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

• 批准号: 10450882
• 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
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-01 至 2024-06-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10450882
• 关键词: Authorship; Collaborations; Data; Disadvantaged; Ensure; Ethnic Origin; Ethnic group; Faculty; Fellowship; Funding; Gender; Instruction; Mentors; Mentorship; Minority Health Research; Outcome; Persons; Play; Policies; Policy Maker; Positioning Attribute; Postdoctoral Fellow; Publications; Race; Research; Research Personnel; Research Project Grants; Role; Science; Scientist; Time; Training; Underrepresented Minority; Underrepresented Populations; United States National Institutes of Health; Woman; Work; ethnic minority; graduate student; improved; large scale data; member; racial and ethnic; racial minority

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.

这个跨学科项目将极大地增强我们对科学贡献的理解 妇女、少数族裔和族裔成员 (URM) 以及研究人员并告知 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 项目的独特新数据以及 NIH 申请的分数。 UMETRICS 数据使我们能够识别所有受雇于研究项目的人员，而不仅仅是那些受雇于研究项目的人员。 被列为出版物的作者。借助这些强大的数据，我们将提供新的视角 妇女和 URM 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性不足的种族和族裔成员 就其对科学的贡献而获得的作者身份而言，群体处于不利地位。 因为我们的 UMETRICS 数据可以识别所有从事项目的人员，所以我们可以研究 首次将女性和 URM 纳入控制性出版物中 所扮演的角色以及为项目投入的精力。我们对工作人员的分析也很及时，因为 NIH 已经 多次考虑增加对在职科学家的支持。如果工作人员不太可能作为合著者出现 与教师、博士后或研究生相比，它对政策变得至关重要 能够找到其他方法来量化他们对科学的贡献。 还有混合证据表明，女学员在女性指导下表现更好 导师。除了得出不同的结论之外，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，而不是因果性的。我们将使用独特的大规模数据 NIH 奖学金申请的分数来估计性别匹配对女性学员的因果影响。 相关性（参见说明）： 政策制定者力求确保我们最优秀和最聪明的人，无论性别、种族和民族如何 以科学为代表。但不幸的是，通常很难量化对 代表性不足的群体成员和研究人员的科学。该项目将使用新数据 更好地量化妇女、代表性不足的种族和少数民族以及研究获得的信用 科学工作人员，并提供与政策相关的指导，以改善他们的培训和资助。