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<p>Interdisciplinary graduate training is essential for preparing a STEM workforce to address complex scientific and societal challenges, yet systematic data describing how such training is articulated at the program level remain limited. Here, we describe a longitudinal dataset of 731 National Science Foundation awards from the Integrative Graduate Education and Research Traineeship (IGERT) and National Research Traineeship (NRT) programs funded between 1996 and 2026, based on project start dates. The dataset integrates administrative award metadata with structured information derived from grant abstracts and, where available, final outcome reports. These data include reported research fields, pedagogical and training-related terms, workforce skill references mapped to established taxonomies (Lightcast and O*NET), and bibliometric metadata for 3,253 publications linked to these awards via the SciSciNet data lake. Using a combination of natural language processing, large language models, and manual validation, we generate standardized, reproducible representations of disciplinary scope, training strategies, skills, and research outputs at the program level. This dataset is designed for reuse across science policy, higher education research, and workforce studies.</p>

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National Science Foundation interdisciplinary graduate training awards from 1996 to 2026

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年6月26日

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  • Monica Marion,
  • Staša Milojević

摘要

Interdisciplinary graduate training is essential for preparing a STEM workforce to address complex scientific and societal challenges, yet systematic data describing how such training is articulated at the program level remain limited. Here, we describe a longitudinal dataset of 731 National Science Foundation awards from the Integrative Graduate Education and Research Traineeship (IGERT) and National Research Traineeship (NRT) programs funded between 1996 and 2026, based on project start dates. The dataset integrates administrative award metadata with structured information derived from grant abstracts and, where available, final outcome reports. These data include reported research fields, pedagogical and training-related terms, workforce skill references mapped to established taxonomies (Lightcast and O*NET), and bibliometric metadata for 3,253 publications linked to these awards via the SciSciNet data lake. Using a combination of natural language processing, large language models, and manual validation, we generate standardized, reproducible representations of disciplinary scope, training strategies, skills, and research outputs at the program level. This dataset is designed for reuse across science policy, higher education research, and workforce studies.