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Measuring the stability of scientific collaboration

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Abstract

Stability has long been regarded as an important characteristic of many natural and social processes. In regards to scientific collaborations, we define stability to reflect the consistent investment of a certain amount of effort into a relationship. In this paper, we provide an explicit definition of a new indicator of stability, based on the year-to-year publication output of collaborations. We conduct a large-scale analysis of stability among collaborations between authors publishing in the field of computer science. Collaborations with medium–high degree of stability tend to occur most frequently, and on average, have the highest average scientific impact. We explore other “circumstances”, reflecting the composition of collaborators, that may interact with the relationship between stability and impact, and show that (1) Transdisciplinary collaborations with low stability leads to high impact publications; (2) Stable collaboration with the collaborative author pairs showing greater difference in scientific age or career impact can produce high impact publications; and (3) Highly-cited collaborators whose publications have a large number of co-authors do not keep stable collaborations. We also demonstrate how our indicator for stability can be used alongside other similar indicators, such as persistence, to better understand the nature of scientific collaboration, and outline a new taxonomy of collaborations.

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Notes

  1. When \(\hbox{max} \left( {p_{mn} } \right) \to + \infty ,\;S_{mn\_i} \to \left( {1 - \frac{1}{N}} \right)\).

References

  • Abramo, G., D’Angelo, C. A., & Costa, F. D. (2009). Research collaboration and productivity: Is there correlation? Higher Education, 57(2), 155–171.

    Article  Google Scholar 

  • Akgün, A. E., & Lynn, G. S. (2002). Antecedents and consequences of team stability on new product development performance. Journal of Engineering and Technology Management, 19(3), 263–286.

    Article  Google Scholar 

  • Bogomol’Nyi, E.B. (1976). The stability of classical solutions. Soviet Journal of Nuclear Physics (English Translation), 24(4).

  • Bromham, L., Dinnage, R., & Hua, X. (2016). Interdisciplinary research has consistently lower funding success. Nature, 534(7609), 684–687.

    Article  Google Scholar 

  • Bu, Y., Ding, Y., Liang, X., & Murray, D. S. (2017). Understanding persistent scientific collaboration. Journal of the Association for Information Science and Technology. https://doi.org/10.1002/asi.23966.

    Google Scholar 

  • Cannon, W. B. (1932). Homeostasis. The wisdom of the body. New York: Norton.

    Google Scholar 

  • Curral, L. A., Forrester, R. H., Dawson, J. F., & West, M. A. (2001). It’s what you do and the way that you do it: Team task, team size, and innovation-related group processes. European Journal of Work and Organizational Psychology, 10(2), 187–204.

    Article  Google Scholar 

  • Eitzen, D. S., & Yetman, N. R. (1972). Managerial change, longevity, and organizational effectiveness. Administrative Science Quarterly, 17(1), 110–116.

    Article  Google Scholar 

  • Ellingsen, T., & Palm, E. (1975). Stability of linear flow. The Physics of Fluids, 18(4), 487–488.

    Article  MATH  Google Scholar 

  • Emmons, K. M., Viswanath, K., & Colditz, G. A. (2008). The role of transdisciplinary collaboration in translating and disseminating health research: Lessons learned and exemplars of success. American Journal of Preventive Medicine, 35(2), S204–S210.

    Article  Google Scholar 

  • Finholt, T. (1999). Collaboratory life: Challenges of Internet-mediated science for chemists. In National Research Council (Ed.), Impact of advances in computing and communications technologies on chemical science and technology. Washington, DC: The National Academies Press.

    Google Scholar 

  • Fridman, A. M., & Polyachenko, V. L. V. (2012). Physics of gravitating systems I: Equilibrium and stability. Berlin: Springer.

    MATH  Google Scholar 

  • Grusky, O. (1963). Managerial succession and organizational effectiveness. American Journal of Sociology, 69(1), 21–31.

    Article  Google Scholar 

  • Guns, R., & Rousseau, R. (2009). Simulating growth of the h-index. Journal of the American Society for Information Science and Technology, 60(2), 410–417.

    Article  Google Scholar 

  • Guzzo, R. A., & Dickson, M. W. (1996). Teams in organizations: Recent research on performance and effectiveness. Annual Review of Psychology, 47, 307–333.

    Article  Google Scholar 

  • Hakala, J. (2009a). Socialization of junior researchers in new academic research environments: Two case studies from Finland. Studies in Higher Education, 34(5), 501–516.

    Article  Google Scholar 

  • Hakala, J. (2009b). The future of the academic calling? Junior researchers in the entrepreneurial university. Higher Education, 57(2), 173.

    Article  Google Scholar 

  • Hannan, M. T., & Freeman, J. (1984). Structural inertia and organizational change. American Sociological Review, 49(2), 149–164.

    Article  Google Scholar 

  • Hara, N., Solomon, P., Kim, S. L., & Sonnenwald, D. H. (2003). An emerging view of scientific collaboration: Scientists’ perspectives on collaboration and factors that impact collaboration. Journal of the American Society for Information Science and Technology, 54(10), 952–965.

    Article  Google Scholar 

  • Hirsch, J. E. (2005). An index to quantify an individual’s scientific research output. Proceedings of the National Academy of Sciences of the United States of America, 102(46), 16569–16572.

    Article  MATH  Google Scholar 

  • Ioannidis, J. P. (2011). More time for research: Fund people not projects. Nature, 477(7366), 529–531.

    Article  Google Scholar 

  • Ioannidis, J. P. A., Boyack, K. W., & Klavans, R. (2014). Estimates of the continuously publishing core in the scientific workforce. PLoS ONE, 9(7), e101698.

    Article  Google Scholar 

  • Katz, R. (1982). The effects of group longevity on project communication and performance. Administrative Science Quarterly, 33(1), 81–104.

    Article  Google Scholar 

  • Kerr, N. L., & Tindale, R. S. (2004). Group performance and decision making. Annual Review of Psychology, 55, 623–655.

    Article  Google Scholar 

  • Kling, R., & McKim, G. (2000). Not just a matter of time: Field differences and the shaping of electronic media in supporting scientific communication. Journal of the American Society for Information Science, 51(14), 1306–1320.

    Article  Google Scholar 

  • Larivière, V., Gingras, Y., Sugimoto, C. R., & Tsou, A. (2015). Team size matters: Collaboration and scientific impact since 1900. Journal of the Association for Information Science and Technology, 66(7), 1323–1332.

    Article  Google Scholar 

  • Lee, S., & Bozeman, B. (2005). The impact of research collaboration on scientific productivity. Social Studies of Science, 35(5), 673–702.

    Article  Google Scholar 

  • Leydesdorff, L., Bornmann, L., Comins, J., & Milojević, S. (2016). Citations: Indicators of quality? The impact fallacy. Retrieved from http://arxiv.org/abs/1603.08452.

  • Milojević, S. (2010). Modes of collaboration in modern science: Beyond power laws and preferential attachment. Journal of the American Society for Information Science and Technology, 61(7), 1410–1423.

    Article  Google Scholar 

  • Molina-Morales, F. X., & Martínez-Fernández, M. T. (2009). Too much love in the neighborhood can hurt: How an excess of intensity and trust in relationships may produce negative effects on firms. Strategic Management Journal, 30(9), 1013–1023.

    Article  Google Scholar 

  • Nystrom, P. C., & Starbuck, W. H. (1984). To avoid organizational crises, unlearn. Organizational Dynamics, 12(4), 53–65.

    Article  Google Scholar 

  • 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.

    Article  Google Scholar 

  • Petersen, A. M. (2015). Quantifying the impact of weak, strong, and super ties in scientific careers. Proceedings of the National Academy of Sciences of the United of America, 112(34), E4671–E4680.

    Article  Google Scholar 

  • Schaltegger, S., Beckmann, M., & Hansen, E. G. (2013). Transdisciplinarity in corporate sustainability: Mapping the field. Business Strategy and the Environment, 22(4), 219–229.

    Article  Google Scholar 

  • Simberloff, D. (2014). The “Balance of Nature”—Evolution of a panchreston. PLoS Biology. https://doi.org/10.1371/journal.pbio.1001963.

    Google Scholar 

  • Smith, E. D., & Nyman, R. C. (1939). Technology and labor: A study of the human problems of labor saving. New Haven, CT: Yale University Press.

    Google Scholar 

  • Stokols, D., Hall, K. L., Taylor, B. K., & Moser, R. P. (2008a). The science of team science: Overview of the field and introduction to the supplement. American Journal of Preventive Medicine, 35(2), S77–S89.

    Article  Google Scholar 

  • Stokols, D., Misra, S., Moser, R. P., Hall, K. L., & Taylor, B. K. (2008b). The ecology of team science: Understanding contextual influences on transdisciplinary collaboration. American Journal of Preventive Medicine, 35(2), S96–S115.

    Article  Google Scholar 

  • Straus, S. E., Chatur, F., & Taylor, M. (2009). Issues in the mentor–mentee relationship in academic medicine: A qualitative study. Academic Medicine, 84(1), 135–139.

    Article  Google Scholar 

  • Tang, J., Fong, A. C. M., Wang, B., & Zhang, J. (2012). A unified probabilistic framework for name disambiguation in digital library. IEEE Transaction on Knowledge and Data Engineering, 24(6), 975–987.

    Article  Google Scholar 

  • Tang, J., Jin, R., & Zhang, J. (2008b). A topic modeling approach and its integration into the random walk framework for academic search. In Proceeding of the eighth IEEE international conference on data mining (pp. 1055–1060). Pisa, Italy.

  • Tang, J., Zhang, J., Yao, L., Li, J., Zhang, L., & Su, Z. (2008a). ArnetMiner: Extraction and mining of academic social networks. In Proceedings of the fourteenth ACM SIGKDD international conference on knowledge discovery and data mining (pp. 990–998). Las Vegas, NV.

  • Uzzi, B. (1996). The sources and consequences of embeddedness for the economic performance of organizations: The network effect. American Sociological Review, 61(4), 674–698.

    Article  Google Scholar 

  • Uzzi, B., Mukherjee, S., Stringer, M., & Jones, B. F. (2013). Atypical combinations and scientific impact. Science, 342(6157), 468–472.

    Article  Google Scholar 

  • Van Noorden, R. (2015). Interdisciplinary research by the numbers. Nature News, 525(7569), 306.

    Article  Google Scholar 

  • Wray, K. B. (2002). The epistemic significance of collaborative research. Philosophy of Science, 69(1), 150–168. https://doi.org/10.1086/338946.

    Article  Google Scholar 

  • Wu, S., Xu, J., & Ding, Y. (2017). Discover citation topic distribution patterns of highly cited papers. In iConference 2017 global collaboration across the information community (pp. 739–743). Wuhan, Hubei, China.

  • Wu, Y., Venkatramanan, S., & Chiu, D. M. (2016). Research collaboration and topic trends in Computer Science based on top active authors. PeerJ Computer Science, 2, e41.

    Article  Google Scholar 

  • Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (Grant Numbers: 71420107026 and 71403190).

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Authors and Affiliations

  1. School of Informatics, Computing, and Engineering, Indiana University, Bloomington, IN, USA

    Yi Bu, Dakota S. Murray & Ying Ding

  2. School of Information Management, Wuhan University, Wuhan, Hubei, China

    Ying Ding & Yong Huang

  3. Center for Studies of Information Resources, Wuhan University, Wuhan, Hubei, China

    Yiming Zhao

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  1. Yi Bu
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  2. Dakota S. Murray
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  3. Ying Ding
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  4. Yong Huang
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Correspondence to Yi Bu.

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Bu, Y., Murray, D.S., Ding, Y. et al. Measuring the stability of scientific collaboration. Scientometrics 114, 463–479 (2018). https://doi.org/10.1007/s11192-017-2599-0

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