RFAs: Unlocking Dataset Value for AI-Enabled Scientific Discovery (US)

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The Unlocking Dataset Value for AI-Enabled Scientific Discovery program is a National Science Foundation (NSF) funding opportunity that supports projects to improve the usability, quality, and interoperability of existing scientific datasets for artificial intelligence (AI)-enabled research. With an estimated $100 million in total funding, the program encourages researchers to enhance datasets, develop AI-ready data pipelines, strengthen data governance, and accelerate scientific discovery across multiple disciplines.

Overview of the Unlocking Dataset Value for AI-Enabled Scientific Discovery Program

The Unlocking Dataset Value for AI-Enabled Scientific Discovery program is an NSF initiative designed to increase the scientific value of existing research datasets through artificial intelligence and advanced data management techniques.

Rather than funding the creation of entirely new datasets, the programme focuses on improving existing scientific data so it can support new discoveries, interdisciplinary research, and AI-driven innovation.

Projects should help researchers maximise the long-term value of scientific data by making it easier to analyse, integrate, and reuse.

Purpose of the Program

The programme seeks to unlock new scientific knowledge by improving how existing datasets are organised, enriched, and analysed using AI technologies.

Its objectives include:

  • Enhancing existing scientific datasets for AI applications.
  • Increasing the long-term value and usability of research data.
  • Supporting interdisciplinary scientific research.
  • Enabling discoveries beyond the original purpose of the data.
  • Developing AI-ready data infrastructure.
  • Promoting responsible and secure data management.
  • Advancing scientific excellence through high-quality datasets.

Why This Program Matters

Scientific organisations have generated enormous volumes of valuable research data over many years. However, much of this information cannot easily be reused because datasets may be incomplete, inconsistent, or lack sufficient metadata.

This programme helps researchers:

  • Improve dataset quality.
  • Enable automated AI analysis.
  • Increase research reproducibility.
  • Support collaboration across scientific disciplines.
  • Accelerate scientific discovery.
  • Reduce duplication of research efforts.
  • Extend the value of existing publicly funded research.

By making datasets more accessible and AI-compatible, researchers can uncover insights that were previously difficult or impossible to identify.

Key Focus Areas

Projects should address one or more of the programme’s priority areas.

Advancing Scientific Community Datasets

Projects may improve datasets used by scientific communities by:

  • Enhancing quality.
  • Increasing accessibility.
  • Improving documentation.
  • Supporting long-term reuse.

AI-Based Feature Extraction

Applicants are encouraged to use AI techniques to:

  • Identify new data features.
  • Extract meaningful information.
  • Improve dataset quality.
  • Generate additional research value.

Metadata Generation

Projects may develop automated systems that:

  • Generate metadata.
  • Improve dataset descriptions.
  • Standardise data documentation.
  • Increase dataset discoverability.

Dataset Integration

The programme supports projects that combine information from multiple datasets to create richer resources for AI-enabled research.

Examples include:

  • Multi-source scientific datasets.
  • Cross-disciplinary research databases.
  • Integrated research repositories.

AI Data Pipelines

Applicants may develop robust data pipelines capable of:

  • Automated data processing.
  • AI-assisted analysis.
  • Continuous dataset improvement.
  • Scalable scientific workflows.

Dataset Harmonisation

Projects may:

  • Standardise data formats.
  • Improve compatibility between datasets.
  • Prepare datasets for machine learning applications.
  • Enhance interoperability.

Data Security and Integrity

Projects should include measures that protect:

  • Dataset integrity.
  • Data quality.
  • Secure access.
  • Responsible data management.

Data Governance

Applicants are encouraged to establish governance models that support:

Leveraging Existing Research Infrastructure

The NSF encourages applicants to build upon existing national research infrastructure where appropriate.

Examples include:

  • NSF data platforms.
  • NSF Integrated Data Systems and Services (IDSS).
  • National AI Research Resource (NAIRR).
  • Genesis Mission platform.
  • Other national scientific research infrastructure.

Applicants may also collaborate with:

  • Philanthropic organisations.
  • Private industry.
  • Non-profit organisations.
  • Scientific research networks.

These partnerships can strengthen proposals and expand opportunities for AI-enabled scientific discovery.

Funding Information

The programme provides:

  • Estimated total funding available: $100,000,000

The NSF expects to support multiple projects that improve scientific datasets and AI research infrastructure.

Who Is Eligible?

Eligible applicants include:

  • U.S.-based for-profit organisations, including small businesses.
  • Non-profit non-academic organisations.
  • Independent museums.
  • Observatories.
  • Research laboratories.
  • Professional societies.
  • Other Federal Agencies.
  • Federally Funded Research and Development Centers (FFRDCs).
  • State governments.
  • Local governments.
  • Accredited two-year colleges.
  • Accredited four-year colleges and universities.
  • Community colleges.
  • Federally recognized Tribal Nations.

International Branch Campuses

Proposals involving international branch campuses of U.S. higher education institutions must:

  • Demonstrate clear benefits to the project.
  • Explain why the proposed activities cannot reasonably be conducted at the U.S. campus.

How the Program Works

Step 1: Identify Existing Scientific Datasets

Applicants should select existing datasets that have significant potential for improvement through AI technologies.

Step 2: Design an AI Enhancement Strategy

Projects should describe how they will:

  • Improve dataset quality.
  • Generate metadata.
  • Extract new features.
  • Integrate datasets.
  • Support automated AI analysis.

Step 3: Build AI-Compatible Data Infrastructure

Applicants may develop:

  • Automated workflows.
  • Data pipelines.
  • AI-ready repositories.
  • Dataset management systems.

Step 4: Ensure Responsible Data Management

Projects should include plans for:

  • Security.
  • Data integrity.
  • Governance.
  • Community participation.
  • Long-term sustainability.

Step 5: Advance Scientific Discovery

Enhanced datasets should enable researchers to:

  • Conduct new analyses.
  • Explore interdisciplinary questions.
  • Generate new scientific knowledge.
  • Support future AI research.

How to Apply

Applicants should prepare a proposal that clearly demonstrates how the project will improve existing scientific datasets and advance AI-enabled research.

Step 1: Confirm Organisational Eligibility

Verify that your organisation falls within one of the eligible applicant categories.

Step 2: Identify the Scientific Dataset

Describe:

  • The dataset.
  • Its current limitations.
  • Its scientific value.
  • Opportunities for AI enhancement.

Step 3: Develop a Technical Proposal

The proposal should explain:

  • AI methods.
  • Dataset enhancement strategy.
  • Metadata generation.
  • Data integration.
  • Security measures.
  • Governance framework.
  • Expected scientific outcomes.

Step 4: Demonstrate Collaboration

Where appropriate, explain partnerships with:

  • Research institutions.
  • Scientific communities.
  • Industry.
  • Non-profit organisations.
  • National research infrastructure providers.

Step 5: Submit the Proposal

Complete the application according to NSF requirements and include all required technical, administrative, and budget documentation.

Common Mistakes Applicants Should Avoid

1. Proposing New Data Collection Instead of Dataset Enhancement

The programme focuses primarily on increasing the value of existing scientific datasets.

2. Weak AI Integration

Clearly explain how artificial intelligence will improve dataset usability and scientific discovery.

3. Insufficient Data Governance

Strong proposals include detailed plans for governance, stewardship, and community participation.

4. Ignoring Security and Data Integrity

Applicants should describe how datasets will remain secure, accurate, and trustworthy.

5. Limited Scientific Impact

Explain how improved datasets will enable discoveries beyond the original purpose of the data.

Tips for a Strong Application

Applicants can strengthen their proposals by:

  • Selecting high-value scientific datasets with broad research potential.
  • Demonstrating clear AI applications.
  • Including interdisciplinary collaborations.
  • Leveraging existing NSF research infrastructure.
  • Providing robust plans for metadata generation and dataset harmonisation.
  • Explaining long-term sustainability and governance.
  • Demonstrating measurable scientific impact.

Frequently Asked Questions (FAQ)

1. What is the Unlocking Dataset Value for AI-Enabled Scientific Discovery program?

It is an NSF funding programme that supports projects to improve existing scientific datasets for artificial intelligence-enabled research and scientific discovery.

2. How much funding is available?

The programme has an estimated total funding of $100,000,000.

3. What types of projects are supported?

Projects that enhance existing datasets through AI, metadata generation, dataset integration, automated analysis pipelines, harmonisation, governance, and secure data management are eligible.

4. Can applicants create entirely new datasets?

The primary focus is on improving and expanding the value of existing scientific datasets rather than creating entirely new datasets.

5. Who is eligible to apply?

Eligible applicants include U.S.-based universities, community colleges, Tribal Nations, government agencies, research laboratories, museums, professional societies, nonprofit organisations, and for-profit organisations, including small businesses.

6. What role does artificial intelligence play in the programme?

AI is expected to support feature extraction, metadata generation, automated analysis, dataset integration, and the development of AI-ready scientific data pipelines.

7. Can organisations collaborate with external partners?

Yes. The NSF encourages collaboration with philanthropic organisations, private industry, nonprofit organisations, and existing national research infrastructure to strengthen projects and advance AI-enabled scientific discovery.

Conclusion

The Unlocking Dataset Value for AI-Enabled Scientific Discovery program represents a major investment in the future of AI-driven research. By improving the quality, interoperability, security, and accessibility of existing scientific datasets, the programme enables researchers to unlock new discoveries, accelerate interdisciplinary innovation, and maximise the long-term value of scientific data. Organisations with expertise in artificial intelligence, data science, research infrastructure, and scientific collaboration are well positioned to contribute to this transformative initiative.

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