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Plots for visualizing paper impact and journal impact of single researchers in a single graph

Author

Listed:
  • Lutz Bornmann

    (Administrative Headquarters of the Max Planck Society)

  • Robin Haunschild

    (Max Planck Institute for Solid State Research)

Abstract

In research evaluation of single researchers, the assessment of paper and journal impact is of interest. High journal impact reflects the ability of researchers to convince strict reviewers, and high paper impact reflects the usefulness of papers for future research. In many bibliometric studies, metrics for journal and paper impact are separately presented. In this paper, we introduce two graph types, which combine both metrics in a single graph. The graphs can be used in research evaluation to visualize the performance of single researchers comprehensively.

Suggested Citation

  • Lutz Bornmann & Robin Haunschild, 2018. "Plots for visualizing paper impact and journal impact of single researchers in a single graph," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 385-394, April.
    • Handle: RePEc:spr:scient:v:115:y:2018:i:1:d:10.1007_s11192-018-2658-1
    DOI: 10.1007/s11192-018-2658-1
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    References listed on IDEAS

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    1. Bornmann, Lutz & Leydesdorff, Loet & Mutz, Rüdiger, 2013. "The use of percentiles and percentile rank classes in the analysis of bibliometric data: Opportunities and limits," Journal of Informetrics, Elsevier, vol. 7(1), pages 158-165.
    2. Lutz Bornmann & Werner Marx, 2014. "The wisdom of citing scientists," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1288-1292, June.
    3. Lutz Bornmann & Werner Marx, 2014. "Distributions instead of single numbers: Percentiles and beam plots for the assessment of single researchers," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(1), pages 206-208, January.
    4. Bornmann, Lutz & Mutz, Rüdiger & Hug, Sven E. & Daniel, Hans-Dieter, 2011. "A multilevel meta-analysis of studies reporting correlations between the h index and 37 different h index variants," Journal of Informetrics, Elsevier, vol. 5(3), pages 346-359.
    5. Lutz Bornmann & Werner Marx, 2014. "How to evaluate individual researchers working in the natural and life sciences meaningfully? A proposal of methods based on percentiles of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 487-509, January.
    6. 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.
    7. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
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    Cited by:

    1. Rojko, Katarina & Lužar, Borut, 2022. "Scientific performance across research disciplines: Trends and differences in the case of Slovenia," Journal of Informetrics, Elsevier, vol. 16(2).
    2. Robin Haunschild & Lutz Bornmann & Jonathan Adams, 2019. "R package for producing beamplots as a preferred alternative to the h index when assessing single researchers (based on downloads from Web of Science)," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 925-927, August.
    3. Lutz Bornmann & Richard Williams, 2020. "An evaluation of percentile measures of citation impact, and a proposal for making them better," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(2), pages 1457-1478, August.
    4. Nadia Simoes & Nuno Crespo, 2020. "A flexible approach for measuring author-level publishing performance," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 331-355, January.

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