PROJECT SUMMARY - COMMUNITY ENGAGEMENT CORE Significance: Energy, transportation, land use and infrastructure policies and choices are hampered by a lack of transdisciplinary and community-informed data, fragmented health surveillance systems, and insufficient local and regional environmental health data. Community engagement is pivotal for meaningful data collection and effective decision making, yet challenges arise from the digital divide. An integrative data approach, and advanced data science methods, can address these complexities and lead to effective policies and ensure their adoption with community involvement. Innovation: Central to our Data Science Core (DSC) is a groundbreaking initiative: the union of data science with deep, reciprocal community interaction. Our team aims to embed community feedback into every facet of research, including data collection, synthesis, and dissemination of results. Historically, community-focused research and bioinformatics have been distinct fields. This initiative strives to bridge the gap by “bringing the power of data to all the stakeholders.” Our proposed approach includes: 1) a revolutionary, crowdsourced, community-based data collection system, in which data is relevant and actionable, 2) a holistic approach to data integration, combining diverse disciplines, 3) advanced data analysis using Artificial Intelligence (AI) and Machine Learning (ML), and 4) effective communication of results to all stakeholders, including community, academia, government, and public. Our team has extensive experience in developing and applying analytical techniques and public interfaces that can enrich research projects facilitated by the Center. Building upon prior work of community-engaged data science from national to local scales, the proposed research activities will consist of three primary aims: 1) Community Integration with Data which underscores the importance of embedding community feedback into our data foundations. The DSC will develop state-of-the-art informatics tools to effectively engage communities, including, an AI-assisted tool translating findings into digestible insights for the wider public, 2) Real-World Data Commons which revolves around the creation of a prototype integrating community insights with multifaceted data on the environment, policy, and health. It will also serve as a public resource, providing scripts, datasets, and visualization tools that ensures community voices are central, through collaboration with the Community Engagement Core, and 3) Analytical Support which provides robust analytics for the center's various projects. This includes advanced air quality monitoring and modeling, with integration of ML to enhance forecasting accuracy. Overall impact: The DSC’s proposed infrastructure is poised to set a precedent whereby it serves as a national blueprint for environmental health policy researchers. Adhering to NIH data sharing principles, this infrastructure will act as a comp...