SUMMARY Significant disparities in preventing, treating, and managing multiple chronic diseases exist along intersecting racial, cultural, socio-economic, and vulnerable population contextual lines. Meaningful community engagement and culturally informed multilevel approaches are required to effectively reduce these disparities. The UCSF Research Coordinating Center (RCC) leverages our significant expertise, experience, and capacity in community-engaged research, disparities research, program implementation with underserved and vulnerable populations, data science, and major chronic diseases to serve and support a nationwide disparities research consortium through coordinating and technical assistance activities. This Supplement will contribute to multiple goals of the National Artificial Intelligence Research Resource (NAIRR) (e.g. capacity building, trustworthy AI, workforce diversity) by enhancing these two existing RCC Specific Aims: Aim-2) Implement RD-MCD Consortium-wide Common Data Element Development, Collection, Integration, Curation, Analysis, and Sharing; and Aim-4) Facilitate and Monitor Vibrant Community-Engaged Research. During this Supplement, the RCC will (a) Expand data on chronic disease and social determinant of health data in health disparity populations. (b) Test the community (neighborhood) de-identification process's robustness via AI-assisted re-identification testing and risk analysis. (c) Demonstrate a case study of using core common data elements to enhance data set interoperability and utilization of resulting aggregated data sets. (d) Develop community-facing training materials and conduct training sessions to enhance broader community awareness of AI. (e) Participate in ScHARe Think-a-Thons to highlight using HEAN data in research.