Abstract
Exploring the temporal research features of Nobel laureates’ papers based on the semantic measurement indexes is helpful to understand the successful mode of scientists. For the public dataset of Nobel laureates in Physics, this study analyzes the semantic relationship between the Prize-winning papers and the other papers published by Nobel laureates in three different periods, which are the period before the laureate published the Prize-winning papers (T1), the period from publishing the Prize-winning papers to the award time (T2), and the period after winning the award (T3). We obtain the top k papers that are semantically close to the Prize-winning papers by the BERT model and use four indexes based on semantic characteristics to analyze the temporal research features of Nobel laureates’ papers. The laureates generally pay attention to the Prize-winning research at the mid-term of the T1 period, who spend an average of 1.55 times as much as the T2 period for further study in the Prize-winning field, and most of them continue for about 15 years on the Prize-winning research. In addition, we find that a few laureates published the paper semantically closest to the Prize-winning paper when they are as the Ph.D. Candidates.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bjørk, R. (2019). The age at which Noble Prize research is conducted. Scientometrics, 119(2), 931–939. https://doi.org/10.1007/s11192-019-03065-4
Bjørk, R. (2020). The journals in physics that publish Nobel Prize research. Scientometrics, 122(2), 817–823. https://doi.org/10.1007/s11192-019-03312-8
Chan, H. F., Mixon, F. G., & Torgler, B. (2018). Relation of early career performance and recognition to the probability of winning the Nobel Prize in economics. Scientometrics, 114, 1069–1086. https://doi.org/10.1007/s11192-017-2614-5
Chan, H. F., Önder, A. S., & Torgler, B. (2015). Do Nobel laureates change their patterns of collaboration following prize reception?. CREMA Working Paper Series, 105(3), 2215–2235. https://doi.org/10.1007/s11192-015-1738-8.
Chan, H. F., & Torgler, B. (2013). Science prizes: Time-lapsed awards for excellence. Nature, 500(7460), 29. https://doi.org/10.1038/500029c
Chan, H. F., & Torgler, B. (2015a). The implications of educational and methodological background for the career success of Nobel laureates: An investigation of major awards. Scientometrics, 102, 847–863. https://doi.org/10.1007/s11192-014-1367-7
Chan, H. F., & Torgler, B. (2015b). Do great minds appear in batches? Scientometrics, 104, 475–488. https://doi.org/10.1007/s11192-015-1620-8
Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). BERT: Pretraining of deep bidirectional transformers for language understanding. ArXiv Preprint ArXiv:1810.04805. https://arxiv.org/abs/1810.04805.
Frandsen, T. F., & Nicolaisen, J. (2013). The ripple effect: Citation chain reactions of a nobel prize. Journal of the American Society for Information Science and Technology, 64(3), 437–447. https://doi.org/10.1002/asi.22785
Gingras, Y., & Wallace, M. L. (2010). Why it has become more difficult to predict Nobel Prize winners: A bibliometric analysis of Nominees and Winners of the Chemistry and Physics Prizes (1901–2007). Scientometrics, 82(2), 401–412. https://doi.org/10.1007/s11192-009-0035-9
Hansson, N., & Tuffs, A. (2016). Nominee and nominator, but never Nobel Laureate: Vincenz Czerny and the Nobel Prize. Langenbeck’s Archives of Surgery, 401, 1093–1096. https://doi.org/10.1007/s00423-016-1511-3
Kawaguchi, D., Kondo, A., & Saito, A. (2016). Researchers’ career transitions over the life cycle. Scientometrics, 109(3), 1435–1454. https://doi.org/10.1007/s11192-016-2131-y
Kosmulski, M. (2020). Nobel laureates are not hot. Scientometrics, 123, 487–495. https://doi.org/10.1007/s11192-020-03378-9
Kreutz, C. K., Sahitaj, P., & Schenkel, R. (2020). Evaluating semantometrics from computer science publications. Scientometrics, 125(3), 2915–2954. https://doi.org/10.1007/s11192-020-03409-5
Kuhn, T. S. (1962). The structure of scientific revolutions. University of Chicago Press.
Kuhn, T. S. (1979). The essential tension. Selected Studies in Scientific Tradition and Change. University of Chicago Press.
Li, J., Yin, Y., Fortunato, S., & Wang, D. (2019). A dataset of publication records for Nobel laureates. Scientific Data, 6, 33. https://doi.org/10.1038/s41597-019-0033-6
Li, J., Yin, Y., Fortunato, S., & Wang, D. (2020). Scientific elite revisited: Patterns of productivity, collaboration, authorship and impact. Journal of the Royal Society Interface, 17(165), 20200135. https://doi.org/10.1098/rsif.2020.0135
Liang, G., Hou, H., Ding, Y., & Hu, Z. (2020). Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country. Journal of Informetrics, 14(3), 101053. https://doi.org/10.1016/j.joi.2020.101053
Liu, Y., & Rousseau, R. (2014). Citation analysis and the development of science: A case study using articles by some Nobel prize winners. Journal of the American Society for Information Science & Technology, 65(2), 281–289. https://doi.org/10.1002/asi.22978
Ma, C., Cheng, S., Yuan, J., & Wu, Y. (2012). Papers written by Nobel Prize winners in physics before they won the prize: An analysis of their language and journal of paper. Scientometrics, 93(3), 1151–1163. https://doi.org/10.1007/s11192-012-0748-z
Magee, R. M., & Simpson, A. T. (2019). Understanding early research experiences through the lens of connected learning. Proceedings of the Association for Information Science and Technology, 56(1), 206–215. https://doi.org/10.1002/pra2.66
Mazloumian, Amin, Eom, Y. H., Helbing, Dirk, Lozano, et al. (2011). How citation boosts promote scientific paradigm shifts and Nobel Prizes. PloS one, 6(5), 8975. https://doi.org/10.1371/journal.pone.0018975
Merton, R. K. (1973). The sociology of science: Theoretical and empirical investigations. Chicago University Press.
Michael, P., Erin, L., Russell, J. F. (2021). Dynamics of Disruption in Science and Technology. ArXiv Preprint ArXiv:2106.11184. https://arxiv.org/abs/2106.11184v1.
Min, C., Bu, Y., Wu, D., Ding, Y., & Zhang, Y. (2021). Identifying citation patterns of scientific breakthroughs: A perspective of dynamic citation process. Information Processing & Management, 58(1), 102428. https://doi.org/10.1016/j.ipm.2020.102428
Molina, J. A., Iiguez, D., Ruiz, G., & Tarancón, A. (2020). Leaders among the leaders in economics: A network analysis of the nobel prize laureates. Applied Economics Letters, 28(7), 584–589. https://doi.org/10.1080/13504851.2020.1764478
Mu, R. P., Liao, Y., & Chi, K. W. (2022). Research on the growth law of outstanding scientists: Case on Nobel Science Prize winners and academicians of Chinese Academy of Sciences. Science Research Management, 43(10), 160–171. (In Chinese).
Nakamura, S. (2015). Biography of Nobel laureate Shuji Nakamura. Annalen Der Physik, 527(56), 350–357. https://doi.org/10.1002/andp.201500804
Packalen, M., & Bhattacharya, J. (2019). Age and the trying out of new ideas. Journal of Human Capital, 13(2), 341–373. https://doi.org/10.1086/703160
Santo, F. (2014). Growing time lag threatens Nobels. Nature, 508(7495), 186–186. https://doi.org/10.1038/508186a
Savino, T., Petruzzelli, A. M., & Albino, V. (2015). Search and recombination process to innovate: A review of the empirical evidence and a research agenda. International Journal of Management Reviews, 19(1), 54–75. https://doi.org/10.1111/ijmr.12081
Schlagberger, E. M., Bornmann, L., & Bauer, J. (2016). At what institutions did Nobel laureates do their prize-winning work? An analysis of biographical information on Nobel laureates from 1994 to 2014. Scientometrics, 109(2), 723–767. https://doi.org/10.1007/s11192-016-2059-2
Schumpeter, J. (1939). Business cycles: A theoretical, historical and statistical analysis of capitalist process. McGraw-Hill.
Sturm, T. (2019). Scientific innovation: A conceptual explication and a dilemma. Theoria, 34(3), 321–341. https://doi.org/10.1387/theoria.20652
Toubia, O., Berger, J., & Eliashberg, J. (2021). How quantifying the shape of stories predicts their success. Proceedings of the National Academy of Sciences, 118(26), 2011695118. https://doi.org/10.1073/pnas.2011695118
Wang, C., Guo, F., & Wu, Q. (2021). The influence of academic advisors on academic network of Physics doctoral students: Empirical evidence based on scientometrics analysis. Scientometrics, 126(6), 4899–4925. https://doi.org/10.1007/s11192-021-03974-3
Xie, Q., Zhang, X., Ding, Y., & Song, M. (2020). Monolingual and multilingual topic analysis using LDA and BERT embeddings. Journal of Informetrics, 14(3), 101055. https://doi.org/10.1016/j.joi.2020.101055
Yan, E., Chen, Z., & Li, K. (2020). Authors’ status and the perceived quality of their work: Measuring citation sentiment change in Nobel articles. Journal of the Association for Information Science and Technology, 71(3), 314–324. https://doi.org/10.1002/asi.24237
Zeng, A., Shen, Z., Zhou, J., Fan, Y., Di, Z., Wang, Y., et al. (2018). Increasing trend of scientists to switch between topics. Nature Communications, 10(1), 3439. https://doi.org/10.1038/s41467-019-11401-8
Zhou, Y., Wang, R., Zeng, A., & Zhang, Y. C. (2020). Identifying prize-winning scientists by a competition-aware ranking. Journal of Informetrics, 14(3), 101038. https://doi.org/10.1016/j.joi.2020.101038
Zuckerman, H. (1977). Scientific elite: Nobel laureates in the United States. Library Quarterly, 67(3), 306–308. https://doi.org/10.1086/629960
Acknowledgements
This work was supported by grants from the National Social Science Foundation of China (No.21BTQ010).
Author information
Authors and Affiliations
Contributions
JD was involved in conceptualization, writing-original draft, writing-review and editing, funding acquisition. YF was involved in methodology, software, validation, formal analysis, visualization, writing—original draft. CL was involved in writing—review aand editing.
Corresponding authors
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Appendix
Appendix
Because most of the indexes do not conform to the normal distribution, we used the Mann–Whitney test to compare whether the same index has statistically significant in different periods or different categories. In the figure, ‘ns’ means that there are no significant differences, ‘*’ means that there is a significant difference at the 0.05 level (\(0.01 < p \le 0.05\)), and ‘**’ means that there is a significant difference at the 0.01 level (\(0.001 < p \le 0.01\)).
The statistical differences of SI ratio of T1 to T2 in different periods; A.2 The statistical differences of SI ratio of T3 to T2 in different periods; A.3 The statistical differences between the two types of SI ratios in the same period. The solid line represents the distribution of the SI ratio of T1 to T2, and the dotted line represents the distribution of the SI ratio of T3 to T2
The statistical differences of CI in different periods
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ding, J., Chen, Y. & Liu, C. Exploring the research features of Nobel laureates in Physics based on the semantic similarity measurement. Scientometrics 128, 5247–5275 (2023). https://doi.org/10.1007/s11192-023-04786-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-023-04786-3