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Measuring the impact of health research data in terms of data citations by scientific publications

Author

Listed:
  • Yongmei Bai

    (Peking University Health Science Center, Peking University
    Peking University)

  • Jian Du

    (Peking University)

Abstract

Health is a representative domain data-driven research since health research data are growingly generated at a massive scale. There is an intuitive logic that the degree to which disease burden and the number of data resources align. In order to figure out disease-specific data sharing and reuse level, we took the number of data records and their citations in the scientific literature in the Data Citation Index platform as approximate indicators. The results indicated that only a small percentage (7.5%) of health data records had received documented citations by scientific publications. We find the level of data sharing and reuse varies across diseases. Our study suggested that the more socioeconomic burden and the more research funding, the more likely scientific data for diseases will be produced and made available. But such a correlation could not be observed for the activity of data reuse. Secondary reuse of scientific data is a complex behavior.

Suggested Citation

  • Yongmei Bai & Jian Du, 2022. "Measuring the impact of health research data in terms of data citations by scientific publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6881-6893, December.
    • Handle: RePEc:spr:scient:v:127:y:2022:i:12:d:10.1007_s11192-022-04559-4
    DOI: 10.1007/s11192-022-04559-4
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    References listed on IDEAS

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    1. Zhang, Lin & ZHAO, Wenjing & Liu, Jianhua & Sivertsen, Gunnar & HUANG, Ying, 2020. "Do national funding organizations properly address the diseases with the highest burden? - Observations from China and the UK," SocArXiv ckpf8, Center for Open Science.
    2. Lin Zhang & Wenjing Zhao & Jianhua Liu & Gunnar Sivertsen & Ying Huang, 2020. "Do national funding organizations properly address the diseases with the highest burden?: Observations from China and the UK," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(2), pages 1733-1761, November.
    3. Hyoungjoo Park & Sukjin You & Dietmar Wolfram, 2018. "Informal data citation for data sharing and reuse is more common than formal data citation in biomedical fields," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 69(11), pages 1346-1354, November.
    4. Barend Mons, 2020. "Invest 5% of research funds in ensuring data are reusable," Nature, Nature, vol. 578(7796), pages 491-491, February.
    5. Panagopoulos, George & Tsatsaronis, George & Varlamis, Iraklis, 2017. "Detecting rising stars in dynamic collaborative networks," Journal of Informetrics, Elsevier, vol. 11(1), pages 198-222.
    6. Hyoungjoo Park & Dietmar Wolfram, 2017. "An examination of research data sharing and re-use: implications for data citation practice," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 443-461, April.
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