Unlocking Dataset Value for AI-Enabled Scientific Discovery (AI Datasets)
U.S. National Science Foundation
This program will advance scientific community datasets to enable scientific discovery and innovation using artificial intelligence (AI) and other methods. Its goal is to increase the value that can be derived from existing scientific datasets by leveraging novel methods and artificial intelligence (AI). This will unlock new AI-driven insights. It can enable interdisciplinary research. It also enables investigations outside of the original motivation for data collection and analysis. The program seeks to 1) apply AI-based capabilities to feature extraction and metadata generation, and the integration of multiple datasets. 2) Develop robust data pipelines necessary for automated analysis of existing datasets and similar use-cases by AI tools and systems. 3) Augment and/or harmonize existing datasets to better enable use by AI data pipelines and automated analysis. Proposals should address dataset security and integrity. Governance and the process for scientific communities to contribute to the datasets should also be addressed. Proposals are encouraged to leverage existing resources. These may include NSF data platforms , the NSF Integrated Data Systems and Services program , the NS
Deadline
2026-11-04
Who can apply
*Who May Submit Proposals: Proposals may only be submitted by the following: -For-profit organizations: U.S.-based commercial organizations, including small businesses, with strong capabilities in scientific or engineering research or education and a passion for innovation. -Non-profit, non-academic organizations: Independent museums, observatories, research laboratories, professional societies and similar organizations located in the U.S. that are directly associated with educational or research activities. -Other Federal Agencies and Federally Funded Research and Development Centers (FFRDCs)
Sources
Grants.gov. Last updated 2026-10-26.
Always confirm eligibility and deadlines on the funder's own page before applying.