirus disease 2019><coronavirus disease-19><coronavirus infectious disease-19><data dissemination><large datasets><large data sets><privacy preservation><data privacy><COVID-19 patient><COVID infected patient><COVID patient><COVID positive patient><COVID-19 infected patient><COVID-19 positive patient><COVID19 patient><COVID19 positive patient><SARS-CoV-2 infected patient><SARS-CoV-2 patient><SARS-CoV-2 positive patient><coronavirus disease 2019 infected patient><coronavirus disease 2019 patient><coronavirus disease 2019 positive patient><coronavirus disease infected patient><coronavirus disease patient><coronavirus disease positive patient><coronavirus disease-19 patient><coronavirus patient><patient infected with COVID><patient infected with COVID-19><patient infected with SARS-CoV-2><patient infected with coronavirus disease><patient infected with coronavirus disease 2019><patient infected with severe acute respiratory syndrome coronavirus 2><patient with COVID><patient with COVID-19><patient with COVID19><patient with SARS-CoV-2><patient with coronavirus disease><patient with coronavirus disease 2019><patient with severe acute respiratory distress syndrome coronavirus 2><severe acute respiratory syndrome coronavirus 2 infected patient><severe acute respiratory syndrome coronavirus 2 patient><severe acute respiratory syndrome coronavirus 2 positive patient><artificial intelligence algorithm><AI algorithm><COVID-19 risk><COVID-19 related risk><COVID-19 risk factor><coronavirus disease 2019 risk><coronavirus disease 2019 risk factor><risk associated with COVID-19><risk factor associated with COVID-19><risk factor related to COVID-19><risk related to COVID-19><federated learning>\",\"pref_terms\":\"Acute;Address;Algorithms;Artificial Intelligence;COVID-19;COVID-19 patient;COVID-19 risk;California;Cessation of life;Chronic;Chronic Disease;Clinical;Communication;Consent;Country;County;Data;Data Analyses;Data Discovery;Data Linkages;Decentralization;Development;Disease;Disease Outbreaks;Equilibrium;Evaluation;Event;Future;Genome;Geography;Goals;Health;Health Insurance Portability and Accountability Act;Health Sciences;Healthcare;Heterogeneity;Hospitalization;Image;Incidence;Institution;Intervention;Knowledge;Laboratories;Learning;Linear Models;Link;Literature;Medical center;Methodology;Methods;Modality;Modeling;Morbidity - disease rate;Outcome;Parents;Patient-Focused Outcomes;Patients;Pattern;Performance;Persons;Phase;Phenotype;Population;Predictive Analytics;Privacy;Privatization;Publishing;Reaction;Records;Recovery;Registries;Research;Research Personnel;Resolution;Resource Allocation;Resources;Running;Sample Size;Security;Site;Source;Structure;Supervision;Techniques;Testing;Texas;Time;Training;Underrepresented Minority;Universities;Visit;analytical tool;artificial intelligence algorithm;base;clinical decision support;clinical decision-making;combat;combinatorial;cost;data dissemination;data integration;data privacy;data sharing;design;distributed data;federated computing;federated learning;hospital readmission;individual patient;large datasets;mortality;multimodality;novel;novel strategies;outcome prediction;pandemic disease;patient privacy;predictive modeling;privacy preservation;privacy protection;profiles in patients;repository;software development;statistical learning;supervised learning;unsupervised learning;virtual\",\"abstract_text\":\"Project Summary\\nLarge data sets are important in the development and evaluation of artificial intelligence (AI) and\\nstatistical learning models to predict morbidity, mortality, and other important health outcomes.\\nHealthcare institutions are stewards of their patients’ data, and want to contribute to the\\ndevelopment, evaluation, and utilization of predictive analytics tools. However, they also know\\nthat simple “de-identification” per HIPAA rules is not sufficient to protect patient privacy.\\nAdditionally, other factors such as protection of market share, lack of control about who uses\\nshared data for what purposes, and concerns about patients’ reactions to having their data shared\\nwithout explicit consent make initiatives such as certain registries and centralized repositories\\ndifficult to implement. We have shown that it is possible to decompose algorithms so that they\\ncan run on data that stays at each healthcare center, thus mitigating the concerns about control\\nand potential misuse. In the first phase of this project, we concentrated on demonstrating the\\naccuracy and performance of these algorithms for the study of chronic diseases in which (1)\\nacquisition of new knowledge about the condition is slow (i.e., the disease is well understood, so\\nscientific discoveries are not being published at a rapid pace); and (2) the incidence and\\npresentation of the disease do not vary dramatically from place to place, and from person to\\nperson. In this competitive renewal, we propose to develop decentralized predictive models that\\nmeet all requirements for chronic diseases, but the methods are also applicable to rapidly evolving\\nacute conditions such as COVID-19. We propose new approaches to deal with sites that may be\\nmissing certain patient profiles or certain variables but can still participate in model learning,\\nevaluation and implementation. These new AI algorithms will permit supervised and unsupervised\\nlearning across institutions, using data from multiple modalities (e.g., imaging, genomes,\\nlaboratory tests), and will allow privacy-protecting record linkage. We will test these algorithms\\nand approaches in data from three highly diverse medical centers across the US: Emory\\nUniversity in Atlanta, University of Texas Health Science Center at Houston, and University of\\nCalifornia, San Diego.\",\"project_title\":\"Decentralized differentially-private methods for dynamic data release and analysis\",\"phr_text\":\"Narrative\\nProtecting the privacy of patients while also allowing data to be used for research is difficult. We\\nwill develop new methods that allow researchers to compute with patient data that can remain\\nphysically at originating healthcare institutions (and thus be kept under their control), and that can\\nstill be used for research involving both chronic and acute, rapidly evolving health conditions such\\nas COVID-19.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"879\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":441869,\"indirect_cost_amt\":171874,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10740597\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10548825,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01MH131335-02\",\"project_serial_num\":\"MH131335\",\"organization\":{\"org_name\":\"NEW JERSEY INSTITUTE OF TECHNOLOGY\",\"city\":null,\"country\":null,\"org_city\":\"NEWARK\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NJ\",\"org_state_name\":null,\"dept_type\":\"BIOMEDICAL ENGINEERING\",\"fips_country_code\":null,\"org_duns\":[\"075162990\"],\"org_ueis\":[\"SGBMHQ7VXNH5\"],\"primary_duns\":\"075162990\",\"primary_uei\":\"SGBMHQ7VXNH5\",\"org_fips\":\"US\",\"org_ipf_code\":\"6004001\",\"org_zipcode\":\"071021982\",\"external_org_id\":6004001},\"award_type\":\"5\",\"activity_code\":\"R01\",\"award_amount\":478425,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"MH\",\"serial_num\":\"131335\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":7354388,\"first_name\":\"Bharat\",\"middle_name\":\"Bhusan\",\"last_name\":\"Biswal\",\"is_contact_pi\":true,\"full_name\":\"Bharat Bhusan Biswal\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"BISWAL, BHARAT BHUSAN\",\"program_officers\":[{\"first_name\":\"Yvonne\",\"middle_name\":\"\",\"last_name\":\"Bennett\",\"full_name\":\"Yvonne  Bennett\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":478425.0}],\"cong_dist\":\"NJ-10\",\"spending_categories\":null,\"project_start_date\":\"2022-01-10T12:01:00Z\",\"project_end_date\":\"2025-10-31T12:10:00Z\",\"organization_type\":{\"name\":\"BIOMED ENGR/COL ENGR/ENGR STA\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"EITN\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Emerging Imaging Technologies in Neuroscience Study Section[EITN]\"},\"award_notice_date\":\"2022-11-14T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01MH131335\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><Affect><Age><ages><Anesthesia procedures><Anesthesia><Atlases><Mental disorders><Mental health disorders><Psychiatric Disease><Psychiatric Disorder><mental illness><psychiatric illness><psychological disorder><Brain><Brain Nervous System><Encephalon><Brain Diseases><Brain Disorders><Encephalon Diseases><Intracranial CNS Disorders><Intracranial Central Nervous System Disorders><Communities><Corpus Callosum><Corpus Callosums><Diagnosis><Disease><Disorder><Foundations><Future><Goals><gray matter><substantia grisea><Growth><Generalized Growth><Tissue Growth><ontogeny><Human><Modern Man><Institutes><Intelligence><Light><Photoradiation><Magnetic Resonance Imaging><MR Imaging><MR Tomography><MRI><MRIs><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><NMR Imaging><NMR Tomography><Nuclear Magnetic Resonance Imaging><Zeugmatography><Short-Term Memory><Immediate Memory><Shortterm Memory><working memory><Methods><Mining><Statistical Models><Probabilistic Models><Probability Models><statistical linear mixed models><statistical linear models><United States National Institutes of Health><NIH><National Institutes of Health><Noise><Phenotype><Validity and Reliability><Rest><Signal Transduction><Cell Communication and Signaling><Cell Signaling><Intracellular Communication and Signaling><Signal Transduction Systems><Signaling><biological signal transduction><Sleep><Testing><Time><Vocabulary><Vocabulary Words><Work><Measures><Data Set><Dataset><Comprehension><base><improved><Area><Clinical><Neurologic><Neurological><Series><Individual><Disease Progression><Liquid substance><fluid><liquid><tool><Diagnostic><Functional Magnetic Resonance Imaging><Functional MRI><fMRI><Knowledge><cognitive function><Investigation><Dimensions><Sensory><System><age group><experience><cohort><trait><neuroimaging><neuro-imaging><neurological imaging><Graph><white matter><substantia alba><Reporting><Property><response><brain volume><DWI (diffusion weighted imaging)><DWI-MRI><Diffusion MRI><Diffusion Weighted MRI><Diffusion weighted imaging><Diffusion-weighted Magnetic Resonance Imaging><dMRI><diffusion tensor imaging><Diffusion Magnetic Resonance Imaging><Brain region><Address><Data><Grant Proposals><Applications Grants><Motor><Reproducibility><Cognitive><Monitor><Transmission><transmission process><Characteristics><Process><developmental><Development><blood oxygenation level dependent><blood oxygen level dependent><vector><neglect><Population><analytical tool><Affective><Algorithmic Tools><Software Algorithm><Algorithmic Software><Algorithmic Analyses><Analyses of Algorithms><Analysis of Algorithms><Algorithmic Analysis><neuropsychiatric disorder><neuropsychiatric disease><connectome><gender fluid><gender fluidity><genderfluid><secondary analysis><imaging study><advanced analytics><machine learning method><machine learning based method><machine learning methodologies>\",\"pref_terms\":\"Address;Adult;Affect;Affective;Age;Algorithmic Analysis;Algorithmic Software;Anesthesia procedures;Applications Grants;Area;Atlases;Brain;Brain Diseases;Brain region;Characteristics;Clinical;Cognitive;Communities;Comprehension;Corpus Callosum;Data;Data Set;Development;Diagnosis;Diagnostic;Diffusion Magnetic Resonance Imaging;Dimensions;Disease;Disease Progression;Foundations;Functional Magnetic Resonance Imaging;Future;Goals;Graph;Growth;Human;Individual;Institutes;Intelligence;Investigation;Knowledge;Light;Liquid substance;Magnetic Resonance Imaging;Measures;Mental disorders;Methods;Mining;Monitor;Motor;Neurologic;Noise;Phenotype;Population;Process;Property;Reporting;Reproducibility;Rest;Sensory;Series;Short-Term Memory;Signal Transduction;Sleep;Statistical Models;System;Testing;Time;United States National Institutes of Health;Validity and Reliability;Vocabulary;Work;advanced analytics;age group;analytical tool;base;blood oxygen level dependent;brain volume;cognitive function;cohort;connectome;experience;gender fluid;gray matter;imaging study;improved;machine learning method;neglect;neuroimaging;neuropsychiatric disorder;response;secondary analysis;tool;trait;transmission process;vector;white matter\",\"abstract_text\":\"PROJECT ABSTRACT\\n The discovery of functional brain connectivity (FC) and functional networks (FNs) have propelled the\\nneuroimaging field, particularly in functional magnetic resonance imaging (fMRI), which has experienced an\\nexponential growth in recent years. FNs have allowed us to better understand extrinsic and intrinsic brain\\nproperties in various disease and healthy states, leading to better characterization of neuropsychiatric\\ndisorders. However, current fMRI analyses are constrained to the gray matter (GM) region of the brain and\\nfMRI data from the white matter (WM) region are often discarded, which makes up approximately 50% of the\\nbrain by volume. Many brain disorders have been associated with WM deficiencies, since WM is critical for the\\ntransmission of information to the GM cortical areas. Despite findings of blood-oxygen-level-dependent\\n(BOLD) signals in the WM, WM-FNs are yet to be fully characterized, and neither the mechanism by which\\nWM-FNs may affect GM-FNs, nor how WM-FNs are associated with phenotypic traits are known.\\nThe long-term goal of this project is to better understand the effect of WM-FNs on normal cognitive\\nfunctions of the human brain and apply fMRI data from various healthy and diseased populations for more\\nreliable diagnostics and monitoring. The rationale for this study is based on our preliminary studies which\\ninvestigated WM-FNs using the Human Connectome Project dataset. We found that WM-FNs are correlated\\nwith subregions of the corpus callosum, a critical WM region relaying information between the two cortical\\nhemispheres. Furthermore, we determined an overlap between the WM-FNs and tracts from diffusion tensor\\nimaging (DTI). In this study we will examine WM-FNs of the whole brain using resting fMRI data from two large\\nindependent cohorts. We hypothesize that the FN measures derived from WM will be similar to that of GM and\\nthe metrics can be used to reliably predict phenotypic traits. The hypothesis will be tested with the following\\nspecific aims: Aim1: To develop and evaluate the time-series, FC and FN characteristics of WM of the whole -\\nbrain; Aim 2: To investigate WM-phenotype associations and the predictability of phenotypes using WM-FNs;\\nand Aim 3: To develop and disseminate a WM-FN toolbox. To the best of our knowledge, this study will be the\\nfirst to examine the reliability and validity of WM-FNs in resting fMRI data, and its relation to brain function. The\\nproposed work holds significant contribution since it will facilitate the use of WM-FN methods for the\\nneuroimaging community, which currently lacks the necessary analytic tools to reliably characterize WM\\nfunction. This study will provide a strong foundation f or future clinical use of both WM-FNs and GM-FNs, to\\nunderstand brain function more comprehensively, in addition to facilitating the use of reliable and reproducible\\nWM-FC methods.\",\"project_title\":\"Functional Connectivity and Baseline Networks of the White Matter Brain: Development and Dissemination of Algorithms and Tools\",\"phr_text\":\"PROJECT NARRATIVE\\nFunctional connectivity and functional networks of the brain’s cortical gray matter have allowed us to better\\nunderstand how different regions of the human brain are connected. In this project we will investigate\\nfunctional networks of the brain’s white matter, a critical region of the brain that is often overlooked in functional\\nmagnetic resonance imaging studies. Findings from this study will help to understand the brain as an\\nintegrated system of both white matter and gray matter and help to better diagnose and treat neuropsychiatric\\ndisorders.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-11-01T12:11:00Z\",\"budget_end\":\"2023-10-31T12:10:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":341313,\"indirect_cost_amt\":137112,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10548825\",\"date_added\":\"2022-11-19T04:11:40Z\"},{\"appl_id\":10551207,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K23DK131341-02\",\"project_serial_num\":\"DK131341\",\"organization\":{\"org_name\":\"VANDERBILT UNIVERSITY MEDICAL CENTER\",\"city\":null,\"country\":null,\"org_city\":\"NASHVILLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"TN\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"079917897\"],\"org_ueis\":[\"GYLUH9UXHDX5\"],\"primary_duns\":\"079917897\",\"primary_uei\":\"GYLUH9UXHDX5\",\"org_fips\":\"US\",\"org_ipf_code\":\"10040927\",\"org_zipcode\":\"372320011\",\"external_org_id\":10040927},\"award_type\":\"5\",\"activity_code\":\"K23\",\"award_amount\":172913,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K23\",\"ic_code\":\"DK\",\"serial_num\":\"131341\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":12074183,\"first_name\":\"Girish\",\"middle_name\":\"\",\"last_name\":\"Hiremath\",\"is_contact_pi\":true,\"full_name\":\"Girish  Hiremath\",\"title\":\"ASSISTANT PROFESSOR OF PEDIATRICS\"}],\"contact_pi_name\":\"HIREMATH, GIRISH \",\"program_officers\":[{\"first_name\":\"DAVID\",\"middle_name\":\"E\",\"last_name\":\"SASLOWSKY\",\"full_name\":\"DAVID E SASLOWSKY\"}],\"agency_ic_admin\":{\"code\":\"DK\",\"abbreviation\":\"NIDDK\",\"name\":\"National Institute of Diabetes and Digestive and Kidney Diseases\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"DK\",\"name\":\"National Institute of Diabetes and Digestive and Kidney Diseases\",\"abbreviation\":\"NIDDK\",\"total_cost\":172913.0}],\"cong_dist\":\"TN-05\",\"spending_categories\":null,\"project_start_date\":\"2022-02-01T12:02:00Z\",\"project_end_date\":\"2025-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"PA-20-205\",\"full_study_section\":{\"srg_code\":\"DDK\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Digestive Diseases and Nutrition C Study Section[DDK-C]\"},\"award_notice_date\":\"2022-12-20T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K23DK131341\",\"terms\":\"<supportive environment><enthusiastic atmosphere><enthusiastic environment><supportive atmosphere><machine learning algorithm><machine learned algorithm><machine learning based algorithm><tissue injury><injury to tissue><inhibitor therapy><inhibitor drug><inhibitor therapeutic><Eosinophilic Gastrointestinal Disease><Eosinophilic Gastrointestinal Disorder><Affect><Allergens><Award><Biometry><Biometrics><Biostatistics><Biopsy><Career Choice><Career Path><career aspiration><career interest><career pathway><career track><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Clinical Research><Clinical Study><Clinical Trials><Deglutition><Swallowing><Disease><Disorder><Endoscopy><endoscopic imaging><eosinophil><Blood Eosinophil><Eosinophilic Granulocyte><Eosinophilic Leukocyte><Marrow Eosinophil><Esophagus><Food><Food or Food Product><Future><Glycogen><Goals><Grant><Histology><indexing><Inflammation><Allergic inflammation><Lasers><Laser Electromagnetic><Laser Radiation><Lead><Pb element><heavy metal Pb><heavy metal lead><Leadership><Libraries><Lipids><Mentors><Mentorship><Methods><Morbidity - disease rate><Morbidity><Pathology><Patients><Program Development><Prospective Studies><Proteins><Publications><Scientific Publication><Publishing><Research><Research Personnel><Investigators><Researchers><Role><social role><Raman Spectrum Analysis><IR/UV/Raman Spectroscopy><Raman Spectroscopy><Raman imaging><Raman spectrometry><Testing><Time><Tissues><Body Tissues><United States><Universities><Work><Writing><Mediating><base><career><Chronic><Clinical><Variant><Variation><Histologic><Histologically><Biochemical><Epithelial><pediatric><Childhood><insight><Individual><Development Plans><inflammatory mediator><Inflammation Mediators><Gastroenterologist><Therapeutic><Topical Corticosteroids><Inflammatory><Eosinophilic Esophagitis><Proton Pump Inhibitors><Machine Learning><machine learned><Knowledge><programs><Severities><Techniques><System><gastrointestinal><chemical fingerprinting><experience><success><skills><novel><Pathogenesis><Positioning Attribute><Position><career development><patient oriented study><patient oriented research><Bio-Informatics><Bioinformatics><Biophotonics><Data><K23 Award><K23 Mechanism><K23 Program><Mentored Patient-Oriented Research Career Development Award (K23)><Mentored Patient-Oriented Research Career Development Award><NIDDK><National Institute of Diabetes and Digestive and Kidney Diseases><predict therapeutic response><predict therapy response><predict treatment response><therapy prediction><treatment prediction><treatment response prediction><Prediction of Response to Therapy><Esophageal Tissue><Translational Science><translation research><Translational Research><Characteristics><developmental><Development><disease phenotype><healing><feeding><clinical relevance><clinically relevant><multidisciplinary><clinical care><response to therapy><response to treatment><therapeutic response><therapy response><treatment response><precision-based medicine><precision medicine><Precision care><individualized care><individualized patient care><personalized patient care><personalized care><Data Science><Data Analytics><Personalized medical approach><individualized approach><precision approach><tailored approach><personalized approach><prediction of response><predictive response><predictor of response><response prediction><predicting response>\",\"pref_terms\":\"Affect;Allergens;Allergic inflammation;Award;Biochemical;Bioinformatics;Biometry;Biophotonics;Biopsy;Career Choice;Characteristics;Child;Childhood;Chronic;Clinical;Clinical Research;Clinical Trials;Data;Data Analytics;Data Science;Deglutition;Development;Development Plans;Disease;Endoscopy;Eosinophilic Esophagitis;Eosinophilic Gastrointestinal Disease;Epithelial;Esophageal Tissue;Esophagus;Food;Future;Gastroenterologist;Glycogen;Goals;Grant;Histologic;Histology;Individual;Inflammation;Inflammation Mediators;Inflammatory;Knowledge;Lasers;Lead;Leadership;Libraries;Lipids;Machine Learning;Mediating;Mentored Patient-Oriented Research Career Development Award;Mentors;Mentorship;Methods;Morbidity - disease rate;National Institute of Diabetes and Digestive and Kidney Diseases;Pathogenesis;Pathology;Patients;Positioning Attribute;Prediction of Response to Therapy;Program Development;Prospective Studies;Proteins;Proton Pump Inhibitors;Publications;Publishing;Raman Spectrum Analysis;Research;Research Personnel;Role;Severities;System;Techniques;Testing;Therapeutic;Time;Tissues;Topical Corticosteroids;Translational Research;United States;Universities;Variant;Work;Writing;base;career;career development;chemical fingerprinting;clinical care;clinically relevant;disease phenotype;eosinophil;experience;feeding;gastrointestinal;healing;indexing;inhibitor therapy;insight;machine learning algorithm;multidisciplinary;novel;patient oriented research;personalized approach;personalized care;precision medicine;predicting response;programs;skills;success;supportive environment;tissue injury;treatment response\",\"abstract_text\":\"PROJECT SUMMARY\\n The overarching goal of this K23 Mentored Patient-Oriented Research Career Development Award is to\\nprovide essential skills and mentored research experience to prepare Dr. Girish Hiremath as an independent\\ninvestigator leading a multi-disciplinary team of researchers focused on eosinophilic esophagitis (EoE). EoE is\\na chronic, allergen-mediated, inflammatory condition of the esophagus. As one of the most common cause of\\nupper gastrointestinal morbidity, it affects 1 out of 2000 individuals in the United States. Our understanding of\\nthe pathogenesis of EoE is incomplete and this has contributed towards burdensome approaches to treat EoE\\npatients. There is a critical unmet need to understand all factors related with development of EoE, including\\ndetermining the biochemical changes in the esophagus that leads to transformation of an unaffected\\nesophagus to an inflamed esophagus in EoE. As a Pediatric Gastroenterologist with clinical and research\\nfocus on EoE for more than a decade, the candidate proposes to fill this important knowledge gap by building\\non his published work through: 1) Validating the esophageal biochemical changes in EoE using Raman\\nspectroscopy (a uniquely suited, highly sensitive, laser-based technique) and machine learning, 2) Determining\\nthe correlation between the Raman peaks and the disease activity, and the operating characteristics of the key\\nRaman peaks in distinguishing EoE activity states, and 3) Determining if a combination of the baseline Raman,\\nhistologic, endoscopic, and clinical features can predict response to EoE therapy. By defining the key\\nesophageal biochemical alterations this study will offer novel insights into pathogenesis of EoE, and by\\nidentifying the baseline predictors of the treatment response it will lay a framework for personalized care of\\nEoE patients. This cutting-edge and clinically relevant research will be conducted in an intellectually stimulating\\nand supportive environment at the Vanderbilt University. Throughout the award period, the candidate will be\\nmentored by Dr. Anita Mahadevan-Jansen (primary mentor, expert in Raman Spectroscopy), Dr. Evan Dellon\\n(co-mentor, expert in EoE), and a team of exceptional mentors in pathology, allergic inflammation, biostatistics\\nand bioinformatics, and data science who mirror the blend of researchers that the candidate will lead in the\\nfuture Their distinguished career paths will serve as a template for the candidate's success. To achieve his\\nlong-term career goal, in conjunction with his mentoring team, the candidate has methodically carved out a 4-\\nyear career development program. This program will provide him essential skills in Raman spectroscopy,\\nintegration of multidimensional biomedical data, clinical translational research, EoE, grant writing, and\\nleadership. The preeminent mentorship, rigorous career development plan, and clinical time under the\\nauspices of this K23 award will lead to publications, data, and experience that will uniquely situate the\\ncandidate as an independent investigator at the intersection of EoE, biophotonics, and data science.\",\"project_title\":\"Precision Medicine Approaches for Eosinophilic Esophagitis Using Raman Spectroscopy and Machine Learning\",\"phr_text\":\"PROJECT NARRATIVE\\n As a debilitating disease, eosinophilic esophagitis (EoE) affects 1 in 2000 individuals in the United\\nStates. We will use Raman spectroscopy and machine learning to define esophageal biochemical composition\\nin EoE. This would add to our understanding of the pathobiology of EoE and promote personalized care of EoE\\npatients.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"847\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":160105,\"indirect_cost_amt\":12808,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10551207\",\"date_added\":\"2022-12-24T04:12:06Z\"},{\"appl_id\":10404414,\"subproject_id\":\"9997\",\"fiscal_year\":2023,\"project_num\":\"1P50AR080596-01\",\"project_serial_num\":\"AR080596\",\"organization\":{\"org_name\":\"MASSACHUSETTS GENERAL HOSPITAL\",\"city\":null,\"country\":null,\"org_city\":\"BOSTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"073130411\"],\"org_ueis\":[\"FLJ7DQKLL226\"],\"primary_duns\":\"073130411\",\"primary_uei\":\"FLJ7DQKLL226\",\"org_fips\":\"US\",\"org_ipf_code\":\"4907701\",\"org_zipcode\":\"021142621\",\"external_org_id\":4907701},\"award_type\":\"1\",\"activity_code\":\"P50\",\"award_amount\":90709,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"P50\",\"ic_code\":\"AR\",\"serial_num\":\"080596\",\"support_year\":\"01\",\"full_support_year\":\"01\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9206112,\"first_name\":\"Yi-Hsiang\",\"middle_name\":\"\",\"last_name\":\"Hsu\",\"is_contact_pi\":true,\"full_name\":\"Yi-Hsiang  Hsu\",\"title\":\"\"}],\"contact_pi_name\":\"HSU, YI-HSIANG \",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"AR\",\"abbreviation\":\"NIAMS\",\"name\":\"National Institute of Arthritits and Musculoskeletal and Skin Diseases\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"AR\",\"name\":\"National Institute of Arthritits and Musculoskeletal and Skin Diseases\",\"abbreviation\":\"NIAMS\",\"total_cost\":90709.0}],\"cong_dist\":\"MA-08\",\"spending_categories\":null,\"project_start_date\":\"2023-01-01T12:01:00Z\",\"project_end_date\":\"2027-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"RFA-AR-22-001\",\"full_study_section\":{\"srg_code\":\"ZAR1\",\"srg_flex\":\"\",\"sra_designator_code\":\"KM\",\"sra_flex_code\":\"\",\"group_code\":\"M2\",\"name\":\"Special Emphasis Panel[ZAR1 KM (M2)]\"},\"award_notice_date\":\"2022-12-27T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P50AR080596\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><Affect><inhibitor><Antibodies><Architecture><Engineering / Architecture><Biology><Biopsy><bone><Bone Density><Bone Mineral Density><Bone Marrow><Bone Marrow Reticuloendothelial System><Cells><Cell Body><Clinical Research><Clinical Study><Data Analyses><Data Analysis><data interpretation><Data Collection><Pharmaceutical Preparations><Drugs><Medication><Pharmaceutic Preparations><drug/agent><Environment><Female><Femur><Foundations><Fracture><bone fracture><Future><Gene Expression><Genes><Human Genome><human whole genome><Goals><Human><Modern Man><Human Genetics><Lead><Pb element><heavy metal Pb><heavy metal lead><Mus><Mice><Mice Mammals><Murine><Osteoblasts><Osteocalcin><Bone 4-Carboxyglutamic Protein><Bone Gla Protein><Bone gamma-Carboxyglutamic Acid Protein><Vitamin K-Dependent Bone Protein><Vitamin K-Dependent Calcium-Binding Protein><Osteocytes><Osteogenesis><Bone Formation><bone tissue formation><Osteoporosis><Postmenopausal Osteoporosis><Perimenopausal Bone Loss><Post-Menopausal Osteoporosis><Postmenopausal Bone Loss><Phenotype><Pilot Projects><pilot study><Quality Control><Research Personnel><Investigators><Researchers><Resources><Research Resources><tibia><Time><Genetic Variation><Genetic Diversity><Woman><Data Set><Dataset><Clinical><Postmenopause><Post-Menopause><Post-menopausal Period><Postmenopausal Period><post-menopausal><postmenopausal><KO mice><Knock-out Mice><Null Mouse><Knockout Mice><long bone><bone cell><data quality><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Biological Process><Biological Function><Morphology><Aspirate substance><Aspirate><Physiologic pulse><Pulse><cell type><Pattern><Techniques><osteoporosis with pathological fracture><Fracture due to osteoporosis><Osteoporosis with fracture><Osteoporotic fracture><osteoporosis associated fracture><osteoporosis related fracture><skeletal><data management><human data><novel><Reporting><PTH(1-34)><parathyroid hormone (1-34)><Skeleton><Modeling><Sampling><response><Bio-Informatics><Bioinformatics><Teriparatide><hPTH (1-34)><Forteo><Molecular Mechanisms of Action><Anabolic Agents><preventing><prevent><genetic association><genome sequencing><Adverse Late Effects><Late Effects><Data><International><Molecular Fingerprinting><molecular profile><molecular signature><Molecular Profiling><Mouse Strains><computational analyses><computational analysis><computer analyses><Computer Analysis><Translational Science><translation research><Translational Research><Molecular><Process><developmental><Development><pathway><Pathway interactions><preclinical><pre-clinical><pre-clinical study><preclinical study><NHLBI><National Heart, Lung, and Blood Institute><Expression Signature><gene expression pattern><gene expression signature><transcriptional profile><transcriptional signature><Gene Expression Profile><clinical phenotype><time use><Gene variant><allele variant><allelic variant><genomic variant><genetic variant><designing><design><innovate><innovative><innovation><transcriptomics><treatment effect><progenitor><precursor cell><FDA approved><treatment strategy><stem cell biology><biorepository><biobank><RNA Seq><RNA sequencing><RNAseq><transcriptome sequencing><Cloud Infrastructure><cloud computer><Cloud Computing><Genetic study><Data Analytics><global gene expression><global transcription profile><transcriptome><phenotypic data><whole genome><entire genome><full genome><Trans-Omics for Precision Medicine><TOPMed><fracture risk><Bone structure><skeletal structure><data resource><single-cell RNA sequencing><scRNA-seq><single cell RNA-seq><single cell RNAseq><single cell expression profiling><single cell transcriptomic profiling><computational platform><computing platform><side effect><bioinformatics pipeline><bio-informatics pipeline>\",\"pref_terms\":\"Adult;Affect;Anabolic Agents;Antibodies;Architecture;Aspirate substance;Bioinformatics;Biological Process;Biology;Biopsy;Bone Density;Bone Marrow;Bone structure;Cells;Clinical;Clinical Research;Cloud Computing;Computer Analysis;Data;Data Analyses;Data Analytics;Data Collection;Data Set;Development;Environment;FDA approved;Female;Femur;Forteo;Foundations;Fracture;Future;Gene Expression;Gene Expression Profile;Genes;Genetic Variation;Genetic study;Goals;Human;Human Genetics;Human Genome;International;Knockout Mice;Late Effects;Lead;Modeling;Molecular;Molecular Mechanisms of Action;Molecular Profiling;Morphology;Mouse Strains;Mus;National Heart, Lung, and Blood Institute;Osteoblasts;Osteocalcin;Osteocytes;Osteogenesis;Osteoporosis;Pathway interactions;Pattern;Pharmaceutical Preparations;Phenotype;Physiologic pulse;Pilot Projects;Postmenopausal Osteoporosis;Postmenopause;Process;Quality Control;Reporting;Research Personnel;Research Project Grants;Resources;Sampling;Skeleton;Techniques;Time;Trans-Omics for Precision Medicine;Translational Research;Woman;biobank;bioinformatics pipeline;bone;bone cell;cell type;clinical phenotype;computational platform;data management;data quality;data resource;design;fracture risk;genetic association;genetic variant;genome sequencing;human data;inhibitor;innovation;long bone;novel;osteoporosis with pathological fracture;parathyroid hormone (1-34);phenotypic data;pre-clinical;preclinical study;precursor cell;prevent;progenitor;response;side effect;single-cell RNA sequencing;skeletal;stem cell biology;tibia;time use;transcriptome;transcriptome sequencing;transcriptomics;treatment effect;treatment strategy;whole genome\",\"abstract_text\":\"Project Summary – Bioinformatics Core\\nThe objective of this Bioinformatics Core is to support statistical and bioinformatics analyses to\\nstudy the effects of osteoporosis anabolic therapies (romosozumab, a sclerostin inhibitor, Scl-\\nAb) on osteoblast progenitors as well as osteocytes in clinical and pre-clinical studies with\\nanabolic therapies. A short anabolic treatment window from Scl-Ab has been reported; in\\ncontrast to teriparatide (PTH) that has a longer anabolic window. This Bioinformatics Core will\\nserve as the critical foundation for the research projects by analyzing cross-species data from\\nsingle cell RNA-seq (scRNA-seq) and bulk RNA-seq using innovative techniques and approaches.\\nThe capabilities of the Bioinformatics Core will be enhanced by being able to leverage existing\\ndatasets of whole genome sequencing in tens thousands of human samples; and osteocyte\\ngene expression signatures and deeply skeletal phenotyping from a unique mouse knockout\\ndataset from our collaborators. To support Project #1, Project #2 as well as pilot projects, the\\nBioinformatics Core will provide statistical design, computational analysis, data quality control\\nand management support as well as accessing to other external human genetics and\\nbioinformatics data and resource. Specifically, in Aim 1, the Bioinformatics Core seeks to build a\\nsynergistic data management, data analytical and bioinformatics pipelines over cloud\\ncomputing environment which allows interaction within investigators through data collection,\\ndata analyses, information sharing and data interpretation. In Aim 2, the Core will provide\\nstatistical and bioinformatics analyses for scRNA-seq and bulk RNA-seq generated from clinical\\nand pre-clinical studies to identify osteoblast precursor cell clusters over time with either Scl-Ab\\nor PTH treatments; osteoblast lineage tracing; osteoblast progenitors and osteocytes gene\\nexpression patterns; and co-expression pattern over time. In addition, cross-species integrating\\nanalysis will be conducted to identify homologous and divergent cell types between human and\\nmice. In Aim 3, we will prioritize genes regulating bone formation and Identify molecular\\nsignatures affecting treatment effect via human gene expression (in bone biopsies) and genetic\\nvariation associated with bone phenotypes (BMD, HR-PQCT derived bone structure, fracture) in\\nlarge-scale whole genome sequenced studies. In Aim 4, To further characterize skeletal function\\nand phenotypes of the genes identified from project# and project#2 transcriptome analyses, we\\nwill utilize the existing KO mice deep phenotyping data; and generate a new KO mice strain.\\nUltimately, these merged analyses will generate powerfully informed, novel hypotheses\\nregarding bone stem cell biology and responses to bone anabolic agents.\",\"project_title\":\"Bioinformatics Core\",\"phr_text\":\"Project Narrative – Bioinformatics Core\\nThe goal of this Bioinformatics Core is to support statistical and bioinformatics analyses to study\\nthe effects of osteoporosis anabolic therapies on osteoblast progenitors as well as osteocytes in\\nclinical and pre-clinical studies. To accomplish this goal, we will perform computational analysis\\non single-cell RNA-seq and bulk RNA-seq obtained from post-menopausal women and female\\nmice treated with either sclerostin inhibitor or PTH. We will then prioritize genes regulating bone\\nformation via human genetic variation association analyses; and knockout mice with deeply\\nskeletal phenotypes.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-04-01T12:04:00Z\",\"budget_end\":\"2023-03-31T12:03:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":56693,\"indirect_cost_amt\":34016,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10404414\",\"date_added\":\"2022-12-31T04:12:24Z\"},{\"appl_id\":10553208,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K01AI141579-05\",\"project_serial_num\":\"AI141579\",\"organization\":{\"org_name\":\"MARSHFIELD CLINIC RESEARCH FOUNDATION\",\"city\":null,\"country\":null,\"org_city\":\"MARSHFIELD\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WI\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"074776030\"],\"org_ueis\":[\"JCALBPNMAHF3\"],\"primary_duns\":\"074776030\",\"primary_uei\":\"JCALBPNMAHF3\",\"org_fips\":\"US\",\"org_ipf_code\":\"4843101\",\"org_zipcode\":\"544495703\",\"external_org_id\":4843101},\"award_type\":\"5\",\"activity_code\":\"K01\",\"award_amount\":106212,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K01\",\"ic_code\":\"AI\",\"serial_num\":\"141579\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":11948346,\"first_name\":\"Joshua\",\"middle_name\":\"Glenn\",\"last_name\":\"Petrie\",\"is_contact_pi\":true,\"full_name\":\"Joshua Glenn Petrie\",\"title\":\"ASSOCIATE RESEARCH SCIENTIST\"}],\"contact_pi_name\":\"PETRIE, JOSHUA GLENN\",\"program_officers\":[{\"first_name\":\"BROOKE\",\"middle_name\":\"ALLISON\",\"last_name\":\"Bozick\",\"full_name\":\"BROOKE ALLISON Bozick\"}],\"agency_ic_admin\":{\"code\":\"AI\",\"abbreviation\":\"NIAID\",\"name\":\"National Institute of Allergy and Infectious Diseases\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"AI\",\"name\":\"National Institute of Allergy and Infectious Diseases\",\"abbreviation\":\"NIAID\",\"total_cost\":106212.0}],\"cong_dist\":\"WI-07\",\"spending_categories\":null,\"project_start_date\":\"2019-01-02T12:01:00Z\",\"project_end_date\":\"2023-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"20\",\"is_other\":false},\"full_foa\":\"PA-18-369\",\"full_study_section\":{\"srg_code\":\"MID\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Microbiology and Infectious Diseases B Research Study Section[MID-B]\"},\"award_notice_date\":\"2022-12-02T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K01AI141579\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><cost effectiveness><Dangerousness><Diagnosis><Disease Outbreaks><Outbreaks><Epidemiologic Methods><Epidemiological Methods><Epidemiological Techniques><Methods Epidemiology><Epidemiology><epidemiologic><epidemiological><Future><Goals><Hospitalization><Hospital Admission><Hospitals><Incidence><Infection><Influenza><Grippe><Intervention Studies><intervention research><interventional research><interventional study><interventions research><Laboratories><Lead><Pb element><heavy metal Pb><heavy metal lead><Mentors><Methods><Methodology><Michigan><Mission><Morbidity - disease rate><Morbidity><mortality><Patients><Public Health><Research><Research Personnel><Investigators><Researchers><Sensitivity and Specificity><Technology><Testing><Time><Universities><Virus Diseases><Viral Diseases><viral infection><virus infection><virus-induced disease><Virus><Work><Measures><Cost Savings><Health Care Costs><Health Costs><Healthcare Costs><Infection Control><Diagnostic tests><Health Benefit><Healthcare><health care><Outcomes Research><base><career><improved><Acute><Clinical><Molecular Epidemiology><Training><Diagnostic><Specimen><Research Specimen><Knowledge><programs><Techniques><Viral><respiratory><Infection prevention><Prevent infection><respiratory virus><skills><novel><Prevention strategy><Preventative strategy><Preventive strategy><Prevention><respiratory infection virus><Modeling><career development><case-controlled><case control><Math Models><mathematic model><mathematical modeling><mathematical model><Intervention Strategies><interventional strategy><Intervention><Bio-Informatics><Bioinformatics><preventing><prevent><Causality><causation><disease causation><Etiology><cost efficient analysis><cost-effective analysis><Cost Effectiveness Analysis><Data><Ph.D.><PhD><Doctor of Philosophy><Effectiveness of Interventions><NIAID><National Institute of Allergy and Infectious Disease><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Transmission><transmission process><Molecular><developmental><Development><burden to community><community burden><influenza transmission><transmission of the flu virus><flu transmission><Outcome><cost effective><pathogen><innovate><innovative><innovation><multidisciplinary><community setting><NGS Method><NGS system><next gen sequencing><nextgen sequencing><next generation sequencing><screening><child patients><pediatric patients><molecular diagnostics><viral transmission><virus transmission><whole genome><entire genome><full genome><health care settings><healthcare settings><implementation strategy><strategies for implementation><advanced analytics><infection burden><burden of infection><implementation intervention>\",\"pref_terms\":\"Acute;Adult;Bioinformatics;Clinical;Cost Effectiveness Analysis;Cost Savings;Dangerousness;Data;Development;Diagnosis;Diagnostic;Diagnostic tests;Disease Outbreaks;Doctor of Philosophy;Effectiveness of Interventions;Epidemiologic Methods;Epidemiology;Etiology;Future;Goals;Health Benefit;Health Care Costs;Healthcare;Hospitalization;Hospitals;Incidence;Infection;Infection Control;Infection prevention;Influenza;Intervention;Intervention Studies;Knowledge;Laboratories;Lead;Measures;Mentors;Methodology;Methods;Michigan;Mission;Modeling;Molecular;Molecular Epidemiology;Morbidity - disease rate;National Institute of Allergy and Infectious Disease;Outcome;Outcomes Research;Patient-Focused Outcomes;Patients;Prevention;Prevention strategy;Public Health;Research;Research Personnel;Sensitivity and Specificity;Specimen;Techniques;Technology;Testing;Time;Training;Universities;Viral;Virus;Virus Diseases;Work;advanced analytics;base;career;career development;case control;community burden;community setting;cost effective;cost effectiveness;flu transmission;health care settings;implementation intervention;implementation strategy;improved;infection burden;innovation;mathematical model;molecular diagnostics;mortality;multidisciplinary;next generation sequencing;novel;pathogen;pediatric patients;prevent;programs;respiratory;respiratory infection virus;respiratory virus;screening;skills;transmission process;viral transmission;whole genome\",\"abstract_text\":\"PROJECT SUMMARY / ABSTRACT\\nHospital-associated respiratory virus infections (HA-RVI) lead to increased morbidity, mortality, and healthcare\\ncosts, but these infections are often under- or misdiagnosed particularly for respiratory viruses other than\\ninfluenza. HA-RVI are preventable through appropriate treatment and infection control measures, but\\nincomplete diagnosis leads to missed opportunities for intervention. Taking advantage of these opportunities\\nfor intervention might require additional diagnostic testing and expanded infection control measures. The\\ndegree to which these approaches would be cost-effective is unclear. Therefore, there is a need to define the\\nburden and clinical impact of respiratory viruses in the hospital setting to inform optimal diagnostic, treatment\\nand infection prevention strategies. The overall objective of this project is to support Josh Petrie, PhD in the\\ndevelopment of expertise in healthcare epidemiology, state-of-the-art molecular methods, and advanced\\nmodeling techniques. This objective will be completed through focused training and career development\\nactivities in healthcare epidemiology, next generation sequencing, bioinformatics, cost-effectiveness analysis,\\nand mathematical modeling that is overseen by an excellent team of mentors. The skills developed by the\\ntraining and career development objectives will be strengthened by mentored research to accomplish the\\nfollowing Specific Aims: (1) Define the epidemiology and burden of community-acquired and hospital-\\nassociated respiratory virus infections and compare clinical impact by viral species; (2) Improve the sensitivity\\nand specificity of case definitions to identify hospital-associated influenza cases by integrating clinical,\\nepidemiologic, and molecular data; and (3) Determine the cost-effectiveness of increased respiratory virus\\nscreening and expanded infection control measures to reduce HA-RVI using mathematical models. Completion\\nof the training and research programs will represent the next step on Dr. Petrie's path toward achieving his\\nlong term goal of establishing an independent research program that uses multidisciplinary laboratory and\\nanalytic methodologies to investigate the transmission of influenza and other respiratory viruses. The expected\\nresearch outcomes of the proposed project are, 1) identification of respiratory viruses that, in addition to\\ninfluenza, are responsible for the greatest number of HA-RVI and result in the most severe clinical outcomes;\\n2) development of methodology for improved identification of HA-RVI through integration of clinical,\\nepidemiologic, and molecular data; and 3) determination of cost-effective diagnostic testing and intervention\\nimplementation strategies for HA-RVI prevention. The proposed research is significant because it is expected\\nthat the outcomes of this work and future studies that build upon it, will lead to reductions in morbidity,\\nmortality, and healthcare costs associated with HA-RVI. This research is innovative both in its focus on a large\\npanel of important respiratory viruses and its use of cross-disciplinary methodology that allows for new\\navenues of research.\",\"project_title\":\"Hospital-Associated Respiratory Virus Infections: Molecular Epidemiology, Clinical Outcomes, and Cost-Effectiveness of Interventions\",\"phr_text\":\"PROJECT NARRATIVE\\nThe proposed research is relevant to public health because incomplete diagnosis of hospital-associated\\nrespiratory virus infections (HA-RVI) is a barrier to effective infection control and prevention efforts leading to\\nincreased morbidity, mortality, and healthcare costs. We propose to determine the overall impact of HA-RVI,\\nimprove case-definitions for their identification, and determine the cost-effectiveness of interventions for their\\nprevention. This research is relevant to the mission of the National Institute of Allergy and Infectious Diseases\\nin that the outcomes of this work and future studies that build upon it will improve understanding, treatment,\\nand prevention of HA-RVI.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"855\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":98344,\"indirect_cost_amt\":7868,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10553208\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10521278,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K01MH122774-03\",\"project_serial_num\":\"MH122774\",\"organization\":{\"org_name\":\"NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC\",\"city\":null,\"country\":null,\"org_city\":\"NEW YORK\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NY\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"167204994\"],\"org_ueis\":[\"TYVNMW8FNQQ3\"],\"primary_duns\":\"167204994\",\"primary_uei\":\"TYVNMW8FNQQ3\",\"org_fips\":\"US\",\"org_ipf_code\":\"1590919\",\"org_zipcode\":\"100321007\",\"external_org_id\":1590919},\"award_type\":\"5\",\"activity_code\":\"K01\",\"award_amount\":175879,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K01\",\"ic_code\":\"MH\",\"serial_num\":\"122774\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14807882,\"first_name\":\"Xi\",\"middle_name\":\"\",\"last_name\":\"Zhu\",\"is_contact_pi\":true,\"full_name\":\"Xi  Zhu\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"ZHU, XI \",\"program_officers\":[{\"first_name\":\"Mark\",\"middle_name\":\"\",\"last_name\":\"Chavez\",\"full_name\":\"Mark  Chavez\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":175879.0}],\"cong_dist\":\"NY-13\",\"spending_categories\":null,\"project_start_date\":\"2020-12-10T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"PA-19-126\",\"full_study_section\":{\"srg_code\":\"APDA\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Adult Psychopathology and Disorders of Aging Study Section[APDA]\"},\"award_notice_date\":\"2022-11-22T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K01MH122774\",\"terms\":\"<multi-modal neuroimaging><multimodal neuro-imaging><Anxiety><Anxiety Disorders><Brain><Brain Nervous System><Encephalon><Classification><Systematics><Clinical Trials><Cluster Analysis><Cluster Analyses><comorbidity><co-morbid><co-morbidity><Mental Depression><depression><Diagnosis><Disease><Disorder><Pharmaceutical Preparations><Drugs><Medication><Pharmaceutic Preparations><drug/agent><Environment><Fright><Fear><Future><Goals><Grant><gray matter><substantia grisea><Heterogeneity><Magnetic Resonance Imaging><MR Imaging><MR Tomography><MRI><MRIs><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><NMR Imaging><NMR Tomography><Nuclear Magnetic Resonance Imaging><Zeugmatography><Mentorship><Methods><National Institute of Mental Health><NIMH><Neurobiology><neurobiological><Patients><Psychiatry><Psychotherapy><Public Health><Research><Research Personnel><Investigators><Researchers><Research Support><Rest><Post-Traumatic Stress Disorders><PTSD><Post-Traumatic Neuroses><Posttraumatic Neuroses><Posttraumatic Stress Disorders><post-trauma stress disorder><posttrauma stress disorder><traumatic neurosis><Supervision><Work><Measures><Treatment outcome><Selection for Treatments><therapy selection><Data Set><Dataset><base><Brain imaging><brain visualization><Clinical><Biological><biologic><Ensure><Training><DSM-4><DSM4><Diagnostic and Statistical Manual of Mental Disorders, 4th edition><Diagnostic and Statistical Manual of Mental Disorders-IV><DSM-IV><Individual><Fostering><Sample Size><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Functional disorder><Dysfunction><Physiopathology><pathophysiology><Diagnostic><Machine Learning><machine learned><Knowledge><programs><Dimensions><Complex><Dependence><Pattern><Techniques><experience><authority><relating to nervous system><neural><skills><memory process><memory processing><Categories><Modeling><clinical depression><major depression><major depression disorder><Major Depressive Disorder><Address><Symptoms><Data><K01 Award><K01 Mechanism><K01 Program><Mentored Training Award><Research Scientist Development Award><Mentored Research Scientist Development Award><multi-modal imaging><multi-modality imaging><multimodality imaging><Multimodal Imaging><predict therapeutic response><predict therapy response><predict treatment response><therapy prediction><treatment prediction><treatment response prediction><Prediction of Response to Therapy><Reproducibility><Research Training><Subgroup><trial regimen><trial treatment><Clinical Treatment><Pattern Recognition><Translational Science><translation research><Translational Research><developmental><Development><Behavioral><work group><working group><clinical phenotype><Advanced Development><designing><design><Prevalence><Trauma><patient centered><patient oriented><reward processing><develop therapy><intervention development><treatment development><therapy development><neurobiological mechanism><multi-modality><multimodality><response to therapy><response to treatment><therapeutic response><therapy response><treatment response><treatment strategy><bio-markers><biologic marker><biomarker><Biological Markers><BigData><Big Data><customized therapy><customized treatment><individualized patient treatment><individualized therapeutic strategy><individualized therapy><individualized treatment><patient specific therapies><patient specific treatment><tailored medical treatment><tailored therapy><tailored treatment><unique treatment><individualized medicine><anxiety-related disorders><prediction of response><predictive response><predictor of response><response prediction><predicting response><associated symptom><co-morbid symptom><co-occuring symptom><comorbid symptom><concurrent symptom><cooccuring symptom><symptom association><symptom comorbidity><clinical heterogeneity><trauma exposure><exposure to trauma><unsupervised learning><unsupervised machine learning><supervised learning><supervised machine learning><multimodal data><multi-modal data><multi-modal datasets><multimodal datasets><advanced analytics><data fusion><large datasets><large data sets><treatment comparison><compare treatment><treatment research><Psychoses><machine learning model><machine learning based model><multimodal neuroimaging><multi-modal neuro-imaging>\",\"pref_terms\":\"Address;Advanced Development;Anxiety;Anxiety Disorders;Behavioral;Big Data;Biological;Biological Markers;Brain;Brain imaging;Categories;Classification;Clinical;Clinical Treatment;Clinical Trials;Cluster Analysis;Complex;DSM-IV;Data;Data Set;Dependence;Development;Diagnosis;Diagnostic;Dimensions;Disease;Ensure;Environment;Fostering;Fright;Functional disorder;Future;Goals;Grant;Heterogeneity;Individual;Knowledge;Machine Learning;Magnetic Resonance Imaging;Major Depressive Disorder;Measures;Mental Depression;Mentored Research Scientist Development Award;Mentorship;Methods;Modeling;Multimodal Imaging;National Institute of Mental Health;Neurobiology;Patients;Pattern;Pattern Recognition;Pharmaceutical Preparations;Post-Traumatic Stress Disorders;Prediction of Response to Therapy;Prevalence;Psychiatry;Psychoses;Psychotherapy;Public Health;Reproducibility;Research;Research Personnel;Research Project Grants;Research Support;Research Training;Rest;Sample Size;Selection for Treatments;Subgroup;Supervision;Symptoms;Techniques;Training;Translational Research;Trauma;Treatment outcome;Work;advanced analytics;anxiety-related disorders;associated symptom;authority;base;clinical heterogeneity;clinical phenotype;comorbidity;data fusion;design;experience;gray matter;individualized medicine;large datasets;machine learning model;memory process;multimodal data;multimodal neuroimaging;multimodality;neurobiological mechanism;patient oriented;predicting response;programs;relating to nervous system;reward processing;skills;supervised learning;therapy development;trauma exposure;treatment comparison;treatment research;treatment response;treatment strategy;unsupervised learning;working group\",\"abstract_text\":\"Posttraumatic stress disorder (PTSD) is a highly prevalent and debilitating disorder. Despite efforts to\\ncharacterize the pathophysiology of PTSD and its heterogenity, no objective biomarker have been established\\nto aid in diagnosis, and prediction of treatment response. This K01 presents a program for research and training\\nthat will support the applicant on a path towards becoming an independent investigator, focused on utilizing a\\ndata-driven computational approach and machine learning techniques to identify multimodal neural biomarkers\\nof PTSD (supervised) and multimodal biotypes of PTSD (unsupervised) and explore whether such biotypes could\\nbe used to predict response to prolonged exposure (PE), the first line treatment for PTSD. The training plan\\nbuilds on the candidate’s prior training and experience and capitalizes on a mentorship team and a research\\nenvironment to foster development of the candidate’s expertise in 1) the neural and behavioral basis of PTSD\\nand anxiety disorders; 2) multimodal data fusion analysis and latent dimension interpretation with data-driven\\ncomputational approaches and data reproducibility; and 3) patient-oriented translational research in anxiety\\ndisorders. This research project will apply both supervised and unsupervised machine learning techniques on\\nmultimodal MRI data from the largest existing PTSD dataset (N~3000 from the ENIGMA-PTSD working group).\\nBiotypes identified from this large dataset will then be extended to clinical treatment data. The results of the\\nproposed research will be vital to aid in finding neural biomarkers of PTSD and better predict different treatment\\noutcomes through different biotype targets and will lead to a future R01 grant examining brain-symptoms\\nassociation across anxiety and trauma-related disorders, and to use the newly identified PTSD biotypes to inform\\ndifferent treatment outcomes in a following R61/33. Together, the research and training experiences and\\nexpertise developed through this K01 award will support the applicant’s transition to research independence and\\nensure the applicant becomes a leading authority in the application of data-driven computational approaches in\\npsychiatry research, and provide the basis for future NIMH grants to explore biotypes from multimodal brain\\nimaging using data-driven computational approaches across anxiety-related disorders.\",\"project_title\":\"Identification of Distinct Multimodal Biotypes of PTSD Using Data Driven Approach: A Multisite Big Data Study\",\"phr_text\":\"PTSD can occur after a direct or indirect traumatic experience and is a highly prevalent and debilitating disorder.\\nThis research project use a big data and data-driven approaches to study the heterogenity of PTSD. In the long\\nterm, this line of research will aid in finding neural biomarkers of PTSD and better predict different treatment\\noutcomes through different biotype targets, which will advance the development of effective diagnostics and\\ntreatments for PTSD.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":162851,\"indirect_cost_amt\":13028,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10521278\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10542769,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R25GM134978-03\",\"project_serial_num\":\"GM134978\",\"organization\":{\"org_name\":\"OREGON HEALTH & SCIENCE UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"PORTLAND\",\"org_country\":\"UNITED STATES\",\"org_state\":\"OR\",\"org_state_name\":null,\"dept_type\":\"NEUROLOGY\",\"fips_country_code\":null,\"org_duns\":[\"096997515\"],\"org_ueis\":[\"NPSNT86JKN51\"],\"primary_duns\":\"096997515\",\"primary_uei\":\"NPSNT86JKN51\",\"org_fips\":\"US\",\"org_ipf_code\":\"6297007\",\"org_zipcode\":\"972393098\",\"external_org_id\":6297007},\"award_type\":\"5\",\"activity_code\":\"R25\",\"award_amount\":330711,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R25\",\"ic_code\":\"GM\",\"serial_num\":\"134978\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":1959401,\"first_name\":\"GARY\",\"middle_name\":\"L\",\"last_name\":\"WESTBROOK\",\"is_contact_pi\":false,\"full_name\":\"GARY L WESTBROOK\",\"title\":\"SENIOR SCIENTIST\"},{\"profile_id\":9699129,\"first_name\":\"Letisha\",\"middle_name\":\"Renee\",\"last_name\":\"Wyatt\",\"is_contact_pi\":true,\"full_name\":\"Letisha Renee Wyatt\",\"title\":\"ASSISTANT PROFESOR\"}],\"contact_pi_name\":\"WYATT, LETISHA RENEE\",\"program_officers\":[{\"first_name\":\"LAMEESE\",\"middle_name\":\"DANA\",\"last_name\":\"Akacem\",\"full_name\":\"LAMEESE DANA Akacem\"}],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":330711.0}],\"cong_dist\":\"OR-03\",\"spending_categories\":null,\"project_start_date\":\"2021-01-01T12:01:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-20-066\",\"full_study_section\":{\"srg_code\":\"TWD\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Training and Workforce Development Study Section - D[TWD-D]\"},\"award_notice_date\":\"2022-12-21T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"R25GM134978\",\"terms\":\"<Authorship><Biomedical Engineering><bio-engineered><bio-engineers><bioengineering><biological engineering><Biomedical Research><Communication><Communities><Environment><Ethics><ethical><Faculty><Goals><Health><Interest Group><Joints><Mentors><Mentorship><Neurosciences><Oregon><Paper><Professional Competence><career skill><Program Development><Publications><Scientific Publication><Research><Research Personnel><Investigators><Researchers><Rotation><Schools><medical schools><medical college><school of medicine><Science><Students><Time><United States><Universities><Work><Writing><Competence><Immunology><base><career><Area><Time Management><Ensure><Training><Individual><Workshop><Educational workshop><Fostering><Interdisciplinary Research><Multidisciplinary Collaboration><Multidisciplinary Research><Interdisciplinary Study><Development Plans><Funding><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Exposure to><Deposition><Deposit><programs><Oral><Techniques><interest><meetings><Postdoctoral Fellow><Postdoc><Research Associate><post-doc><post-doctoral><college><collegiate><data management><experience><success><Self Efficacy><skills><novel><Participant><research in practice><graduate student><social><career development><outreach program><depository><repository><disparity in health><health disparity><Institution><Address><Ph.D.><PhD><Doctor of Philosophy><Doctorate Degree><doctoral degree><Doctor's Degree><Health Sciences><Reproducibility><Strategic Planning><Cancer Biology><Preparation><developmental><Development><Instruction><imaging informatics><designing><design><resilience><Outcome><Population><skill development><skill acquisition><evidence base><responsible research conduct><STEM class><STEM course><STEM discipline><STEM major><science, technology, engineering and math class><science, technology, engineering and math course><science, technology, engineering and math discipline><science, technology, engineering and math field><science, technology, engineering and math major><science, technology, engineering and mathematics class><science, technology, engineering and mathematics course><science, technology, engineering and mathematics discipline><science, technology, engineering and mathematics field><science, technology, engineering and mathematics major><STEM field><undergrad><undergraduate><undergraduate student><Ph D student><Ph D. student><Ph. D. student><Ph.D student><Ph.D. student><PhD student><PhD. student><doctoral student><Complex thinking><Evaluative Thinking><Critical Thinking><Graduate Degree><Secondary School Student><Secondary Student><High School Student><Underrepresented Students><Academic counseling><Academic guidance><Admissions Counseling><Educational counseling><Academic advising><faculty mentor><faculty research><peer instruction><peer led team learning><peer mentoring><peer teaching><peer coaching><graduate school preparation><GRE Prep><Graduate Record Examination Preparation><GRE Preparation><Study Skills><Workplace Diversity><Diverse Workforce><Data Analytics><Underrepresented Populations><Underrepresented Groups><under representation of groups><under represented groups><under represented people><under represented populations><underrepresentation of groups><underrepresented people><recruit><Immersion><Reduce health disparities><Decrease health disparities><Health disparity mitigation><Health disparity reduction><Lower health disparities><Mitigate health disparities>\",\"pref_terms\":\"Academic advising;Address;Area;Authorship;Biomedical Engineering;Biomedical Research;Cancer Biology;Communication;Communities;Competence;Critical Thinking;Data Analytics;Deposition;Development;Development Plans;Diverse Workforce;Doctor of Philosophy;Doctor&apos;s Degree;Educational workshop;Ensure;Environment;Ethics;Exposure to;Faculty;Fostering;Funding;GRE Preparation;Goals;Graduate Degree;Health;Health Sciences;High School Student;Immersion;Immunology;Individual;Institution;Instruction;Interdisciplinary Study;Interest Group;Joints;Mentors;Mentorship;Neurosciences;Oral;Oregon;Outcome;Paper;Participant;Population;Postdoctoral Fellow;Preparation;Professional Competence;Program Development;Publications;Reduce health disparities;Reproducibility;Research;Research Personnel;Research Project Grants;Rotation;STEM field;Schools;Science;Self Efficacy;Strategic Planning;Students;Study Skills;Techniques;Time;Time Management;Training;Underrepresented Populations;Underrepresented Students;United States;Universities;Work;Writing;base;career;career development;college;data management;design;doctoral student;evidence base;experience;faculty mentor;faculty research;graduate school preparation;graduate student;health disparity;imaging informatics;interest;medical schools;meetings;novel;outreach program;peer coaching;programs;recruit;repository;research in practice;resilience;responsible research conduct;skill acquisition;skills;social;success;undergraduate student\",\"abstract_text\":\"Project Summary\\nStudents from groups underrepresented in science often lack actively mentored, sustained research\\nexperiences during their undergraduate years. Absence of direct support and training can be a barrier when\\nthese students pursue advanced biomedical degrees. Ultimately fewer underrepresented individuals hold\\ndoctoral degrees, have careers in biomedicine, thereby exacerbating health disparities in the United States\\n(U.S.). The objective of this program is to recruit and train at least five participants per year for an intensive,\\nmentored experience in biomedical research with the goal to prepare them for pursuing a graduate degree in\\nbiomedicine. In preparing scholars to be competitive and successful in graduate programs, we will use a\\ncompetency-based educational framework to build their skills in research and self-efficacy. The primary aims of\\nOHSU PREP are to 1) provide a sustained research-intensive experience to prepare students for graduate\\nprograms with research faculty mentors who have received mentor training to ensure success with this\\npopulation of scholars; and 2) provide competency-based professional and career skills development. Scholars\\nwill be paired with a faculty mentor and pursue a year-long independent research project aligned with the\\nscholar’s interests and receive additional mentorship from the Program Directors and Program Coordinator in\\nweekly meetings that build their skills and confidence in self-efficacy and professional identity. Each scholar\\nwill develop an individual development plan in order to understand their career and professional development\\nneeds, participate in similar activities as incoming first year PhD students, and take graduate-level courses with\\nan emphasis on ethics and data analytics. We will offer directed preparation for graduate school, including\\ngraduate school applications, study skills and time management, critical thinking, and resilience. Scholars, with\\ntheir mentors, are expected to develop their research projects for presentation at local and national meetings\\nand prepare a research paper for publication and/or deposition in the OHSU Institutional Repository.\\nParticipants will also receive individualized writing instruction, as well as preparation for the GRE, as needed.\\nAs proposed, OHSU PREP will build on existing opportunities that allow for cultural immersion through\\ninteractions with near-peer mentors (i.e., graduate students and postdoctoral fellows) and OHSU faculty from\\nunderrepresented backgrounds. We expect that at least 75% of our participants will successfully enter and\\ncomplete graduate programs in biomedical science and that the program will function as a bridge to ease the\\ntransition of recent college graduates into PhD-conferring programs.\",\"project_title\":\"OHSU PREP - Guiding Promising Underrepresented Post-Baccalaureates to be Successful Biomedical Scholars\",\"phr_text\":\"Project Narrative\\nReducing health disparities requires a diverse biomedical workforce. This program provides research\\nexperience, skill-building activities, academic advising, and cultural immersion for trainees underrepresented in\\nbiomedical sciences to prepare them for successful pursuit and entry into a career in biomedical research.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"859\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":307214,\"indirect_cost_amt\":23497,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10542769\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10544764,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5U01MD017421-02\",\"project_serial_num\":\"MD017421\",\"organization\":{\"org_name\":\"UNIVERSITY OF UTAH\",\"city\":null,\"country\":null,\"org_city\":\"SALT LAKE CITY\",\"org_country\":\"UNITED STATES\",\"org_state\":\"UT\",\"org_state_name\":null,\"dept_type\":\"MISCELLANEOUS\",\"fips_country_code\":null,\"org_duns\":[\"009095365\"],\"org_ueis\":[\"LL8GLEVH6MG3\"],\"primary_duns\":\"009095365\",\"primary_uei\":\"LL8GLEVH6MG3\",\"org_fips\":\"US\",\"org_ipf_code\":\"514002\",\"org_zipcode\":\"841128906\",\"external_org_id\":514002},\"award_type\":\"5\",\"activity_code\":\"U01\",\"award_amount\":1142661,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"U01\",\"ic_code\":\"MD\",\"serial_num\":\"017421\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":9786619,\"first_name\":\"GUILHERME\",\"middle_name\":\"\",\"last_name\":\"DEL FIOL\",\"is_contact_pi\":true,\"full_name\":\"GUILHERME  DEL FIOL\",\"title\":\"PROFESSOR\"},{\"profile_id\":1903140,\"first_name\":\"David\",\"middle_name\":\"W\",\"last_name\":\"Wetter\",\"is_contact_pi\":false,\"full_name\":\"David W Wetter\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"DEL FIOL, GUILHERME \",\"program_officers\":[{\"first_name\":\"Nathaniel\",\"middle_name\":\"\",\"last_name\":\"Stinson\",\"full_name\":\"Nathaniel  Stinson\"}],\"agency_ic_admin\":{\"code\":\"MD\",\"abbreviation\":\"NIMHD\",\"name\":\"National Institute on Minority Health and Health Disparities\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"OD\",\"name\":\"NIH Office of the Director\",\"abbreviation\":\"OD\",\"total_cost\":1142661.0}],\"cong_dist\":\"UT-02\",\"spending_categories\":null,\"project_start_date\":\"2022-01-01T12:01:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"RFA-OD-21-008\",\"full_study_section\":{\"srg_code\":\"ZMH1\",\"srg_flex\":\"\",\"sra_designator_code\":\"ERB\",\"sra_flex_code\":\"G\",\"group_code\":\"\",\"name\":\"ZMH1-ERB-G(G1)\"},\"award_notice_date\":\"2022-11-28T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"U01MD017421\",\"terms\":\"<Adoption><Adult><21+ years old><Adult Human><adulthood><Communities><Community Health Aides><community health worker><Disease><Disorder><Disease Outbreaks><Outbreaks><Grant><Health><Healthcare Systems><Health Care Systems><Human><Modern Man><Institutes><Patients><Poverty><Impoverished><Primary Health Care><Primary Care><Primary Healthcare><Diagnostic Reagent Kits><diagnostic kit><test kit><Research><Resources><Research Resources><Risk><Running><Rural Population><Surveys><Survey Instrument><Target Populations><Telephone><Phone><Testing><Time><Universities><Utah><Vaccination><Work><Latino><Neighborhood Health Center><Community Health Centers><Satellite Centers><Uninsured><Site><Area><Phase><Variant><Variation><Individual><Rural><Populations at Risk><uptake><Collaborations><Native Americans><Adopted><Hour><Event><Clinic><System><contextual factors><Application Context><experience><cohort><novel><outreach><Touch sensation><Touch><tactile sensation><response><Intervention Strategies><interventional strategy><Intervention><low SES><low socio-economic position><low socio-economic status><low socioeconomic position><low socioeconomic status><Address><Data><health communication><Health Sciences><Clinical Sciences><Translational Science><translation research><Translational Research><Vaccinated><Characteristics><Process><community-based health><Community Health><pandemic><pandemic disease><cost><digital><under served group><under served people><under served population><underserved group><underserved people><Underserved Population><designing><design><Outcome><scale up><Population><frontier><ethnoracial><racial and ethnic><patient centered><patient oriented><implementation science><intervention effect><Health Inequity><Inequalities in Health><Inequities in Health><health inequalities><health information technology><population health><flexible><flexibility><Data Analytics><outcome prediction><predictive outcomes><predictors of outcomes><Text Messaging><short message service><texting><health care settings><healthcare settings><health management><health care management><healthcare management><patient outreach><Infrastructure><COVID-19><COVID19><CV-19><CV19><corona virus disease 2019><coronavirus disease 2019><coronavirus disease-19><coronavirus infectious disease-19><coronavirus disease><COVID><CoV disease><corona virus disease><COVID-19 vaccination><COVID19 vaccination><SARS-CoV-2 vaccination><Severe acute respiratory syndrome coronavirus 2 vaccination><coronavirus disease 2019 vaccination><vaccinate against COVID-19><vaccinate against COVID19><vaccinate against SARS-CoV-2><vaccinate against coronavirus disease 2019><vaccinate against severe acute respiratory syndrome coronavirus 2><vaccination against COVID-19><vaccination against COVID19><vaccination against SARS-CoV-2><vaccination against Severe acute respiratory syndrome coronavirus 2><vaccination against coronavirus disease 2019><RADx Underserved Populations><RADx-UP><Rapid Acceleration of Diagnostics Underserved Populations><vaccine acceptance><accept vaccination><accept vaccine><vaccination acceptability><vaccination acceptance><vaccination confidence><vaccination uptake><vaccination willingness><vaccine acceptability><vaccine confidence><vaccine uptake><vaccine willingness><COVID-19 testing><COVID19 testing><SARS-CoV-2 testing><coronavirus disease 2019 testing><severe acute respiratory syndrome coronavirus 2 testing><home test><home-based test><rapid test><rapid assay><rapid tests><testing uptake><Home><Resource-limited setting><Low-resource area><Low-resource community><Low-resource environment><Low-resource region><Low-resource setting><Resource-constrained area><Resource-constrained community><Resource-constrained environment><Resource-constrained region><Resource-constrained setting><Resource-limited area><Resource-limited community><Resource-limited environment><Resource-limited region><Resource-poor area><Resource-poor community><Resource-poor environment><Resource-poor region><Resource-poor setting><Latino Population><Latino group><Latino individual><Latino people><Latinos><COVID-19 pandemic effects><COVID-19 pandemic affected><COVID-19 pandemic consequence><COVID-19 pandemic impact><COVID-19 pandemic impacted><coronavirus disease 2019 pandemic consequence><coronavirus disease 2019 pandemic impact><effects following the COVID-19 pandemic><unvaccinated><non vaccinated><not vaccinated><ethnic minority>\",\"pref_terms\":\"Address;Adopted;Adoption;Adult;Area;COVID-19;COVID-19 pandemic effects;COVID-19 testing;COVID-19 vaccination;Characteristics;Clinic;Clinical Sciences;Collaborations;Communities;Community Health;Community Health Aides;Data;Data Analytics;Diagnostic Reagent Kits;Disease;Disease Outbreaks;Event;Grant;Health;Health Sciences;Healthcare Systems;Home;Hour;Human;Individual;Infrastructure;Institutes;Intervention;Latino;Latino Population;Native Americans;Neighborhood Health Center;Outcome;Patients;Phase;Population;Populations at Risk;Poverty;Primary Health Care;Process;RADx Underserved Populations;Research;Resource-limited setting;Resources;Risk;Running;Rural;Rural Population;Site;Surveys;System;Target Populations;Telephone;Testing;Text Messaging;Time;Touch sensation;Translational Research;Underserved Population;Uninsured;Universities;Utah;Vaccinated;Vaccination;Variant;Work;cohort;contextual factors;coronavirus disease;cost;design;digital;ethnic minority;experience;flexibility;frontier;health care settings;health communication;health inequalities;health information technology;health management;home test;implementation science;intervention effect;low socioeconomic status;novel;outcome prediction;outreach;pandemic disease;patient oriented;patient outreach;population health;racial and ethnic;rapid test;response;scale up;testing uptake;unvaccinated;uptake;vaccine acceptance\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nRacial/ethnic minority, low socioeconomic status (SES), and rural populations suffer profound health inequi-\\nties across a wide variety of diseases and conditions, as well as a disproportionate burden of the negative health\\nconsequences of the COVID-19 pandemic. As of June 2021, the cumulative COVID case rate in Utah per\\n100,000 was 10,803 among Whites vs. 17,541 among Latinos. The positivity rate was 14% among Whites vs.\\n24% among Latinos. Similar disparities persist across the nation regarding vaccination rates between urban vs.\\nrural, high vs. low SES, and White vs. non-White populations. Low vaccination rates leave underserved popula-\\ntions at risk for local outbreaks, and more contagious and severe variants. Thus, interventions targeting these\\npopulations at the interplay between testing and vaccination are critical for pandemic control. Not only do un-\\nderserved populations experience profound health inequities, but there is also a critical digital divide between\\nhigh and low resource healthcare systems. Low resource settings are far less likely to adopt Health Information\\nTechnology approaches, and often do not have the capacity to implement large scale population health man-\\nagement (PHM) efforts utilizing data analytics and automated patient outreach. Community Health Centers\\n(CHCs) are optimal settings for implementing these PHM interventions to increase the uptake of COVID-19\\ntesting and vaccination among underserved populations. Eleven Utah CHC systems are participating in\\nSCALE-UP II. Their 38 primary care clinics serve over 112,000 unique underserved patients annually. Sup-\\nported by a RADx-UP Phase I grant (SCALE-UP Utah), we have established a flexible text messaging outreach\\nand patient navigation infrastructure with CHCs to increase testing and vaccine uptake. SCALE-UP II will lev-\\nerage this infrastructure as well as long standing partnerships among the University of Utah Clinical and\\nTranslational Science Institute, Association for Utah Community Health, CHCs across the entire state, and the\\nUtah Department of Health. SCALE-UP II will investigate three PHM interventions at the interplay between\\nCOVID-19 vaccination and testing: 1) Text Messaging (TM) – bidirectional text messaging with a one-touch\\nresponse to connect patients to vaccination or mail at-home rapid test kits at no cost for use as needed, 2) Con-\\nversational Agent (CA) – automated, scripted and interactive agent used to mimic human interaction to offer\\naccess to at-home rapid test kits, 3) Patient Navigation (PN) – phone call from a community health worker to\\nhelp address hesitancy and access barriers, and to offer at-home rapid test kits. Two types of PN will be tested:\\nReactive PN (RPN) will reach out only to those patients who reply YES to a TM/CA message. Proactive PN\\n(PPN) will reach out to all patients, including those who do not reply to a TM/CA message. The specific aims\\nare to 1) Conduct a 2 (TM vs. CA) X 3 (No PN vs. RPN vs. PPN) factorial design assessing intervention effects\\non testing uptake among unvaccinated adults 2) Examine factors associated with at-home testing and vaccina-\\ntion uptake over time.\",\"project_title\":\"SCALE UP Utah II: Community-Academic Partnership to Address COVID-19 Testing and Vaccination Among Utah Community Health Centers\",\"phr_text\":\"PROJECT NARRATIVE\\nRacial/ethnic minority, low socioeconomic status (SES), and rural populations suffer profound health\\ninequities across a wide variety of diseases and conditions, as well as a disproportionate burden of the negative\\nhealth consequences of the COVID-19 pandemic. SCALE-UP II will provide critical data regarding the impact\\nof pragmatic, scalable population health management approaches to increasing COVID-19 testing and\\nvaccination, and reducing COVID-19-related health inequities. The interventions will be readily available for\\nadoption by other low-resource healthcare settings, and the data will advance population health and\\nimplementation science.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":[\"C6\"],\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"310\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":751748,\"indirect_cost_amt\":390913,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10544764\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10590116,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1K01MH129828-01A1\",\"project_serial_num\":\"MH129828\",\"organization\":{\"org_name\":\"MCLEAN HOSPITAL\",\"city\":null,\"country\":null,\"org_city\":\"BELMONT\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"046514535\"],\"org_ueis\":[\"MCKWJYCWNVN3\"],\"primary_duns\":\"046514535\",\"primary_uei\":\"MCKWJYCWNVN3\",\"org_fips\":\"US\",\"org_ipf_code\":\"1876801\",\"org_zipcode\":\"024781064\",\"external_org_id\":1876801},\"award_type\":\"1\",\"activity_code\":\"K01\",\"award_amount\":179707,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"K01\",\"ic_code\":\"MH\",\"serial_num\":\"129828\",\"support_year\":\"01\",\"full_support_year\":\"01A1\",\"suffix_code\":\"A1\"},\"principal_investigators\":[{\"profile_id\":14077682,\"first_name\":\"Nathaniel\",\"middle_name\":\"G\",\"last_name\":\"Harnett\",\"is_contact_pi\":true,\"full_name\":\"Nathaniel G Harnett\",\"title\":\"POSTDOCTORAL RESEARCH FELLOW\"}],\"contact_pi_name\":\"HARNETT, NATHANIEL G\",\"program_officers\":[{\"first_name\":\"Mark\",\"middle_name\":\"\",\"last_name\":\"Chavez\",\"full_name\":\"Mark  Chavez\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":179707.0}],\"cong_dist\":\"MA-05\",\"spending_categories\":null,\"project_start_date\":\"2022-12-15T12:12:00Z\",\"project_end_date\":\"2026-11-30T12:11:00Z\",\"organization_type\":{\"name\":null,\"code\":\"30\",\"is_other\":false},\"full_foa\":\"PA-20-190\",\"full_study_section\":{\"srg_code\":\"NPAS\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Neural Basis of Psychopathology, Addictions and Sleep Disorders Study Section[NPAS]\"},\"award_notice_date\":\"2022-12-07T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K01MH129828\",\"terms\":\"<Affect><Architecture><Engineering / Architecture><Arousal><Attention><Boston><Brain><Brain Nervous System><Encephalon><Complement><Complement Proteins><classical conditioning><Associative Learning><Pavlovian conditioning><associative conditioning><Data Analyses><Data Analysis><data interpretation><Data Collection><Statistical Data Interpretation><Statistical Data Analyses><Statistical Data Analysis><statistical analysis><Discrimination><Cognitive Discrimination><Environment><Family><Future><Goals><Hospitals><Lead><Pb element><heavy metal Pb><heavy metal lead><Magnetic Resonance Imaging><MR Imaging><MR Tomography><MRI><MRIs><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><NMR Imaging><NMR Tomography><Nuclear Magnetic Resonance Imaging><Zeugmatography><Maps><Memory><Mentors><Methods><National Institute of Mental Health><NIMH><United States National Institutes of Health><NIH><National Institutes of Health><Neurobiology><neurobiological><neurophysiology><neurophysiological><Neurosciences><Perception><Psychiatry><Public Health><Reaction Time><Response RT><Response Time><psychomotor reaction time><Research><Research Design><Study Type><study design><Research Personnel><Investigators><Researchers><Resources><Research Resources><Rest><Risk><Risk Factors><medical schools><medical college><school of medicine><Standardization><Post-Traumatic Stress Disorders><PTSD><Post-Traumatic Neuroses><Posttraumatic Neuroses><Posttraumatic Stress Disorders><post-trauma stress disorder><posttrauma stress disorder><traumatic neurosis><Training Programs><Visual Pathways><Visual Perception><Work><Generations><Imaging Techniques><Imaging Procedures><Imaging Technics><Mediating><base><career><improved><Procedures><Acute><Chronic><Clinical><Susceptibility><Predisposition><Training><insight><Stimulus><Visual><Individual><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Functional disorder><Dysfunction><Physiopathology><pathophysiology><Exposure to><Functional Magnetic Resonance Imaging><Functional MRI><fMRI><programs><Investigation><Dimensions><Stream><Sensory><System><Chronic Phase><Visual system structure><Visual System><visual processing><Visual attention><data management><visual stimulus><cohort><relating to nervous system><neural><stress related disorder><skills><neuroimaging><neuro-imaging><neurological imaging><novel><Prevention strategy><Preventative strategy><Preventive strategy><Prevention><social><Chronic Post Traumatic Stress Disorder><chronic posttraumatic stress disorder><neural circuitry><neurocircuitry><synaptic circuit><synaptic circuitry><neural circuit><acute stress><Emotional><Modeling><response><visual search><DWI (diffusion weighted imaging)><DWI-MRI><Diffusion MRI><Diffusion Weighted MRI><Diffusion weighted imaging><Diffusion-weighted Magnetic Resonance Imaging><dMRI><diffusion tensor imaging><Diffusion Magnetic Resonance Imaging><Institution><Address><Symptoms><Data><Preventative intervention><intervention for prevention><prevention intervention><preventional intervention strategy><preventive intervention><Reproducibility><Career Development Awards><Career Development Awards and Programs><Career Development Programs K-Series><K-Awards><Research Career Program><K-Series Research Career Programs><Research Training><Cognitive><Neurosciences Research><Process><developmental><Development><imaging><Image><pathway><Pathway interactions><Behavioral Model><computer based prediction><prediction model><predictive modeling><Clinical assessments><conditioning><Early Trauma><Early-life trauma><Population><cognitive neuroscience><Trauma><Coupling><innovate><innovative><innovation><Affective><brain behavior><neurobiological mechanism><multi-modality><multimodality><adverse consequence><adverse outcome><RDoC><Research Domain Criteria><BigData><Big Data><Negative Valence><traumatic event><stress reactivity><negative affect><negative affectivity><stress symptom><individual variation><individual heterogeneity><individual variability><trauma exposure><exposure to trauma><multimodal data><multi-modal data><multi-modal datasets><multimodal datasets><Big Data Methods><Big Data Analytics><Big Data Tools><Computer Models><Computerized Models><computational modeling><computational models><computer based models><computerized modeling><data fusion><traumatic stress><large datasets><large data sets>\",\"pref_terms\":\"Acute;Address;Affect;Affective;Architecture;Arousal;Attention;Behavioral Model;Big Data;Big Data Methods;Boston;Brain;Chronic;Chronic Phase;Chronic Post Traumatic Stress Disorder;Clinical;Clinical assessments;Cognitive;Complement;Computer Models;Coupling;Data;Data Analyses;Data Collection;Development;Diffusion Magnetic Resonance Imaging;Dimensions;Discrimination;Early-life trauma;Emotional;Environment;Exposure to;Family;Functional Magnetic Resonance Imaging;Functional disorder;Future;Generations;Goals;Hospitals;Image;Imaging Techniques;Individual;Institution;Investigation;K-Series Research Career Programs;Lead;Magnetic Resonance Imaging;Maps;Mediating;Memory;Mentors;Methods;Modeling;National Institute of Mental Health;Negative Valence;Neurobiology;Neurosciences;Neurosciences Research;Pathway interactions;Perception;Population;Post-Traumatic Stress Disorders;Predisposition;Prevention;Prevention strategy;Procedures;Process;Psychiatry;Public Health;Reaction Time;Reproducibility;Research;Research Design;Research Domain Criteria;Research Personnel;Research Project Grants;Research Training;Resources;Rest;Risk;Risk Factors;Sensory;Standardization;Statistical Data Interpretation;Stimulus;Stream;Symptoms;System;Training;Training Programs;Trauma;United States National Institutes of Health;Visual;Visual Pathways;Visual Perception;Visual attention;Visual system structure;Work;acute stress;adverse outcome;base;brain behavior;career;classical conditioning;cognitive neuroscience;cohort;conditioning;data fusion;data management;improved;individual variation;innovation;insight;large datasets;medical schools;multimodal data;multimodality;negative affect;neural circuit;neurobiological mechanism;neuroimaging;neurophysiology;novel;predictive modeling;preventive intervention;programs;relating to nervous system;response;skills;social;stress reactivity;stress related disorder;stress symptom;trauma exposure;traumatic event;traumatic stress;visual processing;visual search;visual stimulus\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\n Understanding the neurobiology mediating susceptibility to trauma and stress-related disorders is\\nnecessary for future translational efforts to improve treatment and prevention. This Mentored Career\\nDevelopment Award proposal is tailored to facilitate the candidate’s training in multimodal assessment of brain\\nand behavior, in addition to clinical assessment and computational psychiatry approaches, to elucidate the\\nneurobiology of susceptibility to posttraumatic dysfunction. The proposed research training plan will build the\\ncandidate’s skills in multidimensional assessment of perceptual (i.e., visual) processing in the early aftermath of\\ntrauma, advanced computational modeling of behavioral processes, and ‘big-data’ modeling approaches for\\nfusion of multimodal magnetic resonance imaging (MRI) data. This training is complemented by mentored\\ntraining in clinical assessment of trauma-related dysfunction and research program management necessary for\\ntransition to an independent career as a translational neuroscientist. The completion of the K01 will facilitate the\\ncandidate’s transition to an independent investigator capable of developing new neuroscience-based predictive\\nmodels and preventative strategies to lessen the burden of trauma and stress-related disorders.\\n PTSD is typically conceptualized as a dysfunction of arousal and negative valence systems with the NIH\\nResearch Domain Criteria framework. However, new research demonstrates that neural circuitry of affective\\nvisual perceptual processing, in particular affective visual processing, may be a significant risk factor for PTSD.\\nLimited work to date has probed affective visual circuitry in the early aftermath of trauma. The proposed research\\nwill leverage state-of-the-art MRI data collection and analysis with computational cognitive neuroscience\\napproaches to elucidate impacts of trauma on affective visual processing. The project will thus better\\ncharacterize an understudied aspect of the RDoC perceptual framework in trauma to integrate visual circuitry\\nvariability in both threat-related processes and PTSD-related dysfunction.\\n Previous research in recently traumatized individuals has also not leveraged multimodal data approaches\\nto establish generalizable markers of PTSD susceptibility. This project will address these limitations by focusing\\non perceptual circuits and by integrating pre-existing large datasets of trauma and PTSD with multimodal data\\nfusion analytics to assess the robustness of observed neurobiological signatures of trauma-related dysfunction\\nvulnerability. Further, the proposal seeks to use standardized, open processing pipelines to enhance\\nreproducibility and replicability of observed findings to promote generalizability. The research training plan will\\noccur at McLean Hospital, the leading psychiatric research hospital of Harvard Medical School. The institution,\\nand academic culture in greater Boston provides an outstanding environment and access to resources for\\nsuccessful competition of this proposed Mentored Career Development Award.\",\"project_title\":\"Multimodal characterization of affective visual processing in the early aftermath of trauma\",\"phr_text\":\"PROJECT NARRATIVE/RELEVACE TO PUBLIC HEALTH\\nPosttraumatic stress disorder (PTSD) carries significant emotional, financial, and social burdens for affected\\nindividuals and their families. Identification of neurobiological mechanisms that lead to PTSD development will\\nfacilitate the generation of novel predictive models and preventative interventions. The results of the proposed\\nK01 are thus critical for the development of accurate neuroscience-based treatments for trauma and stress-\\nrelated disorders.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-15T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":166432,\"indirect_cost_amt\":13275,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10590116\",\"date_added\":\"2022-12-17T04:12:51Z\"},{\"appl_id\":10620014,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1R01AI174932-01\",\"project_serial_num\":\"AI174932\",\"organization\":{\"org_name\":\"NEW YORK UNIVERSITY SCHOOL OF MEDICINE\",\"city\":null,\"country\":null,\"org_city\":\"NEW YORK\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NY\",\"org_state_name\":null,\"dept_type\":\"INTERNAL MEDICINE/MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"121911077\"],\"org_ueis\":[\"M5SZJ6VHUHN8\"],\"primary_duns\":\"121911077\",\"primary_uei\":\"M5SZJ6VHUHN8\",\"org_fips\":\"US\",\"org_ipf_code\":\"5998304\",\"org_zipcode\":\"10016\",\"external_org_id\":5998304},\"award_type\":\"1\",\"activity_code\":\"R01\",\"award_amount\":809267,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"R01\",\"ic_code\":\"AI\",\"serial_num\":\"174932\",\"support_year\":\"01\",\"full_support_year\":\"01\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":12069066,\"first_name\":\"Anna\",\"middle_name\":\"\",\"last_name\":\"Bershteyn\",\"is_contact_pi\":true,\"full_name\":\"Anna  Bershteyn\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"BERSHTEYN, ANNA \",\"program_officers\":[{\"first_name\":\"Rosemary\",\"middle_name\":\"G\",\"last_name\":\"McKaig\",\"full_name\":\"Rosemary G McKaig\"}],\"agency_ic_admin\":{\"code\":\"AI\",\"abbreviation\":\"NIAID\",\"name\":\"National Institute of Allergy and Infectious Diseases\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"AI\",\"name\":\"National Institute of Allergy and Infectious Diseases\",\"abbreviation\":\"NIAID\",\"total_cost\":809267.0}],\"cong_dist\":\"NY-12\",\"spending_categories\":null,\"project_start_date\":\"2022-11-04T12:11:00Z\",\"project_end_date\":\"2027-10-31T12:10:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-20-185\",\"full_study_section\":{\"srg_code\":\"PPAH\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Population and Public Health Approaches to HIV/AIDS Study Section[PPAH]\"},\"award_notice_date\":\"2022-11-04T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01AI174932\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><Africa South of the Sahara><Sub-Saharan Africa><Subsaharan Africa><Age><ages><Antibody Avidity><Bioethics><Biomedical Ethics><Botswana><Bechuanaland><Budgets><Cause of Death><Centers for Disease Control and Prevention (U.S.)><CDC><Centers for Disease Control><Centers for Disease Control and Prevention><United States Centers for Disease Control><United States Centers for Disease Control and Prevention><Client><Cost Measures><Cost metrics><cost measurement><cost effectiveness><Decision Making><Diagnosis><Disease Outbreaks><Outbreaks><Epidemic><Epidemiology><epidemiologic><epidemiological><Goals><Health><Health care facility><Health Facilities><Healthcare Facility><care facilities><HIV><AIDS Virus><Acquired Immune Deficiency Syndrome Virus><Acquired Immunodeficiency Syndrome Virus><Human Immunodeficiency Viruses><LAV-HTLV-III><Lymphadenopathy-Associated Virus><Virus-HIV><HIV Antibodies><HIV-Associated Antibodies><HTLV-III Antibodies><HTLV-III-LAV Antibodies><Human T-Lymphotropic Virus Type III Antibodies><LAV Antibodies><Lymphadenopathy-Associated Antibodies><HIV Infections><HTLV-III Infections><HTLV-III-LAV Infections><Human T-Lymphotropic Virus Type III Infections><HIV Seropositivity><Anti-HIV Positivity><HIV Positive><HIV Positivity><HIV Seroconversion><HIV antibody positive><HTLV-III Seroconversion><HTLV-III Seropositivity><Incidence><Infection><Lead><Pb element><heavy metal Pb><heavy metal lead><Lesotho><Basutoland><Mozambique><Portuguese East Africa><Persons><Namibia><South West Africa><Southwest Africa><Paper><Plasma><Blood Plasma><Plasma Serum><Reticuloendothelial System, Serum, Plasma><Policy Making><Public Health><Reaction Time><Response RT><Response Time><psychomotor reaction time><Resources><Research Resources><Risk><Role><social role><Running><Sensitivity and Specificity><South Africa><Specificity><Stigmatization><Testing><Time><Work><Zambia><Northern Rhodesia><Measures><Health Care Costs><Health Costs><Healthcare Costs><Health Benefit><AIDS prevention><HIV Prevention><HIV/AIDS prevention><Social Network><Guidelines><base><improved><Training><Blood Serum><Serum><Intuition><Individual><Policies><Sustainable Development><Exposure to><tool><Venous><Machine Learning><machine learned><Life><programs><Adopted><Radial><Radius><Pattern><Country><Services><heuristics><authority><cohort><novel><prevention service><Modeling><response><Math Models><mathematic model><mathematical modeling><mathematical model><Intervention Strategies><interventional strategy><Intervention><AIDS test><AIDS/HIV test><HIV test><HIV-1 test><HIV-2 test><Human immunodeficiency virus test><Avidity><Data><Data Element><Detection><Infectious Diseases / Laboratory><Infectious Diseases Research><International><Newly Diagnosed><Phylogenetics><Phylogenetic Analysis><Transmission><transmission process><point of care><cost><health economics><designing><design><next generation><blood infection><bloodstream infection><Sepsis><infectious disease model><Population><Prevalence><Network-based><network models><standard of care><HIV-1 genome><HIV1 genome><HIV Genome><BigData><Big Data><testing services><Big Data Methods><Big Data Analytics><Big Data Tools><cost estimate><cost estimation><microcosting><antibody detection><antibody based detection><detect antibodies><rapid test><rapid assay><rapid tests><Home><point of care testing><test strip><machine learning model><machine learning based model>\",\"pref_terms\":\"AIDS prevention;Adopted;Adult;Africa South of the Sahara;Age;Antibody Avidity;Avidity;Big Data;Big Data Methods;Bioethics;Botswana;Budgets;Cause of Death;Centers for Disease Control and Prevention (U.S.);Client;Cost Measures;Country;Data;Data Element;Decision Making;Detection;Diagnosis;Disease Outbreaks;Epidemic;Epidemiology;Exposure to;Goals;Guidelines;HIV;HIV Antibodies;HIV Genome;HIV Infections;HIV Seropositivity;Health;Health Benefit;Health Care Costs;Health care facility;Home;Human immunodeficiency virus test;Incidence;Individual;Infection;Infectious Diseases Research;International;Intervention;Intuition;Lead;Lesotho;Life;Machine Learning;Measures;Modeling;Mozambique;Namibia;Network-based;Newly Diagnosed;Paper;Pattern;Persons;Phylogenetic Analysis;Plasma;Policies;Policy Making;Population;Prevalence;Public Health;Radial;Reaction Time;Resources;Risk;Role;Running;Sensitivity and Specificity;Sepsis;Serum;Services;Social Network;South Africa;Specificity;Stigmatization;Sustainable Development;Testing;Time;Training;Venous;Work;Zambia;antibody detection;authority;base;cohort;cost;cost effectiveness;cost estimate;design;health economics;heuristics;improved;infectious disease model;machine learning model;mathematical model;microcosting;network models;next generation;novel;point of care;point of care testing;prevention service;programs;rapid test;response;standard of care;test strip;testing services;tool;transmission process\",\"abstract_text\":\"ABSTRACT/SUMMARY\\nHIV is a leading cause of death in sub-Saharan Africa (SSA), but rates of new infections in declining. As a result,\\nsome HIV services are becoming less efficient: HIV prevention is averting fewer infections per client served, and\\nHIV testing yield (i.e., the fraction of tests leading to new diagnoses) is declining. Given resource constraints,\\nHIV prevention and testing programs will need to increase efficiency in order to maintain momentum toward\\nepidemic control. Specifically, a precision public health approach could be used to focus services where their\\nhealth benefits would be greatest. Precision public health has been challenging to implement in SSA’s\\ngeneralized HIV epidemics because most transmission is not confined to identifiable key populations, eluding\\nprecise responses. Until recently, the only tool for describing HIV transmission has been phylogenetic analysis\\nof HIV genome sequences, which is too slow and costly for real-time response at scale. Rapid tests for recent\\nHIV infection (RTRI) recently became available in SSA, offering a novel opportunity to identify active transmission\\nclusters and respond with precision. RTRI detects high-avidity antibodies that appear approximately one year\\nafter HIV infection. If only low-avidity antibodies are detected, this infection likely occurred in the past year.\\nZambia is an early adopter of RTRI and followed-up two-thirds of positive HIV tests with RTRI in 2021. Despite\\nthe large scale of Zambia’s RTRI program, it has not been evaluated in terms of impact or cost-effectiveness,\\nand outbreak response thresholds have not been optimized. However, the wealth of data now collected by the\\nRTRI program could guide higher-performing, next-generation outbreak response strategies. Our team has\\nexpertise in Zambian HIV policy, program implementation, health economics, bioethics, geospatial analysis,\\nmachine learning, and infectious disease modeling. We helped to develop the only HIV social network model\\nrigorously validated to predict HIV incidence, prevalence, and transmission patterns in SSA, which has been\\nused for international and country-level HIV policy decision-making for over a decade. We propose to partner\\nwith the Center for Infectious Disease Research in Zambia (CIDRZ), a Zambian-run NGO that has been\\nsupporting MoH for over a decade in HIV policy-making and implementation (including RTRI), to (Aim 1)\\ndetermine whether the RTRI program can help Zambia achieve its epidemic control goals, (Aim 2) measure the\\ncost and estimate the cost-effectiveness of the RTRI program, and (Aim 3) use “big data” methods to design\\nnext-generation outbreak responses, taking into account trade-offs of sensitivity vs. specificity, precision vs.\\nease-of-use, and precision vs. avoidance of collecting potentially stigmatizing data elements. While pursuing\\nscientific Aims and hypotheses aligned with the NOSI “Harnessing Big Data to Halt HIV”, we will also have direct\\npolicy impact by providing real-time analytics, HIV outbreak mapping, and input for RTRI policy decision-making.\",\"project_title\":\"Rapid Tests for Recent Infection (RTRI) for Precision Public Health in Sub-Saharan Africa: Next-Generation Strategies Amid Changing HIV Epidemiology\",\"phr_text\":\"PROJECT NARRATIVE\\nHIV is a top cause of death in sub-Saharan Africa, but rates of new infections are declining, so that HIV\\nprevention services avert fewer infections per client served, and HIV testing services lead to fewer new HIV-\\npositive diagnoses per test conducted. To increase the efficiency of these services, a rapid point-of-care test\\nfor recent HIV infection has been developed and is being used in over two-thirds of people newly diagnosed\\nwith HIV in Zambia in order to identify other people at risk or living with undiagnosed HIV in their social\\nnetworks. We will use mathematical modeling to determine whether recency testing can help Zambia achieve\\nepidemic goals, is a good use of limited resources, and can be improved upon using “big data” methods for\\ngreater precision, ease-of-use, and avoidance of potentially stigmatizing data elements.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-11-04T12:11:00Z\",\"budget_end\":\"2023-10-31T12:10:00Z\",\"cfda_code\":\"855\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":523875,\"indirect_cost_amt\":285392,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10620014\",\"date_added\":\"2022-11-05T04:11:40Z\"},{\"appl_id\":10585124,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1R01CA270276-01A1\",\"project_serial_num\":\"CA270276\",\"organization\":{\"org_name\":\"UNIVERSITY OF ROCHESTER\",\"city\":null,\"country\":null,\"org_city\":\"ROCHESTER\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NY\",\"org_state_name\":null,\"dept_type\":\"PEDIATRICS\",\"fips_country_code\":null,\"org_duns\":[\"041294109\"],\"org_ueis\":[\"F27KDXZMF9Y8\"],\"primary_duns\":\"041294109\",\"primary_uei\":\"F27KDXZMF9Y8\",\"org_fips\":\"US\",\"org_ipf_code\":\"7047101\",\"org_zipcode\":\"146270140\",\"external_org_id\":7047101},\"award_type\":\"1\",\"activity_code\":\"R01\",\"award_amount\":483215,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"R01\",\"ic_code\":\"CA\",\"serial_num\":\"270276\",\"support_year\":\"01\",\"full_support_year\":\"01A1\",\"suffix_code\":\"A1\"},\"principal_investigators\":[{\"profile_id\":8799835,\"first_name\":\"Cynthia\",\"middle_name\":\"M\",\"last_name\":\"Rand\",\"is_contact_pi\":true,\"full_name\":\"Cynthia M Rand\",\"title\":\"ASSISTANT PROFESSOR\"}],\"contact_pi_name\":\"RAND, CYNTHIA M\",\"program_officers\":[{\"first_name\":\"VERONICA\",\"middle_name\":\"\",\"last_name\":\"CHOLLETTE\",\"full_name\":\"VERONICA  CHOLLETTE\"}],\"agency_ic_admin\":{\"code\":\"CA\",\"abbreviation\":\"NCI\",\"name\":\"National Cancer Institute\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"CA\",\"name\":\"National Cancer Institute\",\"abbreviation\":\"NCI\",\"total_cost\":483215.0}],\"cong_dist\":\"NY-25\",\"spending_categories\":null,\"project_start_date\":\"2022-12-01T12:12:00Z\",\"project_end_date\":\"2027-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOL OF MEDICINE & DENTISTRY\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-19-360\",\"full_study_section\":{\"srg_code\":\"HSQE\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Health Services: Quality and Effectiveness Study Section[HSQE]\"},\"award_notice_date\":\"2022-11-24T12:11:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R01CA270276\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><Affect><Age><ages><Black race><Black><Malignant neoplasm of cervix uteri><Cervical Cancer><Cervix Cancer><Malignant Cervical Neoplasm><Malignant Cervical Tumor><Malignant Neoplasm of the Cervix><Malignant Tumor of the Cervix><Malignant Tumor of the Cervix Uteri><Malignant Uterine Cervix Neoplasm><Malignant Uterine Cervix Tumor><Uterine Cervix Cancer><Communication><Cost Measures><Cost metrics><cost measurement><cost effectiveness><Decision Making><Education><Educational aspects><Feedback><Goals><Health><Immunization><Immunologic Sensitization><Immunologic Stimulation><Immunological Sensitization><Immunological Stimulation><Immunostimulation><Human Papillomavirus><HPV><Human Papilloma Virus><Infectious Human Wart Virus><wart virus><influenza virus vaccine><Influenza Vaccines><flu vaccine><flu virus vaccine><vaccine against flu><vaccine against influenza><Inpatients><Interview><Leadership><men><men's><Methods><Motivation><Persons><New York><Nurses><nurse><Nursing Staff><nursing personnel><Parents><Patients><Patients' Rooms><Physicians><Play><Poverty><Impoverished><Primary Health Care><Primary Care><Primary Healthcare><Recommendation><Resources><Research Resources><Running><Rural Population><Schools><Surveys><Survey Instrument><Testing><Time><Vaccination><Vaccines><Woman><Work><Medical Care Team><Health Care Team><Healthcare Team><Hispanic><Caring><malignant oropharynx neoplasm><Malignant Oropharyngeal Neoplasm><Malignant Oropharyngeal Tumor><Oropharnyx Cancers><Oropharyngeal Cancer><Oropharyngeal Carcinoma><Oropharynx Cancer><Oropharynx Carcinoma><malignant oropharynx tumor><base><rural area><rural location><rural region><suburb><suburban><suburbia><improved><Cervical><Left><Area><Clinical><Adolescent><Adolescent Youth><juvenile><juvenile human><Ensure><Training><Rural><Policies><uptake><Educational Intervention><Education for Intervention><Instruction Intervention><Training Intervention><instructional intervention><Exposure to><Human papilloma virus infection><HPV infection><Human papillomavirus infection><programs><Protocols documentation><Protocol><Oropharyngeal><Oropharynx><Oropharynxs><oral pharyngeal><Visit><age group><Structure><member><parent influence><parental influence><Immune reaction><immunoreaction><Intervention Strategies><interventional strategy><Intervention><Provider><disparity in health><health disparity><HPV-Related Malignancy><HPV-associated cancer><HPV-associated malignancy><HPV-related cancer><Human Papilloma Virus-Related Malignancy><Human Papilloma Virus-associated cancer><Human Papilloma Virus-associated malignancy><Human Papilloma Virus-related cancer><Human papillomavirus-Related Malignancy><Human papillomavirus-Related Malignant Neoplasm><human papillomavirus associated malignancy><human papillomavirus-associated cancer><human papillomavirus-related cancer><Human Papilloma Virus-Related Malignant Neoplasm><Dose><Data><Effectiveness of Interventions><HPV Vaccine><Human papillomavirus Vaccine><Human Papilloma Virus Vaccine><Interactive Communication><Vaccinated><pandemic><pandemic disease><cost><under served group><under served people><under served population><underserved group><underserved people><Underserved Population><cost effective><aged><12 years of age><age 12 years><twelve year old><twelve years of age><12 year old><13 years of age><age 13 years><thirteen year old><thirteen years of age><13 year old><17 years of age><age 17 years><seventeen year old><seventeen years of age><17 year old><booster dose><booster shot><booster vaccine><primary care setting><Randomization trial><randomized trial><arm><HPV Vaccination><Human Papillomavirus Vaccination><Human Papilloma Virus Vaccination><child patients><pediatric patients><Cluster randomization trial><Cluster randomized trial><Data Analytics><recruit><treatment arm><intervention arm><data infrastructure><effectiveness evaluation><assess effectiveness><determine effectiveness><effectiveness assessment><evaluate effectiveness><vaccine hesitancy><hesitant to vaccination><vaccination hesitancy><vaccine hesitant><vaccine acceptance><accept vaccination><accept vaccine><vaccination acceptability><vaccination acceptance><vaccination confidence><vaccination uptake><vaccination willingness><vaccine acceptability><vaccine confidence><vaccine uptake><vaccine willingness><organizational readiness><provider factors><clinician factors><clinician-level factors><physician factors><physician-level factors><provider-level factors><practice factors><practice-level factors><implementation measures>\",\"pref_terms\":\"12 year old;13 year old;17 year old;Adolescent;Adult;Affect;Age;Area;Black race;Caring;Cervical;Clinical;Cluster randomized trial;Communication;Cost Measures;Data;Data Analytics;Decision Making;Dose;Education;Educational Intervention;Effectiveness of Interventions;Ensure;Exposure to;Feedback;Goals;Health;Hispanic;Human Papilloma Virus Vaccination;Human Papilloma Virus Vaccine;Human Papilloma Virus-Related Malignant Neoplasm;Human Papillomavirus;Human papilloma virus infection;Immunization;Inpatients;Interactive Communication;Intervention;Interview;Leadership;Left;Malignant neoplasm of cervix uteri;Medical Care Team;Methods;Motivation;New York;Nurses;Nursing Staff;Oropharyngeal;Parents;Patients;Patients&apos; Rooms;Persons;Physicians;Play;Policies;Poverty;Primary Health Care;Protocols documentation;Provider;Recommendation;Resources;Running;Rural;Rural Population;Schools;Structure;Surveys;Testing;Time;Training;Underserved Population;Vaccinated;Vaccination;Vaccines;Visit;Woman;Work;age group;aged;arm;base;booster vaccine;cost;cost effective;cost effectiveness;data infrastructure;effectiveness evaluation;health disparity;immunoreaction;implementation measures;improved;influenza virus vaccine;malignant oropharynx neoplasm;member;men;organizational readiness;pandemic disease;parental influence;pediatric patients;practice factors;primary care setting;programs;provider factors;randomized trial;recruit;rural area;suburb;treatment arm;uptake;vaccine acceptance;vaccine hesitancy\",\"abstract_text\":\"Each year in the U.S., ≥20,000 women and 14,000 men are affected by HPV-related cancers, including\\ncervical and oropharyngeal cancer. However, in 2020, only 59% of U.S. adolescents aged 13-17 were up-to-\\ndate for HPV vaccination, and rates for 11-12 year olds, the primary target age group for HPV vaccination\\n(when the immune reaction is better and before exposure to HPV infection), are even lower. Standing orders\\n(written protocols that authorize designated members of the healthcare team to vaccinate without first obtaining\\na patient-specific physician order) have been shown to work in inpatient settings and for adults, but have not\\nbeen evaluated for HPV vaccine, which some parents consider controversial. Also, the ways in which\\norganizational readiness for change (resources, motivation, staff attributes, leadership support and culture)\\nmoderate the effect of standing orders has not been studied. A physician’s recommendation is correlated with\\nHPV vaccine acceptance, and we have developed a successful online, interactive, communication education\\nprogram that will be adapted to train nurses and staff in addition to physicians. We propose testing standing\\norders for HPV vaccine in an Accountable Care Organization (ACO) in Western New York, and assessing\\nwhich provider and practice factors moderate the effect of standing orders. Advantages of this setting include a\\ndiverse group of rural, urban and suburban practices, and the ACO provides data infrastructure and analytics\\nthat allow practices to evaluate vaccination rates in real time. Our aims are: Aim 1: Apply mixed methods (in-\\ndepth interviews and practice surveys) to identify provider- and practice-level barriers and facilitators to\\nimplementing SOs for adolescent HPV vaccination; Aim 2: Using a 2-arm cluster randomized trial (n=40\\npractices), we will assess the effectiveness of standing orders (SO) + HPV communication education\\n(intervention arm) relative to HPV communication education alone (control arm) on HPV vaccination for 11-17\\nyear-olds. A secondary aim is to measure the implementation of SOs using semi-structured interviews and\\nsurveys; Aim 3: Measure costs and cost-effectiveness of the interventions. If successful, this study will add a\\ndeep understanding of how to make SOs effective to raise HPV vaccination rates.\",\"project_title\":\"Standing Orders to Improve HPV Vaccination\",\"phr_text\":\"Despite an effective HPV vaccine being available in the US since 2006, uptake is well below the Healthy\\nPeople 2030 goal of 80%. We will test the effect of standing orders plus communication training for providers\\nand staff compared to communication training alone on HPV vaccination rates. The goal is to understand how\\nbest to increase HPV vaccination in primary care, to ultimately reduce HPV-related cancers.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"393\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":321216,\"indirect_cost_amt\":161999,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10585124\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10584016,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"2U24ES028502-06\",\"project_serial_num\":\"ES028502\",\"organization\":{\"org_name\":\"UNIVERSITY OF MICHIGAN AT ANN ARBOR\",\"city\":null,\"country\":null,\"org_city\":\"ANN ARBOR\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MI\",\"org_state_name\":null,\"dept_type\":\"PUBLIC HEALTH & PREV MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"073133571\"],\"org_ueis\":[\"GNJ7BBP73WE9\"],\"primary_duns\":\"073133571\",\"primary_uei\":\"GNJ7BBP73WE9\",\"org_fips\":\"US\",\"org_ipf_code\":\"1506502\",\"org_zipcode\":\"481091276\",\"external_org_id\":1506502},\"award_type\":\"2\",\"activity_code\":\"U24\",\"award_amount\":360960,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"2\",\"activity_code\":\"U24\",\"ic_code\":\"ES\",\"serial_num\":\"028502\",\"support_year\":\"06\",\"full_support_year\":\"06\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":2515822,\"first_name\":\"Karen\",\"middle_name\":\"Eileen\",\"last_name\":\"Peterson\",\"is_contact_pi\":true,\"full_name\":\"Karen Eileen Peterson\",\"title\":\"PROFESSOR AND CHAIR\"},{\"profile_id\":9490186,\"first_name\":\"Peter\",\"middle_name\":\"Xuekun\",\"last_name\":\"Song\",\"is_contact_pi\":false,\"full_name\":\"Peter Xuekun Song\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"PETERSON, KAREN EILEEN\",\"program_officers\":[{\"first_name\":\"Melissa\",\"middle_name\":\"M\",\"last_name\":\"Smarr\",\"full_name\":\"Melissa M Smarr\"}],\"agency_ic_admin\":{\"code\":\"ES\",\"abbreviation\":\"NIEHS\",\"name\":\"National Institute of Environmental Health Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"ES\",\"name\":\"National Institute of Environmental Health Sciences\",\"abbreviation\":\"NIEHS\",\"total_cost\":360960.0}],\"cong_dist\":\"MI-12\",\"spending_categories\":null,\"project_start_date\":\"2017-09-30T12:09:00Z\",\"project_end_date\":\"2027-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF PUBLIC HEALTH\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"RFA-ES-22-001\",\"full_study_section\":{\"srg_code\":\"ZES1\",\"srg_flex\":\"\",\"sra_designator_code\":\"LKB\",\"sra_flex_code\":\"K\",\"group_code\":\"U\",\"name\":\"Special Emphasis Panel[ZES1 LKB-K (U)]\"},\"award_notice_date\":\"2022-12-29T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"U24ES028502\",\"terms\":\"<Acculturation><Cultural Assimilation><Achievement><Achievement Attainment><Adolescence><12-20 years old><adolescence (12-20)><Adult><21+ years old><Adult Human><adulthood><Affect><Air Pollution><Biometry><Biometrics><Biostatistics><Child><0-11 years old><Child Youth><Children (0-21)><youngster><Cohort Studies><Concurrent Studies><Communities><Diet><diets><Economics><Education><Educational aspects><Environmental Exposure><Environmental Health><Environmental Health Science><Epidemiology><epidemiologic><epidemiological><Family><Fatty Liver><Liver Steatosis><hepatic steatosis><hepatosteatosis><Future><Geography><Health><Health behavior><health related behavior><Household><Housing><indexing><Internships><intern><Lead><Pb element><heavy metal Pb><heavy metal lead><Life Cycle Stages><Life Cycle><life course><Literature><Maintenance><Methods><Mexico><Michigan><Mothers><Neighborhoods><Noise><Pesticides><Play><Poverty><Impoverished><Pregnancy><Gestation><Public Health><Research><Role><social role><Sleep><Social Conditions><Societal Conditions><Students><Surveys><Survey Instrument><Testing><Toxic Environmental Substances><Environmental Toxin><Toxic Environmental Agents><environmental toxicant><Training Activity><training module><Universities><Weight><Work><Environmental Hazards><Generations><Measures><Research Methodology><Research Methods><Schedule><Socioeconomic Status><Socio-economic status><socio-economic position><socioeconomic position><phthalates><Ethnography><ethnographic><Journals><Magazine><improved><Biological><biologic><Economic Factors><Economical Factors><Training><Physical activity><pediatric><Childhood><insight><Individual><adult youth><young adulthood><young adult><Fostering><Inequality><Reproductive Health><Collaborations><Metabolic><Exposure to><Heavy Metals><Nature><Life><non-alcoholic fatty liver><non-alcohol fatty liver><nonalcohol fatty liver><nonalcoholic fatty liver><Complex><Protocols documentation><Protocol><Source><Techniques><college><collegiate><American><experience><stressor><intergenerational><drinking water><cohort><toxicant><Chemical Exposure><novel><Participant><offspring><Adult Children><Adult Daughters><Adult Offspring><Adult Sons><Environmental Risk Factor><Environmental Factor><environmental risk><epidemiology study><Epidemiologic Research><Epidemiologic Studies><Epidemiological Studies><Epidemiology Research><epidemiologic investigation><social><reproductive><exposed in utero><fetal exposure><in utero exposure><intra-uterine environmental exposure><intrauterine environmental exposure><prenatally exposed><prenatal exposure><vulnerable group><vulnerable people><Vulnerable Populations><Future Generations><disparity in health><health disparity><preventing><prevent><low SES><low socio-economic position><low socio-economic status><low socioeconomic position><low socioeconomic status><Address><qualitative reasoning><Qualitative Methods><Data><Master's Degree><Research Infrastructure><Environmental Epidemiology><life-style factor><lifestyle factors><Transmission><transmission process><Modification><Active Follow-up><active followup><follow up><followed up><followup><follow-up><socio-economic><socio-economically><socioeconomically><socioeconomics><developmental><Development><pathway><Pathway interactions><allostatic load><data integration><designing><design><Exposure to Pb2+><Pb exposed><Pb exposure><Pb2+ Exposure><Pb2+ exposed><exposed to lead><exposure to Pb><exposure to lead><lead exposed><lead exposure><Outcome><Population><aged><Consumption><toxic exposure><Toxicant exposure><minority institution><Minority-Serving Institution><high risk><environmental justice><data sharing><early life exposure><undergrad><undergraduate><undergraduate student><western-style diet><western-type diet><western diet><economic indicator><student training><Degree program><Workplace Diversity><Diverse Workforce><Data Analytics><racial minority><disadvantaged population><recruit><cardiometabolism><cardiometabolic><Infrastructure><social factors><social disadvantage><social structure><social structural><socio-structural><sociostructural><data infrastructure><data harmonization><harmonized data><data repository><Data Banks><Databanks><data depository><data set repository><dataset repository><environmental health disparity><social influence><environmental disparity><environmental inequality><environmental inequity><data communication><marginalized community><community marginalization><ethnic minority>\",\"pref_terms\":\"Acculturation;Achievement;Address;Adolescence;Adult;Adult Children;Affect;Air Pollution;American;Biological;Biometry;Chemical Exposure;Child;Childhood;Cohort Studies;Collaborations;Communities;Complex;Consumption;Data;Data Analytics;Degree program;Development;Diet;Diverse Workforce;Economic Factors;Economics;Education;Environmental Epidemiology;Environmental Exposure;Environmental Hazards;Environmental Health;Environmental Risk Factor;Epidemiology;Ethnography;Exposure to;Family;Fatty Liver;Fostering;Future;Future Generations;Generations;Geography;Health;Health behavior;Heavy Metals;Household;Housing;Individual;Inequality;Infrastructure;Internships;Journals;Lead;Life;Life Cycle Stages;Literature;Maintenance;Master&apos;s Degree;Measures;Metabolic;Methods;Mexico;Michigan;Minority-Serving Institution;Modification;Mothers;Nature;Neighborhoods;Noise;Outcome;Participant;Pathway interactions;Pesticides;Physical activity;Play;Population;Poverty;Pregnancy;Protocols documentation;Public Health;Qualitative Methods;Reproductive Health;Research;Research Infrastructure;Research Methodology;Role;Schedule;Sleep;Social Conditions;Socioeconomic Status;Source;Students;Surveys;Techniques;Testing;Toxic Environmental Substances;Toxicant exposure;Training;Training Activity;Universities;Vulnerable Populations;Weight;Work;aged;allostatic load;cardiometabolism;cohort;college;data communication;data harmonization;data infrastructure;data integration;data repository;data sharing;design;disadvantaged population;drinking water;early life exposure;economic indicator;environmental disparity;environmental health disparity;environmental justice;epidemiology study;ethnic minority;experience;follow-up;health disparity;high risk;improved;indexing;insight;intergenerational;lead exposure;lifestyle factors;low socioeconomic status;marginalized community;non-alcoholic fatty liver;novel;offspring;phthalates;prenatal exposure;prevent;racial minority;recruit;reproductive;social;social disadvantage;social factors;social influence;social structure;socioeconomics;stressor;student training;toxicant;transmission process;undergraduate student;western diet;young adult\",\"abstract_text\":\"E3Gen: Multigenerational influences of social structure on toxicant exposures and life course health in the\\nELEMENT cohort\\nSUMMARY\\nTraditional epidemiological research and methods often focus separately on how social, economic, and environmental\\nfactors affect individuals’ life course health, yet evolving research underscores the importance of considering the\\nintegrated effects of factors that cluster in those who are most socially disadvantaged. Multiple mechanistic pathways\\nwith complex linkages must also be taken into account to understand the effects of social structures and chemical\\nexposures that underlie environmental health disparities. This challenge depends not only on the integration of measures\\nof socioeconomic status into environmental cohort studies, but also qualitative and geographic information on\\nneighborhood infrastructure and social conditions that can illuminate individuals’ lived experiences and facilitate new\\nscientific directions. While substantial research examines the developmental origins and biologic mechanisms underlying\\ntoxicant-health associations, few cohorts have the ability to address the intergenerational legacy of toxicant exposures\\nand social structures on life course health.1 This U24 competitive renewal application leverages the rich data and research\\ninfrastructure of the Early Life Exposure in Mexico to ENvironmental Toxicants (ELEMENT) cohort. Here, we propose\\nexpanding our E3Gen cohort maintenance activities to integrate novel measures of social structure and ethnographic data\\ninto our biologic and data repositories in this unique, multi-generational cohort of mother-child dyads followed for ~28\\nyears. This expansion also will implement streamlined protocols to enhance follow-up and engage the original ELEMENT\\noffspring as they transition to adulthood and begin to have children of their own, setting the stage for research examining\\nthe impact of social and environmental exposures on reproductive and metabolic health and development across 3\\ngenerations. Specific aims are to: 1) Create and collect household and individual qualitative and quantitative data to\\nunderstand the direct effects of social and economic stressors and their potential to modify the multigenerational, life\\ncourse effects of environmental exposures on health outcomes. 2) Encourage participation and prevent loss to follow up\\namong ~600 ELEMENT offspring now aged 16-28 years and establish passive surveillance to recruit their children currently\\nand projected to be born over the next 5 years. 3) Develop and test novel, nonlinear multidimensional methods to\\nintegrate and harmonize qualitative and quantitative data, foster cross-project data communication and novel\\ninterdisciplinary partnerships, and accelerate data sharing with the larger environmental health sciences community.\\nThrough our existing partnerships with minority-serving institutions Hampton and Fisk Universities and Spelman College,\\nwe will train diverse students in environmental epidemiology, global public health and biostatistics via summer\\nundergraduate internships and accelerated master’s degree programs.\\n1 Gochfeld, Michael, and Joanna Burger. \\\"Disproportionate exposures in environmental justice and other populations: the importance of outliers.\\\" American Journal\\nof Public Health 101.S1 (2011): S53-S63.\",\"project_title\":\"E3Gen: Multigenerational Influences of Social Structure on Toxicant Exposures and Life Course Health in the ELEMENT Cohort\",\"phr_text\":\"PROJECT NARRATIVE\\nTraditional epidemiological research and methods often focus separately on how social, economic, and\\nenvironmental factors affect individuals' life course health, yet evolving research underscores the importance of\\nconsidering the integrated effects of factors that cluster in those who are most socially disadvantaged. While\\nsubstantial research examines the developmental origins and biologic mechanisms underlying toxicant-health\\nassociations, few cohorts have the ability to address the intergenerational legacy of toxicant exposures and\\nsocial structures on life course health. In the proposed application we will expand on our E3Gen cohort\\nmaintenance activities by utilizing data integration and analytical techniques to incorporate novel measures of\\nsocial structure, economic indicators and ethnographic data into our biologic and data repositories for 550\\noriginal ELEMENT mothers, 600 of their young adult offspring aged 18-33 years, and establish passive\\nsurveillance to recruit their grandchildren currently and projected to be born over the next 5 years.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"113\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":257639,\"indirect_cost_amt\":103321,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10584016\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10592590,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1R21MH132123-01\",\"project_serial_num\":\"MH132123\",\"organization\":{\"org_name\":\"TRINITY COLLEGE DUBLIN\",\"city\":null,\"country\":null,\"org_city\":\"DUBLIN\",\"org_country\":\"IRELAND\",\"org_state\":null,\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"988662490\"],\"org_ueis\":[\"PMDXT6F1X7E6\"],\"primary_duns\":\"988662490\",\"primary_uei\":\"PMDXT6F1X7E6\",\"org_fips\":\"EI\",\"org_ipf_code\":\"2217301\",\"org_zipcode\":\"2\",\"external_org_id\":2217301},\"award_type\":\"1\",\"activity_code\":\"R21\",\"award_amount\":70838,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"R21\",\"ic_code\":\"MH\",\"serial_num\":\"132123\",\"support_year\":\"01\",\"full_support_year\":\"01\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":78510112,\"first_name\":\"Mark\",\"middle_name\":\"\",\"last_name\":\"Ward\",\"is_contact_pi\":true,\"full_name\":\"Mark  Ward\",\"title\":\"\"}],\"contact_pi_name\":\"WARD, MARK \",\"program_officers\":[{\"first_name\":\"Jovier\",\"middle_name\":\"D\",\"last_name\":\"Evans\",\"full_name\":\"Jovier D Evans\"}],\"agency_ic_admin\":{\"code\":\"MH\",\"abbreviation\":\"NIMH\",\"name\":\"National Institute of Mental Health\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"MH\",\"name\":\"National Institute of Mental Health\",\"abbreviation\":\"NIMH\",\"total_cost\":70838.0}],\"cong_dist\":null,\"spending_categories\":null,\"project_start_date\":\"2023-01-01T12:01:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":null,\"is_other\":true},\"full_foa\":\"RFA-MH-22-136\",\"full_study_section\":{\"srg_code\":\"ZMH1\",\"srg_flex\":\"\",\"sra_designator_code\":\"ERB\",\"sra_flex_code\":\"L\",\"group_code\":\"05\",\"name\":\"Special Emphasis Panel[ZMH1 ERB-L (05)]\"},\"award_notice_date\":\"2022-12-30T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RP\",\"core_project_num\":\"R21MH132123\",\"terms\":\"<Adult><21+ years old><Adult Human><adulthood><Age><ages><Elderly><advanced age><elders><geriatric><late life><later life><older adult><older person><senior citizen><Aging><Alcohol consumption><Alcohol Drinking><EtOH drinking><EtOH use><alcohol ingestion><alcohol intake><alcohol product use><alcohol use><alcoholic beverage consumption><alcoholic drink intake><ethanol consumption><ethanol drinking><ethanol ingestion><ethanol intake><ethanol product use><ethanol use><Anxiety><Centers for Disease Control and Prevention (U.S.)><CDC><Centers for Disease Control><Centers for Disease Control and Prevention><United States Centers for Disease Control><United States Centers for Disease Control and Prevention><Clinical Markers><Communities><Cessation of life><Death><Mental Depression><depression><Economics><Europe><Exhibits><Future><Goals><Growth><Generalized Growth><Tissue Growth><ontogeny><Health><Heart Rate><Cardiac Chronotropism><Hydrocortisone><Aeroseb-HC><Cetacort><Cort-Dome><Cortef><Cortenema><Cortisol><Cortispray><Cortril><Dermacort><Eldecort><Hydrocortone><Hytone><Nutracort><Proctocort><Ireland><Eire><Irish Free State><Loneliness><lonely><Longitudinal Studies><long-term study><longitudinal outcome studies><longterm study><Mental Health><Mental Hygiene><Psychological Health><Persons><National Institute of Mental Health><NIMH><Neurosecretory Systems><Neuroendocrine><Neuroendocrine System><Parasympathetic Nervous System><Perception><Play><Prospective Studies><Public Health><Questionnaires><Research><Research Personnel><Investigators><Researchers><Risk><Risk Factors><Role><social role><Sleep><Smoking><Social Environment><social climate><social context><socioenvironment><socioenvironmental><Social isolation><sociologist><Stress><Suicide><fatal attempt><fatal suicide><intent to die><suicidality><Suicide attempt><non fatal attempt><nonfatal attempt><suicidal attempt><Sulfur><S element><Surveys><Survey Instrument><Testing><Thinking><thoughts><Time><Uranium><U element><Woman><Feeling hopeless><Cannot see a future><Feels there is no future><Loss of hope for the future><Negative about the future><No hope for the future><hopelessness><base><improved><Clinical><Physiological><Physiologic><psychologic><psychological><Link><Training><Physical activity><Individual><Collaborations><Nature><Knowledge><ideation><Feeling suicidal><Suicidal thoughts><suicidal ideation><suicidal thinking><suicide ideation><thoughts about suicide><Immune><Immunes><Complex><Clinic><Techniques><System><physical conditioning><physical health><suicidal risk><suicide risk><experience><field study><field based data><field learning><field test><social integration><cohort><Participant><Reporting><social><Early identification><Impulsivity><Modeling><response><theories><Intervention Strategies><interventional strategy><Intervention><Cellular Immune Function><immune function><preventing><prevent><Address><Biology of Aging><Data><International><Characteristics><Process><depression and suicidality><Depression and Suicide><Behavioral><pathway><Pathway interactions><suicide behavior><suicidal behavior><suicide rate><suicide death><suicide morbidity><suicidal morbidity><aged><multidisciplinary><high risk><community setting><inflammation marker><inflammatory marker><bio-markers><biologic marker><biomarker><Biological Markers><health definition><RDoC><Research Domain Criteria><Data Analytics><Prospective cohort><Prospective cohort study><health care settings><healthcare settings><health assessment><protective factors><cardiometabolism><cardiometabolic><sociodemographics><socio-demographics><social relationships>\",\"pref_terms\":\"Address;Adult;Age;Aging;Alcohol consumption;Anxiety;Behavioral;Biological Markers;Biology of Aging;Centers for Disease Control and Prevention (U.S.);Cessation of life;Characteristics;Clinic;Clinical;Clinical Markers;Collaborations;Communities;Complex;Data;Data Analytics;Depression and Suicide;Early identification;Economics;Elderly;Europe;Exhibits;Feeling hopeless;Feeling suicidal;Future;Goals;Growth;Health;Heart Rate;Hydrocortisone;Immune;Impulsivity;Individual;International;Intervention;Ireland;Knowledge;Link;Loneliness;Longitudinal Studies;Mental Depression;Mental Health;Modeling;National Institute of Mental Health;Nature;Neurosecretory Systems;Parasympathetic Nervous System;Participant;Pathway interactions;Perception;Persons;Physical activity;Physiological;Play;Process;Prospective Studies;Prospective cohort;Prospective cohort study;Public Health;Questionnaires;Reporting;Research;Research Domain Criteria;Research Personnel;Risk;Risk Factors;Role;Sleep;Smoking;Social Environment;Social isolation;Stress;Suicide;Suicide attempt;Sulfur;Surveys;System;Techniques;Testing;Thinking;Time;Training;Uranium;Woman;aged;base;cardiometabolism;cohort;community setting;experience;field study;health assessment;health care settings;health definition;high risk;ideation;immune function;improved;inflammatory marker;multidisciplinary;physical conditioning;prevent;protective factors;psychologic;response;social;social integration;social relationships;sociodemographics;sociologist;suicidal behavior;suicidal morbidity;suicidal risk;suicide rate;theories\",\"abstract_text\":\"ABSTRACT\\nSuicide deaths and social disconnection among older adults are increasingly serious public health concerns. A 'Wish\\nTo Die' (WTD) is an indicator of passive suicide ideation which refers to thoughts of one’s own death, that one would\\nbe better off dead, or wishing for one’s death. The association between social disconnection and suicide is widely\\nrecognised and social disconnection has been shown to be associated with an increased likelihood of WTD.\\nImportantly, social disconnection is a multi-faceted construct and both the quantity (social isolation) and quality\\n(loneliness) of people’s social relationships are strongly related to both suicide ideation and subsequent suicide\\nattempts. While this association is well-established, the mechanistic pathways to explain the association are less well\\nunderstood. The main putative mechanisms proposed are: behavioral (sleep, physical activity, smoking, alcohol\\nconsumption), psychological (depression, anxiety, and ageing perceptions), and physiological (neuroendocrine,\\nimmune function, and cardiometabolic).\\nThis study leverages data from The Irish Longitudinal Study on Ageing (TILDA) to identify the pathways that explain\\nthe association between social disconnection and passive suicidal ideation. TILDA is a large, nationally\\nrepresentative, prospective cohort study that examines the social, economic and health circumstances of 8,500+\\ncommunity-dwelling adults aged 50 years and older in Ireland. TILDA data have been collected every two years since\\n2009 and includes both survey data (CAPI and SCQ) and a comprehensive clinic-based health assessment. Detailed\\nbiomarker data are available from these health assessments and include markers from the neuroendocrine, immune,\\ncardiometabolic, and parasympathetic systems.\\nUsing this longitudinal data, the study aims to: (1) Identify who is at highest risk of social disconnection and WTD\\namong older adults as well as when individuals are most at risk; (2) Examine the nature of the associations between\\nsocial disconnection and WTD, including strength, temporality, and directionality, among older adults; (3) Identify\\nmechanistic pathways that explain the association between social disconnection and WTD. To address these aims,\\nfour waves of survey and two waves of biomarker data will be investigated and advanced longitudinal statistical\\ntechniques (GLMM, latent growth curve, latent class growth analysis, and SEM path analysis) will be applied.\\nGiven the complex nature of this study, we have assembled an outstanding multi-disciplinary team of clinical and\\nacademic researchers with expertise in each of the constituent parts of the project. By establishing the causal\\npathways between social disconnection and WTD, study results will identify important risk and protective factors for\\nsuicidal ideation that are amenable to intervention. In doing so, the study will inform effective clinical and\\ncommunity-based interventions by which the train of thoughts leading to active suicidal ideation can be stopped\\nbefore individuals progress to active ideation and suicidal behavior.\",\"project_title\":\"Pathways from social disconnection to suicidal ideation in later life\",\"phr_text\":\"PROJECT NARRATIVE\\nSocial disconnection and suicidal behaviours in later life are important and related public health concerns. This study\\nleverages longitudinal data from a large, nationally representative cohort of older adults to examine the processes\\nthat link these two phenomena. Study results will identify risk and protective factors to inform both community and\\nclinical interventions.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"242\",\"funding_mechanism\":\"Non-SBIR/STTR\",\"direct_cost_amt\":65591,\"indirect_cost_amt\":5247,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10592590\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10571515,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1K99EB033857-01\",\"project_serial_num\":\"EB033857\",\"organization\":{\"org_name\":\"OHIO STATE UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"COLUMBUS\",\"org_country\":\"UNITED STATES\",\"org_state\":\"OH\",\"org_state_name\":null,\"dept_type\":\"NONE\",\"fips_country_code\":null,\"org_duns\":[\"832127323\"],\"org_ueis\":[\"DLWBSLWAJWR1\"],\"primary_duns\":\"832127323\",\"primary_uei\":\"DLWBSLWAJWR1\",\"org_fips\":\"US\",\"org_ipf_code\":\"6218701\",\"org_zipcode\":\"432101016\",\"external_org_id\":6218701},\"award_type\":\"1\",\"activity_code\":\"K99\",\"award_amount\":99955,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"K99\",\"ic_code\":\"EB\",\"serial_num\":\"033857\",\"support_year\":\"01\",\"full_support_year\":\"01\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":15447209,\"first_name\":\"Colin\",\"middle_name\":\"Lee\",\"last_name\":\"Hisey\",\"is_contact_pi\":true,\"full_name\":\"Colin Lee Hisey\",\"title\":\"POSTDOCTORAL SCHOLAR\"}],\"contact_pi_name\":\"HISEY, COLIN LEE\",\"program_officers\":[{\"first_name\":\"Tina\",\"middle_name\":\"L.\",\"last_name\":\"Gatlin\",\"full_name\":\"Tina L. Gatlin\"}],\"agency_ic_admin\":{\"code\":\"EB\",\"abbreviation\":\"NIBIB\",\"name\":\"National Institute of Biomedical Imaging and Bioengineering\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"EB\",\"name\":\"National Institute of Biomedical Imaging and Bioengineering\",\"abbreviation\":\"NIBIB\",\"total_cost\":99955.0}],\"cong_dist\":\"OH-03\",\"spending_categories\":null,\"project_start_date\":\"2022-12-01T12:12:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PAR-21-271\",\"full_study_section\":{\"srg_code\":\"ZEB1\",\"srg_flex\":\"\",\"sra_designator_code\":\"OSR\",\"sra_flex_code\":\"B\",\"group_code\":\"O1\",\"name\":\"Special Emphasis Panel[ZEB1 OSR-B (O1)]\"},\"award_notice_date\":\"2022-12-01T12:12:00Z\",\"is_new\":false,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K99EB033857\",\"terms\":\"<interest><particle><aptamer><skills><simulation><advanced disease><advanced illness><disease classification><disorder classification><nosology><new technology><novel technologies><Column Chromatography><Devices><Positioning Attribute><Position><Sampling><disease subgroups><disease subtype><disorder subtype><Intervention Strategies><interventional strategy><Intervention><Malignant Ovarian Neoplasm><Malignant Ovarian Tumor><Malignant Tumor of the Ovary><Ovary Cancer><ovarian cancer><Malignant neoplasm of ovary><Size Exclusion Chromatography><Molecular Sieve Chromatography><µfluidic><Microfluidics><preventing><prevent><Causality><causation><disease causation><Etiology><Fatality rate><Microfluidic Device><Microfluidic Lab-On-A-Chip><microfluidic chip><Microfluidic Microchips><Symptoms><Data><Detection><Reproducibility><Resolution><Cancer Patient><Patient outcome><Patient-Centered Outcomes><Patient-Focused Outcomes><Monitor><Process><developmental><Development><imaging><Image><rapid growth><designing><design><nano fabricate><nano fabrication><nanofabricate><nanofabrication><nano meter scale><nano meter sized><nano scale><nanometer scale><nanometer sized><nanoscale><electron beam lithography><nano particle><nano-sized particle><nanosized particle><nanoparticle><Population><innovate><innovative><innovation><analytical tool><data acquisition><multidisciplinary><clinical applicability><clinical application><treatment strategy><bio-markers><biologic marker><biomarker><Biological Markers><disease diagnosis><Geometry><vesicle release><vesicular release><Data Analytics><nanoplasmonic><nano-plasmonic><cancer subtypes><cancer sub-types><extracellular vesicles><experimental study><experiment><experimental research><clinical translation><machine learning algorithm><machine learned algorithm><machine learning based algorithm><mortality risk><death risk><classification algorithm><algorithm training><disease diagnostic><machine learning classification><machine learning based classification><generative adversarial network><generative neural network><machine learning classifier><machine learning based classifier><Surface Antigens><Cell Surface Antigens><Immunologic Surface Markers><Immunological Surface Markers><Architecture><Engineering / Architecture><Bar Codes><barcode><Biological Assay><Assay><Bioassay><Biologic Assays><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><Cell Culture Techniques><cell culture><cell cultures><Cells><Cell Body><Classification><Systematics><Communicable Diseases><Infectious Disease Pathway><Infectious Diseases><Infectious Disorder><Cessation of life><Death><Diagnosis><Disease><Disorder><Engineering><Fingerprint><Goals><Gold><Health><Heterogeneity><Human><Modern Man><Laboratories><Lead><Pb element><heavy metal Pb><heavy metal lead><Lipids><Mentors><Methods><Molecular Biology><DNA Molecular Biology><Diagnostic Neoplasm Staging><Cancer Staging><Neoplasm Staging><Tumor Staging><Optics><optical><Patients><Plasma><Blood Plasma><Plasma Serum><Reticuloendothelial System, Serum, Plasma><Production><Public Health><Research><Signal Transduction><Cell Communication and Signaling><Cell Signaling><Intracellular Communication and Signaling><Signal Transduction Systems><Signaling><biological signal transduction><Raman Spectrum Analysis><IR/UV/Raman Spectroscopy><Raman Spectroscopy><Raman imaging><Raman spectrometry><Sulfhydryl Compounds><Mercaptans><Mercapto Compounds><Thiols><sulfhydryl group><Complementary Feeding><Supplementary Feedings><Survival Rate><Testing><Translating><Translations><Woman><Data Set><Dataset><base><reproductive system disorder><Genital Disorders><Reproductive System Disease><reproductive disease><reproductive disorder><Label><sensor><improved><Lateral><Surface><Clinical><Phase><Biochemical><Chemicals><Training><Liquid substance><fluid><liquid><Staging><tool><Diagnostic><Nature><Machine Learning><machine learned><Bioreactors><microbioreactor><programs><Dependence><Serous><Source><Pattern><Techniques>\",\"pref_terms\":\"Architecture;Bar Codes;Biochemical;Biological Assay;Biological Markers;Bioreactors;Cancer Patient;Cell Culture Techniques;Cells;Cessation of life;Chemicals;Classification;Clinical;Column Chromatography;Communicable Diseases;Complementary Feeding;Data;Data Analytics;Data Set;Dependence;Detection;Development;Devices;Diagnosis;Diagnostic;Diagnostic Neoplasm Staging;Disease;Engineering;Etiology;Fatality rate;Fingerprint;Geometry;Goals;Gold;Health;Heterogeneity;Human;Image;Intervention;Label;Laboratories;Lateral;Lead;Lipids;Liquid substance;Machine Learning;Malignant Neoplasms;Malignant neoplasm of ovary;Mentors;Methods;Microfluidic Microchips;Microfluidics;Molecular Biology;Molecular Sieve Chromatography;Monitor;Nature;Optics;Patient-Focused Outcomes;Patients;Pattern;Phase;Plasma;Population;Positioning Attribute;Process;Production;Public Health;Raman Spectrum Analysis;Reproducibility;Research;Resolution;Sampling;Serous;Signal Transduction;Source;Staging;Sulfhydryl Compounds;Surface;Surface Antigens;Survival Rate;Symptoms;Techniques;Testing;Training;Translating;Translations;Woman;advanced disease;algorithm training;analytical tool;aptamer;base;cancer subtypes;classification algorithm;clinical application;clinical translation;data acquisition;design;disease classification;disease diagnosis;disease diagnostic;disorder subtype;electron beam lithography;experimental study;extracellular vesicles;generative adversarial network;improved;innovation;interest;machine learning algorithm;machine learning classification;machine learning classifier;mortality risk;multidisciplinary;nanofabrication;nanoparticle;nanoplasmonic;nanoscale;new technology;particle;prevent;programs;rapid growth;reproductive system disorder;sensor;simulation;skills;tool;treatment strategy;vesicular release\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nOvarian cancer is a diverse group of malignancies that can vary greatly in molecular biology, etiology, and\\npresentation of symptoms. While it accounts for 2.5% of all cancers among women, it results in roughly 5%\\nof cancer-related deaths due to its high fatality rate. This is because 75% of patients are diagnosed with\\nadvanced disease, largely attributed to its relatively late presentation of symptoms and a lack of reliable\\ndetection and monitoring strategies. Extracellular vesicles (EVs) are released by all cells, including ovarian\\ncancer, and their cargo reflects their cells of origin. They have shown immense potential as stable\\nbiomarkers, however their low abundance compared to EVs from healthy cells and a lack of sufficiently\\nsensitive characterization tools has limited their clinical translation. Surface-enhanced Raman spectroscopy\\n(SERS) is sensitive enough to biochemically fingerprint even single EVs, and the information-rich spectra\\nproduced in EV SERS can be fed into machine learning (ML) algorithms to classify them based on their\\nlatent spectral features. Despite early progress in EV SERS, the highly heterogeneous nature of EVs\\nindicates that their separation into distinct subpopulations prior to SERS analysis may help improve disease\\ndiagnosis, classification, and monitoring. Microfluidic devices are uniquely capable of separating EV s into\\nsubpopulations of interest while simultaneously enabling SERS spectral acquisition in a single device. In the\\nproposed research, during the mentored phase, ML-enabled inverse design will be combined with high\\nresolution nanofabrication techniques to improve the signal strength and spectral quality attainable from EV\\nSERS. Preliminary data indicates that the SERS enhancement is highly dependent on the substrate’s\\nnanoscale geometry, which is particularly important for EVs compared to conventional chemical analysis.\\nTowards the end of the mentored phase, once the improved substrates have been thoroughly tested using\\nbioreactor-produced EVs, they will be incorporated into two distinct microfluidic devices and tested\\nthroughout the independent phase using both cell culture and patient EV samples. One device will capture\\ndifferent subpopulations of EVs directly onto the microfluidic SERS substrates based on specific surface-\\nantigens for multiplexed characterization, while the other will separate EVs precisely by size and flow them\\nover the microfluidic SERS substrates to produce EV SERS barcodes. In parallel, ML algorithms tailored\\nspecifically to these platforms will also be developed to process and classify the acquired spectra. This\\nproposal is multidisciplinary, utilizing advanced ML and micro-nanofabrication techniques as well as EV\\nproduction, isolation, and characterization strategies. These experiments are innovative and significant\\nbecause they will develop ML inverse design architectures specifically for EVs as well as microfluidic EV\\nSERS for characterizing and classifying ovarian cancer.\",\"project_title\":\"Machine Learning-enabled Classification of Extracellular Vesicles Using Nanoplasmonic Microfluidics\",\"phr_text\":\"PROJECT NARRATIVE\\nOvarian cancer is a major public health issue and is often diagnosed in later stages, resulting in an\\nincreased risk of death. Extracellular vesicles are nanoscale particles that circulate throughout bodily fluids\\nand are released by all cells, and their cargo may indicate early signs of ovarian cancer or inform better\\ntreatment strategies. The proposed training and research will create improved methods for characterizing\\nand classifying extracellular vesicles to improve patient outcomes by developing ultrasensitive detection\\ndevices and machine learning analytical tools.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2022-12-01T12:12:00Z\",\"budget_end\":\"2023-11-30T12:11:00Z\",\"cfda_code\":\"286\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":92551,\"indirect_cost_amt\":7404,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10571515\",\"date_added\":\"2022-12-03T04:12:55Z\"},{\"appl_id\":10573152,\"subproject_id\":\"6635\",\"fiscal_year\":2023,\"project_num\":\"5P20GM121327-07\",\"project_serial_num\":\"GM121327\",\"organization\":{\"org_name\":\"UNIVERSITY OF KENTUCKY\",\"city\":null,\"country\":null,\"org_city\":\"LEXINGTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"KY\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"939017877\"],\"org_ueis\":[\"H1HYA8Z1NTM5\"],\"primary_duns\":\"939017877\",\"primary_uei\":\"H1HYA8Z1NTM5\",\"org_fips\":\"US\",\"org_ipf_code\":\"2793601\",\"org_zipcode\":\"405260001\",\"external_org_id\":2793601},\"award_type\":\"5\",\"activity_code\":\"P20\",\"award_amount\":124058,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"P20\",\"ic_code\":\"GM\",\"serial_num\":\"121327\",\"support_year\":\"07\",\"full_support_year\":\"07\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":8575677,\"first_name\":\"Jianhang\",\"middle_name\":\"\",\"last_name\":\"Jia\",\"is_contact_pi\":true,\"full_name\":\"Jianhang  Jia\",\"title\":\"PROFESSOR\"}],\"contact_pi_name\":\"JIA, JIANHANG \",\"program_officers\":[],\"agency_ic_admin\":{\"code\":\"GM\",\"abbreviation\":\"NIGMS\",\"name\":\"National Institute of General Medical Sciences\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"GM\",\"name\":\"National Institute of General Medical Sciences\",\"abbreviation\":\"NIGMS\",\"total_cost\":124058.0}],\"cong_dist\":\"KY-06\",\"spending_categories\":null,\"project_start_date\":\"2017-03-01T12:03:00Z\",\"project_end_date\":\"2026-12-31T12:12:00Z\",\"organization_type\":{\"name\":null,\"code\":\"10\",\"is_other\":true},\"full_foa\":\"PAR-19-312\",\"full_study_section\":{\"srg_code\":\"ZGM1\",\"srg_flex\":\"\",\"sra_designator_code\":\"RCB\",\"sra_flex_code\":\"3\",\"group_code\":\"\",\"name\":\"ZGM1-RCB-3\"},\"award_notice_date\":\"2022-12-23T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"RC\",\"core_project_num\":\"P20GM121327\",\"terms\":\"<Aging><Award><Biomedical Research><Biometry><Biometrics><Biostatistics><Malignant Neoplasms><Cancers><Malignant Tumor><malignancy><neoplasm/cancer><cell motility><Cell Locomotion><Cell Migration><Cell Movement><Cellular Migration><Cellular Motility><Motility><Consultations><Animal Disease Models><Equipment><Faculty><Floor><Fluorescence><Goals><Institutes><Kentucky><Laboratories><Laboratory Research><Medicine><Metabolism><Intermediary Metabolism><Metabolic Processes><Methods><Methodology><Mission><Mus><Mice><Mice Mammals><Murine><Neoplasm Metastasis><Metastasis><Metastasize><Metastatic Lesion><Metastatic Mass><Metastatic Neoplasm><Metastatic Tumor><Secondary Neoplasm><Secondary Tumor><cancer metastasis><tumor cell metastasis><Pathology><Pilot Projects><pilot study><Proteins><Research><Research Personnel><Investigators><Researchers><Research Support><Resources><Research Resources><Role><social role><Computer software><Software><Tissues><Body Tissues><Transfection><Universities><Work><Imaging Techniques><Imaging Procedures><Imaging Technics><Bioluminescence><Organ><Microscope><Site><Area><Clinical><Phase><Link><Evaluation><Training><Individual><Disease Progression><Research Project Grants><R-Series Research Projects><R01 Mechanism><R01 Program><Research Grants><Research Projects><Metabolic><instrument><programs><Scientist><Complex><Distant><Slide><System><Consult><Services><success><tumor initiation><skills><novel><Human Resources><Manpower><personnel><Positioning Attribute><Position><Sampling><animal facility><Cancer Treatment><Malignant Neoplasm Therapy><Malignant Neoplasm Treatment><anti-cancer therapy><anticancer therapy><cancer-directed therapy><cancer therapy><Bio-Informatics><Bioinformatics><image-based method><imaging method><imaging modality><Oncogenesis><tumorigenesis><metabolism measurement><metabonomics><metabolomics><Data><Imaging Instrument><Imaging Tool><Imaging Device><Research Infrastructure><in vivo><Cancer Center><COBRE><Center of Biomedical Research Excellence><Centers of Research Excellence><EXTMR><Extramural><Extramural Activities><Monitor><developmental><Development><imaging><Image><optic imaging><optical imaging><anti-cancer research><cancer research><anticancer research><digital><designing><design><cost effective><analytical tool><multidisciplinary><response to therapy><response to treatment><therapeutic response><therapy response><treatment response><flexible><flexibility><cancer cell metabolism><cancer metabolism><tumor cell metabolism><tumor metabolism><imaging in vivo><in vivo imaging><imaging system><microscopic imaging><microscope imaging><microscopy imaging><experimental study><experiment><experimental research><imaging study><in vivo imaging system><IVIS SpectrumCT><IVIS imaging><IVIS optical imaging><IVIS spectral imaging><IVIS spectrum><IVIS system><imaging capabilities><Infrastructure><advanced analytics>\",\"pref_terms\":\"Aging;Animal Disease Models;Area;Award;Bioinformatics;Bioluminescence;Biomedical Research;Biometry;Cancer Center;Centers of Research Excellence;Clinical;Complex;Computer software;Consult;Consultations;Data;Development;Disease Progression;Distant;Equipment;Evaluation;Extramural Activities;Faculty;Floor;Fluorescence;Goals;Human Resources;Image;Imaging Device;Imaging Techniques;Individual;Infrastructure;Institutes;Kentucky;Laboratories;Laboratory Research;Link;Malignant Neoplasms;Medicine;Metabolic;Metabolism;Methodology;Methods;Microscope;Mission;Monitor;Mus;Neoplasm Metastasis;Organ;Pathology;Phase;Pilot Projects;Positioning Attribute;Proteins;Research;Research Infrastructure;Research Personnel;Research Project Grants;Research Support;Resources;Role;Sampling;Scientist;Services;Site;Slide;System;Tissues;Training;Transfection;Universities;Work;advanced analytics;analytical tool;animal facility;anticancer research;cancer therapy;cell motility;cost effective;design;digital;experimental study;flexibility;imaging capabilities;imaging modality;imaging study;imaging system;in vivo;in vivo imaging;in vivo imaging system;instrument;metabolomics;microscopic imaging;multidisciplinary;novel;optical imaging;programs;skills;success;treatment response;tumor initiation;tumor metabolism;tumorigenesis\",\"abstract_text\":\"PROJECT SUMMARY\\nThe Imaging Core of the Center for Cancer and Metabolism (CCM) is the technical service and support core\\nfor CCM project research laboratories and pilot project recipients. The integrated services of the Core range\\nfrom the imaging capabilities of simple microscopes to imaging analysis by sophisticated and advanced\\nimaging methodologies. The Imaging Core personnel assist users to operate the instruments and analyze\\nimaging data. The Core provides advice for the current PIs in this Center of Biomedical Research Excellence\\n(COBRE) application to use the Imaging Core for their specific studies. The Imaging Core has provided\\ntremendous help to the projects in Phase I of COBRE support and will continue to make its services readily\\navailable and easily accessible to all COBRE laboratories in order to generate high quality data. Toward the\\ngoals of providing efficient and high-quality imaging services and of establishing a robust imaging infrastructure\\nwithin the CCM, we have added new features and new imaging systems to the Core. Our specific aims are: 1)\\nto provide project-specific training and consultation for COBRE investigators; 2) to develop and adapt state-of-\\nthe art imaging techniques to enhance the research of early-stage and pilot project investigators; and 3) to\\ndevelop and implement new methods, to enhance CCM imaging services and make such services available to\\nCOBRE and non-COBRE users. This Core will provide an easily accessible facility where investigators can\\nevaluate their tissue and/or cellular samples using various types of imaging systems. Dr. Jianhang Jia, the\\nDirector of the Imaging Core, is a highly productive investigator who has successfully obtained substantial\\nextramural research support including an NIGMS Maximizing Investigators' Research Award (MIRA) (R35)\\naward for established investigators that relies extensively on sophisticated imaging capabilities. He has worked\\nclosely with all graduated COBRE PIs, making significant contributions to their successes in obtaining\\nindependent research support. Dr. Jia has guided the development of the four CCM research projects in this\\nPhase II application, and he will continue to advise CCM investigators on the design of experiments that use\\nimaging modalities.\",\"project_title\":\"Imaging Core\",\"phr_text\":null,\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":null,\"funding_mechanism\":\"Research Centers\",\"direct_cost_amt\":81083,\"indirect_cost_amt\":42975,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10573152\",\"date_added\":\"2023-01-07T04:01:46Z\"},{\"appl_id\":10533755,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5K23NS119794-03\",\"project_serial_num\":\"NS119794\",\"organization\":{\"org_name\":\"UNIVERSITY OF CALIFORNIA, SAN FRANCISCO\",\"city\":null,\"country\":null,\"org_city\":\"SAN FRANCISCO\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":\"NEUROLOGY\",\"fips_country_code\":null,\"org_duns\":[\"094878337\"],\"org_ueis\":[\"KMH5K9V7S518\"],\"primary_duns\":\"094878337\",\"primary_uei\":\"KMH5K9V7S518\",\"org_fips\":\"US\",\"org_ipf_code\":\"577508\",\"org_zipcode\":\"941432510\",\"external_org_id\":577508},\"award_type\":\"5\",\"activity_code\":\"K23\",\"award_amount\":225652,\"is_active\":true,\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"K23\",\"ic_code\":\"NS\",\"serial_num\":\"119794\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"principal_investigators\":[{\"profile_id\":14200522,\"first_name\":\"EDILBERTO\",\"middle_name\":\"\",\"last_name\":\"AMORIM DE CERQUEIRA\",\"is_contact_pi\":true,\"full_name\":\"EDILBERTO  AMORIM DE CERQUEIRA\",\"title\":\"\"}],\"contact_pi_name\":\"AMORIM DE CERQUEIRA, EDILBERTO \",\"program_officers\":[{\"first_name\":\"Vicky\",\"middle_name\":\"R\",\"last_name\":\"Whittemore\",\"full_name\":\"Vicky R Whittemore\"}],\"agency_ic_admin\":{\"code\":\"NS\",\"abbreviation\":\"NINDS\",\"name\":\"National Institute of Neurological Disorders and Stroke\"},\"agency_ic_fundings\":[{\"fy\":2023,\"code\":\"NS\",\"name\":\"National Institute of Neurological Disorders and Stroke\",\"abbreviation\":\"NINDS\",\"total_cost\":225652.0}],\"cong_dist\":\"CA-12\",\"spending_categories\":null,\"project_start_date\":\"2021-01-01T12:01:00Z\",\"project_end_date\":\"2025-12-31T12:12:00Z\",\"organization_type\":{\"name\":\"SCHOOLS OF MEDICINE\",\"code\":\"10\",\"is_other\":false},\"full_foa\":\"PA-19-119\",\"full_study_section\":{\"srg_code\":\"NST\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"NST-1 Study Section[NST-1]\"},\"award_notice_date\":\"2022-12-13T12:12:00Z\",\"is_new\":true,\"mechanism_code_dc\":\"OR\",\"core_project_num\":\"K23NS119794\",\"terms\":\"<Anesthetics><Anesthestic Drugs><Anesthetic Agents><Anesthetic Drugs><Biometry><Biometrics><Biostatistics><Brain><Brain Nervous System><Encephalon><California><Cause of Death><Clinical Trials><Coma><Comatose><Complication><Computing Methodologies><computational methodology><computational methods><computer based method><computer methods><computing method><Critical Care><Educational Curriculum><Curriculum><lesson plans><Decision Making><Diagnosis><Pharmaceutical Preparations><Drugs><Medication><Pharmaceutic Preparations><drug/agent><Electroencephalography><EEG><Environment><Epilepsy><Epileptic Seizures><Epileptics><Seizure Disorder><epilepsia><epileptiform><epileptogenic><Evolution><Feedback><Future><Goals><Heart Arrest><Asystole><Cardiac Arrest><Hospitals><Human><Modern Man><natural hypothermia><Hypothermia><Magnetic Resonance Imaging><MR Imaging><MR Tomography><MRI><MRIs><Medical Imaging, Magnetic Resonance / Nuclear Magnetic Resonance><NMR Imaging><NMR Tomography><Nuclear Magnetic Resonance Imaging><Zeugmatography><Mentors><Methods><nervous system disorder><Nervous System Diseases><Neurologic Disorders><Neurological Disorders><neurological disease><neurophysiology><neurophysiological><Patients><Phenotype><Physiology><Propofol><Diprivan><Disoprofol><Publishing><Recurrence><Recurrent><Research><Research Personnel><Investigators><Researchers><Resuscitation><San Francisco><Seizures><Specificity><Supervision><Testing><Time><Universities><Weaning><Work><Acute Brain Injuries><Mediating><Data Set><Dataset><Caring><Injury><injuries><improved><Brain imaging><brain visualization><Area><Acute><Clinical><Refractory><Neurologic><Neurological><Series><Survivors><Training><disability><insight><Individual><Neurologist><Recovery><cerebral><Cerebrum><Development Plans><Patient Selection><Brain Injuries><Acquired brain injury><brain damage><brain-injured><Collaborations><Therapeutic><Morphology><Machine Learning><machine learned><Knowledge><programs><Scientist><Hour><Frequencies><Techniques><Infusion procedures><Infusion><American><Performance><authority><Recovery of Function><functional recovery><skills><neuroimaging><neuro-imaging><neurological imaging><Categories><Positioning Attribute><Position><Biomedical Technology><intervention therapy><Therapeutic Intervention><Interventional trial><Intervention Trial><Modeling><career development><Magnetic Resonance Imaging Scan><MRI Scans><DWI (diffusion weighted imaging)><DWI-MRI><Diffusion MRI><Diffusion Weighted MRI><Diffusion weighted imaging><Diffusion-weighted Magnetic Resonance Imaging><dMRI><diffusion tensor imaging><Diffusion Magnetic Resonance Imaging><preventing><prevent><Data><International><predict therapeutic response><predict therapy response><predict treatment response><therapy prediction><treatment prediction><treatment response prediction><Prediction of Response to Therapy><Career Development Awards><Career Development Awards and Programs><Career Development Programs K-Series><K-Awards><Research Career Program><K-Series Research Career Programs><Clinical Trials Design><Validation><Monitor><trend><developmental><Development><Epileptogenesis><resilience><Outcome><Neurologic outcome><Neurological outcome><innovate><innovative><innovation><treatment effect><multidisciplinary><response to therapy><response to treatment><therapeutic response><therapy response><treatment response><treatment strategy><bio-markers><biologic marker><biomarker><Biological Markers><neurological recovery><personalization of treatment><personalized therapy><personalized treatment><personalized medicine><predictive biomarkers><predictive molecular biomarker><predictive marker><applied learning><interactive engagement><interactive learning><hands-on learning><biomarker-driven><response biomarker><response markers><outcome prediction><predictive outcomes><predictors of outcomes><improved outcome><Hypoxic-Ischemic Brain Injury><magnetic resonance imaging biomarker><MRI biomarker><deep learning><Data Scientist><deep learning algorithm><Big Data Methods><Big Data Analytics><Big Data Tools><prognostication><brain magnetic resonance imaging><brain MRI><cerebral MRI><cerebral magnetic resonance imaging>\",\"pref_terms\":\"Acute;Acute Brain Injuries;American;Anesthetics;Area;Big Data Methods;Biological Markers;Biomedical Technology;Biometry;Brain;Brain Injuries;Brain imaging;California;Caring;Categories;Cause of Death;Cerebrum;Clinical;Clinical Trials;Clinical Trials Design;Collaborations;Coma;Complication;Computing Methodologies;Critical Care;Data;Data Scientist;Data Set;Decision Making;Development;Development Plans;Diagnosis;Diffusion Magnetic Resonance Imaging;Educational Curriculum;Electroencephalography;Environment;Epilepsy;Epileptogenesis;Evolution;Feedback;Frequencies;Future;Goals;Heart Arrest;Hospitals;Hour;Human;Hypoxic-Ischemic Brain Injury;Individual;Infusion procedures;Injury;International;Intervention Trial;K-Series Research Career Programs;Knowledge;MRI Scans;Machine Learning;Magnetic Resonance Imaging;Mediating;Mentors;Methods;Modeling;Monitor;Morphology;Neurologic;Neurological outcome;Neurologist;Outcome;Patient Selection;Patients;Performance;Pharmaceutical Preparations;Phenotype;Physiology;Positioning Attribute;Prediction of Response to Therapy;Propofol;Publishing;Recovery;Recovery of Function;Recurrence;Refractory;Research;Research Personnel;Resuscitation;San Francisco;Scientist;Seizures;Series;Specificity;Supervision;Survivors;Techniques;Testing;Therapeutic;Therapeutic Intervention;Time;Training;Universities;Validation;Weaning;Work;authority;biomarker-driven;brain magnetic resonance imaging;career development;deep learning;deep learning algorithm;disability;hands-on learning;improved;improved outcome;innovation;insight;magnetic resonance imaging biomarker;multidisciplinary;natural hypothermia;nervous system disorder;neuroimaging;neurological recovery;neurophysiology;outcome prediction;personalized medicine;predictive marker;prevent;prognostication;programs;resilience;response biomarker;skills;treatment effect;treatment response;treatment strategy;trend\",\"abstract_text\":\"PROJECT SUMMARY/ABSTRACT\\nDr. Edilberto Amorim is a neurologist with subspecialty training in critical care and epilepsy who aims to employ\\nbiomedical technology innovations in brain monitoring to personalize treatment for patients with hypoxic-\\nischemic brain injury post-cardiac arrest. This career development award and its rigorous curriculum will\\nestablish Dr. Amorim as a clinician-scientist with independent expertise in: 1) Deep learning applied to\\nphysiology time-series, 2) Causal inference for observational data, and 3) Quantitative brain imaging. Every\\nyear, more than 500,000 Americans have a cardiac arrest. Brain injury is the number one cause of death for\\npatients surviving initial resuscitation, and refractory seizures and other seizure-like brain activity are\\ndiagnosed in up to 50% of patients. Despite being a common complication, outcomes are dismal and current\\ntreatment strategies for seizures post-cardiac arrest are limited. Dr. Amorim aims to identify physiology-driven\\nbiomarkers of resilience to hypoxic-ischemic brain injury by utilizing state-of-the-art computational methods and\\na massive EEG and neuroimaging dataset with >1,500 subjects. His central hypothesis is that specific time-\\ndependent changes in spike and accompanying EEG activity during cardiac arrest treatment predict seizure\\ncontrol and, ultimately, neurological recovery. The primary objectives of this proposal are: 1) Identify early\\nlongitudinal epileptiform EEG phenotypes predictive of neurological recovery using interpretable and deep\\nlearning algorithms; 2) Establish quantitative EEG biomarkers of seizure treatment response to anesthetics;\\nand 3) Estimate the causal effect of rapid seizure treatment with anesthetics in preventing structural brain\\ninjury quantified with brain MRI. Dr. Amorim has generated preliminary data to demonstrate the feasibility of\\nmodeling EEG phenotypes longitudinally for outcome prediction and has applied quantitative EEG biomarkers\\nto predict degree of brain injury on brain MRI. His primary mentor in this proposal will be Dr. Edward Chang, a\\nneuroscientist and leader in human neurophysiology research. His co-mentors will include Dr. Brandon\\nWestover, an authority in machine learning applied to critical care EEG, and Dr. Donna Ferriero, an\\naccomplished translational and neuroimaging investigator in hypoxic-ischemic brain injury. Additional\\nmentoring in quantitative neuroimaging (Dr. Srikantan Nagarajan) and biostatistics (Dr. Charles McCulloch) will\\nbe essential components of his training. These aims are expected to establish early non-invasive predictive\\nbiomarkers of neurological recovery and seizure control that may: 1) Guide patient selection for clinical trials\\nenrichment and 2) Serve as target to therapeutic interventions after hypoxic-ischemic brain injury. By\\nleveraging the deep expertise of a cross-disciplinary group of world-class mentors and the unparalleled\\ninnovation environments of the University of California, San Francisco and the Bay Area, Dr. Amorim will be\\nideally positioned to uncover fundamental knowledge about epileptogenesis after acute brain injury as well as\\nspearhead clinical trials focused on improving outcomes meaningful to cardiac arrest patients.\",\"project_title\":\"Physiology-driven seizure management post-cardiac arrest\",\"phr_text\":\"PROJECT NARRATIVE\\nCardiac arrest impacts the lives of 500,000 Americans every year, and nearly half of patients\\nsurviving the initial resuscitation will develop seizures or seizure-like brain activity as a complication of\\nhypoxic-ischemic brain injury. We aim to reduce secondary brain injury and neurological disability\\nfrom seizures after cardiac arrest by personalizing seizure treatment using EEG and brain MRI\\nbiomarkers of neuro-recovery. We expect that the outcome of the proposed studies will provide\\ncritical knowledge about epileptogenesis after hypoxic-ischemic brain injury andguide the\\ndevelopment of goal-directed seizure treatment and patient selection in future clinical trials.\",\"spending_categories_desc\":null,\"agency_code\":\"NIH\",\"covid_response\":null,\"arra_funded\":\"N\",\"budget_start\":\"2023-01-01T12:01:00Z\",\"budget_end\":\"2023-12-31T12:12:00Z\",\"cfda_code\":\"853\",\"funding_mechanism\":\"Other Research-Related\",\"direct_cost_amt\":208937,\"indirect_cost_amt\":16715,\"project_detail_url\":\"https://reporter.nih.gov/project-details/10533755\",\"date_added\":\"2023-01-07T04:01:46Z\"}]}\n"
     ]
    }
   ],
   "source": [
    "#Example\n",
    "'''\n",
    "{ \"criteria\": { advanced_text_search: { operator: \"and\", search_field: \"projecttitle,terms\", \n",
    "\"search_text\": \"brain disorder\" } } }\n",
    "'''\n",
    "\n",
    "params = { \"criteria\": { 'advanced_text_search': { 'operator': \"and\", 'search_field': \"abstracts,projecttitle,terms\", \n",
    "\"search_text\": \"data analytics\" } } }\n",
    "\n",
    "response = requests.post(\"https://api.reporter.nih.gov/v2/projects/search\", json=params)\n",
    "\n",
    "print(response.status_code)\n",
    "print(response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "67e4f891",
   "metadata": {},
   "outputs": [],
   "source": [
    "response_data = response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e24fdda1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'org_name': 'EMORY UNIVERSITY',\n",
       " 'city': None,\n",
       " 'country': None,\n",
       " 'org_city': 'ATLANTA',\n",
       " 'org_country': 'UNITED STATES',\n",
       " 'org_state': 'GA',\n",
       " 'org_state_name': None,\n",
       " 'dept_type': 'SURGERY',\n",
       " 'fips_country_code': None,\n",
       " 'org_duns': ['066469933'],\n",
       " 'org_ueis': ['S352L5PJLMP8'],\n",
       " 'primary_duns': '066469933',\n",
       " 'primary_uei': 'S352L5PJLMP8',\n",
       " 'org_fips': 'US',\n",
       " 'org_ipf_code': '2384501',\n",
       " 'org_zipcode': '303221007',\n",
       " 'external_org_id': 2384501}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_data['results'][0]['organization']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c8836d13",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'EMORY UNIVERSITY'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "response_data['results'][0]['organization']['org_name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3c856cd6",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "        text-align: right;\n",
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       "      <th>award_amount</th>\n",
       "      <th>is_active</th>\n",
       "      <th>principal_investigators</th>\n",
       "      <th>...</th>\n",
       "      <th>organization_type.name</th>\n",
       "      <th>organization_type.code</th>\n",
       "      <th>organization_type.is_other</th>\n",
       "      <th>full_study_section.srg_code</th>\n",
       "      <th>full_study_section.srg_flex</th>\n",
       "      <th>full_study_section.sra_designator_code</th>\n",
       "      <th>full_study_section.sra_flex_code</th>\n",
       "      <th>full_study_section.group_code</th>\n",
       "      <th>full_study_section.name</th>\n",
       "      <th>full_study_section</th>\n",
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       "      <th>0</th>\n",
       "      <td>10542758</td>\n",
       "      <td>None</td>\n",
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       "      <td>677235.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9239369, 'first_name': 'Nader'...</td>\n",
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       "      <td>Clinical Data Management and Analysis Study Se...</td>\n",
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       "      <td>[{'profile_id': 10051972, 'first_name': 'Kwan-...</td>\n",
       "      <td>...</td>\n",
       "      <td>BIOMED ENGR/COL ENGR/ENGR STA</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>VH</td>\n",
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       "      <td>Special Emphasis Panel[ZRG1-VH-N(50)R]</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>None</td>\n",
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       "      <td>5</td>\n",
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       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9860649, 'first_name': 'Daniel...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>HSR2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>HSR-2 Behavioral, Social, and Cultural Determi...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10706750</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>3OT2TR003443-01S3</td>\n",
       "      <td>TR003443</td>\n",
       "      <td>3</td>\n",
       "      <td>OT2</td>\n",
       "      <td>609144.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9841070, 'first_name': 'Gustav...</td>\n",
       "      <td>...</td>\n",
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       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>ZTR1</td>\n",
       "      <td></td>\n",
       "      <td>SRC</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZTR1-SRC(99)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10706762</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>3OT2TR003450-01S3</td>\n",
       "      <td>TR003450</td>\n",
       "      <td>3</td>\n",
       "      <td>OT2</td>\n",
       "      <td>532891.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 2089750, 'first_name': 'SERGIO...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>ZTR1</td>\n",
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       "      <td>SRC</td>\n",
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       "      <td></td>\n",
       "      <td>ZTR1-SRC(99)</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10526409</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01NS119825-03</td>\n",
       "      <td>NS119825</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
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       "      <td>True</td>\n",
       "      <td>[{'profile_id': 10790241, 'first_name': 'Jonat...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>BDCN</td>\n",
       "      <td>L</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BDCN-L(90)S]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10579825</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01HL141286-05</td>\n",
       "      <td>HL141286</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>406637.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9574233, 'first_name': 'Rhonda...</td>\n",
       "      <td>...</td>\n",
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       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>IRAP</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Infectious Diseases, Reproductive Health, Asth...</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10545172</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R37MH059105-21</td>\n",
       "      <td>MH059105</td>\n",
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       "      <td>[{'profile_id': 1903458, 'first_name': 'JOEL',...</td>\n",
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       "      <td>DRI</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZMD1-DRI(O1)R</td>\n",
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       "      <td>False</td>\n",
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       "      <td></td>\n",
       "      <td>BST</td>\n",
       "      <td>T</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BST-T]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10551263</td>\n",
       "      <td>None</td>\n",
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       "      <td>...</td>\n",
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       "      <td>False</td>\n",
       "      <td>NSS</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NSS</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>10524021</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R00DA046564-05</td>\n",
       "      <td>DA046564</td>\n",
       "      <td>5</td>\n",
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       "      <td>...</td>\n",
       "      <td>SCHOOLS OF PUBLIC HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZDA1</td>\n",
       "      <td></td>\n",
       "      <td>SXM</td>\n",
       "      <td>M</td>\n",
       "      <td></td>\n",
       "      <td>ZDA1-SXM-M(04)S</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>10556378</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P41GM135019-04</td>\n",
       "      <td>GM135019</td>\n",
       "      <td>5</td>\n",
       "      <td>P41</td>\n",
       "      <td>1136551.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9680244, 'first_name': 'BETH',...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>BST</td>\n",
       "      <td>T</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BST-T(40)P]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>10598542</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5U24CA224370-06</td>\n",
       "      <td>CA224370</td>\n",
       "      <td>5</td>\n",
       "      <td>U24</td>\n",
       "      <td>940050.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 1881486, 'first_name': 'Jeremy...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>GGG</td>\n",
       "      <td>D</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-GGG-D(51)R]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>10580718</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01GM144351-02</td>\n",
       "      <td>GM144351</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>289745.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 11821008, 'first_name': 'Jun',...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NP</td>\n",
       "      <td>False</td>\n",
       "      <td>BMRD</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Biostatistical Methods and Research Design Stu...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>10531222</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K01AI137317-05</td>\n",
       "      <td>AI137317</td>\n",
       "      <td>5</td>\n",
       "      <td>K01</td>\n",
       "      <td>133677.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 14108757, 'first_name': 'Judit...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>MID</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Microbiology and Infectious Diseases B Researc...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>10573145</td>\n",
       "      <td>6634</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P20GM121327-07</td>\n",
       "      <td>GM121327</td>\n",
       "      <td>5</td>\n",
       "      <td>P20</td>\n",
       "      <td>182841.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 7042565, 'first_name': 'ANDREW...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>ZGM1</td>\n",
       "      <td></td>\n",
       "      <td>RCB</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>ZGM1-RCB-3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>10545164</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01CA224537-06</td>\n",
       "      <td>CA224537</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>551120.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9122911, 'first_name': 'Cho', ...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>RPHB</td>\n",
       "      <td>Z</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-RPHB-Z(56)R]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>10556386</td>\n",
       "      <td>8662</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P41GM135019-04</td>\n",
       "      <td>GM135019</td>\n",
       "      <td>5</td>\n",
       "      <td>P41</td>\n",
       "      <td>194290.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8876992, 'first_name': 'Anne',...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>BST</td>\n",
       "      <td>T</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BST-T]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>10533736</td>\n",
       "      <td>7665</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P01HD104436-03</td>\n",
       "      <td>HD104436</td>\n",
       "      <td>5</td>\n",
       "      <td>P01</td>\n",
       "      <td>152806.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 6624857, 'first_name': 'JOSEPH...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>ZHD1</td>\n",
       "      <td></td>\n",
       "      <td>DSR</td>\n",
       "      <td>N</td>\n",
       "      <td></td>\n",
       "      <td>ZHD1-DSR-N</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>10554268</td>\n",
       "      <td>8474</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P30CA023108-44</td>\n",
       "      <td>CA023108</td>\n",
       "      <td>5</td>\n",
       "      <td>P30</td>\n",
       "      <td>21074.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9814873, 'first_name': 'Brian'...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>NCI</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Cancer Centers Study Section (A)[NCI-A]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>10513818</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R24ES030893-04</td>\n",
       "      <td>ES030893</td>\n",
       "      <td>5</td>\n",
       "      <td>R24</td>\n",
       "      <td>381953.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8085044, 'first_name': 'HEATHE...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF PUBLIC HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZES1</td>\n",
       "      <td></td>\n",
       "      <td>LKB</td>\n",
       "      <td>K</td>\n",
       "      <td></td>\n",
       "      <td>ZES1-LKB-K(R)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>10543494</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K24DC012801-10</td>\n",
       "      <td>DC012801</td>\n",
       "      <td>5</td>\n",
       "      <td>K24</td>\n",
       "      <td>183209.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 2409380, 'first_name': 'BONNIE...</td>\n",
       "      <td>...</td>\n",
       "      <td>GRADUATE SCHOOLS</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>CDRC</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Communication Disorders Review Committee[CDRC]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>10570160</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01CA249538-04</td>\n",
       "      <td>CA249538</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>459797.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 14318598, 'first_name': 'Jiana...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ITD</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Imaging Technology Development Study Section[ITD]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>10515332</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R13EY032326-03</td>\n",
       "      <td>EY032326</td>\n",
       "      <td>5</td>\n",
       "      <td>R13</td>\n",
       "      <td>29000.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 6841150, 'first_name': 'Sanjoy...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZEY1</td>\n",
       "      <td></td>\n",
       "      <td>VSN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZEY1-VSN(06)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>10636774</td>\n",
       "      <td>7834</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P30CA015704-48</td>\n",
       "      <td>CA015704</td>\n",
       "      <td>5</td>\n",
       "      <td>P30</td>\n",
       "      <td>374776.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 10082895, 'first_name': 'Phili...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>NP</td>\n",
       "      <td>False</td>\n",
       "      <td>NCI</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Cancer Centers Study Section (A)[NCI-A]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>10543557</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01NS119622-02</td>\n",
       "      <td>NS119622</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>499998.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 1929878, 'first_name': 'JONATH...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>SEIR</td>\n",
       "      <td>A</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-SEIR-A(80)S]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>10541870</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R21GM144947-02</td>\n",
       "      <td>GM144947</td>\n",
       "      <td>5</td>\n",
       "      <td>R21</td>\n",
       "      <td>204659.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9547421, 'first_name': 'Daniel...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>SAT</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Surgery, Anesthesiology and Trauma Study Secti...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>10556382</td>\n",
       "      <td>8660</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P41GM135019-04</td>\n",
       "      <td>GM135019</td>\n",
       "      <td>5</td>\n",
       "      <td>P41</td>\n",
       "      <td>322548.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8876992, 'first_name': 'Anne',...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>BST</td>\n",
       "      <td>T</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BST-T]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>10533289</td>\n",
       "      <td>8101</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P01DA047233-05</td>\n",
       "      <td>DA047233</td>\n",
       "      <td>5</td>\n",
       "      <td>P01</td>\n",
       "      <td>322926.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 10583250, 'first_name': 'Li', ...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>IFCN</td>\n",
       "      <td>L</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-IFCN-L]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>10603832</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1R43NS130849-01</td>\n",
       "      <td>NS130849</td>\n",
       "      <td>1</td>\n",
       "      <td>R43</td>\n",
       "      <td>295365.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 11197513, 'first_name': 'PHILI...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>FP</td>\n",
       "      <td>False</td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>ETTN</td>\n",
       "      <td>S</td>\n",
       "      <td>14</td>\n",
       "      <td>Special Emphasis Panel[ZRG1 ETTN-S (14)]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>10547787</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5IK2HX002567-04</td>\n",
       "      <td>HX002567</td>\n",
       "      <td>5</td>\n",
       "      <td>IK2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 12602197, 'first_name': 'Heath...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>MRA0</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Mentored Research[MRA0]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>10717670</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>3R01NS114259-03S1</td>\n",
       "      <td>NS114259</td>\n",
       "      <td>3</td>\n",
       "      <td>R01</td>\n",
       "      <td>39687.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8587924, 'first_name': 'Mark',...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>10740597</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>7R01LM013712-06</td>\n",
       "      <td>LM013712</td>\n",
       "      <td>7</td>\n",
       "      <td>R01</td>\n",
       "      <td>613743.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 10960996, 'first_name': 'Xiaoq...</td>\n",
       "      <td>...</td>\n",
       "      <td>BIOMED ENGR/COL ENGR/ENGR STA</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>CDMA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Clinical Data Management and Analysis Study Se...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>10548825</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01MH131335-02</td>\n",
       "      <td>MH131335</td>\n",
       "      <td>5</td>\n",
       "      <td>R01</td>\n",
       "      <td>478425.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 7354388, 'first_name': 'Bharat...</td>\n",
       "      <td>...</td>\n",
       "      <td>BIOMED ENGR/COL ENGR/ENGR STA</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>EITN</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Emerging Imaging Technologies in Neuroscience ...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>10551207</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K23DK131341-02</td>\n",
       "      <td>DK131341</td>\n",
       "      <td>5</td>\n",
       "      <td>K23</td>\n",
       "      <td>172913.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 12074183, 'first_name': 'Giris...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>DDK</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Digestive Diseases and Nutrition C Study Secti...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>10404414</td>\n",
       "      <td>9997</td>\n",
       "      <td>2023</td>\n",
       "      <td>1P50AR080596-01</td>\n",
       "      <td>AR080596</td>\n",
       "      <td>1</td>\n",
       "      <td>P50</td>\n",
       "      <td>90709.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9206112, 'first_name': 'Yi-Hsi...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>ZAR1</td>\n",
       "      <td></td>\n",
       "      <td>KM</td>\n",
       "      <td></td>\n",
       "      <td>M2</td>\n",
       "      <td>Special Emphasis Panel[ZAR1 KM (M2)]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>10553208</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K01AI141579-05</td>\n",
       "      <td>AI141579</td>\n",
       "      <td>5</td>\n",
       "      <td>K01</td>\n",
       "      <td>106212.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 11948346, 'first_name': 'Joshu...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>20</td>\n",
       "      <td>False</td>\n",
       "      <td>MID</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Microbiology and Infectious Diseases B Researc...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>10521278</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K01MH122774-03</td>\n",
       "      <td>MH122774</td>\n",
       "      <td>5</td>\n",
       "      <td>K01</td>\n",
       "      <td>175879.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 14807882, 'first_name': 'Xi', ...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>APDA</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Adult Psychopathology and Disorders of Aging S...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>10542769</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R25GM134978-03</td>\n",
       "      <td>GM134978</td>\n",
       "      <td>5</td>\n",
       "      <td>R25</td>\n",
       "      <td>330711.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 1959401, 'first_name': 'GARY',...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>TWD</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Training and Workforce Development Study Secti...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>10544764</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5U01MD017421-02</td>\n",
       "      <td>MD017421</td>\n",
       "      <td>5</td>\n",
       "      <td>U01</td>\n",
       "      <td>1142661.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 9786619, 'first_name': 'GUILHE...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZMH1</td>\n",
       "      <td></td>\n",
       "      <td>ERB</td>\n",
       "      <td>G</td>\n",
       "      <td></td>\n",
       "      <td>ZMH1-ERB-G(G1)</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>10590116</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1K01MH129828-01A1</td>\n",
       "      <td>MH129828</td>\n",
       "      <td>1</td>\n",
       "      <td>K01</td>\n",
       "      <td>179707.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 14077682, 'first_name': 'Natha...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>30</td>\n",
       "      <td>False</td>\n",
       "      <td>NPAS</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Neural Basis of Psychopathology, Addictions an...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>10620014</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1R01AI174932-01</td>\n",
       "      <td>AI174932</td>\n",
       "      <td>1</td>\n",
       "      <td>R01</td>\n",
       "      <td>809267.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 12069066, 'first_name': 'Anna'...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>PPAH</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Population and Public Health Approaches to HIV...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>10585124</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1R01CA270276-01A1</td>\n",
       "      <td>CA270276</td>\n",
       "      <td>1</td>\n",
       "      <td>R01</td>\n",
       "      <td>483215.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8799835, 'first_name': 'Cynthi...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOL OF MEDICINE &amp; DENTISTRY</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>HSQE</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Health Services: Quality and Effectiveness Stu...</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>10584016</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>2U24ES028502-06</td>\n",
       "      <td>ES028502</td>\n",
       "      <td>2</td>\n",
       "      <td>U24</td>\n",
       "      <td>360960.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 2515822, 'first_name': 'Karen'...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF PUBLIC HEALTH</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZES1</td>\n",
       "      <td></td>\n",
       "      <td>LKB</td>\n",
       "      <td>K</td>\n",
       "      <td>U</td>\n",
       "      <td>Special Emphasis Panel[ZES1 LKB-K (U)]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>10592590</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1R21MH132123-01</td>\n",
       "      <td>MH132123</td>\n",
       "      <td>1</td>\n",
       "      <td>R21</td>\n",
       "      <td>70838.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 78510112, 'first_name': 'Mark'...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>True</td>\n",
       "      <td>ZMH1</td>\n",
       "      <td></td>\n",
       "      <td>ERB</td>\n",
       "      <td>L</td>\n",
       "      <td>05</td>\n",
       "      <td>Special Emphasis Panel[ZMH1 ERB-L (05)]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>10571515</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1K99EB033857-01</td>\n",
       "      <td>EB033857</td>\n",
       "      <td>1</td>\n",
       "      <td>K99</td>\n",
       "      <td>99955.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 15447209, 'first_name': 'Colin...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>ZEB1</td>\n",
       "      <td></td>\n",
       "      <td>OSR</td>\n",
       "      <td>B</td>\n",
       "      <td>O1</td>\n",
       "      <td>Special Emphasis Panel[ZEB1 OSR-B (O1)]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>10573152</td>\n",
       "      <td>6635</td>\n",
       "      <td>2023</td>\n",
       "      <td>5P20GM121327-07</td>\n",
       "      <td>GM121327</td>\n",
       "      <td>5</td>\n",
       "      <td>P20</td>\n",
       "      <td>124058.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 8575677, 'first_name': 'Jianha...</td>\n",
       "      <td>...</td>\n",
       "      <td>None</td>\n",
       "      <td>10</td>\n",
       "      <td>True</td>\n",
       "      <td>ZGM1</td>\n",
       "      <td></td>\n",
       "      <td>RCB</td>\n",
       "      <td>3</td>\n",
       "      <td></td>\n",
       "      <td>ZGM1-RCB-3</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>10533755</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5K23NS119794-03</td>\n",
       "      <td>NS119794</td>\n",
       "      <td>5</td>\n",
       "      <td>K23</td>\n",
       "      <td>225652.0</td>\n",
       "      <td>True</td>\n",
       "      <td>[{'profile_id': 14200522, 'first_name': 'EDILB...</td>\n",
       "      <td>...</td>\n",
       "      <td>SCHOOLS OF MEDICINE</td>\n",
       "      <td>10</td>\n",
       "      <td>False</td>\n",
       "      <td>NST</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NST-1 Study Section[NST-1]</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50 rows × 76 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     appl_id subproject_id  fiscal_year        project_num project_serial_num  \\\n",
       "0   10542758          None         2023    5R01HL157323-02           HL157323   \n",
       "1   10544322          None         2023    5R01CA270454-02           CA270454   \n",
       "2   10595672          None         2023    5I01HX003151-03           HX003151   \n",
       "3   10706750          None         2023  3OT2TR003443-01S3           TR003443   \n",
       "4   10706762          None         2023  3OT2TR003450-01S3           TR003450   \n",
       "5   10526409          None         2023    5R01NS119825-03           NS119825   \n",
       "6   10579825          None         2023    5R01HL141286-05           HL141286   \n",
       "7   10545172          None         2023    5R37MH059105-21           MH059105   \n",
       "8   10548840          None         2023    5K99MD016895-02           MD016895   \n",
       "9   10586147          None         2023    5R01MH129493-02           MH129493   \n",
       "10  10556384          8661         2023    5P41GM135019-04           GM135019   \n",
       "11  10551263          None         2023    5R00HG010493-04           HG010493   \n",
       "12  10524021          None         2023    5R00DA046564-05           DA046564   \n",
       "13  10556378          None         2023    5P41GM135019-04           GM135019   \n",
       "14  10598542          None         2023    5U24CA224370-06           CA224370   \n",
       "15  10580718          None         2023    5R01GM144351-02           GM144351   \n",
       "16  10531222          None         2023    5K01AI137317-05           AI137317   \n",
       "17  10573145          6634         2023    5P20GM121327-07           GM121327   \n",
       "18  10545164          None         2023    5R01CA224537-06           CA224537   \n",
       "19  10556386          8662         2023    5P41GM135019-04           GM135019   \n",
       "20  10533736          7665         2023    5P01HD104436-03           HD104436   \n",
       "21  10554268          8474         2023    5P30CA023108-44           CA023108   \n",
       "22  10513818          None         2023    5R24ES030893-04           ES030893   \n",
       "23  10543494          None         2023    5K24DC012801-10           DC012801   \n",
       "24  10570160          None         2023    5R01CA249538-04           CA249538   \n",
       "25  10515332          None         2023    5R13EY032326-03           EY032326   \n",
       "26  10636774          7834         2023    5P30CA015704-48           CA015704   \n",
       "27  10543557          None         2023    5R01NS119622-02           NS119622   \n",
       "28  10541870          None         2023    5R21GM144947-02           GM144947   \n",
       "29  10556382          8660         2023    5P41GM135019-04           GM135019   \n",
       "30  10533289          8101         2023    5P01DA047233-05           DA047233   \n",
       "31  10603832          None         2023    1R43NS130849-01           NS130849   \n",
       "32  10547787          None         2023    5IK2HX002567-04           HX002567   \n",
       "33  10717670          None         2023  3R01NS114259-03S1           NS114259   \n",
       "34  10740597          None         2023    7R01LM013712-06           LM013712   \n",
       "35  10548825          None         2023    5R01MH131335-02           MH131335   \n",
       "36  10551207          None         2023    5K23DK131341-02           DK131341   \n",
       "37  10404414          9997         2023    1P50AR080596-01           AR080596   \n",
       "38  10553208          None         2023    5K01AI141579-05           AI141579   \n",
       "39  10521278          None         2023    5K01MH122774-03           MH122774   \n",
       "40  10542769          None         2023    5R25GM134978-03           GM134978   \n",
       "41  10544764          None         2023    5U01MD017421-02           MD017421   \n",
       "42  10590116          None         2023  1K01MH129828-01A1           MH129828   \n",
       "43  10620014          None         2023    1R01AI174932-01           AI174932   \n",
       "44  10585124          None         2023  1R01CA270276-01A1           CA270276   \n",
       "45  10584016          None         2023    2U24ES028502-06           ES028502   \n",
       "46  10592590          None         2023    1R21MH132123-01           MH132123   \n",
       "47  10571515          None         2023    1K99EB033857-01           EB033857   \n",
       "48  10573152          6635         2023    5P20GM121327-07           GM121327   \n",
       "49  10533755          None         2023    5K23NS119794-03           NS119794   \n",
       "\n",
       "   award_type activity_code  award_amount  is_active  \\\n",
       "0           5           R01      677235.0       True   \n",
       "1           5           R01      245468.0       True   \n",
       "2           5           I01           NaN       True   \n",
       "3           3           OT2      609144.0       True   \n",
       "4           3           OT2      532891.0       True   \n",
       "5           5           R01     1113712.0       True   \n",
       "6           5           R01      406637.0       True   \n",
       "7           5           R37      682943.0       True   \n",
       "8           5           K99      114113.0       True   \n",
       "9           5           R01      726975.0       True   \n",
       "10          5           P41      302439.0       True   \n",
       "11          5           R00      223219.0       True   \n",
       "12          5           R00      248993.0       True   \n",
       "13          5           P41     1136551.0       True   \n",
       "14          5           U24      940050.0       True   \n",
       "15          5           R01      289745.0       True   \n",
       "16          5           K01      133677.0       True   \n",
       "17          5           P20      182841.0       True   \n",
       "18          5           R01      551120.0       True   \n",
       "19          5           P41      194290.0       True   \n",
       "20          5           P01      152806.0       True   \n",
       "21          5           P30       21074.0       True   \n",
       "22          5           R24      381953.0       True   \n",
       "23          5           K24      183209.0       True   \n",
       "24          5           R01      459797.0       True   \n",
       "25          5           R13       29000.0       True   \n",
       "26          5           P30      374776.0       True   \n",
       "27          5           R01      499998.0       True   \n",
       "28          5           R21      204659.0       True   \n",
       "29          5           P41      322548.0       True   \n",
       "30          5           P01      322926.0       True   \n",
       "31          1           R43      295365.0       True   \n",
       "32          5           IK2           NaN       True   \n",
       "33          3           R01       39687.0       True   \n",
       "34          7           R01      613743.0       True   \n",
       "35          5           R01      478425.0       True   \n",
       "36          5           K23      172913.0       True   \n",
       "37          1           P50       90709.0       True   \n",
       "38          5           K01      106212.0       True   \n",
       "39          5           K01      175879.0       True   \n",
       "40          5           R25      330711.0       True   \n",
       "41          5           U01     1142661.0       True   \n",
       "42          1           K01      179707.0       True   \n",
       "43          1           R01      809267.0       True   \n",
       "44          1           R01      483215.0       True   \n",
       "45          2           U24      360960.0       True   \n",
       "46          1           R21       70838.0       True   \n",
       "47          1           K99       99955.0       True   \n",
       "48          5           P20      124058.0       True   \n",
       "49          5           K23      225652.0       True   \n",
       "\n",
       "                              principal_investigators  ...  \\\n",
       "0   [{'profile_id': 9239369, 'first_name': 'Nader'...  ...   \n",
       "1   [{'profile_id': 10051972, 'first_name': 'Kwan-...  ...   \n",
       "2   [{'profile_id': 9860649, 'first_name': 'Daniel...  ...   \n",
       "3   [{'profile_id': 9841070, 'first_name': 'Gustav...  ...   \n",
       "4   [{'profile_id': 2089750, 'first_name': 'SERGIO...  ...   \n",
       "5   [{'profile_id': 10790241, 'first_name': 'Jonat...  ...   \n",
       "6   [{'profile_id': 9574233, 'first_name': 'Rhonda...  ...   \n",
       "7   [{'profile_id': 1903458, 'first_name': 'JOEL',...  ...   \n",
       "8   [{'profile_id': 14315038, 'first_name': 'Sung ...  ...   \n",
       "9   [{'profile_id': 1885436, 'first_name': 'Deanna...  ...   \n",
       "10  [{'profile_id': 8876992, 'first_name': 'Anne',...  ...   \n",
       "11  [{'profile_id': 14416846, 'first_name': 'Luca'...  ...   \n",
       "12  [{'profile_id': 13110137, 'first_name': 'Emily...  ...   \n",
       "13  [{'profile_id': 9680244, 'first_name': 'BETH',...  ...   \n",
       "14  [{'profile_id': 1881486, 'first_name': 'Jeremy...  ...   \n",
       "15  [{'profile_id': 11821008, 'first_name': 'Jun',...  ...   \n",
       "16  [{'profile_id': 14108757, 'first_name': 'Judit...  ...   \n",
       "17  [{'profile_id': 7042565, 'first_name': 'ANDREW...  ...   \n",
       "18  [{'profile_id': 9122911, 'first_name': 'Cho', ...  ...   \n",
       "19  [{'profile_id': 8876992, 'first_name': 'Anne',...  ...   \n",
       "20  [{'profile_id': 6624857, 'first_name': 'JOSEPH...  ...   \n",
       "21  [{'profile_id': 9814873, 'first_name': 'Brian'...  ...   \n",
       "22  [{'profile_id': 8085044, 'first_name': 'HEATHE...  ...   \n",
       "23  [{'profile_id': 2409380, 'first_name': 'BONNIE...  ...   \n",
       "24  [{'profile_id': 14318598, 'first_name': 'Jiana...  ...   \n",
       "25  [{'profile_id': 6841150, 'first_name': 'Sanjoy...  ...   \n",
       "26  [{'profile_id': 10082895, 'first_name': 'Phili...  ...   \n",
       "27  [{'profile_id': 1929878, 'first_name': 'JONATH...  ...   \n",
       "28  [{'profile_id': 9547421, 'first_name': 'Daniel...  ...   \n",
       "29  [{'profile_id': 8876992, 'first_name': 'Anne',...  ...   \n",
       "30  [{'profile_id': 10583250, 'first_name': 'Li', ...  ...   \n",
       "31  [{'profile_id': 11197513, 'first_name': 'PHILI...  ...   \n",
       "32  [{'profile_id': 12602197, 'first_name': 'Heath...  ...   \n",
       "33  [{'profile_id': 8587924, 'first_name': 'Mark',...  ...   \n",
       "34  [{'profile_id': 10960996, 'first_name': 'Xiaoq...  ...   \n",
       "35  [{'profile_id': 7354388, 'first_name': 'Bharat...  ...   \n",
       "36  [{'profile_id': 12074183, 'first_name': 'Giris...  ...   \n",
       "37  [{'profile_id': 9206112, 'first_name': 'Yi-Hsi...  ...   \n",
       "38  [{'profile_id': 11948346, 'first_name': 'Joshu...  ...   \n",
       "39  [{'profile_id': 14807882, 'first_name': 'Xi', ...  ...   \n",
       "40  [{'profile_id': 1959401, 'first_name': 'GARY',...  ...   \n",
       "41  [{'profile_id': 9786619, 'first_name': 'GUILHE...  ...   \n",
       "42  [{'profile_id': 14077682, 'first_name': 'Natha...  ...   \n",
       "43  [{'profile_id': 12069066, 'first_name': 'Anna'...  ...   \n",
       "44  [{'profile_id': 8799835, 'first_name': 'Cynthi...  ...   \n",
       "45  [{'profile_id': 2515822, 'first_name': 'Karen'...  ...   \n",
       "46  [{'profile_id': 78510112, 'first_name': 'Mark'...  ...   \n",
       "47  [{'profile_id': 15447209, 'first_name': 'Colin...  ...   \n",
       "48  [{'profile_id': 8575677, 'first_name': 'Jianha...  ...   \n",
       "49  [{'profile_id': 14200522, 'first_name': 'EDILB...  ...   \n",
       "\n",
       "            organization_type.name organization_type.code  \\\n",
       "0              SCHOOLS OF MEDICINE                     10   \n",
       "1    BIOMED ENGR/COL ENGR/ENGR STA                     10   \n",
       "2                             None                     30   \n",
       "3                             None                     20   \n",
       "4                             None                     20   \n",
       "5              SCHOOLS OF MEDICINE                     10   \n",
       "6                             None                     30   \n",
       "7                             None                     10   \n",
       "8   SCHOOLS OF DENTISTRY/ORAL HYGN                     10   \n",
       "9     SCHOOLS OF ARTS AND SCIENCES                     10   \n",
       "10                            None                     20   \n",
       "11                            None                     30   \n",
       "12        SCHOOLS OF PUBLIC HEALTH                     10   \n",
       "13                            None                     20   \n",
       "14             SCHOOLS OF MEDICINE                     10   \n",
       "15                            None                     NP   \n",
       "16             SCHOOLS OF MEDICINE                     10   \n",
       "17                            None                     10   \n",
       "18             SCHOOLS OF MEDICINE                     10   \n",
       "19                            None                     20   \n",
       "20                            None                     10   \n",
       "21                            None                     10   \n",
       "22        SCHOOLS OF PUBLIC HEALTH                     10   \n",
       "23                GRADUATE SCHOOLS                     10   \n",
       "24             SCHOOLS OF MEDICINE                     10   \n",
       "25             SCHOOLS OF MEDICINE                     10   \n",
       "26                            None                     NP   \n",
       "27             SCHOOLS OF MEDICINE                     10   \n",
       "28             SCHOOLS OF MEDICINE                     10   \n",
       "29                            None                     20   \n",
       "30                            None                     10   \n",
       "31                            None                     FP   \n",
       "32                            None                     30   \n",
       "33             SCHOOLS OF MEDICINE                     10   \n",
       "34   BIOMED ENGR/COL ENGR/ENGR STA                     10   \n",
       "35   BIOMED ENGR/COL ENGR/ENGR STA                     10   \n",
       "36                            None                     30   \n",
       "37                            None                     30   \n",
       "38                            None                     20   \n",
       "39                            None                     30   \n",
       "40             SCHOOLS OF MEDICINE                     10   \n",
       "41             SCHOOLS OF MEDICINE                     10   \n",
       "42                            None                     30   \n",
       "43             SCHOOLS OF MEDICINE                     10   \n",
       "44  SCHOOL OF MEDICINE & DENTISTRY                     10   \n",
       "45        SCHOOLS OF PUBLIC HEALTH                     10   \n",
       "46                            None                   None   \n",
       "47             SCHOOLS OF MEDICINE                     10   \n",
       "48                            None                     10   \n",
       "49             SCHOOLS OF MEDICINE                     10   \n",
       "\n",
       "   organization_type.is_other full_study_section.srg_code  \\\n",
       "0                       False                        CDMA   \n",
       "1                       False                        ZRG1   \n",
       "2                       False                        HSR2   \n",
       "3                       False                        ZTR1   \n",
       "4                       False                        ZTR1   \n",
       "5                       False                        ZRG1   \n",
       "6                       False                        IRAP   \n",
       "7                        True                         NSS   \n",
       "8                       False                        ZMD1   \n",
       "9                       False                        CPDD   \n",
       "10                      False                        ZRG1   \n",
       "11                      False                         NSS   \n",
       "12                      False                        ZDA1   \n",
       "13                      False                        ZRG1   \n",
       "14                      False                        ZRG1   \n",
       "15                      False                        BMRD   \n",
       "16                      False                         MID   \n",
       "17                       True                        ZGM1   \n",
       "18                      False                        ZRG1   \n",
       "19                      False                        ZRG1   \n",
       "20                       True                        ZHD1   \n",
       "21                       True                         NCI   \n",
       "22                      False                        ZES1   \n",
       "23                       True                        CDRC   \n",
       "24                      False                         ITD   \n",
       "25                      False                        ZEY1   \n",
       "26                      False                         NCI   \n",
       "27                      False                        ZRG1   \n",
       "28                      False                         SAT   \n",
       "29                      False                        ZRG1   \n",
       "30                       True                        ZRG1   \n",
       "31                      False                        ZRG1   \n",
       "32                      False                        MRA0   \n",
       "33                      False                         NaN   \n",
       "34                      False                        CDMA   \n",
       "35                      False                        EITN   \n",
       "36                      False                         DDK   \n",
       "37                      False                        ZAR1   \n",
       "38                      False                         MID   \n",
       "39                      False                        APDA   \n",
       "40                      False                         TWD   \n",
       "41                      False                        ZMH1   \n",
       "42                      False                        NPAS   \n",
       "43                      False                        PPAH   \n",
       "44                      False                        HSQE   \n",
       "45                      False                        ZES1   \n",
       "46                       True                        ZMH1   \n",
       "47                      False                        ZEB1   \n",
       "48                       True                        ZGM1   \n",
       "49                      False                         NST   \n",
       "\n",
       "   full_study_section.srg_flex full_study_section.sra_designator_code  \\\n",
       "0                                                                       \n",
       "1                                                                  VH   \n",
       "2                                                                       \n",
       "3                                                                 SRC   \n",
       "4                                                                 SRC   \n",
       "5                                                                BDCN   \n",
       "6                                                                       \n",
       "7                                                                       \n",
       "8                                                                 DRI   \n",
       "9                                                                       \n",
       "10                                                                BST   \n",
       "11                                                                      \n",
       "12                                                                SXM   \n",
       "13                                                                BST   \n",
       "14                                                                GGG   \n",
       "15                                                                      \n",
       "16                                                                      \n",
       "17                                                                RCB   \n",
       "18                                                               RPHB   \n",
       "19                                                                BST   \n",
       "20                                                                DSR   \n",
       "21                                                                      \n",
       "22                                                                LKB   \n",
       "23                                                                      \n",
       "24                                                                      \n",
       "25                                                                VSN   \n",
       "26                                                                      \n",
       "27                                                               SEIR   \n",
       "28                                                                      \n",
       "29                                                                BST   \n",
       "30                                                               IFCN   \n",
       "31                                                               ETTN   \n",
       "32                                                                      \n",
       "33                         NaN                                    NaN   \n",
       "34                                                                      \n",
       "35                                                                      \n",
       "36                                                                      \n",
       "37                                                                 KM   \n",
       "38                                                                      \n",
       "39                                                                      \n",
       "40                                                                      \n",
       "41                                                                ERB   \n",
       "42                                                                      \n",
       "43                                                                      \n",
       "44                                                                      \n",
       "45                                                                LKB   \n",
       "46                                                                ERB   \n",
       "47                                                                OSR   \n",
       "48                                                                RCB   \n",
       "49                                                                      \n",
       "\n",
       "   full_study_section.sra_flex_code full_study_section.group_code  \\\n",
       "0                                                                   \n",
       "1                                 N                                 \n",
       "2                                                                   \n",
       "3                                                                   \n",
       "4                                                                   \n",
       "5                                 L                                 \n",
       "6                                                                   \n",
       "7                                                                   \n",
       "8                                                                   \n",
       "9                                                                   \n",
       "10                                T                                 \n",
       "11                                                                  \n",
       "12                                M                                 \n",
       "13                                T                                 \n",
       "14                                D                                 \n",
       "15                                                                  \n",
       "16                                                                  \n",
       "17                                3                                 \n",
       "18                                Z                                 \n",
       "19                                T                                 \n",
       "20                                N                                 \n",
       "21                                                                  \n",
       "22                                K                                 \n",
       "23                                                                  \n",
       "24                                                                  \n",
       "25                                                                  \n",
       "26                                                                  \n",
       "27                                A                                 \n",
       "28                                                                  \n",
       "29                                T                                 \n",
       "30                                L                                 \n",
       "31                                S                            14   \n",
       "32                                                                  \n",
       "33                              NaN                           NaN   \n",
       "34                                                                  \n",
       "35                                                                  \n",
       "36                                                                  \n",
       "37                                                             M2   \n",
       "38                                                                  \n",
       "39                                                                  \n",
       "40                                                                  \n",
       "41                                G                                 \n",
       "42                                                                  \n",
       "43                                                                  \n",
       "44                                                                  \n",
       "45                                K                             U   \n",
       "46                                L                            05   \n",
       "47                                B                            O1   \n",
       "48                                3                                 \n",
       "49                                                                  \n",
       "\n",
       "                              full_study_section.name  full_study_section  \n",
       "0   Clinical Data Management and Analysis Study Se...                 NaN  \n",
       "1              Special Emphasis Panel[ZRG1-VH-N(50)R]                 NaN  \n",
       "2   HSR-2 Behavioral, Social, and Cultural Determi...                 NaN  \n",
       "3                                        ZTR1-SRC(99)                 NaN  \n",
       "4                                        ZTR1-SRC(99)                 NaN  \n",
       "5            Special Emphasis Panel[ZRG1-BDCN-L(90)S]                 NaN  \n",
       "6   Infectious Diseases, Reproductive Health, Asth...                 NaN  \n",
       "7                                                 NSS                 NaN  \n",
       "8                                       ZMD1-DRI(O1)R                 NaN  \n",
       "9   Child Psychopathology and Developmental Disabi...                 NaN  \n",
       "10                 Special Emphasis Panel[ZRG1-BST-T]                 NaN  \n",
       "11                                                NSS                 NaN  \n",
       "12                                    ZDA1-SXM-M(04)S                 NaN  \n",
       "13            Special Emphasis Panel[ZRG1-BST-T(40)P]                 NaN  \n",
       "14            Special Emphasis Panel[ZRG1-GGG-D(51)R]                 NaN  \n",
       "15  Biostatistical Methods and Research Design Stu...                 NaN  \n",
       "16  Microbiology and Infectious Diseases B Researc...                 NaN  \n",
       "17                                         ZGM1-RCB-3                 NaN  \n",
       "18           Special Emphasis Panel[ZRG1-RPHB-Z(56)R]                 NaN  \n",
       "19                 Special Emphasis Panel[ZRG1-BST-T]                 NaN  \n",
       "20                                         ZHD1-DSR-N                 NaN  \n",
       "21            Cancer Centers Study Section (A)[NCI-A]                 NaN  \n",
       "22                                      ZES1-LKB-K(R)                 NaN  \n",
       "23     Communication Disorders Review Committee[CDRC]                 NaN  \n",
       "24  Imaging Technology Development Study Section[ITD]                 NaN  \n",
       "25                                       ZEY1-VSN(06)                 NaN  \n",
       "26            Cancer Centers Study Section (A)[NCI-A]                 NaN  \n",
       "27           Special Emphasis Panel[ZRG1-SEIR-A(80)S]                 NaN  \n",
       "28  Surgery, Anesthesiology and Trauma Study Secti...                 NaN  \n",
       "29                 Special Emphasis Panel[ZRG1-BST-T]                 NaN  \n",
       "30                Special Emphasis Panel[ZRG1-IFCN-L]                 NaN  \n",
       "31           Special Emphasis Panel[ZRG1 ETTN-S (14)]                 NaN  \n",
       "32                            Mentored Research[MRA0]                 NaN  \n",
       "33                                                NaN                 NaN  \n",
       "34  Clinical Data Management and Analysis Study Se...                 NaN  \n",
       "35  Emerging Imaging Technologies in Neuroscience ...                 NaN  \n",
       "36  Digestive Diseases and Nutrition C Study Secti...                 NaN  \n",
       "37               Special Emphasis Panel[ZAR1 KM (M2)]                 NaN  \n",
       "38  Microbiology and Infectious Diseases B Researc...                 NaN  \n",
       "39  Adult Psychopathology and Disorders of Aging S...                 NaN  \n",
       "40  Training and Workforce Development Study Secti...                 NaN  \n",
       "41                                     ZMH1-ERB-G(G1)                 NaN  \n",
       "42  Neural Basis of Psychopathology, Addictions an...                 NaN  \n",
       "43  Population and Public Health Approaches to HIV...                 NaN  \n",
       "44  Health Services: Quality and Effectiveness Stu...                 NaN  \n",
       "45             Special Emphasis Panel[ZES1 LKB-K (U)]                 NaN  \n",
       "46            Special Emphasis Panel[ZMH1 ERB-L (05)]                 NaN  \n",
       "47            Special Emphasis Panel[ZEB1 OSR-B (O1)]                 NaN  \n",
       "48                                         ZGM1-RCB-3                 NaN  \n",
       "49                         NST-1 Study Section[NST-1]                 NaN  \n",
       "\n",
       "[50 rows x 76 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.json_normalize(response_data[\"results\"])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "84631992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                                      EMORY UNIVERSITY\n",
       "1                     UNIVERSITY OF CALIFORNIA AT DAVIS\n",
       "2              VETERANS ADMIN PALO ALTO HEALTH CARE SYS\n",
       "3                         INSTITUTE FOR SYSTEMS BIOLOGY\n",
       "4                         INSTITUTE FOR SYSTEMS BIOLOGY\n",
       "5                                   STANFORD UNIVERSITY\n",
       "6                     CINCINNATI CHILDRENS HOSP MED CTR\n",
       "7                    OREGON HEALTH & SCIENCE UNIVERSITY\n",
       "8                                HARVARD MEDICAL SCHOOL\n",
       "9                                    HARVARD UNIVERSITY\n",
       "10                                BROAD INSTITUTE, INC.\n",
       "11                 VANDERBILT UNIVERSITY MEDICAL CENTER\n",
       "12             UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON\n",
       "13                                BROAD INSTITUTE, INC.\n",
       "14             UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR\n",
       "15                                MAYO CLINIC ROCHESTER\n",
       "16                           UNIVERSITY OF PENNSYLVANIA\n",
       "17                               UNIVERSITY OF KENTUCKY\n",
       "18                                   UNIVERSITY OF UTAH\n",
       "19                                BROAD INSTITUTE, INC.\n",
       "20                  UNIVERSITY OF CALIFORNIA, SAN DIEGO\n",
       "21                                    DARTMOUTH COLLEGE\n",
       "22                             JOHNS HOPKINS UNIVERSITY\n",
       "23                              NORTHWESTERN UNIVERSITY\n",
       "24                           UNIVERSITY OF PENNSYLVANIA\n",
       "25               UNIVERSITY OF MIAMI SCHOOL OF MEDICINE\n",
       "26                        FRED HUTCHINSON CANCER CENTER\n",
       "27                    UNIVERSITY OF CALIFORNIA AT DAVIS\n",
       "28               WAKE FOREST UNIVERSITY HEALTH SCIENCES\n",
       "29                                BROAD INSTITUTE, INC.\n",
       "30              ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI\n",
       "31                                        CYTODEL, INC.\n",
       "32               VA EASTERN COLORADO HEALTH CARE SYSTEM\n",
       "33                   UNIV OF NORTH CAROLINA CHAPEL HILL\n",
       "34                                      YALE UNIVERSITY\n",
       "35                   NEW JERSEY INSTITUTE OF TECHNOLOGY\n",
       "36                 VANDERBILT UNIVERSITY MEDICAL CENTER\n",
       "37                       MASSACHUSETTS GENERAL HOSPITAL\n",
       "38                MARSHFIELD CLINIC RESEARCH FOUNDATION\n",
       "39    NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEA...\n",
       "40                   OREGON HEALTH & SCIENCE UNIVERSITY\n",
       "41                                   UNIVERSITY OF UTAH\n",
       "42                                      MCLEAN HOSPITAL\n",
       "43               NEW YORK UNIVERSITY SCHOOL OF MEDICINE\n",
       "44                              UNIVERSITY OF ROCHESTER\n",
       "45                  UNIVERSITY OF MICHIGAN AT ANN ARBOR\n",
       "46                               TRINITY COLLEGE DUBLIN\n",
       "47                                OHIO STATE UNIVERSITY\n",
       "48                               UNIVERSITY OF KENTUCKY\n",
       "49              UNIVERSITY OF CALIFORNIA, SAN FRANCISCO\n",
       "Name: organization.org_name, dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.get('organization.org_name')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "412fa3ad",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0                SCHOOLS OF MEDICINE\n",
      "1      BIOMED ENGR/COL ENGR/ENGR STA\n",
      "2                               None\n",
      "3                               None\n",
      "4                               None\n",
      "5                SCHOOLS OF MEDICINE\n",
      "6                               None\n",
      "7                               None\n",
      "8     SCHOOLS OF DENTISTRY/ORAL HYGN\n",
      "9       SCHOOLS OF ARTS AND SCIENCES\n",
      "10                              None\n",
      "11                              None\n",
      "12          SCHOOLS OF PUBLIC HEALTH\n",
      "13                              None\n",
      "14               SCHOOLS OF MEDICINE\n",
      "15                              None\n",
      "16               SCHOOLS OF MEDICINE\n",
      "17                              None\n",
      "18               SCHOOLS OF MEDICINE\n",
      "19                              None\n",
      "20                              None\n",
      "21                              None\n",
      "22          SCHOOLS OF PUBLIC HEALTH\n",
      "23                  GRADUATE SCHOOLS\n",
      "24               SCHOOLS OF MEDICINE\n",
      "25               SCHOOLS OF MEDICINE\n",
      "26                              None\n",
      "27               SCHOOLS OF MEDICINE\n",
      "28               SCHOOLS OF MEDICINE\n",
      "29                              None\n",
      "30                              None\n",
      "31                              None\n",
      "32                              None\n",
      "33               SCHOOLS OF MEDICINE\n",
      "34     BIOMED ENGR/COL ENGR/ENGR STA\n",
      "35     BIOMED ENGR/COL ENGR/ENGR STA\n",
      "36                              None\n",
      "37                              None\n",
      "38                              None\n",
      "39                              None\n",
      "40               SCHOOLS OF MEDICINE\n",
      "41               SCHOOLS OF MEDICINE\n",
      "42                              None\n",
      "43               SCHOOLS OF MEDICINE\n",
      "44    SCHOOL OF MEDICINE & DENTISTRY\n",
      "45          SCHOOLS OF PUBLIC HEALTH\n",
      "46                              None\n",
      "47               SCHOOLS OF MEDICINE\n",
      "48                              None\n",
      "49               SCHOOLS OF MEDICINE\n",
      "Name: organization_type.name, dtype: object\n"
     ]
    }
   ],
   "source": [
    "print(df['organization_type.name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "aa877443",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column Name :  appl_id\n",
      "Column Name :  subproject_id\n",
      "Column Name :  fiscal_year\n",
      "Column Name :  project_num\n",
      "Column Name :  project_serial_num\n",
      "Column Name :  award_type\n",
      "Column Name :  activity_code\n",
      "Column Name :  award_amount\n",
      "Column Name :  is_active\n",
      "Column Name :  principal_investigators\n",
      "Column Name :  contact_pi_name\n",
      "Column Name :  program_officers\n",
      "Column Name :  agency_ic_fundings\n",
      "Column Name :  cong_dist\n",
      "Column Name :  spending_categories\n",
      "Column Name :  project_start_date\n",
      "Column Name :  project_end_date\n",
      "Column Name :  full_foa\n",
      "Column Name :  award_notice_date\n",
      "Column Name :  is_new\n",
      "Column Name :  mechanism_code_dc\n",
      "Column Name :  core_project_num\n",
      "Column Name :  terms\n",
      "Column Name :  pref_terms\n",
      "Column Name :  abstract_text\n",
      "Column Name :  project_title\n",
      "Column Name :  phr_text\n",
      "Column Name :  spending_categories_desc\n",
      "Column Name :  agency_code\n",
      "Column Name :  covid_response\n",
      "Column Name :  arra_funded\n",
      "Column Name :  budget_start\n",
      "Column Name :  budget_end\n",
      "Column Name :  cfda_code\n",
      "Column Name :  funding_mechanism\n",
      "Column Name :  direct_cost_amt\n",
      "Column Name :  indirect_cost_amt\n",
      "Column Name :  project_detail_url\n",
      "Column Name :  date_added\n",
      "Column Name :  organization.org_name\n",
      "Column Name :  organization.city\n",
      "Column Name :  organization.country\n",
      "Column Name :  organization.org_city\n",
      "Column Name :  organization.org_country\n",
      "Column Name :  organization.org_state\n",
      "Column Name :  organization.org_state_name\n",
      "Column Name :  organization.dept_type\n",
      "Column Name :  organization.fips_country_code\n",
      "Column Name :  organization.org_duns\n",
      "Column Name :  organization.org_ueis\n",
      "Column Name :  organization.primary_duns\n",
      "Column Name :  organization.primary_uei\n",
      "Column Name :  organization.org_fips\n",
      "Column Name :  organization.org_ipf_code\n",
      "Column Name :  organization.org_zipcode\n",
      "Column Name :  organization.external_org_id\n",
      "Column Name :  project_num_split.appl_type_code\n",
      "Column Name :  project_num_split.activity_code\n",
      "Column Name :  project_num_split.ic_code\n",
      "Column Name :  project_num_split.serial_num\n",
      "Column Name :  project_num_split.support_year\n",
      "Column Name :  project_num_split.full_support_year\n",
      "Column Name :  project_num_split.suffix_code\n",
      "Column Name :  agency_ic_admin.code\n",
      "Column Name :  agency_ic_admin.abbreviation\n",
      "Column Name :  agency_ic_admin.name\n",
      "Column Name :  organization_type.name\n",
      "Column Name :  organization_type.code\n",
      "Column Name :  organization_type.is_other\n",
      "Column Name :  full_study_section.srg_code\n",
      "Column Name :  full_study_section.srg_flex\n",
      "Column Name :  full_study_section.sra_designator_code\n",
      "Column Name :  full_study_section.sra_flex_code\n",
      "Column Name :  full_study_section.group_code\n",
      "Column Name :  full_study_section.name\n",
      "Column Name :  full_study_section\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m3/8dqdsjld0q7bb_84k7_vv_k80000gn/T/ipykernel_75464/3283286550.py:1: FutureWarning: iteritems is deprecated and will be removed in a future version. Use .items instead.\n",
      "  for (columnName, columnData) in df.iteritems():\n"
     ]
    }
   ],
   "source": [
    "for (columnName, columnData) in df.iteritems():\n",
    "    print('Column Name : ', columnName)\n",
    "    #print('Column Contents : ', columnData.values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "6a7333be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>org_name</th>\n",
       "      <th>city</th>\n",
       "      <th>country</th>\n",
       "      <th>org_city</th>\n",
       "      <th>org_country</th>\n",
       "      <th>org_state</th>\n",
       "      <th>org_state_name</th>\n",
       "      <th>dept_type</th>\n",
       "      <th>fips_country_code</th>\n",
       "      <th>org_duns</th>\n",
       "      <th>org_ueis</th>\n",
       "      <th>primary_duns</th>\n",
       "      <th>primary_uei</th>\n",
       "      <th>org_fips</th>\n",
       "      <th>org_ipf_code</th>\n",
       "      <th>org_zipcode</th>\n",
       "      <th>external_org_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>INSTITUTE FOR SYSTEMS BIOLOGY</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>SEATTLE</td>\n",
       "      <td>UNITED STATES</td>\n",
       "      <td>WA</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>[135646524]</td>\n",
       "      <td>[SMK9PCMKXED6]</td>\n",
       "      <td>135646524</td>\n",
       "      <td>SMK9PCMKXED6</td>\n",
       "      <td>US</td>\n",
       "      <td>4106301</td>\n",
       "      <td>981095263</td>\n",
       "      <td>4106301</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                        org_name  city country org_city    org_country  \\\n",
       "0  INSTITUTE FOR SYSTEMS BIOLOGY  None    None  SEATTLE  UNITED STATES   \n",
       "\n",
       "  org_state org_state_name dept_type fips_country_code     org_duns  \\\n",
       "0        WA           None      None              None  [135646524]   \n",
       "\n",
       "         org_ueis primary_duns   primary_uei org_fips org_ipf_code  \\\n",
       "0  [SMK9PCMKXED6]    135646524  SMK9PCMKXED6       US      4106301   \n",
       "\n",
       "  org_zipcode  external_org_id  \n",
       "0   981095263          4106301  "
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "kdf = pd.json_normalize(response_data['results'][3]['organization'])\n",
    "kdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "bbac766c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile_id</th>\n",
       "      <th>first_name</th>\n",
       "      <th>middle_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>is_contact_pi</th>\n",
       "      <th>full_name</th>\n",
       "      <th>title</th>\n",
       "      <th>project_title</th>\n",
       "      <th>organization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>9239369</td>\n",
       "      <td>Nader</td>\n",
       "      <td>Nabile</td>\n",
       "      <td>Massarweh</td>\n",
       "      <td>True</td>\n",
       "      <td>Nader Nabile Massarweh</td>\n",
       "      <td>ASSOCIATE PROFESSOR OF SURGERY</td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "      <td>{'org_name': 'EMORY UNIVERSITY', 'city': None,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8613963</td>\n",
       "      <td>Alex</td>\n",
       "      <td></td>\n",
       "      <td>Sox-Harris</td>\n",
       "      <td>False</td>\n",
       "      <td>Alex  Sox-Harris</td>\n",
       "      <td></td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "      <td>{'org_name': 'EMORY UNIVERSITY', 'city': None,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10051972</td>\n",
       "      <td>Kwan-Liu</td>\n",
       "      <td></td>\n",
       "      <td>Ma</td>\n",
       "      <td>True</td>\n",
       "      <td>Kwan-Liu  Ma</td>\n",
       "      <td></td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "      <td>{'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6158233</td>\n",
       "      <td>SHIN-PING</td>\n",
       "      <td></td>\n",
       "      <td>TU</td>\n",
       "      <td>False</td>\n",
       "      <td>SHIN-PING  TU</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "      <td>{'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9860649</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Blonigen</td>\n",
       "      <td>True</td>\n",
       "      <td>Daniel Michael Blonigen</td>\n",
       "      <td></td>\n",
       "      <td>Using Data Analytics and Targeted Whole Health...</td>\n",
       "      <td>{'org_name': 'VETERANS ADMIN PALO ALTO HEALTH ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>9490186</td>\n",
       "      <td>Peter</td>\n",
       "      <td>Xuekun</td>\n",
       "      <td>Song</td>\n",
       "      <td>False</td>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "      <td>{'org_name': 'UNIVERSITY OF MICHIGAN AT ANN AR...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>78510112</td>\n",
       "      <td>Mark</td>\n",
       "      <td></td>\n",
       "      <td>Ward</td>\n",
       "      <td>True</td>\n",
       "      <td>Mark  Ward</td>\n",
       "      <td></td>\n",
       "      <td>Pathways from social disconnection to suicidal...</td>\n",
       "      <td>{'org_name': 'TRINITY COLLEGE DUBLIN', 'city':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>15447209</td>\n",
       "      <td>Colin</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Hisey</td>\n",
       "      <td>True</td>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>POSTDOCTORAL SCHOLAR</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "      <td>{'org_name': 'OHIO STATE UNIVERSITY', 'city': ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>8575677</td>\n",
       "      <td>Jianhang</td>\n",
       "      <td></td>\n",
       "      <td>Jia</td>\n",
       "      <td>True</td>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>Imaging Core</td>\n",
       "      <td>{'org_name': 'UNIVERSITY OF KENTUCKY', 'city':...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14200522</td>\n",
       "      <td>EDILBERTO</td>\n",
       "      <td></td>\n",
       "      <td>AMORIM DE CERQUEIRA</td>\n",
       "      <td>True</td>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td></td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "      <td>{'org_name': 'UNIVERSITY OF CALIFORNIA, SAN FR...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile_id first_name middle_name            last_name  is_contact_pi  \\\n",
       "0      9239369      Nader      Nabile            Massarweh           True   \n",
       "1      8613963       Alex                       Sox-Harris          False   \n",
       "2     10051972   Kwan-Liu                               Ma           True   \n",
       "3      6158233  SHIN-PING                               TU          False   \n",
       "4      9860649     Daniel     Michael             Blonigen           True   \n",
       "..         ...        ...         ...                  ...            ...   \n",
       "61     9490186      Peter      Xuekun                 Song          False   \n",
       "62    78510112       Mark                             Ward           True   \n",
       "63    15447209      Colin         Lee                Hisey           True   \n",
       "64     8575677   Jianhang                              Jia           True   \n",
       "65    14200522  EDILBERTO              AMORIM DE CERQUEIRA           True   \n",
       "\n",
       "                         full_name                           title  \\\n",
       "0           Nader Nabile Massarweh  ASSOCIATE PROFESSOR OF SURGERY   \n",
       "1                 Alex  Sox-Harris                                   \n",
       "2                     Kwan-Liu  Ma                                   \n",
       "3                    SHIN-PING  TU                       PROFESSOR   \n",
       "4          Daniel Michael Blonigen                                   \n",
       "..                             ...                             ...   \n",
       "61               Peter Xuekun Song                       PROFESSOR   \n",
       "62                      Mark  Ward                                   \n",
       "63                 Colin Lee Hisey            POSTDOCTORAL SCHOLAR   \n",
       "64                   Jianhang  Jia                       PROFESSOR   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA                                   \n",
       "\n",
       "                                        project_title  \\\n",
       "0   Using Modern Data Science Methods and Advanced...   \n",
       "1   Using Modern Data Science Methods and Advanced...   \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...   \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...   \n",
       "4   Using Data Analytics and Targeted Whole Health...   \n",
       "..                                                ...   \n",
       "61  E3Gen: Multigenerational Influences of Social ...   \n",
       "62  Pathways from social disconnection to suicidal...   \n",
       "63  Machine Learning-enabled Classification of Ext...   \n",
       "64                                       Imaging Core   \n",
       "65  Physiology-driven seizure management post-card...   \n",
       "\n",
       "                                         organization  \n",
       "0   {'org_name': 'EMORY UNIVERSITY', 'city': None,...  \n",
       "1   {'org_name': 'EMORY UNIVERSITY', 'city': None,...  \n",
       "2   {'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...  \n",
       "3   {'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...  \n",
       "4   {'org_name': 'VETERANS ADMIN PALO ALTO HEALTH ...  \n",
       "..                                                ...  \n",
       "61  {'org_name': 'UNIVERSITY OF MICHIGAN AT ANN AR...  \n",
       "62  {'org_name': 'TRINITY COLLEGE DUBLIN', 'city':...  \n",
       "63  {'org_name': 'OHIO STATE UNIVERSITY', 'city': ...  \n",
       "64  {'org_name': 'UNIVERSITY OF KENTUCKY', 'city':...  \n",
       "65  {'org_name': 'UNIVERSITY OF CALIFORNIA, SAN FR...  \n",
       "\n",
       "[66 rows x 9 columns]"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf = pd.json_normalize(response_data[\"results\"],record_path=['principal_investigators'],meta=['project_title','organization'])\n",
    "bdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "43bab9ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     {'org_name': 'EMORY UNIVERSITY', 'city': None,...\n",
       "1     {'org_name': 'EMORY UNIVERSITY', 'city': None,...\n",
       "2     {'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...\n",
       "3     {'org_name': 'UNIVERSITY OF CALIFORNIA AT DAVI...\n",
       "4     {'org_name': 'VETERANS ADMIN PALO ALTO HEALTH ...\n",
       "                            ...                        \n",
       "61    {'org_name': 'UNIVERSITY OF MICHIGAN AT ANN AR...\n",
       "62    {'org_name': 'TRINITY COLLEGE DUBLIN', 'city':...\n",
       "63    {'org_name': 'OHIO STATE UNIVERSITY', 'city': ...\n",
       "64    {'org_name': 'UNIVERSITY OF KENTUCKY', 'city':...\n",
       "65    {'org_name': 'UNIVERSITY OF CALIFORNIA, SAN FR...\n",
       "Name: organization, Length: 66, dtype: object"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bdf.get('organization')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "e2b6efd9",
   "metadata": {},
   "outputs": [],
   "source": [
    "listie = []\n",
    "for x in bdf['organization']:\n",
    "    listie.append(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "425eeb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'org_name': 'EMORY UNIVERSITY',\n",
       " 'city': None,\n",
       " 'country': None,\n",
       " 'org_city': 'ATLANTA',\n",
       " 'org_country': 'UNITED STATES',\n",
       " 'org_state': 'GA',\n",
       " 'org_state_name': None,\n",
       " 'dept_type': 'SURGERY',\n",
       " 'fips_country_code': None,\n",
       " 'org_duns': ['066469933'],\n",
       " 'org_ueis': ['S352L5PJLMP8'],\n",
       " 'primary_duns': '066469933',\n",
       " 'primary_uei': 'S352L5PJLMP8',\n",
       " 'org_fips': 'US',\n",
       " 'org_ipf_code': '2384501',\n",
       " 'org_zipcode': '303221007',\n",
       " 'external_org_id': 2384501}"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "listie\n",
    "listie[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "f11388f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "empty_list=[]\n",
    "for i in listie:\n",
    "    name = i['org_name']\n",
    "    empty_list.append(name)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "e15d0465",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['EMORY UNIVERSITY',\n",
       " 'EMORY UNIVERSITY',\n",
       " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
       " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
       " 'VETERANS ADMIN PALO ALTO HEALTH CARE SYS',\n",
       " 'INSTITUTE FOR SYSTEMS BIOLOGY',\n",
       " 'INSTITUTE FOR SYSTEMS BIOLOGY',\n",
       " 'INSTITUTE FOR SYSTEMS BIOLOGY',\n",
       " 'INSTITUTE FOR SYSTEMS BIOLOGY',\n",
       " 'INSTITUTE FOR SYSTEMS BIOLOGY',\n",
       " 'STANFORD UNIVERSITY',\n",
       " 'STANFORD UNIVERSITY',\n",
       " 'CINCINNATI CHILDRENS HOSP MED CTR',\n",
       " 'OREGON HEALTH & SCIENCE UNIVERSITY',\n",
       " 'HARVARD MEDICAL SCHOOL',\n",
       " 'HARVARD UNIVERSITY',\n",
       " 'HARVARD UNIVERSITY',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'VANDERBILT UNIVERSITY MEDICAL CENTER',\n",
       " 'UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR',\n",
       " 'MAYO CLINIC ROCHESTER',\n",
       " 'MAYO CLINIC ROCHESTER',\n",
       " 'UNIVERSITY OF PENNSYLVANIA',\n",
       " 'UNIVERSITY OF KENTUCKY',\n",
       " 'UNIVERSITY OF UTAH',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'UNIVERSITY OF CALIFORNIA, SAN DIEGO',\n",
       " 'DARTMOUTH COLLEGE',\n",
       " 'JOHNS HOPKINS UNIVERSITY',\n",
       " 'NORTHWESTERN UNIVERSITY',\n",
       " 'UNIVERSITY OF PENNSYLVANIA',\n",
       " 'UNIVERSITY OF MIAMI SCHOOL OF MEDICINE',\n",
       " 'FRED HUTCHINSON CANCER CENTER',\n",
       " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
       " 'UNIVERSITY OF CALIFORNIA AT DAVIS',\n",
       " 'WAKE FOREST UNIVERSITY HEALTH SCIENCES',\n",
       " 'WAKE FOREST UNIVERSITY HEALTH SCIENCES',\n",
       " 'BROAD INSTITUTE, INC.',\n",
       " 'ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI',\n",
       " 'CYTODEL, INC.',\n",
       " 'VA EASTERN COLORADO HEALTH CARE SYSTEM',\n",
       " 'UNIV OF NORTH CAROLINA CHAPEL HILL',\n",
       " 'YALE UNIVERSITY',\n",
       " 'YALE UNIVERSITY',\n",
       " 'NEW JERSEY INSTITUTE OF TECHNOLOGY',\n",
       " 'VANDERBILT UNIVERSITY MEDICAL CENTER',\n",
       " 'MASSACHUSETTS GENERAL HOSPITAL',\n",
       " 'MARSHFIELD CLINIC RESEARCH FOUNDATION',\n",
       " 'NEW YORK STATE PSYCHIATRIC INSTITUTE DBA RESEARCH FOUNDATION FOR MENTAL HYGIENE, INC',\n",
       " 'OREGON HEALTH & SCIENCE UNIVERSITY',\n",
       " 'OREGON HEALTH & SCIENCE UNIVERSITY',\n",
       " 'UNIVERSITY OF UTAH',\n",
       " 'UNIVERSITY OF UTAH',\n",
       " 'MCLEAN HOSPITAL',\n",
       " 'NEW YORK UNIVERSITY SCHOOL OF MEDICINE',\n",
       " 'UNIVERSITY OF ROCHESTER',\n",
       " 'UNIVERSITY OF MICHIGAN AT ANN ARBOR',\n",
       " 'UNIVERSITY OF MICHIGAN AT ANN ARBOR',\n",
       " 'TRINITY COLLEGE DUBLIN',\n",
       " 'OHIO STATE UNIVERSITY',\n",
       " 'UNIVERSITY OF KENTUCKY',\n",
       " 'UNIVERSITY OF CALIFORNIA, SAN FRANCISCO']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "empty_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "517525c7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Organization</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>EMORY UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>EMORY UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>UNIVERSITY OF CALIFORNIA AT DAVIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>UNIVERSITY OF CALIFORNIA AT DAVIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>VETERANS ADMIN PALO ALTO HEALTH CARE SYS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>UNIVERSITY OF MICHIGAN AT ANN ARBOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>TRINITY COLLEGE DUBLIN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>OHIO STATE UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>UNIVERSITY OF KENTUCKY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>UNIVERSITY OF CALIFORNIA, SAN FRANCISCO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 1 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                Organization\n",
       "0                           EMORY UNIVERSITY\n",
       "1                           EMORY UNIVERSITY\n",
       "2          UNIVERSITY OF CALIFORNIA AT DAVIS\n",
       "3          UNIVERSITY OF CALIFORNIA AT DAVIS\n",
       "4   VETERANS ADMIN PALO ALTO HEALTH CARE SYS\n",
       "..                                       ...\n",
       "61       UNIVERSITY OF MICHIGAN AT ANN ARBOR\n",
       "62                    TRINITY COLLEGE DUBLIN\n",
       "63                     OHIO STATE UNIVERSITY\n",
       "64                    UNIVERSITY OF KENTUCKY\n",
       "65   UNIVERSITY OF CALIFORNIA, SAN FRANCISCO\n",
       "\n",
       "[66 rows x 1 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ORG = pd.DataFrame({'Organization':empty_list})\n",
    "ORG"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e49e3eb5",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>profile_id</th>\n",
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       "      <th>middle_name</th>\n",
       "      <th>last_name</th>\n",
       "      <th>is_contact_pi</th>\n",
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       "      <td>Nader Nabile Massarweh</td>\n",
       "      <td>ASSOCIATE PROFESSOR OF SURGERY</td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8613963</td>\n",
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       "      <td>Alex  Sox-Harris</td>\n",
       "      <td></td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10051972</td>\n",
       "      <td>Kwan-Liu</td>\n",
       "      <td></td>\n",
       "      <td>Ma</td>\n",
       "      <td>True</td>\n",
       "      <td>Kwan-Liu  Ma</td>\n",
       "      <td></td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6158233</td>\n",
       "      <td>SHIN-PING</td>\n",
       "      <td></td>\n",
       "      <td>TU</td>\n",
       "      <td>False</td>\n",
       "      <td>SHIN-PING  TU</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9860649</td>\n",
       "      <td>Daniel</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Blonigen</td>\n",
       "      <td>True</td>\n",
       "      <td>Daniel Michael Blonigen</td>\n",
       "      <td></td>\n",
       "      <td>Using Data Analytics and Targeted Whole Health...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>9490186</td>\n",
       "      <td>Peter</td>\n",
       "      <td>Xuekun</td>\n",
       "      <td>Song</td>\n",
       "      <td>False</td>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>78510112</td>\n",
       "      <td>Mark</td>\n",
       "      <td></td>\n",
       "      <td>Ward</td>\n",
       "      <td>True</td>\n",
       "      <td>Mark  Ward</td>\n",
       "      <td></td>\n",
       "      <td>Pathways from social disconnection to suicidal...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>15447209</td>\n",
       "      <td>Colin</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Hisey</td>\n",
       "      <td>True</td>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>POSTDOCTORAL SCHOLAR</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>8575677</td>\n",
       "      <td>Jianhang</td>\n",
       "      <td></td>\n",
       "      <td>Jia</td>\n",
       "      <td>True</td>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>Imaging Core</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14200522</td>\n",
       "      <td>EDILBERTO</td>\n",
       "      <td></td>\n",
       "      <td>AMORIM DE CERQUEIRA</td>\n",
       "      <td>True</td>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td></td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile_id first_name middle_name            last_name  is_contact_pi  \\\n",
       "0      9239369      Nader      Nabile            Massarweh           True   \n",
       "1      8613963       Alex                       Sox-Harris          False   \n",
       "2     10051972   Kwan-Liu                               Ma           True   \n",
       "3      6158233  SHIN-PING                               TU          False   \n",
       "4      9860649     Daniel     Michael             Blonigen           True   \n",
       "..         ...        ...         ...                  ...            ...   \n",
       "61     9490186      Peter      Xuekun                 Song          False   \n",
       "62    78510112       Mark                             Ward           True   \n",
       "63    15447209      Colin         Lee                Hisey           True   \n",
       "64     8575677   Jianhang                              Jia           True   \n",
       "65    14200522  EDILBERTO              AMORIM DE CERQUEIRA           True   \n",
       "\n",
       "                         full_name                           title  \\\n",
       "0           Nader Nabile Massarweh  ASSOCIATE PROFESSOR OF SURGERY   \n",
       "1                 Alex  Sox-Harris                                   \n",
       "2                     Kwan-Liu  Ma                                   \n",
       "3                    SHIN-PING  TU                       PROFESSOR   \n",
       "4          Daniel Michael Blonigen                                   \n",
       "..                             ...                             ...   \n",
       "61               Peter Xuekun Song                       PROFESSOR   \n",
       "62                      Mark  Ward                                   \n",
       "63                 Colin Lee Hisey            POSTDOCTORAL SCHOLAR   \n",
       "64                   Jianhang  Jia                       PROFESSOR   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA                                   \n",
       "\n",
       "                                        project_title  \n",
       "0   Using Modern Data Science Methods and Advanced...  \n",
       "1   Using Modern Data Science Methods and Advanced...  \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...  \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...  \n",
       "4   Using Data Analytics and Targeted Whole Health...  \n",
       "..                                                ...  \n",
       "61  E3Gen: Multigenerational Influences of Social ...  \n",
       "62  Pathways from social disconnection to suicidal...  \n",
       "63  Machine Learning-enabled Classification of Ext...  \n",
       "64                                       Imaging Core  \n",
       "65  Physiology-driven seizure management post-card...  \n",
       "\n",
       "[66 rows x 8 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ndf = pd.json_normalize(response_data[\"results\"],record_path='principal_investigators',meta='project_title')\n",
    "ndf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "30028cb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>Blonigen</td>\n",
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       "      <td>Daniel Michael Blonigen</td>\n",
       "      <td></td>\n",
       "      <td>Using Data Analytics and Targeted Whole Health...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>9490186</td>\n",
       "      <td>Peter</td>\n",
       "      <td>Xuekun</td>\n",
       "      <td>Song</td>\n",
       "      <td>False</td>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "      <td>360960</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>78510112</td>\n",
       "      <td>Mark</td>\n",
       "      <td></td>\n",
       "      <td>Ward</td>\n",
       "      <td>True</td>\n",
       "      <td>Mark  Ward</td>\n",
       "      <td></td>\n",
       "      <td>Pathways from social disconnection to suicidal...</td>\n",
       "      <td>70838</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>15447209</td>\n",
       "      <td>Colin</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Hisey</td>\n",
       "      <td>True</td>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>POSTDOCTORAL SCHOLAR</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "      <td>99955</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>8575677</td>\n",
       "      <td>Jianhang</td>\n",
       "      <td></td>\n",
       "      <td>Jia</td>\n",
       "      <td>True</td>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>Imaging Core</td>\n",
       "      <td>124058</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14200522</td>\n",
       "      <td>EDILBERTO</td>\n",
       "      <td></td>\n",
       "      <td>AMORIM DE CERQUEIRA</td>\n",
       "      <td>True</td>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td></td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "      <td>225652</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 10 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile_id first_name middle_name            last_name  is_contact_pi  \\\n",
       "0      9239369      Nader      Nabile            Massarweh           True   \n",
       "1      8613963       Alex                       Sox-Harris          False   \n",
       "2     10051972   Kwan-Liu                               Ma           True   \n",
       "3      6158233  SHIN-PING                               TU          False   \n",
       "4      9860649     Daniel     Michael             Blonigen           True   \n",
       "..         ...        ...         ...                  ...            ...   \n",
       "61     9490186      Peter      Xuekun                 Song          False   \n",
       "62    78510112       Mark                             Ward           True   \n",
       "63    15447209      Colin         Lee                Hisey           True   \n",
       "64     8575677   Jianhang                              Jia           True   \n",
       "65    14200522  EDILBERTO              AMORIM DE CERQUEIRA           True   \n",
       "\n",
       "                         full_name                           title  \\\n",
       "0           Nader Nabile Massarweh  ASSOCIATE PROFESSOR OF SURGERY   \n",
       "1                 Alex  Sox-Harris                                   \n",
       "2                     Kwan-Liu  Ma                                   \n",
       "3                    SHIN-PING  TU                       PROFESSOR   \n",
       "4          Daniel Michael Blonigen                                   \n",
       "..                             ...                             ...   \n",
       "61               Peter Xuekun Song                       PROFESSOR   \n",
       "62                      Mark  Ward                                   \n",
       "63                 Colin Lee Hisey            POSTDOCTORAL SCHOLAR   \n",
       "64                   Jianhang  Jia                       PROFESSOR   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA                                   \n",
       "\n",
       "                                        project_title award_amount is_active  \n",
       "0   Using Modern Data Science Methods and Advanced...       677235      True  \n",
       "1   Using Modern Data Science Methods and Advanced...       677235      True  \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True  \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True  \n",
       "4   Using Data Analytics and Targeted Whole Health...         None      True  \n",
       "..                                                ...          ...       ...  \n",
       "61  E3Gen: Multigenerational Influences of Social ...       360960      True  \n",
       "62  Pathways from social disconnection to suicidal...        70838      True  \n",
       "63  Machine Learning-enabled Classification of Ext...        99955      True  \n",
       "64                                       Imaging Core       124058      True  \n",
       "65  Physiology-driven seizure management post-card...       225652      True  \n",
       "\n",
       "[66 rows x 10 columns]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf = pd.json_normalize(response_data[\"results\"],record_path='principal_investigators',\n",
    "                        meta=['project_title','award_amount','is_active'])\n",
    "mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6068c98",
   "metadata": {},
   "outputs": [],
   "source": [
    "mdf['Organization']=ORG['Organization']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "357a3828",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "    </tr>\n",
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       "      <td></td>\n",
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       "      <td>False</td>\n",
       "      <td>SHIN-PING  TU</td>\n",
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       "      <td>UNIVERSITY OF CALIFORNIA AT DAVIS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>9860649</td>\n",
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       "      <td>Michael</td>\n",
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       "      <td>Daniel Michael Blonigen</td>\n",
       "      <td></td>\n",
       "      <td>Using Data Analytics and Targeted Whole Health...</td>\n",
       "      <td>None</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
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       "    <tr>\n",
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       "      <td>Peter</td>\n",
       "      <td>Xuekun</td>\n",
       "      <td>Song</td>\n",
       "      <td>False</td>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "      <td>360960</td>\n",
       "      <td>True</td>\n",
       "      <td>UNIVERSITY OF MICHIGAN AT ANN ARBOR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
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       "      <td>15447209</td>\n",
       "      <td>Colin</td>\n",
       "      <td>Lee</td>\n",
       "      <td>Hisey</td>\n",
       "      <td>True</td>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>POSTDOCTORAL SCHOLAR</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "      <td>99955</td>\n",
       "      <td>True</td>\n",
       "      <td>OHIO STATE UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>8575677</td>\n",
       "      <td>Jianhang</td>\n",
       "      <td></td>\n",
       "      <td>Jia</td>\n",
       "      <td>True</td>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>Imaging Core</td>\n",
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       "      <td>True</td>\n",
       "      <td>UNIVERSITY OF KENTUCKY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>14200522</td>\n",
       "      <td>EDILBERTO</td>\n",
       "      <td></td>\n",
       "      <td>AMORIM DE CERQUEIRA</td>\n",
       "      <td>True</td>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td></td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "      <td>225652</td>\n",
       "      <td>True</td>\n",
       "      <td>UNIVERSITY OF CALIFORNIA, SAN FRANCISCO</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 11 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile_id first_name middle_name            last_name  is_contact_pi  \\\n",
       "0      9239369      Nader      Nabile            Massarweh           True   \n",
       "1      8613963       Alex                       Sox-Harris          False   \n",
       "2     10051972   Kwan-Liu                               Ma           True   \n",
       "3      6158233  SHIN-PING                               TU          False   \n",
       "4      9860649     Daniel     Michael             Blonigen           True   \n",
       "..         ...        ...         ...                  ...            ...   \n",
       "61     9490186      Peter      Xuekun                 Song          False   \n",
       "62    78510112       Mark                             Ward           True   \n",
       "63    15447209      Colin         Lee                Hisey           True   \n",
       "64     8575677   Jianhang                              Jia           True   \n",
       "65    14200522  EDILBERTO              AMORIM DE CERQUEIRA           True   \n",
       "\n",
       "                         full_name                           title  \\\n",
       "0           Nader Nabile Massarweh  ASSOCIATE PROFESSOR OF SURGERY   \n",
       "1                 Alex  Sox-Harris                                   \n",
       "2                     Kwan-Liu  Ma                                   \n",
       "3                    SHIN-PING  TU                       PROFESSOR   \n",
       "4          Daniel Michael Blonigen                                   \n",
       "..                             ...                             ...   \n",
       "61               Peter Xuekun Song                       PROFESSOR   \n",
       "62                      Mark  Ward                                   \n",
       "63                 Colin Lee Hisey            POSTDOCTORAL SCHOLAR   \n",
       "64                   Jianhang  Jia                       PROFESSOR   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA                                   \n",
       "\n",
       "                                        project_title award_amount is_active  \\\n",
       "0   Using Modern Data Science Methods and Advanced...       677235      True   \n",
       "1   Using Modern Data Science Methods and Advanced...       677235      True   \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True   \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True   \n",
       "4   Using Data Analytics and Targeted Whole Health...         None      True   \n",
       "..                                                ...          ...       ...   \n",
       "61  E3Gen: Multigenerational Influences of Social ...       360960      True   \n",
       "62  Pathways from social disconnection to suicidal...        70838      True   \n",
       "63  Machine Learning-enabled Classification of Ext...        99955      True   \n",
       "64                                       Imaging Core       124058      True   \n",
       "65  Physiology-driven seizure management post-card...       225652      True   \n",
       "\n",
       "                                Organization  \n",
       "0                           EMORY UNIVERSITY  \n",
       "1                           EMORY UNIVERSITY  \n",
       "2          UNIVERSITY OF CALIFORNIA AT DAVIS  \n",
       "3          UNIVERSITY OF CALIFORNIA AT DAVIS  \n",
       "4   VETERANS ADMIN PALO ALTO HEALTH CARE SYS  \n",
       "..                                       ...  \n",
       "61       UNIVERSITY OF MICHIGAN AT ANN ARBOR  \n",
       "62                    TRINITY COLLEGE DUBLIN  \n",
       "63                     OHIO STATE UNIVERSITY  \n",
       "64                    UNIVERSITY OF KENTUCKY  \n",
       "65   UNIVERSITY OF CALIFORNIA, SAN FRANCISCO  \n",
       "\n",
       "[66 rows x 11 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "093062bb",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9841070</td>\n",
       "      <td>Gustavo</td>\n",
       "      <td></td>\n",
       "      <td>Glusman</td>\n",
       "      <td>True</td>\n",
       "      <td>Gustavo  Glusman</td>\n",
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       "      <td>DOCKET: accelerating knowledge extraction from...</td>\n",
       "      <td>609144</td>\n",
       "      <td>True</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15443354</td>\n",
       "      <td>Jennifer</td>\n",
       "      <td></td>\n",
       "      <td>Hadlock</td>\n",
       "      <td>False</td>\n",
       "      <td>Jennifer  Hadlock</td>\n",
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       "      <td>True</td>\n",
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       "      <th>7</th>\n",
       "      <td>8300690</td>\n",
       "      <td>ILYA</td>\n",
       "      <td></td>\n",
       "      <td>SHMULEVICH</td>\n",
       "      <td>False</td>\n",
       "      <td>ILYA  SHMULEVICH</td>\n",
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       "      <td>609144</td>\n",
       "      <td>True</td>\n",
       "      <td>INSTITUTE FOR SYSTEMS BIOLOGY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2089750</td>\n",
       "      <td>SERGIO</td>\n",
       "      <td>E</td>\n",
       "      <td>BARANZINI</td>\n",
       "      <td>False</td>\n",
       "      <td>SERGIO E BARANZINI</td>\n",
       "      <td></td>\n",
       "      <td>EVIDARA: Automated Evidential Support from Raw...</td>\n",
       "      <td>532891</td>\n",
       "      <td>True</td>\n",
       "      <td>INSTITUTE FOR SYSTEMS BIOLOGY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9454292</td>\n",
       "      <td>Sui</td>\n",
       "      <td></td>\n",
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       "      <td>True</td>\n",
       "      <td>Sui  Huang</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>EVIDARA: Automated Evidential Support from Raw...</td>\n",
       "      <td>532891</td>\n",
       "      <td>True</td>\n",
       "      <td>INSTITUTE FOR SYSTEMS BIOLOGY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10790241</td>\n",
       "      <td>Jonathan</td>\n",
       "      <td></td>\n",
       "      <td>Elmer</td>\n",
       "      <td>False</td>\n",
       "      <td>Jonathan  Elmer</td>\n",
       "      <td></td>\n",
       "      <td>PREcision Care In Cardiac ArrEst - ICECAP (PRE...</td>\n",
       "      <td>1113712</td>\n",
       "      <td>True</td>\n",
       "      <td>STANFORD UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>6270502</td>\n",
       "      <td>KAREN</td>\n",
       "      <td>G</td>\n",
       "      <td>HIRSCH</td>\n",
       "      <td>True</td>\n",
       "      <td>KAREN G HIRSCH</td>\n",
       "      <td>ASSISTANT PROFESSOR</td>\n",
       "      <td>PREcision Care In Cardiac ArrEst - ICECAP (PRE...</td>\n",
       "      <td>1113712</td>\n",
       "      <td>True</td>\n",
       "      <td>STANFORD UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9574233</td>\n",
       "      <td>Rhonda</td>\n",
       "      <td></td>\n",
       "      <td>Szczesniak</td>\n",
       "      <td>True</td>\n",
       "      <td>Rhonda  Szczesniak</td>\n",
       "      <td></td>\n",
       "      <td>R01- Mapping environmental contributions to ra...</td>\n",
       "      <td>406637</td>\n",
       "      <td>True</td>\n",
       "      <td>CINCINNATI CHILDRENS HOSP MED CTR</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>1903458</td>\n",
       "      <td>JOEL</td>\n",
       "      <td>T</td>\n",
       "      <td>NIGG</td>\n",
       "      <td>True</td>\n",
       "      <td>JOEL T NIGG</td>\n",
       "      <td>PROFESSOR</td>\n",
       "      <td>ADHD heterogeneity, mechanisms, and risk profile</td>\n",
       "      <td>682943</td>\n",
       "      <td>True</td>\n",
       "      <td>OREGON HEALTH &amp; SCIENCE UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14315038</td>\n",
       "      <td>Sung Eun</td>\n",
       "      <td>EUN</td>\n",
       "      <td>Choi</td>\n",
       "      <td>True</td>\n",
       "      <td>Sung Eun EUN Choi</td>\n",
       "      <td>INSTRUCTOR</td>\n",
       "      <td>Reducing oral health disparities in children u...</td>\n",
       "      <td>114113</td>\n",
       "      <td>True</td>\n",
       "      <td>HARVARD MEDICAL SCHOOL</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>1885436</td>\n",
       "      <td>Deanna</td>\n",
       "      <td></td>\n",
       "      <td>Barch</td>\n",
       "      <td>False</td>\n",
       "      <td>Deanna  Barch</td>\n",
       "      <td></td>\n",
       "      <td>Characterizing pubertal and age mechanisms of ...</td>\n",
       "      <td>726975</td>\n",
       "      <td>True</td>\n",
       "      <td>HARVARD UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>9763495</td>\n",
       "      <td>Leah</td>\n",
       "      <td>Helene</td>\n",
       "      <td>Somerville</td>\n",
       "      <td>True</td>\n",
       "      <td>Leah Helene Somerville</td>\n",
       "      <td>ASSISTANT PROFESSOR</td>\n",
       "      <td>Characterizing pubertal and age mechanisms of ...</td>\n",
       "      <td>726975</td>\n",
       "      <td>True</td>\n",
       "      <td>HARVARD UNIVERSITY</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>8876992</td>\n",
       "      <td>Anne</td>\n",
       "      <td>E.</td>\n",
       "      <td>Carpenter</td>\n",
       "      <td>True</td>\n",
       "      <td>Anne E. Carpenter</td>\n",
       "      <td>DIRECTOR, IMAGING PLATFORM</td>\n",
       "      <td>Center for Open Bioimage Analysis</td>\n",
       "      <td>302439</td>\n",
       "      <td>True</td>\n",
       "      <td>BROAD INSTITUTE, INC.</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>14416846</td>\n",
       "      <td>Luca</td>\n",
       "      <td></td>\n",
       "      <td>Bonomi</td>\n",
       "      <td>True</td>\n",
       "      <td>Luca  Bonomi</td>\n",
       "      <td>ASSISTANT PROFESSOR</td>\n",
       "      <td>SAFEGENOMES: Strong privacy Assurance For Effe...</td>\n",
       "      <td>223219</td>\n",
       "      <td>True</td>\n",
       "      <td>VANDERBILT UNIVERSITY MEDICAL CENTER</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13110137</td>\n",
       "      <td>Emily</td>\n",
       "      <td>Taylor</td>\n",
       "      <td>Hebert</td>\n",
       "      <td>True</td>\n",
       "      <td>Emily Taylor Hebert</td>\n",
       "      <td>ASSISTANT PROFESSOR</td>\n",
       "      <td>Using Machine Learning to Develop Just-in-Time...</td>\n",
       "      <td>248993</td>\n",
       "      <td>True</td>\n",
       "      <td>UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    profile_id first_name middle_name   last_name  is_contact_pi  \\\n",
       "0      9239369      Nader      Nabile   Massarweh           True   \n",
       "1      8613963       Alex              Sox-Harris          False   \n",
       "2     10051972   Kwan-Liu                      Ma           True   \n",
       "3      6158233  SHIN-PING                      TU          False   \n",
       "4      9860649     Daniel     Michael    Blonigen           True   \n",
       "5      9841070    Gustavo                 Glusman           True   \n",
       "6     15443354   Jennifer                 Hadlock          False   \n",
       "7      8300690       ILYA              SHMULEVICH          False   \n",
       "8      2089750     SERGIO           E   BARANZINI          False   \n",
       "9      9454292        Sui                   Huang           True   \n",
       "10    10790241   Jonathan                   Elmer          False   \n",
       "11     6270502      KAREN           G      HIRSCH           True   \n",
       "12     9574233     Rhonda              Szczesniak           True   \n",
       "13     1903458       JOEL           T        NIGG           True   \n",
       "14    14315038   Sung Eun         EUN        Choi           True   \n",
       "15     1885436     Deanna                   Barch          False   \n",
       "16     9763495       Leah      Helene  Somerville           True   \n",
       "17     8876992       Anne          E.   Carpenter           True   \n",
       "18    14416846       Luca                  Bonomi           True   \n",
       "19    13110137      Emily      Taylor      Hebert           True   \n",
       "\n",
       "                  full_name                            title  \\\n",
       "0    Nader Nabile Massarweh   ASSOCIATE PROFESSOR OF SURGERY   \n",
       "1          Alex  Sox-Harris                                    \n",
       "2              Kwan-Liu  Ma                                    \n",
       "3             SHIN-PING  TU                        PROFESSOR   \n",
       "4   Daniel Michael Blonigen                                    \n",
       "5          Gustavo  Glusman              PRINCIPAL SCIENTIST   \n",
       "6         Jennifer  Hadlock  PRINCIPAL MEDICAL INFORMATICIST   \n",
       "7          ILYA  SHMULEVICH                        PROFESSOR   \n",
       "8        SERGIO E BARANZINI                                    \n",
       "9                Sui  Huang                        PROFESSOR   \n",
       "10          Jonathan  Elmer                                    \n",
       "11           KAREN G HIRSCH              ASSISTANT PROFESSOR   \n",
       "12       Rhonda  Szczesniak                                    \n",
       "13              JOEL T NIGG                        PROFESSOR   \n",
       "14        Sung Eun EUN Choi                       INSTRUCTOR   \n",
       "15            Deanna  Barch                                    \n",
       "16   Leah Helene Somerville              ASSISTANT PROFESSOR   \n",
       "17        Anne E. Carpenter       DIRECTOR, IMAGING PLATFORM   \n",
       "18             Luca  Bonomi              ASSISTANT PROFESSOR   \n",
       "19      Emily Taylor Hebert              ASSISTANT PROFESSOR   \n",
       "\n",
       "                                        project_title award_amount is_active  \\\n",
       "0   Using Modern Data Science Methods and Advanced...       677235      True   \n",
       "1   Using Modern Data Science Methods and Advanced...       677235      True   \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True   \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...       245468      True   \n",
       "4   Using Data Analytics and Targeted Whole Health...         None      True   \n",
       "5   DOCKET: accelerating knowledge extraction from...       609144      True   \n",
       "6   DOCKET: accelerating knowledge extraction from...       609144      True   \n",
       "7   DOCKET: accelerating knowledge extraction from...       609144      True   \n",
       "8   EVIDARA: Automated Evidential Support from Raw...       532891      True   \n",
       "9   EVIDARA: Automated Evidential Support from Raw...       532891      True   \n",
       "10  PREcision Care In Cardiac ArrEst - ICECAP (PRE...      1113712      True   \n",
       "11  PREcision Care In Cardiac ArrEst - ICECAP (PRE...      1113712      True   \n",
       "12  R01- Mapping environmental contributions to ra...       406637      True   \n",
       "13   ADHD heterogeneity, mechanisms, and risk profile       682943      True   \n",
       "14  Reducing oral health disparities in children u...       114113      True   \n",
       "15  Characterizing pubertal and age mechanisms of ...       726975      True   \n",
       "16  Characterizing pubertal and age mechanisms of ...       726975      True   \n",
       "17                  Center for Open Bioimage Analysis       302439      True   \n",
       "18  SAFEGENOMES: Strong privacy Assurance For Effe...       223219      True   \n",
       "19  Using Machine Learning to Develop Just-in-Time...       248993      True   \n",
       "\n",
       "                                Organization  \n",
       "0                           EMORY UNIVERSITY  \n",
       "1                           EMORY UNIVERSITY  \n",
       "2          UNIVERSITY OF CALIFORNIA AT DAVIS  \n",
       "3          UNIVERSITY OF CALIFORNIA AT DAVIS  \n",
       "4   VETERANS ADMIN PALO ALTO HEALTH CARE SYS  \n",
       "5              INSTITUTE FOR SYSTEMS BIOLOGY  \n",
       "6              INSTITUTE FOR SYSTEMS BIOLOGY  \n",
       "7              INSTITUTE FOR SYSTEMS BIOLOGY  \n",
       "8              INSTITUTE FOR SYSTEMS BIOLOGY  \n",
       "9              INSTITUTE FOR SYSTEMS BIOLOGY  \n",
       "10                       STANFORD UNIVERSITY  \n",
       "11                       STANFORD UNIVERSITY  \n",
       "12         CINCINNATI CHILDRENS HOSP MED CTR  \n",
       "13        OREGON HEALTH & SCIENCE UNIVERSITY  \n",
       "14                    HARVARD MEDICAL SCHOOL  \n",
       "15                        HARVARD UNIVERSITY  \n",
       "16                        HARVARD UNIVERSITY  \n",
       "17                     BROAD INSTITUTE, INC.  \n",
       "18      VANDERBILT UNIVERSITY MEDICAL CENTER  \n",
       "19  UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mdf.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "e224eda8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Organization</th>\n",
       "      <th>Project</th>\n",
       "      <th>Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Nader Nabile Massarweh</td>\n",
       "      <td>EMORY UNIVERSITY</td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "      <td>677235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alex  Sox-Harris</td>\n",
       "      <td>EMORY UNIVERSITY</td>\n",
       "      <td>Using Modern Data Science Methods and Advanced...</td>\n",
       "      <td>677235</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Kwan-Liu  Ma</td>\n",
       "      <td>UNIVERSITY OF CALIFORNIA AT DAVIS</td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "      <td>245468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>SHIN-PING  TU</td>\n",
       "      <td>UNIVERSITY OF CALIFORNIA AT DAVIS</td>\n",
       "      <td>SCH: Smart EHR Data Analytics to Enhance Cance...</td>\n",
       "      <td>245468</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Daniel Michael Blonigen</td>\n",
       "      <td>VETERANS ADMIN PALO ALTO HEALTH CARE SYS</td>\n",
       "      <td>Using Data Analytics and Targeted Whole Health...</td>\n",
       "      <td>None</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>UNIVERSITY OF MICHIGAN AT ANN ARBOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "      <td>360960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Mark  Ward</td>\n",
       "      <td>TRINITY COLLEGE DUBLIN</td>\n",
       "      <td>Pathways from social disconnection to suicidal...</td>\n",
       "      <td>70838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>OHIO STATE UNIVERSITY</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "      <td>99955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>UNIVERSITY OF KENTUCKY</td>\n",
       "      <td>Imaging Core</td>\n",
       "      <td>124058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td>UNIVERSITY OF CALIFORNIA, SAN FRANCISCO</td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "      <td>225652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>66 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name                              Organization  \\\n",
       "0           Nader Nabile Massarweh                          EMORY UNIVERSITY   \n",
       "1                 Alex  Sox-Harris                          EMORY UNIVERSITY   \n",
       "2                     Kwan-Liu  Ma         UNIVERSITY OF CALIFORNIA AT DAVIS   \n",
       "3                    SHIN-PING  TU         UNIVERSITY OF CALIFORNIA AT DAVIS   \n",
       "4          Daniel Michael Blonigen  VETERANS ADMIN PALO ALTO HEALTH CARE SYS   \n",
       "..                             ...                                       ...   \n",
       "61               Peter Xuekun Song       UNIVERSITY OF MICHIGAN AT ANN ARBOR   \n",
       "62                      Mark  Ward                    TRINITY COLLEGE DUBLIN   \n",
       "63                 Colin Lee Hisey                     OHIO STATE UNIVERSITY   \n",
       "64                   Jianhang  Jia                    UNIVERSITY OF KENTUCKY   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA   UNIVERSITY OF CALIFORNIA, SAN FRANCISCO   \n",
       "\n",
       "                                              Project Funding  \n",
       "0   Using Modern Data Science Methods and Advanced...  677235  \n",
       "1   Using Modern Data Science Methods and Advanced...  677235  \n",
       "2   SCH: Smart EHR Data Analytics to Enhance Cance...  245468  \n",
       "3   SCH: Smart EHR Data Analytics to Enhance Cance...  245468  \n",
       "4   Using Data Analytics and Targeted Whole Health...    None  \n",
       "..                                                ...     ...  \n",
       "61  E3Gen: Multigenerational Influences of Social ...  360960  \n",
       "62  Pathways from social disconnection to suicidal...   70838  \n",
       "63  Machine Learning-enabled Classification of Ext...   99955  \n",
       "64                                       Imaging Core  124058  \n",
       "65  Physiology-driven seizure management post-card...  225652  \n",
       "\n",
       "[66 rows x 4 columns]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIH_df = pd.DataFrame()\n",
    "NIH_df[\"Name\"] = mdf[\"full_name\"]\n",
    "NIH_df[\"Organization\"] = mdf[\"Organization\"]\n",
    "NIH_df[\"Project\"] = mdf[\"project_title\"]\n",
    "NIH_df[\"Funding\"] = mdf[\"award_amount\"]\n",
    "NIH_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "649bd2f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#If you have very specific criterias\n",
    "\n",
    "test1 = {\n",
    "     \"criteria\":\n",
    "     {\n",
    "        \"include_active_projects\": True,\n",
    "        \"fiscal_years\":[],\n",
    "        \"po_names\": [{\"any_name\": \"\"},{\"first_name\": \"\"}], \n",
    "        \"pi_names\": [{\"any_name\": \"\"},{\"first_name\": \"\"}],\n",
    "        \"org_names\":[\"\"],\n",
    "        \"org_cities\":[\"\"],\n",
    "        \"org_states\": [\"\"],\n",
    "        \"agencies\": [\"\"],\n",
    "        \"is_agency_funding\": True,\n",
    "        \"organization_type\":[\"\"],\n",
    "        \"award_amount_range\":{\"min_amount\":0, \"max_amount\": \"\"}\n",
    "\n",
    "         \n",
    "     },\n",
    "     \"include_fields\": [\n",
    "         \"ApplId\",\"SubprojectId\",\"FiscalYear\",\"Organization\", \"ProjectNum\",\"OrgCountry\",\n",
    "         \"ProjectNumSplit\",\"ContactPiName\",\"AllText\",\"FullStudySection\",\n",
    "         \"ProjectStartDate\",\"ProjectEndDate\"\n",
    "     ],\n",
    "     \"offset\":0,\n",
    "     \"limit\":10\n",
    " }\n",
    "test1_response = requests.post(\"https://api.reporter.nih.gov/v2/projects/search\", json=test1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "81a5f948",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "200\n",
      "{\"meta\":{\"search_id\":\"92DXq0G3OEKeHVJGVKoiAQ\",\"total\":96782,\"offset\":0,\"limit\":10,\"sort_field\":null,\"sort_order\":\"ASC\",\"sorted_by_relevance\":true,\"properties\":{\"URL\":\"https:/reporter.nih.gov/search/92DXq0G3OEKeHVJGVKoiAQ/projects\"}},\"results\":[{\"appl_id\":10181056,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5I01HX002778-02\",\"organization\":{\"org_name\":\"VA SALT LAKE CITY HEALTHCARE SYSTEM\",\"city\":null,\"country\":null,\"org_city\":\"SALT LAKE CITY\",\"org_country\":\"UNITED STATES\",\"org_state\":\"UT\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"009094756\"],\"org_ueis\":[\"YYULHSE8AL99\"],\"primary_duns\":\"009094756\",\"primary_uei\":\"YYULHSE8AL99\",\"org_fips\":\"US\",\"org_ipf_code\":\"481089\",\"org_zipcode\":\"841480001\",\"external_org_id\":481089},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"I01\",\"ic_code\":\"HX\",\"serial_num\":\"002778\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"contact_pi_name\":\"GAWRON, ANDREW J\",\"project_start_date\":\"2020-06-01T12:06:00Z\",\"project_end_date\":\"2023-05-31T12:05:00Z\",\"full_study_section\":{\"srg_code\":\"HS8A\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[HS8A]\"}},{\"appl_id\":10307103,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01NS100847-05\",\"organization\":{\"org_name\":\"JOHNS HOPKINS UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"BALTIMORE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"MD\",\"org_state_name\":null,\"dept_type\":\"PSYCHIATRY\",\"fips_country_code\":null,\"org_duns\":[\"001910777\"],\"org_ueis\":[\"FTMTDMBR29C7\"],\"primary_duns\":\"001910777\",\"primary_uei\":\"FTMTDMBR29C7\",\"org_fips\":\"US\",\"org_ipf_code\":\"4134401\",\"org_zipcode\":\"212182680\",\"external_org_id\":4134401},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"NS\",\"serial_num\":\"100847\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"contact_pi_name\":\"COUGHLIN, JENNIFER MARIE\",\"project_start_date\":\"2018-01-01T12:01:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"full_study_section\":{\"srg_code\":\"ZRG1\",\"srg_flex\":\"\",\"sra_designator_code\":\"BDCN\",\"sra_flex_code\":\"W\",\"group_code\":\"\",\"name\":\"Special Emphasis Panel[ZRG1-BDCN-W(02)M]\"}},{\"appl_id\":10316433,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"1I01HX002941-01A2\",\"organization\":{\"org_name\":\"VETERANS ADMIN PALO ALTO HEALTH CARE SYS\",\"city\":null,\"country\":null,\"org_city\":\"PALO ALTO\",\"org_country\":\"UNITED STATES\",\"org_state\":\"CA\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"046017455\"],\"org_ueis\":[\"ZN2TPL7TCAY7\"],\"primary_duns\":\"046017455\",\"primary_uei\":\"ZN2TPL7TCAY7\",\"org_fips\":\"US\",\"org_ipf_code\":\"481014\",\"org_zipcode\":\"943041207\",\"external_org_id\":481014},\"project_num_split\":{\"appl_type_code\":\"1\",\"activity_code\":\"I01\",\"ic_code\":\"HX\",\"serial_num\":\"002941\",\"support_year\":\"01\",\"full_support_year\":\"01A2\",\"suffix_code\":\"A2\"},\"contact_pi_name\":\"TRIVEDI, RANAK \",\"project_start_date\":\"2022-10-01T12:10:00Z\",\"project_end_date\":\"2026-09-30T12:09:00Z\",\"full_study_section\":{\"srg_code\":\"HSR5\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"HSR-5 Health Care System Organization and Delivery[HSR5]\"}},{\"appl_id\":10335235,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5T37MD014208-04\",\"organization\":{\"org_name\":\"UNIVERSITY OF WASHINGTON\",\"city\":null,\"country\":null,\"org_city\":\"SEATTLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WA\",\"org_state_name\":null,\"dept_type\":\"NONE\",\"fips_country_code\":null,\"org_duns\":[\"605799469\"],\"org_ueis\":[\"HD1WMN6945W6\"],\"primary_duns\":\"605799469\",\"primary_uei\":\"HD1WMN6945W6\",\"org_fips\":\"US\",\"org_ipf_code\":\"9087701\",\"org_zipcode\":\"981959472\",\"external_org_id\":9087701},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"T37\",\"ic_code\":\"MD\",\"serial_num\":\"014208\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"contact_pi_name\":\"WALTERS, KARINA L.\",\"project_start_date\":\"2019-07-19T12:07:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"full_study_section\":{\"srg_code\":\"ZMD1\",\"srg_flex\":\"\",\"sra_designator_code\":\"RP\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"ZMD1-RP(M1)\"}},{\"appl_id\":10379076,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5I21HX003227-02\",\"organization\":{\"org_name\":\"MICHAEL E DEBAKEY VA MEDICAL CENTER\",\"city\":null,\"country\":null,\"org_city\":\"HOUSTON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"TX\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"078446044\"],\"org_ueis\":[\"CEAXWSLGMMV7\"],\"primary_duns\":\"078446044\",\"primary_uei\":\"CEAXWSLGMMV7\",\"org_fips\":\"US\",\"org_ipf_code\":\"481087\",\"org_zipcode\":\"770304298\",\"external_org_id\":481087},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"I21\",\"ic_code\":\"HX\",\"serial_num\":\"003227\",\"support_year\":\"02\",\"full_support_year\":\"02\",\"suffix_code\":\"\"},\"contact_pi_name\":\"KAMDAR, NIPA P\",\"project_start_date\":\"2021-04-01T12:04:00Z\",\"project_end_date\":\"2022-09-30T12:09:00Z\",\"full_study_section\":{\"srg_code\":\"HSR2\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"HSR-2 Behavioral, Social, and Cultural Determinants of Health[HSR2]\"}},{\"appl_id\":10384063,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"4R37AI053067-22\",\"organization\":{\"org_name\":\"UNIVERSITY OF WISCONSIN-MADISON\",\"city\":null,\"country\":null,\"org_city\":\"MADISON\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WI\",\"org_state_name\":null,\"dept_type\":\"MICROBIOLOGY/IMMUN/VIROLOGY\",\"fips_country_code\":null,\"org_duns\":[\"161202122\"],\"org_ueis\":[\"LCLSJAGTNZQ7\"],\"primary_duns\":\"161202122\",\"primary_uei\":\"LCLSJAGTNZQ7\",\"org_fips\":\"US\",\"org_ipf_code\":\"578503\",\"org_zipcode\":\"537151218\",\"external_org_id\":578503},\"project_num_split\":{\"appl_type_code\":\"4\",\"activity_code\":\"R37\",\"ic_code\":\"AI\",\"serial_num\":\"053067\",\"support_year\":\"22\",\"full_support_year\":\"22\",\"suffix_code\":\"\"},\"contact_pi_name\":\"SPERANDIO, VANESSA \",\"project_start_date\":\"2023-01-01T12:01:00Z\",\"project_end_date\":\"2027-12-31T12:12:00Z\",\"full_study_section\":{\"srg_code\":\"NSS\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"NSS\"}},{\"appl_id\":10356810,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R38AG065762-03\",\"organization\":{\"org_name\":\"DUKE UNIVERSITY\",\"city\":null,\"country\":null,\"org_city\":\"DURHAM\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NC\",\"org_state_name\":null,\"dept_type\":\"FAMILY MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"044387793\"],\"org_ueis\":[\"TP7EK8DZV6N5\"],\"primary_duns\":\"044387793\",\"primary_uei\":\"TP7EK8DZV6N5\",\"org_fips\":\"US\",\"org_ipf_code\":\"2221101\",\"org_zipcode\":\"277054673\",\"external_org_id\":2221101},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R38\",\"ic_code\":\"AG\",\"serial_num\":\"065762\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"contact_pi_name\":\"VIERA, ANTHONY J\",\"project_start_date\":\"2020-02-01T12:02:00Z\",\"project_end_date\":\"2024-11-30T12:11:00Z\",\"full_study_section\":{\"srg_code\":\"ZHL1\",\"srg_flex\":\"\",\"sra_designator_code\":\"CSR\",\"sra_flex_code\":\"I\",\"group_code\":\"\",\"name\":\"ZHL1-CSR-I(O1)\"}},{\"appl_id\":10371019,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01MD011596-05\",\"organization\":{\"org_name\":\"UNIVERSITY OF WASHINGTON\",\"city\":null,\"country\":null,\"org_city\":\"SEATTLE\",\"org_country\":\"UNITED STATES\",\"org_state\":\"WA\",\"org_state_name\":null,\"dept_type\":\"PUBLIC HEALTH & PREV MEDICINE\",\"fips_country_code\":null,\"org_duns\":[\"605799469\"],\"org_ueis\":[\"HD1WMN6945W6\"],\"primary_duns\":\"605799469\",\"primary_uei\":\"HD1WMN6945W6\",\"org_fips\":\"US\",\"org_ipf_code\":\"9087701\",\"org_zipcode\":\"981959472\",\"external_org_id\":9087701},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"MD\",\"serial_num\":\"011596\",\"support_year\":\"05\",\"full_support_year\":\"05\",\"suffix_code\":\"\"},\"contact_pi_name\":\"FRETTS, AMANDA MAE\",\"project_start_date\":\"2018-04-01T12:04:00Z\",\"project_end_date\":\"2023-11-30T12:11:00Z\",\"full_study_section\":{\"srg_code\":\"CIHB\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Community Influences on Health Behavior Study Section[CIHB]\"}},{\"appl_id\":10392938,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5IK6HX003161-03\",\"organization\":{\"org_name\":\"DURHAM VA MEDICAL CENTER\",\"city\":null,\"country\":null,\"org_city\":\"DURHAM\",\"org_country\":\"UNITED STATES\",\"org_state\":\"NC\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"043241082\"],\"org_ueis\":[\"FWJ7MMLUN2M1\"],\"primary_duns\":\"043241082\",\"primary_uei\":\"FWJ7MMLUN2M1\",\"org_fips\":\"US\",\"org_ipf_code\":\"481065\",\"org_zipcode\":\"277053875\",\"external_org_id\":481065},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"IK6\",\"ic_code\":\"HX\",\"serial_num\":\"003161\",\"support_year\":\"03\",\"full_support_year\":\"03\",\"suffix_code\":\"\"},\"contact_pi_name\":\"BOSWORTH, HAYDEN B\",\"project_start_date\":\"2020-04-01T12:04:00Z\",\"project_end_date\":\"2027-02-28T12:02:00Z\",\"full_study_section\":{\"srg_code\":\"HSR7\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"HSR-7 Health Services Research Special Emphasis[HSR7]\"}},{\"appl_id\":10447787,\"subproject_id\":null,\"fiscal_year\":2023,\"project_num\":\"5R01AR073831-04\",\"organization\":{\"org_name\":\"CARLE FOUNDATION\",\"city\":null,\"country\":null,\"org_city\":\"URBANA\",\"org_country\":\"UNITED STATES\",\"org_state\":\"IL\",\"org_state_name\":null,\"dept_type\":null,\"fips_country_code\":null,\"org_duns\":[\"079151171\"],\"org_ueis\":[\"DCBCUBQLAEN1\"],\"primary_duns\":\"079151171\",\"primary_uei\":\"DCBCUBQLAEN1\",\"org_fips\":\"US\",\"org_ipf_code\":\"1659101\",\"org_zipcode\":\"618012529\",\"external_org_id\":1659101},\"project_num_split\":{\"appl_type_code\":\"5\",\"activity_code\":\"R01\",\"ic_code\":\"AR\",\"serial_num\":\"073831\",\"support_year\":\"04\",\"full_support_year\":\"04\",\"suffix_code\":\"\"},\"contact_pi_name\":\"DAMON, BRUCE M.\",\"project_start_date\":\"2019-05-07T12:05:00Z\",\"project_end_date\":\"2024-12-31T12:12:00Z\",\"full_study_section\":{\"srg_code\":\"SMEP\",\"srg_flex\":\"\",\"sra_designator_code\":\"\",\"sra_flex_code\":\"\",\"group_code\":\"\",\"name\":\"Skeletal Muscle and Exercise Physiology Study Section[SMEP]\"}}]}\n"
     ]
    }
   ],
   "source": [
    "print(test1_response.status_code)\n",
    "print(test1_response.text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0d15ae37",
   "metadata": {},
   "outputs": [],
   "source": [
    "test1_response_data = test1_response.json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6ec2629b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>appl_id</th>\n",
       "      <th>subproject_id</th>\n",
       "      <th>fiscal_year</th>\n",
       "      <th>project_num</th>\n",
       "      <th>contact_pi_name</th>\n",
       "      <th>project_start_date</th>\n",
       "      <th>project_end_date</th>\n",
       "      <th>organization.org_name</th>\n",
       "      <th>organization.city</th>\n",
       "      <th>organization.country</th>\n",
       "      <th>...</th>\n",
       "      <th>project_num_split.serial_num</th>\n",
       "      <th>project_num_split.support_year</th>\n",
       "      <th>project_num_split.full_support_year</th>\n",
       "      <th>project_num_split.suffix_code</th>\n",
       "      <th>full_study_section.srg_code</th>\n",
       "      <th>full_study_section.srg_flex</th>\n",
       "      <th>full_study_section.sra_designator_code</th>\n",
       "      <th>full_study_section.sra_flex_code</th>\n",
       "      <th>full_study_section.group_code</th>\n",
       "      <th>full_study_section.name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10181056</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5I01HX002778-02</td>\n",
       "      <td>GAWRON, ANDREW J</td>\n",
       "      <td>2020-06-01T12:06:00Z</td>\n",
       "      <td>2023-05-31T12:05:00Z</td>\n",
       "      <td>VA SALT LAKE CITY HEALTHCARE SYSTEM</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>002778</td>\n",
       "      <td>02</td>\n",
       "      <td>02</td>\n",
       "      <td></td>\n",
       "      <td>HS8A</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[HS8A]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10307103</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01NS100847-05</td>\n",
       "      <td>COUGHLIN, JENNIFER MARIE</td>\n",
       "      <td>2018-01-01T12:01:00Z</td>\n",
       "      <td>2023-11-30T12:11:00Z</td>\n",
       "      <td>JOHNS HOPKINS UNIVERSITY</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>100847</td>\n",
       "      <td>05</td>\n",
       "      <td>05</td>\n",
       "      <td></td>\n",
       "      <td>ZRG1</td>\n",
       "      <td></td>\n",
       "      <td>BDCN</td>\n",
       "      <td>W</td>\n",
       "      <td></td>\n",
       "      <td>Special Emphasis Panel[ZRG1-BDCN-W(02)M]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10316433</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>1I01HX002941-01A2</td>\n",
       "      <td>TRIVEDI, RANAK</td>\n",
       "      <td>2022-10-01T12:10:00Z</td>\n",
       "      <td>2026-09-30T12:09:00Z</td>\n",
       "      <td>VETERANS ADMIN PALO ALTO HEALTH CARE SYS</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>002941</td>\n",
       "      <td>01</td>\n",
       "      <td>01A2</td>\n",
       "      <td>A2</td>\n",
       "      <td>HSR5</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>HSR-5 Health Care System Organization and Deli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10335235</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5T37MD014208-04</td>\n",
       "      <td>WALTERS, KARINA L.</td>\n",
       "      <td>2019-07-19T12:07:00Z</td>\n",
       "      <td>2024-11-30T12:11:00Z</td>\n",
       "      <td>UNIVERSITY OF WASHINGTON</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>014208</td>\n",
       "      <td>04</td>\n",
       "      <td>04</td>\n",
       "      <td></td>\n",
       "      <td>ZMD1</td>\n",
       "      <td></td>\n",
       "      <td>RP</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>ZMD1-RP(M1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10379076</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5I21HX003227-02</td>\n",
       "      <td>KAMDAR, NIPA P</td>\n",
       "      <td>2021-04-01T12:04:00Z</td>\n",
       "      <td>2022-09-30T12:09:00Z</td>\n",
       "      <td>MICHAEL E DEBAKEY VA MEDICAL CENTER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>003227</td>\n",
       "      <td>02</td>\n",
       "      <td>02</td>\n",
       "      <td></td>\n",
       "      <td>HSR2</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>HSR-2 Behavioral, Social, and Cultural Determi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10384063</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>4R37AI053067-22</td>\n",
       "      <td>SPERANDIO, VANESSA</td>\n",
       "      <td>2023-01-01T12:01:00Z</td>\n",
       "      <td>2027-12-31T12:12:00Z</td>\n",
       "      <td>UNIVERSITY OF WISCONSIN-MADISON</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>053067</td>\n",
       "      <td>22</td>\n",
       "      <td>22</td>\n",
       "      <td></td>\n",
       "      <td>NSS</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>NSS</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10356810</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R38AG065762-03</td>\n",
       "      <td>VIERA, ANTHONY J</td>\n",
       "      <td>2020-02-01T12:02:00Z</td>\n",
       "      <td>2024-11-30T12:11:00Z</td>\n",
       "      <td>DUKE UNIVERSITY</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>065762</td>\n",
       "      <td>03</td>\n",
       "      <td>03</td>\n",
       "      <td></td>\n",
       "      <td>ZHL1</td>\n",
       "      <td></td>\n",
       "      <td>CSR</td>\n",
       "      <td>I</td>\n",
       "      <td></td>\n",
       "      <td>ZHL1-CSR-I(O1)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>10371019</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01MD011596-05</td>\n",
       "      <td>FRETTS, AMANDA MAE</td>\n",
       "      <td>2018-04-01T12:04:00Z</td>\n",
       "      <td>2023-11-30T12:11:00Z</td>\n",
       "      <td>UNIVERSITY OF WASHINGTON</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>011596</td>\n",
       "      <td>05</td>\n",
       "      <td>05</td>\n",
       "      <td></td>\n",
       "      <td>CIHB</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Community Influences on Health Behavior Study ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>10392938</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5IK6HX003161-03</td>\n",
       "      <td>BOSWORTH, HAYDEN B</td>\n",
       "      <td>2020-04-01T12:04:00Z</td>\n",
       "      <td>2027-02-28T12:02:00Z</td>\n",
       "      <td>DURHAM VA MEDICAL CENTER</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>003161</td>\n",
       "      <td>03</td>\n",
       "      <td>03</td>\n",
       "      <td></td>\n",
       "      <td>HSR7</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>HSR-7 Health Services Research Special Emphasi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10447787</td>\n",
       "      <td>None</td>\n",
       "      <td>2023</td>\n",
       "      <td>5R01AR073831-04</td>\n",
       "      <td>DAMON, BRUCE M.</td>\n",
       "      <td>2019-05-07T12:05:00Z</td>\n",
       "      <td>2024-12-31T12:12:00Z</td>\n",
       "      <td>CARLE FOUNDATION</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>073831</td>\n",
       "      <td>04</td>\n",
       "      <td>04</td>\n",
       "      <td></td>\n",
       "      <td>SMEP</td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td></td>\n",
       "      <td>Skeletal Muscle and Exercise Physiology Study ...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 37 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    appl_id subproject_id  fiscal_year        project_num  \\\n",
       "0  10181056          None         2023    5I01HX002778-02   \n",
       "1  10307103          None         2023    5R01NS100847-05   \n",
       "2  10316433          None         2023  1I01HX002941-01A2   \n",
       "3  10335235          None         2023    5T37MD014208-04   \n",
       "4  10379076          None         2023    5I21HX003227-02   \n",
       "5  10384063          None         2023    4R37AI053067-22   \n",
       "6  10356810          None         2023    5R38AG065762-03   \n",
       "7  10371019          None         2023    5R01MD011596-05   \n",
       "8  10392938          None         2023    5IK6HX003161-03   \n",
       "9  10447787          None         2023    5R01AR073831-04   \n",
       "\n",
       "            contact_pi_name    project_start_date      project_end_date  \\\n",
       "0          GAWRON, ANDREW J  2020-06-01T12:06:00Z  2023-05-31T12:05:00Z   \n",
       "1  COUGHLIN, JENNIFER MARIE  2018-01-01T12:01:00Z  2023-11-30T12:11:00Z   \n",
       "2           TRIVEDI, RANAK   2022-10-01T12:10:00Z  2026-09-30T12:09:00Z   \n",
       "3        WALTERS, KARINA L.  2019-07-19T12:07:00Z  2024-11-30T12:11:00Z   \n",
       "4            KAMDAR, NIPA P  2021-04-01T12:04:00Z  2022-09-30T12:09:00Z   \n",
       "5       SPERANDIO, VANESSA   2023-01-01T12:01:00Z  2027-12-31T12:12:00Z   \n",
       "6          VIERA, ANTHONY J  2020-02-01T12:02:00Z  2024-11-30T12:11:00Z   \n",
       "7        FRETTS, AMANDA MAE  2018-04-01T12:04:00Z  2023-11-30T12:11:00Z   \n",
       "8        BOSWORTH, HAYDEN B  2020-04-01T12:04:00Z  2027-02-28T12:02:00Z   \n",
       "9           DAMON, BRUCE M.  2019-05-07T12:05:00Z  2024-12-31T12:12:00Z   \n",
       "\n",
       "                      organization.org_name organization.city  \\\n",
       "0       VA SALT LAKE CITY HEALTHCARE SYSTEM              None   \n",
       "1                  JOHNS HOPKINS UNIVERSITY              None   \n",
       "2  VETERANS ADMIN PALO ALTO HEALTH CARE SYS              None   \n",
       "3                  UNIVERSITY OF WASHINGTON              None   \n",
       "4       MICHAEL E DEBAKEY VA MEDICAL CENTER              None   \n",
       "5           UNIVERSITY OF WISCONSIN-MADISON              None   \n",
       "6                           DUKE UNIVERSITY              None   \n",
       "7                  UNIVERSITY OF WASHINGTON              None   \n",
       "8                  DURHAM VA MEDICAL CENTER              None   \n",
       "9                          CARLE FOUNDATION              None   \n",
       "\n",
       "  organization.country  ... project_num_split.serial_num  \\\n",
       "0                 None  ...                       002778   \n",
       "1                 None  ...                       100847   \n",
       "2                 None  ...                       002941   \n",
       "3                 None  ...                       014208   \n",
       "4                 None  ...                       003227   \n",
       "5                 None  ...                       053067   \n",
       "6                 None  ...                       065762   \n",
       "7                 None  ...                       011596   \n",
       "8                 None  ...                       003161   \n",
       "9                 None  ...                       073831   \n",
       "\n",
       "  project_num_split.support_year project_num_split.full_support_year  \\\n",
       "0                             02                                  02   \n",
       "1                             05                                  05   \n",
       "2                             01                                01A2   \n",
       "3                             04                                  04   \n",
       "4                             02                                  02   \n",
       "5                             22                                  22   \n",
       "6                             03                                  03   \n",
       "7                             05                                  05   \n",
       "8                             03                                  03   \n",
       "9                             04                                  04   \n",
       "\n",
       "  project_num_split.suffix_code full_study_section.srg_code  \\\n",
       "0                                                      HS8A   \n",
       "1                                                      ZRG1   \n",
       "2                            A2                        HSR5   \n",
       "3                                                      ZMD1   \n",
       "4                                                      HSR2   \n",
       "5                                                       NSS   \n",
       "6                                                      ZHL1   \n",
       "7                                                      CIHB   \n",
       "8                                                      HSR7   \n",
       "9                                                      SMEP   \n",
       "\n",
       "  full_study_section.srg_flex full_study_section.sra_designator_code  \\\n",
       "0                                                                      \n",
       "1                                                               BDCN   \n",
       "2                                                                      \n",
       "3                                                                 RP   \n",
       "4                                                                      \n",
       "5                                                                      \n",
       "6                                                                CSR   \n",
       "7                                                                      \n",
       "8                                                                      \n",
       "9                                                                      \n",
       "\n",
       "  full_study_section.sra_flex_code full_study_section.group_code  \\\n",
       "0                                                                  \n",
       "1                                W                                 \n",
       "2                                                                  \n",
       "3                                                                  \n",
       "4                                                                  \n",
       "5                                                                  \n",
       "6                                I                                 \n",
       "7                                                                  \n",
       "8                                                                  \n",
       "9                                                                  \n",
       "\n",
       "                             full_study_section.name  \n",
       "0                       Special Emphasis Panel[HS8A]  \n",
       "1           Special Emphasis Panel[ZRG1-BDCN-W(02)M]  \n",
       "2  HSR-5 Health Care System Organization and Deli...  \n",
       "3                                        ZMD1-RP(M1)  \n",
       "4  HSR-2 Behavioral, Social, and Cultural Determi...  \n",
       "5                                                NSS  \n",
       "6                                     ZHL1-CSR-I(O1)  \n",
       "7  Community Influences on Health Behavior Study ...  \n",
       "8  HSR-7 Health Services Research Special Emphasi...  \n",
       "9  Skeletal Muscle and Exercise Physiology Study ...  \n",
       "\n",
       "[10 rows x 37 columns]"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rdf = pd.json_normalize(test1_response_data[\"results\"])\n",
    "rdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "e21427f9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "type(awards): <class 'dict'>\n"
     ]
    }
   ],
   "source": [
    "nsf_awards_json_api = 'http://api.nsf.gov/services/v1/awards.json'\n",
    "keywords = 'keyword=data analytics'\n",
    "\n",
    "# Make the URL request\n",
    "res = requests.get(nsf_awards_json_api + '?' + keywords)\n",
    "\n",
    "# Get the awards data\n",
    "json_response = res.json()[\"response\"]\n",
    "print(f'type(awards): {type(json_response)}')\n",
    "awards = json_response[\"award\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "ba8f3781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency</th>\n",
       "      <th>awardeeCity</th>\n",
       "      <th>awardeeName</th>\n",
       "      <th>awardeeStateCode</th>\n",
       "      <th>fundsObligatedAmt</th>\n",
       "      <th>id</th>\n",
       "      <th>piFirstName</th>\n",
       "      <th>piLastName</th>\n",
       "      <th>publicAccessMandate</th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSF</td>\n",
       "      <td>IRVINE</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>CA</td>\n",
       "      <td>600000</td>\n",
       "      <td>1954962</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Carey</td>\n",
       "      <td>1</td>\n",
       "      <td>08/13/2020</td>\n",
       "      <td>III: Medium: Collaborative Research: Supportin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA BARBARA</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>CA</td>\n",
       "      <td>400000</td>\n",
       "      <td>1618118</td>\n",
       "      <td>Li-Chung</td>\n",
       "      <td>Wang</td>\n",
       "      <td>0</td>\n",
       "      <td>06/10/2016</td>\n",
       "      <td>SHF: Small: End-To-End Test Data Analytics For...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NSF</td>\n",
       "      <td>New York</td>\n",
       "      <td>Teachers College, Columbia University</td>\n",
       "      <td>NY</td>\n",
       "      <td>499623</td>\n",
       "      <td>1560720</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Bowers</td>\n",
       "      <td>0</td>\n",
       "      <td>05/11/2016</td>\n",
       "      <td>Building Community and Capacity for Data-Inten...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NSF</td>\n",
       "      <td>TALLAHASSEE</td>\n",
       "      <td>Florida State University</td>\n",
       "      <td>FL</td>\n",
       "      <td>204767</td>\n",
       "      <td>1561041</td>\n",
       "      <td>Weikuan</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>09/21/2015</td>\n",
       "      <td>EAGER: Tadoop: A Dual-Purpose Framework Taming...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NSF</td>\n",
       "      <td>NORMAN</td>\n",
       "      <td>University of Oklahoma Norman Campus</td>\n",
       "      <td>OK</td>\n",
       "      <td>1381958</td>\n",
       "      <td>1541165</td>\n",
       "      <td>Kash</td>\n",
       "      <td>Barker</td>\n",
       "      <td>0</td>\n",
       "      <td>09/04/2015</td>\n",
       "      <td>CRISP Type 2/Collaborative Research: Resilienc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MENLO PARK</td>\n",
       "      <td>SRI International</td>\n",
       "      <td>CA</td>\n",
       "      <td>608024</td>\n",
       "      <td>1522990</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Bienkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>08/25/2015</td>\n",
       "      <td>EXP: Understanding Computational Thinking Proc...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA CRUZ</td>\n",
       "      <td>University of California-Santa Cruz</td>\n",
       "      <td>CA</td>\n",
       "      <td>14500</td>\n",
       "      <td>1464671</td>\n",
       "      <td>Ramakrishna</td>\n",
       "      <td>Akella</td>\n",
       "      <td>0</td>\n",
       "      <td>04/28/2015</td>\n",
       "      <td>Planning Grant: I/UCRC for Computation Intensi...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>49992</td>\n",
       "      <td>1527370</td>\n",
       "      <td>Alan</td>\n",
       "      <td>Porter</td>\n",
       "      <td>0</td>\n",
       "      <td>03/25/2015</td>\n",
       "      <td>Forecasting Innovation Pathways of  Big Data &amp;...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>University of Nebraska-Lincoln</td>\n",
       "      <td>NE</td>\n",
       "      <td>405378</td>\n",
       "      <td>1423487</td>\n",
       "      <td>Hongfeng</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>07/28/2014</td>\n",
       "      <td>III: CGV: Small: A Scalable Visual Analytics F...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MONTGOMERY</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>AL</td>\n",
       "      <td>399976</td>\n",
       "      <td>1436871</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Pettis</td>\n",
       "      <td>0</td>\n",
       "      <td>07/16/2014</td>\n",
       "      <td>Targeted Infusion Project: Infusing Data-Enabl...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>IL</td>\n",
       "      <td>709342</td>\n",
       "      <td>1409601</td>\n",
       "      <td>Alok</td>\n",
       "      <td>Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>05/21/2014</td>\n",
       "      <td>SHF: Medium: Collaborative Research: Scalable ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NSF</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>Carnegie-Mellon University</td>\n",
       "      <td>PA</td>\n",
       "      <td>8914035</td>\n",
       "      <td>1261721</td>\n",
       "      <td>Nicholas</td>\n",
       "      <td>Nystrom</td>\n",
       "      <td>0</td>\n",
       "      <td>09/26/2013</td>\n",
       "      <td>CIF21 DIBBS: The Data Exacell</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLACKSBURG</td>\n",
       "      <td>Virginia Polytechnic Institute and State Unive...</td>\n",
       "      <td>VA</td>\n",
       "      <td>199943</td>\n",
       "      <td>1141096</td>\n",
       "      <td>Leanna</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>08/23/2012</td>\n",
       "      <td>Critical Thinking with Data Visualization (CTDV)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NSF</td>\n",
       "      <td>University Park</td>\n",
       "      <td>Pennsylvania State Univ University Park</td>\n",
       "      <td>PA</td>\n",
       "      <td>2999713</td>\n",
       "      <td>1144860</td>\n",
       "      <td>Burt</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>0</td>\n",
       "      <td>06/01/2012</td>\n",
       "      <td>IGERT:  Big Data Social Science - An Integrati...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NSF</td>\n",
       "      <td>ATLANTA</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>3539333</td>\n",
       "      <td>0808863</td>\n",
       "      <td>Haesun</td>\n",
       "      <td>Park</td>\n",
       "      <td>0</td>\n",
       "      <td>05/22/2008</td>\n",
       "      <td>FODAVA-Lead: Dimension Reduction and Data Redu...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NSF</td>\n",
       "      <td>STORRS</td>\n",
       "      <td>University of Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>16662</td>\n",
       "      <td>2219336</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Yan</td>\n",
       "      <td>1</td>\n",
       "      <td>04/29/2022</td>\n",
       "      <td>Conference: UConn Sports Analytics Symposium: ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NSF</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>AIRMETTLE, INC.</td>\n",
       "      <td>TX</td>\n",
       "      <td>276000</td>\n",
       "      <td>2135007</td>\n",
       "      <td>Donpaul</td>\n",
       "      <td>Stephens</td>\n",
       "      <td>1</td>\n",
       "      <td>02/09/2022</td>\n",
       "      <td>STTR Phase I:  Real-Time Smart Data Lake</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLUMBUS</td>\n",
       "      <td>Ohio State University</td>\n",
       "      <td>OH</td>\n",
       "      <td>311411</td>\n",
       "      <td>2110259</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/16/2021</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NSF</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>Sinclair Community College</td>\n",
       "      <td>OH</td>\n",
       "      <td>576315</td>\n",
       "      <td>2055411</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Hansford</td>\n",
       "      <td>1</td>\n",
       "      <td>03/15/2021</td>\n",
       "      <td>Expanding the Data Analytics Technician Pipeli...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BOCA RATON</td>\n",
       "      <td>Florida Atlantic University</td>\n",
       "      <td>FL</td>\n",
       "      <td>95751</td>\n",
       "      <td>2027890</td>\n",
       "      <td>Borko</td>\n",
       "      <td>Furht</td>\n",
       "      <td>1</td>\n",
       "      <td>06/09/2020</td>\n",
       "      <td>NSF RAPID: Modeling Corona Spread Using Big Da...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NSF</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>University of Louisiana at Lafayette</td>\n",
       "      <td>LA</td>\n",
       "      <td>187477</td>\n",
       "      <td>2027688</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Gottumukkala</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2020</td>\n",
       "      <td>RAPID: Visual Analytics Approach to Real-Time ...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NSF</td>\n",
       "      <td>AMES</td>\n",
       "      <td>Iowa State University</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>1943226</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/12/2020</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>IN</td>\n",
       "      <td>45398</td>\n",
       "      <td>1934942</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Herzog</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2019</td>\n",
       "      <td>Workshop:  Addressing Technology and Skills Ga...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>WA</td>\n",
       "      <td>124657</td>\n",
       "      <td>1929901</td>\n",
       "      <td>Huijia</td>\n",
       "      <td>Lin</td>\n",
       "      <td>0</td>\n",
       "      <td>04/18/2019</td>\n",
       "      <td>TWC: Medium: Collaborative: Seal: Secure Engin...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLLEGE STATION</td>\n",
       "      <td>Texas A&amp;M Engineering Experiment Station</td>\n",
       "      <td>TX</td>\n",
       "      <td>449380</td>\n",
       "      <td>1922969</td>\n",
       "      <td>Shuiwang</td>\n",
       "      <td>Ji</td>\n",
       "      <td>0</td>\n",
       "      <td>02/22/2019</td>\n",
       "      <td>CAREER: Towards the Next Generation of Data-Dr...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   agency      awardeeCity                                        awardeeName  \\\n",
       "0     NSF           IRVINE                    University of California-Irvine   \n",
       "1     NSF    SANTA BARBARA             University of California-Santa Barbara   \n",
       "2     NSF         New York              Teachers College, Columbia University   \n",
       "3     NSF      TALLAHASSEE                           Florida State University   \n",
       "4     NSF           NORMAN               University of Oklahoma Norman Campus   \n",
       "5     NSF       MENLO PARK                                  SRI International   \n",
       "6     NSF       SANTA CRUZ                University of California-Santa Cruz   \n",
       "7     NSF          Atlanta                  Georgia Tech Research Corporation   \n",
       "8     NSF          Lincoln                     University of Nebraska-Lincoln   \n",
       "9     NSF       MONTGOMERY                           Alabama State University   \n",
       "10    NSF          Chicago                            Northwestern University   \n",
       "11    NSF       PITTSBURGH                         Carnegie-Mellon University   \n",
       "12    NSF       BLACKSBURG  Virginia Polytechnic Institute and State Unive...   \n",
       "13    NSF  University Park            Pennsylvania State Univ University Park   \n",
       "14    NSF          ATLANTA                  Georgia Tech Research Corporation   \n",
       "15    NSF           STORRS                          University of Connecticut   \n",
       "16    NSF          HOUSTON                                    AIRMETTLE, INC.   \n",
       "17    NSF         COLUMBUS                              Ohio State University   \n",
       "18    NSF           DAYTON                         Sinclair Community College   \n",
       "19    NSF       BOCA RATON                        Florida Atlantic University   \n",
       "20    NSF        LAFAYETTE               University of Louisiana at Lafayette   \n",
       "21    NSF             AMES                              Iowa State University   \n",
       "22    NSF      BLOOMINGTON                                 Indiana University   \n",
       "23    NSF          Seattle                           University of Washington   \n",
       "24    NSF  COLLEGE STATION           Texas A&M Engineering Experiment Station   \n",
       "\n",
       "   awardeeStateCode fundsObligatedAmt       id  piFirstName    piLastName  \\\n",
       "0                CA            600000  1954962      Michael         Carey   \n",
       "1                CA            400000  1618118     Li-Chung          Wang   \n",
       "2                NY            499623  1560720         Alex        Bowers   \n",
       "3                FL            204767  1561041      Weikuan            Yu   \n",
       "4                OK           1381958  1541165         Kash        Barker   \n",
       "5                CA            608024  1522990        Marie    Bienkowski   \n",
       "6                CA             14500  1464671  Ramakrishna        Akella   \n",
       "7                GA             49992  1527370         Alan        Porter   \n",
       "8                NE            405378  1423487     Hongfeng            Yu   \n",
       "9                AL            399976  1436871         Carl        Pettis   \n",
       "10               IL            709342  1409601         Alok     Choudhary   \n",
       "11               PA           8914035  1261721     Nicholas       Nystrom   \n",
       "12               VA            199943  1141096       Leanna         House   \n",
       "13               PA           2999713  1144860         Burt        Monroe   \n",
       "14               GA           3539333  0808863       Haesun          Park   \n",
       "15               CT             16662  2219336          Jun           Yan   \n",
       "16               TX            276000  2135007      Donpaul      Stephens   \n",
       "17               OH            311411  2110259          Jia           Liu   \n",
       "18               OH            576315  2055411         Paul      Hansford   \n",
       "19               FL             95751  2027890        Borko         Furht   \n",
       "20               LA            187477  2027688         Raju  Gottumukkala   \n",
       "21               IA                 0  1943226          Jia           Liu   \n",
       "22               IN             45398  1934942     Patricia        Herzog   \n",
       "23               WA            124657  1929901       Huijia           Lin   \n",
       "24               TX            449380  1922969     Shuiwang            Ji   \n",
       "\n",
       "   publicAccessMandate        date  \\\n",
       "0                    1  08/13/2020   \n",
       "1                    0  06/10/2016   \n",
       "2                    0  05/11/2016   \n",
       "3                    0  09/21/2015   \n",
       "4                    0  09/04/2015   \n",
       "5                    0  08/25/2015   \n",
       "6                    0  04/28/2015   \n",
       "7                    0  03/25/2015   \n",
       "8                    0  07/28/2014   \n",
       "9                    0  07/16/2014   \n",
       "10                   0  05/21/2014   \n",
       "11                   0  09/26/2013   \n",
       "12                   0  08/23/2012   \n",
       "13                   0  06/01/2012   \n",
       "14                   0  05/22/2008   \n",
       "15                   1  04/29/2022   \n",
       "16                   1  02/09/2022   \n",
       "17                   1  03/16/2021   \n",
       "18                   1  03/15/2021   \n",
       "19                   1  06/09/2020   \n",
       "20                   1  05/20/2020   \n",
       "21                   1  03/12/2020   \n",
       "22                   1  05/20/2019   \n",
       "23                   0  04/18/2019   \n",
       "24                   0  02/22/2019   \n",
       "\n",
       "                                                title  \n",
       "0   III: Medium: Collaborative Research: Supportin...  \n",
       "1   SHF: Small: End-To-End Test Data Analytics For...  \n",
       "2   Building Community and Capacity for Data-Inten...  \n",
       "3   EAGER: Tadoop: A Dual-Purpose Framework Taming...  \n",
       "4   CRISP Type 2/Collaborative Research: Resilienc...  \n",
       "5   EXP: Understanding Computational Thinking Proc...  \n",
       "6   Planning Grant: I/UCRC for Computation Intensi...  \n",
       "7   Forecasting Innovation Pathways of  Big Data &...  \n",
       "8   III: CGV: Small: A Scalable Visual Analytics F...  \n",
       "9   Targeted Infusion Project: Infusing Data-Enabl...  \n",
       "10  SHF: Medium: Collaborative Research: Scalable ...  \n",
       "11                      CIF21 DIBBS: The Data Exacell  \n",
       "12   Critical Thinking with Data Visualization (CTDV)  \n",
       "13  IGERT:  Big Data Social Science - An Integrati...  \n",
       "14  FODAVA-Lead: Dimension Reduction and Data Redu...  \n",
       "15  Conference: UConn Sports Analytics Symposium: ...  \n",
       "16           STTR Phase I:  Real-Time Smart Data Lake  \n",
       "17  CAREER: Computing-Aware Network Optimization f...  \n",
       "18  Expanding the Data Analytics Technician Pipeli...  \n",
       "19  NSF RAPID: Modeling Corona Spread Using Big Da...  \n",
       "20  RAPID: Visual Analytics Approach to Real-Time ...  \n",
       "21  CAREER: Computing-Aware Network Optimization f...  \n",
       "22  Workshop:  Addressing Technology and Skills Ga...  \n",
       "23  TWC: Medium: Collaborative: Seal: Secure Engin...  \n",
       "24  CAREER: Towards the Next Generation of Data-Dr...  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsf_df = pd.json_normalize(awards)\n",
    "nsf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "16d4c1f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency</th>\n",
       "      <th>awardeeCity</th>\n",
       "      <th>awardeeName</th>\n",
       "      <th>awardeeStateCode</th>\n",
       "      <th>fundsObligatedAmt</th>\n",
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       "      <th>piFirstName</th>\n",
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       "      <th>publicAccessMandate</th>\n",
       "      <th>date</th>\n",
       "      <th>title</th>\n",
       "      <th>Date2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSF</td>\n",
       "      <td>IRVINE</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>CA</td>\n",
       "      <td>600000</td>\n",
       "      <td>1954962</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Carey</td>\n",
       "      <td>1</td>\n",
       "      <td>08/13/2020</td>\n",
       "      <td>III: Medium: Collaborative Research: Supportin...</td>\n",
       "      <td>2020-08-13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA BARBARA</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>CA</td>\n",
       "      <td>400000</td>\n",
       "      <td>1618118</td>\n",
       "      <td>Li-Chung</td>\n",
       "      <td>Wang</td>\n",
       "      <td>0</td>\n",
       "      <td>06/10/2016</td>\n",
       "      <td>SHF: Small: End-To-End Test Data Analytics For...</td>\n",
       "      <td>2016-06-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NSF</td>\n",
       "      <td>New York</td>\n",
       "      <td>Teachers College, Columbia University</td>\n",
       "      <td>NY</td>\n",
       "      <td>499623</td>\n",
       "      <td>1560720</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Bowers</td>\n",
       "      <td>0</td>\n",
       "      <td>05/11/2016</td>\n",
       "      <td>Building Community and Capacity for Data-Inten...</td>\n",
       "      <td>2016-05-11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NSF</td>\n",
       "      <td>TALLAHASSEE</td>\n",
       "      <td>Florida State University</td>\n",
       "      <td>FL</td>\n",
       "      <td>204767</td>\n",
       "      <td>1561041</td>\n",
       "      <td>Weikuan</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>09/21/2015</td>\n",
       "      <td>EAGER: Tadoop: A Dual-Purpose Framework Taming...</td>\n",
       "      <td>2015-09-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NSF</td>\n",
       "      <td>NORMAN</td>\n",
       "      <td>University of Oklahoma Norman Campus</td>\n",
       "      <td>OK</td>\n",
       "      <td>1381958</td>\n",
       "      <td>1541165</td>\n",
       "      <td>Kash</td>\n",
       "      <td>Barker</td>\n",
       "      <td>0</td>\n",
       "      <td>09/04/2015</td>\n",
       "      <td>CRISP Type 2/Collaborative Research: Resilienc...</td>\n",
       "      <td>2015-09-04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MENLO PARK</td>\n",
       "      <td>SRI International</td>\n",
       "      <td>CA</td>\n",
       "      <td>608024</td>\n",
       "      <td>1522990</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Bienkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>08/25/2015</td>\n",
       "      <td>EXP: Understanding Computational Thinking Proc...</td>\n",
       "      <td>2015-08-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA CRUZ</td>\n",
       "      <td>University of California-Santa Cruz</td>\n",
       "      <td>CA</td>\n",
       "      <td>14500</td>\n",
       "      <td>1464671</td>\n",
       "      <td>Ramakrishna</td>\n",
       "      <td>Akella</td>\n",
       "      <td>0</td>\n",
       "      <td>04/28/2015</td>\n",
       "      <td>Planning Grant: I/UCRC for Computation Intensi...</td>\n",
       "      <td>2015-04-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>49992</td>\n",
       "      <td>1527370</td>\n",
       "      <td>Alan</td>\n",
       "      <td>Porter</td>\n",
       "      <td>0</td>\n",
       "      <td>03/25/2015</td>\n",
       "      <td>Forecasting Innovation Pathways of  Big Data &amp;...</td>\n",
       "      <td>2015-03-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>University of Nebraska-Lincoln</td>\n",
       "      <td>NE</td>\n",
       "      <td>405378</td>\n",
       "      <td>1423487</td>\n",
       "      <td>Hongfeng</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>07/28/2014</td>\n",
       "      <td>III: CGV: Small: A Scalable Visual Analytics F...</td>\n",
       "      <td>2014-07-28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MONTGOMERY</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>AL</td>\n",
       "      <td>399976</td>\n",
       "      <td>1436871</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Pettis</td>\n",
       "      <td>0</td>\n",
       "      <td>07/16/2014</td>\n",
       "      <td>Targeted Infusion Project: Infusing Data-Enabl...</td>\n",
       "      <td>2014-07-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>IL</td>\n",
       "      <td>709342</td>\n",
       "      <td>1409601</td>\n",
       "      <td>Alok</td>\n",
       "      <td>Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>05/21/2014</td>\n",
       "      <td>SHF: Medium: Collaborative Research: Scalable ...</td>\n",
       "      <td>2014-05-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NSF</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>Carnegie-Mellon University</td>\n",
       "      <td>PA</td>\n",
       "      <td>8914035</td>\n",
       "      <td>1261721</td>\n",
       "      <td>Nicholas</td>\n",
       "      <td>Nystrom</td>\n",
       "      <td>0</td>\n",
       "      <td>09/26/2013</td>\n",
       "      <td>CIF21 DIBBS: The Data Exacell</td>\n",
       "      <td>2013-09-26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLACKSBURG</td>\n",
       "      <td>Virginia Polytechnic Institute and State Unive...</td>\n",
       "      <td>VA</td>\n",
       "      <td>199943</td>\n",
       "      <td>1141096</td>\n",
       "      <td>Leanna</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>08/23/2012</td>\n",
       "      <td>Critical Thinking with Data Visualization (CTDV)</td>\n",
       "      <td>2012-08-23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NSF</td>\n",
       "      <td>University Park</td>\n",
       "      <td>Pennsylvania State Univ University Park</td>\n",
       "      <td>PA</td>\n",
       "      <td>2999713</td>\n",
       "      <td>1144860</td>\n",
       "      <td>Burt</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>0</td>\n",
       "      <td>06/01/2012</td>\n",
       "      <td>IGERT:  Big Data Social Science - An Integrati...</td>\n",
       "      <td>2012-06-01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NSF</td>\n",
       "      <td>ATLANTA</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>3539333</td>\n",
       "      <td>0808863</td>\n",
       "      <td>Haesun</td>\n",
       "      <td>Park</td>\n",
       "      <td>0</td>\n",
       "      <td>05/22/2008</td>\n",
       "      <td>FODAVA-Lead: Dimension Reduction and Data Redu...</td>\n",
       "      <td>2008-05-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NSF</td>\n",
       "      <td>STORRS</td>\n",
       "      <td>University of Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>16662</td>\n",
       "      <td>2219336</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Yan</td>\n",
       "      <td>1</td>\n",
       "      <td>04/29/2022</td>\n",
       "      <td>Conference: UConn Sports Analytics Symposium: ...</td>\n",
       "      <td>2022-04-29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NSF</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>AIRMETTLE, INC.</td>\n",
       "      <td>TX</td>\n",
       "      <td>276000</td>\n",
       "      <td>2135007</td>\n",
       "      <td>Donpaul</td>\n",
       "      <td>Stephens</td>\n",
       "      <td>1</td>\n",
       "      <td>02/09/2022</td>\n",
       "      <td>STTR Phase I:  Real-Time Smart Data Lake</td>\n",
       "      <td>2022-02-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLUMBUS</td>\n",
       "      <td>Ohio State University</td>\n",
       "      <td>OH</td>\n",
       "      <td>311411</td>\n",
       "      <td>2110259</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/16/2021</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>2021-03-16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NSF</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>Sinclair Community College</td>\n",
       "      <td>OH</td>\n",
       "      <td>576315</td>\n",
       "      <td>2055411</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Hansford</td>\n",
       "      <td>1</td>\n",
       "      <td>03/15/2021</td>\n",
       "      <td>Expanding the Data Analytics Technician Pipeli...</td>\n",
       "      <td>2021-03-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BOCA RATON</td>\n",
       "      <td>Florida Atlantic University</td>\n",
       "      <td>FL</td>\n",
       "      <td>95751</td>\n",
       "      <td>2027890</td>\n",
       "      <td>Borko</td>\n",
       "      <td>Furht</td>\n",
       "      <td>1</td>\n",
       "      <td>06/09/2020</td>\n",
       "      <td>NSF RAPID: Modeling Corona Spread Using Big Da...</td>\n",
       "      <td>2020-06-09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NSF</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>University of Louisiana at Lafayette</td>\n",
       "      <td>LA</td>\n",
       "      <td>187477</td>\n",
       "      <td>2027688</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Gottumukkala</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2020</td>\n",
       "      <td>RAPID: Visual Analytics Approach to Real-Time ...</td>\n",
       "      <td>2020-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NSF</td>\n",
       "      <td>AMES</td>\n",
       "      <td>Iowa State University</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>1943226</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/12/2020</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>2020-03-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>IN</td>\n",
       "      <td>45398</td>\n",
       "      <td>1934942</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Herzog</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2019</td>\n",
       "      <td>Workshop:  Addressing Technology and Skills Ga...</td>\n",
       "      <td>2019-05-20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>WA</td>\n",
       "      <td>124657</td>\n",
       "      <td>1929901</td>\n",
       "      <td>Huijia</td>\n",
       "      <td>Lin</td>\n",
       "      <td>0</td>\n",
       "      <td>04/18/2019</td>\n",
       "      <td>TWC: Medium: Collaborative: Seal: Secure Engin...</td>\n",
       "      <td>2019-04-18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLLEGE STATION</td>\n",
       "      <td>Texas A&amp;M Engineering Experiment Station</td>\n",
       "      <td>TX</td>\n",
       "      <td>449380</td>\n",
       "      <td>1922969</td>\n",
       "      <td>Shuiwang</td>\n",
       "      <td>Ji</td>\n",
       "      <td>0</td>\n",
       "      <td>02/22/2019</td>\n",
       "      <td>CAREER: Towards the Next Generation of Data-Dr...</td>\n",
       "      <td>2019-02-22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   agency      awardeeCity                                        awardeeName  \\\n",
       "0     NSF           IRVINE                    University of California-Irvine   \n",
       "1     NSF    SANTA BARBARA             University of California-Santa Barbara   \n",
       "2     NSF         New York              Teachers College, Columbia University   \n",
       "3     NSF      TALLAHASSEE                           Florida State University   \n",
       "4     NSF           NORMAN               University of Oklahoma Norman Campus   \n",
       "5     NSF       MENLO PARK                                  SRI International   \n",
       "6     NSF       SANTA CRUZ                University of California-Santa Cruz   \n",
       "7     NSF          Atlanta                  Georgia Tech Research Corporation   \n",
       "8     NSF          Lincoln                     University of Nebraska-Lincoln   \n",
       "9     NSF       MONTGOMERY                           Alabama State University   \n",
       "10    NSF          Chicago                            Northwestern University   \n",
       "11    NSF       PITTSBURGH                         Carnegie-Mellon University   \n",
       "12    NSF       BLACKSBURG  Virginia Polytechnic Institute and State Unive...   \n",
       "13    NSF  University Park            Pennsylvania State Univ University Park   \n",
       "14    NSF          ATLANTA                  Georgia Tech Research Corporation   \n",
       "15    NSF           STORRS                          University of Connecticut   \n",
       "16    NSF          HOUSTON                                    AIRMETTLE, INC.   \n",
       "17    NSF         COLUMBUS                              Ohio State University   \n",
       "18    NSF           DAYTON                         Sinclair Community College   \n",
       "19    NSF       BOCA RATON                        Florida Atlantic University   \n",
       "20    NSF        LAFAYETTE               University of Louisiana at Lafayette   \n",
       "21    NSF             AMES                              Iowa State University   \n",
       "22    NSF      BLOOMINGTON                                 Indiana University   \n",
       "23    NSF          Seattle                           University of Washington   \n",
       "24    NSF  COLLEGE STATION           Texas A&M Engineering Experiment Station   \n",
       "\n",
       "   awardeeStateCode fundsObligatedAmt       id  piFirstName    piLastName  \\\n",
       "0                CA            600000  1954962      Michael         Carey   \n",
       "1                CA            400000  1618118     Li-Chung          Wang   \n",
       "2                NY            499623  1560720         Alex        Bowers   \n",
       "3                FL            204767  1561041      Weikuan            Yu   \n",
       "4                OK           1381958  1541165         Kash        Barker   \n",
       "5                CA            608024  1522990        Marie    Bienkowski   \n",
       "6                CA             14500  1464671  Ramakrishna        Akella   \n",
       "7                GA             49992  1527370         Alan        Porter   \n",
       "8                NE            405378  1423487     Hongfeng            Yu   \n",
       "9                AL            399976  1436871         Carl        Pettis   \n",
       "10               IL            709342  1409601         Alok     Choudhary   \n",
       "11               PA           8914035  1261721     Nicholas       Nystrom   \n",
       "12               VA            199943  1141096       Leanna         House   \n",
       "13               PA           2999713  1144860         Burt        Monroe   \n",
       "14               GA           3539333  0808863       Haesun          Park   \n",
       "15               CT             16662  2219336          Jun           Yan   \n",
       "16               TX            276000  2135007      Donpaul      Stephens   \n",
       "17               OH            311411  2110259          Jia           Liu   \n",
       "18               OH            576315  2055411         Paul      Hansford   \n",
       "19               FL             95751  2027890        Borko         Furht   \n",
       "20               LA            187477  2027688         Raju  Gottumukkala   \n",
       "21               IA                 0  1943226          Jia           Liu   \n",
       "22               IN             45398  1934942     Patricia        Herzog   \n",
       "23               WA            124657  1929901       Huijia           Lin   \n",
       "24               TX            449380  1922969     Shuiwang            Ji   \n",
       "\n",
       "   publicAccessMandate        date  \\\n",
       "0                    1  08/13/2020   \n",
       "1                    0  06/10/2016   \n",
       "2                    0  05/11/2016   \n",
       "3                    0  09/21/2015   \n",
       "4                    0  09/04/2015   \n",
       "5                    0  08/25/2015   \n",
       "6                    0  04/28/2015   \n",
       "7                    0  03/25/2015   \n",
       "8                    0  07/28/2014   \n",
       "9                    0  07/16/2014   \n",
       "10                   0  05/21/2014   \n",
       "11                   0  09/26/2013   \n",
       "12                   0  08/23/2012   \n",
       "13                   0  06/01/2012   \n",
       "14                   0  05/22/2008   \n",
       "15                   1  04/29/2022   \n",
       "16                   1  02/09/2022   \n",
       "17                   1  03/16/2021   \n",
       "18                   1  03/15/2021   \n",
       "19                   1  06/09/2020   \n",
       "20                   1  05/20/2020   \n",
       "21                   1  03/12/2020   \n",
       "22                   1  05/20/2019   \n",
       "23                   0  04/18/2019   \n",
       "24                   0  02/22/2019   \n",
       "\n",
       "                                                title       Date2  \n",
       "0   III: Medium: Collaborative Research: Supportin...  2020-08-13  \n",
       "1   SHF: Small: End-To-End Test Data Analytics For...  2016-06-10  \n",
       "2   Building Community and Capacity for Data-Inten...  2016-05-11  \n",
       "3   EAGER: Tadoop: A Dual-Purpose Framework Taming...  2015-09-21  \n",
       "4   CRISP Type 2/Collaborative Research: Resilienc...  2015-09-04  \n",
       "5   EXP: Understanding Computational Thinking Proc...  2015-08-25  \n",
       "6   Planning Grant: I/UCRC for Computation Intensi...  2015-04-28  \n",
       "7   Forecasting Innovation Pathways of  Big Data &...  2015-03-25  \n",
       "8   III: CGV: Small: A Scalable Visual Analytics F...  2014-07-28  \n",
       "9   Targeted Infusion Project: Infusing Data-Enabl...  2014-07-16  \n",
       "10  SHF: Medium: Collaborative Research: Scalable ...  2014-05-21  \n",
       "11                      CIF21 DIBBS: The Data Exacell  2013-09-26  \n",
       "12   Critical Thinking with Data Visualization (CTDV)  2012-08-23  \n",
       "13  IGERT:  Big Data Social Science - An Integrati...  2012-06-01  \n",
       "14  FODAVA-Lead: Dimension Reduction and Data Redu...  2008-05-22  \n",
       "15  Conference: UConn Sports Analytics Symposium: ...  2022-04-29  \n",
       "16           STTR Phase I:  Real-Time Smart Data Lake  2022-02-09  \n",
       "17  CAREER: Computing-Aware Network Optimization f...  2021-03-16  \n",
       "18  Expanding the Data Analytics Technician Pipeli...  2021-03-15  \n",
       "19  NSF RAPID: Modeling Corona Spread Using Big Da...  2020-06-09  \n",
       "20  RAPID: Visual Analytics Approach to Real-Time ...  2020-05-20  \n",
       "21  CAREER: Computing-Aware Network Optimization f...  2020-03-12  \n",
       "22  Workshop:  Addressing Technology and Skills Ga...  2019-05-20  \n",
       "23  TWC: Medium: Collaborative: Seal: Secure Engin...  2019-04-18  \n",
       "24  CAREER: Towards the Next Generation of Data-Dr...  2019-02-22  "
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsf_df['Date2'] = nsf_df['date'].apply(lambda x: datetime.datetime.strptime(x, '%m/%d/%Y').strftime('%Y-%m-%d'))\n",
    "nsf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "78fc2f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>agency</th>\n",
       "      <th>awardeeCity</th>\n",
       "      <th>awardeeName</th>\n",
       "      <th>awardeeStateCode</th>\n",
       "      <th>fundsObligatedAmt</th>\n",
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       "      <th>date</th>\n",
       "      <th>title</th>\n",
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       "      <th>NAME</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NSF</td>\n",
       "      <td>IRVINE</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>CA</td>\n",
       "      <td>600000</td>\n",
       "      <td>1954962</td>\n",
       "      <td>Michael</td>\n",
       "      <td>Carey</td>\n",
       "      <td>1</td>\n",
       "      <td>08/13/2020</td>\n",
       "      <td>III: Medium: Collaborative Research: Supportin...</td>\n",
       "      <td>2020-08-13</td>\n",
       "      <td>Michael Carey</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA BARBARA</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>CA</td>\n",
       "      <td>400000</td>\n",
       "      <td>1618118</td>\n",
       "      <td>Li-Chung</td>\n",
       "      <td>Wang</td>\n",
       "      <td>0</td>\n",
       "      <td>06/10/2016</td>\n",
       "      <td>SHF: Small: End-To-End Test Data Analytics For...</td>\n",
       "      <td>2016-06-10</td>\n",
       "      <td>Li-Chung Wang</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NSF</td>\n",
       "      <td>New York</td>\n",
       "      <td>Teachers College, Columbia University</td>\n",
       "      <td>NY</td>\n",
       "      <td>499623</td>\n",
       "      <td>1560720</td>\n",
       "      <td>Alex</td>\n",
       "      <td>Bowers</td>\n",
       "      <td>0</td>\n",
       "      <td>05/11/2016</td>\n",
       "      <td>Building Community and Capacity for Data-Inten...</td>\n",
       "      <td>2016-05-11</td>\n",
       "      <td>Alex Bowers</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NSF</td>\n",
       "      <td>TALLAHASSEE</td>\n",
       "      <td>Florida State University</td>\n",
       "      <td>FL</td>\n",
       "      <td>204767</td>\n",
       "      <td>1561041</td>\n",
       "      <td>Weikuan</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>09/21/2015</td>\n",
       "      <td>EAGER: Tadoop: A Dual-Purpose Framework Taming...</td>\n",
       "      <td>2015-09-21</td>\n",
       "      <td>Weikuan Yu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NSF</td>\n",
       "      <td>NORMAN</td>\n",
       "      <td>University of Oklahoma Norman Campus</td>\n",
       "      <td>OK</td>\n",
       "      <td>1381958</td>\n",
       "      <td>1541165</td>\n",
       "      <td>Kash</td>\n",
       "      <td>Barker</td>\n",
       "      <td>0</td>\n",
       "      <td>09/04/2015</td>\n",
       "      <td>CRISP Type 2/Collaborative Research: Resilienc...</td>\n",
       "      <td>2015-09-04</td>\n",
       "      <td>Kash Barker</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MENLO PARK</td>\n",
       "      <td>SRI International</td>\n",
       "      <td>CA</td>\n",
       "      <td>608024</td>\n",
       "      <td>1522990</td>\n",
       "      <td>Marie</td>\n",
       "      <td>Bienkowski</td>\n",
       "      <td>0</td>\n",
       "      <td>08/25/2015</td>\n",
       "      <td>EXP: Understanding Computational Thinking Proc...</td>\n",
       "      <td>2015-08-25</td>\n",
       "      <td>Marie Bienkowski</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>NSF</td>\n",
       "      <td>SANTA CRUZ</td>\n",
       "      <td>University of California-Santa Cruz</td>\n",
       "      <td>CA</td>\n",
       "      <td>14500</td>\n",
       "      <td>1464671</td>\n",
       "      <td>Ramakrishna</td>\n",
       "      <td>Akella</td>\n",
       "      <td>0</td>\n",
       "      <td>04/28/2015</td>\n",
       "      <td>Planning Grant: I/UCRC for Computation Intensi...</td>\n",
       "      <td>2015-04-28</td>\n",
       "      <td>Ramakrishna Akella</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Atlanta</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>49992</td>\n",
       "      <td>1527370</td>\n",
       "      <td>Alan</td>\n",
       "      <td>Porter</td>\n",
       "      <td>0</td>\n",
       "      <td>03/25/2015</td>\n",
       "      <td>Forecasting Innovation Pathways of  Big Data &amp;...</td>\n",
       "      <td>2015-03-25</td>\n",
       "      <td>Alan Porter</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Lincoln</td>\n",
       "      <td>University of Nebraska-Lincoln</td>\n",
       "      <td>NE</td>\n",
       "      <td>405378</td>\n",
       "      <td>1423487</td>\n",
       "      <td>Hongfeng</td>\n",
       "      <td>Yu</td>\n",
       "      <td>0</td>\n",
       "      <td>07/28/2014</td>\n",
       "      <td>III: CGV: Small: A Scalable Visual Analytics F...</td>\n",
       "      <td>2014-07-28</td>\n",
       "      <td>Hongfeng Yu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>NSF</td>\n",
       "      <td>MONTGOMERY</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>AL</td>\n",
       "      <td>399976</td>\n",
       "      <td>1436871</td>\n",
       "      <td>Carl</td>\n",
       "      <td>Pettis</td>\n",
       "      <td>0</td>\n",
       "      <td>07/16/2014</td>\n",
       "      <td>Targeted Infusion Project: Infusing Data-Enabl...</td>\n",
       "      <td>2014-07-16</td>\n",
       "      <td>Carl Pettis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Chicago</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>IL</td>\n",
       "      <td>709342</td>\n",
       "      <td>1409601</td>\n",
       "      <td>Alok</td>\n",
       "      <td>Choudhary</td>\n",
       "      <td>0</td>\n",
       "      <td>05/21/2014</td>\n",
       "      <td>SHF: Medium: Collaborative Research: Scalable ...</td>\n",
       "      <td>2014-05-21</td>\n",
       "      <td>Alok Choudhary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NSF</td>\n",
       "      <td>PITTSBURGH</td>\n",
       "      <td>Carnegie-Mellon University</td>\n",
       "      <td>PA</td>\n",
       "      <td>8914035</td>\n",
       "      <td>1261721</td>\n",
       "      <td>Nicholas</td>\n",
       "      <td>Nystrom</td>\n",
       "      <td>0</td>\n",
       "      <td>09/26/2013</td>\n",
       "      <td>CIF21 DIBBS: The Data Exacell</td>\n",
       "      <td>2013-09-26</td>\n",
       "      <td>Nicholas Nystrom</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLACKSBURG</td>\n",
       "      <td>Virginia Polytechnic Institute and State Unive...</td>\n",
       "      <td>VA</td>\n",
       "      <td>199943</td>\n",
       "      <td>1141096</td>\n",
       "      <td>Leanna</td>\n",
       "      <td>House</td>\n",
       "      <td>0</td>\n",
       "      <td>08/23/2012</td>\n",
       "      <td>Critical Thinking with Data Visualization (CTDV)</td>\n",
       "      <td>2012-08-23</td>\n",
       "      <td>Leanna House</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>NSF</td>\n",
       "      <td>University Park</td>\n",
       "      <td>Pennsylvania State Univ University Park</td>\n",
       "      <td>PA</td>\n",
       "      <td>2999713</td>\n",
       "      <td>1144860</td>\n",
       "      <td>Burt</td>\n",
       "      <td>Monroe</td>\n",
       "      <td>0</td>\n",
       "      <td>06/01/2012</td>\n",
       "      <td>IGERT:  Big Data Social Science - An Integrati...</td>\n",
       "      <td>2012-06-01</td>\n",
       "      <td>Burt Monroe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>NSF</td>\n",
       "      <td>ATLANTA</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>GA</td>\n",
       "      <td>3539333</td>\n",
       "      <td>0808863</td>\n",
       "      <td>Haesun</td>\n",
       "      <td>Park</td>\n",
       "      <td>0</td>\n",
       "      <td>05/22/2008</td>\n",
       "      <td>FODAVA-Lead: Dimension Reduction and Data Redu...</td>\n",
       "      <td>2008-05-22</td>\n",
       "      <td>Haesun Park</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>NSF</td>\n",
       "      <td>STORRS</td>\n",
       "      <td>University of Connecticut</td>\n",
       "      <td>CT</td>\n",
       "      <td>16662</td>\n",
       "      <td>2219336</td>\n",
       "      <td>Jun</td>\n",
       "      <td>Yan</td>\n",
       "      <td>1</td>\n",
       "      <td>04/29/2022</td>\n",
       "      <td>Conference: UConn Sports Analytics Symposium: ...</td>\n",
       "      <td>2022-04-29</td>\n",
       "      <td>Jun Yan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>NSF</td>\n",
       "      <td>HOUSTON</td>\n",
       "      <td>AIRMETTLE, INC.</td>\n",
       "      <td>TX</td>\n",
       "      <td>276000</td>\n",
       "      <td>2135007</td>\n",
       "      <td>Donpaul</td>\n",
       "      <td>Stephens</td>\n",
       "      <td>1</td>\n",
       "      <td>02/09/2022</td>\n",
       "      <td>STTR Phase I:  Real-Time Smart Data Lake</td>\n",
       "      <td>2022-02-09</td>\n",
       "      <td>Donpaul Stephens</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLUMBUS</td>\n",
       "      <td>Ohio State University</td>\n",
       "      <td>OH</td>\n",
       "      <td>311411</td>\n",
       "      <td>2110259</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/16/2021</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>2021-03-16</td>\n",
       "      <td>Jia Liu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>NSF</td>\n",
       "      <td>DAYTON</td>\n",
       "      <td>Sinclair Community College</td>\n",
       "      <td>OH</td>\n",
       "      <td>576315</td>\n",
       "      <td>2055411</td>\n",
       "      <td>Paul</td>\n",
       "      <td>Hansford</td>\n",
       "      <td>1</td>\n",
       "      <td>03/15/2021</td>\n",
       "      <td>Expanding the Data Analytics Technician Pipeli...</td>\n",
       "      <td>2021-03-15</td>\n",
       "      <td>Paul Hansford</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BOCA RATON</td>\n",
       "      <td>Florida Atlantic University</td>\n",
       "      <td>FL</td>\n",
       "      <td>95751</td>\n",
       "      <td>2027890</td>\n",
       "      <td>Borko</td>\n",
       "      <td>Furht</td>\n",
       "      <td>1</td>\n",
       "      <td>06/09/2020</td>\n",
       "      <td>NSF RAPID: Modeling Corona Spread Using Big Da...</td>\n",
       "      <td>2020-06-09</td>\n",
       "      <td>Borko Furht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>NSF</td>\n",
       "      <td>LAFAYETTE</td>\n",
       "      <td>University of Louisiana at Lafayette</td>\n",
       "      <td>LA</td>\n",
       "      <td>187477</td>\n",
       "      <td>2027688</td>\n",
       "      <td>Raju</td>\n",
       "      <td>Gottumukkala</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2020</td>\n",
       "      <td>RAPID: Visual Analytics Approach to Real-Time ...</td>\n",
       "      <td>2020-05-20</td>\n",
       "      <td>Raju Gottumukkala</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>NSF</td>\n",
       "      <td>AMES</td>\n",
       "      <td>Iowa State University</td>\n",
       "      <td>IA</td>\n",
       "      <td>0</td>\n",
       "      <td>1943226</td>\n",
       "      <td>Jia</td>\n",
       "      <td>Liu</td>\n",
       "      <td>1</td>\n",
       "      <td>03/12/2020</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>2020-03-12</td>\n",
       "      <td>Jia Liu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>NSF</td>\n",
       "      <td>BLOOMINGTON</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>IN</td>\n",
       "      <td>45398</td>\n",
       "      <td>1934942</td>\n",
       "      <td>Patricia</td>\n",
       "      <td>Herzog</td>\n",
       "      <td>1</td>\n",
       "      <td>05/20/2019</td>\n",
       "      <td>Workshop:  Addressing Technology and Skills Ga...</td>\n",
       "      <td>2019-05-20</td>\n",
       "      <td>Patricia Herzog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>NSF</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>WA</td>\n",
       "      <td>124657</td>\n",
       "      <td>1929901</td>\n",
       "      <td>Huijia</td>\n",
       "      <td>Lin</td>\n",
       "      <td>0</td>\n",
       "      <td>04/18/2019</td>\n",
       "      <td>TWC: Medium: Collaborative: Seal: Secure Engin...</td>\n",
       "      <td>2019-04-18</td>\n",
       "      <td>Huijia Lin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>NSF</td>\n",
       "      <td>COLLEGE STATION</td>\n",
       "      <td>Texas A&amp;M Engineering Experiment Station</td>\n",
       "      <td>TX</td>\n",
       "      <td>449380</td>\n",
       "      <td>1922969</td>\n",
       "      <td>Shuiwang</td>\n",
       "      <td>Ji</td>\n",
       "      <td>0</td>\n",
       "      <td>02/22/2019</td>\n",
       "      <td>CAREER: Towards the Next Generation of Data-Dr...</td>\n",
       "      <td>2019-02-22</td>\n",
       "      <td>Shuiwang Ji</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   agency      awardeeCity                                        awardeeName  \\\n",
       "0     NSF           IRVINE                    University of California-Irvine   \n",
       "1     NSF    SANTA BARBARA             University of California-Santa Barbara   \n",
       "2     NSF         New York              Teachers College, Columbia University   \n",
       "3     NSF      TALLAHASSEE                           Florida State University   \n",
       "4     NSF           NORMAN               University of Oklahoma Norman Campus   \n",
       "5     NSF       MENLO PARK                                  SRI International   \n",
       "6     NSF       SANTA CRUZ                University of California-Santa Cruz   \n",
       "7     NSF          Atlanta                  Georgia Tech Research Corporation   \n",
       "8     NSF          Lincoln                     University of Nebraska-Lincoln   \n",
       "9     NSF       MONTGOMERY                           Alabama State University   \n",
       "10    NSF          Chicago                            Northwestern University   \n",
       "11    NSF       PITTSBURGH                         Carnegie-Mellon University   \n",
       "12    NSF       BLACKSBURG  Virginia Polytechnic Institute and State Unive...   \n",
       "13    NSF  University Park            Pennsylvania State Univ University Park   \n",
       "14    NSF          ATLANTA                  Georgia Tech Research Corporation   \n",
       "15    NSF           STORRS                          University of Connecticut   \n",
       "16    NSF          HOUSTON                                    AIRMETTLE, INC.   \n",
       "17    NSF         COLUMBUS                              Ohio State University   \n",
       "18    NSF           DAYTON                         Sinclair Community College   \n",
       "19    NSF       BOCA RATON                        Florida Atlantic University   \n",
       "20    NSF        LAFAYETTE               University of Louisiana at Lafayette   \n",
       "21    NSF             AMES                              Iowa State University   \n",
       "22    NSF      BLOOMINGTON                                 Indiana University   \n",
       "23    NSF          Seattle                           University of Washington   \n",
       "24    NSF  COLLEGE STATION           Texas A&M Engineering Experiment Station   \n",
       "\n",
       "   awardeeStateCode fundsObligatedAmt       id  piFirstName    piLastName  \\\n",
       "0                CA            600000  1954962      Michael         Carey   \n",
       "1                CA            400000  1618118     Li-Chung          Wang   \n",
       "2                NY            499623  1560720         Alex        Bowers   \n",
       "3                FL            204767  1561041      Weikuan            Yu   \n",
       "4                OK           1381958  1541165         Kash        Barker   \n",
       "5                CA            608024  1522990        Marie    Bienkowski   \n",
       "6                CA             14500  1464671  Ramakrishna        Akella   \n",
       "7                GA             49992  1527370         Alan        Porter   \n",
       "8                NE            405378  1423487     Hongfeng            Yu   \n",
       "9                AL            399976  1436871         Carl        Pettis   \n",
       "10               IL            709342  1409601         Alok     Choudhary   \n",
       "11               PA           8914035  1261721     Nicholas       Nystrom   \n",
       "12               VA            199943  1141096       Leanna         House   \n",
       "13               PA           2999713  1144860         Burt        Monroe   \n",
       "14               GA           3539333  0808863       Haesun          Park   \n",
       "15               CT             16662  2219336          Jun           Yan   \n",
       "16               TX            276000  2135007      Donpaul      Stephens   \n",
       "17               OH            311411  2110259          Jia           Liu   \n",
       "18               OH            576315  2055411         Paul      Hansford   \n",
       "19               FL             95751  2027890        Borko         Furht   \n",
       "20               LA            187477  2027688         Raju  Gottumukkala   \n",
       "21               IA                 0  1943226          Jia           Liu   \n",
       "22               IN             45398  1934942     Patricia        Herzog   \n",
       "23               WA            124657  1929901       Huijia           Lin   \n",
       "24               TX            449380  1922969     Shuiwang            Ji   \n",
       "\n",
       "   publicAccessMandate        date  \\\n",
       "0                    1  08/13/2020   \n",
       "1                    0  06/10/2016   \n",
       "2                    0  05/11/2016   \n",
       "3                    0  09/21/2015   \n",
       "4                    0  09/04/2015   \n",
       "5                    0  08/25/2015   \n",
       "6                    0  04/28/2015   \n",
       "7                    0  03/25/2015   \n",
       "8                    0  07/28/2014   \n",
       "9                    0  07/16/2014   \n",
       "10                   0  05/21/2014   \n",
       "11                   0  09/26/2013   \n",
       "12                   0  08/23/2012   \n",
       "13                   0  06/01/2012   \n",
       "14                   0  05/22/2008   \n",
       "15                   1  04/29/2022   \n",
       "16                   1  02/09/2022   \n",
       "17                   1  03/16/2021   \n",
       "18                   1  03/15/2021   \n",
       "19                   1  06/09/2020   \n",
       "20                   1  05/20/2020   \n",
       "21                   1  03/12/2020   \n",
       "22                   1  05/20/2019   \n",
       "23                   0  04/18/2019   \n",
       "24                   0  02/22/2019   \n",
       "\n",
       "                                                title       Date2  \\\n",
       "0   III: Medium: Collaborative Research: Supportin...  2020-08-13   \n",
       "1   SHF: Small: End-To-End Test Data Analytics For...  2016-06-10   \n",
       "2   Building Community and Capacity for Data-Inten...  2016-05-11   \n",
       "3   EAGER: Tadoop: A Dual-Purpose Framework Taming...  2015-09-21   \n",
       "4   CRISP Type 2/Collaborative Research: Resilienc...  2015-09-04   \n",
       "5   EXP: Understanding Computational Thinking Proc...  2015-08-25   \n",
       "6   Planning Grant: I/UCRC for Computation Intensi...  2015-04-28   \n",
       "7   Forecasting Innovation Pathways of  Big Data &...  2015-03-25   \n",
       "8   III: CGV: Small: A Scalable Visual Analytics F...  2014-07-28   \n",
       "9   Targeted Infusion Project: Infusing Data-Enabl...  2014-07-16   \n",
       "10  SHF: Medium: Collaborative Research: Scalable ...  2014-05-21   \n",
       "11                      CIF21 DIBBS: The Data Exacell  2013-09-26   \n",
       "12   Critical Thinking with Data Visualization (CTDV)  2012-08-23   \n",
       "13  IGERT:  Big Data Social Science - An Integrati...  2012-06-01   \n",
       "14  FODAVA-Lead: Dimension Reduction and Data Redu...  2008-05-22   \n",
       "15  Conference: UConn Sports Analytics Symposium: ...  2022-04-29   \n",
       "16           STTR Phase I:  Real-Time Smart Data Lake  2022-02-09   \n",
       "17  CAREER: Computing-Aware Network Optimization f...  2021-03-16   \n",
       "18  Expanding the Data Analytics Technician Pipeli...  2021-03-15   \n",
       "19  NSF RAPID: Modeling Corona Spread Using Big Da...  2020-06-09   \n",
       "20  RAPID: Visual Analytics Approach to Real-Time ...  2020-05-20   \n",
       "21  CAREER: Computing-Aware Network Optimization f...  2020-03-12   \n",
       "22  Workshop:  Addressing Technology and Skills Ga...  2019-05-20   \n",
       "23  TWC: Medium: Collaborative: Seal: Secure Engin...  2019-04-18   \n",
       "24  CAREER: Towards the Next Generation of Data-Dr...  2019-02-22   \n",
       "\n",
       "                  NAME  \n",
       "0        Michael Carey  \n",
       "1        Li-Chung Wang  \n",
       "2          Alex Bowers  \n",
       "3           Weikuan Yu  \n",
       "4          Kash Barker  \n",
       "5     Marie Bienkowski  \n",
       "6   Ramakrishna Akella  \n",
       "7          Alan Porter  \n",
       "8          Hongfeng Yu  \n",
       "9          Carl Pettis  \n",
       "10      Alok Choudhary  \n",
       "11    Nicholas Nystrom  \n",
       "12        Leanna House  \n",
       "13         Burt Monroe  \n",
       "14         Haesun Park  \n",
       "15             Jun Yan  \n",
       "16    Donpaul Stephens  \n",
       "17             Jia Liu  \n",
       "18       Paul Hansford  \n",
       "19         Borko Furht  \n",
       "20   Raju Gottumukkala  \n",
       "21             Jia Liu  \n",
       "22     Patricia Herzog  \n",
       "23          Huijia Lin  \n",
       "24         Shuiwang Ji  "
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nsf_df['NAME'] = nsf_df['piFirstName'] + \" \" + nsf_df['piLastName']\n",
    "nsf_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d5dda2a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Organization</th>\n",
       "      <th>Project</th>\n",
       "      <th>Funding</th>\n",
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       "      <th>0</th>\n",
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       "      <td>600000</td>\n",
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       "      <td>400000</td>\n",
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       "      <th>2</th>\n",
       "      <td>Alex Bowers</td>\n",
       "      <td>Teachers College, Columbia University</td>\n",
       "      <td>Building Community and Capacity for Data-Inten...</td>\n",
       "      <td>499623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Weikuan Yu</td>\n",
       "      <td>Florida State University</td>\n",
       "      <td>EAGER: Tadoop: A Dual-Purpose Framework Taming...</td>\n",
       "      <td>204767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kash Barker</td>\n",
       "      <td>University of Oklahoma Norman Campus</td>\n",
       "      <td>CRISP Type 2/Collaborative Research: Resilienc...</td>\n",
       "      <td>1381958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Marie Bienkowski</td>\n",
       "      <td>SRI International</td>\n",
       "      <td>EXP: Understanding Computational Thinking Proc...</td>\n",
       "      <td>608024</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Ramakrishna Akella</td>\n",
       "      <td>University of California-Santa Cruz</td>\n",
       "      <td>Planning Grant: I/UCRC for Computation Intensi...</td>\n",
       "      <td>14500</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Alan Porter</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>Forecasting Innovation Pathways of  Big Data &amp;...</td>\n",
       "      <td>49992</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Hongfeng Yu</td>\n",
       "      <td>University of Nebraska-Lincoln</td>\n",
       "      <td>III: CGV: Small: A Scalable Visual Analytics F...</td>\n",
       "      <td>405378</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Carl Pettis</td>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Targeted Infusion Project: Infusing Data-Enabl...</td>\n",
       "      <td>399976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Alok Choudhary</td>\n",
       "      <td>Northwestern University</td>\n",
       "      <td>SHF: Medium: Collaborative Research: Scalable ...</td>\n",
       "      <td>709342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>Nicholas Nystrom</td>\n",
       "      <td>Carnegie-Mellon University</td>\n",
       "      <td>CIF21 DIBBS: The Data Exacell</td>\n",
       "      <td>8914035</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>Leanna House</td>\n",
       "      <td>Virginia Polytechnic Institute and State Unive...</td>\n",
       "      <td>Critical Thinking with Data Visualization (CTDV)</td>\n",
       "      <td>199943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>Burt Monroe</td>\n",
       "      <td>Pennsylvania State Univ University Park</td>\n",
       "      <td>IGERT:  Big Data Social Science - An Integrati...</td>\n",
       "      <td>2999713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>Haesun Park</td>\n",
       "      <td>Georgia Tech Research Corporation</td>\n",
       "      <td>FODAVA-Lead: Dimension Reduction and Data Redu...</td>\n",
       "      <td>3539333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Jun Yan</td>\n",
       "      <td>University of Connecticut</td>\n",
       "      <td>Conference: UConn Sports Analytics Symposium: ...</td>\n",
       "      <td>16662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>Donpaul Stephens</td>\n",
       "      <td>AIRMETTLE, INC.</td>\n",
       "      <td>STTR Phase I:  Real-Time Smart Data Lake</td>\n",
       "      <td>276000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>Jia Liu</td>\n",
       "      <td>Ohio State University</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>311411</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Paul Hansford</td>\n",
       "      <td>Sinclair Community College</td>\n",
       "      <td>Expanding the Data Analytics Technician Pipeli...</td>\n",
       "      <td>576315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>Borko Furht</td>\n",
       "      <td>Florida Atlantic University</td>\n",
       "      <td>NSF RAPID: Modeling Corona Spread Using Big Da...</td>\n",
       "      <td>95751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Raju Gottumukkala</td>\n",
       "      <td>University of Louisiana at Lafayette</td>\n",
       "      <td>RAPID: Visual Analytics Approach to Real-Time ...</td>\n",
       "      <td>187477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>Jia Liu</td>\n",
       "      <td>Iowa State University</td>\n",
       "      <td>CAREER: Computing-Aware Network Optimization f...</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>Patricia Herzog</td>\n",
       "      <td>Indiana University</td>\n",
       "      <td>Workshop:  Addressing Technology and Skills Ga...</td>\n",
       "      <td>45398</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>Huijia Lin</td>\n",
       "      <td>University of Washington</td>\n",
       "      <td>TWC: Medium: Collaborative: Seal: Secure Engin...</td>\n",
       "      <td>124657</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>Shuiwang Ji</td>\n",
       "      <td>Texas A&amp;M Engineering Experiment Station</td>\n",
       "      <td>CAREER: Towards the Next Generation of Data-Dr...</td>\n",
       "      <td>449380</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  Name                                       Organization  \\\n",
       "0        Michael Carey                    University of California-Irvine   \n",
       "1        Li-Chung Wang             University of California-Santa Barbara   \n",
       "2          Alex Bowers              Teachers College, Columbia University   \n",
       "3           Weikuan Yu                           Florida State University   \n",
       "4          Kash Barker               University of Oklahoma Norman Campus   \n",
       "5     Marie Bienkowski                                  SRI International   \n",
       "6   Ramakrishna Akella                University of California-Santa Cruz   \n",
       "7          Alan Porter                  Georgia Tech Research Corporation   \n",
       "8          Hongfeng Yu                     University of Nebraska-Lincoln   \n",
       "9          Carl Pettis                           Alabama State University   \n",
       "10      Alok Choudhary                            Northwestern University   \n",
       "11    Nicholas Nystrom                         Carnegie-Mellon University   \n",
       "12        Leanna House  Virginia Polytechnic Institute and State Unive...   \n",
       "13         Burt Monroe            Pennsylvania State Univ University Park   \n",
       "14         Haesun Park                  Georgia Tech Research Corporation   \n",
       "15             Jun Yan                          University of Connecticut   \n",
       "16    Donpaul Stephens                                    AIRMETTLE, INC.   \n",
       "17             Jia Liu                              Ohio State University   \n",
       "18       Paul Hansford                         Sinclair Community College   \n",
       "19         Borko Furht                        Florida Atlantic University   \n",
       "20   Raju Gottumukkala               University of Louisiana at Lafayette   \n",
       "21             Jia Liu                              Iowa State University   \n",
       "22     Patricia Herzog                                 Indiana University   \n",
       "23          Huijia Lin                           University of Washington   \n",
       "24         Shuiwang Ji           Texas A&M Engineering Experiment Station   \n",
       "\n",
       "                                              Project  Funding  \n",
       "0   III: Medium: Collaborative Research: Supportin...   600000  \n",
       "1   SHF: Small: End-To-End Test Data Analytics For...   400000  \n",
       "2   Building Community and Capacity for Data-Inten...   499623  \n",
       "3   EAGER: Tadoop: A Dual-Purpose Framework Taming...   204767  \n",
       "4   CRISP Type 2/Collaborative Research: Resilienc...  1381958  \n",
       "5   EXP: Understanding Computational Thinking Proc...   608024  \n",
       "6   Planning Grant: I/UCRC for Computation Intensi...    14500  \n",
       "7   Forecasting Innovation Pathways of  Big Data &...    49992  \n",
       "8   III: CGV: Small: A Scalable Visual Analytics F...   405378  \n",
       "9   Targeted Infusion Project: Infusing Data-Enabl...   399976  \n",
       "10  SHF: Medium: Collaborative Research: Scalable ...   709342  \n",
       "11                      CIF21 DIBBS: The Data Exacell  8914035  \n",
       "12   Critical Thinking with Data Visualization (CTDV)   199943  \n",
       "13  IGERT:  Big Data Social Science - An Integrati...  2999713  \n",
       "14  FODAVA-Lead: Dimension Reduction and Data Redu...  3539333  \n",
       "15  Conference: UConn Sports Analytics Symposium: ...    16662  \n",
       "16           STTR Phase I:  Real-Time Smart Data Lake   276000  \n",
       "17  CAREER: Computing-Aware Network Optimization f...   311411  \n",
       "18  Expanding the Data Analytics Technician Pipeli...   576315  \n",
       "19  NSF RAPID: Modeling Corona Spread Using Big Da...    95751  \n",
       "20  RAPID: Visual Analytics Approach to Real-Time ...   187477  \n",
       "21  CAREER: Computing-Aware Network Optimization f...        0  \n",
       "22  Workshop:  Addressing Technology and Skills Ga...    45398  \n",
       "23  TWC: Medium: Collaborative: Seal: Secure Engin...   124657  \n",
       "24  CAREER: Towards the Next Generation of Data-Dr...   449380  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "DA_nsfdf = pd.DataFrame()\n",
    "DA_nsfdf[\"Name\"] = nsf_df[\"NAME\"]\n",
    "DA_nsfdf[\"Organization\"] = nsf_df[\"awardeeName\"]\n",
    "DA_nsfdf[\"Project\"] = nsf_df[\"title\"]\n",
    "DA_nsfdf[\"Funding\"] = nsf_df[\"fundsObligatedAmt\"]\n",
    "DA_nsfdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ea9af5f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/m3/8dqdsjld0q7bb_84k7_vv_k80000gn/T/ipykernel_75464/2713745722.py:1: FutureWarning: The frame.append method is deprecated and will be removed from pandas in a future version. Use pandas.concat instead.\n",
      "  NIH_NSF = DA_nsfdf.append(NIH_df)\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>Organization</th>\n",
       "      <th>Project</th>\n",
       "      <th>Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Michael Carey</td>\n",
       "      <td>University of California-Irvine</td>\n",
       "      <td>III: Medium: Collaborative Research: Supportin...</td>\n",
       "      <td>600000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Li-Chung Wang</td>\n",
       "      <td>University of California-Santa Barbara</td>\n",
       "      <td>SHF: Small: End-To-End Test Data Analytics For...</td>\n",
       "      <td>400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Alex Bowers</td>\n",
       "      <td>Teachers College, Columbia University</td>\n",
       "      <td>Building Community and Capacity for Data-Inten...</td>\n",
       "      <td>499623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Weikuan Yu</td>\n",
       "      <td>Florida State University</td>\n",
       "      <td>EAGER: Tadoop: A Dual-Purpose Framework Taming...</td>\n",
       "      <td>204767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Kash Barker</td>\n",
       "      <td>University of Oklahoma Norman Campus</td>\n",
       "      <td>CRISP Type 2/Collaborative Research: Resilienc...</td>\n",
       "      <td>1381958</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61</th>\n",
       "      <td>Peter Xuekun Song</td>\n",
       "      <td>UNIVERSITY OF MICHIGAN AT ANN ARBOR</td>\n",
       "      <td>E3Gen: Multigenerational Influences of Social ...</td>\n",
       "      <td>360960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>Mark  Ward</td>\n",
       "      <td>TRINITY COLLEGE DUBLIN</td>\n",
       "      <td>Pathways from social disconnection to suicidal...</td>\n",
       "      <td>70838</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63</th>\n",
       "      <td>Colin Lee Hisey</td>\n",
       "      <td>OHIO STATE UNIVERSITY</td>\n",
       "      <td>Machine Learning-enabled Classification of Ext...</td>\n",
       "      <td>99955</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64</th>\n",
       "      <td>Jianhang  Jia</td>\n",
       "      <td>UNIVERSITY OF KENTUCKY</td>\n",
       "      <td>Imaging Core</td>\n",
       "      <td>124058</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65</th>\n",
       "      <td>EDILBERTO  AMORIM DE CERQUEIRA</td>\n",
       "      <td>UNIVERSITY OF CALIFORNIA, SAN FRANCISCO</td>\n",
       "      <td>Physiology-driven seizure management post-card...</td>\n",
       "      <td>225652</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>91 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Name                             Organization  \\\n",
       "0                    Michael Carey          University of California-Irvine   \n",
       "1                    Li-Chung Wang   University of California-Santa Barbara   \n",
       "2                      Alex Bowers    Teachers College, Columbia University   \n",
       "3                       Weikuan Yu                 Florida State University   \n",
       "4                      Kash Barker     University of Oklahoma Norman Campus   \n",
       "..                             ...                                      ...   \n",
       "61               Peter Xuekun Song      UNIVERSITY OF MICHIGAN AT ANN ARBOR   \n",
       "62                      Mark  Ward                   TRINITY COLLEGE DUBLIN   \n",
       "63                 Colin Lee Hisey                    OHIO STATE UNIVERSITY   \n",
       "64                   Jianhang  Jia                   UNIVERSITY OF KENTUCKY   \n",
       "65  EDILBERTO  AMORIM DE CERQUEIRA  UNIVERSITY OF CALIFORNIA, SAN FRANCISCO   \n",
       "\n",
       "                                              Project  Funding  \n",
       "0   III: Medium: Collaborative Research: Supportin...   600000  \n",
       "1   SHF: Small: End-To-End Test Data Analytics For...   400000  \n",
       "2   Building Community and Capacity for Data-Inten...   499623  \n",
       "3   EAGER: Tadoop: A Dual-Purpose Framework Taming...   204767  \n",
       "4   CRISP Type 2/Collaborative Research: Resilienc...  1381958  \n",
       "..                                                ...      ...  \n",
       "61  E3Gen: Multigenerational Influences of Social ...   360960  \n",
       "62  Pathways from social disconnection to suicidal...    70838  \n",
       "63  Machine Learning-enabled Classification of Ext...    99955  \n",
       "64                                       Imaging Core   124058  \n",
       "65  Physiology-driven seizure management post-card...   225652  \n",
       "\n",
       "[91 rows x 4 columns]"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIH_NSF = DA_nsfdf.append(NIH_df)\n",
    "NIH_NSF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b1bca74",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Name             object\n",
       "Organization     object\n",
       "Project          object\n",
       "Funding         float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "NIH_NSF['Funding']=NIH_NSF['Funding'].astype(float)\n",
    "NIH_NSF.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "e047294e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>Organization</th>\n",
       "      <th>Name</th>\n",
       "      <th>Funding</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>AIRMETTLE, INC.</td>\n",
       "      <td>Donpaul Stephens</td>\n",
       "      <td>276000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Alabama State University</td>\n",
       "      <td>Carl Pettis</td>\n",
       "      <td>399976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>BROAD INSTITUTE, INC.</td>\n",
       "      <td>Anne E. Carpenter</td>\n",
       "      <td>1955828.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>BROAD INSTITUTE, INC.</td>\n",
       "      <td>BETH  CIMINI</td>\n",
       "      <td>1136551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>BROAD INSTITUTE, INC.</td>\n",
       "      <td>Kevin William Eliceiri</td>\n",
       "      <td>1136551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>Virginia Polytechnic Institute and State Unive...</td>\n",
       "      <td>Leanna House</td>\n",
       "      <td>199943.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>WAKE FOREST UNIVERSITY HEALTH SCIENCES</td>\n",
       "      <td>Cristina Maria Furdui</td>\n",
       "      <td>204659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>WAKE FOREST UNIVERSITY HEALTH SCIENCES</td>\n",
       "      <td>Daniel Clark Files</td>\n",
       "      <td>204659.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>YALE UNIVERSITY</td>\n",
       "      <td>LUCILA  OHNO-MACHADO</td>\n",
       "      <td>613743.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>87</th>\n",
       "      <td>YALE UNIVERSITY</td>\n",
       "      <td>Xiaoqian  Jiang</td>\n",
       "      <td>613743.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>88 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Organization                    Name  \\\n",
       "0                                     AIRMETTLE, INC.        Donpaul Stephens   \n",
       "1                            Alabama State University             Carl Pettis   \n",
       "2                               BROAD INSTITUTE, INC.       Anne E. Carpenter   \n",
       "3                               BROAD INSTITUTE, INC.            BETH  CIMINI   \n",
       "4                               BROAD INSTITUTE, INC.  Kevin William Eliceiri   \n",
       "..                                                ...                     ...   \n",
       "83  Virginia Polytechnic Institute and State Unive...            Leanna House   \n",
       "84             WAKE FOREST UNIVERSITY HEALTH SCIENCES   Cristina Maria Furdui   \n",
       "85             WAKE FOREST UNIVERSITY HEALTH SCIENCES      Daniel Clark Files   \n",
       "86                                    YALE UNIVERSITY    LUCILA  OHNO-MACHADO   \n",
       "87                                    YALE UNIVERSITY         Xiaoqian  Jiang   \n",
       "\n",
       "      Funding  \n",
       "0    276000.0  \n",
       "1    399976.0  \n",
       "2   1955828.0  \n",
       "3   1136551.0  \n",
       "4   1136551.0  \n",
       "..        ...  \n",
       "83   199943.0  \n",
       "84   204659.0  \n",
       "85   204659.0  \n",
       "86   613743.0  \n",
       "87   613743.0  \n",
       "\n",
       "[88 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_name = NIH_NSF[['Name','Organization','Funding']].groupby(['Organization','Name']).agg('sum')\n",
    "agg_name_v1 = agg_name.reset_index()\n",
    "agg_name_v1.columns = [\"Organization\",\"Name\",\"Funding\"]\n",
    "agg_name_v1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "63a484d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>AIRMETTLE, INC.</th>\n",
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       "      <td>276000.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Alabama State University</th>\n",
       "      <th>Carl Pettis</th>\n",
       "      <td>399976.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">BROAD INSTITUTE, INC.</th>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>BETH  CIMINI</th>\n",
       "      <td>1136551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Kevin William Eliceiri</th>\n",
       "      <td>1136551.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CINCINNATI CHILDRENS HOSP MED CTR</th>\n",
       "      <th>Rhonda  Szczesniak</th>\n",
       "      <td>406637.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CYTODEL, INC.</th>\n",
       "      <th>PHILIP Arthur BAND</th>\n",
       "      <td>295365.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Carnegie-Mellon University</th>\n",
       "      <th>Nicholas Nystrom</th>\n",
       "      <td>8914035.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DARTMOUTH COLLEGE</th>\n",
       "      <th>Brian P Jackson</th>\n",
       "      <td>21074.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">EMORY UNIVERSITY</th>\n",
       "      <th>Alex  Sox-Harris</th>\n",
       "      <td>677235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Nader Nabile Massarweh</th>\n",
       "      <td>677235.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>FRED HUTCHINSON CANCER CENTER</th>\n",
       "      <th>Philip R Gafken</th>\n",
       "      <td>374776.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida Atlantic University</th>\n",
       "      <th>Borko Furht</th>\n",
       "      <td>95751.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Florida State University</th>\n",
       "      <th>Weikuan Yu</th>\n",
       "      <td>204767.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">Georgia Tech Research Corporation</th>\n",
       "      <th>Alan Porter</th>\n",
       "      <td>49992.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Haesun Park</th>\n",
       "      <td>3539333.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>HARVARD MEDICAL SCHOOL</th>\n",
       "      <th>Sung Eun EUN Choi</th>\n",
       "      <td>114113.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">HARVARD UNIVERSITY</th>\n",
       "      <th>Deanna  Barch</th>\n",
       "      <td>726975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Leah Helene Somerville</th>\n",
       "      <td>726975.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI</th>\n",
       "      <th>Li  Shen</th>\n",
       "      <td>322926.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                                  Funding\n",
       "Organization                            Name                             \n",
       "AIRMETTLE, INC.                         Donpaul Stephens         276000.0\n",
       "Alabama State University                Carl Pettis              399976.0\n",
       "BROAD INSTITUTE, INC.                   Anne E. Carpenter       1955828.0\n",
       "                                        BETH  CIMINI            1136551.0\n",
       "                                        Kevin William Eliceiri  1136551.0\n",
       "CINCINNATI CHILDRENS HOSP MED CTR       Rhonda  Szczesniak       406637.0\n",
       "CYTODEL, INC.                           PHILIP Arthur BAND       295365.0\n",
       "Carnegie-Mellon University              Nicholas Nystrom        8914035.0\n",
       "DARTMOUTH COLLEGE                       Brian P Jackson           21074.0\n",
       "EMORY UNIVERSITY                        Alex  Sox-Harris         677235.0\n",
       "                                        Nader Nabile Massarweh   677235.0\n",
       "FRED HUTCHINSON CANCER CENTER           Philip R Gafken          374776.0\n",
       "Florida Atlantic University             Borko Furht               95751.0\n",
       "Florida State University                Weikuan Yu               204767.0\n",
       "Georgia Tech Research Corporation       Alan Porter               49992.0\n",
       "                                        Haesun Park             3539333.0\n",
       "HARVARD MEDICAL SCHOOL                  Sung Eun EUN Choi        114113.0\n",
       "HARVARD UNIVERSITY                      Deanna  Barch            726975.0\n",
       "                                        Leah Helene Somerville   726975.0\n",
       "ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI Li  Shen                 322926.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "agg_name.head(20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f3c829b7",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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