DOI: 10.5061/dryad.djh9w0w98
title: The extent of gender and race/ethnicity imbalance in infectious disease dynamics research

description: Publication practices accumulate to affect credibility and career
 advancement. Understanding authorship and citation practices is critical
 to addressing inequities. While citation bias has been demonstrated in
 several fields, it remains uncharacterized in infectious disease dynamics
 (IDD), a quantitative, interdisciplinary domain highly visible during the
 COVID-19 pandemic. We analyze IDD articles and their bibliographies from
 2000-2019 using machine-learning algorithms to infer the gender and
 race/ethnicity of each article's lead and senior author. We examine
 authorship and citation patterns by gender and racial group across
 geographic scales, including characterizing the author composition of each
 article's bibliography relative to the field. Our analysis reveals
 persistent gender and race imbalances in IDD research. Man-authored and
 White-authored publications dominate the field, with little progress in
 racial diversification of US and UK publications over the last two
 decades. Woman-authored articles have the most representative citation
 practices but are undercited, especially when women are senior authors. In
 the US and UK, most citations feature White lead and senior authors, even
 when citing articles have lead or senior authors of color. These findings
 underscore the urgent need for more inclusive IDD research practices. We
 discuss possible mechanisms and solutions to create opportunities for
 researchers from underrepresented groups.
description: # The extent of gender and race/ethnicity imbalance in infectious disease
 dynamics research
 [https://doi.org/10.5061/dryad.djh9w0w98](https://doi.org/10.5061/dryad.djh9w0w98) ## Description of the data and file structure This repository provides the source code for the following study: "The extent of gender and race/ethnicity imbalance in infectious disease dynamics research." We also provide anonymized data to reproduce the figures in the text in compliance with our data-sharing agreement with Clarivate. The data files are already embedded with the appropriate directory structure in the zip file. These data comprise articles in the field of infectious disease dynamics, the inferred gender and race of their authors, the author's country of affiliation, and the Journal Impact Factor where the article was published. We analyze patterns in these data to understand how authorship and citation practices vary across the field. Data are first pulled from Web of Science using the workflow and files outlined in the `pull_data` folder. Data are processed using the files in `process_data` folder; this requires some files published with Dworkin et al.'s paper on citation practices in neuroscience which are copied into this repository. Finally, figures in the main text can be reproduced with the code in `make_figures` and the data available in the `coreidd` folder. The IDD_bib_analysis directory contains the code for our tool to analyze one's bibliography relative to the makeup of the field. Directions are provided in the cleanBib Jupyter Notebook. ### Files and variables Files are labeled numerically according to the order in which they were run within each folder. Across folders the order is 1) pull data, 2) process data, 3) make figures. Data dictionaries for the data files are provided below. `coreidd/df9_articledata_0.7_public.csv` * `article_id`: a unique identifier for each article * `PY`: year of publication * `PD`: month of publication, contains NAs * `AG`: gender pairing of first and last authors of the article using `genderize.io`, `M` stands for man, `W` for woman, and `U` for unknown; first letter is first author, second letter is last author * `ARbin_ethni`: race pairing of first and last authors of the article using `ethnicolr` and binned into White (`W`) and non-White (`N`); first letter is first author, second letter is last author * `FA_genderize`: first author gender from `genderize.io`, `M` stands for man, `W` for woman, and `U` for unknown * `LA_genderize`: last author gender from `genderize.io`, `M` stands for man, `W` for woman, and `U` for unknown * `FA_ethni_race`: first author race from `ethnicolr`, `HL` stands for Hispanic, `W_NL` for non-Hispanic White, `B_NL` for non-Hispanic Black, `A` for Asian, and `U` for unknown * `LA_ethni_race`: last author race from `ethnicolr`, `HL` stands for Hispanic, `W_NL` for non-Hispanic White, `B_NL` for non-Hispanic Black, `A` for Asian, and `U` for unknown * `FA_binrace_ethni`: first author binary race from `ethnicolr`, `W` stands for White, `N` for non-White (including Hispanic, Asian, and Black non-Hispanic), `U` is unknown * `LA_binrace_ethni`: last author binary race from `ethnicolr`, `W` stands for White, `N` for non-White (including Hispanic, Asian, and Black non-Hispanic), `U` is unknown * `Country`: country of affiliation for last author of article (string) * `global_north`: indicator variable for whether the last author's country of affiliation falls in the Global North (0 or 1) * `has_single_author`: boolean variable for whether the article has only a single author `coreidd/df9_consensus_authorship_public.csv` This file contains the article ids and publication years (PY) for articles in the authorship dataset. `coreidd/df9_consensus_citation_public.csv` This file contains the articles in the citation dataset including which article they were cited by, and whether they should be included in the analysis depending on the threshold of inclusion. * `article_id`: the article's unique identifier so it can be linked to the full dataset * `in_bib_of_article_id`: article id of the citing article * `PY`: publication year of cited article * `include_95`: boolean for whether article should be included with 95th percentile consensus citation threshold * `include_90`: boolean for whether article should be included with 90th percentile consensus citation threshold. The main analysis uses this threshold; see the paper for a more detailed description of the construction and interpretation of these thresholds * `include_75`: boolean for whether article should be included with 75th percentile consensus citation threshold * `include_50`: boolean for whether article should be included with 50th percentile consensus citation threshold * `is_self_cite`: indicator for whether the citation is a self citation by the first or last author (0 or 1) `input_jif_regression_nosf_20241212_public.csv`, This file contains data necessary for the impact factor regression in the main text. * `pair_gender`: gender pairing of first and last authors of the article using `genderize.io`, `M` stands for man, `W` for woman; first letter is first author, second letter is last author * `pair_race`: race pairing of first and last authors of the article using `ethnicolr`, `W` stands for White, `N` for non-White; first letter is first author, second letter is last author * `FA_gender`: first author gender from `genderize.io`, `M` stands for man, `W` for woman * `LA_gender`: last author gender from `genderize.io`, `M` stands for man, `W` for woman * `FA_race`: first author race from `ethincolr`, `W` stands for White, `N` for non-White * `LA_race`: last author race from `ethincolr`, `W` stands for White, `N` for non-White * `JIF`: 2023 Journal Impact Factor of journal where article was published * `year`: year of publication * `num_citations`: number of times article was cited by articles in authorship dataset * `log_num_citations`: natural log of `num_citations` column * `citation_rate`: number of citations divided by years since publication * `log_citation_rate`: natural log of `citation_rate` column * `WC`: Web of Science category * `num_cited_refs`: number of references in the article's bibliography * `WC_1st`: first entry in `WC` * `Country`: country of affiliation for last author of article (string) * `global_north`: indicator variable for whether the last author's country of affiliation falls in the Global North (0 or 1) * `article_id`: article unique identifier ## Code/software This code uses both R and Python, depending on the file. ## Access information Other publicly accessible locations of the data: * [https://github.com/bansallab/IDD_imbalance](https://github.com/bansallab/IDD_imbalance) Data was derived from the following sources: * Clarivate Web of Science