The domination of scientific publishing in the Global North by major commercial publishers is harmful to science. We need the most powerful members of the research community, funders, governments and Universities, to lead the drive to re-communalise publishing to serve science not the market.

April 10, 2026

This article presents the results of a survey conducted in 2024 among research performing organizations (RPOs) in Germany on how they collect data on publication costs. Of the 583 invitees, 258 (44.3%) completed the questionnaire. This survey is the first comprehensive study on the recording of publication costs at RPOs in Germany. The results show that the majority of surveyed RPOs recorded publication costs at least in part. However, procedures in this regard were often non-binding. Respondents' ratings of the reliability of the collection of data on publication costs varied by the source of publication funding. Eighty percent of respondents rated the contribution of collecting data on publication costs to shaping the open access transformation as "very important" or "important." Yet, these data were used as a basis for strategic decisions in only 59% of the surveyed RPOs. Moreover, most respondents considered the implementation of an information budget at their institutions by 2025 unlikely. We discuss the implications of these findings for the open access transformation.

April 2, 2026

Preprint of a manuscript developed by Park, M; Yi, H; Warnow, T; and Chacko, G concerning very large scale simulations of the growth of citation networks using an agent based modeling approach. A document containing supplemental material accompanies the manuscript. The manuscript contains a link to a public Github repository where the code generated for this project is available. Data generated during this project is being made available via the Illinois Data Bank under repository IDB-9265079.

March 26, 2026

Contributorship refers to indicating who did what in a project, going beyond a simple list of authors. In scholarly journal articles about a project, the Contributor Roles Taxonomy (CRediT) has become a popular way to provide individual contribution information, often with accompanying machine-readable metadata. While CRediT is used by hundreds of scientific journals, the official version of CRediT exists only in English. To support scientific publishers and researchers writing in other languages, we have created translations of the fourteen CRediT roles and their descriptions into thirty-six languages. To ensure high quality, at least one speaker fluent in the target language drafted the translation, with additional involvement of a second fluent person. Because hundreds of scientific journals publish non-English work, the use of our translations could improve the recognition of the associated researchers’ contributions. We have contacted relevant publishers and academic organizations to make them more aware of CRediT, of our translations, and of contributorship generally. To conclude, we discuss the potential for CRediT and other ontologies to be applied more broadly, for example to facilitate greater recognition of people who are not co-authors but are named in Acknowledgments sections.

March 24, 2026

Drawing on two in-depth cases of research projects that have received societally targeted funding and appear to have involved highly intensive academic/non-academic engagements, this study examines processes leading from research funding towards societal outcomes. We trace causal linkages from the specific research funding to the societally relevant outcomes of the research they fund. Using process-tracing, we aim to explore how societally targeted funding and its specific characteristics can be linked to societal outcomes, with particular focus on collaboration/productive interactions. Through interviews and document analysis, we trace how the funding shaped the research projects and how research was conducted, and subsequently how the project design promoted the development of societally relevant research results.

March 23, 2026

The integration of generative artificial intelligence (GenAI) and large language models (LLMs) into scientific research and higher education presents a paradigm shift, offering revolutionizing opportunities while simultaneously raising profound ethical, legal, and regulatory questions. This study examines the complex intersection of AI and science, with a specific focus on the challenges posed to copyright law and the principles of open science. The author argues that current regulatory frameworks in key jurisdictions like the United States, China, the European Union, and the United Kingdom, while aiming to foster innovation, contain significant gaps, particularly concerning the use of copyrighted works and open science outputs for AI training. Widely adopted licensing mechanisms, such as Creative Commons, fail to adequately address the nuances of AI training, and the pervasive lack of attribution within AI systems fundamentally challenges established notions of originality. While current doctrine treats AI training as potentially fair use, this paper argues such mechanisms are inadequate and that copyright holders should retain explicit opt-out rights regardless of fair use doctrine. Instead, the author advocates for upholding authors' rights to refuse the use of their works for AI training and proposes that universities assume a leading role in shaping responsible AI governance. The conclusion is that a harmonized international legislative effort is urgently needed to ensure transparency, protect intellectual property, and prevent the emergence of an oligopolistic market structure that could prioritize commercial profit over scientific integrity and equitable knowledge production. This is a substantially expanded and revised version of a work originally presented at the 20th International Conference on Scientometrics & Informetrics (Kochetkov, 2025).

March 12, 2026

Search engines play a crucial role in discovering and disseminating academic work, making academic search engine optimisation (A-SEO) vital for enhancing research visibility. Consequently, the collection of robust A-SEO data is essential. This study compares four leading SEO tools—Ahrefs, Semrush, Serpstat, and Ubersuggest—to evaluate their performance in measuring the organic traffic of gold open access academic publishers, using MDPI and Frontiers as case studies. The findings reveal significant discrepancies in the web traffic metrics reported by each platform, likely attributable to their diverse and often opaque traffic estimation methodologies. These differences may lead to divergent interpretations, thereby limiting the replicability and reproducibility of studies, and hindering the development of standardised web traffic indicators. This study highlights the need for greater methodological rigour and standardisation in academic SEO research, offering both theoretical insights and practical guidance to improve the online visibility of research within a platform-driven scholarly ecosystem.

March 11, 2026

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.

March 9, 2026

Currently, much medical research is wasted due to inappropriate study design or analysis. Qualified statisticians are essential for ensuring ideal study design and analysis, and the ethical review process is an ideal stage for their input. We estimated the percentage of Australian ethics committees with access to a qualified statistician. Sixty percent of committees reported access to a qualified statistician, either as a full committee member or as a non-member who could be consulted when needed; however, when accounting for statistical qualifications this dropped to 35%. Many committees rely on “highly numerate” researchers in place of qualified statisticians, as they viewed research experience and advanced statistical training as equivalent. Many committees felt that improving study designs was not part of their remit. Some committee chairs viewed formal statistical input as essential; however, there was also a common belief that statistical review was only applicable to some study designs and that “simple” studies did not need review. There was a surprising variance in practice and attitudes towards the involvement of statisticians on research ethics committees. The high number of research studies that receive approval without statistical review risks approving studies that could cause harm due to flawed evidence.

March 4, 2026

Bartoš et al. propose a new article format — Synchronous Robustness Reports (SRRs) — in which journals invite researchers to conduct alternative analyses of recently accepted studies. The goal is to assess the robustness of the original results by exploring how they vary across different analysis choices. However, the proposal does not distinguish between SRRs planned before versus after analysts have seen the data. This distinction is crucial because data-dependent analyses introduce a risk of bias from selective reporting. In this commentary, I argue that without safeguards, SRRs could inadvertently create more confusion than clarity about robustness.

March 4, 2026