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Testing for universality of Mendeley readership distributions

Author

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  • D’Angelo, Ciriaco Andrea
  • Di Russo, Samuele

Abstract

Altmetrics promise useful support for assessing the impact of scientific works, including beyond the scholarly community and with very limited citation windows. Unfortunately, altmetrics scores are currently available only for recent articles and cannot be used as covariates in predicting long term impact of publications. However, the study of their statistical properties is a subject of evident interest to scientometricians. Applying the same approaches used in the literature to assess the universality of citation distributions, the intention here is to test whether the universal distribution also holds for Mendeley readerships. Results of the analysis carried out on a sample of publications randomly extracted from the Web of Science confirm that readerships seem to share similar shapes across fields and can be rescaled to a common and universal form. Such rescaling results as not particularly effective on the right tails. In other regions, rescaling causes a good collapse of field specific distributions, even for very recent publications.

Suggested Citation

  • D’Angelo, Ciriaco Andrea & Di Russo, Samuele, 2019. "Testing for universality of Mendeley readership distributions," Journal of Informetrics, Elsevier, vol. 13(2), pages 726-737.
    • Handle: RePEc:eee:infome:v:13:y:2019:i:2:p:726-737
    DOI: 10.1016/j.joi.2019.03.011
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    Citations

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    Cited by:

    1. S. Ravikumar & Bidyut Bikash Boruah & M. N. Ravikumar, 2022. "Correlation study between citation count and Mendeley readership of the articles of Sri Lankan authors," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4873-4885, August.
    2. Zhichao Fang & Rodrigo Costas & Wencan Tian & Xianwen Wang & Paul Wouters, 2020. "An extensive analysis of the presence of altmetric data for Web of Science publications across subject fields and research topics," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2519-2549, September.
    3. Ortega, José Luis, 2020. "Proposal of composed altmetric indicators based on prevalence and impact dimensions," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Tan Jin & Huiqiong Duan & Xiaofei Lu & Jing Ni & Kai Guo, 2021. "Do research articles with more readable abstracts receive higher online attention? Evidence from Science," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8471-8490, October.

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