2025/01/14
paper · doi:10.18130/xtmk-f634
A vast amount of information on federally funded research and development (R&D) is available and can be utilized by researchers, policymakers, and the public to uncover insights on the directionality and extent of government R&D funding. In this work, we use natural language processing (NLP), machine learning, and information retrieval techniques to classify broad research topics and pandemic-related research topics contained within Federal RePORTER grant abstracts, a typical example of a scientific award database. In collaboration with the National Center for Science and Engineering Statistics (NCSES), we examine these topics, their trends over time, and how the topics and their trends change as a result of the number of topics produced by the model. The methods described in this paper show promise to supplement the information currently collected through the NCSES Federal Survey of Funds for Research and Development (FFS) and Survey of Federal Science and Engineering Support to Universities, Colleges, and Nonprofit Institutions (FSS) by providing information that the surveys do not collect.