U.S. National Science FoundationRallyReport

Unlocking Dataset Value for AI-Enabled Scientific Discovery

Closes November 4, 2026

Up to $100,000,000

50 awards expected

Generated July 22, 2026

Technical Objective

This NSF program aims to increase the scientific and innovation value derived from existing datasets by applying artificial intelligence and novel analytical methods. The initiative focuses on enabling new AI-driven insights, supporting interdisciplinary research, and facilitating investigations beyond the original data collection scope through advanced data engineering and AI integration.

Core Technologies

Artificial intelligence and machine learningFeature extraction and metadata generationMulti-dataset integration and harmonizationAutomated data pipelinesData augmentation techniquesDataset security and integrity protocols

Who Should Apply

Academic institutions, research consortia, and organizations with expertise in scientific data management, AI/ML engineering, and cross-disciplinary research. Applicants should demonstrate capability in dataset curation, AI pipeline development, and experience working with large-scale scientific data repositories.

Evaluation Criteria

1Technical merit of AI/ML approach to dataset value extraction
2Robustness and scalability of proposed data pipelines
3Quality of dataset augmentation/harmonization methodology
4Data governance, security, and integrity safeguards
5Potential for interdisciplinary scientific impact and broader applicability
6Team expertise and institutional resources

Key Dates

Application Deadline

2026-11-04

Submission Mechanics

Proposals should be submitted through the NSF grants portal in accordance with NSF Proposal and Award Policies. Submissions must address dataset security, integrity, and governance processes. Page limits and specific formatting requirements follow standard NSF guidelines.

BD Strategic Notes

This opportunity is well-suited for research institutions with established scientific data repositories and strong AI/ML teams capable of building production-grade data systems. Organizations positioned to demonstrate cross-domain dataset interoperability and AI-driven discovery have competitive advantage; the ~$2M average award suggests mid-scale collaborative efforts rather than single-PI projects.

Watch Out For

Data governance and scientific community consent processes must be clearly defined in proposals
Dataset security and integrity assurance requirements may necessitate compliance with discipline-specific data standards

Generated by RallyProp.ai

Source Opportunity detail