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Discovering temporal scientometric knowledge in COVID-19 scholarly production.
Breno Santana Santos1,2, Ivanovitch Silva1, Luciana Lima3
1Postgraduate Program in Electrical and Computer Engineering, Federal University of Rio Grande do Norte, Natal, RN Brazil.
Scientometrics
|January 24, 2022
Summary
This study introduces a data-driven methodology using machine learning and network analysis to map scientific knowledge, aiding strategic decisions in research fields like COVID-19 studies.
Area of Science:
- Bibliometrics and Scientometrics
- Data Science
- Complex Network Analysis
Background:
- Scientific knowledge mapping supports strategic research decisions using bibliometric indicators.
- Exponential growth in scientific output necessitates systematic, data-oriented analysis methods.
Purpose of the Study:
- To propose a data-oriented methodology for extracting implicit knowledge from scientific production databases.
- To validate the methodology through a case study on COVID-19 research.
Main Methods:
- Combined Data Analysis, Machine Learning, and Complex Network Analysis techniques.
- Utilized the Data Version Control (DVC) tool for knowledge extraction.
- Analyzed a dataset of 199,895 COVID-19 manuscripts from multiple repositories.
Main Results:
- Demonstrated the feasibility of the proposed data-oriented methodology.
- Identified key active countries and prevalent research themes during the COVID-19 pandemic.
- Provided insights into the dynamics of scientific production related to COVID-19.
Conclusions:
- The methodology effectively extracts implicit knowledge from large scientific datasets.
- Findings can inform and expand strategic decision-making for the scientific community.
- The approach supports the global effort to combat the COVID-19 pandemic through enhanced knowledge extraction.
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