Adoption of Preprinting Across Scientific Disciplines and Geographical Regions (1991-2023) is a research paper (2025). On theSindex it has a DataRank of 0.104. It has been cited 1 time.
Preprinting has become an increasingly important component of the scholarly communication system, facilitating rapid open dissemination of scientific knowledge. This study investigates the adoption of preprinting over time, focusing on how it varies across scientific disciplines and geographical regions. We analyzed bibliometric data on 4M preprints and 105M peer-reviewed outputs in the period 1991-2023. Peer-reviewed outputs were linked to preprints using data from Dimensions, OpenAlex, and Crossref, resulting in 2.2M peer-reviewed outputs linked to a preprint. Our findings indicate a strong growth in preprinting, with a nearly threefold increase in the number of preprints published between 2017 and 2022. The adoption of preprinting is highest in the physical and mathematical sciences, particularly among researchers in the Americas and Europe. In recent years, preprinting has also increased notably in the information and computing sciences and the life and medical sciences, driven primarily by researchers in North America and Western and Northern Europe. Preprinting remains relatively uncommon in the humanities and the social and behavioral sciences. Asia demonstrates low preprint adoption, with Eastern Asia showing a modest increase in recent years. Preprint adoption in specific disciplines varies significantly across regions, showing that preprint adoption is shaped by the interplay between disciplines and regions.
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
Base Score Contribution
0.104
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →DataRank blends this paper's own citation count with the influence of the papers that cite it. Here, roughly 100% comes from its base citations and 0% from the citation network.
Citers are pulled from OpenAlex sorted by cited_by_count:descand capped per paper, so when the cap binds we keep the highest-signal references and the score is reproducible across reruns.