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

HHS-NIH11

Status:

Active

October 6, 2022

Posted:

Deadline: 

January 7, 2026

Funding

Program:

Award Floor:

Ceiling:

250000

Match Required?

No

Eligibility

All

States:

Entity Types:

State governments, County governments, City or township governments, Special district governments, Independent school districts, Public & State controlled institutions of higher education, Native American tribal governments (Federally recognized), Public housing authorities, Native American tribal organizations, Nonprofits (with 501(c)(3) status), Nonprofits (without 501(c)(3) status)

Other Eligible Applicants include the following: Alaska Native and Native Hawaiian Serving Institutions; Asian American Native American Pacific Islander Serving Institutions (AANAPISISs); Eligible Agencies of the Federal Government; Faith-based or Community-based Organizations; Hispanic-serving Institutions; Historically Black Colleges and Universities (HBCUs); Indian/Native American Tribal Governments (Other than Federally Recognized); Non-domestic (non-U.S.) Entities (Foreign Organizations); Regional Organizations; Tribally Controlled Colleges and Universities (TCCUs) ; U.S. Territory or Possession.

Contact

Email:

Phone:

Source Type:

Federal

The National Library of Medicine (NLM) supports innovative research and development in biomedical informatics and data science. This funding opportunity focuses on biomedical discovery and data-powered health, integrating streams of complex and interconnected research outputs that can be translated into scientific insights, clinical care, public health practices, and personal wellness to ensure the research is scalable, reproducible, and generalizable. The scope of NLM's interest in these research domains is broad, with emphasis on new and innovative methods and approaches to foster data driven discovery in the biomedical and clinical health sciences as well as domain-independent, scalable, and reusable approaches to discovery, curation, analysis, organization, and management of health-related data and digital objects.

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