On predicting research grants productivity

Abstract

Understanding the reasons associated with successful proposals is 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.

References in corpus (7)

• Patterns of authors contribution in scientific manuscripts
• On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks
• How relevant is the predictive power of the h-index? A case study of the time-dependent Hirsch index
• Unveiling the relationship between complex networks metrics and word senses
• The Gene of Scientific Success
• Analyzing the relationship between text features and research proposal productivity
• Interdisciplinary researchers attain better performance in funding