# On predicting research grants productivity

arXiv: https://arxiv.org/abs/2106.10700 · HTML: https://mlsanity.com/paper/2106.10700/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 54 references that are in the map.
- First posted on arXiv 20 Jun 2021.

## Builds on (most cited first, 11 of 11)

- [Clustering Algorithms: A Comparative Approach](https://mlsanity.com/paper/1612.08388/) (2016)
- [Patterns of authors contribution in scientific manuscripts](https://mlsanity.com/paper/1609.05545/) (2016)
- [Early-career setback and future career impact](https://mlsanity.com/paper/1903.06958/) (2019)
- [A systematic comparison of supervised classifiers](https://mlsanity.com/paper/1311.0202/) (2013)
- [Analyzing Linguistic Complexity and Scientific Impact](https://mlsanity.com/paper/1907.11843/) (2019)
- [Extractive Multi-document Summarization Using Multilayer Networks](https://mlsanity.com/paper/1711.02608/) (2017)
- [Using network science and text analytics to produce surveys in a scientific topic](https://mlsanity.com/paper/1506.05690/) (2015)
- [Authorship attribution via network motifs identification](https://mlsanity.com/paper/1607.06961/) (2016)
- [Topological-collaborative approach for disambiguating authors' names in collaborative networks](https://mlsanity.com/paper/1311.1266/) (2013)
- [Unveiling the relationship between complex networks metrics and word senses](https://mlsanity.com/paper/1302.4465/) (2013)
- [On the use of topological features and hierarchical characterization for disambiguating names in collaborative networks](https://mlsanity.com/paper/1302.4504/) (2013)

## Built on by (most cited first, 1 of 1)

- [Using Full-Text Content to Characterize and Identify Best Seller Books](https://mlsanity.com/paper/2210.02334/) (2022)

## Abstract (arXiv)

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.