Enhancing Data Science Skills Through Bio-OCS - Biomedical Open Case Studies

Fred Hutchinson Cancer Center, Seattle WA

Investigators

Abstract

Project Summary/Abstract Dramatic advances in artificial intelligence, single cell sequencing, spatial transcriptomics, and temporal data hold great potential for propelling biomedical understanding forward. However, access to training for these cutting-edge emerging technologies and data science best practices remains limited, particularly for researchers and trainees at under-resourced institutions. Furthermore, researchers often struggle to find time to apply newly acquired skills to real-world data in a research context. To address these challenges, we propose expanding the Open Case Study (OCS) project to create Biomedical OCS (Bio-OCS), a comprehensive skills development program augmented by synchronous events and community support for research scientists and trainees. This initiative will focus on building bioinformatics skills using cutting-edge methodologies and tools, like AI and spatial transcriptomics, as the basis for case studies. Case studies demonstrating applications with real-world data have proven useful for teaching statistical thinking; the OCS project takes this model further by providing a free archive of case studies, guiding learners in working with data from start to finish and allowing them to demonstrate the necessary computational and problem-solving skills. To support uptake of these skills, the Bio-OCS program will also include virtual workshops for learners to work through our case studies with peers, launch a Slack workspace to facilitate a peer community, hold office hours and check-ins to assist in problem solving, and host in-person “Case-A-Thons” to support the application of case study lessons to participants' own data. Additionally, we will collaboratively adapt our current OCS Instructor Guide by hosting Reciprocal Instructor Summits where we convene instructors from a diverse set of institutions and communities. The new case studies, coupled with the supporting workshops, events, and resources, will enable us to better support diverse learner communities in building biomedical skills. Lessons learned through evaluation of our events and from Bio-OCS learner survey responses will be published in research papers to further support biomedical data science education.