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Distributional characteristics of Dimensions concepts: An Empirical Analysis using Zipf’s law

Author

Listed:
  • Solanki Gupta

    (Banaras Hindu University)

  • Vivek Kumar Singh

    (Banaras Hindu University
    University of Delhi)

Abstract

The massive growth in scholarly outputs during the last few decades has resulted into the creation of several scholarly databases to index the outputs. These scholarly databases index publication records and provide different metadata fields for different kinds of usage ranging from retrieval and research evaluation to various scientometric analysis. The ‘author keywords’ is one such important metadata field provided by many databases and used for different text-based and thematic structure analysis. The Dimensions database, however, does not provide ‘author keywords’ metadata field, instead it provides automatically generated terms from the article full texts, called ‘concepts’. Therefore, it is not clear whether different text-based analysis can be done with data provided by Dimensions database. Therefore, this article explores the distributional characteristics of Dimensions concepts. The Dimensions concept data obtained for a sufficiently large sample of scholarly articles is analysed through rank frequency distribution plots in the log–log space. Existence of Zipfian distribution is explored. The results indicate that Dimensions concepts adhere to the Zipfian properties which in turn indicates that Dimensions concepts have similar distributional characteristics as author keywords and hence they may have the same expressive power as that of author or index keywords for scientometric exercises. The study is novel as it is the first study to explore the distributional characteristics of the Dimensions concepts, particularly with respect to Zipfian properties, which provide the statistical foundation for understanding the Dimensions concepts and help to model and analyse them.

Suggested Citation

  • Solanki Gupta & Vivek Kumar Singh, 2024. "Distributional characteristics of Dimensions concepts: An Empirical Analysis using Zipf’s law," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(2), pages 1037-1053, February.
    • Handle: RePEc:spr:scient:v:129:y:2024:i:2:d:10.1007_s11192-023-04899-9
    DOI: 10.1007/s11192-023-04899-9
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    References listed on IDEAS

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    1. Michal Brzezinski, 2015. "Power laws in citation distributions: evidence from Scopus," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(1), pages 213-228, April.
    2. Banshal, Sumit Kumar & Gupta, Solanki & Lathabai, Hiran H & Singh, Vivek Kumar, 2022. "Power Laws in altmetrics: An empirical analysis," Journal of Informetrics, Elsevier, vol. 16(3).
    3. Hiran H. Lathabai & Abhirup Nandy & Vivek Kumar Singh, 2021. "x-index: Identifying core competency and thematic research strengths of institutions using an NLP and network based ranking framework," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(12), pages 9557-9583, December.
    4. Juan Zhang & Qi Yu & Fashan Zheng & Chao Long & Zuxun Lu & Zhiguang Duan, 2016. "Comparing keywords plus of WOS and author keywords: A case study of patient adherence research," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(4), pages 967-972, April.
    5. Vivek Kumar Singh & Prashasti Singh & Mousumi Karmakar & Jacqueline Leta & Philipp Mayr, 2021. "The journal coverage of Web of Science, Scopus and Dimensions: A comparative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5113-5142, June.
    6. Lu, Wei & Liu, Zhifeng & Huang, Yong & Bu, Yi & Li, Xin & Cheng, Qikai, 2020. "How do authors select keywords? A preliminary study of author keyword selection behavior," Journal of Informetrics, Elsevier, vol. 14(4).
    7. Zi-Ke Zhang & Linyuan Lü & Jian-Guo Liu & Tao Zhou, 2008. "Empirical analysis on a keyword-based semantic system," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(4), pages 557-561, December.
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