Research Project
10405988
ABSTRACT There is an urgent need to leverage existing scholarly data to develop data-informed interventions to reduce health disparities. Stakeholders associated with the production and use of data have developed a set of principles to make research data findable, accessible, interoperative, and reusable (FAIR). The FAIR guiding principles can facilitate biomedical advancements by bolstering data labeling and management practices to enable artificial intelligence and machine learning innovations. The application of FAIR addresses challenges associated with data annotation and management and can support greater efficiency and effectiveness of data used in this area. Adding training to the FAIR principles to early-career faculty members from groups underrepresented in the biomedical sciences will bolster a diverse scientific workforce capable of understanding and redressing cardiovascular health disparities, such as disparities in obesity and related areas crucial to minority health in the FAIR principles. The three aims of the proposed project are to (1) apply an Educational Design Research approach to the development, refinement, and finalization of online training modules for biomedical scientists to enhance their knowledge and skills in the competencies needed to make research data FAIR and AI/ML-ready; (2) assemble a multi-disciplinary advisory committee consisting of ethicists, legal scholars, policy analysts, biomedical investigators, data scientists, and learning designers to provide feedback; and (3) conduct formative and summative assessments. The training modules developed and tested in the proposed project can inform the development of data-informed interventions to prevent obesity, mitigate its current high rates, and slow their projected rise as crucial steps in reducing cardiovascular disease.
