Analysis model of scientific production in Postgraduate Programs based on Interaction Networks: A Case Study in Environmental Sciences

Author(s)

Sant'Anna de Franca-Rocha, Washington de Jesus ; Vasconcelos, Rodrigo Nogueira ; Chaves, Joselisa Maria ; Bilotta, Patricia ; Grimm, Isabel Jurema ; Carvalho Ribeiro, Sonia Maria ; de Albuquerque Nobrega, Rodrigo Affonso ; Martins Sobral, Maria do Carmo ; Philippi Junior, Arlindo ; Cioce Sampaio, Carlos Alberto

• Document type: Journal article
• Source: REVISTA BRASILEIRA DE CIENCIAS AMBIENTAIS; v. 58, n. 3, p. 12-pg., 2023-09-01.

Abstract

The ongoing evaluation of graduate programs (GP) is an important tool for improving the landscape of scientific knowledge and technological development in Brazil. The objective of this article was to develop a model for analyzing the scientific production of GP, based on interaction network resources with a focus on environmental sciences, for the purpose of detecting patterns and connections existing among these networks. The scope of the analysis was broad, encompassing all 112 GP, in addition to courses in the field of Environmental Sciences offered by the Coordination for the Improvement of Higher Education Personnel (CAPES), during the period from 2013 to 2016. The methodology was divided into four stages: 1. data collection and database construction (public information obtained by consulting the Sucupira Platform - CAPES); 2. data mining and processing, and the creation of an overarching network to represent the most relevant terms and themes common to all GP materials produced (theses, dissertations, research projects, articles, books, and book chapters); 3. analysis of semantic networks; and 4. generation of outcomes. The analysis yielded results such as geographical proximity and cluster maps, which allowed for an integrated analysis of GP production in the field of Environmental Sciences with respect to their central themes. The methodology employed proved to be robust and suitable for evaluating graduate programs in Brazil, as well as for identifying research gaps and emerging areas on a national scale, culminating in a proposed model based on semantic networks that analyze scientific production for four-year periods. (AU)