Computational Approaches to Curation at Scale for Biomedical Research Assets (R01 Clinical Trial Not Allowed)
Grant funds computational methods to enhance curation processes for biomedical data, facilitating rapid access to datasets for transformative research.
Browse similar grants
Funder: National Institutes of Health
Due Dates: January 15, 2027 (New) | February 17, 2027 (Renewal / Resubmission / Revision) | April 15, 2027 (Closing date)
Funding Amounts: $250,000 award ceiling; budgets limited to $250,000 direct costs annually; maximum 4 years
Summary: Supports novel computational methods for automated, at-scale curation of biomedical datasets, models, and tools.
Key Information: Projects must produce open-access datasets or tools using preexisting input data; clinical trials are not allowed.
Description
The National Library of Medicine (NLM), part of NIH, supports research developing novel computational approaches to automate curation of biomedical digital assets. The program seeks to increase curation speed, quality, security, and scale while improving FAIR—Findable, Accessible, Interoperable, and Reusable—data access and management. Projects may develop new methods or extend existing open-source tools and pipelines to support data integration, harmonization, annotation, preservation, and discovery.
Relevant assets include public health and epidemiological data, molecular and metabolomic data, biomedical literature and metadata, imaging, environmental measurements, electronic health records, and personal-sensor readings. The goal is durable, reliable datasets and tools that advance biomedical discovery and open science. Approaches must be tested, validated, and designed for broad use, with clear advantages over existing curation methods.
