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</script>AbstractPreprints 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.
[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], COVID-19, Data linking, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Article, 510, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Publication, Living systematic review, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Preprint
[INFO.INFO-SI] Computer Science [cs]/Social and Information Networks [cs.SI], COVID-19, Data linking, [INFO.INFO-SI]Computer Science [cs]/Social and Information Networks [cs.SI], Article, 510, [INFO.INFO-IR]Computer Science [cs]/Information Retrieval [cs.IR], Publication, Living systematic review, [INFO.INFO-IR] Computer Science [cs]/Information Retrieval [cs.IR], Preprint
| citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 32 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 10% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
