PreprintToPaper dataset: connecting bioRxiv preprints with journal publications

The PreprintToPaper dataset connects bioRxiv preprints with their corresponding journal publications, enabling large-scale analysis of the preprint-to-publication process. It comprises metadata for 145,517 preprints from two periods, 2016-2018 (pre-pandemic) and 2020-2022 (pandemic), retrieved via the bioRxiv and Crossref APIs. We selected the two periods to capture preprint-publication dynamics before and during the COVID-19 pandemic while avoiding transitional years. Each record includes bibliographic information such as titles, abstracts, authors, institutions, submission dates, licenses, and subject categories, alongside enriched publication metadata including journal names, publication dates, author lists, and further information. In addition to the main dataset, a version-history subset provides all available versions of preprints within the two selected periods, enabling analysis of how preprints evolve over time. Preprints are categorized into three groups: Published (formally linked to a journal article), Preprint Only (posted on a preprint server), and Gray Zone (potentially published in a journal but unlinked). To enhance reliability, title and author similarity scores were computed, and a human-annotated subset of 299 records was created to evaluate Gray Zone cases. The dataset supports diverse applications, including studies of scholarly communication, open science policies, bibliometric tool development, and natural language processing research on textual changes between preprints and their corresponding journal articles.

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Publication Details

Journal
Scientific Data
Published
2026-02-24
DOI
https://doi.org/10.1038/s41597-026-06867-3
Primary Topic
Academic Publishing and Open Access
Type
article
Field-Weighted Citation Impact
0.00

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PreprintToPaper dataset: connecting bioRxiv preprints with journal publications

Julian Sienkiewicz, Philipp Mayr, Fidan Badalova
Scientific Data
Academic Publishing and Open Access
article

PreprintToPaper dataset: connecting bioRxiv preprints with journal publications

Julian Sienkiewicz, Philipp Mayr, Fidan Badalova
article en

Abstract

The PreprintToPaper dataset connects bioRxiv preprints with their corresponding journal publications, enabling large-scale analysis of the preprint-to-publication process. It comprises metadata for 145,517 preprints from two periods, 2016-2018 (pre-pandemic) and 2020-2022 (pandemic), retrieved via the bioRxiv and Crossref APIs. We selected the two periods to capture preprint-publication dynamics before and during the COVID-19 pandemic while avoiding transitional years. Each record includes bibliographic information such as titles, abstracts, authors, institutions, submission dates, licenses, and subject categories, alongside enriched publication metadata including journal names, publication dates, author lists, and further information. In addition to the main dataset, a version-history subset provides all available versions of preprints within the two selected periods, enabling analysis of how preprints evolve over time. Preprints are categorized into three groups: Published (formally linked to a journal article), Preprint Only (posted on a preprint server), and Gray Zone (potentially published in a journal but unlinked). To enhance reliability, title and author similarity scores were computed, and a human-annotated subset of 299 records was created to evaluate Gray Zone cases. The dataset supports diverse applications, including studies of scholarly communication, open science policies, bibliometric tool development, and natural language processing research on textual changes between preprints and their corresponding journal articles.

Scientific DataVol. 13(1)
Warsaw University of Technology (PL), GESIS - Leibniz Institute for the Social Sciences (DE)
European Commission, Deutsche Forschungsgemeinschaft, Politechnika Warszawska, HORIZON EUROPE Framework Programme, Research Executive Agency
Openalex Percentile: Top 100%
Academic Publishing and Open Access
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PreprintToPaper dataset: connecting bioRxiv preprints with journal publications — Julian Sienkiewicz, Philipp Mayr, et al. · Scientific Data (2026) | TGRS Research Map | TGRS