<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in Web of Science and Scopus databases. It employs multiple methods to analyse and describe the collected corpus, including co-authorship and keyword co-occurrence networks to investigate patterns of collaboration and predominant research themes. Co-authorship analysis identified several robust research clusters, while keyword later spotlighted key thematic areas in the field. Countries, types of documents, categories, year of publication, citations and other metrics were also produced, and implications discussed. The findings present a structured overview of the non-probabilistic data research landscape, delineating the research trends, prominent authors, and emerging themes.</p>
</abstract>

This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in Web of Science and Scopus databases. It employs multiple methods to analyse and describe the collected corpus, including co-authorship and keyword co-occurrence networks to investigate patterns of collaboration and predominant research themes. Co-authorship analysis identified several robust research clusters, while keyword later spotlighted key thematic areas in the field. Countries, types of documents, categories, year of publication, citations and other metrics were also produced, and implications discussed. The findings present a structured overview of the non-probabilistic data research landscape, delineating the research trends, prominent authors, and emerging themes.

Theory and Practice on Non-Probabilistic Data and Analysis: a bibliometric review

Foundations of Computing and Decision Sciences

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

This bibliometric study aims to summarize the academic landscape of non-probabilistic data research, based on an examination of scientific output indexed in...

Theory and Practice on Non-Probabilistic Data and Analysis: a bibliometric review

Publié en ligne: 26 mai 2024

Pages: 161 - 180

Reçu: 09 juil. 2023

Accepté: 24 avr. 2024

DOI: https://doi.org/10.2478/fcds-2024-0010

Mots clés
Non-Probabilistic, Non-Random, Statistics, Bibliometric, Review

© 2024 Jeanfrank Teodoro Dantas Sartori, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Theory and Practice on Non-Probabilistic Data and Analysis: a bibliometric review

Publié en ligne: 26 mai 2024

Pages: 161 - 180

Reçu: 09 juil. 2023

Accepté: 24 avr. 2024

DOI: https://doi.org/10.2478/fcds-2024-0010

Mots clésNon-Probabilistic, Non-Random, Statistics, Bibliometric, Review

© 2024 Jeanfrank Teodoro Dantas Sartori, published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Mots clés
Non-Probabilistic, Non-Random, Statistics, Bibliometric, Review