NSF Human Networks and Data Science - deadlines Jan. and Feb.


National Science Foundation Dir. for Social, Behavioral, Economic Sciences

UI Contact: 

The Human Networks and Data Science program (HNDS)
NSF 22-505  https://www.nsf.gov/pubs/2022/nsf22505/nsf22505.htm
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, and heterogeneous data can provide novel answers to fundamental questions about individual and group behavior. HNDS is especially interested in proposals that provide data-rich insights about human networks to support improved health, prosperity, and security.

Deadline for direct submissions to HNDS-R only:  January 13, 2022  and  Second Thursday in January  Annually Thereafter.
Deadline for HNDS-I proposals only:    February 03, 2022  and  First Thursday in February  Annually Thereafter.

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 proposals will construct user-friendly large-scale next-generation data resources and relevant analytic techniques and produce a finished product that will enable new types of data-intensive research. The databases or techniques should have significant impacts, either across multiple fields or within broad disciplinary areas, by enabling 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.