Day-to-day discovery of preprint-publication links.
Day-to-day discovery of preprint-publication links.
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DOI:
10.1007/s11192-021-03900-7
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发表时间:
2021
期刊:
影响因子:
3.9
通讯作者:
Boutron I
中科院分区:
文献类型:
--
作者:
Cabanac G;Oikonomidi T;Boutron I
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.
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DOI:
10.1002/asi.23044
发表时间:
2014-06-01
影响因子:
3.5
作者:
Lariviere, Vincent;Sugimoto, Cassidy R.;Thelwall, Mike
通讯作者:
Thelwall, Mike
影响因子:
3.9
作者:
Homolak, J.;Kodvanj, I.;Virag, D.
通讯作者:
Virag, D.
影响因子:
64.8
作者:
LEVANDOWSKY, M;WINTER, D
通讯作者:
WINTER, D
影响因子:
8.6
作者:
Gao, Yong;Wu, Qiang;Zhu, Linna
通讯作者:
Zhu, Linna
影响因子:
7.7
作者:
Himmelstein DS;Romero AR;Levernier JG;Munro TA;McLaughlin SR;Greshake Tzovaras B;Greene CS
通讯作者:
Greene CS