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

Jinyang Dong, Jiamou Liu, Tiezhong Liu*

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    2 Citations (Scopus)

    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.

    Original languageEnglish
    Pages (from-to)8561-8579
    Number of pages19
    JournalScientometrics
    Volume126
    Issue number10
    DOIs
    Publication statusPublished - Oct 2021

    Keywords

    • Basic research development
    • Community evolution
    • Community life-cycle model
    • Dynamic scientific collaboration network
    • Research funding

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