NSF announces $83 million in IDSS (Integrated Data Systems and Services) awards to support AI-driven data infrastructure for research.
On July 22, 2026, the U.S. National Science Foundation announced $83 million in awards through its Integrated Data Systems and Services (IDSS) program. These funds will expand researchers’ access to data infrastructure resources that can be deployed alongside computing and artificial intelligence capabilities to accelerate scientific discovery and innovation.
The IDSS program advances national priorities to strengthen U.S. leadership in artificial intelligence, cultivate an AI-ready workforce, and equip researchers with the tools necessary to compete globally. By linking scientific data with computing, instruments, software, and AI resources, the initiative bolsters the national AI research ecosystem. It complements the NSF-led National Artificial Intelligence Research Resource (NAIRR) and other cyberinfrastructure investments while supporting the White House’s Genesis Mission by fortifying the data infrastructure that enables AI and advanced computing-driven discoveries 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 targets national-scale data systems designed to advance data-intensive and AI-driven science, engineering research, innovation, and education. Recognizing data as essential to scientific progress, these investments will enhance how scientific data is collected, stored, shared, and analyzed across NSF facilities, research centers, and national laboratories.
The awards also integrate data systems, AI models, and computing resources to operate more cohesively, while establishing shared platforms that simplify how researchers locate, utilize, and repurpose data across disciplines and projects. Additionally, they grant access to AI tools and advanced computing systems that help researchers apply AI more effectively in their work. Collectively, these investments allow scientists to focus on generating new insights from data rather than managing underlying infrastructure.
Under Category I: National-Scale Integrated Data Systems and Services, two major initiatives were funded. Fabric for AI-Driven Science (FabAID), led by the Morgridge Institute for Research in Madison, Wisconsin, will develop a national data fabric connecting repositories, computing resources, and cyberinfrastructure—including NAIRR—to streamline access and curation of large-scale scientific datasets for AI-driven research. The National Data Platform (NDP), led by UC San Diego, will create an AI-ready ecosystem that integrates distributed data, computing facilities, and AI resources nationwide to support interoperable, scalable, and reproducible AI-driven scientific workflows.
Category II: Transition to National-Scale Operations includes four projects. The Interactive Discovery Laboratory (iDLab), led by UCLA, will launch a unified web-based platform linking researchers to computing and data resources across NSF-supported high-performance computing sites and cloud systems, enabling streamlined AI-enabled research workflows and expanding STEM education through browser-based environments. The 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. The 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 via standardized frameworks, enabling secure, reproducible workflows and supporting workforce development in data science and AI. Finally, the Multidisciplinary Environment for Scientific Advancement (MESA), led by The University of Arizona, will develop an AI-driven data platform leveraging metadata automation and intelligent agents to connect and organize cross-disciplinary scientific data, enabling interoperable and reproducible AI workflows.
Several planning grants were also awarded to support the development of future data infrastructure proposals under both categories. By improving access to scientific data and enabling its more effective integration with AI and computing resources, the IDSS program strengthens the national research data infrastructure. These investments are expected to accelerate scientific discovery, advance innovation, and expand AI-enabled research capabilities, ultimately translating federally supported research into tangible benefits for the American public.
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Source: NSF