Project Summary / Abstract This project brings together Drexel University and the Children’s Hospital of Philadelphia (CHOP) to create an adaptive Biomedical Data Science education program to empower researchers to learn and use emerging data science methods. We propose an in-line program to prepare researchers for data-driven work directly in their current field, while also identifying avenues for interdisciplinary collaboration. We will develop novel curricula pathways, leverage existing educational resources, create bridge materials, and provide practicum “lab” activities matching domain-specific projects to participants for hands-on experience. Educational Pathways in Biomedical Data Science will collaborate with the CHOP Office of Academic Training and Outreach Programs to engage a diverse learner audience in novel pedagogical research. Learners will be recruited from active participants in many existing CHOP training initiatives, including a novel data science education program, graduate student training, postdoc mentorship, physician fellowship research, and clinical research staff training. After enrollment, education program managers will cluster participants into collaborative communities of practice where they will receive mentorship and contribute to development of pathways. We acknowledge that we are still learning the most effective interventions for biomedical data science education, both within our specific communities and broadly within medical education (e.g. Federer et al., 2015; Rowhani-Faarid, Allen, and Barnett, 2017). We will develop new evidence of gaps in knowledge, skills, and attitudes among learners. We will develop and implement biomedical data science literacy instruments based on emerging scholarship. We will also gather learner feedback via mixed methods to adapt and evolve our modular resources, ensure robust learner outcomes, and align deliverables with the NIH Strategic Plan for Data Science. Data Science instruction for researchers outside of traditional computer and information sciences is a concrete step toward data-driven scientific literacy for all. We will ensure that participants emerge from this program with computational and algorithmic literacy solidified through hands-on experience. For non-computing researchers, we also will provide the foundational data fluencies necessary for individuals to contribute meaningfully to machine learning research, which will enable data-driven systems, insight-to-decision transformation, decision- making, and data-driven decision management. Finally, for all participants, we will strive to help individuals from a broad spectrum of backgrounds and identities identify new directions in which to develop their careers.