SHARE API Webinar.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Calling the SHARE API\n",
    "----\n",
    "Here are some working examples of how to query the current scrAPI database for metrics of results coming through the SHARE Notifiation Service.\n",
    "\n",
    "*these slides and example notebooks*:  https://osf.io/bygau/\n",
    "\n",
    "slides also here: https://github.com/erinspace/share_tutorials"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Setup\n",
    "\n",
    "- All of the examples I use here will be using python and some basic libraries\n",
    "    - [Here's a basic guide to getting started with python](https://wiki.python.org/moin/BeginnersGuide)\n",
    "- Code and setup instructions on github at:\n",
    "     - https://github.com/erinspace/share_tutorials\n",
    "\n",
    "- To run these examples on your machine, you'll need to install some basic python packages\n",
    "    - Make sure to use a virtual enviornment to install python packages:\n",
    "        - https://virtualenv.readthedocs.org/en/latest/\n",
    "    - Using your terminal, run ```pip install -r requirements.txt``` inside your virtual enviornment\n",
    "- Run the Jupyter notebook server from the command line:\n",
    "    ```jupyter notebook```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Get a List of the Current SHARE Providers\n",
    "----\n",
    "We'll make an API call to find:\n",
    "- The official name of each SHARE Provider\n",
    "- The URL for the home page of each SHARE Provider\n",
    "- The shortname, or nickname of the SHARE provider for internal use\n",
    "    - We'll use this name when querying for documents from this source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import requests\n",
    "\n",
    "data = requests.get('https://osf.io/api/v1/share/providers/').json()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[u'doepages',\n",
       " u'scholarsphere',\n",
       " u'scholarsarchiveosu',\n",
       " u'utaustin',\n",
       " u'scholarworks_umass']"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Let's kook at the outer layer of data\n",
    "data['providerMap'].keys()[:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### This is a lot of information!\n",
    "\n",
    "Let's display this in a way that looks a little nicer."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
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9sOg3eWg64PgoZvDE0PMntE/lhg1GzEaNnqk9F31BfLH6cmbs%0A2avb4ydfZ08ETTpNWU9bvbF5a/PO/L36DPvMxGzBnPLcxPyZhdQPph%2BpPzZ8Mvk0vXhyKfmz1xeb%0Ar1bLoStd3/f%2BaNsw3Nz8nX8FNAY9i5nATuMWqdE0Kvhg2irCBFGKPonhLhMncwrLUzZF9iyOV1wk%0A7lyeQT4ufleBYsFOoTHhZZEV0Tmxh%2BInJWIltaWopZ5KH98WKkOS%2BSl7V%2B6AvIsCv8J7xSalRGUt%0AEkLqUSlQtVFjVhtRr9Dw0OTVHIOjwFObU3tU57Cuh56Y3pr%2BsMFFw31GAcbbTRhN3pl2mpWZJ1oE%0AWPpaBVtH2UTY%2BtrZ2Gs6SDlyOxGdUc4rLu9dR9zuuDd5VHoW7Ej1CvF29TEky/ly%2BCF%2Bc/7DAd2B%0ALUF1weUhOaFRYW7huhHikXRwJExGj8d8jROM906oSLyd9Cx5OmUhdXUn7S6%2B3RLpAhm4jNeZLVlF%0A2bE5Xnuccl33huRl51cXnC9s2de6/8qBi0XnDzYUny45caiytKysqDz/cNaRlIqoo0GVIVUZ1TeP%0AS544Uyt%2BsuTUk9Or9cQzXA3CjVJwHKie124ybLa64HYx/FLu5ZNXbrQMtY63zbR/6UBfY%2B%2BUvq55%0AQ/em6i3B26jbU129d1q663sq7x7s3XMvtS/2fvyD/P7OAbZHuwZfPeZ6ovPUcThwJGP09LNHz7%2B%2B%0AZBqTeWU5Hv368MS1ycdT49NTb%2BbfYWH2M2eH5hkX5D%2BQPop9ov/0ffHd0ujn%2B1%2Bufq1ZTl9x/ib%2B%0AbeV752rqD801wrrhxuzv/Msi86hqtBdGEkuFXcLNUs1RT9Es0eIJonR6RHf6TIZzjENMmyyirEZs%0Aoex7OI5zXuHq4b7Hc5f3Gl8Nf7KAvsAPwVNCFkLzwnki4iJdol6iq2Kl4vLi9yWCJKkk66VMpd5L%0A526T2NYj4ycLZKvltss9k0%2BAXzfNipaKM0rZynzK7SR70oLKHlV%2B1Tb41TKjnq7BpnFWU0/z8Xa/%0A7R%2B10rSptCt1lHVGdFP1%2BPTa9W30nxsEG2wa1hrZGtMY3zHZaapsOmdWa%2B5pwWExYllm5WBNb91n%0Ak22rafvVrtk%2BzEHc4Y1jjdMOZ07npy5Frqaum24t7uEeIh6vPMt3WO9Y8Sr1FvW%2B4qPn84Kc7Cvk%0A%2BwyuI8EBJoGqQerBZiHk0IgwcrhOBEPEWOTpqIhoUvR6zJ3YgjjbeNb4lwnHE/2TxJLeJR9LMUoZ%0ASw1PY0l7svParhu7u9PvZFzNbMgqz87OidrjkWu0VyoPm/c0v6LAvVCkcG3fxP6HB64WnTi4u9ij%0AROMQ16HV0pGyS%2BWHD%2B8/UlJRc/Ry5d2qZ9Vzx9ZO0NUI1CqdND3leTqqbnd9/pl9DRmN5LOq54jn%0APp//0LR6gXCR75LiZdsraS1XWr%2B3q1%2BN7qi4dqGz/fq1G303l2%2BbdF3tduhZ7i3vU7r/tH//gM%2Bg%0A2WO9p/oj4c%2BJY/NTA3PLX1cp%2Bf9Ve6O8E3AqABzKhDvUXACcdAAo7gZAbBjuO/EA2NIB4KgOUGKB%0AAEXoB4jG5N/3BwLQAAdoASOs3/ADcSAPNGDdxQa4w1pIHNxdFoFjoAncAI/AJPgKd448iAJigngj%0ASUgxcg65h7xD4VASKEtUHKoa7vM24b4uEX0V/RNjgjmEmcIqYfOwr3EauArcGtxh3adWpa6n4aYp%0AxtPi82nxtAcJXIR6OmW6TqIWsYNejf4agynDS8Z4Jgam88yGzEMsjixDrDasj9m82b6zV3BocYxz%0A7uLi5urg9uKh4enkTeRT5vvCf0kgVpAkuC7UK1wuEiy6XYwoNiF%2BWSJP0ldKT1psG3HbmsxH2Tdy%0Aw/ItCmmKCorjSnnKJOVPpHaVEtUUNX91Sw15TfbtRC1Z7Updab39%2Bn0Gn4yojVlNOE15zETMlS2s%0ALWOsjlh323y2E7Z3cTjg2OuMcTF0zXXr92Dz9N3R6PXGB0dm9MX5Lvu99R8LmAuiD7YIKQt9H749%0AojTyY7R5TGMcIT4m4UWScXJ7qkxa3S6B3ZUZbJnF2ficzD3Le0Pz5gsK90UcaClmPMRV%2BqG84Yj3%0AUbbKwer9x01OLNcWnWI5nVe3cias4fPZg%2BeNmhkvLF16d2Wmdb79fcd059JN9tsGd7x6fHod%2BnQe%0AyD2UfKQyFPnk%2ByjmBc3Y8dfMkzfeEmd2zut9aP609lnlq/EK/tv%2B7/dXZ368XXu%2BfmXj4E/fTfmt%0A9YOSfypY32OCNQdBIAWUgBYwhXUGH1hhSAP5oAI0gKuwjvAKLCJYhAuR38p%2BClKKXEAGkA8oepQS%0Ayh2VjbqEeovmR3ujT6EXMCqYHMwwVhKbiR2Dua%2BkAlTBVMPURtTtNHI0jXhJ/DlaZdqbBFvCNF0y%0AkYZYRi9IfwHuX18yJjGxMbUxOzN/YNnFimc9wibDdp89ioOd4xZnCBcL1y3uKB4RnjHeCj5Xfg7%2B%0A5wLVgv5C8sJA%2BKnIWdEcMU9xZbiXm5Psl7oM32JFMtmyO%2BXi5f0UdBUJigNKBcoWJHbSkspz1V61%0AVvVajX2aqdsTtfK123W%2B6Snp%2BxsUGtYZtRpfM7lmet2sz3zSEmUlZe1ss8e2zW7BQcTR26naedxV%0AyC3UvdWTaoeL11HvHp8hcpdvg1%2Bef0iAfaBpkFtwVsitMLpw34jOKK7o1JhXcfrxDYn0SdHJD1IF%0A0xJ3Du4mpZ/K5M4qzcHvSctdyCPnTxWm7pcvQh18VXKxNLFc%2BfDniouVCdUax36cqKtVPFl96n2d%0AeH3wmQuN7Gerzms1fbhQcUn98kALuXWtvbbDrhNcb7hpeWup63i3712Ne4L3MQ8ePkx8hBsseEx4%0AUjvsPWr1PPxl/av3E/xTtm8y392YZZ8/%2BFFs8eGX0pV9q2ZriuvHNt78XPqdfwyggTVdDph9aVhr%0AMgC2sMIUAXbBmV8DroB7YBzOewIihugiO5A0pBK5jkyiaGDWyagy1CCaFR2Avo7hwWRg5rBu2Ic4%0AA9x1WE%2B5TW1J/YomDk%2BPv0DrTEAT2uhiiArE7/Q9DBWMCUxuzGYs5qx2bObsqhySnCQub%2B4Unnhe%0AXz5HfmsBK0ErIUthKxF7UW%2BxOPH9Eo2S96Rmt9HJqMoGyh2VH1HkUvJXbiatqdqqPdTI3%2B6mjdU5%0AqLuub2GQDTPYZtxpcsN0wGzNwsKy1VrW5pydrH2ro4HTiEuEG979nKezF6MPra%2B3v0fAmyDN4MKQ%0Ad2H24f2RVlGPYzxiZ%2BLTEvmSxlPupt3aVZ3ulPEjqybHOZd/72L%2B9cJ9%2BwOLTIo5Sx6UBpatHM6u%0AYDxaW6VS/fB4YA1SW3VK7fRwfUIDd%2BO9c%2BlNJhfkLhlfSW%2BtbS/qcOtkvz56s/K22x2q7tN3lXuv%0A9RndH%2B1PHpAbRA8tPpkZHhotfi7%2Bovrlz1dG4wWvH0zSTzlNn3gz%2B07hfdjMidl7c3ML2A88H%2BU/%0AGS66LJE/%2B3%2Bx/Sr0dXl5/wrPSuM39W9Hv61%2Bd/neusq2Grvaurr2Q/dHzo%2B%2BNeKaw9rhtcF16nXd%0A9eT1i%2BuzG4IbbhslG/c3Nn4q/PT/efjng58/NxU2AzaPbPZT8h8XqKRIeXsAhKAPy4/jm5tfxACg%0AKgFgo3hzc612c3PjJNxsjAFwK/zX/zkUZcr/RFWLFPSg/UcG5f7v4/8ArLvFYb7R18kAAAAJcEhZ%0AcwAACxMAAAsTAQCanBgAAAGbaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHht%0AbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI%2BCiAgIDxyZGY6%0AUkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5z%0AIyI%2BCiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5z%0AOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxYRGltZW5zaW9uPjIwPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxZRGltZW5zaW9uPjE2PC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgPC9yZGY6RGVzY3Jp%0AcHRpb24%2BCiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE%2BCkKAfvUAAAH6SURBVCgVZVM9SFtRFP4S%0AHjUxRhFMwSsIoQXtG6R0UAQX6RPM4CwErQidGujoEMTBBqHpIog4iQhi6dJFSlN0cXB0aDMoGRTa%0A8IrYVqJWI3n4es55ec9rc%2BDee953ft893w25JNcOcH4CdO29h0ofw15OQGVOwfK9nEUlWkFbqAmo%0AXgAPE4L7W%2Biq5rrxj14gg/bEIdRGr2%2BHvZlEIe1C4YVgpkPVNDGiBqQqY3YuCjV7Fyx%2B6Ul8I0XR%0AMu2fAumbdNATWdAx0W3MSWBf3XJElbsbvIDwjXGJsal4YFrfmgYH5wkp0FKYlzVkLIDv6n8J8SX%2B%0Apbt50u51oVfmYF04%2BXCqU4cQ9r/sz88aKvOFdmBEXFifGltr6EISxNwqCqk/0vYMuS9SZRklTeMX%0AtgOdM61%2B/SIJ/c3r4CoiYxollNuWUdZ5wHpf5qn4s54f2L/XhYHzKrD0ThzMsoMPB2cYH1nxAoRQ%0AkC4ESCbluL2mo37v4Yp7g523j8SA5ioGh9tF17tgwJ6/hXpDbCSpxS/l5C38INYG5MoBwKMq/s7c%0AZyO1jlIp8GlyWgJdmGi9eo0dgkzierQ1AnDSunjvwqO4j%2BknEZmEgizLopfzAye1BI5KZ7qP6Pw%2B%0AOFmx/yWY/r4IkfwPPne3gOfFHNRcQBExf3KyeEy/pwez4R8F671ONJUvpAAAAABJRU5ErkJggg%3D%3D%0A\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Department of Energy Pages\n",
      "http://www.osti.gov/pages/\n",
      "doepages\n",
      "\n"
     ]
    },
    {
     "data": {
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      "text/plain": [
       "<IPython.core.display.Image object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ScholarSphere @ Penn State University\n",
      "https://scholarsphere.psu.edu/\n",
      "scholarsphere\n",
      "\n"
     ]
    },
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      "http://130.179.67.140\n",
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      "http://repository.lib.fit.edu\n",
      "fit\n",
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      "The OAKTrust Digital Repository at Texas A&M\n",
      "http://oaktrust.library.tamu.edu\n",
      "oaktrust\n",
      "\n"
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      "Deep Blue @ University of Michigan\n",
      "http://deepblue.lib.umich.edu\n",
      "umich\n",
      "\n"
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      "Cognitive Sciences ePrint Archive\n",
      "http://www.cogprints.org/\n",
      "cogprints\n",
      "\n"
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      "Trace: Tennessee Research and Creative Exchange\n",
      "http://trace.tennessee.edu\n",
      "utktrace\n",
      "\n"
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      "The repository at St Cloud State\n",
      "http://stcloudstate.edu/\n",
      "stcloud\n",
      "\n"
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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Smithsonian Digital Repository\n",
      "http://repository.si.edu\n",
      "smithsonian\n",
      "\n"
     ]
    },
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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cleveland State University's EngagedScholarship@CSU\n",
      "http://engagedscholarship.csuohio.edu\n",
      "csuohio\n",
      "\n"
     ]
    },
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    {
     "name": "stdout",
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     "text": [
      "BioMed Central\n",
      "http://www.springer.com/us/\n",
      "biomedcentral\n",
      "\n"
     ]
    },
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2Bg7%0A1//mJcUryQGdQaMhvddKbwTeEt%2Buvpsefv3%2Bwcj5D%2BmjfmN24%2BYTZh8tPllMGk8pTzNNj87kzMrN%0Ajs5dmk/8bPiF7EvNV4OvEwvnF%2BO/uX23WDJbDlxp/3n4V/OG7ubmbvylYTQ8gx7FTGAXyGByBbw/%0ARRlhlChKFUf9iJaFLoH%2BFaMMUxrze1Z5tkz2Pk5WLkfufJ423mG%2BZf4VgVnBZ0LnhSNF1EXJRF%2BJ%0And0TKC4v/lvikeQxKQdpLukpmTrZWDk1eUi%2BUyFH0UKJTmlQuUTFRZVDdRjJAld1FvUhjZOaLlqC%0AWmvaAzrXdY/o%2BejvNaAxmDRsMyoyjjXxMfU08zcPswix9LSysFa1EbVlsyPao%2BxXHKYcB50eOte5%0AlLrm7Et0C3B39NAlSXoye0Fes94DPh2%2BjX5V/sUBGYFhQU7BmiFCoZRIJoyFj0QsRfFEu8eUxD6I%0Aex0/kTCfuLqf4gDnQeFk7hRsyofUxrS89MgMt0N2mY6HA7LSs8tzruY2Hmk62nDset7V4zX5FwvO%0AnSgtLCrKK84%2BmXYqoSTstF9pQFlK%2Bb2zIucuVQqdL7jw8uJqNfESaw1frSiSB4pX1et0682uOV0P%0AvpF583zD3cb%2BppHm6ZbvrfBtpjaxO6p3Ne8p3ud5gHow3t71sLGjurP00fGuQ48TuyOfRD/N7mnr%0AZXx%2BoO/9C9aXGq9sB3wHU4Yuvn7%2BZukd7bD4e9OR8A8nR2%2BPvRgfmRj/ODeJQaKfOtM/RzMv9Vn%2B%0Ai%2BBXqq8/FyYXh749%2BX5rqWI5ecX%2Bh9CPlZ9tq4m/VNcI67obM7vxl4DmUOWwG1oEg8MsYmdws2Tj%0A5IsUeIIApRbRmSqV%2BgpNP%2B0mvQCDHmMg0yHmsywNrJ1sj9kfcdzmrOCK59bm/sVzgdeEd44vi1%2BI%0Av13ATWBVsFBISuiJsJ8ITqRa1FB0Sixzj/CeTnEvCSBRLrlX8rVUDPJ2Uy9jKjMtmy7HKdciby0/%0Ar3BIkUuxGXlrmVZOVmFUuayqpfpir9feL2pJ6jj1Ug05jUHNRC1OrRZtC%2B03Ov46m7qVepb65PoP%0ADfYbyhnOGlUau5owmwyaFpnZmFOZd1ukW6paLlnVWwfZCNl8tK2w22fPYv/KIc/R0HHTqdE52IXf%0A5b1r8T7zfStuhe4C7g0eWh5vSfGevJ6vkX3E38fAV9FP2d8ogBQYEkQK1gihDhkOvRgWEi4fvh7x%0AMDInyjKaIfpdzNlY7zjBuMn4Mwl6CcOJwUn0SS/33z5w92BH8sOUW6k1acXp6Rlhh1wy9Q6LZmGy%0AXmWX5Djn8ueuHRk9%2BuzYrbxzxw/muxSonGA9sVo4WHSj%2BOTJo6cKSipO3yx9VPa6fPbM2jnKCu5K%0A2fOGF1wvhlUdrM6%2BdKQmpZZ0WfEK8cq3q5/rVq8RrnPekLlp2ZDU2ND0s0X5Vnhrye1rbS13bt/t%0Avrf8wKD9VodN53JXcbfsk1c9R3s9%2BoxeaL3SHgx%2BQxyeG%2B%2BdXV5a3Yr/zv/hthoWqU5PpCIVaiYA%0AdhoA5HcgdeYAUnfiAbCkBMBWGaAEfQGK0AMglbG/5weEnDZYQAFoADPgAkJACqggtbEFcAa%2BSE2c%0ACvLAGVAH7oLnYAwsIZUjOyQNGUDuUByUD12BHkOTKCxKGGWKikKVI3XeJlLXxcK34N9oA/QJ9DhG%0AFpOF%2BYBVwZZg15AK6wmZIlk1ORt5Pp4Cn02BpzhOYCVUU8pRthHViK1USlS3qQ2p39FE01LTXqXT%0Apeunt6XvZ7BgeMHozviTqYRZjXmE5QArG2srmxs7OXsbRyynHOd3rhvckTzyPOu8XXzF/P4CewWJ%0AgqNCN4WzRDxFtcQE9xD3rIl/kfgoOSDVKJ0kIy0zIpslJy/3Vb5FoUAxQclb2VRFSpVpL1FNQr1U%0AU0zrqHa3zlc9Mn0GAxZDdiN%2BYzkTc9MIs1PmHRbfrPisHWyO2XbZox10HTOdelwYXT331bp99MCS%0AaDyxnsten7yHfWb9qPxNAooCp4L3hhSGfgk3jqiNIkRHxLyN049vSRRPqjrAfbA0hTE1Px2fkXpo%0A%2BXBg1lxO7pGQY435NCdYCz8X15xyP81Y2ld%2B9KzBueXKvAv0F7OqVi4F1Xy7fPyqXj3NtcUbkw3T%0ATXMtU60TbYv3mB7oPHTr9Oiy6dZ4KvlM5LlCf%2BjLn0Pot%2BTDZz/Qjd39RJzeP6f1uf7r2jeFJf0V%0A/I%2BjP5%2BsTv/6tPZmvWHj%2BG/PTant/WMr/jhAALSABfAAUSAL1IAhsAUeIAQkgWxQAmrALfAUvAcL%0AEAZihaS2o58AFULXoF7oM4oKJYtyRqWjbqA%2BwVywO3wBnkcroDPQAxgRTCpmGIl9KQ7g/HEDZHpk%0ALeSS5LV4EfwVCjmKewRLwgRlPJGcWETFQ3UNqV/f0cTRMtI209nTfaY/wIBnOMUozviEKYyZifk%2B%0ASwArPet9tjB2fvZhjhJORy5mrjfc5TzevFJ8gO8V/2WBDEFXITmklpsV6RG9iZxieeLpEvslo6W8%0ApDVlCDK9sjlyJvJM8osKbxS7lJqUK1WOqCbujVXLVm/R%2BKElq%2B2tk6tbpdekf9vgtuEdo27jMVOU%0Amai5vcUhy2areRt%2BW3e7cvsRR16nQOcmV9w%2BB7fT7p0e/aR2zxqvLO8AH2tfQz8n/7SA%2B0GUwZ4h%0AbWGs4YkR76O0o2tiqeLC458m8iTF7u87KJ98IZUtrTADfygpcz6LlD2em3hUKg91/H3B9cLYYrmT%0A30qul8aUq5z5da6qUuZ8%2BYWpKqFq/0vXapkul11Vq/t8reSG8s3eRlLTWktlq1UbuFNzz/T%2BYvvZ%0ADs9HKo95nqCfPnsW%2Bxzbl/OC8LJywH3I7E3wu%2Br3U6Nc45YfUyfvzjDNHf8iuPDse%2BHKkVWjNZn1%0AMxsffy/uxh8NyAE1svp5gBhQADrAErghsT%2BArPwK0AAegxFk3RMgQUgT2gclQaXQHWgMRY5EnYQq%0AQvXBDLAPfAfNjk5Bz2KcMM%2BwOtg7ODXcAzJTsvfkUXgq/DUKewJMaKaMIEoTf1J1UpfQxNA60RnR%0AGzNYMRozKTKLsMizurMlsEdzeHLacplzm/GY8ZrymfFbC7gLRgkdFa4VeSw6s4dSXFHCV/K01KAM%0Aq6y3XL38mqKl0jOV7L1O6hiN45rr2iY66UgEm/XbDO4a9hqtmZiYNplLWFyxkrBustWxG3QIccI7%0AX3G1d6PxoPB093bx%2Bein6p8bMBlkHdwTahb2IsIlcjo6KZYzbiThUdL9A%2BXJdim/0ioy7DO5Di9k%0A38k9ctQ3zyCfpeBpoW/Rysn0EprTlWUK5c/O%2BlZAlWUXlC4OVMfUsNU%2BvpJcZ3BN8oZ%2BQ3JTZUte%0Aq1Mb052he6UPnB7iOi4%2Bkuu63a33ZKgnvleyD%2B5feDk90D%2BU/0bobfm73%2B/1RnI%2BPB2jGrebOPdx%0AZlJ6Kmj63Mzj2dl5zGf2L1JfdRccFknfvL9bLvEuLS8fXWFfqf2h/OP0j9WfDj%2BbVhlXI1ebVtd%2B%0Aaf7K%2BNW9RlyzWTu51rdOtq65Hr9%2BfX1mg2fDaaNg48nGxm/p396/T/5%2B%2Bvv3pvSmz%2BapzZ6t%2BEf5%0AyspsHx8QQRsAzMjm5ndBAHAFAGzkb26uVW5ubpxHio1hAO4H73zb2T5rqAEo2/rGA562/Er59zeW%0A/wIUIMa8oH4K5AAAAZtpVFh0WE1MOmNvbS5hZG9iZS54bXAAAAAAADx4OnhtcG1ldGEgeG1sbnM6%0AeD0iYWRvYmU6bnM6bWV0YS8iIHg6eG1wdGs9IlhNUCBDb3JlIDUuNC4wIj4KICAgPHJkZjpSREYg%0AeG1sbnM6cmRmPSJodHRwOi8vd3d3LnczLm9yZy8xOTk5LzAyLzIyLXJkZi1zeW50YXgtbnMjIj4K%0AICAgICAgPHJkZjpEZXNjcmlwdGlvbiByZGY6YWJvdXQ9IiIKICAgICAgICAgICAgeG1sbnM6ZXhp%0AZj0iaHR0cDovL25zLmFkb2JlLmNvbS9leGlmLzEuMC8iPgogICAgICAgICA8ZXhpZjpQaXhlbFhE%0AaW1lbnNpb24%2BNDI8L2V4aWY6UGl4ZWxYRGltZW5zaW9uPgogICAgICAgICA8ZXhpZjpQaXhlbFlE%0AaW1lbnNpb24%2BNDQ8L2V4aWY6UGl4ZWxZRGltZW5zaW9uPgogICAgICA8L3JkZjpEZXNjcmlwdGlv%0Abj4KICAgPC9yZGY6UkRGPgo8L3g6eG1wbWV0YT4K3cK3yQAACphJREFUWAnFmGWoltsSx2fra3cn%0AdrdiiwkGFnaB%2BMFuQf2koGKB4FFRbBELG7%2BoqNiNjWLHtru715nfyLw8%2B9x7FPR6HVj7fZ6VM//5%0Az6x5dkLp0qVDlixZBEmWLJn9/sk/IQT5%2BvWr8JuQkCDp06eXvXv3SsKCBQtC9%2B7dJVWqVPL58%2Bc/%0AqaMB9eXLF1MwefLkpuyzZ8%2BkXbt2EgNFlPz48aMw%2BCcFJNEHJFEYSZ06tbx580aS0cGEFClSmAU%2B%0AgQX0Ry38VSM%2BffpkijhaUcXYmzN9Dq6PxWLy4sUL%2B43RgcuZxAAT/6kccz58%2BGDjv6Is6HAW%2B3MW%0AjWca5yMpU6aU9%2B/f2/PNmzdl9erVcvHiRYmBJALcTmBfhMUI72zg79b5E38AgTPYC0EhaAf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     "text": [
      "United States Geological Survey\n",
      "https://pubs.er.usgs.gov/\n",
      "usgs\n",
      "\n"
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      "http://www.crossref.org\n",
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      "\n"
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mjH1KBQ6n1WT8hed1MTdPy4RThi0b6/pRU9NMO%2BLuEwBpDgW1bjNG4b%0ARHCyPAHQJ7cG7CLXv/WaCF3tzs4TD0nG0UdCxi%2BPAu%2BB%2B%2BtT5HU0Ne8%2Be0PJvDL48eop2JC6dH0e%0A7BUFt94yHXYnUXB4flDJzC8Kkp2663D6c%2BlMLHf/nzBdWgq4MjLBU1gIw/77CBxOTYa0gw8Mqq%2B8%0AXV9rKzS99wHUv/4mtKIbdlddnSAqXA2h30xojQT%2BDNgvROp%2BJENQ/H0S3bob6qF5TS3sWrMSqpc9%0AAt799oWcs06HrEnHhz00asqXdfIJUHjdVbD9zrswQ32fv6ieLV%2BtBeoJ7HbfXRDSDaXpw2ZfaATL%0ASJrGgBva0dfUCG2N9RhDcT3sfOZ5yJ58MhTeOAV7ecUD3h7vBU3v/wvtsgTnX6xG5HyIXyAuo%2BYi%0AQk2LNxlLX2dsP1k2dNQlwzcNPSjU0Pwd7dD8%2BUrYNnUabDjjXCGkRfrZyy5EZHoFV16ODXgy9gJa%0AI38c5BkhCr7xOtZlaeSdJAraYamHn%2Bzo%2BcmOtS89D5t%2B8wdo/iI8rHokDAOewW78jgcfgR8uuAwf%0A/i9Ez8yVlBIYpmH7sUPXfkAMei6wFwGE17qHDDQ0bseWH2H7H%2B%2BAjWedBzVPPAU%2BXJNN1aZ5SBSc%0ABd6JB0kUBZdB/WuvR1QhoH1YRBSMKH2ME2hHIr62HzbBliuuhbZvv4txYXynax5/AioW3h4YlpE3%0AHxGOQzfH1JzG0Bo2IiKC8pmlsPl/L4KWteuUmd2dnwej7lgAnrwCbHid%2BvLFB8Lf1QHls2ZD63/W%0Ah6UV0APCTtri0IUPa3vlFjEE8rUOjcApBmPl4jsDIdjpbe/wzTEE0GvnABEkw65PP4ZNvzsfVekn%0AUDvoX5TqvVfv/96JE6Bk7izR1SQPRj2bEAVra2DbzVOhq6Y2NCn63CaGA6Gn7XDkcqVA078/hvoo%0AwVMGqp8fvTNpklV3S6MuL82B8rHS744jgF7j0LCgu3EXlOMS5SSqqRoSZJ%2BCYta1VyLpoKuwzo20%0Ajub1AVHQ3xnWq8BurS0XGqF64b%2Bap/4yaJu1rFkHuz78CNfiVPdpUaeJE367YwmAkKXIvPR5aseT%0Af0J329lCNEw44phBwdVXQPbJp8gTBV//G1Tfvyyy6HYRBcNqprnc0PbNd1FCqoddGHbY8OYK6O5q%0AcfSYPwwSO0jG4VUa5DG%2BUeiNsPOF52B7tDn4g0wunsvp813JvFLwTjhAmihYdd9S8S07PH9bioJo%0AM193CzR/%2Bll4dWMe%2B9racWFWVPx1foqNmYFFf3B0D%2BAnm4nuMq7Ws%2BxRaHr/w59OJ3LHnZ8Po9BT%0A0JOTJ08UxF5M6/qvwoptTz2AHHGaV4fHTwyretBh986d0L5pk221kaCqDmqXCaAXLhwK%2BLra0Od%2B%0AGQ4FwsbTvddI/t%2B730QUBUtxGIJuwrpnDmJMwdpqsdBIFzb2kM2WoqAGHZt/QB2gf4%2B9Xhw6yitQ%0A82HxrxeP3v%2BZAHqRwP9d%2BCC2fL4KWr/6OuhsYnezTz0FNYHLMFKOJFHwy9Uoas4B%2B4uCOAxobgZ6%0As8ezdVRUIseq%2BdoTT3nMcg0TQLAlaGzZhWNFmuihcCu89irIPvEkHNdK8hR87VWoXvpQZA2EKGgP%0AJyHs1IieGk3aiWfr3lmHl%2Bn79BpPPla7hgkgisW6GxqinE3cKS05WUwa8o6fiHqAnOnDFD%2Bg4c2/%0ARxQ6EK/QHiRAM/XiDczhV%2Bj5GQG6iU8wAZjEOB4M%2BTXqjwuBZjNGTEEdbBnJU7CzA7ZNL4PWr8OH%0AM/YUBQeCyIfzQniLRIAJIBITw86kHbA/FM/B6bI4G02KKFhThZ6C08UU15BK2VIUDKlh5IEib8/I%0AjM19hgnAZPbJOf1UKLxCoii4bhXOGUBRkIKUBm/i0yebPxgSJ%2B4bFxBEMDKKMjp94iOMRuoQNm4r%0Ab4VTroG2Dd9D/YrXxSw4PXUhJ6f6V16B1L32hMJr0AU5eCNRUNhhAHW89xqn24o%2B1UrvSeAzID3N%0AYCP3v28YAZDwleTNAVdqav8lHOSv9KbzYSgnK29aCoqC88ugHb9zt234Bl2WccrqUDfxpk/CSTD3%0AQSouNEJRi4O3eBYacQ3LgKRh2RiARG5zITuRVmEVwqZ1DWKGMwsGdRD7Wlsq0LNg1CbXooOoRcZR%0AR8Le77%2BDxperSDfjd/wt19wQ6FlYuCfgKSxAURBjCv7hYujCrxI0b2HIW68oOK1MhEtL3Xt8X1IC%0Af%2BoJhA0Req6gBj/q7sX4yY0eVLm22jZ1JjS8swJ7OSafnENvfsRwJE7nTj/0kD7sZOzh2z8pO1tG%0ASkNKwzACcGGwR1dR4ZAK3d9NtPiEXba0gw6E4rIZsPWGm/EBxUaog9DE9OGa7bj68DSMKfhYSKPT%0A8MHGZoh5RP9OTgFOE7EFen9YL4ts1LaImO20WXuwHM0SPus0qGjFDz9H8QyHX34pPv/63ZPF9OG1%0An6MoODdCFAxMHXYhESjcDBz7DqmWNmtbhIH9CGBIljX3TYU3XAuZx02S4ymIomDdy3/FiU%2BPRFSa%0AnIRorj1vzkFA3RBATHZJQOPS0S22ipldKSkwcsFsEbO%2BbcO3UkRBmvpMXwYyJx0XCoPAE3tR4u0s%0A0V4iKRu%2Bb0S7DoVQ/5G6fpgyi/gRKFrMUvYftlT9eFsgBU9RkYgpmJSZI8dTsKMNPQVLoX3jpoja%0A07Mv3172GpoFQMO1DhPQpgl7VZsyAlBVITvnk3bIQVBcOh3FQKylzkZComBnVSVsu3VaYJpsEHBC%0AD7BpTMGgavIuIsAEoKMZtG38ftBhqXRkJ27NPedMKLj0YmmiYNPKT6Bi3qKIXkWABLh56LWX2e9n%0AC%2BuwkK%2BhEd%2BgMzDU%2BBYdqQz%2B1qIbr4PMY46VJgrufO5ZqP3zUxEFCUQWlqgDROTAJ4xGgAlAhwXI%0AM67t%2B42w7baZ6H2IwSYVbRp6T5YsnAvePcbjm1vn9GEU/ehBr7z9j2KNvPAq2DW8eHg9nXrMBKDT%0A8q6UVGj88D3YfsedPcq5zgTjvD25eAR6pi2EpIysiO57nEn0XYaf/3ztrUhkMzBu3ua%2B87RH3n%2Ba%0Auo9FoZnzUaIRYAKQgLBLS4Ydjz8Jtc%2B9ICG1%2BJNIP%2BwQKJ45LXCDBFGwo2ILDmmm48Kcu0IKQZ6C%0A%2BO0x5Bwf2AMBJgBJdqRnpHLu7bji0EpJKcaXTO5558DwSy6UJAriqjuffgQV82/HuRShn6JYFIzP%0AHla7iglAlsWwG929q0G8QSkCrcqt6KYpkHH0MSgK6o96Q9OHa595BhdQfTqiCiwKRkBi%2BRNMABJN%0ASL72bZu%2BhXKc5eZrVTcl2eX1wqjb50Hq2HHyRMEFi6HpXx9FoMOiYAQklj7BBCDZfLSMdeN7/4Dt%0Ai%2B%2BWnHL/yXlKitFTcD7O28%2BUJAo2i94MxSQI2VgUDIHD6gdMAAmwIC30seOxP8HO519KQOqxk0z/%0A2eEwYsbUwAW6RcFk6Cj/UZBA9y4WBWOjbu1fmAASYT%2BaUIP%2B9BVzF0LzylWJyCFmmnm//TXkX3i%2B%0APFHw3x9C5YI7oouC7Ega0w5W%2BYEJIEGWogg%2B3Y11%2BAadBp2VlQnKJXqyI265ATKOPFqeKPj0X6Dm%0Ayb9EZGbLhUcjamnvE0wACbQviYKtG78BCn3la21LYE6hSbvS0mAkiYJj9pAkCrqgcuEd0PThx6EZ%0AUfQA9g8Iw8Rah0wACbaXEAX/8S5U3XVvgnMKTT551EgYuWgeJKVlyBEFW0kUnAbtP2wJzYidhELx%0AsNgRE4ACgwlR8OFHYedLyxXk1pfFsCN%2BjqLgbXgCnXokiILtW3%2BE8tvQUzBCFMRmxD2BPuAttMcE%0AoMJYKApSkI2KsgXQvGq1ihx/yiPv9%2BdB/vl/QMc%2BCU5CGL238eN/4XBgMYuCPyFs7R0mAEX2C4iC%0AO2HbLSgKbq9SlCtlo0HRrTdBxi/kiYI1Tz0NtU8/G1EHFgUjIDH9CSYAhSYSouCGrzAU1yzwtel/%0AI8db9KRh6agHzIeU3cbKEwUX4LyHjz4JKwKLgmGAmP6QCUCxiYQo%2BPYKsVKPyqxTdhsFo1AUdHlR%0AFNS7xBfNe2jp8RT8cUtoNVgUDMXD5EdMAAYYSIiCyx6GuuWvKM192C/%2BC0ZM71lkRIIo2LZlc2De%0AQ3NzSD0Cawzw9OEQUEx6wARghGFIFPT5URScBy1r1iotQf7//hbyfv97zF//EISW9Gr68D2MJhQZ%0ADCWgB3DzUmrcIWTGFhoCaDJuIVGwq74WtuFSXZ3V1TKSjC8NJJ/iaTfDsCOOlOIpqOH04ZonnmBR%0AMD70TXcVE4CBJiFRsOXb/0D5tDIMyaX/jRxvVVzDhgVEwZGjURTUueQYEgp1%2BSvnL4Jdn3waVgQW%0ABcMAMd0hE4DBJiFRsGHFW1B97wNKS5IyZrQgAVeKF7/pR18UNO4CCVGwCT9xToeOrVtDb2NRMBQP%0Akx0xAZjAIBoG3axeiqLgy68pLU3Gkb%2BAohuniNVtUBTQlbcIhvLj94EIyc2hEZKph6DR6sO8mQ4B%0AJgAzmMRFomA3VJTOgZZ1XyotUf6lF0Lu2Wdj/jqHAlhqEgUbP/gnBkOJFAVFdGGlNePM4kGACSAe%0AlBRcI0TBuoAo2FVToyDHQBak1heXzoD0gw/V7ySESVJMQREh%2BRm1EZKVAWazjJgATGRQIQp%2BjaLg%0AjDLwd%2Bhc8GMQ9UrKysQ1BhZAclGJFFGQsq6ch8FQ/v3ZIErBlxqBABOAEaj3kyd1o%2BvfeAOqlizt%0A5yr5P9FS4SXzy0CGKCjmPTQ3wlac99CxrVx%2BYTlFaQgwAUiDUl5CFGSj%2BoGHoP61N%2BQlGkdKmccf%0AC4XXXyNPFPwBRUEKhqJw2bQ4qsmXBCHABBAEhml28bMarflXPqsMWr9cr7RYwy%2B7GHLOPFOep%2BD7%0A7yqPkKwUMItnxgRgUgNqSR7oqq3BbvRU/H%2BnslJS9724bCakHXiYFFGQPAV3PPZnqH2WRUFlRhxE%0ARkwAgwBL9aVi%2BvD6LwOiYKf%2Bz3Txlt%2BdnYVrDCwAT%2BEIeaIgRUj%2B7PN4i8DXKUKACUAR0EPNhmYO%0A1r/%2BN6i%2Bf9lQkxjSfal77wUl81AUTE7V7SkoREFcNk2IgoqXTRtS5R10ExOA2Y0tfO1RFLxvKTS8%0A8ZbS0madcDwUXHe1PFEQl00LREhWt2yaUsAsmBkTgBWMhqKgrwtFwZmzoXX910pLXHDlpZBz2umS%0APAW90PTeO1B1Z0%2BEZJonwJuhCDABGAp//JmTKNi5oyqwVNfOuvhv1HmlEAXnzIK0/Q%2BSJgpWP/oY%0A1L24HDSPR2fp%2BHa9CDAB6EVQ4f3CU3DdKvw8OAf8KkXBnGwYuRhFwYIiKaKgRhGS0VOwZc06nCSU%0ArBBBziocASaAcERMfkyf1epefQWqlz2itKTeffaGkrml4PLgAytj%2BnB9PS4yshmXE%2BBZgkoNGZYZ%0AE0AYIKY/7BEFq%2B65DxreWqG0uFknnQAF114lRRTEqYOg4R9vxiLABGAs/kPLnTwFO1EUxElDbd98%0AO7Q0hnhXwVWXQ/bkyVIWGhliEfg2iQgwAUgEU2VSQhSs3g5bMaZgV129sqw1txtK5pRC2kQ5oqCy%0AgnNGURFgAogKizVOkijYvPZz7AmUgr%2BrS1mh3Xm5sNtdi1AUlOApqKzUnFE0BJgAoqFioXMUgKP%2B%0AtdcM8BQcj56CpfgpT4IoaCG87VZUJgCrW/QnT8EH1HsKnjgJiqbImT5sdTNYtfxMAEZbTmcwTlF8%0A8hQkURD9A1q/UuspOPzySyH7tNOkTB822hROzJ8JwEir48OveVLws5q%2BiLxUhV5RkEJzd9cp9BT0%0AuGHkbPQU3O9gKZ6CRprDiXkzARhodb%2BvCzKO%2BgVkn3SinFV6aKGRdauxJzAXH0adsf4HgUtSbg56%0ACi4ET36hfk/BQeTLl%2BpHgAlAP4ZDTsEP3eBKSxMedmn7TJTyBqXpw3WvvCzWGRhywYZwo3cCegqS%0AKOhmUXAI8Bl2CxOAYdAHMqbPd%2B78PIzKOx%2BSsvL0v0F7RcF7l6Cn4NtKa5d18olQKMtTUGnJnZsZ%0AE4BJbJ92wP4wcu5MDKyPM%2BQk%2BNr7MKx4%2BUwjPAUvg%2BxTJrMoaJJ2NVAxmAAGQkjh79mnnwqFOP/e%0A70enHp3CoBAFqypFFJ5unHijaqMpvjQU8E48QMqQRlW5nZoPE4DJLF9w3VWQNekkKb72YvrwGvQU%0ALJuHD6M6UdCdm4sxBW8Hd16%2B/iGNyexjt%2BIwAZjMoq6UFLFAh3cvWaIgTh9evhx2LHtUaU29Eyfg%0AnIEyFgWVoj74zJgABo9Zwu/wFBbAqD8uAHdWrv43d%2B/04buXQOPb7ya87MEZZE8%2BGWj2oN%2BPvQ8M%0AAsKb%2BRBgAjCfTUSJ0g48AIpnz8Q58xg3T6ceQHPvfR1tUD4dRcHvNiitceE1V0D2yf8jZUijtOAO%0AyYwJwMSGzjnzNCBXWxlLd5Mo2LG9HISnYEOjslpryckivHjahP1YFFSGevwZMQHEj5UhVxZOuRqy%0Ajj8BvwzqD6Utpg9/sRIqZs9HUtHvfhwvIOTnMAr9HNw55OegToyMt3xOvo4JwOTWd6Wmoig4G7x7%0ATpDyBiVPwZ0vvQg1Dz%2BmtObe/fcTgUREDECF5KO0khbMjAnAAkbzFBX2iII5%2Bt%2BgPaLgdozN3/ju%0AP5XWPvu0U6DgyssCfg5Kc%2BbMYiHABBALGQPPC9U8TDZPO%2BggKC6dIU8UbG%2BF8mmlGJn3R6U1JVfh%0ArBPlTH5SWnCbZsYEYErD%2BqOO0XPOPgNo%2BW5ZomB75Tax0Ihv1y5lKGgpJArOhrTx%2B0oZ0igruE0z%0AYgIwrWF9UUmg8PprIfPY46SIgi6cPrzrk4%2BgYuFi/Z8aB4Gjp2A4Th9GUTCbRcFBwJaQS5kAEgKr%0ArES7I4KFkCg4csFc8I6TJwrWPvU01Dz1rKxCx5WOVD%2BHuHLki6IhwAQQDRUznRNedKFudJ4RRWKp%0ArqRMOaIgCgtQueAO2PXhJ0prnnPGqdL8HJQW3EaZMQFYwJjRRMH0Qw6CktLpGAsMK6DTU5BW6PG1%0A7IJtt02H9i1blSJSeP01PX4O7Urz5cwCCDABWKIlxBAFzzkThl9yET7/%2Bh8e8hRs27IZym/FmILN%0AzcpQEZOfFpCfwz4sCipDvS8jJoA%2BLEy%2Bh6JglDd90Y3XQeavjpcSU9CVlAKNH30A2xfdiViEDjsS%0ACY6nEP0cSBSUMfkpkQW1YdpMAFYyKuoB4STg8noDouAee0p5g7rQU7Dmz09A7dPPKUUm7WDyc5gO%0AmoQhjdKCWzwzJgCrGTCaKFg8AmMKLoCkYVn6w4mhIIhxyaBy/u34ifBTpegE/BwukeLnoLTgFs6M%0ACcCCxosqCh52qHiDio57lKHCYKpJ/vrdzU3CSahj67bB3Kr72sIb5Pk56C6MAxJgArCkkaOLgrm/%0APhsKLrpQytx7mjnY9sP3%2BGVgBviaW5ShJNvPQVnBLZoRE4BFDYd9/Qg9gKpSePMUyPqlLE/BFGj6%0A4H2oXHyXUpSEnwOFSZfh56C05NbLjAnAejbrK3EMUbBk4VxI3X28FFGQpg/XPI6i4DPP9%2BWrYC/9%0A0IOheNY0KX4OCopr2SyYACxrup6CRxEFk0uK8bOaTFEQoHLeQmj%2BbKVStHLPPQv9HC5kUTCBqDMB%0AJBBcVUkHRMHQ3NJRFBwxcyoOE/C8DFFwVyNsvXk6dG6rCM0owUdFN14PmcccK2XyU4KLasnkmQAs%0AabbwQqMoSD2BsC3vvHNg%2BEX/J8lTEEXBzRtg69SZ4GvVH54srKgxD11emvw0B1L3kDOkiZmRQ39g%0AArCL4fEtH%2B4kRF41RTffABlHy3mDkqdg0/v/gO2L71GKmoeGNItwSJMhwc9BacnNnxkTgPltFH8J%0AhSgY6sLrSkNPQRIFx%2B4lTRTc8ejjsPO5F%2BMvl4Qr0w9HP4cZcoY0EopjmySYAGxjyp6KiHUFQ0kg%0AeWSJiMqblJ4pyVMQoGLuAmheuUoperk/DWk6leZr58yYAGxo3Wh6QPrPDpcrCjY14BoDU6GzslId%0AgmJIcz1kHPVL8Pk71OVr45yYAGxp3Bii4G/OheEXni/NU7D1%2B29hG4qC/rY2ZSi60tJwSDMPUkeP%0AA38n9wT0As8EoBdBs94fSxS85QbIPPIYSdOHvdD4j3eBQoyr3JJHleDkp/ngyshAXUPdAicq66gq%0ALyYAVUgbkU9UURDfoLfjG3TMHtJEweqHH4W6F5crreGwI36OouCtAO4kpfnaLTMmALtZNLw%2B0UTB%0AUSOFKOhKy5AjCqLmWI7LjbWsWh2ee0KPc846A7x7753QPOyeOBOA3S2M9YsqCv78Z/gGvU2ep2Bj%0AHXoKToPO7VVKEdU8bqX52S0zJgC7WTRqfWKIgr89D/J%2B82t5ouDGr3C1oVnga9MfozBqNfikdASU%0A0afmoqwo3hNvAyGgaYiVdKjEpADMOihhlwbFM2%2BD9k2boenTj4E8/fRsriQvNLyzAqruXgIjpt6s%0AJymL3KthRHVPAspK8R66EpBuZJLcA4jExKZn8MEXXwZCnYRcw4YJRT1l5GhpouCOZQ9D3fJXbIqj%0AvarFBGAvew5cmyiiYMrYMcJd2OVNlyIK0gzE8tK50LJ67cDl4SsMRcB2BOBK1deNVW0Nir%2Bneosm%0ACmYcfaSYOCQmFGFPQc8mYgo27IStN6GnYFW1nqRMda/V2lY84CnTAMIL0/r1N1D3/EugeSSOodBV%0AtKMc56vTK0hEtw3P1VzHGrih9av1GIF3kdSC%2Bbu7IWXMaMj9/W8QhmgcHxAFNS2UfMhLsH3DRqh5%0A9mnUA7y6ykQxBVs3rIfy6aUweum9oCUn60rP0JupLWGb2vHYE5D81tuB9iWpQOTNmIOxHL37GPM5%0A0zACaMOGtv2xJeDS9DW0cDtokATU%2BKywidV4Nm6G1o1LpRbXD52QceARkPvbX2OE72gEgNmRHoCC%0AoBZMlHjtCJxx177xe9i18lPEUV9vikikfsVbkHrP/VCEHohW3%2BpfexUxi4y7oKdePn8reA8%2B0HkE%0AQN1Eevj1vmn0gG%2BGewkHDeSSoL8b00yOo2clgoigOChW4wigkZRBouAC2PS7C6CjYhuSQBzp9AMk%0ALTRSvXQZpOw1DnJOP7WfK83/E71Ygr6hyCkw8okRw8Dewsd4PfT%2BzP/bHQF/NFFwj90DomBKmhxR%0A0OeHilkoCq5dZ3c4LVc/JgDLmUx%2BgaOKgscchd3263Hoi4KgBFGwq74Wtt6CMQWrd8ivAKc4ZASY%0AAKJBF9Qljvaz/c5F9xTMv%2Bh8yD33XPQU1D/tVoiCX6%2BD8pmzwd/Bc/nN0oaYAKJYgpasjmfT3Dg%2B%0ARm86W2xR3vSaKwmXG5sGww49HJ2E9Lv3ClHwzdeh6j65omc8%2BGvJ8dk0nrTsdA0TQIQ1NSDvuHg2%0ACk6hud2BCTXx3GDya8RQgD6hBm1JOOd%2B5B0LIXnEKCQBCT0BEgXvXwb1r74elEvid1NQ1wD8QsRb%0AKAJMAKF4iM9i7sKCsLPRD5NysoHWsrPTFo0EUseRKDgHXCn4tcKn7zMYfXYkIqGhQOuX65VBl37I%0AgUBLjpGPBG99CDAB9GEhxC7NkwwpOF8%2Bno3eju6C4XipnRpVdD0g41e/hKKbpkgSBT3QWVeD04en%0AQldNTTxQ677GnZ8Pueechd/xWX8IBpMJIAgNP36uchcOB0%2BcPQAa/3snTJDuHBJUJIN2cRgQZaGR%0A/IsvwIfoHCmioIs8Bb9aiz2BOcpi%2Bw3H8qdPPAirpl/PMMgw0rNlAgiC1A9d6JE1AZKycAGKOLf0%0Aww5G5xD7wRjt8x85rBSXTodhhxyGD5H%2BN6lGnoKv/w01gQfjRFvfZUnZWVCyaD54CoqllF9facxx%0At/1arg5c0TEWMo45Mrb7bJS0h2FkHXfecGxQdhoGBCoaTQ9IyswQq/R4ikrkPEQ4H6FqyYNIBG9G%0AQVf%2BqbT99oXRD9wDnqIRUgKjyi%2Bh2hSZAHrwJmHKM6IEMo8/dlAW8OCiG5nHHWvbsWU0EiC3Xlqv%0AzyXBU5A%2BNVJvonwGioLrvx4U9kO9OP1nh8HYPz8KaRMPCCw6qtPRaajlMMN9TAA9VqDufx7OnvMU%0AxPcFINh4NLZ0Z%2BTasheAYgAKf5G9m8zjjoHCG68L/Bb65TAYmrj2ab5BZ211QBSsrY3rHr0XeSfs%0AA7s/9Rjk/%2B4PIikZQxq9ZTLifiYARN3f3QoZh/9CLJoxFCOkjt8LCqdcg7diGCdbvk2ii4LDL7kQ%0Acs86G0XB1qHAFnKPEAX/sxoqSueBv0tNOCx3Xp6Y8zD6oaUo5u4rhgQyfB1CKmbyA2cTAD6sPnz4%0AU/fcV8yAc6VjRJwhbvkXnA/DL7gIn/9OW/YEYoqCZSgKHkyegnJEwbpXX4HqBx4aohWGdlvWCcfB%0AHs8/DSUzZkDKbqOxLu2B%2BtiSzEMxciYBoDMLNVgKvJh17CQcDz4CKbuPCUVmkEcaLlAxAgNsjrj1%0AVnClpQtisZswGE0PoC8mNH2YRDUpb08UBavvvR8a3lwxSAvou5zqMfzSi2Dc8uehZNYs8I7Hz7v4%0AWdgnyACHQDYlA1sQAD1o1Phi/%2BHDjg88GZPe%2BBp676UfdhjstuQeGPPIg0BLTcnYyC244KrLYdwL%0AT0PuqWeCGz0FKT/6E4TTbxkD5Td7Q4tGAql77Qkl81EUTEavSL2egigK%2BrpIFCyFNowapXojxy7y%0Adxj3ygsw9tFlkHPaGUhuBWiW7hA76q2n6nrFys%2BwiECxCjSU8x40mui%2B%2B3DqavhMPoxy40LvvqS8%0AXEgZvRuO9fYB7wH7AX0OggTF4/PuPxF2u/9uEW67GVfLaV37Je5vgq4dteDrxK4ylTPahn74tLCG%0AqjFwtCIMfC4gCorQ5UEXZ%2BHXk8IbroXK2xfhx1R9PvckCnbsqBILjYx98jFw5%2BYE5aRml%2BZ5ZE46%0AVvxRXMOW1Wug%2BbPPMabBWujavgO6mxrxb5eawiQwF8sTAL31R9x6E2RNPhk9crGrFkwAuC8i7lDc%0AweDzCQQ0OOmU3cfi0GIswDlnBk7jA04x4PxdpKqHS%2BeamCa78cxzoW3jRt2ReILLIX%2B/RxQMjyl4%0A6cXQ9t1G2Ln8Bd2RnkgUbFm3CirK5sFudy3CNQCNa6rkGZp14iTxR1jSg1%2B3/GX0Yiw1uZ0Gtrxx%0AqA5ctriv0DASsCUm5RAhYXBMLUbIQr8gKmuMykgUJMep4OCrpIOUlM0QPZ%2BWNavw4dA3BVdzpcDO%0Al/8KNMQouPryuNtDoi%2BksGlu7FHaYbNGaxsI6bAprANdbtrfLVaPgH9AaE%2BG3G1HLe51t9U5fRhn%0ADrqwl7H97nuh4e9vm8tssYZx5irlgKWxBQEYGVRxQIQHcYFY6NKAocogihhxKYlj4cOZ1PHj0VNw%0ANvZ09IuCOJbAYROKgtPLoO3b7yLyN%2BqEXdqcDYYALoxeWyFCWRvVGGTl6%2B/s6gmXZSVeRl0DSSCw%0A9mMfEpmTjoOiKVdD5R2L5YiC1ZWwDWMKlswvM8Vwj9ocThrpq7BF9yxPAMTElQsWiz%2BL2iCi2NZ7%0Au8QQBS%2B/JCAKvvySFFGwGZX4DaecEaI7RICn8IT17BQJjvUpLLJOfMYABKJ6CqJyXzx7JqQfcKjw%0Ag9BbLPHABS9kojdBvt8GfRg2omkQiCYKkjPUqMXoKVhA4bh0ioKmqal9CsI9APvY0hQ1iSoK7j0e%0ASubh2B0dsuziQWcKsCUUgglAAoicRDACAVEw%2BAztkyNNAc6YDPQSwn/lY6MQYAIwCnlb59sjCobV%0AseCKS9G3/nThUx/2Ex8ahAATgEHA2z3b2KLgDEjf/xApoqDdMVRRPyYAFSg7NI%2BoomBuLk4fRk/B%0A/EIWBU3QLpgATGAEOxchmihIMzKL0aGH1mBgUdBY6zMBGIu/A3KPLgpmn3QCFF57FYuCBrcAJgCD%0ADeCM7GOIgldeBtmTT2VR0MBGwARgIPhOyjqqKIjTn0vmoKfgfrRaj/6Ygk7CU1ZdmQBkIcnpDIhA%0AVFGQIvPi6sPuPAy7xZ6CA2Io%2BwImANmIcnr9IhBVFNx3Hxg5F6PruFkU7Be8BPzIBJAAUDnJ/hCI%0ALgpmnXISFFxzBYuC/UGXgN%2BYABIAKic5EALRRcHCq6%2BA7FMmsyg4EHwSf2cCkAgmJxU/Av2JgrRm%0AH4uC8WOp50omAD3o8b26EIgqCubnwygSBXPyWRTUhW58NxsWEYgW8/D5cU05CinHm1QE/IALobRb%0A47NaIJwYrSOAEYZ7Nu/EfUXory1XXYfDAYq9r2%2Bdgd50zfg/PQNGriCl%2Bbo7cECW%2BC0QM67PyB0V%0AlbjQwkoRtz/xuTssB1zSyp2fC8P%2B6wh8rvowx5aGIluMRUmMhIjCpWuR76JdH34MXbRaMC7uYteN%0AHv70ww%2BD5OIRQVUkoVTNAqmGEUBQbXlXFQJmJQCsv0ahvsIWGlEFi/nyUUcA9qVW81mVS9QPAtFE%0AwX4u558kIcAEIAlITkY/AtFEQf2pcgr9IRA58Orval2/KZEadJWQbzYegT5R0PiyGFcCdc%2BKMg3A%0AODA5Z0aAEYiFAA8BYiHD5xkBByDABOAAI3MVGYFYCDABxEKGzzMCDkCACcABRuYqMgKxEGACiIUM%0An2cEHIAAE4ADjMxVZARiIcAEEAsZPs8IOAABJgAHGJmryAjEQoAJIBYyfJ4RcAACTAAOMDJXkRGI%0AhQATQCxk%2BDwj4AAEmAAcYGSuIiMQCwEmgFjI8HlGwAEIMAE4wMhcRUYgFgJMALGQ4fOMgAMQYAJw%0AgJG5ioxALASYAGIhw%2BcZAQcgwATgACNzFRmBWAgwAcRChs8zAg5AgAnAAUbmKjICsRBgAoiFDJ9n%0ABByAABOAA4zMVWQEYiHABBALGT7PCDgAASYABxiZq8gIxEKACSAWMnyeEXAAAkwADjAyV5ERiIUA%0AE0AsZPg8I%2BAABJgAHGBkriIjEAsBJoBYyPB5RsABCPw/PG6fVaaOw2sAAAAASUVORK5CYII%3D%0A\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DigitalCommons@University of Nebraska - Lincoln\n",
      "http://digitalcommons.unl.edu\n",
      "unl_digitalcommons\n",
      "\n"
     ]
    },
    {
     "data": {
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jo6OgY6JgYEpghMHThEJFCYmSkK6mJiEmJh0EczX%0ALYLZ9g9/cQsunDhxYeESYDUsgTgWFpb4o4UlvnsV%2BR7D%2B/3HEVVNACLozzOGQVQBqAikwUQgVdd1%0AT75UTYdY3fMakO3%2BcrK5588RDUSiY%2BDAeXf1E1QAKAyO2JG3OhI4BAaFQxFIEB0BwzS7QUVgcBVR%0AE4RLCEVMNaWLKSjehhxyiquaukToFKqZeWrXS9yQGkPDCktKaGkWwkNP7aZ0HxZO92hTD0s3Sl4a%0AsZRRelr3aZdlLCtzag4/vMfpI6Zfvrz8iDNmXLGiIki2ZOfgwZOTFxeLlp6RzMyeI488c%2BaVKyvl%0AppDeBfZNq2wgwuKlhIqJKPymQ4qqGMK0yxDxhwxTQi8pmKlTl6S2rkPdwiRoaeYlGuBDBaTSYuql%0ANzUOk6R7RBjTGUsfQrSXEv%2Bsw3cZNB9CtF2JqusAMLElXQSwAsozK3IGOeHWz2utNATVkovWZtiJ%0AOs%2ByY2ae4r3CDjnj6pWnTubh0mNdaZRpeY4rLM9aZaKZfkzLGLzaeXo/daXFimmD5xhr8VxnXVdy%0AVaqvNUZfpTivi3Y6pM6xfBWuzOOYFcxqKWtVAUE9rrr0jBpGm1blNK06T7Iu5yjLXgVo1Tiq6hrb%0A/1227XG8zquPGVaAy%2B20wQKot9OHAggA7Lfv6UBjAWnbx%2Bdy8eckLKB9JVEAbkDY%2B7l7W1X2k3cC%0A6ACkPYbS/gc9LfLzx64OtQAAAwV6VFh0UmF3IHByb2ZpbGUgdHlwZSBleGlmAAB42pVTWY7lOAz7%0AzynqCKRW%2BzjeBMwN5vjzkbx%2BVdXVA7SEIJZkmTKZXOfff%2Br6%2BPj4%2BOiJyzxb9AgAsG0bkIHbGoAE%0AKQD0yfF5AHIA8Ff%2But8hIOfnwpMfoLRvB8m90vcaXM9BKaDKp4I9Q52W0QHYV4DxxPtJW7u%2BbrD2%0Aio1hUSHhAke0qLSo1GhRCYGpq4rAM3qcsCsqMxVQUSjVdChUdSgVAqW6DoVSh0IDL9Nxg%2BtnjggA%0A477WtysQ4978W/6bxQVCv5EZP6ny5M6PolDi%2BqMqAv42CeVAfphcelxfJ3yT/T2u2q3qACBsWFi3%0AAMAb1tr12lx1NoD1VsvL1PZ9yDkAzrsGxC8MsGrPC2gGYKGhoaGjYaGjY5GYGJgkHYWFhaKzUal0%0AKpWNxLr9wsJ6h3/txMbExL5wsAkcHIKGg4NDe61waJ9hyK%2Bg3yb6G69aAEi0X3%2BwgiIXACFBKAlW%0A7f2Iy6ploNajooOf/KspcIEQGByBREPHwHyg/q/1/t703XndrbyLBkIhMAgciUCDQ7FU71YSChNS%0AlHCjM0gVSV6NKgha5%2BCkiagY3WUxRFUtpcmmKVK8i2vhsBiaqs4YMqWphNnV1S3MfMnQNA3mFvcj%0A4SVTmy3dmn601EKy27Dm07ov23asbPj05duv4%2BUeERkteoyYsWLHiQpNS8/IzJY9R85cufNkJW%2BG%0A5OHXoLDrISsRcPU3E0IlBXbzkBShwlUaO2kvGhZdNguql1jIYUqTLqau9NBUtaI44sUCUkJ9yRaz%0Arm5DmWHm7hpp4Ueu35n4Mw9/pkHy%2BomJqj0ALHwSnQS0gLLM8lwesWDa5j4nFX6FaMYJXa4TNWfp%0AWJmT1sp1cPpulVNW5DA2Pzs1uDRn364565RSMu0aS9N77DmtTTmpfnxpj9n7OTHPrL0zTqXYOb23%0AU4K5d%2Bg0sGY/dgo78xpjlUdW8pwqwEPGri3Tq2vo0ioLlao5I2pb9NJsVYBU9VFVu1//AYe5xc4Y%0A4AkXAAAHDnpUWHRSYXcgcHJvZmlsZSB0eXBlIHhtcAAAeNrtXU2O6zwO3OsU7wgSiyxax3HL1m6A%0Ab/kdfxZyEiVt56f7DZAMhCxMwxLJokhaUQVI%2BPc//4Q/f/4kzVkCCqpPHpkIftFcJVJodGauWETW%0A%2BvX1VUUkMlOxiJjDdEHUxaOCxok56OSzC83gs66mhM/8IpCpIqhY44ziE2afuHjkQnNlkugTFxau%0ADsw%2BBSwiTFRWCg1ze3AeXkUkXtS48guLCCArkqxIsiAhIQURrHGWKFEiiguVxGyiqryx2p4tIpxc%0AXT1ilirV3d09yOoOyMrqQpeKhIyMLBURIhUCkaUpkcXhmasrk0%2BycHUImiuh%2BXLrkSrNQbgwe3Rl%0AZuUq8fScicLq5opFRORGUf/pQVGJHUjRzYWloQl0qVRCKo2FxoXG1dUhdQtn7eN4jYBKYCZZgwsX%0AJleszFyY6DSpnD3S6CjNTBt8GXutFkVquB%2BV71OOxofHYXwmij4FJo/fA8nVIeslUWUlWLlI9YnJ%0A4xa/eIlf%2BB7ANhSzwoXG5MrqYsrKyoJVL%2BqjRU6srqzBwYnVo0SFASLRnJkrldXdgXLy4lj18x6Z%0AartihSAiQWVBBGRBQjRFCjDMiEiYEUFZEOGYEREPfOmU9gsRdjxZWKnMpOfHnpwcCQ882VW6lxLh%0AyhNzZWKbkJ73BjNiuOvNgeLvBeIS/k6BMIe/UyBMwaMuuvymSLhwkRqetgpXE9pe1%2BbqCM9ZdZh6%0A9kiTakJzsLq5ELSmPpya917/a%2Bp00QWzKivh5w6qWRWrmxszFXOAABA1CAhVIis1YVIDkJA0KRAV%0ACiQ1NUwQTAAAh6qdEibct/iEp5mrwzUwcSVot65CoK94Go5cvbV4bfC7p2EnqObC7KRRlS6sXC4p%0A65HJxUE7rSMWEZNwvZRHK3iOziWbZl75GnajI6%2BvY7iJzqHF4%2Bj0MeqnP0i8I3/DS4l3x2C4l3g3%0AibC7nqe54brP/bzNhde2U8cNJ3QdR5nJo35z0G62OVLD7WZgJwlPBmatLswtuEwXyEgo4aL1eOAj%0AAzadS4TFrbPgEMT2kYp0klF6szadeiWLW3jBbu4n3poND%2B0eTL81G77Z3ZlmxpXZFSvkxsx5dNgb%0A/nD11rYUTpoqM50WHJslA6VAYPDtOm3XLAUCQqRA4NDt2sZNUCmwACBKgQFI2wPZrpACQQalAIhI%0AUgAkTNs1S4FB2jUAkG0gN0txu6btenpum4J2BRSA4AumohKQIWqqamoKUBWuoq3rJAVcE6gGqGnC%0ApFRVUdsayzluYS9wLW57u6Dj7hBe2wUdd4fw/C6I5hPzi7uR06TvXxV2vmqZT8yBxGyR2Scai02q%0AzFsfRtcmiHJRvzcjXE%2B5Z/NGQb42GZ6wmfe8PCjaiwIFKwuri2E7n7DWA3sz30eFo538lfLDXANA%0ATCA0SIEiIoOIndzqypEgV7J0Y3oZYRtkSFeKVMrZmnfy1NWZdrI3RYoJhtjJ0snWydwMY2sjJ3kK%0A3U0%2By9OmtMmpk6WT0ckauhvr5Ivl1o8UCbpNPsnayQzdjXfydJbzFtQmo5Ntkx3SoJ1u8lnGFviE%0AvHl6ktnJ3slnRRnTWVFG7hRlpE6WTkYna%2BhurJOb5fbNkVJa%2B5XSGr0UUApcCiYpyFKQAqIUJCQp%0ASBApSIAUJKiUrT83VQkuZWv9svX19pqAFEjYXi7WXjmt3be3yNbyvyCgmqK9EBSYkKEAIrIqooqK%0ApgBR23aKWU0NhEEA%2BE2ZPujt7731O5%2B8nA5eXtkEhu/Tn2n432cFzDoxO9pRjmepz0/uO2XYTj8M%0AK9pZ0d3hl9FHG6296VmhsOjCSjKz0LA6TNRVJGpuuwUkZLCdjYhs7f15hbepEJ7Pnj7T2qFwO6KW%0A6hPmcH1Kfe/74/1P%2BNm0/62in4J5R2i7x4efCu14YT4Q2rNZ9hHQflYybwrtb9T/u0C7YPn46j9e%0AmI%2BE9lyWfQi0n5TM20L7ff2/D7QTlv%2BD6j9amA%2BF9kyWfQy010vmjaH9tv7fCdrNAcJnN7bdXyB8%0AKrTHWfZB0F4tmbeG9rv6fy9ouz9k%2BlRou7%2BJ%2BFRoj7Lso6C9VjJvDu039f9u0O78HPbzoO2esH8q%0AtPtZ9mHQXimZt4f28/p/Q2h/h8x4D2jXWD68sd1bmI%2BD9nyWfQC0QWYOMnOQmYPMHGTmIDMHmTnI%0AzEFmDjJzkJmDzBxk5iAzB5k5yMxBZg4yc5CZg8wcZOYgMweZOcjMQWYOMnOQmYPMHGTmIDMHmTnI%0AzEFmDjJzkJmDzBxk5iAzB5k5yMxBZg4yc5CZg8wcZOYgMweZOcjMQWYOMnOQmYPMHGTmIDMHmTnI%0AzEFmDjJzkJmDzBxk5iAzB5k5yMxBZg4y88Gatj%2BmDTf/TGtc23/Qurc/lQ3/Begpa277EhXzAAAA%0ACXZwQWcAAAAQAAAAEABcxq3DAAACG0lEQVQ4y02SP2gUYRDFfzN3F40axZhGDaQVGyHGLqiFRRAs%0AjcT0CgYhja2NIthYWdiJnQqeaSz8g0QQTSMBC1GCIoKJGMwlQXJesjvP4tu97Da73/DNm/d2fibl%0AAgcBFoADxVGkbwNj%2BxHpLAKXDCJV8u6VSBcsg25zABlUhC0MU0iywKouwtJUs6IRkFecFDW89FtK%0ApmLZHKgbKcUr/OMIBwLHAlkxnVLHIITLirJvWy9%2BhhVCdWavsLr4mf6JWQS0Ho7RODhCrX%2BIv29v%0AYfVedo9eZ8fQKGtPJ%2BmffEOwxcrMBQYOHMYZvszW0hwiB4J/P57jA0dYfzHN/nMP2HN8mtXmRfLF%0Aj3QW3yPlOA20NI9Wv1Bn7zGybJONuTuo1qBmPbS/PcOzDo3BkzQGR9k5fAmtfcdMmNUq6wzq4PQc%0AOkF7/i4hY9fIVeLPVzYo8s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      "VCU Scholars Compass\n",
      "http://scholarscompass.vcu.edu\n",
      "scholarscompass_vcu\n",
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     "text": [
      "DataONE: Data Observation Network for Earth\n",
      "https://www.dataone.org/\n",
      "dataone\n",
      "\n"
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      "Carnegie Mellon University Research Showcase\n",
      "http://repository.cmu.edu/\n",
      "cmu\n",
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      "https://materialsdata.nist.gov\n",
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      "\n"
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      "University of Delaware Institutional Repository\n",
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      "udel\n",
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      "http://calhoun.nps.edu\n",
      "calhoun\n",
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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "University of Tennessee at Chattanooga\n",
      "http://scholar.utc.edu\n",
      "ut_chattanooga\n",
      "\n"
     ]
    },
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      "Arizona State University Digital Repository\n",
      "http://www.asu.edu/\n",
      "asu\n",
      "\n"
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      "Zenodo\n",
      "https://zenodo.org/oai2d\n",
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     "text": [
      "Digital Commons @ CalPoly\n",
      "http://digitalcommons.calpoly.edu/\n",
      "calpoly\n",
      "\n"
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      "WHOAS at MBLWHOI Library\n",
      "http://darchive.mblwhoilibrary.org\n",
      "mblwhoilibrary\n",
      "\n"
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      "bhl\n",
      "\n"
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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digital Access to Scholarship at Harvard\n",
      "http://dash.harvard.edu\n",
      "dash\n",
      "\n"
     ]
    },
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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "London School of Hygiene and Tropical Medicine Research Online\n",
      "http://researchonline.lshtm.ac.uk\n",
      "lshtm\n",
      "\n"
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9sOg3eWg64PgoZvDE0PMntE/lhg1GzEaNnqk9F31BfLH6cmbs%0A2avb4ydfZ08ETTpNWU9bvbF5a/PO/L36DPvMxGzBnPLcxPyZhdQPph%2BpPzZ8Mvk0vXhyKfmz1xeb%0Ar1bLoStd3/f%2BaNsw3Nz8nX8FNAY9i5nATuMWqdE0Kvhg2irCBFGKPonhLhMncwrLUzZF9iyOV1wk%0A7lyeQT4ufleBYsFOoTHhZZEV0Tmxh%2BInJWIltaWopZ5KH98WKkOS%2BSl7V%2B6AvIsCv8J7xSalRGUt%0AEkLqUSlQtVFjVhtRr9Dw0OTVHIOjwFObU3tU57Cuh56Y3pr%2BsMFFw31GAcbbTRhN3pl2mpWZJ1oE%0AWPpaBVtH2UTY%2BtrZ2Gs6SDlyOxGdUc4rLu9dR9zuuDd5VHoW7Ej1CvF29TEky/ly%2BCF%2Bc/7DAd2B%0ALUF1weUhOaFRYW7huhHikXRwJExGj8d8jROM906oSLyd9Cx5OmUhdXUn7S6%2B3RLpAhm4jNeZLVlF%0A2bE5Xnuccl33huRl51cXnC9s2de6/8qBi0XnDzYUny45caiytKysqDz/cNaRlIqoo0GVIVUZ1TeP%0AS544Uyt%2BsuTUk9Or9cQzXA3CjVJwHKie124ybLa64HYx/FLu5ZNXbrQMtY63zbR/6UBfY%2B%2BUvq55%0AQ/em6i3B26jbU129d1q663sq7x7s3XMvtS/2fvyD/P7OAbZHuwZfPeZ6ovPUcThwJGP09LNHz7%2B%2B%0AZBqTeWU5Hv368MS1ycdT49NTb%2BbfYWH2M2eH5hkX5D%2BQPop9ov/0ffHd0ujn%2B1%2Bufq1ZTl9x/ib%2B%0AbeV752rqD801wrrhxuzv/Msi86hqtBdGEkuFXcLNUs1RT9Es0eIJonR6RHf6TIZzjENMmyyirEZs%0Aoex7OI5zXuHq4b7Hc5f3Gl8Nf7KAvsAPwVNCFkLzwnki4iJdol6iq2Kl4vLi9yWCJKkk66VMpd5L%0A526T2NYj4ycLZKvltss9k0%2BAXzfNipaKM0rZynzK7SR70oLKHlV%2B1Tb41TKjnq7BpnFWU0/z8Xa/%0A7R%2B10rSptCt1lHVGdFP1%2BPTa9W30nxsEG2wa1hrZGtMY3zHZaapsOmdWa%2B5pwWExYllm5WBNb91n%0Ak22rafvVrtk%2BzEHc4Y1jjdMOZ07npy5Frqaum24t7uEeIh6vPMt3WO9Y8Sr1FvW%2B4qPn84Kc7Cvk%0A%2BwyuI8EBJoGqQerBZiHk0IgwcrhOBEPEWOTpqIhoUvR6zJ3YgjjbeNb4lwnHE/2TxJLeJR9LMUoZ%0ASw1PY0l7svParhu7u9PvZFzNbMgqz87OidrjkWu0VyoPm/c0v6LAvVCkcG3fxP6HB64WnTi4u9ij%0AROMQ16HV0pGyS%2BWHD%2B8/UlJRc/Ry5d2qZ9Vzx9ZO0NUI1CqdND3leTqqbnd9/pl9DRmN5LOq54jn%0APp//0LR6gXCR75LiZdsraS1XWr%2B3q1%2BN7qi4dqGz/fq1G303l2%2BbdF3tduhZ7i3vU7r/tH//gM%2Bg%0A2WO9p/oj4c%2BJY/NTA3PLX1cp%2Bf9Ve6O8E3AqABzKhDvUXACcdAAo7gZAbBjuO/EA2NIB4KgOUGKB%0AAEXoB4jG5N/3BwLQAAdoASOs3/ADcSAPNGDdxQa4w1pIHNxdFoFjoAncAI/AJPgKd448iAJigngj%0ASUgxcg65h7xD4VASKEtUHKoa7vM24b4uEX0V/RNjgjmEmcIqYfOwr3EauArcGtxh3adWpa6n4aYp%0AxtPi82nxtAcJXIR6OmW6TqIWsYNejf4agynDS8Z4Jgam88yGzEMsjixDrDasj9m82b6zV3BocYxz%0A7uLi5urg9uKh4enkTeRT5vvCf0kgVpAkuC7UK1wuEiy6XYwoNiF%2BWSJP0ldKT1psG3HbmsxH2Tdy%0Aw/ItCmmKCorjSnnKJOVPpHaVEtUUNX91Sw15TfbtRC1Z7Updab39%2Bn0Gn4yojVlNOE15zETMlS2s%0ALWOsjlh323y2E7Z3cTjg2OuMcTF0zXXr92Dz9N3R6PXGB0dm9MX5Lvu99R8LmAuiD7YIKQt9H749%0AojTyY7R5TGMcIT4m4UWScXJ7qkxa3S6B3ZUZbJnF2ficzD3Le0Pz5gsK90UcaClmPMRV%2BqG84Yj3%0AUbbKwer9x01OLNcWnWI5nVe3cias4fPZg%2BeNmhkvLF16d2Wmdb79fcd059JN9tsGd7x6fHod%2BnQe%0AyD2UfKQyFPnk%2ByjmBc3Y8dfMkzfeEmd2zut9aP609lnlq/EK/tv%2B7/dXZ368XXu%2BfmXj4E/fTfmt%0A9YOSfypY32OCNQdBIAWUgBYwhXUGH1hhSAP5oAI0gKuwjvAKLCJYhAuR38p%2BClKKXEAGkA8oepQS%0Ayh2VjbqEeovmR3ujT6EXMCqYHMwwVhKbiR2Dua%2BkAlTBVMPURtTtNHI0jXhJ/DlaZdqbBFvCNF0y%0AkYZYRi9IfwHuX18yJjGxMbUxOzN/YNnFimc9wibDdp89ioOd4xZnCBcL1y3uKB4RnjHeCj5Xfg7%2B%0A5wLVgv5C8sJA%2BKnIWdEcMU9xZbiXm5Psl7oM32JFMtmyO%2BXi5f0UdBUJigNKBcoWJHbSkspz1V61%0AVvVajX2aqdsTtfK123W%2B6Snp%2BxsUGtYZtRpfM7lmet2sz3zSEmUlZe1ss8e2zW7BQcTR26naedxV%0AyC3UvdWTaoeL11HvHp8hcpdvg1%2Bef0iAfaBpkFtwVsitMLpw34jOKK7o1JhXcfrxDYn0SdHJD1IF%0A0xJ3Du4mpZ/K5M4qzcHvSctdyCPnTxWm7pcvQh18VXKxNLFc%2BfDniouVCdUax36cqKtVPFl96n2d%0AeH3wmQuN7Gerzms1fbhQcUn98kALuXWtvbbDrhNcb7hpeWup63i3712Ne4L3MQ8ePkx8hBsseEx4%0AUjvsPWr1PPxl/av3E/xTtm8y392YZZ8/%2BFFs8eGX0pV9q2ZriuvHNt78XPqdfwyggTVdDph9aVhr%0AMgC2sMIUAXbBmV8DroB7YBzOewIihugiO5A0pBK5jkyiaGDWyagy1CCaFR2Avo7hwWRg5rBu2Ic4%0AA9x1WE%2B5TW1J/YomDk%2BPv0DrTEAT2uhiiArE7/Q9DBWMCUxuzGYs5qx2bObsqhySnCQub%2B4Unnhe%0AXz5HfmsBK0ErIUthKxF7UW%2BxOPH9Eo2S96Rmt9HJqMoGyh2VH1HkUvJXbiatqdqqPdTI3%2B6mjdU5%0AqLuub2GQDTPYZtxpcsN0wGzNwsKy1VrW5pydrH2ro4HTiEuEG979nKezF6MPra%2B3v0fAmyDN4MKQ%0Ad2H24f2RVlGPYzxiZ%2BLTEvmSxlPupt3aVZ3ulPEjqybHOZd/72L%2B9cJ9%2BwOLTIo5Sx6UBpatHM6u%0AYDxaW6VS/fB4YA1SW3VK7fRwfUIDd%2BO9c%2BlNJhfkLhlfSW%2BtbS/qcOtkvz56s/K22x2q7tN3lXuv%0A9RndH%2B1PHpAbRA8tPpkZHhotfi7%2Bovrlz1dG4wWvH0zSTzlNn3gz%2B07hfdjMidl7c3ML2A88H%2BU/%0AGS66LJE/%2B3%2Bx/Sr0dXl5/wrPSuM39W9Hv61%2Bd/neusq2Grvaurr2Q/dHzo%2B%2BNeKaw9rhtcF16nXd%0A9eT1i%2BuzG4IbbhslG/c3Nn4q/PT/efjng58/NxU2AzaPbPZT8h8XqKRIeXsAhKAPy4/jm5tfxACg%0AKgFgo3hzc612c3PjJNxsjAFwK/zX/zkUZcr/RFWLFPSg/UcG5f7v4/8ArLvFYb7R18kAAAAJcEhZ%0AcwAACxMAAAsTAQCanBgAAAGbaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHht%0AbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI%2BCiAgIDxyZGY6%0AUkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5z%0AIyI%2BCiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5z%0AOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxYRGltZW5zaW9uPjIxPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxZRGltZW5zaW9uPjE3PC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgPC9yZGY6RGVzY3Jp%0AcHRpb24%2BCiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE%2BCnl91vkAAAGPSURBVDgRxVK7SkNBED33%0A3phobnxAfERUTETIB2ghCkEDfoCgTQorK3u/wX/Q3kbEJwimEQUVtLG0MeaBaIIQowkxuQ935uau%0AQWxCCgeGnd2Zc/bM7Cq2MLRhahtYhv4/gSdz5IFZcRrxBiMYmk3Co4f/7Mysv%2BPlfBq1txTnNT/A%0ALVgGoHghwVRoW1/SaU%2BudfQiNH8HAhKGXO0aTjAbqahXMhxX8kmkD3RkTxp%2BHMTH4xbnasV7qdg3%0AEIMaCK9xAuIxXy/jMMpP6B5Zhj6WgLiUnQr7ohusonAdh1V3IIGJdaFAJHuiDRVlQXK1yIVGNedU%0AKUJqNctn%2BZslJqQEYegixf1IhdtVlB52QEo0HTAF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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Digital Commons @ Wayne State\n",
      "http://digitalcommons.wayne.edu\n",
      "waynestate\n",
      "\n"
     ]
    },
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      "OpenSIUC at the Southern Illinois University Carbondale\n",
      "http://opensiuc.lib.siu.edu/\n",
      "opensiuc\n",
      "\n"
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    {
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     "text": [
      "Hacettepe University DSpace on LibLiveCD\n",
      "http://bbytezarsivi.hacettepe.edu.tr\n",
      "hacettepe\n",
      "\n"
     ]
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SfxsOt7zKx7Axbfqy1j6qWqtca/7QdULHR6VXaj%0AMbAtrqSubmuRbWHHY5VlfacccxKNWAxiczCzTMgs7rKPuHKnHm4ITKU5xfRksklLVCZOTRtlfoyJ%0ABe4lZ11O%2BovqfXOwuqW5p%2B4U%2BDm7FQdc2qx0uxu41qqw1iVjWo5UZYSb9X3Mc3M5jmuHYRESJ0Mf%0ArOhafHx8yglkjqoRCRxGQmZ26H85UArkzWXM7ph0G1n2h6DtntgmJKL2NbZiwsKrfZBuu1I1nE1q%0A/unGYapVh5JNm7KNfvGkkaSU2K0eO1zhUkeelAx49GsrpKausLYoxYbjsxXbaRd3R5quROxdTLe%2B%0A%2BHTvVuiPpzw8YSPi7nsXTq6fAxG3XsE3ibg%2BaWDYjh5KxynQXR3A6xgttmks4o7t22ZDIj1qUZHu%0AzShqpZtIE93GOa74L5ZN%2BrV28SCbub9X18FYbWe53vXv6S1Q29FiGWaqel2CK97xoM3FZrHZMVOt%0AOnICJzhw1DPTkeUo84%2BCDDlH8eXlznkhao0ocT7CPPzRa5e6yizOZN3qonQDePSjXmnXdw33PmtP%0AYLa03aJrZ8/bdP7V2TKFbFm5SPi4lFla0CSZumB4cQXrnDZwv3HmCidKWsCBh6q6GtkmYIBwwRs2%0AAWMG4ezcUe98nsy%2BP0/73CVD6hW59f6SVMZ607bbWWbqjE9ZtVcgWkpFMWdojjxcTYYpueHyzQuz%0ARYknE3wVr68eK/W1wma8HOghOa3hENhLNifs8PodQW5nt%2Bvmv0Q88JSnCLiL9cr/AKetR/8AzMP/%0AAMItXPY0N99N5n7hSLefyuuk1P7HaQ/2fYXditkVdOtoynxb2SsOZNtlLA7aHEQ8I6ZJJk47Mkoi%0AB/G%2Bv71ThPoQFRc4Qrzjp5nqCh5t%2BcJ935%2Bb42xQ9822OuWf0qtb2m37p7K95LLFOKrUNsSd%2BZ0o%0AcmjLQcuzuWxEX61SoFHVjGYiPPCxbRLnGVBKZTwSF/LQvPS0nIIQ01EL4jhZr%2BhrN67ob3Fh6/5U%0AWReytdtvrdWe5OL1UAVB1Hgh29rNY4gdaLKo65V%2BCVHpnVu/tfvfzjU7LA/b5ZeD%2B2TjLjxRzV4z%0AfQwhgfHfdtrfeO%2B75qtZ%2BbZdv%2BwNzsla67bev2sFjlbRD6nuNopLqNQGXE4kGtYfSUPJx4RYWOXS%0AnOAuAjTgiXPrQjGFpWlKvFgASniCV8IkQi/rWVsxYs7Liz9Ku49XIyibN3xvjYVAX2HxfZZ5M27b%0AdnjDXRjWRQ0Q8jpKuKs7nLkxnL9c1grljgjtwUKGi15SMAeezpMKlyigp4y8Hw6og2V/Gt%2B5amzs%0ATMzZLXPp97c18w%2Boh3At9puMVT4bY7nZcrS3N/eopjiZY2DbcdY681A2tSmpRPy1tw1OJmtKT4Al%0AXwL4FnxmvikegpAYHJ4rYrZ4bBYt3vUluis19YC51FfYXqKkVmgjkp0zMO7YJtKsnR62BN0pJFEm%0AwNzLXGfpGSGfgyUKz9iT%2BP0VyNEgfMVWq%2Bs2Xfk6gd0vL810/wC8OzNdL6X7vkxXynuI66alubGn%0Avm1kh3TS1u3LHMWFvWjt3ikTZMyLxqHOG2S%2BK3CcZ8fLnnUUcj1kLYHuJNiy2eXq%2BSq33jfXSoE%2B%0AkFe6S86f0alNrbXTXGJsuwkSVVTNR/8AUTTLmzys03UWFUf7jAlxToJ0rwPKMjVnPz/C/jbSwG1W%0AZuD4CYc%2BjYrPtfyrIfV%2Bs1cY9M7vW3s/DNLFNWDXpIeBdSbIM1KDb3KOduCR8aQ2DvBobR74i1DQ%0ApKUMyqyr4Rn4jRLE9ZGTC%2BEWfW9CqO%2B/lZaDqmkxfaD6SrPS9Dn4OeuGNSMWv4ZhMMivI%2B7VO1Lt%0AlegJwQnWFwh3c1WG4U5dYGjKCKNnzCnPleU3ptKPMYuIY/dJsLuP4SUvfG7/AFtWmfSv7d6vqWlU%0A9Z9yWuJ1RsnUdjtse0i9iv21RRKw8pYZKfcNBPZ8gECsLCdk5lq5jiqQ5GFuFY8FR7sNr6TpJSm8%0AJhF5I5mHZrdGHh4SHpUltu2xdD612ypOx98MdL6eah2vGxddmbBtLaFSnmLyi6zWFKA1iBNKtgmb%0AWaySckk4VMmrpBmQ2%2BXC0mwJ2hpwFSnHA80r825PYAds36y7hHrVdjZurY851KcIuDv1xbtT3ept%0AVUdpZ4J3cWm0iTD2sNZRm5nGEU1qE01M/kI0BVFYA%2B4lo9KVGSj2ZdJ8PL4V8exoUDaWU3B8GHb0%0AbyQdP11K9Grut/QzckTXtlVTVmgtgSGYuAfSUnARlbmGWJQ8cFxn%2BoYSPXloiUWX3KOF81wdREr9%0A6PanPxxyz10JHEUskbXfexe6/wDCq5E1839ivJiGiExH4BMVG4gfx%2BYn8Ili1xD/AIpTfLRUX%2BOw%0AL0/j8tcqFkPh68j/AE%2BPH%2BOced8V9ZWUFf7IfU//ANMnXj/6W1t/%2Bb5v4ZV/9xL%2Bcv6lXGXWSnaI%0AiImvxMXAwMXHQkFCRzKIhYWIZNo2JiImNbCZx0XFxzMSAx8c3ZhCIABIQIQgoQhCUJxjnO7uTuRP%0AiIlZV6Z9NuqjC4KvrTr7qkVqU9JJYks06JIIUksuHGZJtFFbqaNpHDjHsScbdBUF/wB4heF5znnS%0A9XUuGB6gsPlUXbrWyWrrN16vN4Dsq5aX1rZ72H7XObRN1GHkJNwRiNsNg4kCOWuUyrtuJq1Q3M5S%0AUrcbdAwrQhKU8oNTUCHNhMQh1MSrie9sWW366F/ZzrF1rs8xJ2Ky9etG2GwTT08jMTs5qahS0xLS%0ADomSun8nJv4Ahn70hVKUQpVrWtSvLKs54GpqBZhGoMRHhYy/lM%2B38F7JLrn17mYGtVSX0TpuVq1M%0A/Mf0fWpLWFIfQFU/qJ6OSsH9NQ7qDW2gsv5EQ3Dz7UYvujDwU3mvCVchqioYiLnzEi3ixviK2y%2Bt%0AnhTPt/BfWo9edA6/nW1ooWjdPUizMhugs7FUdZ0uuTrQLwBGrwTaXhoQDgAzNSkGRKCYwQZFIX5J%0AypPB1E0g2Oc5Bfhd3JvijE75uVvrvWQvOkNL7QkGcvsvUOr9iSsey/HMJO80CqWyQYx%2BDlc4YM30%0A/EuCtmX3JzE9SFJR5mWv48s5zyBmmiZ2jlKNu4ib4I5O2bFf67l7KFqLU%2Bqvyv8AhfrHXmt/z32P%0A5z%2BgqVW6f%2Ba/F/efjMyn9PRjf8h9t%2BRkPR7vP0/fG9Xj7F%2BUHLLLbnZSkw7MRE/6ke77xMy0LZfV%0ATrhuKY/qLZultfW6wZShBbBIV9qKcdDEj1hC%2BmWKROZAKE/wNBykQP8A0Jxy8dVURDhCYhDqvqo1%0A23SZ1JtD1zQdWwAapremVii1wBVuBw1UhI%2BCjlOipQkzwzaOAhLh8TAx%2Bw5PIpMpTla8q5mckspO%0AUhvIXW5YkdyfeyZbxyFdOEUI2brb12us7IWi56D0tbbNMEGaVsVm1ZRp6dkzBAJqEshLykEZw9Ih%0AoAQ0qIRWUjChGP1TjHNBqKgBYQnMRHhYyYfiqZ9v4LaqHqnVurAyTfWOtaBrkEyRsaXDQ6bXagGV%0AMySZDMsiOvxzfD4gUOXGBKLhWR4cLSjx88/NTllm%2B9kKTDsxET/qRid83K313qF%2BxXbWk9ZHVWTs%0ACkbTlIq5SUZAQNlp1bhpmvuLXLklMMKiV07srQrewEbRLhwgagYEsCkqGZeUGQPWnpTqWLmzESFs%0ARMRa2Hr2bFLM20cl96H231ldNpA0lLRV%2B1ftt/BkssJQ9p1M1XlrJBgQ9K5kK29bunbCaQIEc%2BIt%0AAXii4QxcqwhX2brDcdLKEXPM4yRXs5A97F43EosVr8Py/svHu7txUNC3el0S1653BOSWxZFtCUaS%0AptVhZmDstgc%2Bvxr7B85tLZY5lGTD%2BQmELywrzHkqMLUmYaQ54zMZAFo965Zs3XsRmYmu%2Bbuslrnt%0AjrDYmzH%2BlzsbxrvbjGDHZUa92hVXFVnZSBXhS1ycEdJ3LGcANGMqXhq8KvCRmylCktnXog6WSONp%0AtWSK9riWIcXjcQpYrX4fl/Zejsj2fp3VqsM7rsKpbGl6ec6Wj%2ByUmAjZyMr7s7hu1j28%2Bt3PNCMM%0AunDlKG68DIJS0%2BpS0LyNC4p6Y6knGMhE%2By72v5uSMxPu5MtRlu49drM1rSIuOmd%2B09tte50%2BhVKy%0AzdQqziqqsN4kQxlebys1A3p6iLwQpvZlJE%2B71CWtAV/Hxm7Uhk0jhMBPGzu7XfFZt7aynC2fepp3%0ABuekaQrsXYLoaUMuxWWIpdRrldi3E5aLlcZ8hBwtXrMM1/aQlj%2BlwpOFKGJCG6yGINCVZ5jFCcxE%0AIW1WxE76rMI9LqGZia75u6jCl9sKpadrwukJ3XO39YbOnoKXtEdXNi1iDZhPXYYY1nl29iq9plYu%0AQbrItQkpaPXBhmCsbkbdWP51OlIYnmGQJYxe12fp80mZ/YrPlnezLb9p9g6nq%2B01bX46/ddi7NuU%0AdKzkBrfW8VGStnNXIPGEytlkTz01GxkBBCcqC3QeQkWuHLk2ANUuCIKlFIqc5hKTEMcY6pEWy5cO%0AVycvIyqzX81eDSvZWlbysuyKXA16/wBTuGpP6WFsCs7BrSK7KQT63rsuYiPzgUi5FIky1rB3OTtS%0AnZEbSDQrZ0dJV%2BqZqcoWjMnEgmxYSYr4rW/qR2YWu2TsrF8xV04ROEThE4ROEXKH6uCnaNOdeCMA%0AN3L5HcHUqmTZ46KxaOHaKtsnLYDp6Fm4WzbqNgaVlQ3OoaVZWkJM48FenorDzlTfZzJ/EUj6fNUu%0AsOtG1dldsKL2f3q41vWW%2Bn6xKwGs9c6xnLJc8HezbSUbPp65XazU2vkdLH%2BafZbtGsYMSVBbLyT5%0Aw5S5xepiipSpoMRFM93J8I7OgWF3/Uq3Zmdm6FGX1FiWIOzOhZqizhZG0D7MMVwLGxSb6GgXcqlm%0AyyzBLysZEvnEfHqN44KYLJ0RCfLKAkz/ABzTR%2BHm67G74ebztt%2BSsPFi6m%2BvWtD6xuXG%2BO92z9ob%0A7WPXvYzQFVVrWq6Iiw5zER%2Bu3mXy/wDEFrb3J0l2E1dOrfMeJBsmQmwrAwMpJEOGfhepZoKGKOD/%0AADKeoe5H39WHo3evhdNjWbYpV%2Brj/wBDOy//AHDrj/zuB5TRX/Gxen4KB338rLdKe83TD7JaWLsS%0A61s167VjrsztcZIxA5cdNr11rFtqM0OfuTm2qUFvamcczC7jHQvX9uhu4WDxMNZV5G0JRuNOxeEF%0AJh8YhdiyG3R1qC6LbbrOd2NOsd8R2pqHAbaTqLeUNeXG09GTC2RJAchZtdxa3Eq1cBSj4y1ExlwH%0AWpK8lHlsg2GzwIztlxRTvA8plFz0JDgPyOg5XyszKvGrdwbxrHazS2l%2B7GraJJ7Lk4DZDfQ3YTWc%0AhIN4eXDiGbvrvGy0Gsgh5I9YV9j7c5aMctjYj/GKQhz94LolhhKlmmo5SEBccYFt8XP0%2BNxZqXZr%0APhezLf8As7o%2B9bH7CQe1%2Bru7K/Quz%2BnNcRkLYKbbY47yr2jWlum7K/gWsytMef0NzyzC0IzlLZ5j%0AJBty5XGuG7ZyTOmmCKnKKpheSmmLJx2iTCOL5f7pis1nbI19um25bVdd37%2Bou8tMQ2r%2Bz1YretRb%0AGsVQl30hT9h1KJxYCUeSj2LqTcjjTNw219lJBFcEdBlEicnRlgNm2VkIhDAcEzyUxO9mLaxZYvh9%0AXUE1maz2Zl0n5wKycInCJwicInCKqHZjqbWe0oaiwu2y9rVaCpNgjbfC16gPNfxsfi5Q6ZQMZanT%0Auxa8lX5pMLOXeAQLDxDHCFYX9n7vMi%2BimqipsbhGJFI1id77OrImH9yhvJb1KyFei30LBx0VJWOZ%0Atz5i3SBzZbCCvNpuYLhSlZeSAKpBRccFxnCk4zhpHtRfCU%2BIk5%2BVZ5yJnd3YWFuyN/mRF7VKrRvf%0AqVDb/umvrtYtw7mqbrVlhY27X8NRnWr46FrlrYqbLxPpzPatknsu5Itm39gJB67ZeKVjQ0QMxkE6%0AIKp4AkAYQJpGwve%2BY9nI29iqz5u1rWXy2n07pO09h6w3AW/bSou4dWQxYGO2lr2QpELabPHlBkSw%0AXNrJUN9FyzfK3EsvLYMa1aZ/PPAqbKarG3HMVYcUcsXNjJDI98BYsI%2BTNi97oRiZmJm3WWY7IdW4%0ALtBQmOs77s7acLTkfYlnoymF1xGkuEhFuGbyOlLA9mNbvzAcheM/akUYuOZqW4X7Gi8IDgNaapKm%0AN5I4xI%2BHFiy8msPvXRns2IWWi37panaNDbav2B2c7K2XXwEwoXNXI60PDt5lnAkalj4%2BwSNc0Mzf%0ATzD2M2iijdOy4OsCSm8y4SvlwrOaN5I6aMZO1rvvdWIyYUxYc7M7qRt%2B9Yqn2Cl9aWKbueyKLZtR%0Ayk3OUewa3noqBk46YnW8a2M%2BcFlK%2B%2Bw7wMUYFKQ5SluVDgwXYnISerGcFSUDSCIDIMzMLsQ4t38T%0AKGdhaz5OyVTrgxY7Lgtw7G2Jdty7Fp8LLQFDlri0pcNF0WPsLYDSyuK3XaFVIpqOakm4EjevnKXJ%0A1AV9s3yAHyPI6h3jKGONoQJ7la%2BdtmJycsm6GS%2BVmG7LDXzqnD2vdK%2BwlZ2ttfWO08VKGo6ZCnSF%0ARcVxxWIl9KyJIiXqtop8g3nWzp1KJIbDrJMiNEszM1NSDJk1o6p2hanKIZIr4s8V8XlYhwq3dbJb%0AtqXQdd1ZYr1fXFhs2wto7MJDZu%2BxrquExNybKvM1MIGBjYyswsdGV2us26yYE1ZMg%2BxRfa7K5MlB%0AcUlnKUQGzRxx7rD37xZ4iIlV3Ynw9CnrmSunCJwicInCJwicInCJwicInCJwicInCJwicInCJwic%0AInCLGv5KOim6nco/ZRrUf/O4fugM26f4%2Bf2M4IlKf4T/AJ55zT1NPSxvLUzhTxD0mYg35idmWByR%0Axi5yyDGHW7iw%2Bt1EE92Q0XW8rTJbPrBVIz4rRDOjWRaVfz8pyiuAdKwrGUqxnHx84z%2Buf558lXfa%0AJyI0c7tU8paYnH/omVSXqgGVePUcpNBU2UulYit2H5z/AExNQzNd79KxvkmNa3Kwr/jKFx8K1Zts%0AqznPz5kmJNuRGPhP%2BQlZ/ZP6/wB/H4%2Bs%2B3HkdT38GirNIP2ghAB/NNLG/uF8cPjTcvNCRXaIJqh/%0AFARH3zYvYolmfqHCxhaK/q9as5xnwdTVoSlKVeOfjzYsYZXnjzz8/wAOE/OE/H%2Bryx8rWf4gBe40%0AHJlyfoKapw%2B4EL/6n%2B3jzfaIObU%2Bi/xHJ%2B1g%2BajCV797bd4WiLr1FiBK8vBeY%2BZkHg/nJPHHuPOJ%0AEr4Rkf8Adv8Ayofz/bPgn5mq%2B3flXLjam0fQ0o8P%2BXNKbbekphDs8G1uzkvMl5f6XO7Q08ELeabl%0A7x4fYo9ku5XYSQ8sCuTSKGvC0qFG1qtp%2BMKQnH6mexhyjzjxVnGUkSrGSK/b/l8fAqPtg5ez3waY%0AClEuGOmpv1HGZj%2BEuLyLz5eWfKGRsq1oW8WOP5gT%2B1aO%2B7Hb0kFK%2B42jbR5Vlec/YyOYzHyTHwr4%0AxGoFhOP%2B3GPjCf8AT488Sf7Q%2BW9Q7vJymqx3tyXm9v8A4hD0dnhXBJyj09Ldy0nNfufB%2Biy1J3tb%0AaL/Ocvtk355nI8hzl3cbE4VkWfLPqzk0hn5H%2B6v1/t%2B2f%2B7nlTcqeU8/3/KOvla1tesnfV7OtLu%2B%0AKuU9LaVktj0nUSZcU0r/ALlrri0WV54fd2KddZHlXry4l5A/h5%2BPn4e1xnxznKU/Px/fxxzz5NJ6%0ASltz2kJ5MG7imMsPrJcx1VSbNjqJCw9Zu/7liTuHDomSuDmcFzjCclMVZiZSn%2B2MrIvOfHH%2BX885%0ACM5SxSG8hNxOTu/tWJmRFcncn8ZebhZJwicIswCdmmuRKbTMq3UHCcByCRdByLCceKcCyM2MjxhO%0AfjHx8fGObjXVsTg8dZLE47tpDHD5uEsl0jPOFnGYxceyRLYWOx9kMVITG3y7M1%2B3CxIY2idbq9%2Bf%0AhOCIw3fJz7fhKcfOP5/XHPRg5RcooSZqbTtbC98Q4KmobPtap7y6I9JaSjs0VfPG9%2BGU2z/C6kqz%0Ado98WnJUvNhy8YBeMpS2raWtbSJH8Z8EOIYATr/b/NZlq/b4%2Bfj4xz6LSX2ncudJYhm5QS08ZcNN%0AgpremFgP1m5eMvTqeVOnqq%2BPSBxj2Y7R%2B8AsX5iUHyUrKzLlTyYk5CVdrznK3Uk9cvnKsqz8qyo7%0AoilKz8/zn5zz4qpq6qskearqZKqUuOQyMvWbu68OWWWUnOaUpi6zJyL8xLGcyXOnCJwicInCJwic%0AInCJwicInCJwicInCL6KUpSsqVnKlqzlSlKznKlKzn5znOc/3z845GbuTuWa0Zsebr58lZpwicIn%0ACJwicInCJwicInCJwicInCJwicIv/9kgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAg%0AICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgICAgIC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      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "New Prairie Press at Kansas State University\n",
      "http://newprairiepress.org\n",
      "npp_ksu\n",
      "\n"
     ]
    },
    {
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9sOg3eWg64PgoZvDE0PMntE/lhg1GzEaNnqk9F31BfLH6cmbs%0A2avb4ydfZ08ETTpNWU9bvbF5a/PO/L36DPvMxGzBnPLcxPyZhdQPph%2BpPzZ8Mvk0vXhyKfmz1xeb%0Ar1bLoStd3/f%2BaNsw3Nz8nX8FNAY9i5nATuMWqdE0Kvhg2irCBFGKPonhLhMncwrLUzZF9iyOV1wk%0A7lyeQT4ufleBYsFOoTHhZZEV0Tmxh%2BInJWIltaWopZ5KH98WKkOS%2BSl7V%2B6AvIsCv8J7xSalRGUt%0AEkLqUSlQtVFjVhtRr9Dw0OTVHIOjwFObU3tU57Cuh56Y3pr%2BsMFFw31GAcbbTRhN3pl2mpWZJ1oE%0AWPpaBVtH2UTY%2BtrZ2Gs6SDlyOxGdUc4rLu9dR9zuuDd5VHoW7Ej1CvF29TEky/ly%2BCF%2Bc/7DAd2B%0ALUF1weUhOaFRYW7huhHikXRwJExGj8d8jROM906oSLyd9Cx5OmUhdXUn7S6%2B3RLpAhm4jNeZLVlF%0A2bE5Xnuccl33huRl51cXnC9s2de6/8qBi0XnDzYUny45caiytKysqDz/cNaRlIqoo0GVIVUZ1TeP%0AS544Uyt%2BsuTUk9Or9cQzXA3CjVJwHKie124ybLa64HYx/FLu5ZNXbrQMtY63zbR/6UBfY%2B%2BUvq55%0AQ/em6i3B26jbU129d1q663sq7x7s3XMvtS/2fvyD/P7OAbZHuwZfPeZ6ovPUcThwJGP09LNHz7%2B%2B%0AZBqTeWU5Hv368MS1ycdT49NTb%2BbfYWH2M2eH5hkX5D%2BQPop9ov/0ffHd0ujn%2B1%2Bufq1ZTl9x/ib%2B%0AbeV752rqD801wrrhxuzv/Msi86hqtBdGEkuFXcLNUs1RT9Es0eIJonR6RHf6TIZzjENMmyyirEZs%0Aoex7OI5zXuHq4b7Hc5f3Gl8Nf7KAvsAPwVNCFkLzwnki4iJdol6iq2Kl4vLi9yWCJKkk66VMpd5L%0A526T2NYj4ycLZKvltss9k0%2BAXzfNipaKM0rZynzK7SR70oLKHlV%2B1Tb41TKjnq7BpnFWU0/z8Xa/%0A7R%2B10rSptCt1lHVGdFP1%2BPTa9W30nxsEG2wa1hrZGtMY3zHZaapsOmdWa%2B5pwWExYllm5WBNb91n%0Ak22rafvVrtk%2BzEHc4Y1jjdMOZ07npy5Frqaum24t7uEeIh6vPMt3WO9Y8Sr1FvW%2B4qPn84Kc7Cvk%0A%2BwyuI8EBJoGqQerBZiHk0IgwcrhOBEPEWOTpqIhoUvR6zJ3YgjjbeNb4lwnHE/2TxJLeJR9LMUoZ%0ASw1PY0l7svParhu7u9PvZFzNbMgqz87OidrjkWu0VyoPm/c0v6LAvVCkcG3fxP6HB64WnTi4u9ij%0AROMQ16HV0pGyS%2BWHD%2B8/UlJRc/Ry5d2qZ9Vzx9ZO0NUI1CqdND3leTqqbnd9/pl9DRmN5LOq54jn%0APp//0LR6gXCR75LiZdsraS1XWr%2B3q1%2BN7qi4dqGz/fq1G303l2%2BbdF3tduhZ7i3vU7r/tH//gM%2Bg%0A2WO9p/oj4c%2BJY/NTA3PLX1cp%2Bf9Ve6O8E3AqABzKhDvUXACcdAAo7gZAbBjuO/EA2NIB4KgOUGKB%0AAEXoB4jG5N/3BwLQAAdoASOs3/ADcSAPNGDdxQa4w1pIHNxdFoFjoAncAI/AJPgKd448iAJigngj%0ASUgxcg65h7xD4VASKEtUHKoa7vM24b4uEX0V/RNjgjmEmcIqYfOwr3EauArcGtxh3adWpa6n4aYp%0AxtPi82nxtAcJXIR6OmW6TqIWsYNejf4agynDS8Z4Jgam88yGzEMsjixDrDasj9m82b6zV3BocYxz%0A7uLi5urg9uKh4enkTeRT5vvCf0kgVpAkuC7UK1wuEiy6XYwoNiF%2BWSJP0ldKT1psG3HbmsxH2Tdy%0Aw/ItCmmKCorjSnnKJOVPpHaVEtUUNX91Sw15TfbtRC1Z7Updab39%2Bn0Gn4yojVlNOE15zETMlS2s%0ALWOsjlh323y2E7Z3cTjg2OuMcTF0zXXr92Dz9N3R6PXGB0dm9MX5Lvu99R8LmAuiD7YIKQt9H749%0AojTyY7R5TGMcIT4m4UWScXJ7qkxa3S6B3ZUZbJnF2ficzD3Le0Pz5gsK90UcaClmPMRV%2BqG84Yj3%0AUbbKwer9x01OLNcWnWI5nVe3cias4fPZg%2BeNmhkvLF16d2Wmdb79fcd059JN9tsGd7x6fHod%2BnQe%0AyD2UfKQyFPnk%2ByjmBc3Y8dfMkzfeEmd2zut9aP609lnlq/EK/tv%2B7/dXZ368XXu%2BfmXj4E/fTfmt%0A9YOSfypY32OCNQdBIAWUgBYwhXUGH1hhSAP5oAI0gKuwjvAKLCJYhAuR38p%2BClKKXEAGkA8oepQS%0Ayh2VjbqEeovmR3ujT6EXMCqYHMwwVhKbiR2Dua%2BkAlTBVMPURtTtNHI0jXhJ/DlaZdqbBFvCNF0y%0AkYZYRi9IfwHuX18yJjGxMbUxOzN/YNnFimc9wibDdp89ioOd4xZnCBcL1y3uKB4RnjHeCj5Xfg7%2B%0A5wLVgv5C8sJA%2BKnIWdEcMU9xZbiXm5Psl7oM32JFMtmyO%2BXi5f0UdBUJigNKBcoWJHbSkspz1V61%0AVvVajX2aqdsTtfK123W%2B6Snp%2BxsUGtYZtRpfM7lmet2sz3zSEmUlZe1ss8e2zW7BQcTR26naedxV%0AyC3UvdWTaoeL11HvHp8hcpdvg1%2Bef0iAfaBpkFtwVsitMLpw34jOKK7o1JhXcfrxDYn0SdHJD1IF%0A0xJ3Du4mpZ/K5M4qzcHvSctdyCPnTxWm7pcvQh18VXKxNLFc%2BfDniouVCdUax36cqKtVPFl96n2d%0AeH3wmQuN7Gerzms1fbhQcUn98kALuXWtvbbDrhNcb7hpeWup63i3712Ne4L3MQ8ePkx8hBsseEx4%0AUjvsPWr1PPxl/av3E/xTtm8y392YZZ8/%2BFFs8eGX0pV9q2ZriuvHNt78XPqdfwyggTVdDph9aVhr%0AMgC2sMIUAXbBmV8DroB7YBzOewIihugiO5A0pBK5jkyiaGDWyagy1CCaFR2Avo7hwWRg5rBu2Ic4%0AA9x1WE%2B5TW1J/YomDk%2BPv0DrTEAT2uhiiArE7/Q9DBWMCUxuzGYs5qx2bObsqhySnCQub%2B4Unnhe%0AXz5HfmsBK0ErIUthKxF7UW%2BxOPH9Eo2S96Rmt9HJqMoGyh2VH1HkUvJXbiatqdqqPdTI3%2B6mjdU5%0AqLuub2GQDTPYZtxpcsN0wGzNwsKy1VrW5pydrH2ro4HTiEuEG979nKezF6MPra%2B3v0fAmyDN4MKQ%0Ad2H24f2RVlGPYzxiZ%2BLTEvmSxlPupt3aVZ3ulPEjqybHOZd/72L%2B9cJ9%2BwOLTIo5Sx6UBpatHM6u%0AYDxaW6VS/fB4YA1SW3VK7fRwfUIDd%2BO9c%2BlNJhfkLhlfSW%2BtbS/qcOtkvz56s/K22x2q7tN3lXuv%0A9RndH%2B1PHpAbRA8tPpkZHhotfi7%2Bovrlz1dG4wWvH0zSTzlNn3gz%2B07hfdjMidl7c3ML2A88H%2BU/%0AGS66LJE/%2B3%2Bx/Sr0dXl5/wrPSuM39W9Hv61%2Bd/neusq2Grvaurr2Q/dHzo%2B%2BNeKaw9rhtcF16nXd%0A9eT1i%2BuzG4IbbhslG/c3Nn4q/PT/efjng58/NxU2AzaPbPZT8h8XqKRIeXsAhKAPy4/jm5tfxACg%0AKgFgo3hzc612c3PjJNxsjAFwK/zX/zkUZcr/RFWLFPSg/UcG5f7v4/8ArLvFYb7R18kAAAAJcEhZ%0AcwAACxMAAAsTAQCanBgAAAGbaVRYdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHht%0AbG5zOng9ImFkb2JlOm5zOm1ldGEvIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI%2BCiAgIDxyZGY6%0AUkRGIHhtbG5zOnJkZj0iaHR0cDovL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5z%0AIyI%2BCiAgICAgIDxyZGY6RGVzY3JpcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5z%0AOmV4aWY9Imh0dHA6Ly9ucy5hZG9iZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxYRGltZW5zaW9uPjIwPC9leGlmOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4%0AZWxZRGltZW5zaW9uPjE2PC9leGlmOlBpeGVsWURpbWVuc2lvbj4KICAgICAgPC9yZGY6RGVzY3Jp%0AcHRpb24%2BCiAgIDwvcmRmOlJERj4KPC94OnhtcG1ldGE%2BCkKAfvUAAAH6SURBVCgVZVM9SFtRFP4S%0AHjUxRhFMwSsIoQXtG6R0UAQX6RPM4CwErQidGujoEMTBBqHpIog4iQhi6dJFSlN0cXB0aDMoGRTa%0A8IrYVqJWI3n4es55ec9rc%2BDee953ft893w25JNcOcH4CdO29h0ofw15OQGVOwfK9nEUlWkFbqAmo%0AXgAPE4L7W%2Biq5rrxj14gg/bEIdRGr2%2BHvZlEIe1C4YVgpkPVNDGiBqQqY3YuCjV7Fyx%2B6Ul8I0XR%0AMu2fAumbdNATWdAx0W3MSWBf3XJElbsbvIDwjXGJsal4YFrfmgYH5wkp0FKYlzVkLIDv6n8J8SX%2B%0Apbt50u51oVfmYF04%2BXCqU4cQ9r/sz88aKvOFdmBEXFifGltr6EISxNwqCqk/0vYMuS9SZRklTeMX%0AtgOdM61%2B/SIJ/c3r4CoiYxollNuWUdZ5wHpf5qn4s54f2L/XhYHzKrD0ThzMsoMPB2cYH1nxAoRQ%0AkC4ESCbluL2mo37v4Yp7g523j8SA5ioGh9tF17tgwJ6/hXpDbCSpxS/l5C38INYG5MoBwKMq/s7c%0AZyO1jlIp8GlyWgJdmGi9eo0dgkzierQ1AnDSunjvwqO4j%2BknEZmEgizLopfzAye1BI5KZ7qP6Pw%2B%0AOFmx/yWY/r4IkfwPPne3gOfFHNRcQBExf3KyeEy/pwez4R8F671ONJUvpAAAAABJRU5ErkJggg%3D%3D%0A\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DoE's SciTech Connect Database\n",
      "http://www.osti.gov/scitech\n",
      "scitech\n",
      "\n"
     ]
    },
    {
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      "DigitalCommons@PCOM\n",
      "http://digitalcommons.pcom.edu\n",
      "pcom\n",
      "\n"
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      "Columbia Academic Commons\n",
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      "\n"
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      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "eLife Sciences\n",
      "http://elifesciences.org/\n",
      "elife\n",
      "\n"
     ]
    },
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9sOg3eWg64PgoZvDE0PMntE/lhg1GzEaNnqk9F31BfLH6cmbs%0A2avb4ydfZ08ETTpNWU9bvbF5a/PO/L36DPvMxGzBnPLcxPyZhdQPph%2BpPzZ8Mvk0vXhyKfmz1xeb%0Ar1bLoStd3/f%2BaNsw3Nz8nX8FNAY9i5nATuMWqdE0Kvhg2irCBFGKPonhLhMncwrLUzZF9iyOV1wk%0A7lyeQT4ufleBYsFOoTHhZZEV0Tmxh%2BInJWIltaWopZ5KH98WKkOS%2BSl7V%2B6AvIsCv8J7xSalRGUt%0AEkLqUSlQtVFjVhtRr9Dw0OTVHIOjwFObU3tU57Cuh56Y3pr%2BsMFFw31GAcbbTRhN3pl2mpWZJ1oE%0AWPpaBVtH2UTY%2BtrZ2Gs6SDlyOxGdUc4rLu9dR9zuuDd5VHoW7Ej1CvF29TEky/ly%2BCF%2Bc/7DAd2B%0ALUF1weUhOaFRYW7huhHikXRwJExGj8d8jROM906oSLyd9Cx5OmUhdXUn7S6%2B3RLpAhm4jNeZLVlF%0A2bE5Xnuccl33huRl51cXnC9s2de6/8qBi0XnDzYUny45caiytKysqDz/cNaRlIqoo0GVIVUZ1TeP%0AS544Uyt%2BsuTUk9Or9cQzXA3CjVJwHKie124ybLa64HYx/FLu5ZNXbrQMtY63zbR/6UBfY%2B%2BUvq55%0AQ/em6i3B26jbU129d1q663sq7x7s3XMvtS/2fvyD/P7OAbZHuwZfPeZ6ovPUcThwJGP09LNHz7%2B%2B%0AZBqTeWU5Hv368MS1ycdT49NTb%2BbfYWH2M2eH5hkX5D%2BQPop9ov/0ffHd0ujn%2B1%2Bufq1ZTl9x/ib%2B%0AbeV752rqD801wrrhxuzv/Msi86hqtBdGEkuFXcLNUs1RT9Es0eIJonR6RHf6TIZzjENMmyyirEZs%0Aoex7OI5zXuHq4b7Hc5f3Gl8Nf7KAvsAPwVNCFkLzwnki4iJdol6iq2Kl4vLi9yWCJKkk66VMpd5L%0A526T2NYj4ycLZKvltss9k0%2BAXzfNipaKM0rZynzK7SR70oLKHlV%2B1Tb41TKjnq7BpnFWU0/z8Xa/%0A7R%2B10rSptCt1lHVGdFP1%2BPTa9W30nxsEG2wa1hrZGtMY3zHZaapsOmdWa%2B5pwWExYllm5WBNb91n%0Ak22rafvVrtk%2BzEHc4Y1jjdMOZ07npy5Frqaum24t7uEeIh6vPMt3WO9Y8Sr1FvW%2B4qPn84Kc7Cvk%0A%2BwyuI8EBJoGqQerBZiHk0IgwcrhOBEPEWOTpqIhoUvR6zJ3YgjjbeNb4lwnHE/2TxJLeJR9LMUoZ%0ASw1PY0l7svParhu7u9PvZFzNbMgqz87OidrjkWu0VyoPm/c0v6LAvVCkcG3fxP6HB64WnTi4u9ij%0AROMQ16HV0pGyS%2BWHD%2B8/UlJRc/Ry5d2qZ9Vzx9ZO0NUI1CqdND3leTqqbnd9/pl9DRmN5LOq54jn%0APp//0LR6gXCR75LiZdsraS1XWr%2B3q1%2BN7qi4dqGz/fq1G303l2%2BbdF3tduhZ7i3vU7r/tH//gM%2Bg%0A2WO9p/oj4c%2BJY/NTA3PLX1cp%2Bf9Ve6O8E3AqABzKhDvUXACcdAAo7gZAbBjuO/EA2NIB4KgOUGKB%0AAEXoB4jG5N/3BwLQAAdoASOs3/ADcSAPNGDdxQa4w1pIHNxdFoFjoAncAI/AJPgKd448iAJigngj%0ASUgxcg65h7xD4VASKEtUHKoa7vM24b4uEX0V/RNjgjmEmcIqYfOwr3EauArcGtxh3adWpa6n4aYp%0AxtPi82nxtAcJXIR6OmW6TqIWsYNejf4agynDS8Z4Jgam88yGzEMsjixDrDasj9m82b6zV3BocYxz%0A7uLi5urg9uKh4enkTeRT5vvCf0kgVpAkuC7UK1wuEiy6XYwoNiF%2BWSJP0ldKT1psG3HbmsxH2Tdy%0Aw/ItCmmKCorjSnnKJOVPpHaVEtUUNX91Sw15TfbtRC1Z7Updab39%2Bn0Gn4yojVlNOE15zETMlS2s%0ALWOsjlh323y2E7Z3cTjg2OuMcTF0zXXr92Dz9N3R6PXGB0dm9MX5Lvu99R8LmAuiD7YIKQt9H749%0AojTyY7R5TGMcIT4m4UWScXJ7qkxa3S6B3ZUZbJnF2ficzD3Le0Pz5gsK90UcaClmPMRV%2BqG84Yj3%0AUbbKwer9x01OLNcWnWI5nVe3cias4fPZg%2BeNmhkvLF16d2Wmdb79fcd059JN9tsGd7x6fHod%2BnQe%0AyD2UfKQyFPnk%2ByjmBc3Y8dfMkzfeEmd2zut9aP609lnlq/EK/tv%2B7/dXZ368XXu%2BfmXj4E/fTfmt%0A9YOSfypY32OCNQdBIAWUgBYwhXUGH1hhSAP5oAI0gKuwjvAKLCJYhAuR38p%2BClKKXEAGkA8oepQS%0Ayh2VjbqEeovmR3ujT6EXMCqYHMwwVhKbiR2Dua%2BkAlTBVMPURtTtNHI0jXhJ/DlaZdqbBFvCNF0y%0AkYZYRi9IfwHuX18yJjGxMbUxOzN/YNnFimc9wibDdp89ioOd4xZnCBcL1y3uKB4RnjHeCj5Xfg7%2B%0A5wLVgv5C8sJA%2BKnIWdEcMU9xZbiXm5Psl7oM32JFMtmyO%2BXi5f0UdBUJigNKBcoWJHbSkspz1V61%0AVvVajX2aqdsTtfK123W%2B6Snp%2BxsUGtYZtRpfM7lmet2sz3zSEmUlZe1ss8e2zW7BQcTR26naedxV%0AyC3UvdWTaoeL11HvHp8hcpdvg1%2Bef0iAfaBpkFtwVsitMLpw34jOKK7o1JhXcfrxDYn0SdHJD1IF%0A0xJ3Du4mpZ/K5M4qzcHvSctdyCPnTxWm7pcvQh18VXKxNLFc%2BfDniouVCdUax36cqKtVPFl96n2d%0AeH3wmQuN7Gerzms1fbhQcUn98kALuXWtvbbDrhNcb7hpeWup63i3712Ne4L3MQ8ePkx8hBsseEx4%0AUjvsPWr1PPxl/av3E/xTtm8y392YZZ8/%2BFFs8eGX0pV9q2ZriuvHNt78XPqdfwyggTVdDph9aVhr%0AMgC2sMIUAXbBmV8DroB7YBzOewIihugiO5A0pBK5jkyiaGDWyagy1CCaFR2Avo7hwWRg5rBu2Ic4%0AA9x1WE%2B5TW1J/YomDk%2BPv0DrTEAT2uhiiArE7/Q9DBWMCUxuzGYs5qx2bObsqhySnCQub%2B4Unnhe%0AXz5HfmsBK0ErIUthKxF7UW%2BxOPH9Eo2S96Rmt9HJqMoGyh2VH1HkUvJXbiatqdqqPdTI3%2B6mjdU5%0AqLuub2GQDTPYZtxpcsN0wGzNwsKy1VrW5pydrH2ro4HTiEuEG979nKezF6MPra%2B3v0fAmyDN4MKQ%0Ad2H24f2RVlGPYzxiZ%2BLTEvmSxlPupt3aVZ3ulPEjqybHOZd/72L%2B9cJ9%2BwOLTIo5Sx6UBpatHM6u%0AYDxaW6VS/fB4YA1SW3VK7fRwfUIDd%2BO9c%2BlNJhfkLhlfSW%2BtbS/qcOtkvz56s/K22x2q7tN3lXuv%0A9RndH%2B1PHpAbRA8tPpkZHhotfi7%2Bovrlz1dG4wWvH0zSTzlNn3gz%2B07hfdjMidl7c3ML2A88H%2BU/%0AGS66LJE/%2B3%2Bx/Sr0dXl5/wrPSuM39W9Hv61%2Bd/neusq2Grvaurr2Q/dHzo%2B%2BNeKaw9rhtcF16nXd%0A9eT1i%2BuzG4IbbhslG/c3Nn4q/PT/efjng58/NxU2AzaPbPZT8h8XqKRIeXsAhKAPy4/jm5tfxACg%0AKgFgo3hzc612c3PjJNxsjAFwK/zX/zkUZcr/RFWLFPSg/UcG5f7v4/8ArLvFYb7R18kAAAGbaVRY%0AdFhNTDpjb20uYWRvYmUueG1wAAAAAAA8eDp4bXBtZXRhIHhtbG5zOng9ImFkb2JlOm5zOm1ldGEv%0AIiB4OnhtcHRrPSJYTVAgQ29yZSA1LjQuMCI%2BCiAgIDxyZGY6UkRGIHhtbG5zOnJkZj0iaHR0cDov%0AL3d3dy53My5vcmcvMTk5OS8wMi8yMi1yZGYtc3ludGF4LW5zIyI%2BCiAgICAgIDxyZGY6RGVzY3Jp%0AcHRpb24gcmRmOmFib3V0PSIiCiAgICAgICAgICAgIHhtbG5zOmV4aWY9Imh0dHA6Ly9ucy5hZG9i%0AZS5jb20vZXhpZi8xLjAvIj4KICAgICAgICAgPGV4aWY6UGl4ZWxYRGltZW5zaW9uPjE5PC9leGlm%0AOlBpeGVsWERpbWVuc2lvbj4KICAgICAgICAgPGV4aWY6UGl4ZWxZRGltZW5zaW9uPjE2PC9leGlm%0AOlBpeGVsWURpbWVuc2lvbj4KICAgICAgPC9yZGY6RGVzY3JpcHRpb24%2BCiAgIDwvcmRmOlJERj4K%0APC94OnhtcG1ldGE%2BCsLDxvwAAAHdSURBVCgVnVJbK0RRFN77GNcmNSfTmAeDXDoIOUxuiRdGlEJy%0AKXnxwg/wA7x74UWjKZlM4/YglxxP5JJL80JJeSBeaBTjGibj22eNwyur1Zq1v/N9%2B1t77%2BFjE172%0ArzDxf8kgMjHObbHm97fw7x0aWmvNZvPyrFbXVD2zqGXIKaHbJyLExZtuPkRv4pyN9mm/Zegd2fbQ%0A/SPwd3eYx7CxtpUX9rN1l6caHAnTqi57Esz1QKM4ZYvVsjt/BOBg4QQEhzOZCPQVCFLinCudWfVD%0A%2BaQESR3It6VaA74rIKdasKPFNb43XNqThiVo6CFB4JxCkl5gZ0yYXB4%2BqINsfytA46FurO7cfj6f%0A%2Bq7xVanIdE/5SSLxCEfmqbliA8ZArSkroVEJ2f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    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ResearchWorks @ University of Washington\n",
      "https://digital.lib.washington.edu/\n",
      "uwashington\n",
      "\n"
     ]
    },
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     "text": [
      "University of Illinois at Urbana-Champaign, IDEALS\n",
      "https://www.ideals.illinois.edu\n",
      "uiucideals\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from IPython.display import Image, display\n",
    "\n",
    "for source in data['providerMap'].keys():\n",
    "    display(Image(url=data['providerMap'][source]['favicon']))\n",
    "    print(\n",
    "        '{}\\n{}\\n{}\\n'.format(\n",
    "            data['providerMap'][source]['long_name'].encode('utf-8'),\n",
    "            data['providerMap'][source]['url'],\n",
    "            data['providerMap'][source]['short_name']\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SHARE Schema\n",
    "\n",
    "Required fields:\n",
    "- title\n",
    "- contributors\n",
    "- uris\n",
    "- providerUpdatedDateTime\n",
    "\n",
    "We add some information after each document is harvested inside the field shareProperties, including:\n",
    "- source (where the document was originally harvested)\n",
    "- docID  (a unique identifier for that object from that source)\n",
    "\n",
    "These two fields can be combined to make a unique document identifier.\n",
    "\n",
    "See more details about the SHARE Schema, including examples of documents with all of the fields, here:\n",
    "https://osf.io/wunk7/wiki/home/?view"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Simple Queries\n",
    "\n",
    "- We need a URL to use to access the SHARE API.\n",
    "- We will add arguments to this URL to shape our request\n",
    "    - size: how many results we'll return\n",
    "    - sort: how we want the results to be sorted\n",
    "    - from: where to start in the resutls returned"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "OSF_APP_URL = 'https://osf.io/api/v1/share/search/'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "import furl\n",
    "\n",
    "search_url = furl.furl(OSF_APP_URL)\n",
    "search_url.args['size'] = 3\n",
    "search_url.args['sort'] = 'providerUpdatedDateTime'\n",
    "search_url.args['from'] = 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Our Query URL So far"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The request URL is https://osf.io/api/v1/share/search/?size=3&sort=providerUpdatedDateTime&from=5\n"
     ]
    }
   ],
   "source": [
    "print('The request URL is {}'.format(search_url.url))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Our results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Protein-targeted corona phase molecular recognition -- from mit -- updated on March 17 2016\n",
      "Endothelial protein kinase MAP4K4 promotes vascular inflammation and atherosclerosis -- from mit -- updated on March 17 2016\n",
      "Molecular sled is an eleven-amino acid vehicle facilitating biochemical interactions via sliding components along DNA -- from mit -- updated on March 17 2016\n"
     ]
    }
   ],
   "source": [
    "from datetime import datetime\n",
    "\n",
    "recent_results = requests.get(search_url.url).json()\n",
    "\n",
    "for result in recent_results['results']:\n",
    "    print(\n",
    "        '{} -- from {} -- updated on {}'.format(\n",
    "            result['title'].encode('utf-8'),\n",
    "            result['shareProperties']['source'],\n",
    "            datetime.strftime(datetime.strptime(result['providerUpdatedDateTime'], \"%Y-%m-%dT%H:%M:%S+00:00\"), '%B %d %Y')\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Narrowing Results by Source"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The request URL is https://osf.io/api/v1/share/search/?size=3&sort=providerUpdatedDateTime&from=5&q=shareProperties.source:mit\n",
      "---------\n",
      "Logic circuit prototypes for three-terminal magnetic tunnel junctions with mobile domain walls -- from mit -- updated on March 17 2016\n",
      "A microfluidic platform enabling single-cell RNA-seq of multigenerational lineages -- from mit -- updated on March 17 2016\n",
      "Genetically engineering self-organization of human pluripotent stem cells into a liver bud-like tissue using Gata6 -- from mit -- updated on March 17 2016\n"
     ]
    }
   ],
   "source": [
    "search_url.args['q'] = 'shareProperties.source:mit'\n",
    "recent_results = requests.get(search_url.url).json()\n",
    "\n",
    "print('The request URL is {}'.format(search_url.url))\n",
    "print('---------')\n",
    "for result in recent_results['results']:\n",
    "    print(\n",
    "        '{} -- from {} -- updated on {}'.format(\n",
    "            result['title'].encode('utf-8'),\n",
    "            result['shareProperties']['source'],\n",
    "            datetime.strftime(datetime.strptime(result['providerUpdatedDateTime'], \"%Y-%m-%dT%H:%M:%S+00:00\"), '%B %d %Y')\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Complex Queries\n",
    "- The SHARE Search API runs on elasticsearch\n",
    "- More information on how to format elasticsearch queries: \n",
    "    - https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Defining a Helper Function\n",
    "- We'll use this helper function in later examples."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "import json\n",
    "\n",
    "def query_share(url, query):\n",
    "    # A helper function that will use the requests library,\n",
    "    # pass along the correct headers,\n",
    "    # and make the query we want\n",
    "    headers = {'Content-Type': 'application/json'}\n",
    "    data = json.dumps(query)\n",
    "    return requests.post(url, headers=headers, data=data, verify=False).json()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Building a Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "sponsorship_query = {\n",
    "    \"size\": 5,\n",
    "    \"query\": {\n",
    "        \"filtered\": {\n",
    "            \"filter\": {\n",
    "                \"exists\": {\n",
    "                    \"field\": \"sponsorships\"\n",
    "                }\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Running the Query and Printing Results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A Phase III, Randomized, Comparative, Open-label Study of Intravenous Iron Isomaltoside 1000 (Monofer®) Administered as Maintenance Therapy by Single or Repeated Bolus Injections in Comparison With Intravenous Iron Sucrose in Subjects With Stage 5 Chronic Kidney Disease on Dialysis Therapy (CKD-5D) -- from source clinicaltrials -- sponsored by Pharmacosmos A/S \n",
      "-------------------\n",
      "Phase IB Study of FOLFIRINOX Plus PF-04136309 in Patients With Borderline Resectable and Locally Advanced Pancreatic Adenocarcinoma -- from source clinicaltrials -- sponsored by Washington University School of Medicine National Cancer Institute (NCI)\n",
      "-------------------\n",
      "Discontinuation of Infliximab Therapy in Patients With Crohn's Disease During Sustained Complete Remission: A National Multi-center, Double Blinded, Randomized, Placebo Controlled Study -- from source clinicaltrials -- sponsored by Copenhagen University Hospital at Herlev \n",
      "-------------------\n",
      "Temperature Evaluation by MRI Thermometry During Cervical Cooling -- from source clinicaltrials -- sponsored by University of Vermont Cryothermic Systems. Inc.\n",
      "-------------------\n",
      "Combination of Lenalidomide and Ofatumumab in Patients With Previously Treated Chronic Lymphocytic Leukemia and Small Lymphocytic Lymphoma (CLL/SLL) -- from source clinicaltrials -- sponsored by M.D. Anderson Cancer Center GlaxoSmithKline\n",
      "-------------------\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.py:768: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n",
      "  InsecureRequestWarning)\n"
     ]
    }
   ],
   "source": [
    "results = query_share(search_url.url, sponsorship_query)\n",
    "\n",
    "for item in results['results']:\n",
    "    print('{} -- from source {} -- sponsored by {}'.format(\n",
    "            item['title'].encode('utf-8'),\n",
    "            item['shareProperties']['source'].encode('utf-8'),\n",
    "            ' '.join(\n",
    "                [sponsor['sponsor']['sponsorName'] for sponsor in item['sponsorships']]\n",
    "            )\n",
    "        )\n",
    "    )\n",
    "    print('-------------------')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### New Query\n",
    "\n",
    "How many results *do not* have subjects?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "no_subjects_query = {\n",
    "    \"query\": {\n",
    "        \"query_string\": {\n",
    "            \"analyze_wildcard\": True, \n",
    "            \"query\": \"NOT subjects:*\"\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.py:768: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n",
      "  InsecureRequestWarning)\n"
     ]
    }
   ],
   "source": [
    "results_with_no_subjects = query_share(search_url.url, no_subjects_query)\n",
    "total_results = requests.get(OSF_APP_URL).json()['count']\n",
    "results_percent = (float(results_with_no_subjects['count'])/total_results)*100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3623349 results out of 5297121, or 68.40%, do not have subjects.\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    '{} results out of {}, or {}%, do not have subjects.'.format(\n",
    "        results_with_no_subjects['count'],\n",
    "        total_results,\n",
    "        format(results_percent, '.2f')\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Using sharepa for SHARE Parsing and Analysis\n",
    "\n",
    "- sharepa - short for SHARE Parsing and Analysis\n",
    "    - https://github.com/CenterForOpenScience/sharepa#sharepa"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic Actions\n",
    "\n",
    "A basic search will provide access to all documents in SHARE in 10 document slices.\n",
    "\n",
    "#### Count\n",
    "You can use sharepa and the basic search to get the total number of documents in SHARE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5297121"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sharepa import basic_search\n",
    "\n",
    "basic_search.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Iterating Through Results\n",
    "\n",
    "A basic iteration through results will yield 10 at a time, starting from the first documents collected.\n",
    "\n",
    "Let's do a basic search and iterate through the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Avian community structure and incidence of human West Nile infection\n",
      "Rat12_a\n",
      "Non compact continuum limit of two coupled Potts models\n",
      "\n",
      "Simultaneous Localization, Mapping, and Manipulation for Unsupervised\n",
      "  Object Discovery\n",
      "Synthesis of High-Temperature Self-lubricating Wear Resistant Composite Coating on Ti6Al4V Alloy by Laser Deposition\n",
      "Comparative Studies of Silicon Dissolution in Molten Aluminum Under Different Flow Conditions, Part I: Single-Phase Flow\n",
      "Scrambling of data in all-optical domain\n",
      "Step behaviour and autonomic nervous system activity in multiparous dairy cows during milking in a herringbone milking system\n",
      "<p>Typical features of the constant velocity forced dissociation process in the SGP-3-ligated 1G1Q 2CR complex system.</p>\n"
     ]
    }
   ],
   "source": [
    "results = basic_search.execute()\n",
    "\n",
    "for hit in results:\n",
    "    print(hit.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Slicing Results\n",
    "\n",
    "You can use slices to access a different set of results.\n",
    "\n",
    "Let's print out 5 results, starting from the 20th and going until the 25th."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Elements of Trust in Named-Data Networking\n",
      "Effect of Perceived Attributions about Ostracism on Social Pain and Task Performance\n",
      "Millimeter Wave MIMO Channel Tracking Systems\n",
      "Metric Dimension and Zero Forcing Number of Two Families of Line Graphs\n",
      "The Glassey conjecture on asymptotically flat manifolds\n"
     ]
    }
   ],
   "source": [
    "results = basic_search[20:25].execute()\n",
    "for hit in results:\n",
    "    print(hit.title)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Sorting Results\n",
    "\n",
    "By default, the oldest results are returned first.\n",
    "\n",
    "You can instead sort results by ```ProviderUpdatedDateTime``` to get the most recent items in the SHARE dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Probing\n",
      "the Energy Level Alignment\n",
      "and the Correlation with Open-Circuit Voltage in Solution-Processed\n",
      "Polymeric Bulk Heterojunction Photovoltaic Devices - Last updated on March 17 2016\n",
      "A Review of Cry Protein Detection with Enzyme-Linked\n",
      "Immunosorbent Assays - Last updated on March 17 2016\n",
      "Subtle balance of tropoelastin molecular shape and flexibility regulates dynamics and hierarchical assembly - Last updated on March 17 2016\n",
      "Experimental database on connections for composite special moment frames (C-SMF) [version 1.0] - Last updated on March 17 2016\n",
      "研究者向けソーシャル・ネットワーク・サービスの概説 - Last updated on March 17 2016\n",
      "Protein-targeted corona phase molecular recognition - Last updated on March 17 2016\n",
      "Endothelial protein kinase MAP4K4 promotes vascular inflammation and atherosclerosis - Last updated on March 17 2016\n",
      "Molecular sled is an eleven-amino acid vehicle facilitating biochemical interactions via sliding components along DNA - Last updated on March 17 2016\n",
      "Genomic insights into the Ixodes scapularis tick vector of Lyme disease - Last updated on March 17 2016\n",
      "Logic circuit prototypes for three-terminal magnetic tunnel junctions with mobile domain walls - Last updated on March 17 2016\n"
     ]
    }
   ],
   "source": [
    "results = basic_search.sort('-providerUpdatedDateTime').execute()\n",
    "\n",
    "for hit in results:\n",
    "    print('{} - Last updated on {}'.format(\n",
    "            hit.title.encode('utf-8'), \n",
    "            datetime.strftime(datetime.strptime(hit.providerUpdatedDateTime, \"%Y-%m-%dT%H:%M:%S+00:00\"), '%B %d %Y')\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Advanced Search with sharepa\n",
    "\n",
    "Queries are formed using lucene query syntax \n",
    "    - https://www.elastic.co/guide/en/elasticsearch/reference/current/query-dsl-query-string-query.html#query-string-syntax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"query\": {\n",
      "        \"query_string\": {\n",
      "            \"analyze_wildcard\": true, \n",
      "            \"query\": \"subjects:*\"\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "from sharepa import ShareSearch\n",
    "from sharepa.helpers import pretty_print\n",
    "\n",
    "my_search = ShareSearch()\n",
    "\n",
    "my_search = my_search.query(\n",
    "    'query_string',\n",
    "    query='subjects:*',\n",
    "    analyze_wildcard=True\n",
    ")\n",
    "\n",
    "pretty_print(my_search.to_dict())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Experimental database on connections for composite special moment frames (C-SMF) [version 1.0] - with subjects ['cft', 'civil engineering', 'composite special moment frames', 'concrete-filled tube columns', 'moment connections']\n",
      "\n",
      "\n",
      "Service-oriented computing in manufacturing automation: a SWOT analysis - with subjects ['manufacturing systems', 'swot', 'automation', 'service-oriented architectures']\n",
      "\n",
      "\n",
      "Industrial agent technology - with subjects ['multi-agent systems', 'industrial agents']\n",
      "\n",
      "\n",
      "Simulation of multi-agent manufacturing systems using agent-based modelling platforms - with subjects ['manufacturing systems', 'multi-agent systems', 'simulation', 'self-organization']\n",
      "\n",
      "\n",
      "An autonomous intelligent gateway infrastructure for in-field processing in precision viticulture - with subjects ['wireless sensor networks;', 'precision agriculture', 'site-specific management']\n",
      "\n",
      "\n",
      "Combining adaptation and optimization in bio-inspired multi-agent manufacturing systems - with subjects ['bio-inspiration', 'manufacturing systems', 'multi-agent systems', 'self-organization']\n",
      "\n",
      "\n",
      "Effects of gamma irradiation on sugars, fatty acids and tocopherols of chestnuts (Castanea sativa Miller) - with subjects ['fatty acids', 'gamma irradiation', 'irradiated chestnuts', 'tocopherols', 'sugars']\n",
      "\n",
      "\n",
      "Prática de ensino supervisionada em Ensino  Pré-Escolar e Ensino do 1º Ciclo do Ensino Básico - with subjects ['educa\\xc3\\xa7\\xc3\\xa3o pr\\xc3\\xa9-escolar', 'participa\\xc3\\xa7\\xc3\\xa3o ativa', 'ensino b\\xc3\\xa1sico', 'experi\\xc3\\xaancias de ensino-aprendizagem', 'crian\\xc3\\xa7as']\n",
      "\n",
      "\n",
      "Mycorrhizal induction of phenolic compounds and antioxidant properties of fungi and seedlings during the early steps of symbiosis. - with subjects ['antioxidant activity', 'phenolic compounds', 'symbiosis', 'ectomycorrhizal fungi', 'pinus pinaster seedlings']\n",
      "\n",
      "\n",
      "Infusions and decoctions of mixed herbs used in folk medicine: synergism in antioxidant potential - with subjects ['antioxidant activity', 'herbal mixtures', 'infusions/decoctions', 'synergistic effects']\n",
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "new_results = my_search.sort('-providerUpdatedDateTime').execute()\n",
    "\n",
    "for hit in new_results:\n",
    "    print(\n",
    "        '{} - with subjects {}\\n\\n'.format(\n",
    "            hit.title.encode('utf-8'),\n",
    "            [sub.encode('utf-8') for sub in hit.subjects]\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Debugging and Problem Solving\n",
    "\n",
    "Not everything always goes as planned when querying an unfamillar API.\n",
    "\n",
    "Here are some debugging and problem solving strategies when you're querying the SHARE API."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Start forming a search we're not too sure about\n",
    "\n",
    "We are interested in seeing how many results are specified as being in a language other than English"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "language_search = ShareSearch()\n",
    "\n",
    "language_search = language_search.query(\n",
    "    'query_string',\n",
    "    query='NOT languages=english'\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'Result' object has no attribute 'languages'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-23-f7e8b99a9f64>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mhit\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mresults\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m     \u001b[0;32mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mhit\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlanguages\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/elasticsearch_dsl/utils.pyc\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, attr_name)\u001b[0m\n\u001b[1;32m    107\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    108\u001b[0m             raise AttributeError(\n\u001b[0;32m--> 109\u001b[0;31m                 '%r object has no attribute %r' % (self.__class__.__name__, attr_name))\n\u001b[0m\u001b[1;32m    110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    111\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'Result' object has no attribute 'languages'"
     ]
    }
   ],
   "source": [
    "results = language_search.execute()\n",
    "\n",
    "for hit in results:\n",
    "    print(hit.languages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### That didn't look right.\n",
    "\n",
    "Let's look at our error:\n",
    "\n",
    "```AttributeError: 'Result' object has no attribute 'languages' ```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Building up the correct query\n",
    "\n",
    "Let's ry narrowing our query to only results that have a language attribute\n",
    "\n",
    "(Language is not required, so many results won't have this information."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "language_search = ShareSearch()\n",
    "\n",
    "language_search = language_search.filter(\n",
    "    'exists',\n",
    "    field=\"languages\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 213917/5297121 - or 4.04% documents with languages specified\n"
     ]
    }
   ],
   "source": [
    "results = language_search.execute()\n",
    "results_percent = (float(language_search.count())/basic_search.count())*100\n",
    "\n",
    "print('There are {}/{} - or {}% documents with languages specified'.format(\n",
    "        language_search.count(),\n",
    "        basic_search.count(),\n",
    "        format(results_percent, '.2f')\n",
    "    )\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the languages for the first 10 results:\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n",
      "[u'eng']\n"
     ]
    }
   ],
   "source": [
    "print('Here are the languages for the first 10 results:')\n",
    "\n",
    "for hit in results:\n",
    "    print(hit.languages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Referencing the SHARE Schema\n",
    "\n",
    "Simplified form here: https://github.com/CenterForOpenScience/SHARE-Schema/blob/master/share.yaml\n",
    "\n",
    "#### Section on languages:\n",
    "        languages:\n",
    "            description: |-\n",
    "                The primary languages in which the content of the resource is presented. Values used for this element MUST conform to ISO 639-3. This offers three letter tags e.g. \"eng\" for English.\n",
    "            type: array\n",
    "            items:\n",
    "                type: string\n",
    "                pattern: \"[a-z][a-z][a-z]\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Continuing to Refine our Query"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "from elasticsearch_dsl import Q\n",
    "\n",
    "language_search = language_search.query(~Q(\"term\", languages=\"eng\"))\n",
    "\n",
    "results = language_search.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17686 documents that do not have \"eng\" listed.\n",
      "Here are the languages for the first 10 results:\n",
      "[u'ger']\n",
      "[u'ger']\n",
      "[u'fre']\n",
      "[u'ita']\n",
      "[u'fre']\n",
      "[u'fre']\n",
      "[u'fre']\n",
      "[u'fre', u'lat']\n",
      "[u'ger']\n",
      "[u'ger']\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    'There are {} documents that do not have \"eng\" listed.'.format(\n",
    "        language_search.count()\n",
    "    )\n",
    ")\n",
    "\n",
    "print('Here are the languages for the first 10 results:')\n",
    "\n",
    "for hit in results:\n",
    "    print(hit.languages)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Complex Queries and Basic Visualization\n",
    "\n",
    "- How to use both basic HTTP requests and sharepa\n",
    "- Aggregations, or queries that will return summary statistics about the whole dataset.\n",
    "- simple data visualizations using pandas and matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Aggregations\n",
    "\n",
    "Aggregations let you quickly get summary statistics for all of SHARE results in one query."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Documents Per Source Missing Descriptions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "missing_descriptions_aggregation = {\n",
    "    \"query\": {\n",
    "        \"query_string\": {\n",
    "            \"analyze_wildcard\": True, \n",
    "            \"query\": \"NOT description:*\"\n",
    "        }\n",
    "    },\n",
    "    \"aggs\": {\n",
    "        \"sources\": {\n",
    "            \"terms\": {\n",
    "                \"field\": \"_type\", # A field where the SHARE source is stored                \n",
    "                \"min_doc_count\": 0, \n",
    "                \"size\": 0  # Will return all sources, regardless if there are results\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "crossref has 1006927 documents without descriptions\n",
      "datacite has 794719 documents without descriptions\n",
      "dataone has 267745 documents without descriptions\n",
      "pubmedcentral has 140986 documents without descriptions\n",
      "bhl has 36342 documents without descriptions\n",
      "rcaap has 24595 documents without descriptions\n",
      "biomedcentral has 22541 documents without descriptions\n",
      "iowaresearch has 20014 documents without descriptions\n",
      "scitech has 17131 documents without descriptions\n",
      "cyberleninka has 14124 documents without descriptions\n",
      "lshtm has 9300 documents without descriptions\n",
      "citeseerx has 8866 documents without descriptions\n",
      "uiucideals has 7604 documents without descriptions\n",
      "noaa_nodc has 7275 documents without descriptions\n",
      "smithsonian has 6674 documents without descriptions\n",
      "ucescholarship has 5643 documents without descriptions\n",
      "ghent has 5135 documents without descriptions\n",
      "springer has 3324 documents without descriptions\n",
      "doepages has 3302 documents without descriptions\n",
      "tdar has 3290 documents without descriptions\n",
      "plos has 2717 documents without descriptions\n",
      "uwashington has 2457 documents without descriptions\n",
      "figshare has 2426 documents without descriptions\n",
      "nih has 2061 documents without descriptions\n",
      "shareok has 1777 documents without descriptions\n",
      "scholarworks_umass has 1753 documents without descriptions\n",
      "erudit has 1734 documents without descriptions\n",
      "calpoly has 1360 documents without descriptions\n",
      "duke has 1176 documents without descriptions\n",
      "osf has 927 documents without descriptions\n",
      "umich has 926 documents without descriptions\n",
      "kent has 911 documents without descriptions\n",
      "wash_state_u has 836 documents without descriptions\n",
      "scholarsphere has 730 documents without descriptions\n",
      "scholarsbank has 678 documents without descriptions\n",
      "krex has 656 documents without descriptions\n",
      "cmu has 516 documents without descriptions\n",
      "vtech has 447 documents without descriptions\n",
      "upennsylvania has 371 documents without descriptions\n",
      "uow has 304 documents without descriptions\n",
      "mblwhoilibrary has 263 documents without descriptions\n",
      "neurovault has 253 documents without descriptions\n",
      "iastate has 252 documents without descriptions\n",
      "dash has 241 documents without descriptions\n",
      "clinicaltrials has 216 documents without descriptions\n",
      "valposcholar has 176 documents without descriptions\n",
      "waynestate has 168 documents without descriptions\n",
      "asu has 167 documents without descriptions\n",
      "scholarsarchiveosu has 158 documents without descriptions\n",
      "uky has 156 documents without descriptions\n",
      "chapman has 140 documents without descriptions\n",
      "texasstate has 123 documents without descriptions\n",
      "csuohio has 113 documents without descriptions\n",
      "calhoun has 101 documents without descriptions\n",
      "digitalhoward has 86 documents without descriptions\n",
      "opensiuc has 86 documents without descriptions\n",
      "trinity has 73 documents without descriptions\n",
      "npp_ksu has 70 documents without descriptions\n",
      "icpsr has 53 documents without descriptions\n",
      "oaktrust has 50 documents without descriptions\n",
      "udel has 50 documents without descriptions\n",
      "cuscholar has 49 documents without descriptions\n",
      "cuny has 43 documents without descriptions\n",
      "u_south_fl has 41 documents without descriptions\n",
      "caltech has 34 documents without descriptions\n",
      "pcom has 33 documents without descriptions\n",
      "iwu_commons has 32 documents without descriptions\n",
      "wustlopenscholarship has 30 documents without descriptions\n",
      "cambridge has 26 documents without descriptions\n",
      "ukansas has 22 documents without descriptions\n",
      "mit has 19 documents without descriptions\n",
      "unl_digitalcommons has 19 documents without descriptions\n",
      "addis_ababa has 17 documents without descriptions\n",
      "elife has 17 documents without descriptions\n",
      "utktrace has 17 documents without descriptions\n",
      "zenodo has 16 documents without descriptions\n",
      "ncar has 14 documents without descriptions\n",
      "huskiecommons has 12 documents without descriptions\n",
      "dryad has 10 documents without descriptions\n",
      "scholarscompass_vcu has 8 documents without descriptions\n",
      "stcloud has 7 documents without descriptions\n",
      "lwbin has 4 documents without descriptions\n",
      "pdxscholar has 3 documents without descriptions\n",
      "utaustin has 3 documents without descriptions\n",
      "columbia has 2 documents without descriptions\n",
      "hacettepe has 1 documents without descriptions\n",
      "usgs has 1 documents without descriptions\n",
      "arxiv_oai has 0 documents without descriptions\n",
      "cogprints has 0 documents without descriptions\n",
      "dailyssrn has 0 documents without descriptions\n",
      "fit has 0 documents without descriptions\n",
      "harvarddataverse has 0 documents without descriptions\n",
      "mason has 0 documents without descriptions\n",
      "nist has 0 documents without descriptions\n",
      "pcurio has 0 documents without descriptions\n",
      "purdue has 0 documents without descriptions\n",
      "sldr has 0 documents without descriptions\n",
      "spdataverse has 0 documents without descriptions\n",
      "ut_chattanooga has 0 documents without descriptions\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.py:768: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n",
      "  InsecureRequestWarning)\n"
     ]
    }
   ],
   "source": [
    "results_without_descriptions = query_share(OSF_APP_URL, missing_descriptions_aggregation)\n",
    "\n",
    "missing_descriptions_counts = results_without_descriptions['aggregations']['sources']['buckets']\n",
    "\n",
    "for source in missing_descriptions_counts:\n",
    "    print('{} has {} documents without descriptions'.format(source['key'], source['doc_count']))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Making Results More Useful\n",
    "\n",
    "Let's do that same query, but this time find the percentages of documents from each source insead of the numbers alone.\n",
    "\n",
    "We'll also leave out sources that have all of their descriptions to make the list more manageable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "sig_terms_agg = {\n",
    "    \"query\": {\n",
    "        \"query_string\": {\n",
    "            \"analyze_wildcard\": True, \n",
    "            \"query\": \"NOT description:*\"\n",
    "        }\n",
    "    },\n",
    "    \"aggs\": {\n",
    "        \"sources\":{\n",
    "            \"significant_terms\":{\n",
    "                \"field\": \"_type\", # A field where the SHARE source is stored                \n",
    "                \"min_doc_count\": 1, # Only results with more than one document\n",
    "                \"percentage\": {} # This will make the \"score\" parameter a percentage\n",
    "            }\n",
    "        }\n",
    "    }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/requests/packages/urllib3/connectionpool.py:768: InsecureRequestWarning: Unverified HTTPS request is being made. Adding certificate verification is strongly advised. See: https://urllib3.readthedocs.org/en/latest/security.html\n",
      "  InsecureRequestWarning)\n"
     ]
    }
   ],
   "source": [
    "docs_with_no_description_results = query_share(OSF_APP_URL, sig_terms_agg)\n",
    "docs_with_no_description = docs_with_no_description_results['aggregations']['sources']['buckets']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.00% (or 2061/2061) of documents from nih have no description\n",
      "92.85% (or 1006927/1084484) of documents from crossref have no description\n",
      "91.44% (or 22541/24651) of documents from biomedcentral have no description\n",
      "82.35% (or 14/17) of documents from ncar have no description\n",
      "76.97% (or 267745/347844) of documents from dataone have no description\n",
      "64.46% (or 927/1438) of documents from osf have no description\n",
      "61.64% (or 1734/2813) of documents from erudit have no description\n",
      "60.33% (or 730/1210) of documents from scholarsphere have no description\n",
      "59.86% (or 176/294) of documents from valposcholar have no description\n",
      "58.18% (or 32/55) of documents from iwu_commons have no description\n"
     ]
    }
   ],
   "source": [
    "for source in docs_with_no_description:\n",
    "    print(\n",
    "        '{}% (or {}/{}) of documents from {} have no description'.format(\n",
    "            format(source['score']*100, '.2f'),\n",
    "            source['doc_count'],\n",
    "            source['bg_count'],\n",
    "            source['key']\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Aggregations with sharepa\n",
    "\n",
    "Let's use sharepa to find out how many documents per source that do not have subjects"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificantTerms(field='_type', min_doc_count=1, percentage={}, size=0)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "no_subjects_search = ShareSearch()\n",
    "\n",
    "no_subjects_search = no_subjects_search.query(\n",
    "    'query_string',\n",
    "    query='NOT subjects:*',\n",
    "    analyze_wildcard=True  # This will make elasticsearch pay attention to the asterisk (which matches anything)\n",
    ")\n",
    "\n",
    "no_subjects_search.aggs.bucket(\n",
    "    'sources',  # Every aggregation needs a name\n",
    "    'significant_terms',  # There are many kinds of aggregations\n",
    "    field='_type',  # We store the source of a document in its type, so this will aggregate by source\n",
    "    min_doc_count=1,\n",
    "    percentage={},\n",
    "    size=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Examining the query\n",
    "\n",
    "Let's take a look at the query that sharepa generated, and we'll see that it looks a lot like the query we made by hand"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"query\": {\n",
      "        \"query_string\": {\n",
      "            \"analyze_wildcard\": true, \n",
      "            \"query\": \"NOT subjects:*\"\n",
      "        }\n",
      "    }, \n",
      "    \"aggs\": {\n",
      "        \"sources\": {\n",
      "            \"significant_terms\": {\n",
      "                \"field\": \"_type\", \n",
      "                \"percentage\": {}, \n",
      "                \"min_doc_count\": 1, \n",
      "                \"size\": 0\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pretty_print(no_subjects_search.to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "#### Executing the query\n",
    "\n",
    "Run the query and check out the results"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "scrolled": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100.00% of documents from fit do not have subjects\n",
      "100.00% of documents from nih do not have subjects\n",
      "99.77% of documents from dailyssrn do not have subjects\n",
      "98.37% of documents from dataone do not have subjects\n",
      "98.02% of documents from biomedcentral do not have subjects\n",
      "97.34% of documents from plos do not have subjects\n",
      "97.11% of documents from calhoun do not have subjects\n",
      "95.69% of documents from crossref do not have subjects\n",
      "95.66% of documents from figshare do not have subjects\n",
      "95.60% of documents from harvarddataverse do not have subjects\n",
      "94.78% of documents from osf do not have subjects\n",
      "89.16% of documents from springer do not have subjects\n",
      "87.02% of documents from digitalhoward do not have subjects\n",
      "82.56% of documents from caltech do not have subjects\n",
      "82.35% of documents from ncar do not have subjects\n",
      "76.36% of documents from erudit do not have subjects\n",
      "75.98% of documents from smithsonian do not have subjects\n",
      "72.16% of documents from clinicaltrials do not have subjects\n",
      "68.12% of documents from tdar do not have subjects\n",
      "66.51% of documents from datacite do not have subjects\n",
      "65.32% of documents from umich do not have subjects\n",
      "62.34% of documents from citeseerx do not have subjects\n",
      "60.85% of documents from lshtm do not have subjects\n",
      "60.33% of documents from scholarsphere do not have subjects\n",
      "59.34% of documents from ukansas do not have subjects\n",
      "58.08% of documents from scitech do not have subjects\n",
      "56.84% of documents from doepages do not have subjects\n",
      "48.68% of documents from waynestate do not have subjects\n",
      "47.09% of documents from dash do not have subjects\n",
      "45.10% of documents from shareok do not have subjects\n",
      "44.06% of documents from duke do not have subjects\n",
      "43.73% of documents from udel do not have subjects\n",
      "37.78% of documents from hacettepe do not have subjects\n",
      "37.37% of documents from cambridge do not have subjects\n",
      "36.62% of documents from uow do not have subjects\n",
      "36.18% of documents from scholarsarchiveosu do not have subjects\n",
      "34.69% of documents from mblwhoilibrary do not have subjects\n",
      "33.51% of documents from ucescholarship do not have subjects\n",
      "31.16% of documents from pcurio do not have subjects\n",
      "30.44% of documents from uiucideals do not have subjects\n",
      "25.81% of documents from u_south_fl do not have subjects\n",
      "24.98% of documents from bhl do not have subjects\n",
      "24.87% of documents from usgs do not have subjects\n",
      "24.23% of documents from uwashington do not have subjects\n",
      "21.43% of documents from cyberleninka do not have subjects\n",
      "20.25% of documents from opensiuc do not have subjects\n",
      "20.00% of documents from mason do not have subjects\n",
      "19.84% of documents from mit do not have subjects\n",
      "19.84% of documents from spdataverse do not have subjects\n",
      "18.63% of documents from unl_digitalcommons do not have subjects\n",
      "16.97% of documents from wash_state_u do not have subjects\n",
      "16.13% of documents from utktrace do not have subjects\n",
      "16.02% of documents from neurovault do not have subjects\n",
      "15.56% of documents from lwbin do not have subjects\n",
      "14.94% of documents from rcaap do not have subjects\n",
      "12.82% of documents from scholarsbank do not have subjects\n",
      "12.14% of documents from cuny do not have subjects\n",
      "11.48% of documents from wustlopenscholarship do not have subjects\n",
      "10.55% of documents from cuscholar do not have subjects\n",
      "9.38% of documents from texasstate do not have subjects\n",
      "9.11% of documents from calpoly do not have subjects\n",
      "9.01% of documents from oaktrust do not have subjects\n",
      "8.99% of documents from cmu do not have subjects\n",
      "8.70% of documents from iwu_commons do not have subjects\n",
      "8.09% of documents from upennsylvania do not have subjects\n",
      "7.75% of documents from vtech do not have subjects\n",
      "6.12% of documents from valposcholar do not have subjects\n",
      "5.53% of documents from krex do not have subjects\n",
      "4.98% of documents from scholarworks_umass do not have subjects\n",
      "4.59% of documents from scholarscompass_vcu do not have subjects\n",
      "4.50% of documents from asu do not have subjects\n",
      "3.99% of documents from chapman do not have subjects\n",
      "3.97% of documents from addis_ababa do not have subjects\n",
      "3.88% of documents from dryad do not have subjects\n",
      "3.85% of documents from iowaresearch do not have subjects\n",
      "3.69% of documents from iastate do not have subjects\n",
      "2.33% of documents from pdxscholar do not have subjects\n",
      "2.31% of documents from trinity do not have subjects\n",
      "2.20% of documents from ghent do not have subjects\n",
      "1.72% of documents from pcom do not have subjects\n",
      "1.69% of documents from ut_chattanooga do not have subjects\n",
      "1.20% of documents from uky do not have subjects\n",
      "0.99% of documents from csuohio do not have subjects\n",
      "0.98% of documents from pubmedcentral do not have subjects\n",
      "0.74% of documents from columbia do not have subjects\n",
      "0.61% of documents from icpsr do not have subjects\n",
      "0.59% of documents from zenodo do not have subjects\n",
      "0.56% of documents from huskiecommons do not have subjects\n",
      "0.44% of documents from kent do not have subjects\n",
      "0.03% of documents from utaustin do not have subjects\n"
     ]
    }
   ],
   "source": [
    "aggregated_results = no_subjects_search.execute()\n",
    "\n",
    "for source in aggregated_results.aggregations['sources']['buckets']:\n",
    "    print(\n",
    "        '{}% of documents from {} do not have subjects'.format(\n",
    "            format(source['score']*100, '.2f'),\n",
    "            source['key'] \n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Top Subjects Aggregation\n",
    "\n",
    "Let's do an elasticsearch query to find out what the most used subjects are used in the dataset across all sources."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Terms(exclude='of|and|or', field='subjects', min_doc_count=1, size=10)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "top_subjects_search = ShareSearch()\n",
    "\n",
    "top_subjects_search.aggs.bucket(\n",
    "    'subjectsTermFilter',  # Every aggregation needs a name\n",
    "    'terms',  # There are many kinds of aggregations\n",
    "    field='subjects',  # We store the source of a document in its type, so this will aggregate by source\n",
    "    min_doc_count=1,\n",
    "    exclude= \"of|and|or\",\n",
    "    size=10\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[\n",
      "    {\n",
      "        \"key\": \"article\", \n",
      "        \"doc_count\": 174865\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"physics\", \n",
      "        \"doc_count\": 125573\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"science\", \n",
      "        \"doc_count\": 92664\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"research\", \n",
      "        \"doc_count\": 87282\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"mathematics\", \n",
      "        \"doc_count\": 72899\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"high\", \n",
      "        \"doc_count\": 69610\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"engineering\", \n",
      "        \"doc_count\": 67738\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"astrophysics\", \n",
      "        \"doc_count\": 67017\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"energy\", \n",
      "        \"doc_count\": 66978\n",
      "    }, \n",
      "    {\n",
      "        \"key\": \"system\", \n",
      "        \"doc_count\": 63449\n",
      "    }\n",
      "]\n"
     ]
    }
   ],
   "source": [
    "top_subjects_results_executed = top_subjects_search.execute()\n",
    "top_subjects_results = top_subjects_results_executed.aggregations.subjectsTermFilter.to_dict()['buckets']\n",
    "\n",
    "pretty_print(top_subjects_results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Plotting\n",
    "\n",
    "Here are some simple plots using pandas and matplotlib\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Creating a Dataframe\n",
    "\n",
    "To create a plot, first we need to get the data into an appropriae format.\n",
    "\n",
    "Pandas, a python plotting library, has the DataFrame format, which is a lot like a spreadsheet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doc_count</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>174865</td>\n",
       "      <td>article</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>125573</td>\n",
       "      <td>physics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>92664</td>\n",
       "      <td>science</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>87282</td>\n",
       "      <td>research</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>72899</td>\n",
       "      <td>mathematics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>69610</td>\n",
       "      <td>high</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>67738</td>\n",
       "      <td>engineering</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>67017</td>\n",
       "      <td>astrophysics</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>66978</td>\n",
       "      <td>energy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>63449</td>\n",
       "      <td>system</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doc_count           key\n",
       "0     174865       article\n",
       "1     125573       physics\n",
       "2      92664       science\n",
       "3      87282      research\n",
       "4      72899   mathematics\n",
       "5      69610          high\n",
       "6      67738   engineering\n",
       "7      67017  astrophysics\n",
       "8      66978        energy\n",
       "9      63449        system"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "top_subjects_dataframe = pd.DataFrame(top_subjects_results)\n",
    "top_subjects_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plotting the Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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t7e2btG+3koKkDwHHsa65B1INYHjd/T1JZ/jLWNfEVF9ee85ewJOS+gE7R8QqScuAlrrn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\naaHb4wnKo6IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fd5e9d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib import pyplot\n",
    "%matplotlib inline\n",
    "\n",
    "top_subjects_dataframe.plot(kind='bar', x='key', y='doc_count')\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Complex Queries and Dataframes\n",
    "\n",
    "Let's make a new search, for all documents updated in the years 2012 to 2015 that contain the subject \"science.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "science_search = ShareSearch() #create search object\n",
    "science_search = science_search.filter( #apply filter to search\n",
    "    \"range\", #applied a range type filter\n",
    "    providerUpdatedDateTime={ #the feild in the data we compare\n",
    "        'gte':'2012-01-01', #hits must be greater than or equal to this date and...\n",
    "        'lte':'2015-12-31' #hits must be less than or equal to this date\n",
    "    }\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SignificantTerms(field='_type', min_doc_count=1, percentage={}, size=0)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "science_search = science_search.filter(\n",
    "     \"prefix\",\n",
    "     subjects=\"science\"\n",
    ")\n",
    "\n",
    "science_search.aggs.bucket(\n",
    "    'sources',\n",
    "    'significant_terms',\n",
    "    field='_type',\n",
    "    min_doc_count=1,\n",
    "    percentage={},\n",
    "    size=0\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Take a look at the query we've built"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\n",
      "    \"query\": {\n",
      "        \"filtered\": {\n",
      "            \"filter\": {\n",
      "                \"bool\": {\n",
      "                    \"must\": [\n",
      "                        {\n",
      "                            \"range\": {\n",
      "                                \"providerUpdatedDateTime\": {\n",
      "                                    \"gte\": \"2012-01-01\", \n",
      "                                    \"lte\": \"2015-12-31\"\n",
      "                                }\n",
      "                            }\n",
      "                        }, \n",
      "                        {\n",
      "                            \"prefix\": {\n",
      "                                \"subjects\": \"science\"\n",
      "                            }\n",
      "                        }\n",
      "                    ]\n",
      "                }\n",
      "            }, \n",
      "            \"query\": {\n",
      "                \"match_all\": {}\n",
      "            }\n",
      "        }\n",
      "    }, \n",
      "    \"aggs\": {\n",
      "        \"sources\": {\n",
      "            \"significant_terms\": {\n",
      "                \"field\": \"_type\", \n",
      "                \"percentage\": {}, \n",
      "                \"min_doc_count\": 1, \n",
      "                \"size\": 0\n",
      "            }\n",
      "        }\n",
      "    }\n",
      "}\n"
     ]
    }
   ],
   "source": [
    "pretty_print(science_search.to_dict())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Make the query, and graph the result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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QJYTCDoIg6BJCYQdBEHQJobCDIAi6hPASGQbCBTAIgnbQdIRtZtPM7FIzu8PMbjez/dP+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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10ff8f3d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "from matplotlib import pyplot\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "science_search_results = science_search.execute()\n",
    "\n",
    "science_results = science_search_results.aggregations.sources.to_dict()  \n",
    "science_data_frame = pd.DataFrame(science_results['buckets']) \n",
    "\n",
    "science_data_frame['percents'] = (science_data_frame['score'] * 100)\n",
    "\n",
    "science_data_frame[:30].plot(kind='bar', x='key', y='percents') # Limit to the first 30 results for readability\n",
    "\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Plot Number of Documents by Source\n",
    "\n",
    "We'll limit it to the top 30 sources to make sure that the graph is readable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Terms(field='_type', min_doc_count=0, size=0)"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sharepa import bucket_to_dataframe\n",
    "\n",
    "all_results = ShareSearch()\n",
    "\n",
    "all_results = all_results.query(\n",
    "    'query_string',\n",
    "    query='*',\n",
    "    analyze_wildcard=True\n",
    ")\n",
    "\n",
    "all_results.aggs.bucket(\n",
    "    'sources',\n",
    "    'terms',\n",
    "    field='_type',\n",
    "    size=0,\n",
    "    min_doc_count=0\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/erin/.virtualenvs/tuts/lib/python2.7/site-packages/ipykernel/__main__.py:10: FutureWarning: sort(columns=....) is deprecated, use sort_values(by=.....)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110062210>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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+wnpj3GTlOM7KjiuLz5X08Q3HcZyVHVcWKyHeC3EcZ2XDlcVKi/dCHMdZefAB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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11006c0d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_results = all_results.execute()\n",
    "\n",
    "all_results_frame = bucket_to_dataframe(\n",
    "    '# documents by source',\n",
    "    all_results.aggregations.sources.buckets\n",
    ")\n",
    "\n",
    "all_results_frame_sorted = all_results_frame.sort(\n",
    "    ascending=False,\n",
    "    columns='# documents by source'\n",
    ")\n",
    "\n",
    "all_results_frame_sorted[:30].plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Different Kinds of Charts\n",
    "\n",
    "Let's make a pie chart\n",
    "\n",
    "- Limited to 10 sources"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x110321c50>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gaX/+ZQlwr6puA/4kIsv9fUuBNyKnijreNOAbvEn65cDDgF3JfrVCLBlHiEc4hyza1vUo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\nvV5ElonIFyLyN3+f80Rkkb/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zVXWK/5p1QPRC+jZAH1Wt01SriOQ2g7dHw+AXIStUl4Mk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\npfm83QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11032be50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "all_results_frame_sorted[:10].plot(kind='pie', y=\"# documents by source\", legend=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## SHARE Data in the Wide World\n",
    "\n",
    "Here are some examples of how to get SHARE data into different formats"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exporting a DataFrame to csv and Excel\n",
    "\n",
    "Let's do a query and then export the results to different formats.\n",
    "\n",
    "We're interested in the number of documents from each source that have a description."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "description_search = ShareSearch()\n",
    "\n",
    "description_search = description_search.query(\n",
    "    'query_string', \n",
    "    query='description:*',\n",
    "    analyze_wildcard=True\n",
    ")\n",
    "\n",
    "description_search.aggs.bucket(\n",
    "    'sources',\n",
    "    'significant_terms',\n",
    "    field='_type',\n",
    "    min_doc_count=0,\n",
    "    percentage={},\n",
    "    size=0\n",
    ")\n",
    "\n",
    "description_results = description_search.execute()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Cleaning up our dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "description_dataframe = pd.DataFrame(description_results.aggregations.sources.to_dict()['buckets'])\n",
    "\n",
    "# We will add our own \"percent\" column to make things clearer\n",
    "description_dataframe['percent'] = (description_dataframe['score'] * 100)\n",
    "# And, drop the old score column\n",
    "description_dataframe = description_dataframe.drop('score', 1)\n",
    "\n",
    "# Let's set the source name as the index, and then drop the old column\n",
    "description_dataframe = description_dataframe.set_index(description_dataframe['key'])\n",
    "description_dataframe = description_dataframe.drop('key', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>bg_count</th>\n",
       "      <th>doc_count</th>\n",
       "      <th>percent</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>key</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mason</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>fit</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>nist</th>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>100.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dailyssrn</th>\n",
       "      <td>6894</td>\n",
       "      <td>6878</td>\n",
       "      <td>99.767914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pcurio</th>\n",
       "      <td>12070</td>\n",
       "      <td>12041</td>\n",
       "      <td>99.759735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ut_chattanooga</th>\n",
       "      <td>280</td>\n",
       "      <td>279</td>\n",
       "      <td>99.642857</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>addis_ababa</th>\n",
       "      <td>1916</td>\n",
       "      <td>1878</td>\n",
       "      <td>98.016701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>columbia</th>\n",
       "      <td>1885</td>\n",
       "      <td>1846</td>\n",
       "      <td>97.931034</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarscompass_vcu</th>\n",
       "      <td>414</td>\n",
       "      <td>402</td>\n",
       "      <td>97.101449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>krex</th>\n",
       "      <td>30028</td>\n",
       "      <td>29054</td>\n",
       "      <td>96.756361</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>asu</th>\n",
       "      <td>13392</td>\n",
       "      <td>12804</td>\n",
       "      <td>95.609319</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>harvarddataverse</th>\n",
       "      <td>1932</td>\n",
       "      <td>1847</td>\n",
       "      <td>95.600414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>hacettepe</th>\n",
       "      <td>45</td>\n",
       "      <td>43</td>\n",
       "      <td>95.555556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>calhoun</th>\n",
       "      <td>4288</td>\n",
       "      <td>4085</td>\n",
       "      <td>95.265858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>figshare</th>\n",
       "      <td>571002</td>\n",
       "      <td>543820</td>\n",
       "      <td>95.239596</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mblwhoilibrary</th>\n",
       "      <td>8058</td>\n",
       "      <td>7670</td>\n",
       "      <td>95.184909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>oaktrust</th>\n",
       "      <td>1665</td>\n",
       "      <td>1569</td>\n",
       "      <td>94.234234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarsbank</th>\n",
       "      <td>11610</td>\n",
       "      <td>10759</td>\n",
       "      <td>92.670112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>citeseerx</th>\n",
       "      <td>204638</td>\n",
       "      <td>189221</td>\n",
       "      <td>92.466209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>elife</th>\n",
       "      <td>515</td>\n",
       "      <td>476</td>\n",
       "      <td>92.427184</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>plos</th>\n",
       "      <td>44742</td>\n",
       "      <td>40834</td>\n",
       "      <td>91.265478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unl_digitalcommons</th>\n",
       "      <td>1589</td>\n",
       "      <td>1439</td>\n",
       "      <td>90.560101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarsarchiveosu</th>\n",
       "      <td>3148</td>\n",
       "      <td>2850</td>\n",
       "      <td>90.533672</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>vtech</th>\n",
       "      <td>7789</td>\n",
       "      <td>7013</td>\n",
       "      <td>90.037232</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dash</th>\n",
       "      <td>5649</td>\n",
       "      <td>5073</td>\n",
       "      <td>89.803505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>zenodo</th>\n",
       "      <td>23609</td>\n",
       "      <td>21052</td>\n",
       "      <td>89.169385</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>caltech</th>\n",
       "      <td>15198</td>\n",
       "      <td>13401</td>\n",
       "      <td>88.176076</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>purdue</th>\n",
       "      <td>8</td>\n",
       "      <td>7</td>\n",
       "      <td>87.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>arxiv_oai</th>\n",
       "      <td>345590</td>\n",
       "      <td>301627</td>\n",
       "      <td>87.278856</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cyberleninka</th>\n",
       "      <td>126463</td>\n",
       "      <td>110034</td>\n",
       "      <td>87.008848</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rcaap</th>\n",
       "      <td>208936</td>\n",
       "      <td>118826</td>\n",
       "      <td>56.871961</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tdar</th>\n",
       "      <td>8690</td>\n",
       "      <td>4934</td>\n",
       "      <td>56.777906</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>texasstate</th>\n",
       "      <td>341</td>\n",
       "      <td>189</td>\n",
       "      <td>55.425220</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>npp_ksu</th>\n",
       "      <td>385</td>\n",
       "      <td>207</td>\n",
       "      <td>53.766234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scitech</th>\n",
       "      <td>253571</td>\n",
       "      <td>130142</td>\n",
       "      <td>51.323692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>umich</th>\n",
       "      <td>1975</td>\n",
       "      <td>991</td>\n",
       "      <td>50.177215</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uwashington</th>\n",
       "      <td>25230</td>\n",
       "      <td>12508</td>\n",
       "      <td>49.575902</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>shareok</th>\n",
       "      <td>41689</td>\n",
       "      <td>20359</td>\n",
       "      <td>48.835424</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ucescholarship</th>\n",
       "      <td>35781</td>\n",
       "      <td>16838</td>\n",
       "      <td>47.058495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lshtm</th>\n",
       "      <td>49292</td>\n",
       "      <td>20695</td>\n",
       "      <td>41.984501</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iwu_commons</th>\n",
       "      <td>55</td>\n",
       "      <td>22</td>\n",
       "      <td>40.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ghent</th>\n",
       "      <td>9136</td>\n",
       "      <td>3601</td>\n",
       "      <td>39.415499</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>noaa_nodc</th>\n",
       "      <td>13942</td>\n",
       "      <td>5442</td>\n",
       "      <td>39.033137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>datacite</th>\n",
       "      <td>1485691</td>\n",
       "      <td>542769</td>\n",
       "      <td>36.533101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>valposcholar</th>\n",
       "      <td>294</td>\n",
       "      <td>107</td>\n",
       "      <td>36.394558</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>scholarworks_umass</th>\n",
       "      <td>3634</td>\n",
       "      <td>1272</td>\n",
       "      <td>35.002752</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>iowaresearch</th>\n",
       "      <td>36325</td>\n",
       "      <td>12131</td>\n",
       "      <td>33.395733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>osf</th>\n",
       "      <td>1438</td>\n",
       "      <td>436</td>\n",
       "      <td>30.319889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kent</th>\n",
       "      <td>1762</td>\n",
       "      <td>466</td>\n",
       "      <td>26.447219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>smithsonian</th>\n",
       "      <td>12548</td>\n",
       "      <td>3262</td>\n",
       "      <td>25.996175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>duke</th>\n",
       "      <td>2106</td>\n",
       "      <td>544</td>\n",
       "      <td>25.830959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>doepages</th>\n",
       "      <td>10220</td>\n",
       "      <td>2507</td>\n",
       "      <td>24.530333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>usgs</th>\n",
       "      <td>193</td>\n",
       "      <td>47</td>\n",
       "      <td>24.352332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>erudit</th>\n",
       "      <td>2813</td>\n",
       "      <td>670</td>\n",
       "      <td>23.817988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataone</th>\n",
       "      <td>347844</td>\n",
       "      <td>74422</td>\n",
       "      <td>21.395223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icpsr</th>\n",
       "      <td>49511</td>\n",
       "      <td>9446</td>\n",
       "      <td>19.078589</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lwbin</th>\n",
       "      <td>270</td>\n",
       "      <td>38</td>\n",
       "      <td>14.074074</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>biomedcentral</th>\n",
       "      <td>24651</td>\n",
       "      <td>1623</td>\n",
       "      <td>6.583911</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neurovault</th>\n",
       "      <td>2485</td>\n",
       "      <td>145</td>\n",
       "      <td>5.835010</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>crossref</th>\n",
       "      <td>1084484</td>\n",
       "      <td>30778</td>\n",
       "      <td>2.838032</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>96 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                     bg_count  doc_count     percent\n",
       "key                                                 \n",
       "mason                      25         25  100.000000\n",
       "fit                         5          5  100.000000\n",
       "nist                        3          3  100.000000\n",
       "dailyssrn                6894       6878   99.767914\n",
       "pcurio                  12070      12041   99.759735\n",
       "ut_chattanooga            280        279   99.642857\n",
       "addis_ababa              1916       1878   98.016701\n",
       "columbia                 1885       1846   97.931034\n",
       "scholarscompass_vcu       414        402   97.101449\n",
       "krex                    30028      29054   96.756361\n",
       "asu                     13392      12804   95.609319\n",
       "harvarddataverse         1932       1847   95.600414\n",
       "hacettepe                  45         43   95.555556\n",
       "calhoun                  4288       4085   95.265858\n",
       "figshare               571002     543820   95.239596\n",
       "mblwhoilibrary           8058       7670   95.184909\n",
       "oaktrust                 1665       1569   94.234234\n",
       "scholarsbank            11610      10759   92.670112\n",
       "citeseerx              204638     189221   92.466209\n",
       "elife                     515        476   92.427184\n",
       "plos                    44742      40834   91.265478\n",
       "unl_digitalcommons       1589       1439   90.560101\n",
       "scholarsarchiveosu       3148       2850   90.533672\n",
       "vtech                    7789       7013   90.037232\n",
       "dash                     5649       5073   89.803505\n",
       "zenodo                  23609      21052   89.169385\n",
       "caltech                 15198      13401   88.176076\n",
       "purdue                      8          7   87.500000\n",
       "arxiv_oai              345590     301627   87.278856\n",
       "cyberleninka           126463     110034   87.008848\n",
       "...                       ...        ...         ...\n",
       "rcaap                  208936     118826   56.871961\n",
       "tdar                     8690       4934   56.777906\n",
       "texasstate                341        189   55.425220\n",
       "npp_ksu                   385        207   53.766234\n",
       "scitech                253571     130142   51.323692\n",
       "umich                    1975        991   50.177215\n",
       "uwashington             25230      12508   49.575902\n",
       "shareok                 41689      20359   48.835424\n",
       "ucescholarship          35781      16838   47.058495\n",
       "lshtm                   49292      20695   41.984501\n",
       "iwu_commons                55         22   40.000000\n",
       "ghent                    9136       3601   39.415499\n",
       "noaa_nodc               13942       5442   39.033137\n",
       "datacite              1485691     542769   36.533101\n",
       "valposcholar              294        107   36.394558\n",
       "scholarworks_umass       3634       1272   35.002752\n",
       "iowaresearch            36325      12131   33.395733\n",
       "osf                      1438        436   30.319889\n",
       "kent                     1762        466   26.447219\n",
       "smithsonian             12548       3262   25.996175\n",
       "duke                     2106        544   25.830959\n",
       "doepages                10220       2507   24.530333\n",
       "usgs                      193         47   24.352332\n",
       "erudit                   2813        670   23.817988\n",
       "dataone                347844      74422   21.395223\n",
       "icpsr                   49511       9446   19.078589\n",
       "lwbin                     270         38   14.074074\n",
       "biomedcentral           24651       1623    6.583911\n",
       "neurovault               2485        145    5.835010\n",
       "crossref              1084484      30778    2.838032\n",
       "\n",
       "[96 rows x 3 columns]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finally, we'll show the results!\n",
    "description_dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Exporting to CSV and Excel formats\n",
    "\n",
    "Pandas has handy tools built in that makes converting a dataframe very easy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "description_dataframe.to_csv('exported_data/SHARE_Counts_with_Descriptions.csv')\n",
    "description_dataframe.to_excel('exported_data/SHARE_Counts_with_Descriptions.xlsx')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Working with Outside Data\n",
    "\n",
    "Here's a quick example of how you could work with a list of names, and use them to see what information is in SHARE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "names = [\"Susan Jones\", \"Ravi Patel\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "name_search = ShareSearch()\n",
    "\n",
    "for name in names:\n",
    "    name_search = name_search.query(\n",
    "        {\n",
    "            \"bool\": {\n",
    "                \"should\": [\n",
    "                    {\n",
    "                        \"match\": {\n",
    "                            \"contributors.name\": {\n",
    "                                \"query\": name, \n",
    "                                \"operator\": \"and\",\n",
    "                                \"type\" : \"phrase\"\n",
    "                            }\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    )\n",
    "\n",
    "\n",
    "name_results = name_search.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 38 documents with contributors who have any of those names.\n",
      "Here are the first 10:\n",
      "---------\n",
      "Short- and Long-Term Outcomes for Extremely Preterm Infants -- with contributors Ravi Patel\n",
      "\"Prospective, Randomized, Multi-Center, Efficacy Non-inferiority Study of MEDIHONEY® Gel Versus Collagenase for Wound Debridement\" -- with contributors Ravi Patel, MD\n",
      "Obstetrical and Neonatal Perspectives on Prematurity -- with contributors Tracy Manuck, Ravi Patel\n",
      "Representative structures of bHLH proteins from the Protein Data Bank -- with contributors Susan Jones\n",
      "Evolutionary tree for SMC proteins, created using PHYLIP 69,70 -- with contributors Susan Jones, John Sgouros\n",
      "Assessing the Fitness of an Academic Library for Doctoral Research -- with contributors Edwards, Susan ; Jones, Lynn ;\n",
      "‘It’s not what it looks like. I’m Santa’: Connecting Community through Film -- with contributors Susan Jones, Joanna McIntyre\n",
      "The cohesion interaction network -- with contributors Susan Jones, John Sgouros\n",
      "Structure of an SMC protein -- with contributors Susan Jones, John Sgouros\n",
      "Sequence alignments for three motifs shared by proteins in the cohesion network -- with contributors Susan Jones, John Sgouros\n"
     ]
    }
   ],
   "source": [
    "print(\n",
    "    'There are {} documents with contributors who have any of those names.'.format(\n",
    "        name_search.count()\n",
    "    )\n",
    ")\n",
    "\n",
    "print('Here are the first 10:')\n",
    "print('---------')\n",
    "for result in name_results:\n",
    "    print(\n",
    "        '{} -- with contributors {}'.format(\n",
    "            result.title.encode('utf-8'),\n",
    "            ', '.join([contributor.name.encode('utf-8') for contributor in result.contributors])\n",
    "        )\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Where did these results come from?\n",
    "\n",
    "We can add an aggregation!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>doc_count</th>\n",
       "      <th>key</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>12</td>\n",
       "      <td>datacite</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7</td>\n",
       "      <td>pubmedcentral</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>clinicaltrials</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>arxiv_oai</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1</td>\n",
       "      <td>citeseerx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1</td>\n",
       "      <td>figshare</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>mblwhoilibrary</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>utaustin</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   doc_count             key\n",
       "0         12        datacite\n",
       "1         11        crossref\n",
       "2          7   pubmedcentral\n",
       "3          3  clinicaltrials\n",
       "4          1       arxiv_oai\n",
       "5          1       citeseerx\n",
       "6          1        figshare\n",
       "7          1  mblwhoilibrary\n",
       "8          1        utaustin"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "name_search.aggs.bucket(\n",
    "    'sources',  # Every aggregation needs a name\n",
    "    'terms',  # There are many kinds of aggregations, terms is a pretty useful one though\n",
    "    field='_type',  # We store the source of a document in its type, so this will aggregate by source\n",
    "    size=0,  # These are just to make sure we get numbers for all the sources, to make it easier to combine graphs\n",
    "    min_doc_count=1\n",
    ")\n",
    "\n",
    "name_results = name_search.execute()\n",
    "\n",
    "pd.DataFrame(name_results.aggregations.sources.to_dict()['buckets'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Searching by ORCID "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "orcids = [\n",
    "    'http://orcid.org/0000-0003-1942-4543',\n",
    "    'http://orcid.org/0000-0003-4875-1447',\n",
    "    'http://orcid.org/0000-0002-6085-4433',\n",
    "    'http://orcid.org/0000-0002-7995-9948',\n",
    "    'http://orcid.org/0000-0002-2170-853X',\n",
    "    'http://orcid.org/0000-0002-8899-9087'\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "orcid_search = ShareSearch()\n",
    "\n",
    "for orcid in orcids:\n",
    "    orcid_search = orcid_search.query(\n",
    "        {\n",
    "            \"bool\": {\n",
    "                \"should\": [\n",
    "                    {\n",
    "                        \"match\": {\n",
    "                            \"contributors.sameAs\": {\n",
    "                                \"query\": orcid, \n",
    "                                \"operator\": \"and\",\n",
    "                                \"type\" : \"phrase\"\n",
    "                            }\n",
    "                        }\n",
    "                    }\n",
    "                ]\n",
    "            }\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [],
   "source": [
    "orcid_search.aggs.bucket(\n",
    "    'sources',\n",
    "    'terms',\n",
    "    field='_type',\n",
    "    size=0,\n",
    "    min_doc_count=1\n",
    ")\n",
    "\n",
    "orcid_results = orcid_search.execute()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": false,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 12 documents with contributors who have any of those orcids.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>title</th>\n",
       "      <th>docID</th>\n",
       "      <th>source</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Widespread shortening of 3' untranslated regio...</td>\n",
       "      <td>10.1101/026831</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>COMADRE: a global database of animal demography</td>\n",
       "      <td>10.1101/027821</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A psychometric analysis of outcome measures in...</td>\n",
       "      <td>10.1136/annrheumdis-2014-207235</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Light-induced indeterminacy alters shade avoid...</td>\n",
       "      <td>10.1101/024018</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Optimisation of a treat-to-target approach in ...</td>\n",
       "      <td>10.1136/annrheumdis-2015-208324</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>A selfish genetic element drives recurring sel...</td>\n",
       "      <td>10.1101/024851</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Pharmacological treatment of psoriatic arthrit...</td>\n",
       "      <td>10.1136/annrheumdis-2015-208466</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>The many evolutionary fates of a large segment...</td>\n",
       "      <td>10.1101/043687</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>In wealthier countries, patients perceive wors...</td>\n",
       "      <td>10.1136/annrheumdis-2015-207738</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>The changing landscape of biosimilars in rheum...</td>\n",
       "      <td>10.1136/annrheumdis-2016-209166</td>\n",
       "      <td>crossref</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                               title  \\\n",
       "0  Widespread shortening of 3' untranslated regio...   \n",
       "1    COMADRE: a global database of animal demography   \n",
       "2  A psychometric analysis of outcome measures in...   \n",
       "3  Light-induced indeterminacy alters shade avoid...   \n",
       "4  Optimisation of a treat-to-target approach in ...   \n",
       "5  A selfish genetic element drives recurring sel...   \n",
       "6  Pharmacological treatment of psoriatic arthrit...   \n",
       "7  The many evolutionary fates of a large segment...   \n",
       "8  In wealthier countries, patients perceive wors...   \n",
       "9  The changing landscape of biosimilars in rheum...   \n",
       "\n",
       "                             docID    source  \n",
       "0                   10.1101/026831  crossref  \n",
       "1                   10.1101/027821  crossref  \n",
       "2  10.1136/annrheumdis-2014-207235  crossref  \n",
       "3                   10.1101/024018  crossref  \n",
       "4  10.1136/annrheumdis-2015-208324  crossref  \n",
       "5                   10.1101/024851  crossref  \n",
       "6  10.1136/annrheumdis-2015-208466  crossref  \n",
       "7                   10.1101/043687  crossref  \n",
       "8  10.1136/annrheumdis-2015-207738  crossref  \n",
       "9  10.1136/annrheumdis-2016-209166  crossref  "
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(\n",
    "    'There are {} documents with contributors who have any of those orcids.'.format(\n",
    "        orcid_search.count()\n",
    "    )\n",
    ")\n",
    "\n",
    "all_agg_df = pd.DataFrame()\n",
    "all_agg_df['title'] = [result.title for result in orcid_results]\n",
    "all_agg_df['docID'] = [result.shareProperties.docID for result in orcid_results]\n",
    "all_agg_df['source'] = [result.shareProperties.source for result in orcid_results]\n",
    "all_agg_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## This is just the surface!\n",
    "\n",
    "The SHARE API has the potential to answer many questions about our data\n",
    "\n",
    "Data curation and enhancement will only make these analasyes more interesting."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Thank you!\n",
    "\n",
    "## Questions?\n",
    "\n",
    "**email**: erin@cos.io\n",
    "\n",
    "*SHARE Technical Documentation and Information*: https://osf.io/t3j94/\n",
    "\n",
    "*these slides and example notebooks*:  https://osf.io/bygau/"
   ]
  }
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