Research Misconduct News A compilation of articles regarding Research Misconduct issues.

Nature
By Diana Kwon

How much is the artificial intelligence (AI) revolution altering the process of communicating science? With generative AI tools such as ChatGPT improving so rapidly, attitudes about using them to write research papers are also evolving. The number of papers with signs of AI use is rising rapidly (D. Kobak et al. Preprint at arXiv https://doi.org/pkhp; 2024), raising questions around plagiarism and other ethical concerns.

To capture a sense of researchers’ thinking on this topic, Nature posed a variety of scenarios to some 5,000 academics around the world, to understand which uses of AI are considered ethically acceptable.

The survey results suggest that researchers are sharply divided on what they feel are appropriate practices. Whereas academics generally feel it’s acceptable to use AI chatbots to help to prepare manuscripts, relatively few report actually using AI for this purpose — and those who did often say they didn’t disclose it.

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Nature
By Sara Reardon

Illustration: The Project Twins

A decade ago, Helena Jambor found herself struggling to understand the figures in the scientific papers she was reading during her postdoc. Jambor was studying how fluorescently labelled messenger RNA arranges itself inside embryos of the fruit fly Drosophila melanogaster and wanted to choose the images that would most clearly convey her findings. But she couldn’t find many helpful guidelines — or even good examples in the literature. “When you start out, you think you’re the one who’s stupid,” she says. “But after ten years in science, you realize, okay, now I’m an expert and I still don’t understand some of these figures.”

Jambor, now a biologist and visualization specialist at the Dresden University of Technology in Germany, realized that image presentation wasn’t just a problem in her own field. In 2021, when she and her colleagues analysed the figures of 580 biology papers in 15 top journals, they found that most contained poorly presented images (H. Jambor et al. PLoS Biol. 19, e3001161; 2021). Many panels lacked labels or scale bars, and annotation features, such as descriptive text and arrows, were often missing from the imaged objects. “I just think it’s sad if someone spends four years researching an amazing issue and publishes a paper and then has a figure that is not fully understandable,” she says. “It will inevitably reduce the readership.”

Whereas scientists receive extensive training on how to collect data, less work goes into teaching them how to showcase the information in publications, presentations and grant applications. 

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startupdaily.
Timothy Hugh Barker

Earlier this year I received comments on an academic manuscript of mine as part of the usual peer review process, and noticed something strange.

My research focuses on ensuring trustworthy evidence is used to inform policy, practice and decision making. I often collaborate with groups like the World Health Organisation to conduct systematic reviews to inform clinical and public health guidelines or policy. The paper I had submitted for peer review was about systematic review conduct.

What I noticed raised my concerns about the growing role artificial intelligence (AI) is playing in the scientific process.

A service to the community

Peer review is fundamental to academic publishing, ensuring research is rigorously critiqued prior to publication and dissemination. In this process researchers submit their work to a journal where editors invite expert peers to provide feedback. This benefits all involved.

For peer reviewers, it is favourably considered when applying for funding or promotion as it is seen as a service to the community. For researchers, it challenges them to refine their methodologies, clarify their arguments, and address weaknesses to prove their work is publication worthy. For the public, peer review ensures that the findings of research are trustworthy.

Even at first glance the comments I received on my manuscript in January this year seemed odd.

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PLOS ONE
Lulin Chen, Yizhao Li, Xiaoyan Guo, Wei Liu

Abstract

As scientific outputs continue to surge, research misconduct has garnered global attention. Case-based learning (CBL), an active student-centered learning strategy, possesses many advantages but has not been widely used in China due to resource constraints. This study aimed to address the research gap regarding the impact of CBL and traditional lecture on residents’ knowledge and attitudes towards research misconduct. This controlled before-and-after study was conducted at two tertiary hospitals in southwest China from November 2022 through March 2023. All medical residents at the two hospitals were defined as participants. Residents participating in CBL course at one hospital comprised the experimental group, whereas those engaging in traditional lecture at another hospital constituted the control group. The CBL and control group included 202 and 205 individuals, respectively. A total of 298 subjects were successfully matched after propensity score matching, with 149 individuals in each group. After the courses, the participants’ knowledge on research misconduct, perceived consequences for research misconduct, and their agreement rate regarding research misconduct improved in the CBL and control group (P < 0.05), but certain aspects of their perceived consequences and agreement rate did not show significant improvement in the control group. The results revealed that there is a marked enhancement in residents’ knowledge about research misconduct, their perception of its consequences, and their overall disapproval of such behavior in the CBL group. This underscores the effectiveness of CBL in fostering a deeper understanding and stronger aversion towards research misconduct among residents.

Chen, L., Li, Y., Guo, X., & Liu, W. (2025). Comparison of case-based learning and traditional lecture in teaching residents on research misconduct: a controlled before-and-after study. PLoS ONE, 20(4), e0322336. https://doi.org/10.1371/journal.pone.0322336

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Nature
By Rodrigo de Oliveira Andrade

In an unprecedented effort, a coalition of more than 50 research teams has surveyed a swathe of Brazilian biomedical studies to double-check their findings — with dismaying results.

The teams were able to replicate the results of less than half of the tested experiments¹. That rate is in keeping with that found by other large-scale attempts to reproduce scientific findings. But the latest work is unique in focusing on papers that use specific methods and in examining the research output of a specific country, according to the research teams.

The results provide an impetus to strengthen the country’s science, the study’s authors say. “We now have the material to start making changes from within — whether through public policies or within universities,” says Mariana Boechat de Abreu, a metascience researcher at the Federal University of Rio de Janeiro (UFRJ) in Brazil and one of the coordinators of the project.

The work was posted on 8 April to the bioRxiv preprint server and has not yet been peer reviewed.

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Nature
By Diana Kwon

“As of my last knowledge update”, “regenerate response”, “as an AI language model” — these are just a few of the telltale signs of researchers’ use of artificial intelligence (AI) that science-integrity watchers have found sprinkled through papers in the scholarly literature.

Generative AI tools such as ChatGPT have quickly transformed academic publishing. Scientists are increasingly using them to prepare and review manuscripts, and publishers have scrambled to create guidelines for their ethical use. Although policies vary, many publishers require authors to disclose the use of AI in the preparation of scientific papers.

But science sleuths have identified hundreds of cases in which AI tools seem to have been used without disclosure. In some cases, the papers have been silently corrected — the hallmark AI phrases removed without acknowledgement. This type of quiet change is a potential threat to scientific integrity, say some researchers.

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Published online by Taylor & Francis Online
Elisa Reverman

Abstract

In this article, I explore the idea of increased transparency in the context of research misconduct. I begin with a brief introduction of how increased transparency across the research enterprise has gained momentum and shepherded in the current Open Science movement. I then introduce general endorsements for greater transparency within research misconduct, which propose that increased transparency will achieve a range of aims. Using existing taxonomies of transparency, I break these general endorsements down into more specific mechanisms of transparency, and in doing so exhibit the wide range of forms and structures that transparency can take. Following this, I argue that while transparency for purposes such as quality improvement or third-party auditing may be justifiable, public-facing transparency for the purposes of trust-building and accountability generates unique concerns and requires more evidence to justify. In detailing these concerns, I argue for greater caution and consideration of the epistemic and practical effects of public transparency with research misconduct reports and point out a disanalogy between Open Science and matters of research misconduct. I ultimately conclude that research misconduct proceedings and reports ought not default to public-facing transparency without further evidence to support that such an effort would achieve their intended aims.

Reverman, E. (2025). The justified limits of transparency in research misconduct reports. Accountability in Research, 1–20. https://doi.org/10.1080/08989621.2025.2495790

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Published online by Cambridge University Press
Nathaniel Jaffe, Minal Caron, Lauren Walsh, Barbara Bierer and Mark Barnes

ABSTRACT

Researchers involved in research misconduct proceedings are increasingly threatening or bringing legal defamation claims against the institutions, complainants, and publications involved in the proceedings. Although defamation claims do not often succeed, they can nevertheless be costly and lengthy. This article analyzes certain defamation cases in the research misconduct space and provides advice for institutions and other involved parties seeking to minimize potential defamation liability associated with research misconduct proceedings.

Jaffe N, Caron M, Walsh L, Bierer B, Barnes M. Defamation Claims Arising from Research Misconduct Cases: Best Practices for Institutions. Journal of Law, Medicine & Ethics. Published online 2025:1-8. doi:10.1017/jme.2025.37

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Nature
Dalmeet Singh Chawla

Research-integrity analysts are warning that ‘journal snatchers’ — companies that acquire scholarly journals from reputable publishers — are turning legitimate titles into predatory, low-quality publications with questionable practices.

In an analysis published on the preprint repository Zenodo in January, researchers identified three dozen journals that have been caught in this predicament after being bought by what they describe as a network of recently established international companies with no track record in the publishing industry. The scholarly database Scopus has removed the titles from its index after an investigation.

“We found at least 36 journals but we think that there may be more,” says study co-author Alberto Martín-Martín, an information scientist at the University of Granada in Spain. Nature was able to reach one of the companies named in the study, Oxbridge Publishing House, which disputes the allegations.

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West Coast Biotech & Life Sciences Reporter
Jonathan Wosen 

In a stark sign of scientists’ escalating frustration with how academic journals operate, researchers are taking on six publishing behemoths in court, arguing that the system is exploitative and overly expensive, and that it relies on illegal and anticompetitive practices.

Four researchers have sued six of the world’s biggest publishers: Elsevier, John Wiley & Sons, Sage Publications, Springer Nature, Taylor & Francis, and Wolters Kluwer. The scientists allege that these publishers violated federal antitrust law by colluding not to pay researchers for peer reviewing manuscripts, preventing them from submitting papers to more than one journal at a time, and blocking authors from publicly discussing or sharing work once they’ve submitted it to a journal.

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Nature
Elizabeth Gibney

Late last year, media outlets worldwide warned that black plastic cooking utensils contained worrying levels of cancer-linked flame retardants. The risk was found to be overhyped — a mathematical error in the underlying research suggested a key chemical exceeded the safe limit when in fact it was ten times lower than the limit. Keen-eyed researchers quickly showed that an artificial intelligence (AI) model could have spotted the error in seconds.

The incident has spurred two projects that use AI to find mistakes in the scientific literature. The Black Spatula Project is an open-source AI tool that has so far analysed around 500 papers for errors. The group, which has around eight active developers and hundreds of volunteer advisers, hasn’t made the errors public yet; instead, it is approaching the affected authors directly, says Joaquin Gulloso, an independent AI researcher based in Cartagena, Colombia, who helps to coordinate the project. “Already, it’s catching many errors,” says Gulloso. “It’s a huge list. It’s just crazy.”

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The Chronicle of Higher Education
Stephanie M. Lee

Billions of dollars have been spent on researching Alzheimer’s disease, which afflicts more than six million Americans and is the fifth-leading cause of death among the nation’s elderly. Yet there is no cure, and the few treatments available show only modest evidence of slowing cognitive decline.

Fraudulent science may have steered the field in the wrong direction for decades, according to Doctored: Fraud, Arrogance, and Tragedy in the Quest to Cure Alzheimer’s, which was published Tuesday.

Charles Piller, an investigative journalist at Science, chronicles how the field coalesced around a central explanation for what causes the disease: biological chain reactions set off by clumps of amyloid-beta proteins. These sticky plaques have been found between the brain cells of deceased patients with Alzheimer’s since the disease was coined in the early 20th century. From the 1990s onwards, the “amyloid hypothesis” virtually crowded out all others, even though drug after drug failed to improve cognition while successfully clearing plaques from the brain. Doctored presents evidence that this hypothesis was reinforced by hundreds of important Alzheimer’s papers — led by star researchers, published in leading journals, financed by federal grants — that relied on falsified data.

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Times Higher Education
Leslie McIntosh, René Aquarius Dorothy Bishop

For centuries, science has served as a bedrock of societal progress. Its principles of inquiry and evidence-based reasoning underpin everything from medical breakthroughs to technological innovation. Yet today, headlines paint a troubling picture of manipulated data and fabricated research findings, resulting in thousands of retractions. And that is just what has been uncovered so far.

As scientific “sleuths”, who focus on cleaning up the research literature, we are often accused of weakening public trust in science. But blame for mistrust lies squarely with the bad actors who have contaminated the scientific literature with what our colleague Smut Clyde has termed “parascience”: outputs that superficially resemble science, with its field-specific jargon, images and tables, but lack its core features, such as peer review or any actual underlying experiments.

The ripple effects are profound when we can no longer rely on the integrity of the scientific record. Medical advancements stall, infrastructure innovations falter and public health initiatives lose credibility. But is science itself in crisis? Hardly.

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Nature
Jackson Ryan

More than 8,000 of the world’s most-cited scientists have at least one retraction, according to a database that links retractions to top-cited papers¹.

An analysis of the database, published in PLOS Biology on 30 January, attempts to map the scale of retractions and understand how they manifest. “Not every retraction is a sign of misconduct,” says John Ioannidis, an epidemiologist at Stanford University in California, who led the study. “But it is important to have a bird’s eye view, across all scientific fields, [of] people who are most influential in science.”

Retracted papers had a higher number of self-citations than did non-retracted papers. And papers with higher co-authorship numbers were more likely to be retracted than were those with fewer co-authors.

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Inside Higher Ed
Raquel Y. Salinas

As a trained scientist, I had a fantastic research mentor. We talked about my research project, which experiments to prioritize and what the data meant, and we even sometimes discussed personal things like family and ties to home. When I joined his lab, I was open with my mentor about my interest in a teaching career and my desire to run a small research program working primarily with undergraduates. However, my career aspirations evolved over the course of my graduate training, and I found myself hesitant to share my new career goals. Though I recognized that my interactions with my mentor were quite positive and supportive, I still feared that sharing my non–academic scientist aspirations would somehow disappoint him, or worse, that I wouldn’t get the fullest support for my research training.

Now, as a career development professional who advises biomedical Ph.D. students, I see this same pattern often. Students express feeling comfortable discussing their research and academic endeavors with their research mentors but hesitate when it comes to discussing career plans outside academic research. They fear not receiving the same level of support and training, letting their mentor down, or being seen as less committed to their research and academic pursuits.

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KelloggInsight
John Pavlus

BASED ON THE RESEARCH OF Ryan Hill and Carolyn Stein

SUMMARY 

When it comes to discoveries, being first matters. And new research underscores just how much. Ryan Hill of the Kellogg School and his colleague investigated what happens when scientists competed to discover the three-dimensional shapes of individual proteins. They found that scientists who were second to complete their projects were nearly 20 percent less likely to have their work published in a top journal, and their papers received 21 percent fewer citations from future work. Furthermore, they found that the more competitive a particular race was, the more the scientists rushed their work—resulting in lower-quality findings.

What motivates a scientist to make discoveries? An intrinsic desire to expand human knowledge, of course. But there’s often another force at work: the desire to be first. Even a luminary like Charles Darwin wasn’t exempt from it. Upon learning that his colleague had conceived a nearly identical theory of evolution, Darwin quickly published On the Origin of Species to avoid getting scooped. From the discovery of DNA a century later to the present-day race to master AI, this competitive pressure still shapes the way science gets done.

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