Computational Approaches to Curation at Scale for Biomedical Research Assets (R01 Clinical Trial Not Allowed)
This NIH/NLM research grant funds development of computational, at-scale approaches to curate biomedical research data and models. It is aimed at researchers at institutions such as universities, n…
- Deadline
- Apr 15, 2027
- Posted
- Oct 29, 2024
- Award amount
- Up to $250,000
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We’ll email you before Apr 15, 2027 so you have time to apply.
In plain English
This NIH/NLM research grant funds development of computational, at-scale approaches to curate biomedical research data and models. It is aimed at researchers at institutions such as universities, nonprofits, and other eligible organizations; foreign organizations are not eligible. The listed budget cap is about $250,000, and clinical trials are not allowed under this opportunity.
AI-generated summary to help you decide quickly — verify the official eligibility rules before applying.
Who can apply
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; Non-domestic (non-U.S.) Entities (Foreign Organizations) are not eligible to apply. Non-domestic (non-U.S.) components of U.S. Organizations are not eligible to apply.
About this grant
NLM wishes to accelerate access to, and availability of, secure, complete datasets and computational models that can serve as the basis for transformative biomedical discoveries. Innovative at-scale computational approaches that increase the speed and scope of curation processes are needed for data mining and knowledge discovery from growing quantities of biomedical data being produced from ongoing data science advances.
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