Abstract
In this contribution, we conduct a multi-angular analysis of the interdisciplinarity of Nobel Prize winning research compared to non-Nobel Prize winning articles, based on a large data set. Here interdisciplinarity is measured by the diversity of references, using two true diversity indicators. Articles mentioned by the Nobel Prize committee in Physiology or Medicine (in short: NP articles) awarded during the period from 1900 to 2016 are the focus of our research. These articles are compared with those in a dataset of articles that do not include a Nobel Prize winner among their authors. Moreover, these non-NPs articles were not only published in the same year and in the same research field as the NP ones but were also dealing with the same research topic (such articles are referred to as non-NP articles). The results suggest that the topic-related knowledge included in Nobel Prize winning work is higher than that in non-NPs, hence with lower interdisciplinarity than the latter. Our findings provide useful clues to better understand the characteristics of transformative research, here represented by key publications by Nobel Prize laureates in Physiology or Medicine, and their pattern of knowledge integration.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Ahlgren, P., Jarneving, B., & Rousseau, R. (2003). Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science and Technology, 54(6), 550–560. https://doi.org/10.1002/asi.10242
Chen, C. M., Chen, Y., Horowitz, M., Hou, H. Y., Liu, Z. Y., & Pellegrino, D. (2009). Towards an explanatory and computational theory of scientific discovery. Journal of Informetrics, 3(3), 191–209. https://doi.org/10.1016/j.joi.2009.03.004
Chen, S. J., Arsenault, C., & Larivière, V. (2015). Are top-cited papers more interdisciplinary? Journal of Informetrics, 9(4), 1034–1046. https://doi.org/10.1016/j.joi.2015.09.003
Chubin, D. E. (1994). Grants peer-review in theory and practice. Evaluation Review, 18(1), 20–30. https://doi.org/10.1177/0193841X9401800103
Du, J., Li, P. X., Haunschild, R., Sun, Y. N., & Tang, X. L. (2020). Paper-patent citation linkages as early signs for predicting delayed recognized knowledge: Macro and micro evidence. Journal of Informetrics, 14(2), 15. https://doi.org/10.1016/j.joi.2020.101017
Fontana, M., Iori, M., Montobbio, F., & Sinatra, R. (2020). New and atypical combinations: An assessment of novelty and interdisciplinarity. Research Policy, 49(7), 28. https://doi.org/10.1016/j.respol.2020.104063
Hu, X. J., & Rousseau, R. (2016). Scientific influence is not always visible: The phenomenon of under-cited influential publications. Journal of Informetrics, 10(4), 1079–1091. https://doi.org/10.1016/j.joi.2016.10.002
Hu, X. J., & Rousseau, R. (2017). Nobel Prize winners 2016: Igniting or sparking foundational publications? Scientometrics, 110(2), 1053–1063. https://doi.org/10.1007/s11192-016-2205-x
Hu, X. J., Luo, J. H., & Rousseau, R. (2018). A warning for Chinese academic evaluation systems: Short-term bibliometric measures misjudge the value of pioneering contributions. Journal of Zhejiang University-Science B, 19(1), 1–5. https://doi.org/10.1631/jzus.B1700569
Huang, Y. H., Hsu, C. N., & Lerman, K. (2013). Identifying transformative scientific research. Proceedings of 2013 IEEE 13th International Conference on Data Mining, (pp. 291–300). New York.
Ioannidis, J. P. A., Cristea, I. A., & Boyack, K. W. (2020). Work honored by Nobel prizes clusters heavily in a few scientific fields. PLoS ONE, 15(7), 11. https://doi.org/10.1371/journal.pone.0234612
Jost, L. (2009). Mismeasuring biological diversity: Response to Hoffmann and Hoffmann (2008). Ecological Economics, 68(4), 925–928. https://doi.org/10.1016/j.ecolecon.2008.10.015
Koshland, D. E. (2007). The cha-cha-cha theory of scientific discovery. Science, 317(5839), 761–762. https://doi.org/10.1126/science.1147166
Kuhn, T. S. (1962). The structure of scientific revolutions. The University of Chicago Press.
Larivière, V., & Gingras, Y. (2010). On the relationship between interdisciplinarity and scientific impact. Journal of the American Society for Information Science and Technology, 61(1), 126–131. https://doi.org/10.1002/asi.21226
Leinster, T., & Cobbold, C. A. (2012). Measuring diversity: The importance of species similarity. Ecology, 93(3), 477–489. https://doi.org/10.1890/10-2402.1
Leydesdorff, L., & Ivanova, I. (2021). The measurement of “interdisciplinarity” and “synergy” in scientific and extra-scientific collaborations. Journal of the Association of Information Science and Technology, 72(4), 387–402.
Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2019a). Interdisciplinarity as diversity in citation patterns among journals: Rao-Stirling diversity, relative variety, and the f coefficient. Journal of Informetrics, 13(1), 255–269. https://doi.org/10.1016/j.joi.2018.12.006
Leydesdorff, L., Wagner, C. S., & Bornmann, L. (2019b). Diversity measurement: Steps towards the measurement of interdisciplinarity? Journal of Informetrics, 13(3), 904–905. https://doi.org/10.1016/j.joi.2019.03.016
Li, J. C., Yin, Y., Fortunato, S., & Wang, D. S. (2019). A dataset of publication records for Nobel laureates. Scientific Data, 6, 10. https://doi.org/10.1038/s41597-019-0033-6
Li, X., Zhao, D. Z., & Hu, X. J. (2020). Gatekeepers in knowledge transfer between science and technology: An exploratory study in the area of gene editing. Scientometrics, 124(2), 1261–1277. https://doi.org/10.1007/s11192-020-03537-y
Liang, G. Q., Hou, H. Y., Ren, P. L., Bu, Y., Kong, X. J., & Hu, Z. G. (2019). Understanding Nobel Prize winning articles: A bibliometric analysis. Current Science, 116(3), 379–385. https://doi.org/10.18520/cs/v116/i3/379-385
Liang, G. Q., Hou, H. Y., Ding, Y., & Hu, Z. G. (2020). Knowledge recency to the birth of Nobel Prize winning articles: Gender, career stage, and country. Journal of Informetrics, 14(2), 1–14. https://doi.org/10.1016/j.joi.2020.101053
Marshall, A. W., Olkin, I., & Arnold, B. C. (2011). Inequalities: Theory of Majorization and its Applications. Springer.
Min, C., Bu, Y., & Sun, J. J. (2021). Predicting scientific breakthroughs based on knowledge structure variations. Technological Forecasting and Social Change, 164, 120502. https://doi.org/10.1016/j.techfore.2020.120502
Academies, N. (2019). Fostering Transformative Research in the Geographical Sciences. The National Academies Press. https://doi.org/10.17226/21881
Pennington, D. D., Simpson, G. L., McConnell, M. S., Fair, J. M., & Baker, R. J. (2013). Transdisciplinary research, transformative learning, and transformative science. BioScience, 63(7), 564–573. https://doi.org/10.1525/bio.2013.63.7.9
Ponomarev, I. V., Williams, D. E., Hackett, C. J., Schnell, J. D., & Haak, L. L. (2014). Predicting highly cited papers: A method for early detection of candidate breakthroughs. Technological Forecasting and Social Change, 81, 49–55. https://doi.org/10.1016/j.techfore.2012.09.017
Porter, A. L., Cohen, A. S., Roessner, J. D., & Perreault, M. (2007). Measuring researcher interdisciplinarity. Scientometrics, 72(1), 117–147. https://doi.org/10.1007/s11192-007-1700-5
Prabhakaran, T., Lathabai, H. H., & Changat, M. (2015). Detection of paradigm shifts and emerging fields using scientific network: A case study of Information Technology for Engineering. Technological Forecasting and Social Change, 91, 124–145. https://doi.org/10.1016/j.techfore.2014.02.003
Prabhakaran, T., Lathabai, H. H., George, S., & Changat, M. (2018). Towards prediction of paradigm shifts from scientific literature. Scientometrics, 117(3), 1611–1644. https://doi.org/10.1007/s11192-018-2931-3
Rafols, I., & Meyer, M. (2010). Diversity and network coherence as indicators of interdisciplinarity: Case studies in bionanoscience. Scientometrics, 82(2), 263–287. https://doi.org/10.1007/s11192-009-0041-y
Reiter, P. R., & Leone, S. (2019). Facilitating creativity in interdisciplinary design teams using cognitive processes: A review. Proceedings of the Institution of Mechanical Engineers Part C - Journal of Mechanical Engineering Science, 233(2), 385–394. https://doi.org/10.1177/0954406217753236
Rousseau, R. (1992). Concentration and DIVersity in informetric research. Doctoral thesis Antwerp University.
Rousseau, R. (2019). On the Leydesdorff-Wagner-Bornmann proposal for diversity measurement. Journal of Informetrics, 13(3), 906–907. https://doi.org/10.1016/j.joi.2019.03.015
Rousseau, R., Egghe, L., & Guns, R. (2018). Becoming Metric-Wise. A bibliometric guide for researchers. Kidlington (UK): Chandos (Elsevier).
Rousseau, R., & Hu, X. J. (2018). Under-cited influential work by Eugene Garfield. Scientometrics, 114(2), 651–657. https://doi.org/10.1007/s11192-017-2530-8
Rousseau, R., Zhang, L., & Hu, X. J., et al. (2019). Knowledge integration: its meaning and measurement. In W. Glänzel (Ed.), Handbook of science and technology indicators. New York: Springer Handbooks.
Schilling, M. A., & Green, E. (2011). Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences. Research Policy, 40(10), 1321–1331. https://doi.org/10.1016/j.respol.2011.06.009
Sebastian, Y., & Chen, C. M. (2021). The boundary-spanning mechanisms of Nobel Prize winning papers. PLoS ONE, 16(8), e0254744. https://doi.org/10.1371/journal.pone.0254744
Solomon, G. E., & A., Carley, S., & Porter, A. L. (2016). How multidisciplinary are the multidisciplinary journals Science and Nature? PLoS ONE, 11(4), 12. https://doi.org/10.1371/journal.pone.0152637
Stirling, A. (1998).On the economics and analysis of DIVersity. Science Policy Research Unit (SPRU). Electronic Working Papers Series.
Stirling, A. (2007). A general framework for analysing diversity in science, technology and society. Journal of the Royal Society Interface, 4(15), 707–719. https://doi.org/10.1098/rsif.2007.0213
Szell, M., Ma, Y. F., & Sinatra, R. (2018). A Nobel opportunity for interdisciplinarity. Nature Physics, 14(11), 1075–1078. https://doi.org/10.1038/s41567-018-0314-6
Trevors, J. T., Pollack, G. H., Saier, M. H., & Masson, L. (2012). Transformative research: Definitions, approaches and consequences. Theory in Biosciences, 131(2), 117–123. https://doi.org/10.1007/s12064-012-0154-3
Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. (2013). Atypical Combinations and Scientific Impact. Science, 342(6157), 468–472. https://doi.org/10.1126/science.1240474
Waltman, L., & van Eck, N. J. (2012). A new methodology for constructing a publication-level classification system of science. Journal of the American Society for Information Science and Technology, 63(12), 2378–2392. https://doi.org/10.1002/asi.22748
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80–83.
Winnink, J. J., Tijssen, R. J. W., & van Raan, A. F. J. (2016). Theory-changing breakthroughs in science: The impact of research teamwork on scientific discoveries. Journal of the Association for Information Science and Technology, 67(5), 1210–1223. https://doi.org/10.1002/asi.23505
Winnink, J. J., Tijssen, R. J. W., & van Raan, A. F. J. (2019). Searching for new breakthroughs in science: How effective are computerised detection algorithms? Technological Forecasting and Social Change, 146, 673–686. https://doi.org/10.1016/j.techfore.2018.05.018
Wuestman, M., Hoekman, J., & Frenken, K. (2020). A typology of scientific breakthroughs. Quantitative Science Studies, 1(3), 1203–1222. https://doi.org/10.1162/qss_a_00079
Xi, F. J., Rousseau, R., & Hu, X. J. (2021). “Sparking” and “igniting” key publications of 2020 Nobel Prize laureates. Journal of Data and Information Science, 6(2), 28–42. https://doi.org/10.2478/jdis-2021-0016
Yaqub, O. (2018). Serendipity: Towards a taxonomy and a theory. Research Policy, 47(1), 169–179.
Zhang, H. H., Wang, R. W., Zhang, R. J., & Ye, F. Y. (2019). Characterizing interdisciplinarity of Nobel laureates’ key publications. Current Science, 117(7), 1148–1152. https://doi.org/10.18520/cs/v117/i7/1148-1152
Zhang, L., Rousseau, R., & Glänzel, W. (2016). Diversity of references as an indicator of the interdisciplinarity of journals: Taking similarity between subject fields into account. Journal of the Association for Information Science and Technology, 67(5), 1257–1265. https://doi.org/10.1002/asi.23487
Zimmermann, H. (1980). OSI reference model - the ISO model of architecture for open systems interconnection. IEEE Transactions on Communications, 28(4), 425–432. https://doi.org/10.1109/tcom.1980.1094702
Acknowledgements
This work was supported by the National Natural Science Foundation of China, Grant Numbers 71974167 and 71573225. We further thank Loet Leydesdorff and the anonymous reviewers for their helpful comments.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
Ronald Rousseau is a member of the editorial board of the journal Scientometrics.
Rights and permissions
About this article
Cite this article
Li, X., Rousseau, R., Liang, L. et al. Is low interdisciplinarity of references an unexpected characteristic of Nobel Prize winning research?. Scientometrics 127, 2105–2122 (2022). https://doi.org/10.1007/s11192-022-04290-0
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11192-022-04290-0