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AI Datasets is NSF's answer to a bottleneck that has become obvious as foundation models spread through the sciences: the limiting factor is rarely model capacity and increasingly the state of the underlying data.
Vast quantities of scientific data already exist in repositories, instrument archives and lab collections, but most of it was assembled for a specific original purpose and is not structured, labeled, documented or integrated well enough for machine learning to use.
This program funds the work of making that existing data AI-ready rather than collecting new data - feature extraction, metadata generation, dataset integration across sources, and construction of durable data pipelines - so that datasets can support discovery well beyond what they were originally gathered to answer. The three-tier structure is the key strategic feature.
Planning awards of up to $200,000 let teams scope a dataset problem and build the partnerships needed before committing; Impact awards of up to $2,000,000 support focused enhancement of a specific community dataset; and Flagship awards of up to $5,000,000 target datasets of broad, cross-disciplinary consequence where the payoff justifies a major investment.
Groups without an established data-engineering track record should strongly consider entering at the Planning tier rather than reaching for Flagship, since the Flagship competition (5 to 10 awards) will be dominated by teams with existing repository infrastructure and demonstrated community uptake.
Six NSF directorates co-sponsor the program, so proposals that credibly serve more than one scientific community have a structural advantage. Eligibility is unusually broad for NSF - for-profit organizations, state and local governments, tribal nations and federal agencies may all submit alongside universities and non-profits - and there are no limits on the number of proposals per organization or on PI qualifications.
The November 4, 2026 deadline recurs on the first Wednesday in November annually thereafter, making this a program worth planning against over multiple cycles.
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Or search similar grants →According to the current listing, eligibility includes: Eligible proposers include accredited U.S. institutions of higher education, non-profit organizations, for-profit organizations, state and local governments, tribal nations, and federal agencies and FFRDCs. There are no restrictions on PI qualifications and no limit on the number of proposals per organization, which is unusually permissive for an NSF program of this size. Proposals must focus on enhancing the value of existing scientific datasets through AI-based methods rather than on new data collection. Applicants select one of three tiers - Planning (up to $200,000), Impact (up to $2,000,000, up to three years), or Flagship (up to $5,000,000, up to three years). The full proposal deadline is November 4, 2026, with subsequent deadlines on the first Wednesday in November annually thereafter. Submission is through Research.gov or Grants.gov per NSF Proposal and Award Policies and Procedures Guide requirements. Confirm the full requirements in the official notice before applying.
The current listing shows the program offers three award tiers: Planning awards of up to $200,000 per award, Impact awards of up to $2,000,000 per award over up to three years, and Flagship awards of up to $5,000,000 per award over up to three years. NSF anticipates 10 to 20 Planning awards, 10 to 20 Impact awards, and 5 to 10 Flagship awards, for a total of 25 to 50 awards. Total program funding is $60,000,000 to $100,000,000, subject to the availability of funds. Verify award ceilings, matching requirements, and allowable costs in the official notice.
Applications for NSF 26-512 Unlocking Dataset Value for AI-Enabled Scientific Discovery (AI Datasets) for Making Scientific Data AI-Ready are due November 4, 2026. Build your timeline backwards from this date to cover registrations, approvals, and final submission checks.
NSF 26-512 Unlocking Dataset Value for AI-Enabled Scientific Discovery (AI Datasets) for Making Scientific Data AI-Ready is funded by U.S. National Science Foundation (NSF), Directorates for Computer and Information Science and Engineering, Biological Sciences, Engineering, Geosciences, Mathematical and Physical Sciences, and Technology, Innovation and Partnerships. Verify program details on the funder's official page before applying.
Start from the official opportunity page linked in this listing — it carries the sponsor's submission instructions.
The NSF Computer and Information Science and Engineering: Future Computing Research (Future CoRe) program (NSF 25-543) supports foundational computing research and education with a strong emphasis on AI and machine learning. The program funds research that advances the foundations of computing including AI/ML theory, algorithms, systems, and applications. Future CoRe supports small to medium-sized research projects that can have significant impact on computing foundations. The program has target dates of February 5, 2026 and September 10, 2026 for proposal submissions (note: these are target dates, not deadlines, meaning proposals may be submitted at any time but will be reviewed in batches around these dates). Investigators may not serve as PI, co-PI, or Senior/Key Personnel on more than two proposals submitted within any consecutive 12-month period across all Future CoRe programs. This is one of NSF's primary mechanisms for funding fundamental AI and machine learning research at U.S. academic institutions, covering areas such as machine learning theory, natural language processing, computer vision, robotics foundations, and human-AI interaction.
The ONR Long Range Broad Agency Announcement (N00014-25-S-B001) is the Office of Naval Research's primary mechanism for soliciting research proposals across all naval science and technology priority areas. The BAA accepts proposals on a rolling basis through September 30, 2026 and covers ONR's full spectrum of research interests with particular emphasis on AI-related topics including autonomous maritime systems, human-machine teaming, machine learning for sensor fusion, cooperative autonomous swarm technology, undersea autonomy, and AI-enabled decision superiority. Proposals can be funded through multiple mechanisms including individual investigator grants, the Young Investigator Program (~$510K over 3 years for early-career faculty), and Multidisciplinary University Research Initiative (MURI) awards ($1.5M/year for 3-5 years for research teams). ONR recommends contacting relevant program officers before submitting to discuss alignment with current research priorities. The BAA supports basic research (6.1), applied research (6.2), and advanced technology development (6.3) across the full range of naval-relevant science and engineering disciplines.
DE-FOA-0003600 is the DOE Office of Science's FY 2026 open, rolling solicitation for financial assistance, providing roughly $500 million across seven program areas: Advanced Scientific Computing Research, Basic Energy Sciences, Biological and Environmental Research, Fusion Energy Sciences, High Energy Physics, Nuclear Physics, and Isotope R&D and Production. AI and machine learning research is supported directly through Advanced Scientific Computing Research, while Biological and Environmental Research funds atmospheric process research, environmental systems process research, and earth-energy systems modeling — making this a major channel for AI-enhanced climate and earth system modeling work. DOE anticipates 200 to 350 new awards ranging from $5,000 to $5 million each, with project periods from six months to five years. The FOA opened September 30, 2025 and accepts applications on a rolling basis through the end of FY 2026.
NSF's Arctic Research Opportunities solicitation funds roughly 75 awards a year — up to $50 million — across six program areas from natural sciences to social sciences to the Arctic Observing Network. The July 15, 2026 target date is not a hard deadline, and understanding that distinction is the first strategic decision an Arctic researcher makes. Here is how the six doors differ and how to choose the right one.
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