This article discusses European copyright law as applied to the development and training of generative AI and natural language processing in public interest research institutions and libraries. The article focuses on the scope of the new exceptions from copyright law for text and data mining (TDM) for research purposes and discusses them from the perspective of research ethics and principles of open science in publicly financed research. The public interest mission of research institutions and libraries includes the open dissemination of research results but the exceptions from copyright are focused only on the training phase in AI development. Regulation on data transparency is fragmented. The article finds that while new exceptions open for developing language models under research institutions and libraries’ public interest mission to preserve national languages, the regulation is not adapted to principles of research ethics and open science, and legal uncertainty remains.

Open science is good for both epistemic and social reasons, but in nonobvious ways, it can have detrimental epistemic side effects. Drawing on case studies and the social epistemology of science, I show how practices intended to increase transparency, communication, and information sharing in science can backfire. We should not reject Open Science, just implement it carefully. I argue that we can do so by treating openness as a governing value in science, and thus, that our pursuit of openness needs to be balanced against our pursuit of the whole scheme of values that govern science.

The use of programming languages in archaeological research has witnessed a notable surge in the last decade, particularly with R, a versatile statistical computing language that fosters the development of specialized packages. This article introduces the tesselle project (https://www.tesselle.org/), a comprehensive collection of R packages tailored for archaeological research and education. The tesselle packages are centered on quantitative analysis methods specifically crafted for archaeology. They are designed to complement both general-purpose and other specialized statistical packages. These packages serve as a versatile toolbox, facilitating the exploration and analysis of common data types in archaeology—such as count data, compositional data, or chronological data—and enabling the construction of reproducible workflows. Complementary packages for visualization, data preparation, and educational resources augment the tesselle ecosystem. This article outlines the project's inception, its objectives, design principles, and key components, along with reflections on future directions.

Community biology labs are locally organized spaces for research, tinkering and innovation, which are important for improving the accessibility of biological research and the transferability of scientific knowledge. These labs promote citizen science by providing resources and education to community members. For community labs to deliver consistent and reliable results, they would ideally be based on an adaptive and robust foundation: an Enterprise Systems Thinking (EST) framework. This paper follows a descriptive methodology to apply EST to conceptualize the optimal functioning of community biology labs. EST approaches can increase the overall understanding of the community lab system’s context and performance. This supportive tool can aid in successful stakeholder engagement and communications within the lab’s complex structure. It is also adaptive and can be adjusted as Community Bio labs expand in scale and are newly introduced to local communities. The result of this paper is the development of a framework that may help enhance existing community laboratory organizational approaches so that they may provide consistent accessibility, innovation and education to local communities.

Al-Hoorie et al. (2024: Studies in Second Language Acquisition, 1–23) illuminate a validation crisis within the second language (L2) Motivational Self System (L2MSS), revealing empirical flaws in its current measurement. Their analysis indicates a persistent lack of discriminant validity among the system’s constructs, issuing a fundamental challenge in distinguishing the concepts. These findings, echoing previous concerns, underscore a pressing need for theoretical refinement and methodological rigor within the field, leading the authors to advocate for a temporary halt in L2 self-studies to address these issues comprehensively. This commentary discusses the call for a substantive moratorium presented by Al-Hoorie et al. (2024: Studies in Second Language Acquisition, 1–23) as a necessary step toward resolving persistent challenges in the field. By highlighting historical issues and suggesting pathways for theoretical diversification and methodological advancement, I aim to foster a productive dialogue on motivational psychology in language learning while ensuring empirical robustness.

One of the goals of open science is to promote the transparency and accessibility of research. Sharing data and materials used in network research is critical to these goals. In this paper, we present recommendations for whether, what, when, and where network data and materials should be shared. We recommend that network data and materials should be shared, but access to or use of shared data and materials may be restricted if necessary to avoid harm or comply with regulations. Researchers should share the network data and materials necessary to reproduce reported results via a publicly accessible repository when an associated manuscript is published. To ensure the adoption of these recommendations, network journals should require sharing, and network associations and academic institutions should reward sharing.

Blind review is ubiquitous in contemporary science, but there is no consensus among stakeholders and researchers about when or how much or why blind review should be done. In this essay, we explain why blinding enhances the impartiality and credibility of science while also defending a norm according to which blind review is a baseline presumption in scientific peer review.

The point has repeatedly been made that validation is a crucial success factor in demonstrating the scientific contribution and ensuring the adoption of results. Still, researchers in design science validate their research findings too infrequently. We must all evaluate our claimed contributions on open benchmarks to improve validation quality and foster cumulative research. In this paper, we propose a meta-model to standardise and operationalise the concept of open scientific benchmarks in design science and to guide communities of researchers in the co-development of scientific benchmarks.

PEPAdb (Prehistoric Europe's Personal Adornment Database) is a long-term, open-ended project that aims to improve access to archaeological data online. Its website (https://pepadb.us.es) publishes and analyses datasets about prehistoric personal adornment, drawing on the results of various research projects and bibliographic references.

Data compilations expand the scope of research; however, data citation practice lags behind advances in data use. It remains uncommon for data users to credit data producers in professionally meaningful ways. In paleontology, databases like the Paleobiology Database (PBDB) enable assessment of patterns and processes spanning millions of years, up to global scale. The status quo for data citation creates an imbalance wherein publications drawing data from the PBDB receive significantly more citations (median: 4.3 ± 3.5 citations/year) than the publications producing the data (1.4 ± 1.3 citations/year). By accounting for data reuse where citations were neglected, the projected citation rate for data-provisioning publications approached parity (4.2 ± 2.2 citations/year) and the impact factor of paleontological journals (n = 55) increased by an average of 13.4% (maximum increase = 57.8%) in 2019. Without rebalancing the distribution of scientific credit, emerging “big data” research in paleontology—and science in general—is at risk of undercutting itself through a systematic devaluation of the work that is foundational to the discipline.

Most societies witness an ever increasing prevalence of both obesity and dementia, a scenario related to often underestimated individual and public health burden. Overnutrition and weight gain have been linked with abnormal functionality of homoeostasis brain networks and changes in higher cognitive functions such as reward evaluation, executive functions and learning and memory. In parallel, evidence has accumulated that modifiable factors such as obesity and diet impact the gut–brain axis and modulate brain health and cognition through various pathways. Using neuroimaging data from epidemiological studies and randomised clinical trials, we aim to shed light on the underlying mechanisms and to determine both determinants and consequences of obesity and diet at the level of human brain structure and function. We analysed multimodal 3T MRI of about 2600 randomly selected adults (47 % female, 18–80 years of age, BMI 18–47 kg/m2) of the LIFE-Adult study, a deeply phenotyped population-based cohort. In addition, brain MRI data of controlled intervention studies on weight loss and healthy diets acquired in lean, overweight and obese participants may help to understand the role of the gut–brain axis in food craving and cognitive ageing. We find that higher BMI and visceral fat accumulation correlate with accelerated brain age, microstructure of the hypothalamus, lower thickness and connectivity in default mode- and reward-related areas, as well as with subtle grey matter atrophy and white matter lesion load in non-demented individuals. Mediation analyses indicated that higher visceral fat affects brain tissue through systemic low-grade inflammation, and that obesity-related regional changes translate into cognitive disadvantages. Considering longitudinal studies, some, but not all data indicate beneficial effects of weight loss and healthy diets such as plant-based nutrients and dietary patterns on brain ageing and cognition. Confounding effects of concurrent changes in other lifestyle factors or false positives might help to explain these findings. Therefore a more holistic intervention approach, along with open science tools such as data and code sharing, in-depth pre-registration and pooling of data could help to overcome these limitations. In addition, as higher BMI relates to increased head micro-movements during MRI, and as head motion in turn systematically induces image artefacts, future studies need to rigorously control for head motion during MRI to enable valid neuroimaging results. In sum, our results support the view that overweight and obesity are intertwined with markers of brain health in the general population, and that weight loss and plant-based diets may help to promote brain plasticity. Meta-analyses and longitudinal cohort studies are underway to further differentiate causation from correlation in obesity- and nutrition-brain research.

The transition to open data practices is straightforward albeit surprisingly challenging to implement largely due to cultural and policy issues. A general data sharing framework is presented along with two case studies that highlight these challenges and offer practical solutions that can be adjusted depending on the type of data collected, the country in which the study is initiated, and the prevailing research culture. Embracing the constraints imposed by data privacy considerations, especially for biomedical data, must be emphasized for data outside of the United States until data privacy law(s) are established at the Federal and/or State level.