Abstract
The aim of this paper is to collect the most-cited articles of the twenty-first century and to study how this group changed over time. Here the term “most-cited” is operationalized by considering yearly h-cores in the Web of Science. These h-cores are analysed in terms of authors, research areas, countries, institutions, journals and average number of authors per paper. We only consider publications of article or proceedings type. The research of some of the more prolific authors is on genetics and genomes publishing in multidisciplinary journals, such as Nature and Science, while the results show that writing a software tool for crystallography or molecular biology may help collecting large numbers of citations. English is the language of all articles in any h-core. The core institutions are largely those best placed in most rankings of world universities. Some attention is given on the relation between h-core articles and the information sciences. We further introduce the notions of h-core scores and h-core score per publication, leading to new rankings of countries. We conclude by stating that the notions of h-cores and h-core scores provide a new perspective on leading countries, articles and scientists.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Acharya, A., Verstak, A., Suzuki, H., Henderson, S., Iakhiaev, M., Chiung, C., Lin, Y. & Shetty, N. (2014). Rise of the rest: The growing impact of non-elite journals. arXiv:1410:2217v1. 8 Oct 2014.
Berners-Lee, T., Hendler, L., & Lassila, O. (2001). The semantic web. Scientific American, 284(5), 28–37.
Bordons, M., & Gómez, I. (2000). Collaboration networks in science. In B. Cronin & H. B. Atkins (Eds.), The web of knowledge: A Festschrift in honor of Eugene Garfield (pp. 197–213). Medford, NJ: Information Today Inc.
Bornmann, L. (2013). How to analyze percentile citation impact data meaningfully in bibliometrics: The statistical analysis of distributions, percentile rank classes, and top-cited papers. Journal of the American Society for Information Science and Technology, 64(3), 587–595. doi:10.1002/asi.22792.
Bornmann, L., & Marx, W. (2012). How good is research really? EMBO Reports, 14(3), 226–230. doi:10.1038/embor.2013.9.
Burrell, Q. L. (1992a). A note on a result of Rousseau for concentration measures. Journal of the American Society for Information Science, 43(6), 452–454.
Burrell, Q. L. (1992b). A simple-model for linked informetric processes. Information Processing and Management, 28(5), 637–645.
Chuang, K.-Y., Chuang, Y.-C., & Ho, Y.-S. (2015). Global influence of Cancer Statistics articles. Current Science, 109(9), 1552–1554.
Commission of the European Communities, & European Commission. (2001). Towards a European research area: Key figures 2001—Indicators for benchmarking of national research policies (Special ed.). Luxembourg: Office for Official Publications of the European Communities.
Egghe, L. (2004). Positive reinforcement and 3-dimensional informetrics. Scientometrics, 60(3), 497–509.
Egghe, L. (2009). The theorem of Fellman and Jakobsson: A new proof and dual theory. Mathematical and Computer Modelling, 50(11–12), 1595–1605.
Fellman, J. (1976). Effect of transformations on Lorentz (sic) curves. Econometrica, 44(4), 823–824.
Glänzel, W., & Schubert, A. (2005). Analysing scientific networks through co-Authorship. In H. F. Moed, W. Glänzel, & U. Schmoch (Eds.), Handbook of quantitative science and technology research (pp. 257–276). Dordrecht: Kluwer Academic Publishers.
Hirsch, J. E. (2005). An index to quantify an individual’s research output. Proceedings of the National Academy of Sciences of the USA, 102(46), 16569–16572.
Jakobsson, U. (1976). Measurement of degree of progression. Journal of Public Economics, 5(1–2), 161–168.
Jin, B. H., & Rousseau, R. (2005). China’s quantitative expansion phase: Exponential growth but low impact. In P. Ingwersen & B. Larsen (Eds.), Proceedings of ISSI 2005 (pp. 362–370). Stockholm: Karolinska University Press.
Kantha, S. S. (2015). Random thoughts on the 100 most cited papers from 1925 to 2008. Current Science, 109(9), 1545–1548.
King, D. A. (2004). The scientific impact of nations. Nature, 430(6997), 311–316.
Larivière, V., Lozano, G. A., & Gingras, Y. (2014). Are elite journals declining? Journal of the Association for Information Science and Technology, 65(4), 649–655.
Leydesdorff, L. (2012). Alternatives to the journal impact factor: I3 and the top-10 % (or top-25 %?) of the most-highly cited papers. Scientometrics, 92(2), 355–365. doi:10.1007/s11192-012-0660-6.
Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2014). The European Union, China, and the United States in the top-1 % and top-10 % layers of most-frequently cited publications: Competition and collaborations. Journal of Informetrics, 8(3), 606–617.
Lozano, G. A., Larivière, V., & Gingras, Y. (2012). The weakening relationship between the impact factor and papers’ citations in the digital age. Journal of the American Society for Information Science and Technology, 63(11), 2140–2145.
National Science Board (2012). Science and engineering indicators 2012. (No. NSB 12-01). Arlington VA: National Science Foundation.
Rodríguez-Navarro, A. (2013). Response to “How good is research really?”. EMBO Reports, 14(6), 493. doi:10.1038/embor.2013.60.
Rousseau, R. (1992). Concentration and diversity of availability and use in information systems: A positive reinforcement model. Journal of the American Society for Information Science, 43(5), 391–395.
Rousseau, R. (2006). New developments related to the Hirsch index. Science Focus, 1(4), 23–25 (in Chinese). An English version is available at: http://eprints.rclis.org/7616/
Rousseau, R. (2014). Skewness for journal citation curves. In E. Noyons (Ed.), Context counts: Pathways to master big and little data. Proceedings of the STI conference 2014 Leiden, p. 498–501.
Rousseau, R., & Zhang, L. (2014). How to determine the h-index of a set of publications in the WoS? ISSI Newsletter, 10(3), 63–65.
Sanz-Casado, E., García-Zorita, C., & Rousseau, R. (2015). The most-cited articles of the 21st century. In Salah, A. A., Tonta, Y., Salah, A. A. A., & Sugimoto, C., U. Al (Eds.) Proceedings of ISSI 2015, Istanbul; 15th international society of scientometrics and informetrics conference, p. 150–159.
Seglen, P. O. (1992). The skewness of science. Journal of the American Society for Information Science, 43(9), 628–638.
Van Noorden, R., Maher, B., & Nuzzo, R. (2014). The Top-100 papers. Nature, 514(7524), 550–553.
Waltman, L., Calero-Medina, C., Kosten, J., Noyons, E. C. M., Tijssen, R. J. W., van Eck, N. J., et al. (2012). The Leiden ranking 2011/2012: Data collection, indicators, and interpretation. Journal of the American Society for Information Science and Technology, 63(12), 2419–2432. doi:10.1002/asi.22708.
Zhao, S. X., & Li, J. (2015). Citation peaks in modern science: 1900-2010. Current Science, 109(9), 1523–1525.
Zhou, P., & Leydesdorff, L. (2006). The emergence of China as a leading nation in science. Research Policy, 35(1), 83–104.
Zhou, P., & Leydesdorff, L. (2008). China ranks second in scientific publications since 2006. ISSI Newsletter, 4(1), 7–9.
Zhou, Q. J., Rousseau, R., Yang, L. Y., Yue, T., & Yang, G. L. (2012). A general framework for describing diversity within systems and similarity between systems with applications in informetrics. Scientometrics, 93(3), 787–812.
Acknowledgements
The authors thank Raf Guns (University of Antwerp) and ISSI reviewers for helpful comments. Work by RR is supported by NSFC Project number 71573225.
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Sanz-Casado, E., García-Zorita, C. & Rousseau, R. Using h-cores to study the most-cited articles of the twenty-first century. Scientometrics 108, 243–261 (2016). https://doi.org/10.1007/s11192-016-1956-8
Received:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-016-1956-8