GitHub - AEADataEditor/processing-jira-process-data: Code to pre-process data extracted from the AEA's verification system

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Process Data from Reproducibility Service

Lars Vilhuber

2025-12-02

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Lars Vilhuber

Linda Wang

Takshil Sachdev

Note: The PDF version of this document is transformed by manually printing from a browser.

Overview

This README describes how to process data for the AEA Pre-publication Verification Service. The code constructs the analysis file from raw process data extracted from Jira using an API. The replicator should expect the code to run for approximately 2 hours.

Data Availability and Provenance Statements

Data used originates from Jira system used by the AEA data editor and the members of his replication lab.

Statement about Rights

I certify that the author(s) of the manuscript have legitimate access to and permission to use the data used in this manuscript.
I certify that the author(s) of the manuscript have documented permission to redistribute/publish the data contained within this replication package.

Summary of Availability

Some data cannot be made publicly available.
Confidential data used are not provided as part of the public replication package.

Details on the Data

Raw process data

Raw process data from each step of the workflow is extracted from Jira using API (see Instructions to Replicators for details), and saved as issue_history_MM-DD-YYYY.csv (for detailed transaction-level data)

The data is not made available outside of the organization, as it contains names of replicators, manuscript numbers, and verbatim email correspondence. An anonymized version without identifying information is made available instead.

To obtain, run programs/01_download_issues.py. This will use the fields as specified in data/metadata/jira-fields.xlsx. If fields need to be updated (they are keyed on names), run programs/00_jira_fields.py to obtain a new CSV file, and mark the to-be-included fields with "True".

A full download of JIRA issues as of 2025 would be

> python3 01_download_issues.py -s 2018-01-01 -e 2024-11-30
Summary:
- Start Date: 2018-01-01
- End Date: 2024-11-30

 About to extract all issue history between these dates from https://aeadataeditors.atlassian.net.
 The output will be written to /home/rstudio/data/confidential/issue_history_2024-12-03.csv.

At this time, the latest extract was made 2025-12-02.

Anonymized data

We subset the raw data to variables of interest, and substitute random numbers for sensitive strings. This is done by running 02_jira_anonymize.R. The programs saves both the confidential version and the anonymized version.

source(file.path(programs,"02_jira_anonymize.R"),echo=TRUE)

## 
## > source(here::here("programs", "config.R"), echo = TRUE)
## 
## > process_raw <- TRUE
## 
## > download_raw <- TRUE
## 
## > extractday <- "2025-12-02"
## 
## > firstday <- "2024-12-01"
## 
## > lastday <- "2025-11-30"
## 
## > basepath <- here::here()
## 
## > setwd(basepath)
## 
## > jiraconf <- file.path(basepath, "data", "confidential")
## 
## > jiraanon <- file.path(basepath, "data", "anon")
## 
## > jirameta <- file.path(basepath, "data", "metadata")
## 
## > images <- file.path(basepath, "images")
## 
## > tables <- file.path(basepath, "tables")
## 
## > programs <- file.path(basepath, "programs")
## 
## > temp <- file.path(basepath, "data", "temp")
## 
## > for (dir in list(images, tables, programs, temp)) {
## +     if (file.exists(dir)) {
## +     }
## +     else {
## +         dir.create(file.path(dir))
## +     }
##  .... [TRUNCATED] 
## 
## > issue_history.prefix <- "issue_history_"
## 
## > manuscript.lookup <- "mc-lookup"
## 
## > manuscript.lookup.rds <- file.path(jiraconf, paste0(manuscript.lookup, 
## +     ".RDS"))
## 
## > assignee.lookup <- "assignee-lookup"
## 
## > assignee.lookup.rds <- file.path(jiraconf, paste0(assignee.lookup, 
## +     ".RDS"))
## 
## > jira.conf.plus.base <- "jira.conf.plus"
## 
## > jira.conf.plus.rds <- file.path(jiraconf, paste0(jira.conf.plus.base, 
## +     ".RDS"))
## 
## > jira.conf.names.csv <- "jira_conf_names.csv"
## 
## > members.txt <- file.path(jiraanon, "replicationlab_members.txt")
## 
## > jira.anon.base <- "jira.anon"
## 
## > jira.anon.rds <- file.path(jiraanon, paste0(jira.anon.base, 
## +     ".RDS"))
## 
## > jira.anon.csv <- file.path(jiraanon, paste0(jira.anon.base, 
## +     ".csv"))
## 
## > if (file.exists(here::here("programs", "confidential-config.R"))) {
## +     source(here::here("programs", "confidential-config.R"))
## +     message("Con ..." ... [TRUNCATED]

## Confidential config found.

## 
## > source(here::here("global-libraries.R"), echo = TRUE)
## 
## > ppm.date <- "2023-11-01"
## 
## > options(repos = paste0("https://packagemanager.posit.co/cran/", 
## +     ppm.date, "/"))
## 
## > global.libraries <- c("dplyr", "stringr", "tidyr", 
## +     "knitr", "readr", "here", "splitstackshape", "boxr", "jose", 
## +     "rmarkdown", "tidylog" .... [TRUNCATED] 
## 
## > pkgTest <- function(x) {
## +     if (!require(x, character.only = TRUE)) {
## +         install.packages(x, dep = TRUE)
## +         if (!require(x, charact .... [TRUNCATED] 
## 
## > pkgTest.github <- function(x, source) {
## +     if (!require(x, character.only = TRUE)) {
## +         install_github(paste(source, x, sep = "/"))
## +      .... [TRUNCATED] 
## 
## > results <- sapply(as.list(global.libraries), pkgTest)
## 
## > exportfile <- paste0(issue_history.prefix, extractday, 
## +     ".csv")
## 
## > if (!file.exists(file.path(jiraconf, exportfile))) {
## +     process_raw = FALSE
## +     print("Input file for anonymization not found - setting global  ..." ... [TRUNCATED] 
## 
## > if (process_raw == TRUE) {
## +     jira.conf.raw <- read.csv(file.path(jiraconf, exportfile), 
## +         stringsAsFactors = FALSE) %>% rename(ticket = .... [TRUNCATED]

## rename: renamed one variable (ticket)

## mutate: new variable 'mc_number' (character) with 552 unique values and 0% NA

## mutate: changed 87 values (<1%) of 'mc_number' (0 new NA)

## filter: no rows removed
## filter: no rows removed

## select: dropped one variable (Key.1)

## filter: no rows removed

## filter: removed all rows (100%)

## select: dropped 43 variables (DataAccessReplicationTeam, Resolution, Agreement.signed, JournalIssueMonth, JournalIssueYear, …)

## mutate: no changes

## select: dropped one variable (Sub.tasks)

## distinct: no rows removed

## Joining with `by = join_by(ticket)`
## anti_join: added no columns
## > rows only in x 26,940
## > rows only in y ( 0)
## > matched rows ( 0)
## > ========
## > rows total 26,940
## select: dropped 45 variables (DataAccessReplicationTeam, Resolution,
## Agreement.signed, JournalIssueMonth, JournalIssueYear, …)
## filter: removed 1,565 rows (6%), 25,375 rows remaining
## distinct: removed 24,822 rows (98%), 553 rows remaining
## mutate: new variable 'rand' (double) with one unique value and 0% NA
## mutate: new variable 'mc_number_anon' (integer) with 553 unique values and 0%
## NA
## select: dropped one variable (rand)
## select: dropped 45 variables (DataAccessReplicationTeam, Resolution,
## Agreement.signed, JournalIssueMonth, JournalIssueYear, …)
## filter: removed 10,160 rows (38%), 16,780 rows remaining
## filter: no rows removed
## filter: removed 1,181 rows (7%), 15,599 rows remaining
## distinct: removed 15,540 rows (>99%), 59 rows remaining
## mutate: new variable 'rand' (double) with 2 unique values and 0% NA
## mutate: new variable 'assignee_anon' (integer) with 59 unique values and 0% NA
## select: dropped one variable (rand)
## Rows: 23 Columns: 2
## ── Column specification
## ──────────────────────────────────────────────────────── Delimiter: "," chr
## (2): name, label
## ℹ Use `spec()` to retrieve the full column specification for this data. ℹ
## Specify the column types or set `show_col_types = FALSE` to quiet this message.
## filter: no rows removed
## select: dropped one variable (label)
## left_join: added one column (mc_number_anon)
## > rows only in x 1,565
## > rows only in y ( 0)
## > matched rows 25,375
## > ========
## > rows total 26,940
## left_join: added one column (assignee_anon)
## > rows only in x 11,341
## > rows only in y ( 0)
## > matched rows 15,599
## > ========
## > rows total 26,940
## mutate: new variable 'date_created' (Date) with 220 unique values and 0% NA
## new variable 'date_asof' (Date) with 349 unique values and 0% NA
## rename: renamed one variable (reason.failure)
## rename: renamed one variable (external)
## rename: renamed one variable (subtask)
## mutate: new variable 'date_resolved' (Date) with 249 unique values and 6% NA
## mutate: new variable 'received' (character) with 2 unique values and 0% NA
## mutate: new variable 'has_subtask' (character) with 2 unique values and 0% NA
## rename: renamed one variable (External.party.name.conf)
## filter: removed 7,814 rows (29%), 19,126 rows remaining
## select: dropped 51 variables (DataAccessReplicationTeam, Resolution,
## Agreement.signed, JournalIssueMonth, JournalIssueYear, …)
## mutate: changed 26,338 values (58%) of 'subtask' (0 new NA)
## select: dropped one variable (subtask)
## distinct: removed 44,483 rows (98%), 981 rows remaining
## filter: removed 1,565 rows (6%), 25,375 rows remaining
## Joining with `by = join_by(ticket)`
## anti_join: added no columns
## > rows only in x 25,375
## > rows only in y ( 981)
## > matched rows ( 0)
## > ========
## > rows total 25,375
## select: dropped 26 variables (DataAccessReplicationTeam, JournalIssueMonth,
## JournalIssueYear, Original.OS, External.party.name.conf, …)
## select: dropped 4 variables (Manuscript.Central.identifier, mc_number,
## Assignee, openICPSR.Project.Number)

Publishing data

Some additional cleaning and matching, and then we write out the file

source(file.path(programs,"10_jira_anon_publish.R"),echo=TRUE)

Finally, we push the confidential data to Box, using the following code, which we specifically run manually:

cd programs
R CMD BATCH 99_push_box.R

Describing the Data

The anonymized data has 23 columns.

Variables

name	label
ticket	The tracking number within the system. Project specific. Sequentially assigned upon receipt.
mc_number_anon	The (anonymized) number assigned by the editorial workflow system (Manuscript Central/ ScholarOne) to a manuscript. This is purged by a script of any revision suffixes.
assignee_anon	Anonymized assignee name (time-varying)
date_created	Creation date of issue
received	An indicator for whether the issue is just created and has not been assigned to a replicator yet.
Journal	Journal associated with an issue and manuscript. Derived from the manuscript number. Possibly updated by hand
Status	Status associated with a ticket at any point in time. The schema for these has changed over time.
external	An indicator for whether the issue required external validation.
Resolution	Resolution associated with a ticket at the end of the reproducibility check.
reason.failure	A list of reasons for failure to fully reproduce.
MCRecommendation	Decision status when the issue is Revise and Resubmit.
MCRecommendationV2	Decision status when the issue is conditionally accepted.
External.party.name	Name of the external party. Usually only institutional names.
Non.compliant	An indicator for whether the issue is non-compliant for some reason.
DCAF_Access_Restrictions	Category of Access Restrictions (2 categories)
DCAF_Access_Restrictions_V2	Category of Access Restrictions (4 categories)
Update.type	Who initiated the need to update the replication package
Software.used	Manually coded software used in the replication package
Agreement.signed	Type of agreements signed by Data Editor to obtain private data
MCStatus	Status of the manuscript in the editorial workflow system.
As.Of.Date	Date and time stamp of the issue transaction
date_asof	Date part of the issue transaction
date_resolved	The date the issue was resolved.

Lab members during this period

We list the lab members active at some point during this period. This still requires confidential data as an input.

There were a total of 56 lab members over the course of the 12 month period.

R session info

sessionInfo()

## R version 4.2.3 (2023-03-15)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 22.04.4 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] tidylog_1.0.2         rmarkdown_2.21        jose_1.2.0           
##  [4] openssl_2.0.6         boxr_0.3.6            splitstackshape_1.4.8
##  [7] here_1.0.1            readr_2.1.4           knitr_1.42           
## [10] tidyr_1.3.0           stringr_1.5.0         dplyr_1.1.1          
## 
## loaded via a namespace (and not attached):
##  [1] bslib_0.4.2       jquerylib_0.1.4   pillar_1.9.0      compiler_4.2.3   
##  [5] tools_4.2.3       bit_4.0.5         digest_0.6.31     jsonlite_1.8.4   
##  [9] evaluate_0.20     lifecycle_1.0.3   tibble_3.2.1      pkgconfig_2.0.3  
## [13] rlang_1.1.0       cli_3.6.1         parallel_4.2.3    yaml_2.3.7       
## [17] xfun_0.38         fastmap_1.1.1     withr_2.5.0       sass_0.4.5       
## [21] generics_0.1.3    vctrs_0.6.2       askpass_1.1       hms_1.1.3        
## [25] bit64_4.0.5       rprojroot_2.0.3   tidyselect_1.2.0  glue_1.6.2       
## [29] data.table_1.14.8 R6_2.5.1          fansi_1.0.4       vroom_1.6.1      
## [33] tzdb_0.3.0        purrr_1.0.1       magrittr_2.0.3    clisymbols_1.2.0 
## [37] htmltools_0.5.5   utf8_1.2.3        stringi_1.7.12    cachem_1.0.7     
## [41] crayon_1.5.2

Software Requirements

R (last run with R 4.2.3)
- package here (>=0.1)
Python
- module venv

Other packages will be installed automatically by the programs, as long as the requirements above are met, see Session Info. R (last run with R r paste0(R.Version()$major, ".", R.Version()$minor))

R packages

Package	Version
dplyr	1.1.1
stringr	1.5.0
tidyr	1.3.0
knitr	1.42
readr	2.1.4
here	1.0.1
splitstackshape	1.4.8
boxr	0.3.6
jose	1.2.0
rmarkdown	2.21
tidylog	1.0.2

Python packages

Modules
jira>=3.8.0
requests
python-dotenv
pandas

Docker

These requirements are satisfied in the Docker image created by Dockerfile, see description below

Controlled Randomness

No Pseudo random generator is used in the analysis described here.

Memory, Runtime, Storage Requirements

The code was last run successfully on GitHub Codespaces on a 2-core machine with 8GB RAM and 32GB storage. Approximate time needed to reproduce the analysis varies depending on how much data is downloaded from the Jira API. Downloading the variables listed above took approximately 5 seconds for each case.

Description of Programs/Code

00_get_fields.py: Marks the to-be-extracted JIRA fields with "True" and outputs file data/metadata/jira-fields.xlsx
01_download_issues.py: Extracts raw process data from Jira using API
02_jira_anonymize.R: Subsets the raw data to variables of interest, and substitute random numbers for sensitive strings
03_lab_members.R: Outputs list of lab members active at some point during extracted period
10_jira_anon_publish.R: Does final cleaning and matching and writes out the anonymized file
99_push_box.R: Uploads extracted data to secure Box folder
99_render_README.R: Renders Rmd README file

Instructions to Replicators

Clone this repository onto your device or a GitHub Codespace

Set up Docker

The Dockerfile is used to build the Docker image.
The image is built with the build.sh script, which requires a TAG argument, and will otherwise read parameters from the .myconfig.sh file.

bash ./build.sh TAG

[NOTE]: If working on BioHPC, remember to replace docker with docker1 in the relevant code.

Use ls-tags.sh to list available tags.

bash ./ls-tags.sh

To run the image as a Rstudio interactive development image, use

bash ./start_rstudio.sh TAG

It defaults to the 2025-02-05 image if you don't specify a tag.

Set up JIRA

Obtain the per-individual API Key
The API Key is not stored in this repository.
Go to https://id.atlassian.com/manage-profile/security/api-tokens

Click on "Create API token"
Enter a label for the token (e.g. "JIRA Extract")
Copy the token to the clipboard

Use it with the Python scripts in this repository, in one of the following ways:
- Set the environment variable JIRA_API_KEY to the token value
  - On github codespaces this involves creating a Github secret with the exact name JIRA_API_KEY and value of the key you get from JIRA
- Create a file named .env in the root directory of this project, and add the following line to it: JIRA_API_KEY=<token value>
- Pass the token value to the Python scripts when prompted

Set up Box

Location: https://cornell.app.box.com/folder/143352802492🔒
We use the subfolder jira_exports🔒
In order to up- and download, you need not just an API key, but a JSON file with other credentials. This file is called client_enterprise_id,"_",client_key_id,"_config.json", e.g. 81483_bkgnsg4p_config.json
- The client_enterprise_id is identified in the JSON file itself as well
- The client_key_id is the name of the key in the Box developer console🔒
The JSON file is key
- It is not stored in this repository, but is stored in the Box folder InternalData
- To use this, the file must be downloaded and stored in the root of the project directory
Then the .env file needs to be appropriately adjusted with the relevant numbers as per below entered:

BOX_FOLDER_ID=12345678890
BOX_PRIVATE_KEY_ID=abcdef4g
BOX_ENTERPRISE_ID=123456

Here:
- The BOX_FOLDER_ID is the 12 digit number in the URL of the box folder .../folder/12345678890?...
- The BOX_PRIVATE_KEY_ID refers to the publicKeyID in the JSON file
- The BOX_ENTERPRISE_ID is the number at the beginning of the name of the JSON file
Alternatively, on Github Codespaces, these need to be encoded as secrets.

Start Docker and Set up Environment

Run ./start_rstudio.sh (bash ./start_rstudio.sh from the command line) it should pull the image from the docker and open a port for you to develop in a familiar RStudio environment
- On GitHub Codespaces you can access this port by clicking on ports and then the little globe icon to open it in a new tab
- On a local computer, you may need to open a browser at http://localhost:8787
- To obtain a console in the running Docker container, open a second terminal and connect: `

container_id=$(docker container ls | head -2 | tail -1 | awk ' { print $1 } ')
docker exec -it -u rstudio $container_id /bin/bash`

[NOTE]: The remainder of the instructions assume you are working within the Docker environment. Adjust as necessary if you are only using the code base in your own environment.

Change to the correct working directory:
- Rstudio: click on processing-jira-process-data/processing-jira-process-data.Rproj
- Console: cd to the correct directory
Install any missing Python packages by running pip install -r requirements.txt.
Set up environment variables:
- Ensure an .env file is present in the root project directory (or your GitHub Secrets are set)
- Ensure that the Box JSON file as outlined above is present in the root project directory
- Provide JIRA and BOX information

Template file

JIRA_USERNAME=
JIRA_API_KEY=
BOX_FOLDER_ID=
BOX_PRIVATE_KEY_ID=
BOX_ENTERPRISE_ID=

Define start and end dates:
- Update the extractday, firstday, and lastday fields in the programs/config.R file.
- You will need to manually provide them to the Python programs (for now)

Obtain Extract

cd programs
To obtain extract run python3 01_download_issues.py -s 2024-12-01 -e 2025-11-30 with the relevant dates.
- This will get the fields as specified in data/metadata/jira-fields.xlsx.
- If fields need to be updated (they are keyed on names), edit programs/00_jira_fields.py to obtain the full list of fields, open the resulting Excel file (data/metadata/jira-fields.xlsx) and mark the to-be-included fields with "True"
- Otherwise running programs/00_jira_fields.py is not required.
Run R programs in numerical order to create the confidential and anonymized files used for the report.
- Running with R CMD BATCH name_of_file.R will create the necessary log files.
- This is encapsulated in the main.sh file, for convenience:

cd programs
bash -x ./main.sh

Push the extracted confidential data to Box, using the following code, which we specifically run manually:

cd programs
R CMD BATCH 99_push_box.R

Finally, run 99_render_README.Rout to update the .Rmd README file and output a .md file and .html file.
- Manually print the .html file to obtain a PDF.

Citation

Vilhuber, Lars. 2025. "Process data for the AEA Pre-publication Verification Service." American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2025-12-02. https://doi.org/10.3886/E117876V6

@techreport{10.3886/e117876V6,
  doi = {10.3886/E117876V6},
  url = {https://doi.org/10.3886/E117876V6},
  author = {Vilhuber,  Lars},
  title = {Process data for the AEA Pre-publication Verification Service},
  institution = {American Economic Association [publisher]},
  series = {ICPSR - Interuniversity Consortium for Political and Social Research},
  year = {2025}}

References

Vilhuber, Lars. r YEAR. "Process Data for the AEA Pre-publication Verification Service." American Economic Association [publisher]. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], r Sys.Date(). [r ICPSR_DOI](r ICPSR_DOI).

Acknowledgements

This README was adapted from the social-science-data-editors/template_README template.

Name		Name	Last commit message	Last commit date
Latest commit History 67 Commits
README_files/figure-html		README_files/figure-html
data		data
images		images
programs		programs
.gitignore		.gitignore
.here		.here
.myconfig.sh		.myconfig.sh
Dockerfile		Dockerfile
JIRA_V3_MIGRATION.md		JIRA_V3_MIGRATION.md
LICENSE.txt		LICENSE.txt
README.Rmd		README.Rmd
README.html		README.html
README.md		README.md
README.pdf		README.pdf
build.sh		build.sh
global-libraries.R		global-libraries.R
index.html		index.html
ls-tags.sh		ls-tags.sh
package.sh		package.sh
processing-jira-process-data.Rproj		processing-jira-process-data.Rproj
requirements.txt		requirements.txt
run_docker.sh		run_docker.sh
start_rstudio.sh		start_rstudio.sh
tags.txt		tags.txt

License

AEADataEditor/processing-jira-process-data

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Overview

Data Availability and Provenance Statements

Statement about Rights

Summary of Availability

Details on the Data

Raw process data

Anonymized data

Publishing data

Describing the Data

Variables

Lab members during this period

R session info

Software Requirements

R packages

Python packages

Docker

Controlled Randomness

Memory, Runtime, Storage Requirements

Description of Programs/Code

Instructions to Replicators

Set up Docker

Set up JIRA

Set up Box

Start Docker and Set up Environment

Obtain Extract

Citation

References

Acknowledgements

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