The peer-review system remains central to scientific communication, yet increasing submission volumes, shifts toward open access publishing, and disruptions such as the Covid-19 pandemic have raised concerns about its efficiency and accuracy. This study aims to systematically map trends in acceptance and publication delays across more than six million PubMed-indexed articles published between 2016 and 2025. Using journal metadata and peer-review information, we will examine how journal characteristics, reviewer practices, and field-specific factors shape these delays. To achieve this, we will employ a combination of descriptive statistics, generalized additive models and machine learning algorithms to capture trends, seasonality, and journal-level effects. The project will provide a comprehensive description of how publication timelines have evolved and identify the key determinants driving variations across journals and disciplines.

April 23, 2026

Open Engagement of societal actors in scientific processes is a key pillar of Open Science. This paper explores how the broad spectrum of open engagement approaches can be systematically operationalized to classify research projects. Used terminologies for these approaches, like e.g., citizen science, lack differentiation which complicate comprehensive monitoring, and, therefore, a better understanding and description of open engagement practices are needed. Open Science Monitoring requires an interdisciplinary discussion of terminology and an understanding of the different research processes, practices, and outputs. In this paper, we present our preliminary findings based on a corpus of 91 participatory research projects from the Berlin University Alliance (BUA), collected as part of the development of a Participatory Research Map (PR Map).

April 21, 2026

In this paper we introduce a novel implementation of the Relative Intensity of Collaboration (RIC, (Fuchs et al., 2021): we distinguish between relative intensity of bilaterally coauthored (RICbila) and multilaterally coauthored (RICmulti) papers. We calculate both indicators for Russian collaboration 1995-2024 with Belarus, Ukraine, European countries bordering Russia, Belarus, and/or the Black Sea, five other large European countries, and China and the US. We find that the relative intensity of Russian bilateral collaboration with those countries is mostly decreasing, and rapidly falling for Bulgaria, Germany, and Ukraine, countries with which collaboration used to be intense. In contrast, the relative intensity of bilateral collaboration with China is increasing rapidly. The relative intensity of multilateral collaboration is mostly decreasing, although more gradually. We conclude that distinguishing between different collaboration patterns helps in understanding the consequences of geopolitical developments for research collaboration.

April 20, 2026

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

The global science literature, represented as a network with articles as nodes and citations as edges, is a rich artifact for studies in scientometrics centered around historical, epistemological, sociological, and graph-theoretic perspectives. The structure of this network depends on its count of nodes and distribution of edges. We are interested in how the network has evolved from its origin to its present structure. Since extant theories of citation do not offer much in the way of quantitative explanation, we use a modeling approach that generates synthetic networks. Specifically, we have developed an idealized agent-based model of citations (SASCA-ReS) that can generate synthetic networks of size over 200 million nodes, which is comparable with the size of today’s network. This model allows us to reason in an artificial world and to identify patterns of citation that may explain real-world scenarios. We report results from simulations under this model with different parameter settings and at various scales to explore counterfactual and hypothetical scenarios.

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