Artículo

On predicting research grants productivity via machine learning

Resumen

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.
Ismail, Norashikin (21739318200); Kaliani Sundram, Veera Pandiyan (58169033100); Othman, Nor Azairiah Fatimah (58248098600); Abdul Rahman, Zanariah (59156532700)
Crowdfunding, Social Capital and Sustainability – A Ten-Year Bibliometric Overview
2024
10.5171/2024.177889
https://www.scopus.com/inward/record.uri?eid=2-s2.0-85195212667&doi=10.5171%2f2024.177889&partnerID=40&md5=87ba792c4a2092420ef3c96cb626f5e9
Universiti Teknologi MARA Cawangan, Johor, Malaysia
All Open Access; Gold Open Access
Scopus
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