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Introducing ‘facilitymetrics’: a first review and analysis of commonly used measures of scientific leadership among synchrotron radiation facilities worldwide

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  • Olof Hallonsten

    (University of Gothenburg)

Abstract

Big Science accelerator complexes are no longer mere tools for nuclear and particle physics, but modern-day experimental resources for a wide range of natural sciences and often named instrumental to scientific and technological development for innovation and economic growth. Facilities compete on a global market to attract the best users and facilitate the best science, and advertise the achievement of their users as markers of quality and productivity. Thus a need has risen for (quantitative) quality assessment of science on the level of facilities. In this article, we examine some quantitative performance measurements frequently used by facilities to display quality: technical reliability, competition for access, and publication records. We report data from the world’s three largest synchrotron radiation facilities from the years 2004–2010, and discuss their meaning and significance by placing them in proper context. While we argue that quality is not possible to completely capture in these quantitative metrics, we acknowledge their apparent importance and, hence, we introduce and propose facilitymetrics as a new feature of the study of modern big science, and as a new empirical focus for scientometrical study, in the hope that future studies can contribute to a deeper, much-needed analysis of the topic.

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  • Olof Hallonsten, 2013. "Introducing ‘facilitymetrics’: a first review and analysis of commonly used measures of scientific leadership among synchrotron radiation facilities worldwide," Scientometrics, Springer;Akadémiai Kiadó, vol. 96(2), pages 497-513, August.
    • Handle: RePEc:spr:scient:v:96:y:2013:i:2:d:10.1007_s11192-012-0945-9
    DOI: 10.1007/s11192-012-0945-9
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    References listed on IDEAS

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    1. Aant Elzinga, 2012. "Features of the current science policy regime: Viewed in historical perspective," Science and Public Policy, Oxford University Press, vol. 39(4), pages 416-428, August.
    2. Olof Hallonsten & Thomas Heinze, 2012. "Institutional persistence through gradual organizational adaptation: Analysis of national laboratories in the USA and Germany," Science and Public Policy, Oxford University Press, vol. 39(4), pages 450-463, July.
    3. Merle Jacob & Olof Hallonsten, 2012. "The persistence of big science and megascience in research and innovation policy," Science and Public Policy, Oxford University Press, vol. 39(4), pages 411-415, July.
    4. Catherine Westfall, 2012. "Institutional persistence and the material transformation of the US national labs: The curious story of the advent of the Advanced Photon Source," Science and Public Policy, Oxford University Press, vol. 39(4), pages 439-449, July.
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    Cited by:

    1. Del Bo, Chiara F., 2016. "The rate of return to investment in R&D: The case of research infrastructures," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 26-37.
    2. Laurent Bach & Sandrine Wolff, 2022. "The BETA-EvaRIO impact evaluation method: towards a bridging approach?," The Journal of Technology Transfer, Springer, vol. 47(3), pages 651-672, June.
    3. Kamilla Kohn Rådberg & Hans Löfsten, 2024. "The entrepreneurial university and development of large-scale research infrastructure: exploring the emerging university function of collaboration and leadership," The Journal of Technology Transfer, Springer, vol. 49(1), pages 334-366, February.
    4. Laurent Bach & Sandrine Wolff, 2017. "The BETA-EvaRIO impact evaluation method: towards a bridging approach?," Post-Print hal-02167827, HAL.
    5. Richard Heidler & Olof Hallonsten, 2015. "Qualifying the performance evaluation of Big Science beyond productivity, impact and costs," Scientometrics, Springer;Akadémiai Kiadó, vol. 104(1), pages 295-312, July.
    6. Fabio S. V. Silva & Peter A. Schulz & Everard C. M. Noyons, 2019. "Co-authorship networks and research impact in large research facilities: benchmarking internal reports and bibliometric databases," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 93-108, January.
    7. Wenchao Xu & Yanmei Xu & Junfeng Li, 2017. "A Study of RI Clusters Based on Symbiosis Theory," Sustainability, MDPI, vol. 9(3), pages 1-13, March.
    8. Chiara F. DEL BO, 2014. "The rate of return to investment in R&D infrastructure: an overview," Departmental Working Papers 2014-11, Department of Economics, Management and Quantitative Methods at Università degli Studi di Milano.
    9. Olof Hallonsten, 2014. "How expensive is Big Science? Consequences of using simple publication counts in performance assessment of large scientific facilities," Scientometrics, Springer;Akadémiai Kiadó, vol. 100(2), pages 483-496, August.
    10. D’Ippolito, Beatrice & Rüling, Charles-Clemens, 2019. "Research collaboration in Large Scale Research Infrastructures: Collaboration types and policy implications," Research Policy, Elsevier, vol. 48(5), pages 1282-1296.
    11. Xiyi Yang & Xiaoyu Zhou & Cong Cao, 2024. "Beamtimes and knowledge production times: how big-science research infrastructures shape nations’ domestic and international science production," Palgrave Communications, Palgrave Macmillan, vol. 11(1), pages 1-11, December.

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