Dataset for the analysis of gendered research productivity affected by early COVID-19 pandemic

• Published in: Data in brief Vol. 48; p. 109200
• Main Authors: Kwon, Eunrang, Yun, Jinhyuk, Kang, Jeong-han
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Inc 01.06.2023; The Author(s). Published by Elsevier Inc; Elsevier
• Subjects: Career; Childcare; COVID-19; Data; Gender inequality; Microsoft academic graph; Research productivity; COVID-19; Childcare; Microsoft academic graph; Research productivity; Gender inequality; Career
• ISSN: 2352-3409; 2352-3409
• DOI: 10.1016/j.dib.2023.109200

In many countries, COVID-19 has made it harder for women to study because they are expected to do more housework and care for children. This article encompasses different data sources that can be used to figure out how the early pandemic of COVID-19 affected the number of studies done by females, in comparison with males. This data is add-on metadata that can be used with raw Microsoft Academic Graph (MAG) from 2016 to 2020 of the Feb 6, 2021 dump. We retrieved open-source metadata from various sources, including LinkedIn, the Johns Hopkins Coronavirus Resource Center, and Google's COVID-19 Community Mobility Reports, and linked bibliographic information to characteristics of the author's environments. It consists of published journals and online preprints, including each author's gender and involvement in the publication, their position through time, the h-index of their institutes, and gender equality in the professional labor market at the country level. For each record of papers, the data also includes the information of the papers, e.g., title and field of study. By gathering this evidence, our data can support the fact diversity in science is more than just the number of active members of different groups. It should also examine minority participation in science. Our data may help scholars understand diversity in science and advance it. The article ``The effect of the COVID-19 pandemic on gendered research productivity and its correlates'' uses this data as the principal source (Kwon, Yun & Kang, 2021).