REU Site: Equitable Data Science in Adolescent Development

REU 网站：青少年发展中的公平数据科学

• 批准号: 2243973
• 负责人: Mark Fiecas
• 金额: $ 40.5万
• 依托单位: University of Minnesota-Twin Cities
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-15 至 2026-08-31
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2243973&HistoricalAwards=false
• 关键词: REU; Site; Equitable; Data; Science

This project is funded from the Research Experiences for Undergraduates (REU) Sites program in the Directorate for Social, Behavioral, and Economic Sciences (SBE). Equitable data science investigates how biases in the data, the analysis of the data, and the interpretation of results can reinforce long-standing inequalities in our society and also create new inequities and disparities. Thus, it is critical to train the next generation of data scientists to develop methods and analyze data responsibly. This interdisciplinary project will provide undergraduate students with training in equitable data science to build their research skills in data science, statistical modeling and machine learning, and scientific communication. Through this training the students will learn how data sources, analysis methods, and the interpretation of results can reflect, reinforce, and mitigate systemic inequalities. The students will be trained to prioritize equity when approaching data-centered projects. These projects will lead to publicly-available software and reports that bring awareness to societal inequities and inform discussions on how to address them. Finally, the students will disseminate their work via a community event, which will inspire future research and connect the community with equity-related research. Developing skills in equitable data science are invaluable towards enhancing scientific rigor while balancing fairness and equity in scientific research. During the 10-week program, students will build research skills in equitable data science and answer equity-oriented research questions about adolescent development using data from the Adolescent Brain Cognitive Development (ABCD) Study. Topics on diversity, equity, and inclusion (DEI) and their role in data science form the foundations of this program, emphasized early through DEI modules and discussions. Students will learn the causes and consequences of social inequality and how these inequalities manifest themselves in scientific research, from the data itself to the interpretation of results. The program continues with extensive technical training in R for programming, statistical modeling, and data visualization. Weekly workshops will provide technical training on topics ranging from statistics and machine learning to large-scale programming. Seminars and professional development events will be held every other week so that students will hear from researchers about their work on social and health inequities, prepare students for graduate school or the job market, and provide students with networking opportunities. Hackathons will also be held every other week so that students can gain experiences in equitable data science beyond the data from the ABCD Study. The 10-week program culminates in a public-facing event, giving the students the opportunity to present their work to the community and engage with the community in dialogue around DEI.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.

该项目是从社会，行为和经济科学局（SBE）的本科生（REU）站点计划（SBE）的研究经验中资助的。公平数据科学研究了数据中的偏见，数据分析以及结果的解释如何加强我们社会中长期存在的不平等，并造成新的不平等现象和差异。因此，至关重要的是训练下一代数据科学家以负责任地开发方法并分析数据。这个跨学科项目将为本科生提供公平数据科学的培训，以在数据科学，统计建模和机器学习以及科学沟通方面建立他们的研究技能。通过这项培训，学生将学习数据源，分析方法以及结果的解释如何反映，增强和减轻系统性不平等。在接触以数据为中心的项目时，学生将接受培训以优先考虑权益。这些项目将导致公开可用的软件，并报告对社会不平等的认识，并就如何解决这些问题进行讨论。最后，学生将通过社区活动来传播他们的工作，这将激发未来的研究，并将社区与与股权相关的研究联系起来。发展公平数据科学技能对于增强科学严谨性的同时，在科学研究中平衡公平和公平是无价的。在为期10周的课程中，学生将使用青少年脑认知发展（ABCD）研究的数据来建立公平数据科学的研究技能，并回答有关青少年发展的研究问题。关于多样性，公平和包容性（DEI）的主题及其在数据科学中的作用构成了该计划的基础，并通过DEI模块和讨论很早就强调。学生将学习社会不平等的原因和后果，以及这些不平等如何在科学研究中表现出来，从数据本身到结果的解释。该计划继续进行R的广泛技术培训，用于编程，统计建模和数据可视化。每周研讨会将提供有关统计和机器学习到大规模编程的主题的技术培训。研讨会和专业发展活动将每隔一周举行一次，以便学生会听取研究人员在社会和健康不平等方面的工作，为研究生院或就业市场做好准备，并为学生提供社交机会。黑客马拉松也将每隔一周举行一次，以便学生可以在ABCD研究的数据之外获得公平数据科学的经验。为期10周的计划在面向公众的活动中达到顶峰，使学生有机会向社区展示其作品，并在DEI围绕DEI的对话中与社区互动。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响的评估来评估的支持。