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National Science Foundation interdisciplinary graduate training awards from 1996 to 2026.
Monica Marion1, Staša Milojević2
1Center for Complex Networks and Systems Research, Luddy School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, 47401, USA.
Scientific Data
|June 26, 2026
Summary
This study introduces a dataset of 731 National Science Foundation (NSF) awards for interdisciplinary graduate training programs. It details research fields, training strategies, and skills to inform STEM workforce development.
Area of Science:
- STEM education research
- Interdisciplinary graduate training programs
- Workforce development
Background:
- Limited systematic data exists on interdisciplinary graduate training program structures.
- Preparing a STEM workforce for complex challenges requires effective training.
- National Science Foundation (NSF) programs like IGERT and NRT are key initiatives.
Purpose of the Study:
- To describe a longitudinal dataset of NSF IGERT and NRT awards.
- To provide standardized, reproducible data on program scope, training, skills, and outputs.
- To facilitate research in science policy, higher education, and workforce studies.
Main Methods:
- Compiled data from 731 NSF awards (1996-2026).
- Integrated administrative metadata, grant abstracts, and outcome reports.
- Utilized NLP, LLMs, and manual validation for data extraction and standardization.
Main Results:
- Generated a dataset with research fields, training terms, and workforce skills.
- Mapped skills to Lightcast and O*NET taxonomies.
- Included bibliometric data for 3,253 linked publications.
Conclusions:
- The dataset offers a comprehensive resource for analyzing interdisciplinary graduate training.
- Enables data-driven insights into STEM workforce preparation and policy.
- Supports future research on the impact and evolution of graduate STEM education.

