ABSTRACT Background Early notification of journal readers of the existence of publication integrity concerns by an editorial expression of concern (EoC) can mitigate the adverse effects of unreliable research. Methods We extracted EoCs and retraction notices from the Retraction Watch database up until the end of 2024. We assessed temporal trends in publication of each notice, the proportion of retracted papers with an associated EoC, and the timing of EoCs according to publication date and retraction status. We examined the ratio of EoCs to retractions among authors, including those with multiple retractions, and among both journals and publishers. Results EoCs were rarely published during the 14y assessment period. Only 3.3% of retracted papers had a preceding EoC. For 56% of publications with an EoC, the EoC was the latest notice. 92% of authors had an EoC:retraction ratio < 1. Only 35% of authors with > 5 retractions had an EoC, and 99% had an EoC:retraction ratio < 1. Publication of an EoC was more common in journals which had published > 5 retractions than in those which had published < 5. Conclusions EoCs are rarely employed during the assessment of publication integrity concerns. Journal readers are disadvantaged by delayed notification of potentially unreliable research.

ABSTRACT Authorship practices in collaborative research teams are often complex and influence perceptions surrounding fairness, responsibility, and accountability in scholarly work. This study investigates prevailing authorship norms, the frequency of authorship disagreements, and differences in authorship perceptions across faculty and student roles, genders, and disciplinary contexts. Survey results reveal systematic differences, particularly between faculty and students, in how authorship distribution is perceived and how different types of researcher contributions are valued toward authorship credit. We further assess changes in authorship norms and ethical perceptions through a follow-up survey after a three-year effort to improve authorship ethics on our campus, which included training on ethical authorship practices and adoption of a formal institutional authorship policy. The results show notable shifts in researchers’ awareness, expectations, and attitudes toward authorship ethics and responsibilities. This suggests proactive education and policymaking can promote integrity in collaborative scholarly work and recalibrate local norms. Based on these insights, we offer recommendations for supporting transparent authorship communication and fostering more ethical research environments.

ABSTRACT Three oversight bodies review research proposals to help ensure the safe and responsible conduct of biomedical research, each focusing on unique aspects of research ethics: institutional review boards (IRBs), institutional biosafety committees (IBCs), and institutional animal care and use committees (IACUCs). The role of artificial intelligence (AI) in research oversight is rapidly expanding, specifically when preparing and reviewing applications. Although using AI may reduce administrative costs and burdens, it also may create new concerns since AI tools can make mistakes of fact and reasoning, and are susceptible to bias. Furthermore, outsourcing ethical planning and oversight of research to AI could compromise ethical understanding. Although the arguments for/against using AI in preparation or review of IRB, IBC, or IACUC differ fundamentally from those concerning AI use in manuscript writing/peer review, currently there is minimal guidance about the responsible use of AI in research oversight from government agencies, professional organizations, universities, hospitals, and other entities that conduct research. We argue that 1) to minimize the risks of using AI in research oversight, additional guidance is urgently needed; and 2) humans must always be the final decider because ethical planning and oversight involve value judgments that should not be outsourced to AI.

ABSTRACT Background Research fraud is often seen as a rare event, but evidence from self-report surveys indicates that fabrication and falsification of data are common enough to be a problem. This study assessed attitudes to serious research misconduct, contrasting views of self-appointed research “sleuths” and research integrity (RI) experts. Methods Respondents completed a two-round Delphi survey, rating agreement with statements about prevalence, responses to, and consequences of serious research misconduct. In Round 1, there were 95 respondents (37 sleuths, 33 RI experts and 25 other). For Round 2, there were 79 respondents (36 sleuths, 22 RI experts and 21 other). Results Respondents agreed on the damaging impact of academic incentive structures on research integrity, and the importance of agencies working together to address serious research misconduct and preserve the academic record. There was polarization of views between sleuths and RI experts on the frequency of serious research misconduct, the adequacy of institutional responses, and the suitability of self-regulation by institutions. Conclusion Sleuths and RI experts operate in information silos. They could benefit from working together, but this will only be possible if trust is restored. Institutional self-regulation has drawbacks. Strengths and weaknesses of alternative regulatory models need to be evaluated.

ABSTRACT This study aims to examine how scholarly journals advise authors, peer reviewers, and journal editors about the acceptable use of artificial intelligence (AI) in the scholarly publishing process. Methods: Using a content analysis method, policies from 80 journals were collected in August 2024 from publicly available websites. Data from the journal policies related to AI authorship and allowable AI use by authors, peer reviewers, and editors were entered into a spreadsheet and analyzed using descriptive statistics. Findings: The results show that 62.5% (n = 50) of the journal policies mentioned AI. 96% of these journals did not permit AI to be listed as an author. 54% of journals allowed authors to use AI tools to improve language in their manuscript, whereas using AI to generate images was prohibited by 26% of journals. 64% of journals did not provide any AI-related guidance for peer reviewers/editors. Conclusion: This research advances the literature by detailing journal policies regarding AI authorship, the allowed use of AI for authors and peer reviewers, and disciplinary distinctions in AI treatment. The findings underscore the need for enhanced guidance directed at authors, peer reviewers, and editors regarding the ethical and appropriate application of AI tools.

ABSTRACT Scientific fakery is a centuries old problem. Twinned with the long history of hard-working scientists earning fame for genuine discoveries, runs a tawdry history of those who were willing fabricate results to falsely gain prestige. Fraud in the past relied on bespoke fakery, but today’s fraudsters can exploit the online scientific world to quickly create realistic looking papers on an industrial scale. Fraudsters are using open data sets to create meaningless analyses and combining these results with text from large language models. There has been an explosion of these low value papers using openly available and highly regarded data sets, such as the US National Health and Nutrition Examination Survey (NHANES). The paper miners will likely exploit whatever open data resources they can find until data custodians put more stringent controls in place, or journals and publishers push back. Some scientific data may be too open, even though making research data openly available is a recommended policy for increasing research integrity. Journals and researchers need to be aware of this new threat to research integrity.

ABSTRACT Background Responsible Conduct of Research (RCR) courses seek to heighten awareness of the importance of mentor/mentee interactions and other topics, but questions remain – e.g., how best to train mentors/mentees to establish such relationships. Description of Exercise This paper proposes an approach as a model to strengthen RCR education by more fully, and actively, rather than passively, engaging trainees. A classroom activity was developed that can enhance instructors’ abilities to improve mentor/mentee interactions. The instructor divided classes into groups of roughly four trainees, and had them think of a good mentor they have observed, and to list traits/behaviors they liked. Groups then summarized discussions for the class. The instructors recorded and integrated responses. Each group then considered bad mentors, answering the same questions, and repeating the process regarding bad mentees and good mentees. The class then compared the four discussions. Trainees have commonly had both formal and informal mentors, seen both good and bad mentors and mentees, and often themselves served as mentors. Mentees thus connect abstract principles concerning mentorship to personal experiences; and reflect on their own interactions/roles, preferences, and rights/responsibilities. Conclusion This exercise suggests some benefits of recognizing personal/emotional, not just intellectual components in RCR, and has important implications for education, practice, and research.

ABSTRACT Background: Generative Artificial Intelligence(GenAI) significantly enhances medical research efficiency but raises ethical concerns regarding research integrity. The lack of systematic guidelines for its ethical use underscores the need to investigate GenAI’s impact on researchers’ awareness and behavior concerning integrity. Methods/Materials: A cross-sectional survey of 718 valid responses from Chinese medical researchers assessed GenAI’s impact on research integrity using an extended Unified Theory of Acceptance and Use of Technology(UTAUT) model. Results: The findings reveal that performance expectancy, effort expectancy, technical environment, trust in technology, and supporting conditions positively influence researchers’ awareness of research integrity. Conversely, GenAI anxiety and perceived risks exert a significant negative impact. Furthermore, both supporting conditions and integrity awareness are positively associated with integrity behavior, while GenAI anxiety negatively affects such behavior. Conclusion: The stakeholders in the medical research ecosystem should develop comprehensive guidelines for the responsible use of GenAI. Emphasis should be placed on optimizing the technical environment, enhancing trust and support structures, and embedding integrity safeguards, thereby promoting the synergistic development of technological innovation and ethical research practices.

ABSTRACT Aim: This scoping review aims to investigate the reasons for adopting the Contributor Roles Taxonomy (CRediT) in scholarly publishing, identify barriers to its implementation or concerns about its use, and propose improvements to enhance its effectiveness in attributing individual contributions to research articles. Methods: A comprehensive literature search was conducted following PRISMA guidelines across multiple databases, including ProQuest, LISA, LISTA, EBSCO, PubMed, Scopus, and Web of Science Core Collection. Results: From an initial pool of 732 papers, 45 were selected for inclusion in the review. The findings indicate that, while the adoption of CRediT promotes transparency and recognition of contributions beyond traditional authorship, several challenges remain. Key barriers include limited applicability across different research types, potential ethical concerns, and conflicts among contributors. Factors contributing to slow adoption include low awareness, inconsistent implementation, and cultural resistance within the research community. Additionally, ambiguous role definitions complicate attribution and fairness. Conclusions: This review highlights CRediT’s potential to enhance transparency and equitable recognition of diverse contributions in scholarly publishing. However, it underscores the need to address internal challenges and promote broader acceptance within the research community. Recommendations include establishing clearer role hierarchies, standardizing adoption policies, and integrating CRediT into metadata for improved contribution tracking.

ABSTRACT Reliable scientific knowledge allows us to better understand the natural world, grounds interventions to improve health and wellbeing of humans and other entities, contributes to economic development, and informs policymaking. Ensuring the integrity of scientific research is necessary to advance these various benefits and to safeguard the public’s warranted trust in science. Although a variety of complex factors are likely to play a role in advancing or hindering research integrity, some evidence shows that institutional and national policies can be more or less conducive to promoting research integrity and preventing misconduct. I focus here on Spain. I critically assess Spanish legislation and institutional policies and argue that while they proclaim ethical values consistent with fostering research integrity and preventing misconduct, these values fail to be put into practice. I explain some factors that contribute to these failings, and I also offer some suggestions that could contribute to ensuring the respect of ethical values already enshrined in Spanish policies.