Análise cientométrica orientada a dados não estruturados para avaliação de produções científicas

Abstract

This study seeks to construct a methodology for scientometric analysis from unstructured data, directing its application towards the assessment of academic output. Situated within the scope of the Graduate Program in Electrical and Computer Engineering (PPgEEC) at the Federal University of Rio Grande do Norte (UFRN), the study proposes the use of advanced data science tools, particularly the Python programming language and various associated libraries, as well as the Scopus platform for data collection. Analyses will be developed that highlight the main research areas explored by the program and spotlight the professors with the best scientometric metrics. Additionally, graphics are implemented which extract key insights about the profile of postgraduates and associated researchers. In conclusion, the study's findings will be distilled into a data visualization and exploration platform, aiming to facilitate the dissemination and understanding of the program's advancements.