NSF announces $83M investment in integrated data systems and services to advance AI-driven science and strengthen U.S. research infrastructure

The U.S. National Science Foundation today announced $83 million in awards through the Integrated Data Systems and Services (IDSS) program. The awards will expand access to data infrastructure resources that researchers can use alongside computing and artificial intelligence resources to accelerate scientific discovery and innovation.

IDSS supports national priorities to strengthen U.S. leadership in AI, build an AI-ready workforce and ensure researchers have the tools and infrastructure needed to compete globally. The program advances a national AI research ecosystem by connecting scientific data with computing, instruments, software and AI resources, complementing the NSF-led National Artificial Intelligence Research Resource (NAIRR) and other cyberinfrastructure investments. These investments also support the White House's Genesis Mission by strengthening the data infrastructure that enables researchers to make discoveries, use AI and advanced computing and accelerate innovation across the U.S. research enterprise.

"America's leadership in artificial intelligence depends not only on advanced computing resources but also on the data infrastructure that enables researchers to discover, access, share and analyze scientific data at scale," said Brian Stone, performing the duties of the NSF director. "These investments provide foundational capabilities that empower scientists and engineers to drive transformative AI-enabled discovery, accelerate innovation and strengthen national competitiveness."

The IDSS program invests in national-scale data systems that advance data-intensive and AI-driven science, engineering research, innovation and education. Because data is essential for scientific discovery, these investments will improve how scientific data is collected, stored, shared and analyzed across NSF facilities, research centers and national laboratories.

The awards also connect data systems, AI models and computing resources so they work together more effectively, while creating shared platforms that make it easier for researchers to find, use and reuse data across different projects and fields. In addition, they provide access to AI tools and advanced computing systems that help researchers use AI more effectively in their work.

Collectively, these investments will enable scientists to focus on generating new insights from data rather than managing underlying infrastructure. The IDSS awards and project descriptions are provided below.

Category I: National-Scale Integrated Data Systems and Services

• Fabric for AI-Driven Science (FabAID), led by the Morgridge Institute for Research in Madison, Wisconsin, will build a national data fabric that connects repositories, computing resources and cyberinfrastructure, including NAIRR, to streamline access and curation of large-scale scientific datasets for AI-driven research.
• National Data Platform (NDP), led by UC San Diego, will create an AI-ready ecosystem that integrates distributed data, computing facilities and AI resources across the U.S. to support interoperable, scalable and reproducible AI-driven scientific workflows.

Category II: Transition to National-Scale Operations

• Interactive Discovery Laboratory (iDLab), led by UCLA, will provide a unified web-based platform that connects researchers to computing and data resources across NSF-supported high-performance computing sites and cloud systems, enabling streamlined workflows for AI-enabled research and expanding STEM education and training through browser-based environments.
• BRIDGE National Center, led by the University of California, Irvine, will deliver an open, cloud-based infrastructure for reusable data science workflows, datasets and machine learning models to improve reproducibility and accelerate AI-driven discovery.
• National Science Data Fabric (NSDF), led by The University of Tennessee, Knoxville, will transition a pilot into a national service that connects researchers to distributed scientific data through standardized frameworks that make data easier to find, access, and reuse. It will enable secure and reproducible workflows and support workforce development programs in data science and AI.
• Multidisciplinary Environment for Scientific Advancement (MESA), led by The University of Arizona, will develop an AI-driven data platform that uses metadata automation and intelligent agents to connect and organize scientific data across disciplines, enabling interoperable and reproducible AI workflows.

In addition, several planning grants were awarded to support the development of future data infrastructure proposals under Category I and II.

The IDSS program strengthens the national research data infrastructure by improving access to scientific data and enabling its more effective use in combination with AI and computing resources. By making scientific data more accessible and actionable, these investments are expected to accelerate scientific discovery, advance innovation and expand AI-enabled research capabilities, helping translate federally supported research into benefits for the American public.

Learn more about the IDSS program.