Category III: Planning Federated AI-Ready Cyberinfrastructure for Advanced Microscopy and Imaging: A Teach-Explore-Design Framework for Community-Driven Infrastructure

The project will investigate the feasibility, community acceptance, and design requirements for a federated, cross-domain, AI-ready data cyberinfrastructure. The proposed concept of a national data system serving domains of advanced microscopy, imaging, materials, biology, and environmental sciences connects workflows across institutions without requiring all raw data to be centralized. This effort will help transform isolated local repositories into reusable national resources for more reproducible, AI-enabled discoveries, while maintaining local organizational policies and control of data. The project will examine a federated approach, where data remain close to the instruments and institutions that produce them, while shared services support discovery, provenance, trust, and AI-enabled reuse, with a goal to provide a more scalable and sustainable national infrastructure. The planning work will use a Teach–Explore–Design framework: first helping scientists and cyberinfrastructure stakeholders imagine what such a future system could enable, then studying their workflows, concerns, and constraints, and finally co-designing candidate architectures, operating models, and responsible-AI practices with them. The project will produce community-vetted evidence and design artifacts, including candidate federated architectures, operating models, trust and provenance concepts, and responsible-AI workflow patterns, to guide a future national-scale cyberinfrastructure effort. 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: 2609490 | Program: 01002627DB NSF RESEARCH & RELATED ACTIVIT | Principal Investigator: Lewis Tseng | Institution: University of Massachusetts Lowell, LOWELL, MA | Award Amount: $499,490 View on NSF Award Search: https://www.nsf.gov/awardsearch/show-award/?AWD_ID=2609490 View on Research.gov: https://www.research.gov/awardapi-service/v1/awards/2609490.html