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: 10221744
• 负责人: Jason David Owen-Smith
• 金额: $ 21.15万
• 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/10221744
• 关键词: Authorship; Collaborations; Data; Disadvantaged; Ensure; Ethnic Origin; Ethnic group; Faculty; Fellowship; Funding; Gender; Instruction; Mentors; Mentorship; Outcome; 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 population; 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.

这个跨学科的项目将大大提高我们的科学贡献的理解， 妇女、代表性不足的种族和族裔少数群体成员以及研究人员， 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项目的独特新数据和NIH应用程序的分数。 UMETRICS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。有了这些强有力的数据，我们将提供新的视角， 妇女和统一资源管理机制在科学合作网络中的地位。这一点尤其重要 因为现有的证据表明，妇女和代表性不足的种族和民族的成员， 一些群体因其对科学的贡献而获得著作权方面的荣誉，处于不利地位。 由于我们的UMETRICS数据可以识别所有从事项目的人员，因此我们可以研究 这是第一次将妇女和统一登记机制列入控制人口的出版物， 所发挥的作用和投入项目的努力。我们对员工的分析也是及时的，因为NIH已经 一再考虑增加对科学家的支持。如果工作人员不太可能作为合著者出现， 而不是教师，博士后，或者研究生，这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。 还有各种证据表明，在妇女指导下，女学员的表现更好 导师除了得出喜忧参半的结论外，现有的关于性别匹配的好处的工作 是描述性的，而不是因果关系。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的分数，以估计性别匹配对女性受训者的因果影响。 相关性（参见说明）： 政策制定者寻求确保我们最优秀和最聪明的人，不分性别，种族和民族， 在科学中表现出来。但是，不幸的是，通常最难量化的是， 科学代表性不足的群体和研究人员的成员。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所获得的信贷 科学工作人员，并提供政策相关的指导，以改善他们的培训和资金。