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

这一跨学科项目将极大地增强我们对 妇女、种族和族裔代表不足少数群体成员(URM)以及研究人员和信息 关于这些群体的科学和科学劳动力政策。我们的工作是通过 使用来自UMETRICS项目和NIH申请分数的独特新数据。 UMETRICS的数据使我们能够识别所有受雇于研究项目的人，而不仅仅是那些 在出版物上被列为作者。有了这些强大的数据，我们将提供关于 妇女和城市管理机构在科学合作网络中的地位。这一点尤为重要 因为现有证据表明，妇女和代表不足的种族和民族的成员 就他们对科学的贡献而获得的作者学分而言，群体处于不利地位。 因为我们的UMETRICS数据可以识别所有在项目中工作的人，所以我们可以研究 第一次，妇女和城市居民甚至被纳入控制 所扮演的角色和投入到项目中的工作量。我们对员工的分析也是及时的，就像NIH一样 反复考虑增加对科学家工作人员的支持。如果员工不太可能以合著者的身份出现 在比教师、博士后或研究生更多的文章中，这对政策来说变得至关重要 能够找到其他方法来量化他们对科学的贡献。 也有各种证据表明，女性学员在女性的指导下表现更好。 导师。除了得出喜忧参半的结论外，关于性别匹配的好处的现有工作 学员和导师之间的关系是描述性的，而不是因果的。我们将使用独特的大规模数据 美国国立卫生研究院奖学金申请分数，用于评估性别匹配对女性受训者的因果影响。 相关性(请参阅说明)： 政策制定者试图确保我们最优秀和最聪明的人，无论性别、种族和民族， 是科学界的代表。但不幸的是，通常最难量化的是 代表人数不足的群体成员和研究人员的科学。该项目将使用新数据来 更好地量化妇女、代表性不足的种族和族裔少数群体获得的信贷以及研究 教科文组织还为科学工作人员提供政策指导，以改进他们的培训和资金。

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.