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[Text mining in scientific publications with Argentine authors].
Ricardo A Dorr1, Juan José Casal1, Roxana Toriano1
1Laboratorio de Biomembranas, Instituto de Fisiología y Biofísica Bernardo Houssay (IFIBIO Houssay), Facultad de Medicina, Universidad de Buenos Aires-CONICET, Buenos Aires, Argentina.
Medicina
|April 27, 2021
Summary
Text mining analyzed over 75,000 life science publications by Argentine authors. This research identifies key topics and their relation to health problems, aiding research resource management.
Area of Science:
- Life Sciences
- Biomedical Research
- Scientific Publication Analysis
Background:
- Argentine authors contributed significantly to life sciences literature until 2019.
- A large corpus of over 75,000 articles was available for analysis.
Purpose of the Study:
- To apply text mining for extracting novel information from Argentine life sciences publications.
- To identify main research topics and their connection to health issues in Argentina.
Main Methods:
- Utilized automated text mining tools for analyzing approximately 70,800 abstracts.
- Employed non-supervised digital detection to uncover thematic patterns.
- Database encompassed over 75,000 articles from ~5000 journals, involving ~186,000 authors.
Main Results:
- Identified prevalent research themes and their association with Argentine health problems and treatments.
- Detailed publication trends, including yearly output, prominent journals, and author collaborations.
- Generated predictive insights for future research directions.
Conclusions:
- Text mining provides a powerful method for understanding scientific output.
- The findings can inform strategic resource allocation for basic and clinical research in Argentina.
- This analysis offers a foundation for future bibliometric studies and research policy development.