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

项目摘要/摘要 我们建议创建一个开放的、适应性强的同步和异步多轨课程 提供培训模块以促进可查找、可访问、可互操作和可重复使用(公平)的数据 生物医学数据，作为Passport to AI熟练程度培训计划的一部分。完整的计划将 通过实践学习活动和讨论引导参与者，生成开放的 工作证明了他们的能力，并最终获得了象征所获技能的徽章。这个 课程资源将包括可作为学分课程提供的模块，同步 研讨会或异步在线模块。展会内容将与 公平对待美国，并将从同步研讨会开始，进行计划评估和反馈 指引着我们的持续发展。然后，这些将导致发展异步研讨会 无论是针对基于群体的机会还是针对个人学习者。计划资源将提供 针对学生级别进行定制，通过教师实现本科生培训目标 具有水平内容的。学习结果、参与者评估和徽章将是相同的，但 我们认识到，所服务的不同人群将从不同的成功途径中受益。这 公平计划集成到规模更大、结构类似的人工智能教育计划中，该计划将提供增强的 为寻求精通数据科学和人工智能研究的学员提供学习机会。 此外，该计划将与行业和校园合作伙伴协调，以扩大 方案，评估其结果，并不断完善内容。通过与GO协作 公平的美国团队，我们确保我们的内容将融入他们的最佳实践，变得广泛 可访问，并为公平的数据社区做出贡献。

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.