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On predicting research grants productivity via machine learning

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  • Tohalino, Jorge A.V.
  • Amancio, Diego R.

Abstract

Understanding the reasons associated with successful proposals are of paramount importance to improve evaluation processes. In this context, we analyzed whether bibliometric features are able to predict the success of research grants. We extracted features aiming at characterizing the academic history of Brazilian researchers, including research topics, affiliations, number of publications and visibility. The extracted features were then used to predict grants productivity via machine learning in three major research areas, namely Medicine, Dentistry and Veterinary Medicine. We found that research subject and publication history play a role in predicting productivity. In addition, institution-based features turned out to be relevant when combined with other features. While the best results outperformed text-based attributes, the evaluated features were not highly discriminative. Our findings indicate that predicting grants success, at least with the considered set of bibliometric features, is not a trivial task.

Suggested Citation

  • Tohalino, Jorge A.V. & Amancio, Diego R., 2022. "On predicting research grants productivity via machine learning," Journal of Informetrics, Elsevier, vol. 16(2).
    • Handle: RePEc:eee:infome:v:16:y:2022:i:2:s1751157722000128
    DOI: 10.1016/j.joi.2022.101260
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    Cited by:

    1. Hren, Darko & Pina, David G. & Norman, Christopher R. & Marušić, Ana, 2022. "What makes or breaks competitive research proposals? A mixed-methods analysis of research grant evaluation reports," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Guerreiro, Lucas & Silva, Filipi N. & Amancio, Diego R., 2024. "Recovering network topology and dynamics from sequences: A machine learning approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 638(C).

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