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Fresh teams are associated with original and multidisciplinary research

Author

Listed:
  • An Zeng

    (School of Systems Science, Beijing Normal University)

  • Ying Fan

    (School of Systems Science, Beijing Normal University)

  • Zengru Di

    (School of Systems Science, Beijing Normal University)

  • Yougui Wang

    (School of Systems Science, Beijing Normal University)

  • Shlomo Havlin

    (Department of Physics, Bar-Ilan University)

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.

Suggested Citation

  • An Zeng & Ying Fan & Zengru Di & Yougui Wang & Shlomo Havlin, 2021. "Fresh teams are associated with original and multidisciplinary research," Nature Human Behaviour, Nature, vol. 5(10), pages 1314-1322, October.
    • Handle: RePEc:nat:nathum:v:5:y:2021:i:10:d:10.1038_s41562-021-01084-x
    DOI: 10.1038/s41562-021-01084-x
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    Citations

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    Cited by:

    1. Ruijie Wang & Yuhao Zhou & An Zeng, 2023. "Evaluating scientists by citation and disruption of their representative works," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1689-1710, March.
    2. Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    3. Li, Heyang & Wu, Meijun & Wang, Yougui & Zeng, An, 2022. "Bibliographic coupling networks reveal the advantage of diversification in scientific projects," Journal of Informetrics, Elsevier, vol. 16(3).
    4. Yue Wang & Ning Li & Bin Zhang & Qian Huang & Jian Wu & Yang Wang, 2023. "The effect of structural holes on producing novel and disruptive research in physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1801-1823, March.
    5. Chen, Wei & Yan, Yan, 2023. "New components and combinations: The perspective of the internal collaboration networks of scientific teams," Journal of Informetrics, Elsevier, vol. 17(2).
    6. Kazuki Nakajima & Kazuyuki Shudo & Naoki Masuda, 2023. "Higher-order rich-club phenomenon in collaborative research grant networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2429-2446, April.
    7. Lei Hou & Jiashan Luo & Xue Pan, 2022. "Research Topic Specialization of Universities in Information Science and Library Science and Its Impact on Inter-University Collaboration," Sustainability, MDPI, vol. 14(15), pages 1-14, July.
    8. Liu, Meijun & Jaiswal, Ajay & Bu, Yi & Min, Chao & Yang, Sijie & Liu, Zhibo & Acuña, Daniel & Ding, Ying, 2022. "Team formation and team impact: The balance between team freshness and repeat collaboration," Journal of Informetrics, Elsevier, vol. 16(4).
    9. Jian Gao & Yian Yin & Kyle R. Myers & Karim R. Lakhani & Dashun Wang, 2021. "Potentially long-lasting effects of the pandemic on scientists," Nature Communications, Nature, vol. 12(1), pages 1-6, December.
    10. Ma, Guoshuai & Yuhua, Qian & Zhang, Yayu & Yan, Hongren & Cheng, Honghong & Hu, Zhiguo, 2022. "The recognition of kernel research team," Journal of Informetrics, Elsevier, vol. 16(4).
    11. Meijun Liu & Yi Bu & Chongyan Chen & Jian Xu & Daifeng Li & Yan Leng & Richard B. Freeman & Eric T. Meyer & Wonjin Yoon & Mujeen Sung & Minbyul Jeong & Jinhyuk Lee & Jaewoo Kang & Chao Min & Min Song , 2022. "Pandemics are catalysts of scientific novelty: Evidence from COVID‐19," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 73(8), pages 1065-1078, August.
    12. Lu Liu & Benjamin F. Jones & Brian Uzzi & Dashun Wang, 2023. "Data, measurement and empirical methods in the science of science," Nature Human Behaviour, Nature, vol. 7(7), pages 1046-1058, July.

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