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Computer Science Papers in Web of Science: A Bibliometric Analysis

Author

Listed:
  • Dalibor Fiala

    (Department of Computer Science and Engineering, University of West Bohemia, Pilsen 301 00, Czech Republic)

  • Gabriel Tutoky

    (Department of Cybernetics and Artificial Intelligence, Technical University of Košice, Košice 040 01, Slovakia)

Abstract

In this article we present a bibliometric study of 1.9 million computer science papers published from 1945 to 2014 and indexed in Web of Science. We analyze both the quantity and the impact of these publications according to document types, languages, disciplines, countries, institutions, and publication sources. The most frequent author keywords, cited references, and cited papers as well as the distribution of the number of references and citations per paper and of the age of cited references are also explored. Since conference proceedings play a tremendous role in this scientific field, we investigate the time and place of computer science conferences in terms of the most prolific months and locations. And, last but not least, the production of journal articles and conference papers over the whole time period and the level of collaboration in different computer science disciplines are inspected. One of the main results is the finding that “Artificial Intelligence” is the most productive subfield of computer science, but “Interdisciplinary Applications” has the highest relative impact.

Suggested Citation

  • Dalibor Fiala & Gabriel Tutoky, 2017. "Computer Science Papers in Web of Science: A Bibliometric Analysis," Publications, MDPI, vol. 5(4), pages 1-16, September.
    • Handle: RePEc:gam:jpubli:v:5:y:2017:i:4:p:23-:d:113639
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    References listed on IDEAS

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    Cited by:

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    2. Gu, Jiangwei & Pan, Xuelian & Zhang, Shuxin & Chen, Jiaoyu, 2024. "International mobility matters: Research collaboration and scientific productivity," Journal of Informetrics, Elsevier, vol. 18(2).
    3. Prem Kumar Singh, 2022. "t-index: entropy based random document and citation analysis using average h-index," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(1), pages 637-660, January.
    4. Manvendra Janmaijaya & Amit K. Shukla & Ajith Abraham & Pranab K. Muhuri, 2018. "A Scientometric Study of Neurocomputing Publications (1992–2018): An Aerial Overview of Intrinsic Structure," Publications, MDPI, vol. 6(3), pages 1-22, July.
    5. Ricardo Arencibia-Jorge & Rosa Lidia Vega-Almeida & José Luis Jiménez-Andrade & Humberto Carrillo-Calvet, 2022. "Evolutionary stages and multidisciplinary nature of artificial intelligence research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(9), pages 5139-5158, September.
    6. Liu, Meijun & Hu, Xiao, 2022. "Movers’ advantages: The effect of mobility on scientists’ productivity and collaboration," Journal of Informetrics, Elsevier, vol. 16(3).
    7. Farshad Amiraslani & Deirdre Dragovich, 2022. "A Review of Documentation: A Cross-Disciplinary Perspective," World, MDPI, vol. 3(1), pages 1-20, February.

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