Abstract
The Hirsch or h-index continues to be one of the most popular author-based metrics, despite some of its flaws and limitations. In some cases, citations to some academics’ work are increasing at a phenomenal pace, and in some exceptional cases such as Highly Cited Researchers (HCRs), whether alive or deceased, citation counts to their work have reached tens or hundreds of thousands, with h-indexes in the hundreds. Although the h-index currently has one additional derivative index, the i10-index, which is a measure of the number of publications with 10 or more citations, the i10-index is clearly not a sufficient differentiating factor for academics with very high citation counts such as HCRs. In this letter, an expansion of this metric is proposed to include an i100-index, an i1000-index, and an i10,000-index, which indicate the number of publications with 100, 1000, or 10,000 or more citations, respectively. These three new, expanded and/or modified metrics that are based on the h-index may assist in more effectively differentiating the top echelon of HCRs or leaders in a specific field of study. The i100-, i1000-, and i10,000-indexes for 10 HCRs (Michel Foucault, Ronald C. Kessler, Graham Colditz, Sigmund Freud, JoAnn E. Manson, Shizuo Akira, Pierre Bourdieu, Robert Langer, Eric Lander, and Bert Vogelstein) were calculated. Limitations to these three new proposed h-index-based indexes are noted.
This is a preview of subscription content, log in via an institution to check access.
Notes
https://clarivate.com/news/global-highly-cited-researchers-2019-list-reveals-top-talent-in-the-sciences-and-social-sciences/; https://recognition.webofsciencegroup.com/awards/highly-cited/2019/ (November 19, 2019; last accessed: December 1, 2020).
http://www.webometrics.info/en/hlargerthan100 (April, 2020; last accessed; September 26, 2020).
References
Bornmann, L., & Leydesdorff, L. (2018). Count highly-cited papers instead of papers with h citations: Use normalized citation counts and compare “like with like”! Scientometrics, 115(2), 1119–1123. https://doi.org/10.1007/s11192-018-2682-1.
Brembs, B., Button, K., & Munafò, M. (2013). Deep impact: Unintended consequences of journal rank. Frontiers in Human Neuroscience, 7, 291. https://doi.org/10.3389/fnhum.2013.00291.
Dinsmore, A., Allen, L., & Dolby, K. (2014). Alternative perspectives on impact: The potential of ALMs and altmetrics to inform funders about research impact. PLoS Biology, 12(11), e1002003. https://doi.org/10.1371/journal.pbio.1002003.
Dobránszki, J., & Teixeira da Silva, J. A. (2019). Corrective factors for author- and journal-based metrics impacted by citations to accommodate for retractions. Scientometrics, 121(1), 387–398. https://doi.org/10.1007/s11192-019-03190-0.
Eom, Y.-H., & Fortunato, S. (2011). Characterizing and modeling citation dynamics. PLoS ONE, 6(9), e24926. https://doi.org/10.1371/journal.pone.0024926.
Hirsch, J. E. (2019). hα: An index to quantify an individual’s scientific leadership. Scientometrics, 118(2), 673–686. https://doi.org/10.1007/s11192-018-2994-1.
Hu, G.-Y., Wang, L., Ni, R., & Liu, W.-S. (2020). Which h-index? An exploration within the Web of Science. Scientometrics, 123(3), 1225–1233. https://doi.org/10.1007/s11192-020-03425-5.
Ioannidis, J. P. A., Baas, J., Klavans, R., & Boyack, K. W. (2019). A standardized citation metrics author database annotated for scientific field. PLoS Biology, 17(8), e3000384. https://doi.org/10.1371/journal.pbio.3000384.
Ioannidis, J. P. A., Klavans, R., & Boyack, K. W. (2018). Thousands of scientists publish a paper every five days. Nature, 561, 167–169. https://doi.org/10.1038/d41586-018-06185-8.
Jensenius, F., Htun, M., Samuels, D., Singer, D., Lawrence, A., & Chwe, M. (2018). The benefits and pitfalls of Google Scholar. PS: Political Science & Politics, 51(4), 820–824. https://doi.org/10.1017/S104909651800094X.
Kalcioglu, M. T., Ileri, Y., Ozdamar, O. I., Yilmaz, U., & Tekin, M. (2018). Evaluation of the academic productivity of the top 100 worldwide physicians in the field of otorhinolaryngology and head and neck surgery using the Google Scholar h-index as the bibliometrics ranking system. The Journal of Laryngology & Otology, 132(12), 1097–1101. https://doi.org/10.1017/s0022215118002190.
McCoy, A. B., Sittig, D. F., Lin, J., & Wright, A. (2019). Identification and ranking of biomedical informatics researcher citation statistics through a Google Scholar scraper. In AMIA Annual Symposium Proceedings, 2019 (pp 655–663). PMCID: PMC7153158
Montazerian, M., Zanotto, E. D., & Eckert, H. (2019). A new parameter for (normalized) evaluation of H-index: Countries as a case study. Scientometrics, 118(3), 1065–1078. https://doi.org/10.1007/s11192-018-2996-z.
Nieminen, P., Carpenter, J., Rucker, G., & Schumacher, M. (2006). The relationship between quality of research and citation frequency. BMC Medical Research Methodology, 6, 42. https://doi.org/10.1186/1471-2288-6-42.
O’Kelly, F., Fernandez, N., & Koyle, M. A. (2019). Predatory publishing or a lack of peer review transparency? A contemporary analysis of indexed open and non-open access articles in paediatric urology. Journal of Pediatric Urology, 15(2), 159.e1–159.e7. https://doi.org/10.1016/j.jpurol.2018.08.019.
Roldan-Valadez, E., Salazar-Ruiz, S. Y., Ibarra-Contreras, R., & Rios, C. (2019). Current concepts on bibliometrics: a brief review about impact factor, Eigenfactor score, CiteScore, SCImago Journal Rank, Source-Normalised Impact per Paper, H-index, and alternative metrics. Irish Journal of Medical Science, 188(3), 939–951. https://doi.org/10.1007/s11845-018-1936-5.
Rousseau, R., & Leuven, K. U. (2008). Reflections on recent developments of the h-index and h-type indices. COLLNET Journal of Scientometrics and Information Management, 2(1), 1–8. https://doi.org/10.1080/09737766.2008.10700835.
Schmoch, U. (2020). Mean values of skewed distributions in the bibliometric assessment of research units. Scientometrics. https://doi.org/10.1007/s11192-020-03476-8.
Teixeira da Silva, J. A. (2018). The Google Scholar h-index: Useful but burdensome metric. Scientometrics, 117(1), 631–635. https://doi.org/10.1007/s11192-018-2859-7.
Teixeira da Silva, J. A., & Bernès, S. (2018). Clarivate Analytics: Continued omnia vanitas impact factor culture. Science and Engineering Ethics, 24(1), 291–297. https://doi.org/10.1007/s11948-017-9873-7.
Teixeira da Silva, J. A., & Dobránszki, J. (2018a). Multiple versions of the h-index: Cautionary use for formal academic purposes. Scientometrics, 115(2), 1107–1113. https://doi.org/10.1007/s11192-018-2680-3.
Teixeira da Silva, J. A., & Dobránszki, J. (2018b). Rejoinder to “Multiple versions of the h-index: Cautionary use for formal academic purposes”. Scientometrics, 115(2), 1131–1137. https://doi.org/10.1007/s11192-018-2684-z.
Thoma, B., & Chan, T. M. (2019). Using Google Scholar to track the scholarly output of research groups. Perspectives on Medical Education, 8(3), 201–205. https://doi.org/10.1007/s40037-019-0515-4.
Yasin, A., Fatima, R., Wen, L., Afzal, W., Azhar, M., & Torkar, R. (2020). On using grey literature and Google Scholar in systematic literature reviews in software engineering. IEEE Access, 8, 36226–36243. https://doi.org/10.1109/ACCESS.2020.2971712.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Teixeira da Silva, J.A. The i100-index, i1000-index and i10,000-index: expansion and fortification of the Google Scholar h-index for finer-scale citation descriptions and researcher classification. Scientometrics 126, 3667–3672 (2021). https://doi.org/10.1007/s11192-020-03831-9
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-020-03831-9