Opportunity Information: Apply for 26 512
The National Science Foundation (NSF) grant opportunity titled "Unlocking Dataset Value for AI-Enabled Scientific Discovery" focuses on getting more scientific and practical value out of datasets that already exist, especially community datasets that may have been collected for one purpose but could support many more. The core idea is that large, high-impact datasets often contain insights that are difficult to see with traditional approaches, and that modern AI methods can uncover patterns, relationships, and signals that were previously missed. NSF is aiming to support projects that make those datasets more usable by AI systems, more interoperable across disciplines, and more ready for automated analysis, so researchers can pursue new questions, including questions that were not part of the original reason the data was collected.
The program lays out three main technical goals. First, it seeks efforts that apply AI-based capabilities to feature extraction and metadata generation, as well as the integration of multiple datasets. In practice, this means using AI to derive more informative variables or representations from raw data (feature extraction), and to create richer, more consistent descriptions of the data (metadata) so other researchers and machines can understand what the data contains, how it was produced, and how it should be used. It also includes work that links datasets together, such as connecting measurements from different instruments, studies, institutions, or scientific fields, so that combined analyses become possible rather than siloed.
Second, NSF wants robust data pipelines that enable automated analysis of existing datasets and similar use cases by AI tools and systems. The emphasis here is not just on one-off scripts, but on repeatable and dependable workflows that can ingest data, validate it, transform it into AI-ready formats, run standardized preprocessing, and support downstream modeling and analysis. A strong project under this goal would likely address reliability, documentation, testing, provenance tracking, scalability, and the ability to re-run analyses as data updates. The intent is to reduce friction so AI-enabled discovery can become routine, not bespoke, and so that future users can apply comparable pipelines to related datasets.
Third, the program calls for augmenting and/or harmonizing existing datasets so they work better with AI data pipelines and automated analysis. Augmentation can include adding labels, annotations, derived variables, or supplemental contextual information that increases the dataset's analytical value. Harmonization typically means aligning formats, units, vocabularies, schemas, and ontologies so that data from different sources can be compared or merged without extensive manual cleanup. This is especially important for interdisciplinary research, where different communities may record similar concepts in incompatible ways. By making datasets more consistent and machine-actionable, NSF is trying to unlock broader reuse and more credible, reproducible AI-based results.
Beyond technical improvements, NSF explicitly expects proposals to address dataset security and integrity. This includes protecting the data against unauthorized access or tampering, ensuring that the dataset remains trustworthy over time, and maintaining clear controls around versioning, access policies, and auditability. Integrity also connects to scientific reliability: users need to be able to trace what changed, when it changed, and why it changed, and they need confidence that the dataset they are analyzing is authentic and has not been corrupted. Proposals are also expected to cover governance, including how the broader scientific community will contribute to, maintain, and improve the datasets. That governance component can include rules for contributions, moderation or review processes, quality control standards, stewardship responsibilities, and mechanisms to resolve disputes or manage updates in a transparent way.
NSF encourages proposers to build on existing infrastructure rather than reinventing it. Examples mentioned include NSF data platforms, the NSF Integrated Data Systems and Services program, the NSF-led National AI Research Resource, the Genesis Mission platform, and other national infrastructure. This signals that NSF is looking for projects that plug into widely used ecosystems, leverage established resources, and create outcomes that are sustainable and broadly beneficial. The solicitation also notes that NSF is open to exploring partnerships with philanthropy, private industry, or non-profit organizations to expand support or enable additional collaborative opportunities, as long as those partnerships advance AI-driven discovery by increasing the value of high-impact scientific datasets.
The opportunity aligns with NSF-wide themes such as expanding participation in STEM and adhering to agency priorities established by Congress, the administration, and NSF leadership. Applicants are encouraged to review NSF priorities and align their projects where appropriate. NSF also highlights expectations around "Gold Standard Science," which is essentially a call for high rigor, strong scientific integrity, and practices appropriate to the field and research modality. In a data and AI context, that typically implies careful validation, transparent methods, reproducibility, responsible documentation, and thoughtful treatment of bias, uncertainty, and limitations.
Eligibility is broad across the U.S. research and innovation ecosystem. Eligible applicants include U.S.-based for-profit organizations (including small businesses) with strong scientific or engineering research or education capabilities; non-profit, non-academic organizations such as museums, observatories, independent research labs, and professional societies; other federal agencies and Federally Funded Research and Development Centers (FFRDCs) subject to NSF eligibility guidance; state and local governments; and accredited U.S. institutions of higher education, including two- and four-year institutions and community colleges. Tribal Nations that are federally recognized are also eligible. For U.S. universities with international branch campuses, proposals that involve funding work at a branch campus must clearly explain the benefits to the project and why the work cannot be performed at the U.S. campus.
From the opportunity metadata, this is an NSF discretionary grant competition in the science and technology research and development category, with CFDA numbers listed as 47.041, 47.049, 47.050, 47.070, 47.074, and 47.084. The funding opportunity number is listed as 26 512. The original closing date is 2026-11-04, and NSF anticipates around 50 awards. An award ceiling is not specified in the provided listing, which usually means applicants need to consult the full solicitation or NSF guidance for typical award sizes, budgeting expectations, and any limits that may apply by project type.
Overall, NSF is signaling strong interest in projects that treat datasets as long-term scientific assets: making them AI-ready, easier to integrate, securely managed, and governed in a way that invites community participation. The payoff NSF is aiming for is faster and more reliable AI-enabled discovery, broader reuse of valuable data, and the ability for researchers from different fields to ask new kinds of questions using datasets that have already required significant public investment to collect.Apply for 26 512
- The U.S. National Science Foundation in the science and technology and other research and development sector is offering a public funding opportunity titled "Unlocking Dataset Value for AI-Enabled Scientific Discovery" and is now available to receive applicants.
- Interested and eligible applicants and submit their applications by referencing the CFDA number(s): 47.041, 47.049, 47.050, 47.070, 47.074, 47.084.
- This funding opportunity was created on 2026-07-21.
- Applicants must submit their applications by 2026-11-04. (Agency may still review applications by suitable applicants for the remaining/unused allocated funding in 2026.)
- The number of recipients for this funding is limited to 50 candidate(s).
- Eligible applicants include: Others.
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FAQs: NSF "Unlocking Dataset Value for AI-Enabled Scientific Discovery"
What is the goal of the NSF opportunity "Unlocking Dataset Value for AI-Enabled Scientific Discovery"?
The opportunity focuses on getting more scientific and practical value out of existing datasets, especially community datasets that may have been collected for one purpose but could support many more. NSF is emphasizing that modern AI methods can uncover patterns, relationships, and signals in high-impact datasets that traditional approaches may miss.
What kinds of datasets is NSF most interested in?
NSF emphasizes existing datasets, particularly large, high-impact, and community datasets. The intent is to treat these datasets as long-term scientific assets that can be reused broadly and support new questions beyond the original purpose for which they were collected.
How does AI fit into this program?
AI is central to the program because it can help extract new features, generate richer metadata, integrate multiple datasets, and enable automated analysis through robust pipelines. NSF is aiming for datasets that are more usable by AI systems, more interoperable across disciplines, and more ready for automated analysis.
What are the three main technical goals described in the opportunity?
The program lays out three technical goals: (1) AI-based feature extraction, metadata generation, and multi-dataset integration; (2) robust, repeatable data pipelines that enable automated AI analysis; and (3) augmentation and/or harmonization of existing datasets to work better with AI pipelines and automated analysis.
What does "AI-based feature extraction" mean in the context of this program?
It refers to using AI to derive more informative variables or representations from raw data, so that downstream analysis and modeling can reveal insights that are not easily accessible with conventional methods.
What does NSF mean by "metadata generation"?
Metadata generation means creating richer, more consistent descriptions of datasets so that other researchers and machines can understand what the data contains, how it was produced, and how it should be used. The program highlights using AI to help produce that metadata at scale and with greater consistency.
What does "integration of multiple datasets" involve?
It involves linking datasets together so combined analyses become possible rather than siloed. Examples include connecting measurements across different instruments, studies, institutions, or scientific fields to enable broader, cross-cutting discovery.
What is meant by "robust data pipelines" in this solicitation?
Robust pipelines are repeatable and dependable workflows (not one-off scripts) that can ingest data, validate it, transform it into AI-ready formats, run standardized preprocessing, and support downstream modeling and analysis. The emphasis is on reducing friction so AI-enabled discovery becomes routine and reproducible.
What characteristics does NSF highlight for strong data pipelines?
The opportunity points to reliability, documentation, testing, provenance tracking, scalability, and the ability to re-run analyses as data updates. The aim is to support automated analysis in a dependable way over time.
What does "augmentation" of datasets mean here?
Augmentation includes adding labels, annotations, derived variables, or supplemental contextual information that increases a dataset's analytical value and makes it more useful for AI-enabled discovery.
What does "harmonization" of datasets mean here?
Harmonization means aligning formats, units, vocabularies, schemas, and ontologies so data from different sources can be compared or merged without extensive manual cleanup. This is particularly important for interdisciplinary work where communities represent similar concepts in incompatible ways.
Why is NSF emphasizing interoperability across disciplines?
Because interdisciplinary research often requires combining or comparing datasets that were created under different community standards. By improving interoperability and making data more machine-actionable, NSF is aiming to unlock broader reuse and more credible, reproducible AI-based results.
What does NSF expect regarding dataset security and integrity?
Proposals are expected to address dataset security and integrity, including protection against unauthorized access or tampering, ensuring the dataset remains trustworthy over time, and maintaining controls around versioning, access policies, and auditability.
How does versioning relate to integrity in this program?
Integrity includes being able to trace what changed, when it changed, and why it changed. Clear versioning supports scientific reliability by ensuring users can identify exactly which dataset version was analyzed and confirm the dataset has not been corrupted.
What does NSF mean by governance for datasets?
Governance covers how the broader scientific community will contribute to, maintain, and improve datasets over time. The opportunity mentions elements such as rules for contributions, moderation or review processes, quality control standards, stewardship responsibilities, and transparent mechanisms to resolve disputes or manage updates.
Does NSF encourage building on existing infrastructure?
Yes. NSF explicitly encourages proposers to build on existing infrastructure rather than reinventing it, signaling interest in projects that plug into widely used ecosystems and produce sustainable, broadly beneficial outcomes.
What examples of infrastructure are mentioned?
The opportunity mentions NSF data platforms, the NSF Integrated Data Systems and Services program, the NSF-led National AI Research Resource, the Genesis Mission platform, and other national infrastructure.
Are partnerships with non-NSF entities allowed or encouraged?
NSF notes it is open to exploring partnerships with philanthropy, private industry, or non-profit organizations to expand support or enable additional collaborative opportunities, as long as the partnerships advance AI-driven discovery by increasing the value of high-impact scientific datasets.
How does this opportunity align with NSF-wide priorities?
The opportunity aligns with NSF themes such as expanding participation in STEM and adhering to agency priorities established by Congress, the administration, and NSF leadership. Applicants are encouraged to review NSF priorities and align their projects where appropriate.
What is "Gold Standard Science" in this context?
NSF highlights expectations around "Gold Standard Science," emphasizing high rigor, strong scientific integrity, and practices appropriate to the field and research modality. In a data and AI context, this typically implies careful validation, transparent methods, reproducibility, and responsible documentation, including thoughtful treatment of bias, uncertainty, and limitations.
Who is eligible to apply?
Eligibility is broad across the U.S. research and innovation ecosystem. Eligible applicants include U.S.-based for-profit organizations (including small businesses) with strong scientific or engineering research or education capabilities; non-profit, non-academic organizations (such as museums, observatories, independent research labs, and professional societies); other federal agencies and FFRDCs (subject to NSF eligibility guidance); state and local governments; accredited U.S. institutions of higher education (including two- and four-year institutions and community colleges); and federally recognized Tribal Nations.
Are international branch campuses of U.S. universities addressed?
Yes. For U.S. universities with international branch campuses, proposals that involve funding work at a branch campus must clearly explain the benefits to the project and why the work cannot be performed at the U.S. campus.
What is the funding opportunity number?
The funding opportunity number listed is 26 512.
What is the closing date for this opportunity?
The original closing date listed is 2026-11-04.
How many awards does NSF anticipate making?
NSF anticipates around 50 awards.
Is there an award ceiling listed?
An award ceiling is not specified in the provided listing. That typically means applicants should consult the full solicitation or NSF guidance for typical award sizes, budgeting expectations, and any limits that may apply by project type.
What category is this grant competition listed under?
It is listed as an NSF discretionary grant competition in the science and technology research and development category.
Which CFDA numbers are associated with this opportunity?
The listing includes CFDA numbers 47.041, 47.049, 47.050, 47.070, 47.074, and 47.084.
What kinds of outcomes is NSF ultimately trying to achieve?
NSF is aiming for faster and more reliable AI-enabled discovery, broader reuse of valuable data, and the ability for researchers across fields to ask new kinds of questions using datasets that already required significant public investment to collect.
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