RCN-UBE: Sustainable, nationwide network to promote reproducible big-data analysis in biology programs within community colleges and minority-serving institutions

This project aims to serve the national interest by equipping undergraduate students with essential skills in big data analytics, bridging a gap that exists at some community college and minority-serving institutions. By providing training in this highly relevant and sought-after skill set, this project aims to empower students from diverse backgrounds to thrive in the data-driven landscape of modern life sciences and STEM-related fields. To achieve this objective, we will support and train faculty members from community colleges and minority-serving institutions, working with them in integrating computational skills and big data analytic techniques into their existing life science curricula. Furthermore, this project will establish a sustainable and nationwide network dedicated to developing, adopting, curating, and maintaining computational and pedagogical resources, enabling a wide range of undergraduate students to engage in the analysis of real-world biological data. The rapid advancement of high throughput technologies has transformed the way research is conducted in the life sciences, with computational tools playing a crucial role. In order to effectively navigate and gain insights from large datasets, 21st-century biologists heavily rely on big data analytic techniques (BDAT). However, at teaching-focused institutions such as community colleges and some minority-serving institutions, faculty face numerous challenges to incorporating BDAT into the life sciences curricula. To address these barriers, we propose a targeted effort by a dedicated group of faculty members with appointments both in life science research at four-year universities and in life science units at community colleges and minority-serving institutions. This project will develop a sustainable, nationwide network for implementing effective course-based undergraduate research experiences (CUREs) focused on BDAT, with a particular emphasis on best practices for improving reproducibility in life sciences research. To achieve this overarching goal, the project will focus on three main objectives: 1) Develop, adopt, curate, and maintain computational and pedagogical resources for the Consortium of Biological Data Science Education (CBSE); 2) Develop faculty training strategies for life science instructors who lack departmental integration and institutional support for BDAT; and 3) Evaluate teaching, learning, and assessment strategies for BDAT skills and reproducibility concepts in life science education. By addressing these objectives, this effort will provide an effective approach to teaching BDAT skills at community colleges and minority-serving institutions, empowering undergraduate students with upper-division educational and workforce-ready computational and data science skill sets. This project is being jointly funded by the Directorate for Biological Sciences, Division of Biological Infrastructure, and the Directorate for STEM Education, Division of Undergraduate Education as part of their efforts to address the challenges posed in Vision and Change in Undergraduate Biology Education: A Call to Action (http://visionandchange/finalreport/). 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.