584 ICTR data science initiative: Empowering translational teams to better leverage data science

Objectives/Goals: High-performing translational teams (TTs) effectively draw knowledge from empirical data to develop health solutions. However, some TTs lack rigorous data approaches, resulting in inefficiency. The ICTR data science initiative integrates team-oriented data science for more innovative and reproducible translational research. Methods/Study Population: To help TTs better leverage data science, the Institute for Clinical and Translational Research (ICTR) at the University of Wisconsin-Madison orchestrated a strategic initiative involving four main actions. • Assess needs. Determine how TTs are using data science and identify essential tools for success. • Establish partnerships. Develop strategic relationships to centralize resources and engage data scientists. Provide team science training to ensure effective integration. • Develop educational pathways. Design and implement workshops to demystify novel data science tools and upskill translational scientists. • Facilitate culture change. Identify ways that all ICTR services can help identify needs, foster educational pathways, and encourage partnerships to help TTs better leverage data science. Results/Anticipated Results: Initial assessments indicated that fewer than 25% of TTs receiving pilot awards used data science tools, and only 10% had a data scientist on their team. Data from collaboration planning sessions indicated that few TTs used data science, but all were interested in learning more. To address this deficiency, ICTR partnered with the Data Science Institute and the Section of Applied Clinical Informatics. This expertise informed resource development (e.g., a data science primer, websites) and generated workshops. Educational opportunities include tailored workshops to help TTs better curate data and create more efficient workflows, graduate course modules to improve rigor and reproducibility, and seminars illustrating translational applications of AI, visualizations, and large data integration. Discussion/Significance of Impact: The ICTR Data Science Initiative was designed to empower TTs to more effectively integrate data to power translation. As data science approaches and expertise are embedded within teams, we anticipate continued increases in interest and usage of data science tools, collaborative publications, and data rich applications for extramural funding.