Human Networks and Data Science (HNDS)
🏛 U.S. National Science Foundation (NSF)
✓ Free, no account · Source: Grants.gov · Last verified Oct 8, 2026
Can you apply?
This grant is for academic and research institutions seeking to fund fundamental research on human networks, behavior, and data science. Applicants must be eligible NSF grant recipients, typically including universities, nonprofit research institutions, and some government laboratories. The program has two tracks: infrastructure development and core research using data and network science methods to understand human behavior. Geographic scope is national; international collaborators may participate but lead institutions should be U.S.-based.
HNDS-Infrastructure proposals develop databases, data resources, and analytic techniques for social science research. HNDS-Core proposals apply data and network science to advance theory in social, behavioral, or economic disciplines. Both tracks emphasize data-rich insights about human networks for health, prosperity, or security outcomes.
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Program description
The Human Networks and Data Science program (HNDS) supports research that enhances understanding of human behavior by leveraging data and network science research across a broad range of topics. HNDS research will identify ways in which dynamic, distributed, or heterogeneous data can provide novel answers to fundamental questions about individual or group behavior. HNDS is especially interested in proposals that provide data-rich insights about human networks to support improved health, prosperity, and security.
HNDS has two tracks:
(1) Human Networks and Data Science – Infrastructure (HNDS-I). Infrastructure proposals will address the development of data resources and relevant analytic techniques that support fundamental Social, Behavioral and Economic (SBE) research. Successful infrastructure proposals will construct, within the financial resources provided by the award, databases or relevant analytic techniques and produce a finished product that will enable previously impossible data-intensive research in the social sciences. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by making possible new types of data-intensive research in the SBE sciences.
(2) Human Networks and Data Science – Core Research (HNDS-R). Core research proposals will advance theory in a core SBE discipline by the application of data and network science methods. This includes the leveraging of large data sets with diverse spatio-temporal scales of measurement and linked qualitative and quantitative approaches, as well as multi-scale, multi-level network data and techniques of network analysis. Supported projects are expected to yield results that will enhance, expand, and transform theory and methods, and that generate novel understandings of human behavior – particularly understandings that can lead to significant societal benefits or opportunities. HNDS-R encourages core research proposals that make innovative use of NSF-supported data networks, databases, centers and other forms of scientific infrastructure including those developed by HNDS-I (formerly RIDIR) projects.
Who can apply
Eligible applicants
How to apply
Application links
Key dates & requirements
Required documents
- NSF Form SF-424 (R&R) or equivalent cover sheet
- Project Narrative/Description
- Budget and Budget Justification
- Current and Pending Support documentation
- Biographical sketches of key personnel
- Data management plan
- Letters of support from partner institutions (if applicable)
Program contact
- 👤 U.S. National Science Foundation
- 📧 grantsgovsupport@nsf.gov
- 📞 703-292-4203
Funding track record
Recent awards under CFDA 47.075 from the last 3 years — real organizations that won funding through this same program.
Top 10 Largest Recent Awards
-
$38,357,018
-
$18,499,999
-
$13,999,656
-
$10,999,998
-
$8,993,747
-
$8,043,354
-
$5,500,000
-
$5,237,549
-
$5,200,000
-
$5,047,151
Top States by Funding
- MI 9 awards $94.1M
- DC 6 awards $20.7M
- IL 5 awards $19.5M
- MA 7 awards $18.5M
- NY 9 awards $17.0M
Source: USAspending.gov — federal spending transparency. Data covers last 3 years.
Funding history
Annual funding for this program — Federal obligations (CFDA 47.075). How funding has trended year over year.
| 2024 | $292,390,000 | |
| 2025 | $219,410,000 | |
| 2026 est. | $92,200,000 |
FAQ
Who can apply for HNDS funding?
Eligible institutions include universities, nonprofit research organizations, and NSF-eligible entities. Check NSF's Grant Proposal Guide for detailed recipient eligibility.
What is the deadline and funding range?
The deadline is January 14, 2027 (fixed). Awards range from $10,000 to $1,200,000 depending on project scope and track.
What activities does HNDS support?
HNDS funds research using data and network science to study human behavior. Infrastructure track develops databases and techniques; Core Research track applies these methods to advance social science theory.
Is cost-sharing required?
No cost-sharing is required for HNDS proposals. However, institutional commitment to the project may strengthen competitiveness.
How competitive is this program?
With an $8 million funding pool and broad eligibility, competition is moderate to high. Strong proposals combine innovative methodology with clear societal impact.
💡 Tips for applicants
- Clearly define your research question and explain why data/network science methods are essential to answer it. Generic applications rarely succeed.
- For HNDS-I proposals, demonstrate that your data resource will enable future research beyond your project. Show anticipated impact across disciplines.
- Connect your research to societal benefits. Strong proposals explain how findings improve health, security, or economic opportunity for specific populations.
- Review NSF's Proposal Preparation Guide. Follow formatting, font, and margin requirements exactly. Deviations can result in desk rejection.
- For HNDS-R, explicitly cite NSF-supported data infrastructure you're using or building on. This strengthens alignment with program priorities.
⚠️ Common mistakes
Proposals fail to clearly justify why network science or data science methods are necessary rather than traditional approaches. Lack of detail about data sources, quality, and access creates reviewer doubt about feasibility. Proposals underestimate infrastructure needs or overestimate capacity to deliver promised datasets and tools on time.
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