SYMP 25-4 - The BEDE Network: At the intersection of data science and open source education.

The Biological and Environmental Data Education (BEDE) Network’s (NSF-RCN) main objective is to support instructors as they integrate data science skills across undergraduate biology and environmental science curricula, through instructor training, curricular maps, and a network of supportive colleagues.

We published survey results in 2021 of higher education instructor perceptions, needs, and barriers with respect to teaching data science in life science courses. As part of our results, we found that >50% of respondents use open source online materials and ~25% use their own materials to teach data science in their undergraduate classes. This represents a tremendous opportunity to spread data science OERs to instructors who encounter barriers to teaching data science in their biology courses. Based on the survey results, the greatest barriers were instructor and student background and space in the curriculum. By supporting the construction of OERs from pre-existing homegrown materials, we can publicize the efforts by numerous instructors and provide a more diverse toolkit for instructors interested in and already teaching data science in their biology classrooms. In addition, we can connect instructors facing challenges with those who have found solutions and workflows that are successful in undergraduate classrooms.

The data science community has embraced open education over the years, and there is a great potential to tap into this pre-existing community to help support life science instructors across institutions. In addition to supporting instructors through training and community-building, the BEDE Network hosts a repository of OERs to reduce instructor’s barriers to integrating data science training into their classrooms. The OERs range in scale, from single lessons, to multi-lesson modules, and even full courses. The two open courses target different audiences: one is a general education course, designed to give students from any disciplinary background a foundation in core data literacy skills through a narrative-based, ecological approach to provide training in data-driven decision making; the second course targets undergraduate students in biology programs, teaching them broadly-applicable data science skills such as best practices in using spreadsheets, data quality control and assurance, and computational reproducibility. Through building supportive communities, conducting curricular research, and providing instructors with both data science training and accessible educational resources, the BEDE Network aims to facilitate the expansion of data literacy and data science training in biology and environmental undergraduate curricula.