Author
Listed:
- Schlogl, Gisele de Felippe
(Departamento de Ciência da Informação, UFSC. Brazil.)
- Lima Dutra, Moisés
(Departamento de Ciência da Informação, PGCIN/UFSC. Brazil.)
Abstract
Analyzing correlations between research groups has been increasingly appealing in recent years. The identification of proximity between different research projects can not only contribute to triggering new partnerships, but also optimize resources and share results. In Brazil, the Lattes Curriculum System of the Brazilian National Council for Scientific and Technological Development is a rich source of information about the academic and professional life of professors, researchers, and students. Lattes curricula present information, much of it up-to-date, in a semi-structured text format. This paper intends to identify correlations between Brazilian research groups in Information Science through the analysis of keywords contained in the informative summaries and in the descriptions of the research projects found in the Lattes curricula of the participants of these groups. The analysis presented below was made with the application of text mining techniques to the Lattes curricula of researchers linked to 27 graduate programs in Information Science from 24 Brazilian institutions of higher education, totaling 399 curricula analyzed. Among the results obtained, it was possible to identify some existing research trends between the groups and link them to the areas of Information Science, Archivology, Library Science, and Museology. It was also possible to identify the most used research terms at the moment. In addition, the analysis of the occurrence of the terms allowed to identify the areas that concentrate most of the research in Information Science in Brazil, as well as to realize that there is a propensity of researchers to use certain terms to describe their research and their informative summaries.
Suggested Citation
Handle:
RePEc:prm:awjrnl:v:1:y:2020:i:1:p:e006
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:prm:awjrnl:v:1:y:2020:i:1:p:e006. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Pro-Metrics Editorial Office (email available below). General contact details of provider: https://awari.pro-metrics.org/index.php/a .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.