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Fresh teams are associated with original and multidisciplinary research

Abstract

Teamwork is one of the most prominent features in modern science. It is now well understood that team size is an important factor that affects the creativity of the team. However, the crucial question of how the character of research studies is related to the freshness of a team remains unclear. Here, we quantify the team freshness according to the absence of prior collaboration among team members. Our results suggest that papers produced by fresher teams are associated with greater originality and a greater multidisciplinary impact. These effects are even stronger in larger teams. Furthermore, we find that freshness defined by new team members in a paper is a more effective indicator of research originality and multidisciplinarity compared with freshness defined by new collaboration relationships among team members. Finally, we show that the career freshness of team members is also positively correlated with the originality and multidisciplinarity of produced papers.

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Fig. 1: Illustration of the freshness of teams, disruption of papers and multidisciplinary impact of papers.
Fig. 2: Fresh teams create more original and multidisciplinary research.
Fig. 3: The difference between fresh and old teams is amplified in larger teams.
Fig. 4: Team freshness defined by new team members and new collaboration relationships.
Fig. 5: Freshness of team member’s careers.

Data availability

The APS data can be downloaded at https://journals.aps.org/datasets. The computer science data can be downloaded at https://www.aminer.cn/aminernetwork. The multidisciplinary data were download from https://docs.microsoft.com/en-us/academic-services/graph. Other related, relevant data are available from the corresponding author on reasonable request.

Code availability

Computational codes for data processing and analysis are available from the corresponding author on request.

References

  1. 1.

    Fortunato, S. et al. Science of science. Science 359, eaao0185 (2018).

    Article  CAS  Google Scholar 

  2. 2.

    Zeng, A. et al. The science of science: from the perspective of complex systems. Phys. Rep. 714-715, 1–73 (2017).

    Article  Google Scholar 

  3. 3.

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

    Article  CAS  Google Scholar 

  4. 4.

    Guimera, R., Uzzi, B., Spiro, J. & Amaral, L. Team assembly mechanisms determine collaboration network structure and team performance. Science 308, 697–702 (2005).

    Article  CAS  Google Scholar 

  5. 5.

    Leahey, E. et al. From sole investigator to team scientist: trends in the practice and study of research collaboration. Annu. Rev. Sociol. 42, 81–100 (2016).

    Article  Google Scholar 

  6. 6.

    Milojevic, S. Principles of scientific research team formation and evolution. Proc. Natl Acad. Sci. USA 111, 3984–3989 (2014).

    Article  CAS  Google Scholar 

  7. 7.

    Hunter, L. & Leahey, E. Collaborative research in sociology: trends and contributing factors. Am. Sociol. 39, 290–306 (2008).

    Article  Google Scholar 

  8. 8.

    Xie, Y. ‘Undemocracy’: inequalities in science. Science 344, 809–810 (2014).

    Article  CAS  Google Scholar 

  9. 9.

    Falk-Krzesinski, H. J. et al. Mapping a research agenda for the science of team science. Res. Eval. 20, 145–158 (2011).

    Article  Google Scholar 

  10. 10.

    Barabasi, A. et al. Evolution of the social network of scientific collaborations. Phys. A 311, 590–614 (2002).

    Article  Google Scholar 

  11. 11.

    Newman, M. E. J. The structure of scientific collaboration networks. Proc. Natl Acad. Sci. USA 98, 404–409 (2001).

    Article  CAS  Google Scholar 

  12. 12.

    Petersen, A. M. Quantifying the impact of weak, strong, and super ties in scientific careers. Proc. Natl Acad. Sci. USA 112, E4671–E4680 (2015).

    Article  CAS  Google Scholar 

  13. 13.

    Li, M. et al. Evolving model of weighted networks inspired by scientific collaboration networks. Phys. A 375, 355–364 (2007).

    Article  Google Scholar 

  14. 14.

    Borner, K., Maru, J. T. & Goldstone, R. L. The simultaneous evolution of author and paper networks. Proc. Natl Acad. Sci. USA 101, 5266–5273 (2004).

    Article  CAS  Google Scholar 

  15. 15.

    Redner, S. How popular is your paper? An empirical study of the citation distribution. Eur. Phys. J. B 4, 131–134 (1998).

    Article  CAS  Google Scholar 

  16. 16.

    Klug, M. & Bagrow, J. P. Understanding the group dynamics and success of teams. R. Soc. Open Sci. 3, 160007 (2016).

    Article  Google Scholar 

  17. 17.

    Hsiehchen, D., Espinoza, M. & Hsieh, A. Multinational teams and diseconomies of scale in collaborative research. Sci. Adv. 1, e1500211 (2015).

    Article  Google Scholar 

  18. 18.

    Wu, L., Wang, D. & Evans, J. A. Large teams develop and small teams disrupt science and technology. Nature 566, 378–382 (2019).

    Article  CAS  Google Scholar 

  19. 19.

    Coccia, M. & Wang, L. Evolution and convergence of the patterns of international scientific collaboration. Proc. Natl Acad. Sci. USA 113, 2057–2061 (2016).

    Article  CAS  Google Scholar 

  20. 20.

    Jones, B. F., Wuchty, S. & Uzzi, B. Multi-university research teams: shifting impact, geography, and stratification in science. Science 322, 1259–1262 (2008).

    Article  CAS  Google Scholar 

  21. 21.

    Gazni, A., Sugimoto, C. R. & Didegah, F. Mapping world scientific collaboration: authors, institutions, and countries. J. Am. Soc. Inf. Sci. Technol. 63, 323–335 (2012).

    Article  CAS  Google Scholar 

  22. 22.

    Van Noorden, R. et al. Interdisciplinary research by the numbers. Nature 525, 306–307 (2015).

    Article  CAS  Google Scholar 

  23. 23.

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

    Article  CAS  Google Scholar 

  24. 24.

    Stephan, P. E. & Levin, S. G. Age and the Nobel Prize revisited. Scientometrics 28, 387–399 (1993).

    Article  Google Scholar 

  25. 25.

    Jones, B. F. & Weinberg, B. A. Age dynamics in scientific creativity. Proc. Natl Acad. Sci. USA 108, 18910–18914 (2011).

    Article  Google Scholar 

  26. 26.

    Jones, B. F., Reedy, E. J. & Weinberg, B. A. Age and Scientific Genius (Wiley-Blackwell, 2014).

  27. 27.

    Sinatra, R., Wang, D., Deville, P., Song, C. & Barabasi, A.-L. Quantifying the evolution of individual scientific impact. Science 354, aaf5239 (2016).

    Article  CAS  Google Scholar 

  28. 28.

    Funk, R. J. & Owen-Smith, J. A dynamic network measure of technological change. Manag. Sci. 63, 791–817 (2017).

    Article  Google Scholar 

  29. 29.

    Sinatra, R., Deville, P., Szell, M., Wang, D. & Barabasi, A.-L. A century of physics. Nat. Phys. 11, 791–796 (2015).

    Article  CAS  Google Scholar 

  30. 30.

    Zhou, T., Lu, L. & Zhang, Y.-C. Predicting missing links via local information. Eur. Phys. J. B 71, 623–630 (2009).

    Article  CAS  Google Scholar 

  31. 31.

    Petersen, A. M. et al. Reputation and impact in academic careers. Proc. Natl Acad. Sci. USA 111, 15316–15321 (2014).

    Article  CAS  Google Scholar 

  32. 32.

    Zeng, A. et al. Increasing trend of scientists to switch between topics. Nat. Commun. 10, 3439 (2019).

    Article  CAS  Google Scholar 

  33. 33.

    Jia, T., Wang, D. & Szymanski, B. K. Quantifying patterns of research-interest evolution. Nat. Hum. Behav. 1, 0078 (2017).

    Article  Google Scholar 

  34. 34.

    Tang, J. et al. ArnetMiner: extraction and mining of academic social networks. In Proc. Fourteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD’2008) (eds Li, Y., Liu, B. & Sarawagi, S.) 990–998 (Association for Computing Machinery, 2008).

  35. 35.

    Sinha, A. et al. An overview of Microsoft Academic Service (MA) and applications. In Proc. 24th International Conference on World Wide Web (WWW15 Companion) (eds Gangemi, A., Leonardi, S. & Panconesi, A.) 243–246 (Association for Computing Machinery, 2015).

  36. 36.

    Stirling, A. A general framework for analysing diversity in science, technology and society. J. R. Soc. Interface 4, 707–719 (2007).

    Article  Google Scholar 

  37. 37.

    Porter, A. & Rafols, I. Is science becoming more interdisciplinary? Measuring and mapping six research fields over time. Scientometrics 81, 719–745 (2009).

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China under Grant (71843005 and 71731002). S.H. thanks the Israel Science Foundation and the NSF-BSF for financial support. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.

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Affiliations

Authors

Contributions

A.Z. and S.H. designed the research. A.Z. performed the experiments. Y.F., Z.D. and Y.W. contributed analytical tools. A.Z. and S.H. analysed the data. All of the authors wrote the manuscript.

Corresponding author

Correspondence to Shlomo Havlin.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Filipi Nascimento Silva and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Peer reviewer reports are available.

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Supplementary Figs. 1–24, Supplementary Tables 1–4 and Supplementary References.

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Zeng, A., Fan, Y., Di, Z. et al. Fresh teams are associated with original and multidisciplinary research. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01084-x

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