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Citation analysis with medical subject Headings (MeSH) using the Web of Knowledge: A new routine

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  • Loet Leydesdorff
  • Tobias Opthof

Abstract

Citation analysis of documents retrieved from the Medline database (at the Web of Knowledge) has been possible only on a case‐by‐case basis. A technique is presented here for citation analysis in batch mode using both Medical Subject Headings (MeSH) at the Web of Knowledge and the Science Citation Index at the Web of Science (WoS). This freeware routine is applied to the case of “Brugada Syndrome,” a specific disease and field of research (since 1992). The journals containing these publications, for example, are attributed to WoS categories other than “cardiac and cardiovascular systems”, perhaps because of the possibility of genetic testing for this syndrome in the clinic. With this routine, all the instruments available for citation analysis can now be used on the basis of MeSH terms. Other options for crossing between Medline, WoS, and Scopus are also reviewed.

Suggested Citation

  • Loet Leydesdorff & Tobias Opthof, 2013. "Citation analysis with medical subject Headings (MeSH) using the Web of Knowledge: A new routine," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1076-1080, May.
    • Handle: RePEc:bla:jamist:v:64:y:2013:i:5:p:1076-1080
    DOI: 10.1002/asi.22770
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    Cited by:

    1. Lutz Bornmann & Klaus Wohlrabe, 2019. "Normalisation of citation impact in economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 841-884, August.
    2. Haunschild, Robin & Daniels, Angela D. & Bornmann, Lutz, 2022. "Scores of a specific field-normalized indicator calculated with different approaches of field-categorization: Are the scores different or similar?," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Haunschild, Robin & Schier, Hermann & Marx, Werner & Bornmann, Lutz, 2018. "Algorithmically generated subject categories based on citation relations: An empirical micro study using papers on overall water splitting," Journal of Informetrics, Elsevier, vol. 12(2), pages 436-447.
    4. Loet Leydesdorff & Paul Wouters & Lutz Bornmann, 2016. "Professional and citizen bibliometrics: complementarities and ambivalences in the development and use of indicators—a state-of-the-art report," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 2129-2150, December.
    5. Barbara McGillivray & Paola Marongiu & Nilo Pedrazzini & Marton Ribary & Mandy Wigdorowitz & Eleonora Zordan, 2022. "Deep Impact: A Study on the Impact of Data Papers and Datasets in the Humanities and Social Sciences," Publications, MDPI, vol. 10(4), pages 1-40, October.
    6. Schreiber, Michael, 2014. "How to improve the outcome of performance evaluations in terms of percentiles for citation frequencies of my papers," Journal of Informetrics, Elsevier, vol. 8(4), pages 873-879.
    7. Seolmin Yang & So Young Kim, 2023. "Knowledge-integrated research is more disruptive when supported by homogeneous funding sources: a case of US federally funded research in biomedical and life sciences," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(6), pages 3257-3282, June.
    8. Bornmann, Lutz & Haunschild, Robin, 2016. "Citation score normalized by cited references (CSNCR): The introduction of a new citation impact indicator," Journal of Informetrics, Elsevier, vol. 10(3), pages 875-887.

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