GatorSTAR: A New MARC U*STAR Program at the University of Florida

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Project Summary/Abstract We propose to create an open and adaptable multi-track curriculum for synchronous and asynchronous delivery of training modules to promote findable, accessible, interoperable, and reusable (FAIR) data for biomedical data as part of a Passport to AI Proficiency training program. The complete program will lead participants through hands-on learning activities and discussions, generating an open portfolio of work demonstrating their competency and culminating in badges signifying the skills earned. The program resources will comprise modules that can be delivered as for-credit courses, synchronous workshops, or asynchronous online modules. The FAIR content will be developed in collaboration with GO FAIR US and will begin with synchronous workshops, with program assessments and feedback guiding our continued development. These will then lead to development of asynchronous workshops for both group-based opportunities and individual learners. The program resources will offer customization for the level of students, enabling targeting of undergraduate trainees through faculty with leveled content. The learning outcomes, participant assessment, and badge will be the same, but we recognize that the diverse populations served will benefit from different pathways to success. This FAIR program integrates into a larger, similarly structured AI education program that will offer enhanced learning opportunities for trainees seeking to gain proficiency in data science and AI research. Furthermore, the program will coordinate with industry and campus partners to expand the breadth of the program, assess its outcomes, and constantly improve the content. By collaborating with the GO FAIR US team, we ensure that our content will incorporate their best practices, become widely accessible, and contribute to the FAIR data community.