1. Introduction

The history of preprinting goes back at least to the 1950s and 1960s, when attempts to develop preprinting practices were made both in the US and in the Soviet Union (Till, 2001; Hammarfelt & Dahlin, 2024). A crucial milestone in the history of preprinting is the establishment of the arXiv server in 1991, which automated an existing manual process of circulating papers in hard copy by post, and initially focused on high-energy physics (Ginsparg, 1997; Kling & McKim, 2000). Later arXiv expanded its scope to include other fields such as physics, mathematics, computer science, and more. Since then, numerous platforms hosting preprints have been set up. A study published in 2020 (Kirkham et al., 2020) compiled a list of preprint platforms in biomedical fields, where a preprint platform was defined as any platform where manuscripts are openly available before peer review is complete, or as servers without a dedicated formal peer-review service. Last updated in September 2024, the list (ASAPbio, 2024) comprised 65 preprint platforms. An additional 14 preprint servers were not included in the list because they do not have a biomedical focus. According to Chiarelli et al. (2019a), almost 40 new preprint servers were established between 2016-2019. Chiarelli et al. refer to the increase in number of preprint platforms starting from 2016 as the “second wave” of preprint servers. While some preprint servers have a general focus, others such as bioRxiv, ChemRxiv, EdArXiv, LawArXiv, medRxiv, PsyArXiv, SocArXiv, etc. are tailored to specific disciplines. In addition, increasing interest in preprinting in certain regions has led to the development of preprint servers with a national or regional focus, such as AfricaArxiv, IndiaRxiv, ChinaXiv, Jxiv, INA-Rxiv, etc. (Tennant et al., 2018; Irawan et al., 2022; Chaleplioglou & Koulouris, 2023; ASAPbio, 2024).

The increasing number of preprint servers has provided more platforms for researchers to share their research findings at an early stage. This growth in preprint servers supported a parallel growth in the number of preprints. According to Xie, Shen, & Wang (2021), preprints have exhibited exponential growth, doubling their volume in less than ten years, although still comprising only 4% of research articles in 2021. A study analyzing selected publications from PLOS’ Open Science Indicators and the PMC Open Access Subset datasets (Colavizza et al., 2024) reported a significant rise in preprint posting between 2018 and 2020, followed by a plateau beginning in 2021. However, the authors also noted an increase in preprints in 2023, as seen in the PMC Open Access Subset.

The COVID-19 pandemic highlighted the urgent need for rapid open dissemination of research outputs related to the pandemic. In the early months of the pandemic, the number of COVID-19 preprints surpassed the number of COVID-19 journal articles (Waltman et al., 2021). Moreover, when surveyed, a significant majority of authors of COVID-19 preprints—both those previously familiar with preprints and those who published their first preprint during the pandemic—reported an intention to continue preprinting in the future, at least for some of their work (Rzayeva et al., 2023).

Given the increasing significance of preprints in the dissemination of scientific knowledge and the heightened interest in preprinting among researchers, this study examines the adoption of preprinting over time, focusing on preprint adoption across scientific disciplines and geographical regions. We address the following research questions:

1. Evolving role of preprints in the scholarly communication system: How has the overall adoption of preprinting evolved over time?

2. Preprint adoption across scientific disciplines: How does preprint adoption vary across scientific disciplines?

3. Preprint adoption across geographical regions: How does preprint adoption vary across geographical regions?

4. Preprint adoption across scientific disciplines and geographical regions: How does preprint adoption in specific scientific disciplines vary across geographical regions, and vice versa?

In the next sections of this paper, we will review relevant literature, describe the data collection and the methodology of our research, present our findings, and discuss the key results and the limitations of our work.

2. Literature review

2.1 Disciplinary variation in preprint adoption

An early study by Kling & McKim (2000) suggested that various social and disciplinary forces shape how and why certain scientific fields adopt communication innovations at different rates, leading to a non-uniform transition across disciplines. The factors influencing these differences include disciplinary norms, varying trust mechanisms regarding non-peer-reviewed publications among readers and scholars, and differing perceptions of legitimate communication channels.

It is widely recognized that physics and mathematics were early adopters of preprint practices (Kling & McKim, 2000; Brown, 2001; Larivière et al., 2014; Puebla, Polka, & Rieger, 2021; Ni & Waltman, 2024). These disciplines have also maintained a high rate of preprinting (Larivière et al., 2014; Xie, Shen, & Wang, 2021). According to Kling & McKim (2000), physicists have embraced preprints as a key part of their communication infrastructure due to the community’s established norms of rapid information exchange, trust in informal peer review, and a pre-existing tradition of sharing preprints before formal publication. Another possible reason, suggested by Xie, Shen, & Wang (2021) and Kling and McKim (2000), is that the widespread adoption of preprint servers among scientists in physics and mathematics may stem from their early exposure to Web technologies.

In recent years there has been a surge in the popularity of preprinting in other fields. According to Xie, Shen, & Wang (2021), a significant increase in the adoption of preprinting can be observed in computer science in recent years. However, as reported in their study, a large majority of the papers in this field (95%) remain non- preprints, despite computer science being a major contributor to preprinting. The authors suggested that adoption in this field may be attributed to the high throughput of computer science literature. The same research also highlighted that economics shows a high adoption of preprinting, with a ratio of preprints (called ‘working papers’ in economics) to all papers of 21% from 1991 to 2015.

As also reported by Xie, Shen, & Wang (2021), in biology, preprints accounted for around 7% of the total number of publications in 2020. Some other studies also highlighted the evolving landscape of preprinting in the life sciences (Tennant et al., 2018; Sever et al., 2019; Puebla, Polka, & Rieger, 2021; Fraser et al., 2021).

Nonetheless, the volume of preprints in the life sciences remains relatively small compared to peer-reviewed journal publications. Biesenbender, Toepfer, & Peters (2024) suggested that the challenges around adoption of preprinting in the life sciences may be attributed to unique needs in this field in terms of publishing, dissemination, and discussion of research results, as well as reputation management, which are not adequately addressed by existing preprint servers. Odell, Palmer, & Dill (2017) highlighted significant knowledge gaps and uncertainty in the health sciences regarding open access and scholarly communication, explaining their support for journal publishing over repository archiving. However, there has been a recent increase in the number of life science preprints. As reported by Puebla, Polka, & Rieger (2021) the number of submissions in bioRxiv grew from 1,700 to 20,000 between 2015 to 2018. Levchenko et al. (2024) showed that the share of life science preprints among the total number of journal articles in Europe PMC increased from 1% in 2016 to over 12% by 2023. Some researchers link this to the growing visibility of preprints and recognition of them as valid research outputs by funders and other relevant institutions (Berg et al., 2016; Penfold & Polka, 2020; Puebla, Polka, & Rieger, 2021; Levchenko et al., 2024). Other factors influencing the evolution and adoption of preprinting in biology and medicine have also been discussed, including the launch of dedicated preprint servers like bioRxiv and medRxiv (Kaiser, 2017; Malinda et al., 2024), NIH policy allowing researchers to claim preprints in grant applications (National Institutes of Health, 2017; Tennant et al., 2018), the integration of preprints into databases such as PubMed (National Library of Medicine, 2023), and promotion of preprinting among life science researchers, for instance by organizations such as ASAPbio (Levchenko et al., 2024).

In the field of chemistry, the Chemistry Preprint Server, established in 2000, initially operated as an experimental platform to gauge the chemistry community’s response to preprints (James et al., 2002). In 2017, a renewed interest in preprinting in the chemistry field led to the launch of the Chemistry Research Network by SSRN and the introduction of ChemRxiv by the American Chemical Society (Notman, 2021).

Carà, Criminna, & Pagliaro (2017) noted that chemistry was among the later adopters of preprinting in the natural sciences, but that the field was now primed for widespread acceptance, driven by encouragement from major funding agencies like the NIH and the growing significance of preprints in establishing priority for scholarly discoveries and innovations. According to Coudert (2020), chemistry has experienced a significant increase in preprint adoption in recent years, driven by the emergence of platforms like ChemRxiv and the introduction of preprint-friendly policies by journals. However, as mentioned by the author, preprints still remain a small fraction of the total number of chemistry journal papers. Additionally, Coudert (2020) reported that preprint adoption is particularly prominent in subfields like theoretical chemistry and materials science.

Penfold & Polka (2020) reported significant variation in open science practices, including preprint posting, across different research fields, suggesting that disciplines face unique challenges in implementing open science, and some practices may not be equally applicable across all fields. For a selected set of publications from PLOS’ Open Science Indicators and the PMC Open Access Subset datasets, they found that information and computing sciences, psychology, economics, physical sciences, language, communication and culture, and mathematical sciences showed the highest adoption of preprinting.

Preprinting is less common in other disciplines, as noted by Xie, Shen, & Wang (2021) and Ni & Waltman (2024). For example, in many disciplines in the humanities and the social and behavioral sciences, preprinting has not yet become common practice, despite the existence of preprint servers like SocArXiv, SSRN, and Advance, which host preprints from these fields.

Tmava & Ryza (2023) provided an overview of research on discipline-specific practices in adopting open access repositories. They found correlations between academic discipline and familiarity with open access principles, awareness of open access policies, and researchers’ intention to deposit their works in open access repositories. Other studies (Fry et al., 2015; Chiarelli et al., 2019b; Hadad & Aharony, 2024) also investigated differences in preprint adoption across various disciplines. For example, Fry et al. (2015) explored the cultural characteristics of three disciplines, calling them “open access (OA)-friendly disciplines” because of the wide use of open access repositories in these fields. They showed that for physicists and economists preprints are an essential feature of their respective open access landscapes, whereas for researchers in clinical medicine articles published in journals hold a central role.

2.2 Geographical variation in preprint adoption

Adoption of preprinting by research communities is not solely shaped by discipline; Biesenbender, Toepfer, & Peters (2024) emphasized the role of the survey respondents’ geographical origin and career stage in shaping their attitudes towards preprinting. Abdill, Adamowicz, & Blekhman (2020) demonstrated that in the life sciences the US and UK contribute a disproportionately large number of preprints to bioRxiv compared to other countries. A survey by Ni & Waltman (2024) found that preprint adoption was higher in the US and Europe compared to China and the rest of the world, suggesting greater familiarity with and a stronger commitment to preprinting among researchers in these regions. The authors suggested that the low adoption of preprinting in China can be attributed to distinct characteristics of the Chinese scholarly communication system, such as the reliance on ranked journal articles as a key factor in academic evaluation, career advancement, and material incentives for researchers (Ren, 2013; Wang, Halffman, & Zwart, 2021; Hyland, 2023). As mentioned also by Chinese participants of the survey conducted by Ni & Waltman (2024), accruing recognition can be a powerful incentive for researchers to preprint their work. Recently Biesenbender et al. (2025) also conducted a survey among researchers from Global North countries, including the USA and Europe, and Global South countries, including China and India. The results showed that respondents from the Global South were more likely to agree that posting preprints enhances dissemination and visibility and has a positive impact in terms of citations. They also emphasized that policies and mandates could influence publishing behavior, encouraging a shift toward open access in their region.

As a key open science (OS) practice, the adoption of preprinting may be significantly influenced by open science and open access (OA) policies as well as funder requirements. The variation in policies and funding environments highlights the importance of considering geographical variations in understanding preprint adoption. For instance, countries with strong OA mandates, such as the UK and the Netherlands, have historically fostered a policy and funding landscape conducive to preprint dissemination (Huang et al., 2020; Moskovkin et al., 2021). In contrast, Asian countries such as China, which have historically had less robustOA policies, have experienced significant growth in OA publications more recently, with a 25% increase between 2017 and 2020 (Wang & Campbell, 2023). Wang and Campbell (2023) also highlighted recent initiatives promoting OA, including the establishment of the Open Science Promotion Consortium in 2022 and the strategic plan of the Chinese Academy of Sciences to further develop ChinaXiv. However, the authors emphasized that further efforts are needed to expand OA adoption among Chinese researchers. Their recommendations to do so include strengthening collaboration between funders and research institutions, enhancing expert support, and addressing common misconceptions.

Several other studies have further explored the adoption, motivations, and attitudes towards preprinting as a component of OS across the globe (Heriyanto, 2018; Ciriminna & Pagliaro, 2023; Subaveerapandiyan et al., 2024; Taubert et al., 2024), but to date there are fewer comparative studies examining geographical patterns of preprint adoption. Despite the aforementioned geographical unevenness in preprint adoption, Eckmann & Bandrowski (2023) noted that preprints demonstrate a broader geographic distribution among authors across countries and income groups compared to publications in journals.

2.3 Matching preprints and peer-reviewed outputs

As highlighted in the literature (Berg et al., 2016; Cabanac, Oikonomidi & Boutron, 2021; Avissar-Whiting, 2022; Eckmann & Bandrowski, 2023), preprint servers often lack robust mechanisms for linking preprints to the corresponding peer-reviewed outputs. For preprints that have been published in a journal, arXiv offers a service for “automated DOI and journal reference updates from publishers”. It calls on publishers to cooperate by providing information via an XML feed (arXiv, n.d.-a).

Authors of arXiv preprints can also manually include links to their published journal articles in the metadata of their preprints (arXiv, n.d.-b). Preprints.org claims to assure automatic linking of preprints to peer-reviewed outputs but acknowledges technical issues that may arise. As a precaution, authors are encouraged to manually link their preprints to the corresponding peer-reviewed outputs (Preprints.org, n.d.). Both of these cases demonstrate that the links that preprint servers provide between preprints and peer-reviewed outputs cannot be expected to be complete. Abdill, Adamowicz, & Blekhman (2020) suggested that bioRxiv may fail to report up to 53% of the links between preprints and the corresponding journal articles.

Some journal publishers require authors to provide a reference from their journal article to the preprint version of their work (Ibragimova & Phagava, 2022), but this is relatively uncommon. As noted by Tkaczyk (2023a), although some publishers provide such links when they deposit metadata to Crossref, significant gaps in the metadata persist. Crossref facilitates establishing links between preprints and their eventual journal publications by notifying metadata depositors of potential matches and prompting them to update their metadata accordingly (Crossref, n.d.-a).

Recently Crossref also introduced a new algorithmic strategy (Tkaczyk, 2023a) that doubled the number of matches between preprints and journal publications. This strategy successfully identified 627K matches, which were complemented by an additional 15K matches deposited by Crossref members. Crossref published the resulting dataset of links between preprints and journal publications (Tkaczyk, 2023b).

Several researchers have explored methods for matching preprints to the corresponding peer-reviewed outputs. Eckmann & Bandrowski (2023) introduced a tool named PreprintMatch, which pairs preprints from bioRxiv and medRxiv with papers indexed in PubMed, and compared the effectiveness of this tool with existing methods (Serghiou & Ioannidis, 2018; Fu & Hughey, 2019; Fraser et al., 2020; Cabanac, Oikonomidi, & Boutron, 2021; Crossref, n.d.-b). Bloch, Rückert, & Friedrich (2023) developed PreprintResolver, which uses four literature databases to match arXiv preprints to the corresponding peer-reviewed outputs, achieving a success rate of over 60% in matching 1000 computer science preprints for which arXiv did not provide a link to a peer-reviewed output. Cabanac, Oikonomidi, & Boutron (2021) presented an algorithm utilizing Crossref metadata to identify links between preprints and peer-reviewed outputs in biomedical research, surpassing preprint servers’ matching accuracy by 16%. The authors argued that their algorithm addresses limitations of previous studies (Larivière et al., 2014; Fraser et al., 2020; Lin et al., 2020) associated with downloading, indexing, and updating bibliographic data. Akbaritabar, Stephen, & Squazzoni (2022) matched preprints to journal articles by searching the Dimensions database for preprints posted between 2000 and 2018 and by matching these preprints to publications in Web of Science based on their title. In the study of retractions downstream of preprints Avissar-Whiting (2022) analyzed matching of preprints to the corresponding journal articles for three major preprint servers: ResearchSquare, bioRxiv, and medRxiv. These preprint servers rely on algorithmic matching approaches. The author identified limitations of Crossref’s approach due to its reliance on exact matches of titles and author lists. Avissar-Whiting (2022) also found technical issues hampering Research Square’s matching approach. Levchenko et al. (2024) analyzed the share of preprints with a corresponding journal article in Europe PMC. Some preprints had a link to a journal article via their Crossref metadata, while others were linked by matching titles and first author surnames. However, Levchenko et al. noted that due to limitations of the different matching approaches Europe PMC is likely to underestimate the share of preprints linked to a journal article.

3. Data sources and methodology

3.1 Matching peer-reviewed outputs with preprints

To link peer-reviewed outputs to preprints, we utilized three data sources: Dimensions, OpenAlex and Crossref. Dimensions and OpenAlex were chosen because they are among the largest datasets containing comprehensive bibliometric data on peer-reviewed outputs and preprints (Visser, van Eck, & Waltman, 2021).Crossref recently introduced a dataset containing more than 600K links identified between journal articles and preprints (Tkaczyk, 2023a; Tkaczyk, 2023b). We used Dimensions as the base dataset; thus, a link identified in OpenAlex or Crossref between a peer-reviewed output and a preprint was included in our analysis only if the two publications being linked were also present in Dimensions. Matching of publications in Dimensions, OpenAlex, and Crossref was performed based on DOI or arXiv ID.

Matching peer-reviewed outputs with preprints and categorizing the final collection of linked publications was done using Microsoft SQL Server. We used the database system of the Centre for Science and Technology Studies (CWTS) at Leiden University. Our SQL scripts are available online (Rzayeva, Waltman, & Pinfield, 2025). Below we provide a step-by-step description of the process for matching

peer-reviewed outputs with preprints.

Step 1: We identified peer-reviewed outputs indexed in Dimensions using a snapshot of the Dimensions database from July 2024. To do this, we excluded publications without a DOI and publications classified as “preprint”. We considered only publications from the period 1991-2023. 1991 was chosen as the start year because this is the year in which arXiv was launched. The end year of 2023 was chosen because it was the most recent year for which complete data was available in our snapshot of the Dimensions database. Additionally, we excluded publications on the various F1000 platforms because of the unconventional publishing model of F1000, where it is not easy to distinguish between a preprint and an article.

Step 2: Next, we identified preprints indexed in Dimensions by selecting publications classified as “preprint”. We included only the first version of each preprint. To exclude later versions, we discarded preprints with a DOI that match one of the following regular expressions:

• ‘%.v[2-9]’ or ‘%.v[1-9][0-9]’

• ‘%/v[2-9]’ or ‘%/v[1-9][0-9]’

• ‘%-v[2-9]’ or ‘%-v[1-9][0-9]’

Step 3: In the Dimensions dataset, we found 1.4M peer-reviewed outputs identified in Step 1 that have a link to a preprint identified in Step 2.

Step 4a and Step 4b: In the OpenAlex dataset, we found 1.9M peer-reviewed outputs identified in Step 1 that have a link to a preprint identified in Step 2. We used a snapshot of the OpenAlex database from August 2024.

Step 5: In the Crossref dataset (Tkaczyk, 2023b), we found 0.5M peer-reviewed outputs identified in Step 1 that have a link to a preprint identified in Step 2.

Step 6: By combining the links from Steps 3, 4, and 5, we identified 2.2M unique peer-reviewed outputs with a link to a preprint.

Figure 1 presents the distribution of links, identified in the Dimensions, OpenAlex, and Crossref datasets, between peer-reviewed outputs and preprints.

To assess the accuracy of our matching of peer-reviewed outputs with preprints, we conducted a manual check of 150 randomly selected links from the final set of links obtained in Step 6. To separately assess the accuracy of each dataset, we also checked random samples of 50 links that appeared in only one dataset (e.g., a link provided by OpenAlex, but not by Dimensions and Crossref). The checks involved searching the peer-reviewed output and the associated preprint on the Web and determining whether they indeed represent the same work (e.g., based on title, abstract, and author names). The checks demonstrated a high accuracy of 99%, with only one peer-reviewed output being incorrectly linked to an unrelated preprint.

Our approach to identifying peer-reviewed outputs (step 1 above), preprints (step 2), and links between peer-reviewed outputs and preprints (steps 3 to 6) has significant limitations that lead to an underestimation of the adoption of preprinting. We discuss these limitations in Section 5.3.

3.2 Categorization of peer-reviewed outputs and preprints

To categorize peer-reviewed outputs and preprints by scientific discipline, we used Dimension’s Field of Research (FoR) classification system (Dimensions, n.d.). For our analysis, we then grouped these disciplines into broader disciplinary areas, as shown in Table 1. If a publication was assigned to multiple disciplines, we counted it in each of these disciplines.

Table 1. Scientific disciplines grouped into broader disciplinary areas.

Disciplinary area	Discipline
Physical Sciences and Technology	Built Environment and Design
Physical Sciences and Technology	Chemical Sciences
Physical Sciences and Technology	Earth Sciences
Physical Sciences and Technology	Engineering
Physical Sciences and Technology	Environmental Sciences
Physical Sciences and Technology	Information and Computing Sciences
Physical Sciences and Technology	Mathematical Sciences
Physical Sciences and Technology	Physical Sciences
Physical Sciences and Technology	Technology
Life and Medical Sciences	Agricultural and Veterinary Sciences
Life and Medical Sciences	Biological Sciences
Life and Medical Sciences	Medical and Health Sciences
Social and Behavioral Sciences	Commerce, Management, Tourism and Services
Social and Behavioral Sciences	Economics
Social and Behavioral Sciences	Education
Social and Behavioral Sciences	Law and Legal Studies
Social and Behavioral Sciences	Psychology and Cognitive Sciences
Social and Behavioral Sciences	Studies in Human Society
Humanities	History and Archaeology
Humanities	Language, Communication and Culture
Humanities	Philosophy and Religious Studies
Humanities	Studies in Creative Arts and Writing

Based on data from the Dimensions database, we assigned a publication to a country if at least one author has an affiliation in that country. In addition to country- level statistics, we also compiled statistics at higher levels of aggregation by grouping countries into subregions and regions. Subregions and regions were defined based on the United Nations M49 standard (United Nations, n.d.).

Aggregation from the level of countries to the level of subregions and regions was performed in an additive way, meaning that a publication belonging to multiple countries in a particular subregion or region was counted multiple times, once for eachofthecountriesinvolved.Inthenextsection,wepresentstatisticsatthelevel of subregions and regions. More detailed country-level statistics are available in a dataset in Zenodo (Rzayeva, Waltman, & Pinfield, 2025).

4. Results

4.1 Overall preprint adoption

As previously mentioned, Dimensions served as the primary data source for our analysis. In Dimensions, we identified approximately 105M peer-reviewed outputs published between 1991 and 2023 and over 4M preprints. This yields two collections of outputs—a collection of peer-reviewed outputs and a collection of preprints. Figure 2 presents the size of each collection as well as the overlap of the collections, consisting of peer-reviewed outputs with a link to a preprint in the Dimensions, OpenAlex, and Crossref datasets. As Figure 2 shows, a large majority of peer- reviewed outputs do not have a corresponding preprint. Conversely, nearly half of the preprints are not linked to a peer-reviewed output.

Figure 2. Venn diagram (not at scale) showing the number of peer-reviewed outputs and the number of preprints included in the analysis

Figure 3 presents the growth over time in the number of peer-reviewed outputs, the number of preprints, and the number of peer-reviewed outputs with a link to a preprint. Figure 4 shows the growth in the proportion of peer-reviewed outputs that have a link to a preprint. There is an overall increase in the adoption of preprinting over time. However, the proportion of peer-reviewed outputs linked to a preprint remains relatively low, not exceeding 4% throughout the entire analyzed period. Due to limitations of our data sources, the true proportion of peer-reviewed outputs linked to a preprint will be somewhat higher than in our statistics. However, this does not change the conclusion that even in recent years a large majority of the peer- reviewed outputs have not been preprinted.

Figure 4 shows a decrease in the proportion of peer-reviewed outputs linked to a preprint in 2023, the final year of the analysis. This is likely to be an artifact resulting from missing links between recent peer-reviewed outputs and preprints in the data sources used in the analysis.

4.2 Preprint adoption across scientific disciplines

As shown in Table 1, we have categorized scientific disciplines into four broad areas: Physical Sciences and Technology, Life and Medical Sciences, Social and Behavioral Sciences, and Humanities. Figure 5 illustrates the time trend of preprint adoption, depicting the growth over time in the proportion of peer-reviewed outputs linked to a preprint relative to the total number of peer-reviewed outputs, both across broader disciplinary areas and in their respective disciplines. We once again observe a decreasing proportion of peer-reviewed outputs linked to a preprint in the latest year, which we consider to be an artifact resulting from limitations of our data sources.

Social and Behavioral Sciences

Humanities

Figure 5. Proportion of peer-reviewed outputs with a link to a preprint in different scientific disciplines. Click on the link below a chart to explore it in more detail in the Tableau environment

As shown in Figure 5, the Mathematical Sciences and Physical Sciences have consistently demonstrated a comparatively high adoption of preprinting, with steady growth starting from the very beginning of the analyzed period. These disciplines have achieved the highest adoption of preprinting among all disciplines, with 27% of peer-reviewed outputs in Mathematical Sciences and 20% in Physical Sciences having a link to a preprint in 2022. Information and Computing Sciences has the third highest adoption of preprinting, having 12% of peer-reviewed outputs linked to a preprint in 2022. In this discipline growth in preprint adoption started more recently than in the Mathematical Sciences and Physical Sciences. Most of the growth took place in the recent 15 years.

Around 2020, disciplines in the Life and Medical Sciences demonstrated a significant growth in the proportion of peer-reviewed outputs linked to a preprint. The Biological Sciences more than doubled its proportion of preprinted outputs between 2019 and 2021, peaking at 9% in 2021 and 2022. Similarly, the Medical and Health Sciences increased fivefold, while the Agricultural and Veterinary Sciences saw a fourfold rise, both peaking at 4%–5% of peer-reviewed outputs being preprinted.

Around the same time, Psychology and Cognitive Sciences in the Social and Behavioral Sciences also showed an increase in preprint adoption, with 7% of peer- reviewed outputs being preprinted in 2021 and 2022. A similar preprint adoption was reached by the Earth Sciences and Technology disciplines in Physical Sciences and Technology; however, these disciplines exhibited a more gradual increase in preprint adoption in the analyzed period. The Chemical Sciences, Engineering, and Environmental Sciences disciplines displayed a similar growth in preprint adoption, with the proportion of preprinted outputs peaking at 5%–6% in 2021 and 2022.

Additionally, Economics showed a modest rise in preprint adoption in recent years, with the proportion of peer-reviewed outputs linked to a preprint reaching 4%.

Other disciplines did not show a significant preprint adoption. This includes the Humanities, where preprint adoption remained minimal, with only about 1% of peer- reviewed outputs linked to a preprint across all disciplines in this area.

As discussed in more detail in Section 5.3, the limitations of our methodology lead to an overcounting of peer-reviewed outputs and an undercounting of preprints, potentially causing a significant underestimation of the proportion of peer-reviewed outputs linked to a preprint. This is particularly relevant in disciplines where widely adopted discipline-specific preprinting practices (e.g., working papers in Economics) are not captured in our analysis, or where publications classified as peer-reviewed outputs have not actually undergone peer review (e.g., book reviews in the Humanities).

4.3 Preprint adoption across geographical regions

As mentioned earlier, we used the United Nations Geoscheme (United Nations, n.d.) to group countries into subregions and regions. To achieve some level of comparability in the number of publications per subregion, we merged some subregions within the same region: all subregions in Oceania were merged. The same was done for the subregions in Africa. In Asia, all subregions except for Eastern Asia were merged into a combined subregion called ‘Rest of Asia’.

Nevertheless, even after merging of subregions, significant differences in the number of publications per subregion persisted. To illustrate, the total number of peer- reviewed outputs in the analyzed period is 1.6 million for Africa and 2.2 million for Oceania compared to 21.2 million for North America and 20.8 million for Western Europe.

For a detailed examination of the results at the level of regions, subregions, and countries, interactive plots available in the Tableau environment (Rzayeva, n.d.) can be used. Due to the relatively small number of publications of some countries, we applied a threshold of 15,000 peer-reviewed outputs in the period 1991-2023. Only countries meeting this threshold were included in the interactive plots. However, to provide the full picture, we have made our complete dataset, including all countries, available online (Rzayeva, Waltman, & Pinfield, 2025).

Figure 6 shows the time trend in the adoption of preprinting, demonstrating the variation in the proportion of peer-reviewed outputs linked to a preprint across geographical regions and subregions. In all regions, we observe a decline in 2023 in the proportion of peer-reviewed outputs linked to a preprint. As discussed before, we consider this to be an artifact resulting from limitations of our data sources.

As with disciplines, we observe a growth over time in the proportion of preprinted outputs across nearly all regions, with a peak in 2021–2022. However, there are a few exceptions. Eastern Europe experienced a decline in the proportion of preprinted outputs around the mid-2010s, despite previously leading the European region in preprint adoption with a peak of 8% preprinted outputs in 2012. In recent years, about 5% of the Eastern European outputs were preprinted. When Eastern Europe experienced a decline in preprint adoption, Western Europe steadily increased its proportion of preprinted outputs. In recent years, Western Europe has emerged as the subregion with the highest adoption of preprinting, with 13% of its outputs in 2022 having a link to a preprint, the highest proportion of preprinted outputs among all subregions. With 11% preprinted outputs in 2022, Northern Europe ranks second globally. In Southern Europe, only 8% of peer-reviewed outputs were preprinted in 2022.

Similar observations can be made for other regions and subregions. In Latin America and the Caribbean, the proportion of preprinted outputs increased to 6% in 2022.

North America has seen a growing preprint adoption in recent years, peaking at nearly 10% in 2022. Eastern Asia demonstrated a substantial increase in preprint adoption in recent years, reaching a maximum of 6% preprinted outputs. In the Rest of Asia, the proportion of preprinted outputs showed a gradual increase in the early years and then plateaued in the 2000s, reaching 4% in 2022. The Africa and Oceania regions peaked at, respectively, 5% and 8% preprinted outputs in 2022.

4.4 Preprint adoption across scientific disciplines and geographical regions

To provide a combined perspective on preprint adoption across scientific disciplines and geographical regions, we performed an analysis focused on recent years. Figure 7 shows the proportion of peer-reviewed outputs linked to a preprint across disciplines and regions in the years 2021 and 2022.

As expected, the Mathematical Sciences and Physical Sciences show a high proportion of preprinted outputs across many regions. In the Mathematical Sciences, the highest proportions can be observed in Western Europe (51%), Northern Europe (46%), North America (43%), Southern Europe (42%), and Latin America and the Caribbean (40%). In the Physical Sciences, the highest proportions of preprinted outputs can be found in the same regions: Western Europe (49%), Southern Europe (49%), Northern Europe (48%), Latin America and the Caribbean (48%), and North America (40%). Oceania also has a high proportion of preprinted outputs in the Physical Sciences (38%), while this proportion is a bit lower in the Mathematical Sciences (31%). Eastern Europe has a substantial proportion of preprinted outputs in the Mathematical Sciences (33%) and the Physical Sciences (28%), but it is lagging behind the other European subregions.

Preprint adoption in the Mathematical Sciences and Physical Sciences is lowest in Africa and in the two Asian subregions, with the proportion of preprinted outputs in these regions not exceeding 20%. Importantly, of all subregions, Eastern Asia has the largest number of peer-reviewed outputs in the Mathematical Sciences and Physical Sciences. The low preprint adoption in this subregion therefore has a significant negative effect on the global adoption of preprinting.

The next discipline showing a significant preprint adoption is Information and Computing Sciences, particularly in Western Europe (25%), North America (22%) and Northern Europe (20%). Latin America and the Caribbean, Eastern Europe, Southern Europe, and Oceania have a lower proportion of preprinted outputs in this discipline (between 13% and 18%), while the proportion is lowest for Africa and the Asian subregions (between 7% and 9%).

Other disciplines have a lower adoption of preprinting, but the regional patterns are fairly similar to those in the Mathematical Sciences, Physical Sciences, and Information and Computing Sciences. North America, Northern Europe, and Western Europe typically have the highest proportion of preprinted outputs. Southern Europe and Oceania also have a relatively high proportion of preprinted outputs in some disciplines, such as the Chemical Sciences and Earth Sciences, and in the case of Oceania also Technology and the Biological Sciences. The other regions tend to show a lower preprint adoption. While Latin America and the Caribbean plays a leading role in preprint adoption in the Mathematical Sciences and Physical Sciences, the role of this region in other disciplines is much more modest, although it does show a relatively significant preprint adoption in the Chemical Sciences.

There are interesting exceptions to the general patterns. Most notably, while in many disciplines Africa is among the regions with the lowest adoption of preprinting, it has the highest preprint adoption in the Medical and Health Sciences. 8% of the African outputs in this discipline have a link to a preprint, slightly surpassing Northern Europe and Western Europe, which have 7% preprinted outputs in the Medical and Health Sciences. In the Biological Sciences, Africa also has a decent preprint adoption compared to other regions.

5. Discussion and conclusion

In the context of a growing interest in the role of preprinting in the scholarly communication system, it is important to understand the extent to which different research communities have adopted preprinting over time. The results of our study show the growing absolute number of preprints, with nearly three times more preprints published in 2022 compared to 2017, confirming the increasing trend in preprint adoption. However, in many research communities the proportion of peer- reviewed outputs being preprinted is still quite low. Our findings show that the proportion of preprinted outputs varies significantly across scientific disciplines and geographical regions. Furthermore, we found that the adoption of preprinting depends on the interplay of both factors—scientific discipline and geographical region—rather than being determined by either in isolation.

5.1 Main findings

Since the early 1990s, the adoption of preprinting has grown steadily in the Physical Sciences and Mathematical Sciences. Preprinting has become an accepted practice for sharing research findings in these disciplines (Kling & McKim, 2000; Brown, 2001; Larivière et al., 2014; Xie, Shen, & Wang, 2021; Puebla, Polka, & Rieger, 2021; Ni & Waltman, 2024; Colavizza et al., 2024). Our analysis confirms the high adoption of preprinting in these disciplines. Notably, in our analysis, about 70% of peer-reviewed outputs linked to a preprint were found in the Physical Sciences and Mathematical Sciences. Preprinting is widely embraced in these disciplines worldwide, with no region exhibiting higher preprint adoption in any other discipline than these two. Preprint adoption in these disciplines is particularly prominent among researchers in the Americas and Europe. While Eastern Asia is the region with the largest number of peer-reviewed outputs in the Physical Sciences and Mathematical Sciences, it lags far behind the Americas, Europe and Oceania in terms of the proportion of peer-reviewed outputs linked to a preprint.

In line with the findings of Xie, Shen, & Wang (2021), we also observed increasing preprint adoption in the Information and Computing Sciences in recent years. Less prominent than in the Physical Sciences and Mathematical Sciences, though still notable, adoption of preprinting in this discipline is concentrated mostly among researchers in North America as well as Western Europe and Northern Europe.

Despite a rising trend in the proportion of preprinted outputs in most disciplines, only the three above-mentioned disciplines in the broader Physical Sciences and Technology area had more than 10% of their peer-reviewed outputs in recent years linked to a preprint. Other disciplines in the Physical Sciences and Technology area demonstrate a slower growth in the proportion of preprinted outputs. The adoption of preprinting in these disciplines is led by researchers in North America, Western Europe, and Northern Europe.

Despite the low proportion of preprinted outputs in the Life and Medical Sciences, our findings confirm the increasing adoption of preprinting in this disciplinary area in recent years, in line with earlier studies (Tennant et al., 2018; Sever et al., 2019; Xie, Shen, & Wang, 2021; Puebla, Polka, & Rieger, 2021; Fraser et al., 2021). All three disciplines in this area show a pronounced rise in the proportion of preprinted outputs starting in 2019 and peaking in 2021–2022. The highest proportion was observed in the Biological Sciences. In this discipline, preprinting is particularly popular among researchers in North America, Western Europe, and Northern Europe, consistent with previous research by Abdill, Adamowicz, & Blekhman (2020). The increasing trend in disciplines in the Life and Medical Sciences area was undoubtedly driven by the COVID-19 outbreak, which highlighted the need to disseminate research findings rapidly and openly (Waltman et al., 2021; Rzayeva et al., 2023). A survey we performed of authors who posted COVID-19-related preprints revealed that many of these authors intended to continue posting preprints for other works beyond pandemic-related research (Rzayeva et al., 2023). This may explain the rising trend in preprint adoption in the Life and Medical Sciences in the post- pandemic period.

Disciplines in the Humanities and the Social and Behavioral Sciences show lower proportions of peer-reviewed outputs linked to a preprint, with the exception of the Psychology and Cognitive Sciences discipline, which began to exhibit an increase in preprint adoption around the same time as the Biological Sciences. However, the proportion of preprinted outputs remains relatively low in this discipline across all regions worldwide, with slightly higher adoption among researchers in Northern Europe and Western Europe. Another notable yet not very prominent increase was observed in the Economics discipline, which is surprising when compared to findings from previous studies by Fry et al. (2015), Xie, Shen, & Wang (2021), and Colavizza et al. (2024). We emphasize that the low adoption of preprinting observed in the Humanities and the Social and Behavioral Sciences may be influenced by limitations in our data, as discussed below in Section 5.3.

Preprint adoption has increased in almost all regions. North America, Western Europe, and Northern Europe demonstrate similar patterns in adopting preprinting, with adoption showing a substantial growth in recent years. Among these regions, Western Europe has the highest preprint adoption. Consistent with recent findings from a researcher survey carried out by Ni & Waltman (2024), these three regions play a leading role in preprint adoption. No other region reached the threshold of having at least 10% of their peer-reviewed outputs linked to a preprint. Southern Europe and Latin America and the Caribbean show a stable preprint adoption since the early 2000s, with some limited growth in recent years. Interestingly, Eastern Europe exhibited more pronounced preprint adoption in the early 2010s, but unlike other regions, it experienced a decline in recent years.

In Asia, preprint adoption has been relatively low and stable, with Eastern Asia showing some growth in recent years. Compared to North America, Western Europe, and Northern Europe, the adoption of preprinting in Eastern Asia is substantially lower in most disciplines. This aligns with earlier studies of preprint adoption and scholarly communication trends in China – the biggest contributor of scholarly publications in Eastern Asia (Ren, 2013; Wang, Halffman, & Zwart, 2021; Hyland, 2023; Ni & Waltman, 2024). Increasing trends in preprint adoption are also visible in Africa and Oceania, with significant growth especially in the most recent years.

Our results demonstrate that preprint adoption is shaped by the interplay between scientific disciplines and geographical regions. For instance, while it may not be surprising that preprint adoption is higher in the Physical Sciences and Mathematical Sciences than in other disciplines, our results also show that preprint adoption in these disciplines is significantly higher among researchers in the Americas and Europe compared to researchers in other regions. Similarly, the recent growth in preprint adoption in the Information and Computing Sciences is unevenly distributed, with North America, Western Europe, and Northern Europe showing a higher preprint adoption than other regions. Nevertheless, even in regions with a high adoption of preprinting in the Physical Sciences, Mathematical Sciences, and Information and Computing Sciences, preprint adoption remains fairly low in many other disciplines. These findings make clear that preprint adoption is not shaped solely by either discipline or region, but rather by their interplay.

5.2 Factors influencing preprint adoption

As discussed in Section 1, a variety of factors may influence the adoption of preprinting in a research community. Established publication norms and traditions play a crucial role, as seen in the Physical Sciences and Mathematical Sciences, where preprints are widely used for rapid open dissemination of scientific knowledge. Additionally, external factors, such as the COVID-19 outbreak, may highlight the need for accelerated dissemination of scientific knowledge via preprints. At the national and regional level, open science developments and funder policies may be important factors influencing preprint adoption.

Ni & Waltman (2024) examined researchers’ attitudes toward preprinting across countries and disciplines, including the familiarity of researchers with preprinting and the frequency of reading and posting of preprints. Their survey revealed significant differences across countries and disciplines in the factors that influence preprint adoption. For instance, a substantial percentage of Chinese researchers in the social sciences and humanities reported that receiving recognition for preprinting their work is a very important factor in adopting preprinting. A similar attitude was observed among Chinese researchers in the life and health sciences. European respondents in the physical sciences and engineering indicated that for them encouragement or mandating of preprinting by funders is a key factor in preprint adoption. Similarly, US researchers in the physical sciences and engineering as well as the life and health sciences, along with their European colleagues in the physical sciences and engineering and the social sciences and humanities, emphasized the importance of integrating preprints into journal workflows as a primary means of fostering preprint adoption. The findings of Ni & Waltman indicate the broad range of factors that may contribute to disciplinary and regional differences in preprint adoption.

5.3 Limitations

As mentioned above, the methodology of our study underestimates the proportion of peer-reviewed outputs linked to a preprint, because of overcounting of peer-reviewed outputs, undercounting of preprints, and missing links between peer-reviewed outputs and preprints. This may be particularly relevant for some disciplines. For instance, in Economics, “working papers” or “discussion papers” are widely disseminated through platforms like Research Papers in Economics (RePEc), the National Bureau of Economic Research (NBER), the Asian Development Bank (ADB) Economics Working Papers Series, and the Economic Research Forum Working Papers (ERF), among others. These types of publications play a similar role as preprints, allowing scholars to share research findings prior to publication in a peer-reviewed journal (Frandsen, 2009; Einav et al., 2021). However, in our collection of preprints, we do not cover these repositories. RePEc and other repositories popular in Economics often employ unique repository-specific identifiers rather than DOIs. The Dimensions database that we use indexes mainly outputs with DOIs, excluding publications in RePeC and similar repositories. This is an important limitation in Economics and also in other disciplines where researchers post their work prior to peer review in repositories that use their own unique identifiers.

Additionally, our methodology can lead to an overcounting of peer-reviewed outputs by including publications that have not actually undergone peer review. For example, in the Humanities, many journals publish book reviews (Zuccala & van Leeuwen, 2011). While these book reviews are usually not peer-reviewed, they are nonetheless included in our analysis because they are indexed in the Dimensions database and the database does not provide a straightforward way to distinguish them from peer-reviewed outputs. This causes an overcounting of peer-reviewed outputs in the Humanities and, consequently, an underestimation of the proportion of peer-reviewed outputs linked to a preprint. This issue applies not only to book reviews but also to other types of publications that usually do not undergo peer review and that generally are not expected to be preprinted, such as news articles, editorials, and letters. The Dimensions database does not enable us to distinguish these publications from peer-reviewed outputs.

To estimate the proportion of outputs that are incorrectly classified as peer-reviewed in our analysis, we conducted a manual check of 100 randomly selected publications in our collection of peer-reviewed outputs. Our manual check revealed that 91 of the 100 publications are likely to have undergone peer review, although it is difficult to determine this with certainty, since for most publications there is no explicit evidence proving that the publication has been peer reviewed. Publications that are probably incorrectly classified as peer-reviewed include book reviews, letters, and meeting abstracts. Based on our manual check, the statistics reported in this paper seem to overestimate the number of peer-reviewed outputs by about 10%, leading to a limited underestimation of the proportion of peer-reviewed outputs that have been preprinted.

Finally, we note that for some peer-reviewed outputs it may not be realistic to expect them to be preprinted. For example, in the medical sciences, articles reporting case studies or narrative reviews cannot be posted on the medRxiv preprint server, even though these articles can be peer reviewed by and published in a journal. Because of such restrictions imposed by preprint servers, it would be hard to realize 100% adoption of preprinting.

Author contributions

Conceptualization: Narmin Rzayeva, Stephen Pinfield, Ludo Waltman Data curation: Narmin Rzayeva, Ludo Waltman

Formal Analysis: Narmin Rzayeva, Ludo Waltman

Investigation: Narmin Rzayeva, Stephen Pinfield, Ludo Waltman Methodology: Narmin Rzayeva, Stephen Pinfield, Ludo Waltman Software: Narmin Rzayeva

Supervision: Stephen Pinfield, Ludo Waltman Visualization: Narmin Rzayeva

Writing – original draft: Narmin Rzayeva

Writing – review & editing: Stephen Pinfield, Ludo Waltman

Funding information

The authors did not receive any funding for the research presented in this paper.

Competing interests

Ludo Waltman is president of ASAPbio, an organization that promotes preprinting in the life sciences. The other authors have no competing interest.

Data availability

The data that support the findings of this study are openly available in Zenodo (Rzayeva, Waltman, & Pinfield, 2025).

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