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Revisiting bibliometric issues using new empirical data

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  • Linda Butler

Abstract

Using research funded by the National Health and Medical Research Council of Australia, the classification of the grant that funded the research which led to the publication was contrasted with the use of a common proxy, journal set classification. Frequently, the two measures produce very similar results but major differences can occur. Acknowledgments data appear to accurately reflect a funding body's total research output, but lack the ability to identify individual funding schemes within such bodies. In contrast to the output funded by long-term grants, publications from research funded on a limited, three-year cycle exhibit a very fast publication turn-around — considerably faster than the often-quoted four years. The accuracy with which researchers report links between publications and the grants from which they emanate is examined. Copyright , Beech Tree Publishing.

Suggested Citation

  • Linda Butler, 2001. "Revisiting bibliometric issues using new empirical data," Research Evaluation, Oxford University Press, vol. 10(1), pages 59-65, April.
  • Handle: RePEc:oup:rseval:v:10:y:2001:i:1:p:59-65
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    File URL: http://hdl.handle.net/10.3152/147154401781777141
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    Cited by:

    1. Xianwen Wang & Di Liu & Kun Ding & Xinran Wang, 2012. "Science funding and research output: a study on 10 countries," Scientometrics, Springer;Akadémiai Kiadó, vol. 91(2), pages 591-599, May.
    2. Jue Wang & Philip Shapira, 2015. "Is There a Relationship between Research Sponsorship and Publication Impact? An Analysis of Funding Acknowledgments in Nanotechnology Papers," PLOS ONE, Public Library of Science, vol. 10(2), pages 1-19, February.
    3. Nicola Grassano & Daniele Rotolo & Joshua Hutton & Frédérique Lang & Michael M. Hopkins, 2017. "Funding Data from Publication Acknowledgments: Coverage, Uses, and Limitations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(4), pages 999-1017, April.
    4. Jue Wang & Philip Shapira, 2011. "Funding acknowledgement analysis: an enhanced tool to investigate research sponsorship impacts: the case of nanotechnology," Scientometrics, Springer;Akadémiai Kiadó, vol. 87(3), pages 563-586, June.
    5. John Rigby, 2013. "Looking for the impact of peer review: does count of funding acknowledgements really predict research impact?," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(1), pages 57-73, January.

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