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Discovering temporal scientometric knowledge in COVID-19 scholarly production

Author

Listed:
  • Breno Santana Santos

    (Federal University of Rio Grande do Norte
    Federal University of Sergipe)

  • 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))

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.

Suggested Citation

  • Breno Santana Santos & Ivanovitch Silva & Luciana Lima & Patricia Takako Endo & Gisliany Alves & Marcel da Câmara Ribeiro-Dantas, 2022. "Discovering temporal scientometric knowledge in COVID-19 scholarly production," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(3), pages 1609-1642, March.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:3:d:10.1007_s11192-021-04260-y
    DOI: 10.1007/s11192-021-04260-y
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    References listed on IDEAS

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    1. Ashkan Ebadi & Pengcheng Xi & Stéphane Tremblay & Bruce Spencer & Raman Pall & Alexander Wong, 2021. "Understanding the temporal evolution of COVID-19 research through machine learning and natural language processing," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 725-739, January.
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