Unsupervised learning to profile emerging researchers in LATAM with Elsevier’s data

Abstract

This proposal is being presented in Computer Science. High-impact researchers possess several key features based on their expertise; never theless, it takes time to establish themselves as leaders in their area. The objective of this research is to develop a model that can identify those outstanding researchers by discipline using indicators from the last five years of research and acknowledged databases such as Sco pus and Web of Science. Additionally, it will compare similarities across various disciplines to determine whether it is possible to predict researchers from one or more disciplines using the same model. The main objective of this research is to discover the characteristics that define a ”rising star” based on the concept of an early career researcher as a initial time window. It is important to mention that current metrics measure researchers’ performance through indicators known as H-index and its variants. However, these metrics often do not consider characteristics that differentiate one group from another. Through this unsupervised approach, we aim to f ind different groups that exist in LATAM to measure their characteristics more precisely and fairly, and to identify those high-impact researchers who may not be immediately apparent through indicators like the H-index. This thesis will demonstrate the process from data mining to the statistical analysis of the different groups.