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Normalization of Mendeley reader counts for impact assessment

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  • Haunschild, Robin
  • Bornmann, Lutz

Abstract

A different number of citations can be expected for publications appearing in different subject categories and publication years. For this reason, the citation-based normalized indicator Mean Normalized Citation Score (MNCS) is used in bibliometrics. Mendeley is one of the most important sources of altmetrics data. Mendeley reader counts reflect the impact of publications in terms of readership. Since a significant influence of publication year and discipline has also been observed in the case of Mendeley reader counts, reader impact should not be estimated without normalization. In this study, all articles and reviews of the Web of Science core collection with a publication year of 2012 (and a DOI) are used to normalize their Mendeley reader counts. A new indicator that determines the normalized reader impact is obtained –the Mean Normalized Reader Score (MNRS) – and compared with the MNCS. The MNRS enables us to compare the impact a paper has had on Mendeley across subject categories and publication years. Comparisons on the journal and university level show that the MNRS and MNCS correlate larger for 9601 journals than for 76 German universities.

Suggested Citation

  • Haunschild, Robin & Bornmann, Lutz, 2016. "Normalization of Mendeley reader counts for impact assessment," Journal of Informetrics, Elsevier, vol. 10(1), pages 62-73.
    • Handle: RePEc:eee:infome:v:10:y:2016:i:1:p:62-73
    DOI: 10.1016/j.joi.2015.11.003
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Robin Haunschild & Lutz Bornmann, 2018. "Field- and time-normalization of data with many zeros: an empirical analysis using citation and Twitter data," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 997-1012, August.
    2. Bornmann, Lutz & Haunschild, Robin, 2016. "Normalization of Mendeley reader impact on the reader- and paper-side: A comparison of the mean discipline normalized reader score (MDNRS) with the mean normalized reader score (MNRS) and bare reader ," Journal of Informetrics, Elsevier, vol. 10(3), pages 776-788.
    3. Liwei Zhang & Jue Wang, 2021. "What affects publications’ popularity on Twitter?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9185-9198, November.
    4. Manika Lamba, 2020. "Research productivity of health care policy faculty: a cohort study of Harvard Medical School," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 107-130, July.
    5. Yan, Weiwei & Zhang, Yin, 2018. "Research universities on the ResearchGate social networking site: An examination of institutional differences, research activity level, and social networks formed," Journal of Informetrics, Elsevier, vol. 12(1), pages 385-400.
    6. Lutz Bornmann & Rüdiger Mutz & Robin Haunschild & Felix Moya-Anegon & Mirko Almeida Madeira Clemente & Moritz Stefaner, 2021. "Mapping the impact of papers on various status groups in excellencemapping.net: a new release of the excellence mapping tool based on citation and reader scores," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(11), pages 9305-9331, November.
    7. Lutz Bornmann & Robin Haunschild, 2016. "How to normalize Twitter counts? A first attempt based on journals in the Twitter Index," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1405-1422, June.
    8. Ying Guo & Xiantao Xiao, 2022. "Author-level altmetrics for the evaluation of Chinese scholars," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(2), pages 973-990, February.
    9. Robin Haunschild & Lutz Bornmann, 2017. "How many scientific papers are mentioned in policy-related documents? An empirical investigation using Web of Science and Altmetric data," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1209-1216, March.
    10. Ortega, José Luis, 2018. "The life cycle of altmetric impact: A longitudinal study of six metrics from PlumX," Journal of Informetrics, Elsevier, vol. 12(3), pages 579-589.
    11. Gerson Pech & Catarina Delgado, 2020. "Assessing the publication impact using citation data from both Scopus and WoS databases: an approach validated in 15 research fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 909-924, November.
    12. Houqiang Yu, 2017. "Context of altmetrics data matters: an investigation of count type and user category," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 267-283, April.
    13. Bornmann, Lutz & Haunschild, Robin, 2018. "Normalization of zero-inflated data: An empirical analysis of a new indicator family and its use with altmetrics data," Journal of Informetrics, Elsevier, vol. 12(3), pages 998-1011.
    14. Pech, Gerson & Delgado, Catarina, 2021. "Screening the most highly cited papers in longitudinal bibliometric studies and systematic literature reviews of a research field or journal: Widespread used metrics vs a percentile citation-based app," Journal of Informetrics, Elsevier, vol. 15(3).
    15. Cao, Xuanyu & Chen, Yan & Ray Liu, K.J., 2016. "A data analytic approach to quantifying scientific impact," Journal of Informetrics, Elsevier, vol. 10(2), pages 471-484.

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