Supplemental Training in Making Data FAIR and AI/ML Ready

PROJECT SUMMARY In 2018, the NIH Strategic Plan for Data Science identified a number of goals and cross-cutting themes to address in order to maximize the value of data generated through NIH-funded efforts. This included the enhancement of data sharing, access, and interoperability of NIH-supported data resources. One key barrier to achieving this goal is the lack of biomedical scientists with the ability to apply data science techniques to maximize the usability of the data and metadata produced by their research. Our NIEHS-supported training grant (T32 ES007322) provides a single, unified training program for 18 predoctoral students and 8 postdoctoral fellows within the environmental health sciences. Our program is designed to ensure trainees acquire skills in advanced data analytics to complement their primary training in environmental epidemiology, climate science, molecular mechanisms of disease, and the exposome. The integration of additional training in making diverse epidemiologic, toxicological and clinical data findable, accessible, interoperable and reusable (FAIR) and ready for use with artificial intelligence and machine learning (AI/ML) is a natural progression for our multi-disciplinary training program. We also benefit from the co-location of two other NIH-funded training grants, in nursing informatics and neuroscience, with activities training biomedical researchers in data science. We aim to leverage our collective expertise to develop a multidisciplinary curriculum that enables our trainees to develop the competencies and skills needed to make diverse biomedical data FAIR and AI/ML-ready. This curriculum will be designed to be flexible and module-based so it can be implemented in-full, as part of existing training seminars, or as stand-alone bootcamps, depending upon the needs of individual training programs. Our novel curriculum will combine didactic seminars, guided discussions and hands-on training activities to develop competencies and skills in use of data standards, the FAIR principles and AI/ML-readiness. This module-based curriculum will be centered on core foundational concepts, such as ontologies, common data elements and metadata annotation. To construct these modules, we will draw upon expertise from faculty both internal and external to Columbia University from within the fields of semantic science, information science, environmental health data science, and computer science. We will consult with educational professionals who will advise on evidence-based curricular design and provide independent evaluation of our curriculum and training activities using both quantitative and qualitative measures. Following successful evaluation, we propose to incorporate the developed curriculum and training activities into multiple existing training programs. Recorded lectures, discussion guides and training materials will be made available within a shared resource library. Formalizing supplementary training in the FAIR principles and ML/AI-readiness across our multiple training programs will accelerate the achievement of research training aims and develop a cadre of scientists poised to advance biomedical research through the application of data science.