NLM Research Grants in Biomedical Informatics and Data Science (R01)

Research Focus

The National Library of Medicine (NLM) supports research advancing biomedical informatics and data science—the application of computational, analytical, and information science methods to biomedical data to improve decision-making and human health. This funding opportunity targets novel methods and approaches for data-driven discovery across biomedical and clinical domains, with emphasis on domain-independent, scalable, and reproducible solutions aligned with FAIR (Findable, Accessible, Interoperable, Reusable) standards.

Priority research areas include: computational approaches integrating structured and unstructured data using natural language processing (NLP) and deep learning; analysis and discovery at scale leveraging cloud computing and federated learning; algorithmic bias assessment and fairness; decision support systems generalizable to underserved populations; informatics methods translating basic research to clinical practice; and applications addressing health disparities. The NLM strategic plan emphasizes accelerating discovery through data-powered health across the exposome (intracellular to built environment) and advancing computable biomedical knowledge and decision-analytic models. Research must center on developing and testing novel informatics or data science methods; applications applying existing methods to clinical domains are considered lower priority unless the approach is novel and broadly reproducible.

At a Glance

• Who can apply: Not stated (see NIH standard eligibility)
• Funding & project length: Not stated
• Award mechanism: R01 Research Project Grant; clinical trials optional (small, early-stage to Phase I only)
• Key dates: Applications due February 5, June 5, October 5, 2025 and February 5, 2026 (5:00 PM local time); earliest start December 2025–December 2026 depending on cycle; expires March 6, 2026
• Best fit for: Multidisciplinary teams (computer science, statistics, information science, engineering, social/behavioral sciences) developing novel computational, NLP, machine learning, or data management methods for biomedical discovery or clinical/public health applications