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.

该项目由社会，行为和经济科学理事会（SBE）本科生研究经验（REU）站点计划资助。公平数据科学研究数据中的偏见、数据分析和结果解释如何加剧我们社会中长期存在的不平等，并创造新的不平等和差距。因此，培养下一代数据科学家开发方法并负责任地分析数据是至关重要的。这个跨学科项目将为本科生提供公平数据科学方面的培训，以培养他们在数据科学、统计建模和机器学习以及科学交流方面的研究技能。通过这一培训，学生将学习数据来源、分析方法和结果解释如何反映、加强和减轻系统性不平等。学生将接受培训，在处理以数据为中心的项目时优先考虑公平性。这些项目将带来公开可用的软件和报告，提高人们对社会不平等的认识，并为如何解决这些问题的讨论提供信息。最后，学生们将通过社区活动传播他们的工作，这将激发未来的研究，并将社区与股票相关的研究联系起来。发展公平数据科学技能对于提高科学严谨性，同时平衡科学研究的公平性和公平性是无价的。在为期10周的课程中，学生将建立公平数据科学的研究技能，并使用青少年大脑认知发展（ABCD）研究的数据回答有关青少年发展的公平导向研究问题。关于多样性、公平和包容（DEI）及其在数据科学中的作用的主题构成了本课程的基础，并在早期通过DEI模块和讨论进行了强调。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。该计划继续在R编程，统计建模和数据可视化方面进行广泛的技术培训。每周的研讨会将提供从统计和机器学习到大规模编程等主题的技术培训。研讨会和专业发展活动将每隔一周举行一次，以便学生听取研究人员关于他们在社会和卫生不平等方面的工作，为学生进入研究生院或就业市场做好准备，并为学生提供交流机会。黑客马拉松还将每隔一周举行一次，以便学生们在ABCD研究的数据之外获得公平数据科学的经验。这个为期10周的项目在面向公众的活动中达到高潮，让学生有机会向社区展示他们的作品，并与社区围绕DEI进行对话。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。