An assessment tool for academic research evaluation

Abstract

The academic evaluation of the publication record of researchers is relevant for identifying both relevant topics and talented candidates for promotion and funding. A key tool for this is the use of the indexes provided by Web of Science and Scopus, costly databases that sometimes exceed the possibilities of academic institutions in many parts of the world. We develop a methodology that uses data in one of the databases to infer the most commonly used index of the other one. In this way, access to just one database allows recovering the information contained in both. Using machine learning methods, we select just a few of the hundreds of variables in one database, which are used in a panel regression to infer the main index in the other database. Since the information of Scopus can be freely scraped from the web, this approach allows the inference of the impact factor of publications (the main index in Web of Science), a key index used to assess the quality of academic research around the globe.