PreprintToPaper dataset: connecting bioRxiv preprints with journal publications

Area of Science:

• Bibliometrics and Scholarly Communication
• Open Science and Preprint Research
• Data Science and Computational Linguistics

Background:

• The transition from preprints to published articles is a key aspect of scholarly communication.
• Understanding preprint-to-publication dynamics is crucial for evaluating open science practices.
• The COVID-19 pandemic significantly impacted preprint server usage and publication trends.

Purpose of the Study:

• To introduce the PreprintToPaper dataset, connecting bioRxiv preprints with their journal publications.
• To facilitate large-scale analysis of the preprint-to-publication pathway.
• To enable research on preprint evolution and scholarly communication patterns.

Main Methods:

• Collected metadata for 145,517 bioRxiv preprints from 2016-2018 and 2020-2022 using bioRxiv and Crossref APIs.
• Enriched preprint data with publication metadata, including titles, authors, and dates.
• Developed a version-history subset and categorized preprints into Published, Preprint Only, and Gray Zone.

Main Results:

• The dataset contains comprehensive metadata for preprints and their corresponding publications.
• Includes a version-history subset for analyzing preprint evolution.
• Features similarity scores and a human-annotated subset for quality assessment.

Conclusions:

• The PreprintToPaper dataset provides a valuable resource for studying scholarly communication and open science.
• It supports diverse research applications, including bibliometric analysis and NLP.
• Enables examination of preprint-publication dynamics across different time periods.