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A bibliometric measure of translational science

Author

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  • Yeon Hak Kim

    (Ministry of Science and ICT)

  • Aaron D. Levine

    (Georgia Institute of Technology)

  • Eric J. Nehl

    (Emory University)

  • John P. Walsh

    (Georgia Institute of Technology)

Abstract

Science funders are increasingly requiring evidence of the broader impacts of even basic research. Initiatives such as NIH’s CTSA program are designed to shift the research focus toward more translational research. However, tracking the effectiveness of such programs depends on developing indicators that can track the degree to which basic research is influencing clinical research. We propose a new bibliometric indicator, the TS score, that is relatively simple to calculate, can be implemented at scale, is easy to replicate, and has good reliability and validity properties. This indicator is broadly applicable in settings where the goal is to estimate the degree to which basic research is used in more applied downstream research, relative to use in basic research. The TS score should be of use for a variety of policy analysis and research evaluation purposes.

Suggested Citation

  • Yeon Hak Kim & Aaron D. Levine & Eric J. Nehl & John P. Walsh, 2020. "A bibliometric measure of translational science," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2349-2382, December.
    • Handle: RePEc:spr:scient:v:125:y:2020:i:3:d:10.1007_s11192-020-03668-2
    DOI: 10.1007/s11192-020-03668-2
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    References listed on IDEAS

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    Cited by:

    1. Li, Xin & Tang, Xuli & Cheng, Qikai, 2022. "Predicting the clinical citation count of biomedical papers using multilayer perceptron neural network," Journal of Informetrics, Elsevier, vol. 16(4).
    2. Sang-Min Park & Nicholas S. Vonortas, 2023. "Translational research: from basic research to regional biomedical entrepreneurship," Small Business Economics, Springer, vol. 60(4), pages 1761-1783, April.
    3. Li, Xin & Tang, Xuli & Lu, Wei, 2024. "Investigating clinical links in edge-labeled citation networks of biomedical research: A translational science perspective," Journal of Informetrics, Elsevier, vol. 18(3).
    4. Dongyu Zang & Chunli Liu, 2023. "Exploring the clinical translation intensity of papers published by the world’s top scientists in basic medicine," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(4), pages 2371-2416, April.
    5. Xin Li & Xuli Tang & Wei Lu, 2023. "Tracking biomedical articles along the translational continuum: a measure based on biomedical knowledge representation," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(2), pages 1295-1319, February.

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