On predicting research grants productivity

TL;DR

This study investigates whether bibliometric features can predict research grant success across three major areas, revealing some predictive value but also highlighting the complexity of the task.

Contribution

It introduces a machine learning approach using bibliometric data to predict research grant productivity in multiple scientific fields.

Findings

Research subject and publication history are predictive of grant success.

Institutional features enhance prediction when combined with other data.

Predicting grant success with bibliometric features remains a challenging task.

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.