From the Research Universities edition of October 3, 2026
NSF 26-512 AI Datasets: up to $100M; proposals must address governance, security, integrity; deadline 4 Nov 2026
U.S. National Science Foundation (multi-directorate; cognizant: aidatasets@nsf.gov) · Federal research funding / AI-enabled scientific data infrastructure · United States (eligible IHEs, nonprofits, for-profits, state/local/tribal, FFRDCs per solicitation)
- Publisher
- NSF Program Solicitation 26-512
- Original publication
- Posted July 21, 2026 (NSF 26-512); full proposal deadline November 4, 2026 (newly time-critical relative to Oct 2026 campus proposal cycles)
- Source retrieved
- 2026-10-04
- Event date
- 2026-11-04
What happened
NSF Unlocking Dataset Value for AI-Enabled Scientific Discovery (NSF 26-512) funds enhancing existing scientific datasets for AI pipelines—not de novo collection. Anticipated $60–100M; Impact awards up to $2M, Flagship up to $5M, Planning up to $200k; ~25–50 awards. Full proposals due 4 Nov 2026 (then first Wednesday in November annually). All proposals must address dataset governance (access, retirement, community contribution) and dataset security/integrity (provenance, trust, quality, privacy, authenticity), including research-security issues for sensitive data. Encouraged leverage of NSF data platforms, IDSS, NAIRR, and DOE Genesis Mission / American Science and Security Platform. Success measures include datasets expanded, AI-guided techniques, STEM training, and infrastructure usage. Out of scope: primary new data collection (stays with directorates).
Why it matters
University research administration and research-computing data programs preparing AI-for-science proposals need explicit governance/security/integrity plans aligned to NSF 26-512 and Genesis-adjacent infrastructure—especially with the 4 Nov 2026 deadline.
Evidence and measured results
Opened NSF solicitation page via WebFetch; confirmed deadline, award bands, and mandatory governance/security language; check_sources: not archived (NSF 26-513 hubs solicitation already archived).
Limitations and uncertainty
Solicitation text is requirements/funding guidance, not measured deployment outcomes; award counts and $60–100M subject to appropriations; July posting included now because Nov 4 deadline makes it newly actionable for Oct campus cycles.
Put this evidence to work
Lighthouse Advisory interpretation, grounded in this source. Enriched 2026-10-03; this does not change the original publication date. Each role follows the same checklist so takeaways compare across sources.
Sales
Role takeaway
- Customer problem
- Campuses hold valuable scientific datasets that are not AI-pipeline ready and lack governance/security plans NSF now requires for major AI-data investments.
- Stakeholders
- VPR / sponsored programs
- Research data librarians
- Domain PIs
- Research computing / cyberinfrastructure
- Research security officers
- Discovery questions
- Which existing datasets could yield new AI-driven inquiries without new collection?
- What access, retirement, and contribution policies exist today?
- How will sensitive data and research-security constraints be handled?
- Value hypothesis
- Compliant Impact/Planning proposals that pair dataset enhancement with governance/security can compete for NSF 26-512 while positioning for NAIRR/Genesis interoperability.
- Potential engagement
- Three-week pre-deadline proposal packaging workshop covering dataset inventory, governance/security drafts, and award-track selection.
- Unsupported claims
- Guaranteed NSF award
- Funding for de novo data collection under this solicitation
- Automatic Genesis Mission integration
Pre-sales engineering
Role takeaway
- Fit
- University data platforms enhancing legacy scientific datasets for AI training/automation with explicit governance.
- Architecture and integration
- Feature/metadata/integration pipelines; provenance and access layers; contribution APIs; optional NAIRR/Genesis connectors.
- Prerequisites
- Dataset inventory
- Identity/access model
- Provenance tooling
- Community contribution process
- Constraints
- No de novo collection
- Sensitive-data limits
- Sustainment beyond award
- Single-org lead with subawards only
- Security
- Document provenance, authenticity, privacy, and research-security controls for extraction and sharing; segregate sensitive corpora.
- Proof of value
- Measurable dataset usability improvements
- Documented governance artifacts
- Pilot AI-pipeline consumers
- Adoption metrics from target communities
Delivery
Role takeaway
- Work
- Inventory datasets, draft Sections 1–4 (and Flagship 5 if needed), coordinate keywords/directorates, complete security/governance plans, submit by 4 Nov 2026 local time.
- Dependencies
- Sponsored programs
- Data stewards
- InfoSec/research security
- Community letters of collaboration
- Ownership
- Lead PI owns scientific vision; research computing/libraries own pipeline sustainment; AOR owns institutional submission.
- Skills and adoption
- Train data stewards on AI-pipeline readiness and contribution workflows; brief PIs on Gold Standard Science / integrity expectations.
- Governance checkpoints
- Dataset sensitivity review
- Access/retirement policy sign-off
- Research-security screening
- Pre-submission compliance check
- Acceptance criteria
- Complete governance and security sections
- Named beneficiary communities and keywords
- Clear enhancement methods (not new collection)
- On-time Research.gov/Grants.gov submission
- Risks
- Deadline slip
- Scope creep into de novo collection
- Unresolved sensitive-data sharing
- Weak community commitment letters
Implementation considerations
Lighthouse Advisory interpretation across the operating dimensions a public-sector buyer must settle before this evidence becomes a design. Each note answers the question under its heading for this specific source.
Architecture and integration
What must connect, and where does the AI sit in the workflow?
Design AI-ready dataset pipelines with provenance, access controls, contribution workflows, and hooks to NAIRR/Genesis-class platforms; planning grants for community coordination before Flagship scope.
Governance
Who approves, reviews and stays accountable for outcomes?
Treat governance and security sections as first-class proposal content equal to scientific vision; define access, retirement, and contribution processes before claiming AI-agent readiness.
Security and privacy
What data, permissions and controls need testing?
Address sensitive datasets, authenticity/provenance, extraction risks, and research-security constraints; do not assume open release of all enhanced artifacts.
Accessibility and workforce
Who is affected, and what skills or accommodations follow?
Solicitation explicitly measures undergraduates/graduate students trained in AI use of scientific datasets; planning grants can broaden community participation beyond a single lab.
Procurement
What should contracts, pricing and exit terms secure?
Align campus data-platform procurements and cost proposals with NSF’s no voluntary cost-sharing rule and expected PI-meeting/NAIRR travel budgets.
Operating model
Which teams own the service once it runs?
Treat AI-ready dataset stewardship as an institutional program with governance owners, not a one-off PI grant artifact.
Publication history
- 2026-10-03Research Universities · Issue 263 resources
Stable resource ID: nsf-26-512-ai-datasets-solicitation-20260721