Discovering temporal scientometric knowledge in COVID-19 scholarly production

Breno Santana Santos (), Ivanovitch Silva (), Luciana Lima (), Patricia Takako Endo (), Gisliany Alves () and Marcel da Câmara Ribeiro-Dantas ()
Additional contact information
Breno Santana Santos: Federal University of Rio Grande do Norte
Ivanovitch Silva: Federal University of Rio Grande do Norte
Luciana Lima: Federal University of Rio Grande do Norte
Patricia Takako Endo: University of Pernambuco
Gisliany Alves: Federal University of Rio Grande do Norte
Marcel da Câmara Ribeiro-Dantas: Sorbonne Université (EDITE)

Scientometrics, 2022, vol. 127, issue 3, No 20, 1609-1642

Abstract: Abstract The mapping and analysis of scientific knowledge makes it possible to identify the dynamics and/or growth of a particular field of research or to support strategic decisions related to different research entities, based on bibliometric and/or scientometric indicators. However, with the exponential growth of scientific production, a systematic and data-oriented approach to the analysis of this large set of productions becomes increasingly essential. Thus, in this work, a data-oriented methodology was proposed, combining Data Analysis, Machine Learning and Complex Network Analysis techniques, and Data Version Control (DVC) tool, for the extraction of implicit knowledge in scientific production bases. In addition, the approach was validated through a case study in a COVID-19 manuscripts dataset, which had 199,895 articles published on arXiv, bioRxiv, medRxiv, PubMed and Scopus databases. The results suggest the feasibility of the proposed methodology, indicating the most active countries and the most explored themes in each period of the pandemic. Therefore, this study has the potential to instrument and expand strategic decisions by the scientific community, aiming at extracting knowledge that supports the fight against the COVID-19 pandemic.

Keywords: Scientometrics; Bibliometrics; COVID-19; Pandemic; Data Science; 58-00; 58-06; 68T09; 68T10; 68T99; 94-11; 94A16 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11192-021-04260-y Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:scient:v:127:y:2022:i:3:d:10.1007_s11192-021-04260-y

Ordering information: This journal article can be ordered from
http://www.springer.com/economics/journal/11192

DOI: 10.1007/s11192-021-04260-y

Access Statistics for this article

Scientometrics is currently edited by Wolfgang Glänzel

More articles in Scientometrics from Springer, Akadémiai Kiadó
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().