Computational Approaches to Curation at Scale for Biomedical Research Assets (R01 Clinical Trial Not Allowed)
🏛 National Institutes of Health (HHS-NIH11)
✓ Free, no account · Source: Grants.gov · Last verified Oct 19, 2026
Can you apply?
This grant is for researchers who want to develop computational methods for managing and curating large biomedical datasets. Applicants must hold a Ph.D., M.D., or equivalent research degree and work at eligible institutions. U.S. and international researchers at academic institutions, national laboratories, and research hospitals can apply. Funding supports development of scalable tools and approaches for organizing, annotating, and accessing biomedical research assets. Clinical trials are not an allowable activity under this mechanism.
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Program description
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.
Who can apply
Eligible applicants
- 501(c)(3) Public Charity
- City / Municipal Government
- County Government
- Nonprofits
- Postdoctoral Researcher
- Private University
- Public Authority
- Public K-12 School
- Public University
- Researcher (independent)
- Small Business (SBA-defined)
- Special District
- State Government
- Tribal Nation
- Tribal Organization
How to apply
Application links
Required documents
- SF-424 (R&R) form
- Project Narrative (specific aims, background, methods, timeline)
- Detailed Budget and Budget Justification
- Biographical Sketches of key personnel
- NIH Biosketch forms for all researchers
- Letters of Support from collaborating institutions
- Data Management and Sharing Plan
- Facilities and Resources documentation
Program contact
- 👤 National Institutes of Health
- 📧 grantsinfo@nih.gov
- 📞 301-402-2541
Funding track record
Recent awards under CFDA 93.879 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
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$31,424,450
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$21,310,613
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$18,962,515
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$18,956,268
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$18,759,333
-
$18,422,490
-
$17,519,518
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$16,306,781
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$15,389,486
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$15,377,324
Top States by Funding
- TN 4 awards $65.5M
- MA 10 awards $63.8M
- NY 12 awards $60.2M
- CA 14 awards $59.6M
- PA 8 awards $38.4M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 93.879). How funding has trended year over year.
| 2024 | $78,196,782 | |
| 2025 | $79,391,024 | |
| 2026 est. | $70,230,119 |
FAQ
Who can apply for this grant?
Ph.D., M.D., or equivalent degree holders at academic, laboratory, and hospital research institutions. Postdoctoral researchers may also qualify with sponsor support.
What research activities are funded?
Development of computational and informatics methods for large-scale curation and management of biomedical data. Tool development, infrastructure building, and proof-of-concept demonstrations are typical.
Are clinical trials allowed?
No. This R01 specifically excludes clinical trial research. Focus should be on computational methods and data infrastructure.
What is the typical funding amount?
R01 grants typically range from $250,000 to $750,000 per year for 3-5 years, though actual amounts vary by study design and institutional cost factors.
When should I submit?
The application opens October 29, 2024, with a fixed deadline of April 15, 2027. Submit well before the deadline to allow time for revisions.
💡 Tips for applicants
- Start by clearly defining the biomedical research problem your computational approach will solve. Describe the scale and impact of the asset curation challenge.
- Show preliminary data demonstrating the feasibility of your computational methods. Include pilot results or proof-of-concept examples from real biomedical datasets.
- Emphasize scalability and reusability. Reviewers want methods and tools that can benefit the broader research community, not just your lab.
- Build a strong team with computational experts and domain specialists in relevant biomedical fields. Collaboration strengthens your application.
- Address data standards, interoperability, and long-term sustainability. Explain how your curation approach will remain useful as biomedical science evolves.
⚠️ Common mistakes
Proposing clinical trials or direct clinical applications; reviewers will reject for being outside scope. Focusing narrowly on a single dataset without demonstrating broader impact or scalability. Underestimating the need for strong computational and informatics expertise in the proposal.
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