DCL: INCLUDES: Early Stage Empirical Research (EAGER): Data and the Future of an Empirically Grounded Research Community for Broadening Participation in STEM

美国的科学和工程尚未实现跨种族、性别、民族、地理和/或社会阶层的充分参与。许多研究项目旨在调查参与的障碍和克服这些障碍的办法。然而，这些研究项目可能是孤立进行的，没有跨项目的有益讨论或合作。该项目旨在确定研究人员之间的联系，以扩大参与。研究论文及其引用的计算分析将绘制和阐明这个网络的结构。在这张地图的指导下，项目团队将采访和研究一系列研究人员，重点关注这些研究人员如何生成和存储数据。通过这个项目，研究人员将学习如何更好地共享数据；比较和加强研究成果；并建立社区，以更有效地开展扩大参与的全国性研究。这反过来将有助于更大规模的干预，在科学和工程领域的多样性和包容性方面取得更有意义的进步。这项研究将从计算机分析联邦拨款和由这些奖励产生的出版物开始。项目团队具有管理和分析大型复杂数据集、综合模式、识别知识差距、量化趋势和创建可视化工具的专业知识。这一分析将揭示多样性和包容性研究社区的结构。将从这些群体中招募关键科学家进行进一步研究。这将包括深度访谈和焦点小组聚会。这项研究将揭示建立数据协作的问题和解决方案；研究人员执行数据管理计划的方式；他们如何利用数据集作为稀缺资源，超越假设生成和测试的背景；以及共享数据和数据管理实践如何能够在扩大参与方面培养一个更强大、以经验为基础的研究界。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

Science and engineering in the United States have yet to achieve full participation across race, gender, ethnicity, geography, and/or social class. Many research projects aim to investigate barriers to participation and solutions to overcome those barriers. These research projects, however, can take place in isolation, without useful discussion or collaboration across projects. This project aims to identify connections across researchers in broadening participation. Computational analysis of research papers, and their citations, will map and illuminate the structure of this network. Guided by this map, the project team will interview and study a range of researchers, with focus on how these researchers generate and store data. Through this project, the researchers will learn how to better share data; compare and strengthen research findings; and build community toward more effective national-scale research on broadening participation. This in turn will aid larger scale interventions, making more meaningful strides toward diversity and inclusion across science and engineering. This research will begin with computerized analysis of federal grant awards and publications arising from those awards. The project team has expertise managing and analyzing large complex data sets, synthesizing patterns, identifying gaps in knowledge, quantifying trends, and creating visualization tools. This analysis will reveal the structure of diversity and inclusion research communities. From these communities, key scientists will be recruited for further study. This will include in-depth interviews and a focus group gathering. This study will reveal problems and solutions to build data collaborations; ways researchers execute their data management plans; how they utilize datasets as scarce resources, beyond the contexts of hypothesis generating and testing; and how shared data and data-management practices can foster a stronger, empirically grounded research community in broadening participation.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.