UK Biobank (UKB) is a large-scale, prospective resource offering significant opportunities for mental health research. Data include genetic and biological data, healthcare linkage, and mental health enhancements. Challenges arise from incomplete linkage of some sources and the incomplete coverage for enhancements, which also occur at different times post-baseline. We searched for publications using UKB for mental health research from 2016 to 2023 to describe and inspire future use. Papers were classified by mental health topic, ‘additional’ aspects, and the data used to define the mental health topic. We identified 480 papers, with 338 focusing on mental health disorder topics (affective, anxiety, psychotic, multiple, and transdiagnostic). The most commonly studied disorder was depression (41%). The most common single method of ascertaining mental disorder status was the Mental Health Questionnaire (26%), with genetic risk, for example, using polygenic risk scores, also frequent (21%). Common additional aspects included brain imaging, gene–environment interaction, and the relationship with physical health. The review demonstrates the value of UKB to mental health research. We explore the strengths and weaknesses, producing resources informed by the review. A strength is the flexibility: conventional epidemiological studies are present, but also genomics, imaging, and other tools for understanding mental health. A major weakness is selection effects. UKB continues to hold potential, especially with additional data continuing to become available.

The training of Artificial Intelligence (AI) models relies on extensive amounts of “data,” often sourced from content protected by copyright, related and sui generis rights. The discussion of whether and how to strike a balance between licensing and exceptions under copyright law is one of global relevance. While some countries have adopted or considered adopting specific exceptions to allow text and data mining (TDM), others (most) have not introduced any new legislation. In Europe, much of the attention has so far centred on Article 4 of Directive 2019/790 (DSMD), including in the context of a potential UK reform.

The starting point of this contribution is the following four-fold observation. First, TDM may be part of AI training processes, but it is neither synonymous with AI training nor is it all that AI training entails, including in terms of acts restricted by copyright and related rights. Second, from a European (thus including both the EU and the UK) perspective, limiting the attention to Article 4 DSMD is myopic, as national case law demonstrates. Third, calls have recently been made to relax EU copyright rules to facilitate “research,” seemingly including the President of the European Commission herself, who announced forthcoming legislative proposals “to make Europe the home of innovation again.” Fourth, the UK Government’s Copyright and AI consultation has recently ended: should no reform be ultimately undertaken, the application of the existing TDM exception will depend to a large extent on how courts construe the notions of “research” and the “non-commercial” requirement thereof.

Moving from the above, this study investigates whether and to what extent unlicensed AI training activities could be undertaken by relying, not on Article 4 DSMD as transposed into national law or a hypothetical reform of the UK system of exceptions, but rather on what appear to be so far potentially overlooked defences. Reference is made specifically to research and education exceptions, notably Article 3 DSMD and Article 5(3)(a) of Directive 2001/29 (InfoSoc Directive), also read in light of Article 5 DSMD. The discussion of other jurisdictions – including the US and countries, like South Korea and Singapore, which have adopted open-ended fair use-style defences – is also undertaken. This is done to determine whether unlicensed AI training, including training seemingly done for the purpose of research or education/learning, might be considered lawful.

In light of the context summarized above, the study tackles two key questions: (a) whether unlicensed AI training may be classified as “research” or even “learning” in the context of “teaching,” and (b) whether commercial AI developers may take advantage of the provisions above. Ultimately, both questions are answered in the negative, finding that no exception or open-ended defence fully covers unlicensed AI training activities. As a result, a licensing approach (and culture) appears to be the way for AI training to be undertaken lawfully, including when this is done for “research” and “learning.”

Data governance has emerged as a pivotal area of study over the past decade, yet despite its growing importance, a comprehensive analysis of the academic literature on this subject remains notably absent. This paper addresses this gap by presenting a systematic review of all academic publications on data governance from 2007 to 2024. By synthesizing insights from more than 3500 documents authored by more than 9000 researchers across various sources, this study offers a broad yet detailed perspective on the evolution of data governance research.

Women remain underrepresented in National Institutes of Health (NIH) study sections, panels of scientists who review grant applications to inform national research priorities and funding allocations. This longitudinal, retrospective study examined the representation of women on study sections before and during the COVID-19 pandemic. Overall, 16,902 reviewers served on 1,045 study sections across 2019, 2020, and 2021, of which 40.1% (n = 6,786) were women. The likelihood of reviewers being women significantly increased from 2019 to 2021, except among chairpersons. Understanding the representation of scientists influencing NIH grant decisions is important to ensuring scientific discovery that meets the nation’s pluralistic needs.

Due to the provisions of the Svalbard Treaty, Russia has kept a presence on this Norwegian archipelago – primarily based on coal mining – and has regularly made it clear that ensuring the continuation of this presence is a political goal. Since the late 2000s, Russia has attempted to revitalise its presence, stressing the need for economic efficiency and diversification away from coal. This includes tourism, fish processing and research activities. In recent years, Russia’s official rhetoric on Svalbard has sharpened, i.a. accusing Norway of breaching the treaty’s provisions on military use of the islands. The article contrasts the statements with the concrete actions undertaken by Russia to preserve and develop its presence. Russia’s policy of presence on Svalbard is not particularly well-coordinated or strategic – beyond an increasing openness to exploring new ways to sustain a sufficient presence. Financial limitations have constrained initiatives. The search for new activities and solutions is driven primarily by the need for cost-cutting and consolidating a limited presence deemed necessary for Russian security interest, not as strategies aimed at increasing Russian influence over the archipelago.

Community advisory boards (CABs) have traditionally been formed in the context of discrete projects and served to support community protections within the confines of the associated investigation(s). However, as funding bodies increasingly prioritize health equity, CABs have shifted – evolving into long-running organizations with broader scope and value. An emerging cornerstone of these project-independent boards (PICABs) has been the formation of “Research Review Boards” (RRBs). While unified in their goal of promoting community protection and representation in health research, it is unknown to what degree RRBs differ on key features including membership, leadership, service reach, and – crucially – impact. A scoping review was conducted according to PRISMA-ScR guidelines to analyze current practices for RRBs. Of screened articles (n= 1878), 25 were included, corresponding to 24 unique RRBs. Findings indicated overlaps in the stated missions, funding structures, and processes of most RRBs. Differences in membership composition, location, service-reach, leadership structures, evaluation procedures, and perceived impact were evident. Where data is available, RRBs receive positive endorsement from both internal members and external users. Standardization of evaluation procedures is needed to fully quantify impact. Additional challenges to sustainability, communication, and conflicts (e.g., of interest, commitment, and power differentials) merit further consideration.

Journal editors often deal with allegations of research misconduct, defined by the Office of Research Integrity (ORI) in the United States as fabrication, falsification, and plagiarism. It is important that editors have a transparent and consistent process to deal with these allegations quickly and fairly. This process will include the authors and may include research integrity officers at the sponsoring institution as well as funders. Retractions may not be consistent with the ORI definition, for example, specifying inadequate peer-review and unreported conflict of interest, but nevertheless represent scientific misconduct.

The federal government has a long history of trying to find the right balance in supporting scientific and medical research while protecting the public and other researchers from potential harms. To date, this balance has been generally calibrated differently across contexts – including in clinical care, human subjects research, and research integrity. New challenges continue to face this disparate model of regulation, including novel Generative Artificial Intelligence (GenAI) tools. Because of potential increases in unintentional fabrication, falsification, and plagiarism using GenAI – and challenges establishing both these errors and intentionality in retrospect – this article argues that we should instead move toward a system that sets accepted community standards for the use of GenAI in research as prospective requirements.