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Measuring Co-Authorship and Networking-Adjusted Scientific Impact

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  • John P A Ioannidis

Abstract

Appraisal of the scientific impact of researchers, teams and institutions with productivity and citation metrics has major repercussions. Funding and promotion of individuals and survival of teams and institutions depend on publications and citations. In this competitive environment, the number of authors per paper is increasing and apparently some co-authors don't satisfy authorship criteria. Listing of individual contributions is still sporadic and also open to manipulation. Metrics are needed to measure the networking intensity for a single scientist or group of scientists accounting for patterns of co-authorship. Here, I define I1 for a single scientist as the number of authors who appear in at least I1 papers of the specific scientist. For a group of scientists or institution, In is defined as the number of authors who appear in at least In papers that bear the affiliation of the group or institution. I1 depends on the number of papers authored Np. The power exponent R of the relationship between I1 and Np categorizes scientists as solitary (R>2.5), nuclear (R = 2.25–2.5), networked (R = 2–2.25), extensively networked (R = 1.75–2) or collaborators (R

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  • John P A Ioannidis, 2008. "Measuring Co-Authorship and Networking-Adjusted Scientific Impact," PLOS ONE, Public Library of Science, vol. 3(7), pages 1-8, July.
    • Handle: RePEc:plo:pone00:0002778
    DOI: 10.1371/journal.pone.0002778
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    2. M. P. Rozing & T. N. Leeuwen & P. H. Reitsma & F. R. Rosendaal & N. A. Aziz, 2020. "Freeloading in biomedical research," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 47-55, January.
    3. Meen Chul Kim & Yoo Kyung Jeong & Min Song, 2014. "Investigating the integrated landscape of the intellectual topology of bioinformatics," Scientometrics, Springer;Akadémiai Kiadó, vol. 101(1), pages 309-335, October.
    4. M. Ausloos, 2013. "A scientometrics law about co-authors and their ranking: the co-author core," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(3), pages 895-909, June.
    5. Liu, Jie & Ge, Huilin, 2022. "Collaboration mechanisms and community detection of statisticians based on ERGMs and kNN-walktrap," Computational Statistics & Data Analysis, Elsevier, vol. 168(C).
    6. Juan Luis Sanz-Cabanillas & Juan Ruano & Francisco Gomez-Garcia & Patricia Alcalde-Mellado & Jesus Gay-Mimbrera & Macarena Aguilar-Luque & Beatriz Maestre-Lopez & Marcelino Gonzalez-Padilla & Pedro J , 2017. "Author-paper affiliation network architecture influences the methodological quality of systematic reviews and meta-analyses of psoriasis," PLOS ONE, Public Library of Science, vol. 12(4), pages 1-14, April.
    7. Ronnie Ramlogan & Davide Consoli, 2014. "Dynamics of collaborative research medicine: the case of glaucoma," The Journal of Technology Transfer, Springer, vol. 39(4), pages 544-566, August.
    8. B Ian Hutchins & Matthew T Davis & Rebecca A Meseroll & George M Santangelo, 2019. "Predicting translational progress in biomedical research," PLOS Biology, Public Library of Science, vol. 17(10), pages 1-25, October.

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