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Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data

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  • Bornmann, Lutz
  • Tekles, Alexander
  • Zhang, Helena H.
  • Ye, Fred Y.

Abstract

Lee et al. (2015) – based on Uzzi et al. (2013) – and Wang et al. (2017) proposed scores based on cited references (cited journals) data which can be used to measure the novelty of papers (named as novelty scores U and W in this study). Although previous research has used novelty scores in various empirical analyses, no study has been published up to now – to the best of our knowledge – which quantitatively tested the convergent validity of novelty scores: do these scores measure what they propose to measure? Using novelty assessments by faculty members (FMs) at F1000Prime for comparison, we tested the convergent validity of the two novelty scores (U and W). FMs’ assessments do not only refer to the quality of biomedical papers, but also to their characteristics (by assigning certain tags to the papers): for example, are the presented findings or formulated hypotheses novel (tags “new findings” and “hypothesis”)? We used these and other tags to investigate the convergent validity of both novelty scores. Our study reveals different results for the novelty scores: the results for novelty score U are mostly in agreement with previously formulated expectations. We found, for instance, that for a standard deviation (one unit) increase in novelty score U, the expected number of assignments of the “new finding” tag increase by 7.47%. The results for novelty score W, however, do not reflect convergent validity with the FMs’ assessments: only the results for some tags are in agreement with the expectations. Thus, we propose – based on our results – the use of novelty score U for measuring novelty quantitatively, but question the use of novelty score W.

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  • Bornmann, Lutz & Tekles, Alexander & Zhang, Helena H. & Ye, Fred Y., 2019. "Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data," Journal of Informetrics, Elsevier, vol. 13(4).
  • Handle: RePEc:eee:infome:v:13:y:2019:i:4:s1751157718304371
    DOI: 10.1016/j.joi.2019.100979
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    1. 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.
    2. Jacques Mairesse & Michele Pezzoni, 2018. "Novelty in Science: The Impact of French Physicists' Novel Articles," GREDEG Working Papers 2018-23, Groupe de REcherche en Droit, Economie, Gestion (GREDEG CNRS), Université Côte d'Azur, France.
    3. Salil Gunashekar & Steven Wooding & Susan Guthrie, 2017. "How do NIHR peer review panels use bibliometric information to support their decisions?," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1813-1835, September.
    4. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    5. Jesper W. Schneider & Rodrigo Costas, 2017. "Identifying potential “breakthrough” publications using refined citation analyses: Three related explorative approaches," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(3), pages 709-723, March.
    6. Mikko Packalen & Jay Bhattacharya, 2017. "Neophilia ranking of scientific journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 43-64, January.
    7. Ponomarev, Ilya V. & Williams, Duane E. & Hackett, Charles J. & Schnell, Joshua D. & Haak, Laurel L., 2014. "Predicting highly cited papers: A Method for Early Detection of Candidate Breakthroughs," Technological Forecasting and Social Change, Elsevier, vol. 81(C), pages 49-55.
    8. Martin, Ben R. & Irvine, John, 1993. "Assessing basic research : Some partial indicators of scientific progress in radio astronomy," Research Policy, Elsevier, vol. 22(2), pages 106-106, April.
    9. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    10. Kevin J. Boudreau & Eva C. Guinan & Karim R. Lakhani & Christoph Riedl, 2016. "Looking Across and Looking Beyond the Knowledge Frontier: Intellectual Distance, Novelty, and Resource Allocation in Science," Management Science, INFORMS, vol. 62(10), pages 2765-2783, October.
    11. Jian Du & Xiaoli Tang & Yishan Wu, 2016. "The effects of research level and article type on the differences between citation metrics and F1000 recommendations," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 67(12), pages 3008-3021, December.
    12. Bornmann, Lutz & Leydesdorff, Loet, 2013. "The validation of (advanced) bibliometric indicators through peer assessments: A comparative study using data from InCites and F1000," Journal of Informetrics, Elsevier, vol. 7(2), pages 286-291.
    13. Ehsan Mohammadi & Mike Thelwall, 2013. "Assessing non-standard article impact using F1000 labels," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(2), pages 383-395, November.
    14. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    15. Jinseok Kim & Jana Diesner, 2015. "Coauthorship networks: A directed network approach considering the order and number of coauthors," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2685-2696, December.
    16. Uddin, Shahadat & Khan, Arif, 2016. "The impact of author-selected keywords on citation counts," Journal of Informetrics, Elsevier, vol. 10(4), pages 1166-1177.
    17. Sarah Kaplan & Keyvan Vakili, 2015. "The double-edged sword of recombination in breakthrough innovation," Strategic Management Journal, Wiley Blackwell, vol. 36(10), pages 1435-1457, October.
    18. Lutz Bornmann, 2015. "Interrater reliability and convergent validity of F1000Prime peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(12), pages 2415-2426, December.
    19. Lee, You-Na & Walsh, John P. & Wang, Jian, 2015. "Creativity in scientific teams: Unpacking novelty and impact," Research Policy, Elsevier, vol. 44(3), pages 684-697.
    20. Lutz Bornmann & Julian N. Marewski, 2019. "Heuristics as conceptual lens for understanding and studying the usage of bibliometrics in research evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 419-459, August.
    21. Lutz Bornmann & Alexander Tekles & Loet Leydesdorff, 2019. "How well does I3 perform for impact measurement compared to other bibliometric indicators? The convergent validity of several (field-normalized) indicators," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(2), pages 1187-1205, May.
    22. Wang, Jian & Lee, You-Na & Walsh, John P., 2018. "Funding model and creativity in science: Competitive versus block funding and status contingency effects," Research Policy, Elsevier, vol. 47(6), pages 1070-1083.
    23. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
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    Cited by:

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    4. Hou, Jianhua & Wang, Dongyi & Li, Jing, 2022. "A new method for measuring the originality of academic articles based on knowledge units in semantic networks," Journal of Informetrics, Elsevier, vol. 16(3).
    5. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.
    6. Sandra Rousseau & Ronald Rousseau, 2021. "Bibliometric Techniques And Their Use In Business And Economics Research," Journal of Economic Surveys, Wiley Blackwell, vol. 35(5), pages 1428-1451, December.
    7. Bornmann, Lutz & Tekles, Alexander, 2021. "Convergent validity of several indicators measuring disruptiveness with milestone assignments to physics papers by experts," Journal of Informetrics, Elsevier, vol. 15(3).
    8. Guoqiang Liang & Ying Lou & Haiyan Hou, 2022. "Revisiting the disruptive index: evidence from the Nobel Prize-winning articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(10), pages 5721-5730, October.
    9. Helena H. Zhang & Fred Y. Ye, 2020. "Identifying ‘associated-sleeping-beauties’ in ‘swan-groups’ based on small qualified datasets of physics and economics," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1525-1537, March.
    10. Yulin Yu & Daniel M. Romero, 2024. "Does the Use of Unusual Combinations of Datasets Contribute to Greater Scientific Impact?," Papers 2402.05024, arXiv.org, revised Sep 2024.
    11. Sotaro Shibayama & Deyun Yin & Kuniko Matsumoto, 2021. "Measuring novelty in science with word embedding," PLOS ONE, Public Library of Science, vol. 16(7), pages 1-16, July.
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    Keywords

    Bibliometrics; Novelty; Creativity; Cited references; F1000Prime;
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