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The impact of top scientists on the community development of basic research directed by government funding: evidence from program 973 in China

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  • Jinyang Dong

    (Beijing Institute of Technology)

  • Jiamou Liu

    (The University of Auckland)

  • Tiezhong Liu

    (Beijing Institute of Technology)

Abstract

Basic research progress requires sustainable and healthy development of the academic community. This study aims to examine community development directed by research funding and the impact of top scientists on this development. To complement existing methods of measuring funding performance, which focus mostly on narrow factors such as citation or publications, we provide a new perspective that uses community development to describe the sustainability of both human capital and discipline development in basic research. The clique percolation method, instant-optimal method, and a community life-cycle model are used to describe the development of the academic community based on dynamic scientific collaboration networks. We enhance the existing community life-cycle model with a new stable event for a more realistic and comprehensive view of scientific collaboration. This study uses articles funded by basic research projects to distinguish the output of basic research. Covering all 17 disciplines, 177,909 articles funded by China’s basic research funding Program 973 are used to form a dynamic scientific collaboration network throughout 19 years. Six impacts of top scientists are identified on the development of the basic research community, including attracting collaborators, enhancing the community’s tendency to evolve, making the community exist longer, speeding up the production, strengthening the publishing quality, and enhancing the activeness.

Suggested Citation

  • Jinyang Dong & Jiamou Liu & Tiezhong Liu, 2021. "The impact of top scientists on the community development of basic research directed by government funding: evidence from program 973 in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(10), pages 8561-8579, October.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:10:d:10.1007_s11192-021-04092-w
    DOI: 10.1007/s11192-021-04092-w
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    References listed on IDEAS

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    1. Abramo, Giovanni & D’Angelo, Ciriaco Andrea & Soldatenkova, Anastasiia, 2016. "The ratio of top scientists to the academic staff as an indicator of the competitive strength of universities," Journal of Informetrics, Elsevier, vol. 10(2), pages 596-605.
    2. Gergely Palla & Imre Derényi & Illés Farkas & Tamás Vicsek, 2005. "Uncovering the overlapping community structure of complex networks in nature and society," Nature, Nature, vol. 435(7043), pages 814-818, June.
    3. Star X. Zhao & Wen Lou & Alice M. Tan & Shuang Yu, 2018. "Do funded papers attract more usage?," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 153-168, April.
    4. Jacob, Brian A. & Lefgren, Lars, 2011. "The impact of research grant funding on scientific productivity," Journal of Public Economics, Elsevier, vol. 95(9), pages 1168-1177.
    5. Aimilia Protogerou & Yannis Caloghirou & Evangelos Siokas, 2013. "Twenty-five years of science-industry collaboration: the emergence and evolution of policy-driven research networks across Europe," The Journal of Technology Transfer, Springer, vol. 38(6), pages 873-895, December.
    6. Abramo, Giovanni & Cicero, Tindaro & D’Angelo, Ciriaco Andrea, 2013. "The impact of unproductive and top researchers on overall university research performance," Journal of Informetrics, Elsevier, vol. 7(1), pages 166-175.
    7. Győrffy, Balázs & Herman, Péter & Szabó, István, 2020. "Research funding: past performance is a stronger predictor of future scientific output than reviewer scores," Journal of Informetrics, Elsevier, vol. 14(3).
    8. Abdullah Gök & John Rigby & Philip Shapira, 2016. "The impact of research funding on scientific outputs: Evidence from six smaller European countries," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(3), pages 715-730, March.
    9. Gergely Palla & Albert-László Barabási & Tamás Vicsek, 2007. "Quantifying social group evolution," Nature, Nature, vol. 446(7136), pages 664-667, April.
    10. Giovanni Abramo & Ciriaco Andrea D’Angelo & Anastasiia Soldatenkova, 2017. "How long do top scientists maintain their stardom? An analysis by region, gender and discipline: evidence from Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(2), pages 867-877, February.
    11. Giovanni Abramo & Ciriaco Andrea D’Angelo & Anastasiia Soldatenkova, 2017. "Erratum to: How long do top scientists maintain their stardom? An analysis by region, gender and discipline: evidence from Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(3), pages 1825-1825, December.
    12. Jie Zheng & Jianya Gong & Rui Li & Kai Hu & Huayi Wu & Siluo Yang, 2017. "Community evolution analysis based on co-author network: a case study of academic communities of the journal of “Annals of the Association of American Geographers”," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(2), pages 845-865, November.
    13. Barabási, A.L & Jeong, H & Néda, Z & Ravasz, E & Schubert, A & Vicsek, T, 2002. "Evolution of the social network of scientific collaborations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 311(3), pages 590-614.
    14. Martin Rosvall & Carl T Bergstrom, 2010. "Mapping Change in Large Networks," PLOS ONE, Public Library of Science, vol. 5(1), pages 1-7, January.
    15. Guangchao Charles Feng, 2020. "Research Performance Evaluation in China: A Big Data Analysis," SAGE Open, , vol. 10(1), pages 21582440199, January.
    16. Mu-Hsuan Huang & Mei-Jhen Huang, 2018. "An analysis of global research funding from subject field and funding agencies perspectives in the G9 countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(2), pages 833-847, May.
    17. Liang, Xinning & Liu, Anita M.M., 2018. "The evolution of government sponsored collaboration network and its impact on innovation: A bibliometric analysis in the Chinese solar PV sector," Research Policy, Elsevier, vol. 47(7), pages 1295-1308.
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