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Field classification of publications in Dimensions: a first case study testing its reliability and validity

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  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

Abstract

Dimensions is a research data infrastructure and tool, including grants, publications, citations, clinical trials, and patents in one place. An interesting feature of Dimensions is its field classification scheme, which is not based on journal classification systems, as in the Web of Science or Scopus, but on machine learning. Each publication is assigned to at least one field. Using the set of my own publications, I investigated whether they were reliably and validly assigned to fields. The results put in question the reliability and validity of the scheme. Large scale studies seem necessary to investigate the scheme in more detail.

Suggested Citation

  • Lutz Bornmann, 2018. "Field classification of publications in Dimensions: a first case study testing its reliability and validity," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 637-640, October.
    • Handle: RePEc:spr:scient:v:117:y:2018:i:1:d:10.1007_s11192-018-2855-y
    DOI: 10.1007/s11192-018-2855-y
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    References listed on IDEAS

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    1. Thelwall, Mike, 2018. "Dimensions: A competitor to Scopus and the Web of Science?," Journal of Informetrics, Elsevier, vol. 12(2), pages 430-435.
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    Cited by:

    1. Christian Herzog & Brian Kierkegaard Lunn, 2018. "Response to the letter ‘Field classification of publications in Dimensions: a first case study testing its reliability and validity’," Scientometrics, Springer;Akadémiai Kiadó, vol. 117(1), pages 641-645, October.
    2. Raminta Pranckutė, 2021. "Web of Science (WoS) and Scopus: The Titans of Bibliographic Information in Today’s Academic World," Publications, MDPI, vol. 9(1), pages 1-59, March.
    3. Gabriela F. Nane & Nicolas Robinson-Garcia & François Schalkwyk & Daniel Torres-Salinas, 2023. "COVID-19 and the scientific publishing system: growth, open access and scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(1), pages 345-362, January.
    4. Gerson Pech & Catarina Delgado & Silvio Paolo Sorella, 2022. "Classifying papers into subfields using Abstracts, Titles, Keywords and KeyWords Plus through pattern detection and optimization procedures: An application in Physics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(11), pages 1513-1528, November.
    5. Lin Zhang & Beibei Sun & Fei Shu & Ying Huang, 2022. "Comparing paper level classifications across different methods and systems: an investigation of Nature publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 7633-7651, December.
    6. Michael Gusenbauer, 2022. "Search where you will find most: Comparing the disciplinary coverage of 56 bibliographic databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(5), pages 2683-2745, May.

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