Research Infrastructure: Category II: MESA (Multidisciplinary Environment for Scientific Advancement)

The goal of project MESA (Multidisciplinary Environment for Scientific Advancement) is to build a shared, open-source platform in which scientific data from many fields are automatically described, organized, and connected, so any researcher can find and use them in minutes to hours instead of weeks to months. At its core, metadata-enabled scientific agents read each new dataset, attach descriptions drawn from community-curated standards, and recommend how it can be combined with related information from other disciplines. It will enable researchers spanning diverse disciplines, such as astronomy, biology, environment, public health, and computer science to expedite their discoveries and engage in seamless cross-disciplinary collaboration. The project will be developed and tested with established NSF-supported synthesis centers in environmental data science (ESIIL) and molecular and cellular science (NCEMS), and in AI in agriculture (AIIRA), as well as the international Event Horizon Telescope (EHT) Collaboration. The MESA outreach model ensures that benefits reach real use cases across the range of institutions with diverse levels of research activity and that graduate students, postdoctoral researchers, and professional staff are trained in the design and use of trustworthy, agentic AI for science. MESA will be implemented as a federated, cloud-native data-mesh and data lakehouse coupled to an agentic AI layer operating as an integrated data system and service. There are three coordinated technical objectives: agentic AI-powered metadata generation; cross-domain data integration linking datasets through interoperable APIs, shared ontologies, and policy-driven governance; and agentic orchestration managing workflows and translating user intent into reproducible research objects. MESA will operate on public NSF cyberinfrastructure accessed through ACCESS-CI (Texas Advanced Computing Center, Open Storage Network, CyVerse, and Jetstream-2 Cloud) and the integrated Rule-Oriented Data System (iRODS) from the Renaissance Computing Institute (RENCI). The two-year prototype effort includes automated metadata generation for astronomy simulations, life-science data repositories, multi-omics integration, precision agriculture, sensor networks, and environmental data synthesis to demonstrate platform capability. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria. NSF Award ID: 2608717 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Tyson Swetnam | Institution: University of Arizona, TUCSON, AZ | Award Amount: $4,617,408 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2608717 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2608717.html