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A parameter-free index for identifying under-cited sleeping beauties in science

Author

Listed:
  • Jian Du

    (Chinese Academy of Medical Sciences)

  • Yishan Wu

    (Chinese Academy of Science and Technology for Development)

Abstract

We developed a systematic methodology for identifying the under-cited (or not-so-highly cited) Sleeping Beauty (SB) publications and tried to figure out their key characteristics. Based on the identification framework of “beauty coefficient” (B) introduced by Ke et al. (Proc Natl Acad Sci USA 112:7426–7431, 2015), taking into account the whole citation history of the publications concerned, we substituted yearly citations in “beauty coefficient” with yearly cumulative percentage of citations, and eliminated the denominator in “beauty coefficient” since the curve of a given document’s cumulative citations is always monotonically increasing if only the document is cited. The value of the modified beauty coefficient is denoted as Bcp. We also redefined the awakening year, sleeping length and sleeping depth within the Bcp framework with the intention of avoiding arbitrary thresholds as much as possible. We tested the new index using the data of SB articles identified from Science and Nature. The results showed that Bcp is more sensitive in identifying the “lower level SBs”, which refers to the case when the total citations and the maximum annual citations of SBs are not so high in comparison with other typical SBs. Bcp works better than B in at least two aspects: (1) it “punishes” the situations when the SBs experienced early citations instead of continuous sleeping; (2) it allows for comparing the extent of delayed citation impact of publications in different fields with different citation patterns. We also figured out some key characteristics of such SB publications and pondered some policy implications about the associations of SB publications with transformative research, research front and research evaluation.

Suggested Citation

  • Jian Du & Yishan Wu, 2018. "A parameter-free index for identifying under-cited sleeping beauties in science," Scientometrics, Springer;Akadémiai Kiadó, vol. 116(2), pages 959-971, August.
    • Handle: RePEc:spr:scient:v:116:y:2018:i:2:d:10.1007_s11192-018-2780-0
    DOI: 10.1007/s11192-018-2780-0
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    References listed on IDEAS

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    6. Jian Wang & Bart Thijs & Wolfgang Glänzel, 2015. "Interdisciplinarity and Impact: Distinct Effects of Variety, Balance, and Disparity," PLOS ONE, Public Library of Science, vol. 10(5), pages 1-18, May.
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    8. Hui Fang, 2018. "Analysing the variation tendencies of the numbers of yearly citations for sleeping beauties in science by using derivative analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 1051-1070, May.
    9. Jian Wang, 2013. "Citation time window choice for research impact evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 851-872, March.
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    Cited by:

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    2. Yang, Jinqing & Bu, Yi & Lu, Wei & Huang, Yong & Hu, Jiming & Huang, Shengzhi & Zhang, Li, 2022. "Identifying keyword sleeping beauties: A perspective on the knowledge diffusion process," Journal of Informetrics, Elsevier, vol. 16(1).
    3. Du, Jian & Li, Peixin & Haunschild, Robin & Sun, Yinan & Tang, Xiaoli, 2020. "Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence," Journal of Informetrics, Elsevier, vol. 14(2).
    4. Anthony F. J. van Raan, 2021. "Sleeping beauties gain impact in overdrive mode," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(5), pages 4311-4332, May.
    5. Hou, Jianhua & Yang, Xiucai, 2020. "Social media-based sleeping beauties: Defining, identifying and features," Journal of Informetrics, Elsevier, vol. 14(2).
    6. Li, Xin & Ma, Xiaodi & Feng, Ye, 2024. "Early identification of breakthrough research from sleeping beauties using machine learning," Journal of Informetrics, Elsevier, vol. 18(2).
    7. Jianhua Hou & Hao Li & Yang Zhang, 2023. "Altmetrics-based sleeping beauties: necessity or just a supplement?," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5477-5506, October.
    8. Jianhua Hou & Xiucai Yang, 2019. "Patent sleeping beauties: evolutionary trajectories and identification methods," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 187-215, July.
    9. Helena H. Zhang & Fred Y. Ye, 2020. "Identifying ‘associated-sleeping-beauties’ in ‘swan-groups’ based on small qualified datasets of physics and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1525-1537, March.
    10. Milad Haghani & Pegah Varamini, 2021. "Temporal evolution, most influential studies and sleeping beauties of the coronavirus literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 7005-7050, August.
    11. Chi, Yuxue & Tang, Xianyi & Liu, Yijun, 2022. "Exploring the “awakening effect” in knowledge diffusion: a case study of publications in the library and information science domain," Journal of Informetrics, Elsevier, vol. 16(4).
    12. Hui Fang, 2019. "A transition stage co-citation criterion for identifying the awakeners of sleeping beauty publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(1), pages 307-322, October.
    13. Peter Kokol & Helena Blažun Vošner & Jernej Završnik & Grega Žlahtič, 2022. "Sleeping beauties in health informatics research," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 5073-5081, August.
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    15. Jianhua Hou & Xiucai Yang & Haoyang Song & Haiyue Yao, 2023. "Will patent family be dormant? Research on the identification and characteristics of sleeping beauty’s patent family," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(10), pages 5361-5387, October.

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