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Invisible Collaborators: Underrepresentation, Research Networks, and Outcomes of Biomedical Researchers

隐形合作者：代表性不足、研究网络和生物医学研究人员的成果

基本信息

批准号：
10646413
负责人：
Jason David Owen-Smith
金额：
$ 21.44万
依托单位：
OHIO STATE UNIVERSITY
依托单位国家：
美国
项目类别：
财政年份：
2020
资助国家：
美国
起止时间：
2020-08-01 至 2025-06-30
项目状态：
未结题

项目摘要

This interdisciplinary project will greatly enhance our understanding of the scientific contributions of women, members of racial and ethnic under represented minorities (URMs), and research staff and inform science and scientific workforce policy regarding those groups. Our work is made possible through the use of unique new data from the UMETRICS project and on scores on NIH applications. The UMETRICS data allow us to identify all people employed on research projects, not just those who are listed as authors on publications. With these powerful data, we will provide new perspectives on the positions of women and URMs in the network of scientific collaborations. This is particularly important because existing evidence indicates that women and members of underrepresented racial and ethnic groups are disadvantaged in terms of the authorship credit they receive for their contributions to science. Because our UMETRICS data make it possible to identify all people working on projects, we can study for the first time the extent to which women and URMs are even included on publications controlling for the role played and the amount of effort devoted to projects. Our analysis of staff is also timely as NIH has repeatedly considered increasing support for staff scientists. If staff are less likely to appear as coauthors on articles than faculty, postdocs, or perhaps graduate students, it becomes critically important for policy to be able to find other ways to quantify their contribution to science. There is also mixed evidence that women trainees perform better under the mentorship of women mentors. In addition to coming to mixed conclusions, existing work on the benefits of a gender match between trainees and mentors is descriptive rather than causal. We will use unique large-scale data on scores on NIH fellowship applications to estimate the causal effect of a gender match on women trainees. RELEVANCE (See instructions): Policy makers seek to ensure that our best and brightest regardless of gender, race, and ethnicity are represented in science. But, unfortunately, it is often hardest to quantify the relative contribution to science of members of underrepresented groups and research staff. This project will use new data to better quantify the credit received by women, underrepresented racial and ethnic minorities, and research staff in science and provide policy-relevant guidance for improving their training and funding.

这个跨学科的项目将大大提高我们对科学贡献的认识