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

In many countries, the advent of COVID-19 has decreased the research output of female researchers due to gendered housekeeping and childcare responsibilities. This article covers different data sources for analyzing how the COVID-19 epidemic influenced female research productivity. The primary source is bibliographic data collected from Microsoft Academic Graph (MAG) for the years 2016 to 2020. The data consists of both offline published journal data and online preprint data and offers the titles, academic disciplines, and author information for each work, including the names and affiliations of each author. We extract open-source metadata from LinkedIn, the Johns Hopkins Coronavirus Resource Center, and Google's COVID-19 Community Mobility Reports linking bibliographic information to country-level characteristics to which authors belong. The article “The effect of the COVID-19 pandemic on gendered research productivity and its correlates” utilizes this data (Kwon, Yun & Kang, 2021).在诸多国家，受性别分工下的家务与育儿责任影响，新冠疫情（COVID-19）的暴发致使女性研究者的科研产出有所下滑。本文梳理了用于分析新冠疫情对女性科研生产力影响的多类数据源。核心数据源为2016至2020年间从微软学术图谱（Microsoft Academic Graph, MAG）中采集的文献计量数据。该数据集涵盖线下正式发表的期刊文献与线上预印本两类数据，包含每篇成果的标题、所属学科以及作者信息，其中具体包括每位作者的姓名与所属机构。我们从领英（LinkedIn）、约翰斯·霍普金斯大学冠状病毒资源中心以及谷歌新冠社区流动报告中提取开源元数据，将文献计量信息与作者所属国家的特征进行关联。论文《新冠疫情大流行对性别分化下科研生产力的影响及其相关因素》（Kwon、Yun与Kang，2021）即采用了本数据集。