  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.
