Meta-Research: Individual-level researcher data confirm the widening gender gap in publishing rates during COVID-19
Abstract
Publishing is part and parcel of a successful academic career, and Covid-19 has amplified gender disparities in manuscript submissions and authorships. We used longitudinal publication data on 431,207 scientists in biology, chemistry, and clinical and basic medicine to quantify the differential impact of Covid-19 on women's and men's annual publishing rates. In a difference-in-differences analysis, we estimated that the average gender difference in publication productivity increased from -0.26 in 2019 (corresponding to a 17% lower output for women than men) to -0.35 in 2020 (corresponding to a 24% lower output for women than men). An age-group comparison showed a widening gender gap for both early career and mid-career scientists. The increasing gender gap was most pronounced among highly productive authors and scientists in clinical medicine and biology. Our study demonstrates the importance of reinforcing institutional commitments to diversity through policies that support the inclusion and retention of women researchers.
Data availability
The current manuscript is a computational study, so no data have been generated for this manuscript. Source data and code will be provided on git-hub for all tables and figures.
Article and author information
Author details
Funding
Samfund og Erhverv, Det Frie Forskningsråd (DFF-0133-00165B)
- Emil Bargmann Madsen
- Mathias Wullum Nielsen
- Josefine Bjørnholm
- Jens Peter Andersen
Aarhus Universitets Forskningsfond (AUFF-F-2018-7-5)
- Jens Peter Andersen
Independent Research Fund Denmark (9130-00029B)
- Mathias Wullum Nielsen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Reviewing Editor
- Peter Rodgers, eLife, United Kingdom
Publication history
- Received: December 21, 2021
- Accepted: March 15, 2022
- Accepted Manuscript published: March 16, 2022 (version 1)
- Accepted Manuscript updated: March 17, 2022 (version 2)
- Version of Record published: March 23, 2022 (version 3)
Copyright
© 2022, Madsen et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 1,925
- Page views
-
- 180
- Downloads
-
- 8
- Citations
Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.
Download links
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Further reading
-
eLife’s Executable Research Article lets authors include live code, data and interactive figures in their published paper.
-
- Computational and Systems Biology
- Structural Biology and Molecular Biophysics
The design of compounds that can discriminate between closely related target proteins remains a central challenge in drug discovery. Specific therapeutics targeting the highly conserved myosin motor family are urgently needed as mutations in at least 6 of its members cause numerous diseases. Allosteric modulators, like the myosin-II inhibitor blebbistatin, are a promising means to achieve specificity. However, it remains unclear why blebbistatin inhibits myosin-II motors with different potencies given that it binds at a highly conserved pocket that is always closed in blebbistatin-free experimental structures. We hypothesized that the probability of pocket opening is an important determinant of the potency of compounds like blebbistatin. To test this hypothesis, we used Markov state models (MSMs) built from over 2 milliseconds of aggregate molecular dynamics simulations with explicit solvent. We find that blebbistatin’s binding pocket readily opens in simulations of blebbistatin-sensitive myosin isoforms. Comparing these conformational ensembles reveals that the probability of pocket opening correctly identifies which isoforms are most sensitive to blebbistatin inhibition and that docking against MSMs quantitatively predicts blebbistatin binding affinities (R2=0.82). In a blind prediction for an isoform (Myh7b) whose blebbistatin sensitivity was unknown, we find good agreement between predicted and measured IC50s (0.67 mM vs. 0.36 mM). Therefore, we expect this framework to be useful for the development of novel specific drugs across numerous protein targets.