The project is a collaborative effort among the University of Tennessee Chattanooga, Tuskegee University, Spelman College, and West Virginia University to integrate and automate biological big data into student training and education. Leveraging the team's expertise in computer science and ecology, the project will offer training workshops on using network models to integrate heterogeneous genomic big data and heterogeneous ecological big data to address life sciences questions. The team will engage faculty and students in developing a protocol to automate field data collection. The team also will prototype automated methods to enhance plant digitization, leveraging the collection of digitized plant images and meta-information at the Southeast Regional Network of Expertise and Collections, as well as the ecological datasets in collaboration with the Encyclopedia of Life.
The project objectives are to (1) enhance faculty expertise in big biological data through summer workshops; (2) catalyze interdisciplinary collaboration on big biological data research and education through hackathons, working groups, and community-building via a Video Education Faculty Network; and (3) develop hands-on, constructively peer-evaluated learning modules incorporating high-quality video tutorials. The proposed activities will address challenges surrounding the integration and automation of big biological data into education and training at predominantly undergraduate institutions and Historically Black Colleges and Universities. The project will help bridge the gaps between big biological data and the fields of systems biology, ecology and evolution, and environmental sciences. Overall, the project will catalyze collaborations among diverse institutions and disciplines while increasing diversity in big data.
This award is co-funded by the Improving Undergraduate STEM Education: Education and Human Resources (IUSE): EHR Program (NSF 17-590). IUSE supports projects that are designed to improve student learning through development of new curricular materials and methods of instruction and development of new assessment tools to measure student learning.
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.
