REU Site: Equitable Data Science in Adolescent Development

University Of Minnesota-Twin Cities, Minneapolis MN

Investigators

Abstract

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.