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Usage metrics vs classical metrics: analysis of Russia’s research output


  • Valentina Markusova

    () (All Russian Institute for Scientific and Technical Information (VINITI) of the RAS)

  • Valentin Bogorov

    () (Customer Education Team, Clarivate Analytics)

  • Alexander Libkind

    () (All Russian Institute for Scientific and Technical Information (VINITI) of the RAS)


Abstract This paper discusses the results of a pilot project investigating Russian scholarly publications using the altmetric indicators “Usage Count Last 180 days” (U1) and “Usage Count Since 2013” (U2) introduced by Web of Science. We explored the relationship between citation impact and both types of usage counts. The data set consisted of 37,281 records (publications) indexed by SCI-E in 2015. Seven broad research areas were selected to observe citation patterns and usage counts. A significant difference was found between mean citations and mean usage counts (U2) in a few research areas. We discovered a significant Kendall rank correlation between the citation metrics and usage metrics at the article level. This correlation is particularly strong for the longer period usage metric (U2). We also analyzed the relationship between usage metrics and traditional journal-level citation metrics. Very weak correlation was observed.

Suggested Citation

  • Valentina Markusova & Valentin Bogorov & Alexander Libkind, 2018. "Usage metrics vs classical metrics: analysis of Russia’s research output," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(2), pages 593-603, February.
  • Handle: RePEc:spr:scient:v:114:y:2018:i:2:d:10.1007_s11192-017-2597-2
    DOI: 10.1007/s11192-017-2597-2

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    References listed on IDEAS

    1. Pei-Shan Chi & Wolfgang Glänzel, 2017. "An empirical investigation of the associations among usage, scientific collaboration and citation impact," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 403-412, July.
    2. Maria Karaulova & Abdullah Gök & Oliver Shackleton & Philip Shapira, 2016. "Science system path-dependencies and their influences: nanotechnology research in Russia," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(2), pages 645-670, May.
    3. Xianwen Wang & Zhichao Fang & Xiaoling Sun, 2016. "Usage patterns of scholarly articles on Web of Science: a study on Web of Science usage count," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 917-926, November.
    4. Christian Gumpenberger & Wolfgang Glänzel & Juan Gorraiz, 2016. "The ecstasy and the agony of the altmetric score," Scientometrics, Springer;Akadémiai Kiadó, vol. 108(2), pages 977-982, August.
    5. Mike Thelwall & Paul Wilson, 2016. "Mendeley readership altmetrics for medical articles: An analysis of 45 fields," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(8), pages 1962-1972, August.
    6. Stefanie Haustein & Isabella Peters & Judit Bar-Ilan & Jason Priem & Hadas Shema & Jens Terliesner, 2014. "Coverage and adoption of altmetrics sources in the bibliometric community," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(2), pages 1145-1163, November.
    7. Rodrigo Costas & Zohreh Zahedi & Paul Wouters, 2015. "Do “altmetrics” correlate with citations? Extensive comparison of altmetric indicators with citations from a multidisciplinary perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(10), pages 2003-2019, October.
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    Cited by:

    1. Luis Javier Cabeza Ramírez & Sandra M. Sánchez-Cañizares & Fernando J. Fuentes-García, 2019. "Past Themes and Tracking Research Trends in Entrepreneurship: A Co-Word, Cites and Usage Count Analysis," Sustainability, MDPI, Open Access Journal, vol. 11(11), pages 1-32, June.


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