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Day-to-day discovery of preprint–publication links

Author

Listed:
  • Guillaume Cabanac

    (University of Toulouse)

  • Theodora Oikonomidi

    (Université de Paris, Centre of Research in Epidemiology and Statistics (CRESS))

  • Isabelle Boutron

    (Université de Paris, Centre of Research in Epidemiology and Statistics (CRESS)
    AP-HP, Hôpital Hôtel Dieu
    Cochrane France)

Abstract

Preprints promote the open and fast communication of non-peer reviewed work. Once a preprint is published in a peer-reviewed venue, the preprint server updates its web page: a prominent hyperlink leading to the newly published work is added. Linking preprints to publications is of utmost importance as it provides readers with the latest version of a now certified work. Yet leading preprint servers fail to identify all existing preprint–publication links. This limitation calls for a more thorough approach to this critical information retrieval task: overlooking published evidence translates into partial and even inaccurate systematic reviews on health-related issues, for instance. We designed an algorithm leveraging the Crossref public and free source of bibliographic metadata to comb the literature for preprint–publication links. We tested it on a reference preprint set identified and curated for a living systematic review on interventions for preventing and treating COVID-19 performed by international collaboration: the COVID-NMA initiative ( covid-nma.com ). The reference set comprised 343 preprints, 121 of which appeared as a publication in a peer-reviewed journal. While the preprint servers identified 39.7% of the preprint–publication links, our linker identified 90.9% of the expected links with no clues taken from the preprint servers. The accuracy of the proposed linker is 91.5% on this reference set, with 90.9% sensitivity and 91.9% specificity. This is a 16.26% increase in accuracy compared to that of preprint servers. We release this software as supplementary material to foster its integration into preprint servers’ workflows and enhance a daily preprint–publication chase that is useful to all readers, including systematic reviewers. This preprint–publication linker currently provides day-to-day updates to the biomedical experts of the COVID-NMA initiative.

Suggested Citation

  • Guillaume Cabanac & Theodora Oikonomidi & Isabelle Boutron, 2021. "Day-to-day discovery of preprint–publication links," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5285-5304, June.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:6:d:10.1007_s11192-021-03900-7
    DOI: 10.1007/s11192-021-03900-7
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    References listed on IDEAS

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    1. Adam Palayew & Ole Norgaard & Kelly Safreed-Harmon & Tue Helms Andersen & Lauge Neimann Rasmussen & Jeffrey V. Lazarus, 2020. "Pandemic publishing poses a new COVID-19 challenge," Nature Human Behaviour, Nature, vol. 4(7), pages 666-669, July.
    2. Jialiang Lin & Yao Yu & Yu Zhou & Zhiyang Zhou & Xiaodong Shi, 2020. "How many preprints have actually been printed and why: a case study of computer science preprints on arXiv," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(1), pages 555-574, July.
    3. Vincent Larivière & Cassidy R. Sugimoto & Benoit Macaluso & Staša Milojević & Blaise Cronin & Mike Thelwall, 2014. "arXiv E-prints and the journal of record: An analysis of roles and relationships," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(6), pages 1157-1169, June.
    4. Julian H Elliott & Tari Turner & Ornella Clavisi & James Thomas & Julian P T Higgins & Chris Mavergames & Russell L Gruen, 2014. "Living Systematic Reviews: An Emerging Opportunity to Narrow the Evidence-Practice Gap," PLOS Medicine, Public Library of Science, vol. 11(2), pages 1-6, February.
    5. Alexandra Baumann & Klaus Wohlrabe, 2020. "Where have all the working papers gone? Evidence from four major economics working paper series," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2433-2441, September.
    6. Diana Kwon, 2020. "How swamped preprint servers are blocking bad coronavirus research," Nature, Nature, vol. 581(7807), pages 130-131, May.
    7. J. Homolak & I. Kodvanj & D. Virag, 2020. "Preliminary analysis of COVID-19 academic information patterns: a call for open science in the times of closed borders," Scientometrics, Springer;Akadémiai Kiadó, vol. 124(3), pages 2687-2701, September.
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    Cited by:

    1. Annie Collins & Rohan Alexander, 2022. "Reproducibility of COVID-19 pre-prints," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(8), pages 4655-4673, August.
    2. Marc Bertin & Iana Atanassova, 2022. "Preprint citation practice in PLOS," Scientometrics, Springer;Akadémiai Kiadó, vol. 127(12), pages 6895-6912, December.

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