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Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime

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  • Bornmann, Lutz

Abstract

Can altmetric data be validly used for the measurement of societal impact? The current study seeks to answer this question with a comprehensive dataset (about 100,000 records) from very disparate sources (F1000, Altmetric, and an in-house database based on Web of Science). In the F1000 peer review system, experts attach particular tags to scientific papers which indicate whether a paper could be of interest for science or rather for other segments of society. The results show that papers with the tag “good for teaching” do achieve higher altmetric counts than papers without this tag – if the quality of the papers is controlled. At the same time, a higher citation count is shown especially by papers with a tag that is specifically scientifically oriented (“new finding”). The findings indicate that papers tailored for a readership outside the area of research should lead to societal impact.

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  • Bornmann, Lutz, 2014. "Validity of altmetrics data for measuring societal impact: A study using data from Altmetric and F1000Prime," Journal of Informetrics, Elsevier, vol. 8(4), pages 935-950.
    • Handle: RePEc:eee:infome:v:8:y:2014:i:4:p:935-950
    DOI: 10.1016/j.joi.2014.09.007
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    References listed on IDEAS

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    1. Bornmann, Lutz & Leydesdorff, Loet & Wang, Jian, 2013. "Which percentile-based approach should be preferred for calculating normalized citation impact values? An empirical comparison of five approaches including a newly developed citation-rank approach (P1," Journal of Informetrics, Elsevier, vol. 7(4), pages 933-944.
    2. Lutz Bornmann, 2013. "What is societal impact of research and how can it be assessed? a literature survey," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(2), pages 217-233, February.
    3. Lutz Bornmann & Werner Marx, 2014. "How should the societal impact of research be generated and measured? A proposal for a simple and practicable approach to allow interdisciplinary comparisons," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(1), pages 211-219, January.
    4. Jonathan Grant, 1999. "Evaluating the outcomes of biomedical research on healthcare," Research Evaluation, Oxford University Press, vol. 8(1), pages 33-38, April.
    5. Hausman, Jerry & Hall, Bronwyn H & Griliches, Zvi, 1984. "Econometric Models for Count Data with an Application to the Patents-R&D Relationship," Econometrica, Econometric Society, vol. 52(4), pages 909-938, July.
    6. Ludo Waltman & Rodrigo Costas, 2014. "F1000 Recommendations as a Potential New Data Source for Research Evaluation: A Comparison With Citations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 433-445, March.
    7. Bornmann, Lutz & Williams, Richard, 2013. "How to calculate the practical significance of citation impact differences? An empirical example from evaluative institutional bibliometrics using adjusted predictions and marginal effects," Journal of Informetrics, Elsevier, vol. 7(2), pages 562-574.
    8. Ehsan Mohammadi & Mike Thelwall, 2013. "Assessing non-standard article impact using F1000 labels," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 383-395, November.
    9. Lutz Bornmann, 2014. "Is there currently a scientific revolution in Scientometrics?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(3), pages 647-648, March.
    10. Ehsan Mohammadi & Mike Thelwall, 2014. "Mendeley readership altmetrics for the social sciences and humanities: Research evaluation and knowledge flows," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(8), pages 1627-1638, August.
    11. Jian Wang, 2013. "Citation time window choice for research impact evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 851-872, March.
    12. J. Scott Long & Jeremy Freese, 2006. "Regression Models for Categorical Dependent Variables using Stata, 2nd Edition," Stata Press books, StataCorp LP, edition 2, number long2, March.
    13. Richard Williams, 2012. "Using the margins command to estimate and interpret adjusted predictions and marginal effects," Stata Journal, StataCorp LP, vol. 12(2), pages 308-331, June.
    14. Narin, Francis & Hamilton, Kimberly S. & Olivastro, Dominic, 1997. "The increasing linkage between U.S. technology and public science," Research Policy, Elsevier, vol. 26(3), pages 317-330, October.
    Full references (including those not matched with items on IDEAS)

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    Keywords

    Altmetrics; Bibliometrics; F1000; Twitter; Societal impact;
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