The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications

ChatGPT's ability to generate realistic experimental images poses a new challenge to academic integrity

The rapid advancements in large language models (LLMs) such as ChatGPT have raised concerns about their potential impact on academic integrity. While initial concerns focused on ChatGPT's writing capabilities, recent updates have integrated DALL-E 3's image generation features, extending the risks to visual evidence in biomedical research. Our tests revealed ChatGPT's nearly barrier-free image generation feature can be used to generate experimental result images, such as blood smears, Western Blot, immunofluorescence and so on. Although the current ability of ChatGPT to generate experimental images is limited, the risk of misuse is evident. This development underscores the need for immediate action. We suggest that AI providers restrict the generation of experimental image, develop tools to detect AI-generated images, and consider adding "invisible watermarks" to the generated images. By implementing these measures, we can better ensure the responsible use of AI technology in academic research and maintain the integrity of scientific evidence.

How widespread use of generative AI for images and video can affect the environment and the science of ecology

Generative artificial intelligence (AI) models will have broad impacts on society including the scientific enterprise; ecology and environmental science will be no exception. Here, we discuss the potential opportunities and risks of advanced generative AI for visual material (images and video) for the science of ecology and the environment itself. There are clearly opportunities for positive impacts, related to improved communication, for example; we also see possibilities for ecological research to benefit from generative AI (e.g., image gap filling, biodiversity surveys, and improved citizen science). However, there are also risks, threatening to undermine the credibility of our science, mostly related to actions of bad actors, for example in terms of spreading fake information or committing fraud. Risks need to be mitigated at the level of government regulatory measures, but we also highlight what can be done right now, including discussing issues with the next generation of ecologists and transforming towards radically open science workflows.

Community-developed checklists for publishing images and image analyses

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However, for scientists wishing to publish obtained images and image-analysis results, there are currently no unified guidelines for best practices. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here, we present community-developed checklists for preparing light microscopy images and describing image analyses for publications. These checklists offer authors, readers and publishers key recommendations for image formatting and annotation, color selection, data availability and reporting image-analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby to heighten the quality and explanatory power of microscopy data.

Quantitative research assessment: using metrics against gamed metrics

Quantitative bibliometric indicators are widely used and widely misused for research assessments. Some metrics have acquired major importance in shaping and rewarding the careers of millions of scientists. Given their perceived prestige, they may be widely gamed in the current "publish or perish" or "get cited or perish" environment. This review examines several gaming practices, including authorship-based, citation-based, editorial-based, and journal-based gaming as well as gaming with outright fabrication. Different patterns are discussed, including massive authorship of papers without meriting credit (gift authorship), team work with over-attribution of authorship to too many people (salami slicing of credit), massive self-citations, citation farms, H-index gaming, journalistic (editorial) nepotism, journal impact factor gaming, paper mills and spurious content papers, and spurious massive publications for studies with demanding designs. For all of those gaming practices, quantitative metrics and analyses may be able to help in their detection and in placing them into perspective. A portfolio of quantitative metrics may also include indicators of best research practices (e.g., data sharing, code sharing, protocol registration, and replications) and poor research practices (e.g., signs of image manipulation). Rigorous, reproducible, transparent quantitative metrics that also inform about gaming may strengthen the legacy and practices of quantitative appraisals of scientific work.

When Seeing Isn't Believing: Navigating Visual Health Misinformation through Library Instruction

Visual misinformation poses unique challenges to public health due to its potential for persuasiveness and rapid spread on social media. In this article, librarians at the University of Pittsburgh Health Sciences Library System identify four types of visual health misinformation: misleading graphs and charts, out of context visuals, image manipulation in scientific publications, and AI-generated images and videos. To educate our campus's health sciences audience and wider community on these topics, we have developed a range of instruction about visual health misinformation. We describe our strategies and provide suggestions for implementing visual misinformation programming for a variety of audiences.

Inappropriate image duplications in rhinology research publications

KEY POINTS

Duplicated images in research articles erode integrity and credibility of biomedical science. Forensic software is necessary to detect figures with inappropriately duplicated images. This analysis reveals a significant issue of inappropriate image duplication in our field.

A community-driven approach to enhancing the quality and interpretability of microscopy images

Scientific publications in the life sciences regularly include image data to display and communicate revelations about cellular structure and function. In 2016, a set of guiding principles known as the 'FAIR Data Principles' were put forward to ensure that research data are findable, accessible, interoperable and reproducible. However, challenges still persist regarding the quality, accessibility and interpretability of image data, and how to effectively communicate microscopy data in figures. This Perspective article details a community-driven initiative that aims to promote the accurate and understandable depiction of light microscopy data in publications. The initiative underscores the crucial role of global and diverse scientific communities in advancing the standards in the field of biological images. Additionally, the perspective delves into the historical context of scientific images, in the hope that this look into our past can help ongoing community efforts move forward.

The Dark Side of Artificial Intelligence: The Possible Risk of Falsifying Images for Scientific Articles

This article explores the potential risks associated with using artificial intelligence (AI)-generated images in the field of microscopy. It discusses the current state-of-the-art AI-based image-generation techniques and their limitations. It investigates the potential risks associated with the illegal use of AI-generated images, including their use in creating falsified scientific data and the consequences of such misuse. The article concludes by exploring possible solutions to mitigate these risks, such as implementing robust authentication methods and developing ethical guidelines for using and disseminating AI-generated images in the field of microscopy. Additionally, the article also presents the results of a survey involving 101 professionals, showing that the recognition of authentic and entirely AI-generated images is performed well. But, the detection of hybrid images could be improved.

Community-developed checklists for publishing images and image analyses

Images document scientific discoveries and are prevalent in modern biomedical research. Microscopy imaging in particular is currently undergoing rapid technological advancements. However for scientists wishing to publish the obtained images and image analyses results, there are to date no unified guidelines. Consequently, microscopy images and image data in publications may be unclear or difficult to interpret. Here we present community-developed checklists for preparing light microscopy images and image analysis for publications. These checklists offer authors, readers, and publishers key recommendations for image formatting and annotation, color selection, data availability, and for reporting image analysis workflows. The goal of our guidelines is to increase the clarity and reproducibility of image figures and thereby heighten the quality and explanatory power of microscopy data is in publications.

Gradient-based enhancement attacks in biomedical machine learning

The prevalence of machine learning in biomedical research is rapidly growing, yet the trustworthiness of such research is often overlooked. While some previous works have investigated the ability of adversarial attacks to degrade model performance in medical imaging, the ability to falsely improve performance via recently-developed "enhancement attacks" may be a greater threat to biomedical machine learning. In the spirit of developing attacks to better understand trustworthiness, we developed two techniques to drastically enhance prediction performance of classifiers with minimal changes to features: 1) general enhancement of prediction performance, and 2) enhancement of a particular method over another. Our enhancement framework falsely improved classifiers' accuracy from 50% to almost 100% while maintaining high feature similarities between original and enhanced data (Pearson's r's > 0.99). Similarly, the method-specific enhancement framework was effective in falsely improving the performance of one method over another. For example, a simple neural network outperformed logistic regression by 17% on our enhanced dataset, although no performance differences were present in the original dataset. Crucially, the original and enhanced data were still similar (r = 0.99). Our results demonstrate the feasibility of minor data manipulations to achieve any desired prediction performance, which presents an interesting ethical challenge for the future of biomedical machine learning. These findings emphasize the need for more robust data provenance tracking and other precautionary measures to ensure the integrity of biomedical machine learning research. Code is available at https://github.com/mattrosenblatt7/enhancement_EPIMI.

Connectome-based machine learning models are vulnerable to subtle data manipulations

Neuroimaging-based predictive models continue to improve in performance, yet a widely overlooked aspect of these models is "trustworthiness," or robustness to data manipulations. High trustworthiness is imperative for researchers to have confidence in their findings and interpretations. In this work, we used functional connectomes to explore how minor data manipulations influence machine learning predictions. These manipulations included a method to falsely enhance prediction performance and adversarial noise attacks designed to degrade performance. Although these data manipulations drastically changed model performance, the original and manipulated data were extremely similar (r = 0.99) and did not affect other downstream analysis. Essentially, connectome data could be inconspicuously modified to achieve any desired prediction performance. Overall, our enhancement attacks and evaluation of existing adversarial noise attacks in connectome-based models highlight the need for counter-measures that improve the trustworthiness to preserve the integrity of academic research and any potential translational applications.

Challenges in peer review: how to guarantee the quality and transparency of the editorial process in scientific journals

The editorial process of scientific journals is complex but essential for the dissemination of scientific knowledge. The quality of the process depends on the authors, editors and reviewers, who must have the necessary experience and knowledge to ensure the quality of the published articles. One of the most significant challenges scientific journals face today is the peer review of manuscripts. Editors are responsible for coordinating and overseeing the entire editorial process, from manuscript submission to final publication, and ensuring that articles meet ethical and scientific integrity standards. Editors are also in charge of selecting appropriate reviewers. However, the latter is becoming difficult due to the increasing refusal of expert reviewers to participate in the editorial process. The reasons for it are diverse, but the lack of recognition for review work and reviewer fatigue in the most sought-after reviewers are among the most important. Some of the measures that could be taken to alleviate the problem concern the possibility of professionalizing peer review.

Enhancing Partnerships of Institutions and Journals to Address Concerns About Research Misconduct: Recommendations From a Working Group of Institutional Research Integrity Officers and Journal Editors and Publishers

Importance

Institutions and journals strive to promote and protect the integrity of the research record, and both groups are equally committed to ensuring the reliability of all published data.

Observations

Three US universities coordinated a series of virtual meetings from June 2021 to March 2022 for a working group composed of senior, experienced US research integrity officers (RIOs), journal editors, and publishing staff who are familiar with managing issues of research integrity and publication ethics. The goal of the working group was to improve the collaboration and transparency between institutions and journals to ensure that research misconduct and publication ethics are managed properly and efficiently. Recommendations address the following: identifying proper contacts at institutions and journals, specifying information to share between institutions and journals, correcting the research record, reconsideration of some fundamental research misconduct concepts, and journal policy changes. The working group identified 3 key recommendations to be adopted and implemented to change the status quo for better collaboration between institutions and journals: (1) reconsideration and broadening of the interpretation by institutions of the need-to-know criteria in federal regulations (ie, confidential or sensitive information and data are not disclosed unless there is a need for an individual to know the facts to perform specific jobs or functions), (2) uncoupling the evaluation of the accuracy and validity of research data from the determination of culpability and intent of the individuals involved, and (3) initiating a widespread change for the policies of journals and publishers regarding the timing and appropriateness for contacting institutions, either before or concurrently under certain conditions, when contacting the authors.

Conclusions and Relevance

The working group recommends specific changes to the status quo to enable effective communication between institutions and journals. Using confidentiality clauses and agreements to impede sharing does not benefit the scientific community nor the integrity of the research record. However, a careful and informed framework for improving communications and sharing information between institutions and journals can foster better working relationships, trust, transparency, and most importantly, faster resolution to data integrity issues, especially in published literature.

Toward an Integrated Machine Learning Model of a Proteomics Experiment

In recent years machine learning has made extensive progress in modeling many aspects of mass spectrometry data. We brought together proteomics data generators, repository managers, and machine learning experts in a workshop with the goals to evaluate and explore machine learning applications for realistic modeling of data from multidimensional mass spectrometry-based proteomics analysis of any sample or organism. Following this sample-to-data roadmap helped identify knowledge gaps and define needs. Being able to generate bespoke and realistic synthetic data has legitimate and important uses in system suitability, method development, and algorithm benchmarking, while also posing critical ethical questions. The interdisciplinary nature of the workshop informed discussions of what is currently possible and future opportunities and challenges. In the following perspective we summarize these discussions in the hope of conveying our excitement about the potential of machine learning in proteomics and to inspire future research.

Amending the literature through version control

The ideal of self-correction in science is not well served by the current culture and system surrounding amendments to published literature. Here we describe our view of how amendments could and should work by drawing on the idea of an author-led version control system. We report a survey (n = 132) that highlights academics' dissatisfaction with the status quo and their support for such an alternative approach. Authors would include a link in their published manuscripts to an updatable website (e.g. a GitHub repository) that could be disseminated in the event of any amendment. Such a system is already in place for computer code and requires nothing but buy-in from the scientific community-a community that is already evolving towards open science frameworks. This would remove a number of frictions that discourage amendments leading to an improved scientific literature and a healthier academic climate.

SILA: a system for scientific image analysis

A great deal of the images found in scientific publications are retouched, reused, or composed to enhance the quality of the presentation. In most instances, these edits are benign and help the reader better understand the material in a paper. However, some edits are instances of scientific misconduct and undermine the integrity of the presented research. Determining the legitimacy of edits made to scientific images is an open problem that no current technology can perform satisfactorily in a fully automated fashion. It thus remains up to human experts to inspect images as part of the peer-review process. Nonetheless, image analysis technologies promise to become helpful to experts to perform such an essential yet arduous task. Therefore, we introduce SILA, a system that makes image analysis tools available to reviewers and editors in a principled way. Further, SILA is the first human-in-the-loop end-to-end system that starts by processing article PDF files, performs image manipulation detection on the automatically extracted figures, and ends with image provenance graphs expressing the relationships between the images in question, to explain potential problems. To assess its efficacy, we introduce a dataset of scientific papers from around the globe containing annotated image manipulations and inadvertent reuse, which can serve as a benchmark for the problem at hand. Qualitative and quantitative results of the system are described using this dataset.

A critical path to producing high quality, reproducible data from quantitative western blot experiments

Western blotting experiments were initially performed to detect a target protein in a complex biological sample and more recently, to measure relative protein abundance. Chemiluminescence coupled with film-based detection was traditionally the gold standard for western blotting but accurate and reproducible quantification has been a major challenge from this methodology. The development of sensitive, camera-based detection technologies coupled with an updated technical approach permits the production of reproducible, quantitative data. Fluorescence reagent and detection solutions are the latest innovation in western blotting but there remains questions and debate concerning their relative sensitivity and dynamic range versus chemiluminescence. A methodology to optimize and produce excellent, quantitative western blot results with rigorous data analysis from membranes probed with both fluorescent and chemiluminescent antibodies is described. The data reveal when and how to apply these detection methods to achieve reproducible data with a stepwise approach to data processing for quantitative analysis.

Transforming and extending library services by embracing technology and collaborations: A case study

Technology advances and collaborations with information technology and computer science groups have enabled library services to expand into new domains. Listening to user needs, eliminating administrative burden and saving users time remain strong foundations on which to build new library services enabled by technology. Examples of what is now possible is described, including service to user groups, successes, failures and challenges. Although technology advances have enabled library service enhancements to all user groups, special emphasis on new library services in support of the research enterprise is discussed. As Lindberg and Humphreys predicted in 2015, the research enterprise's need for responsible curation of research data has created new opportunities for library services and examples of those services are discussed. As technology continues to advance, new library services are expected to emerge. These may include regulatory and compliance services. By developing these services with user feedback to save users time and expedite their work, and in collaboration with technology experts, libraries can expect to offer sustainable and valued services for years to come.

Sustained Rise in Retractions in the Life Sciences Literature during the Pandemic Years 2020 and 2021

The COVID-19 pandemic has been devastating to all human endeavors, and scientific research has not been spared. We queried how the retraction of publications might have been affected during the pandemic years 2020–2021. Searches performed with Retraction Watch Database (RWD) revealed that the total number of retractions (as proxied by retraction-related notices) rose steadily from 2013 into the pandemic years 2020–2021. Interestingly, while retractions in the physical and social sciences tapered during 2020–2021, those of the basic life sciences and health sciences showed robust increases in 2020, with the former maintaining a steep rise in 2021. This rise in retractions belied a tapering of total relevant publications in the same year and is confirmed with a complementary search strategy in Scopus. The retraction rate in the medical sciences, particularly those relating to infectious disease, is clearly affected by the anomalous high retraction rate of COVID-19-related papers. However, the sustained increase in the retraction rate of the basic life sciences papers, could be due, at least partly, to retraction spikes in several journals. The rise in retractions in the life and medical sciences could be attributed to heightened post-publication peer review of papers in online platforms such as PubPeer, where numerous problematic papers have been revealed.

Benchmarking Scientific Image Forgery Detectors

The field of scientific image integrity presents a challenging research bottleneck given the lack of available datasets to design and evaluate forensic techniques. The sensitivity of data also creates a legal hurdle that restricts the use of real-world cases to build any accessible forensic benchmark. In light of this, there is no comprehensive understanding on the limitations and capabilities of automatic image analysis tools for scientific images, which might create a false sense of data integrity. To mitigate this issue, we present an extendable open-source algorithm library that reproduces the most common image forgery operations reported by the research integrity community: duplication, retouching, and cleaning. We create a large scientific forgery image benchmark (39,423 images) with enriched ground truth using this library and realistic scientific images. All figures within the benchmark are synthetically doctored using images collected from creative commons sources. While collecting the source images, we ensured that the they did not present any suspicious integrity problems. Because of the high number of retracted papers due to image duplication, this work evaluates the state-of-the-art copy-move detection methods in the proposed dataset, using a new metric that asserts consistent match detection between the source and the copied region. All evaluated methods had a low performance in this dataset, indicating that scientific images might need a specialized copy-move detector. The dataset and source code are available at https://github.com/phillipecardenuto/rsiil .

AI-enabled image fraud in scientific publications

Destroying image integrity in scientific papers may result in serious consequences. Inappropriate duplication and fabrication of images are two common misconducts in this aspect. The rapid development of artificial-intelligence technology has brought to us promising image-generation models that can produce realistic fake images. Here, we show that such advanced generative models threaten the publishing system in academia as they may be used to generate fake scientific images that cannot be effectively identified. We demonstrate the disturbing potential of these generative models in synthesizing fake images, plagiarizing existing images, and deliberately modifying images. It is very difficult to identify images generated by these models by visual inspection, image-forensic tools, and detection tools due to the unique paradigm of the generative models for processing images. This perspective reveals vast risks and arouses the vigilance of the scientific community on fake scientific images generated by artificial intelligence (AI) models.

This perspective reports on the vast risk of potential image fraud based on artificial intelligence (AI) generative technologies in academic publications that have been neglected. This article discusses the scenarios, capabilities, and effects of AI algorithms used in academic fraud. The issue described in this perspective is not only relevant to computer scientists. As members of the scientific community, each of us will be deeply involved in the peer-review process. Each of us may be deceived by the AI image-fraud methods described in this article. Although the algorithm developing itself belongs to the field of computer science, its impact, as mentioned in this perspective, is more related to a wider range of scientific fields, such as biology, medicine, and natural science. Arousing their attention to this threat is a necessary condition to resist this threat. Combined with state-of-the-art AI research, this perspective also discusses possible preventive measures to respond to this potential threat.

Do retraction practices work effectively? Evidence from citations of psychological retracted articles

Scientific retraction practices are intended to help purge the continued use of flawed research and assist in maintaining the integrity, credibility and quality of scientific literature. However, the practical effect of retraction is still vague and needs to be further explored. In this study, we analysed the citation counts and sentiments (positive/negative) of retracted articles in psychology journals from Web of Science to explore the effect of retraction. Causal inference strategies were used to measure the net effect of retractions on citation. Results show that the retraction practices induced the citation counts to reduce as expected. However, the proportion of negative citations also decreased because of retraction, indicating an unsatisfied effect. The retraction practice of high-impact factors and open access journals was more effective than other journals. The study integrated an understanding of the dissemination of erroneous publications and provided implications for liabilities involved in the whole retraction process.

Deepfakes: A new threat to image fabrication in scientific publications?

There is an increasing risk of people using advanced artificial intelligence, particularly the generative adversarial network (GAN), for scientific image manipulation for the purpose of publications. We demonstrated this possibility by using GAN to fabricate several different types of biomedical images and discuss possible ways for the detection and prevention of such scientific misconducts in research communities.

Correction of the Scientific Production: Publisher Performance Evaluation Using a Dataset of 4844 PubMed Retractions

Background. Retraction of problematic scientific articles after publication is one of the mechanisms for correcting the literature available to publishers. The market volume and the business model justify publishers’ ethical involvement in the post-publication quality control (PPQC) of human-health-related articles. The limited information about this subject led us to analyze PubMed-retracted articles and the main retraction reasons grouped by publisher. We propose a score to appraise publisher’s PPQC results. The dataset used for this article consists of 4844 PubMed-retracted papers published between 1.01.2009 and 31.12.2020. Methods. An SDTP score was constructed from the dataset. The calculation formula includes several parameters: speed (article exposure time (ET)), detection rate (percentage of articles whose retraction is initiated by the editor/publisher/institution without the authors’ participation), transparency (percentage of retracted articles available online and the clarity of the retraction notes), and precision (mention of authors’ responsibility and percentage of retractions for reasons other than editorial errors). Results. The 4844 retracted articles were published in 1767 journals by 366 publishers, the average number of retracted articles/journal being 2.74. Forty-five publishers have more than 10 retracted articles, holding 88% of all papers and 79% of journals. Combining our data with data from another study shows that less than 7% of PubMed dataset journals retracted at least one article. Only 10.5% of the retraction notes included the individual responsibility of the authors. Nine of the top 11 publishers had the largest number of retracted articles in 2020. Retraction-reason analysis shows considerable differences between publishers concerning the articles’ ET: median values between 9 and 43 months (mistakes), 9 and 73 months (images), and 10 and 42 months (plagiarism and overlap). The SDTP score shows, from 2018 to 2020, an improvement in PPQC of four publishers in the top 11 and a decrease in the gap between 1st and 11th place. The group of the other 355 publishers also has a positive evolution of the SDTP score. Conclusions. Publishers have to get involved actively and measurably in the post-publication evaluation of scientific products. The introduction of reporting standards for retraction notes and replicable indicators for quantifying publishing QC can help increase the overall quality of scientific literature.

Broad Medical Uncertainty and the ethical obligation for openness

This paper argues that there exists a collective epistemic state of 'Broad Medical Uncertainty' (BMU) regarding the effectiveness of many medical interventions. We outline the features of BMU, and describe some of the main contributing factors. These include flaws in medical research methodologies, bias in publication practices, financial and other conflicts of interest, and features of how evidence is translated into practice. These result in a significant degree of uncertainty regarding the effectiveness of many medical treatments and unduly optimistic beliefs about the benefit/harm profiles of such treatments. We argue for an ethical presumption in favour of openness regarding BMU as part of a 'Corrective Response'. We then consider some objections to this position (the 'Anti-Corrective Response'), including concerns that public honesty about flaws in medical research could undermine trust in healthcare institutions. We suggest that, as it stands, the Anti-Corrective Response is unconvincing.

Graphical integrity issues in open access publications: Detection and patterns of proportional ink violations

Academic graphs are essential for communicating complex scientific ideas and results. To ensure that these graphs truthfully reflect underlying data and relationships, visualization researchers have proposed several principles to guide the graph creation process. However, the extent of violations of these principles in academic publications is unknown. In this work, we develop a deep learning-based method to accurately measure violations of the proportional ink principle (AUC = 0.917), which states that the size of shaded areas in graphs should be consistent with their corresponding quantities. We apply our method to analyze a large sample of bar charts contained in 300K figures from open access publications. Our results estimate that 5% of bar charts contain proportional ink violations. Further analysis reveals that these graphical integrity issues are significantly more prevalent in some research fields, such as psychology and computer science, and some regions of the globe. Additionally, we find no temporal and seniority trends in violations. Finally, apart from openly releasing our large annotated dataset and method, we discuss how computational research integrity could be part of peer-review and the publication processes.

Detecting fabrication in large-scale molecular omics data

Fraud is a pervasive problem and can occur as fabrication, falsification, plagiarism, or theft. The scientific community is not exempt from this universal problem and several studies have recently been caught manipulating or fabricating data. Current measures to prevent and deter scientific misconduct come in the form of the peer-review process and on-site clinical trial auditors. As recent advances in high-throughput omics technologies have moved biology into the realm of big-data, fraud detection methods must be updated for sophisticated computational fraud. In the financial sector, machine learning and digit-frequencies are successfully used to detect fraud. Drawing from these sources, we develop methods of fabrication detection in biomedical research and show that machine learning can be used to detect fraud in large-scale omic experiments. Using the gene copy-number data as input, machine learning models correctly predicted fraud with 58-100% accuracy. With digit frequency as input features, the models detected fraud with 82%-100% accuracy. All of the data and analysis scripts used in this project are available at https://github.com/MSBradshaw/FakeData.

Ad hominem rhetoric in scientific psychology

Ad hominem discourse is largely prohibited in scientific journals. Historically, this prohibition restricted the dissemination of ad hominem discussion, but during the last decade, blogs and social media platforms became popular among researchers. With the use of social media now entrenched among researchers, there are important questions about the role of ad hominems. Ad hominems and other forms of strong criticism became particularly evident in online discussions associated with the recent replication crisis in psychology. Here, these discussions, and a few incidences of ad hominems in journal articles, are situated in the broader history of science. It is argued that explicit codes of conduct should be considered to curb certain kinds of ad hominem comments in certain fora, but that some ad hominem discussions have an important role to play in a healthier science.

The 1-h fraud detection challenge

Publications baring falsified and fabricated images appear frequently in the primary literature. Industrialized forms of image forgery as practiced by the so-called paper mills worsen the current situation even further. Good education and awareness within the scientific society are essential to create an environment in which honesty and trust are the prime values in experimental research. Here I focus on the detection of publication fraud and provide some examples and advice. Finally, my views on the future of fraud detection and prevention are given.

Developing innovation governance readiness in regenerative medicine: lessons learned from the Macchiarini crisis

The generation and clinical adoption of workable therapies in regenerative medicine has been slow, despite its alleged potential to relieve suffering and improve health outcomes. This has been explained by a fundamental difference between advanced cell and gene therapies and conventional drug- and device-based therapies, raising questions about how the readiness of existing healthcare systems to adopt such therapies can be evaluated and improved. In this paper, we use the lessons learned from the Macchiarini crisis at the Karolinska Institute in Sweden to take the first step in formulating the concept of innovation governance readiness. We propose it as a tool to help evaluate and improve the ability of private, public and civil society actors to work together to build and put into practice therapies based on emerging medical technologies such as regenerative medicine.

Requiem for impact factors and high publication charges

Journal impact factors, publication charges and assessment of quality and accuracy of scientific research are critical for researchers, managers, funders, policy makers, and society. Editors and publishers compete for impact factor rankings, to demonstrate how important their journals are, and researchers strive to publish in perceived top journals, despite high publication and access charges. This raises questions of how top journals are identified, whether assessments of impacts are accurate and whether high publication charges borne by the research community are justified, bearing in mind that they also collectively provide free peer-review to the publishers. Although traditional journals accelerated peer review and publication during the COVID-19 pandemic, preprint servers made a greater impact with over 30,000 open access articles becoming available and accelerating a trend already seen in other fields of research. We review and comment on the advantages and disadvantages of a range of assessment methods and the way in which they are used by researchers, managers, employers and publishers. We argue that new approaches to assessment are required to provide a realistic and comprehensive measure of the value of research and journals and we support open access publishing at a modest, affordable price to benefit research producers and consumers.

Retracted articles in the biomedical literature from Indian authors

The aim of the present study is to identify retracted articles in the biomedical literature (co) authored by Indian authors and to examine the features of retracted articles. The PubMed database was searched to find the retracted articles in order to reach the goal. The search yielded 508 records and retrieved for the detailed analysis of: authorships and collaboration type, funding information, who retracts? journals and impact factors, and reasons for retraction. The results show that most of the biomedical articles retracted were published after 2010 and common reasons are plagiarism and fake data for retraction. More than half of the retracted articles were co-authored within the institutions and there is no repeat offender. 25% of retracted articles were published in the top 15 journals and 33% were published in the non-impact factor journals. Average time from publication to retraction is calculated to 2.86 years and retractions due to fake data takes longest period among the reasons. Majority of the funded research was retracted due to fake data whereas it is plagiarism for non-funded.

Creating clear and informative image-based figures for scientific publications

Scientists routinely use images to display data. Readers often examine figures first; therefore, it is important that figures are accessible to a broad audience. Many resources discuss fraudulent image manipulation and technical specifications for image acquisition; however, data on the legibility and interpretability of images are scarce. We systematically examined these factors in non-blot images published in the top 15 journals in 3 fields; plant sciences, cell biology, and physiology (n = 580 papers). Common problems included missing scale bars, misplaced or poorly marked insets, images or labels that were not accessible to colorblind readers, and insufficient explanations of colors, labels, annotations, or the species and tissue or object depicted in the image. Papers that met all good practice criteria examined for all image-based figures were uncommon (physiology 16%, cell biology 12%, plant sciences 2%). We present detailed descriptions and visual examples to help scientists avoid common pitfalls when publishing images. Our recommendations address image magnification, scale information, insets, annotation, and color and may encourage discussion about quality standards for bioimage publishing.

The thin ret(raction) line: biomedical journal responses to incorrect non-targeting nucleotide sequence reagents in human gene knockdown publications

The capacity of the scientific literature to self-correct is of vital importance, but few studies have compared post-publication journal responses to specific error types. We have compared journal responses to a specific reagent error in 31 human gene knockdown publications, namely a non-targeting or negative control nucleotide sequence that is instead predicted to target a human gene. The 31 papers published by 13 biomedical journals generated 26 published responses (14 retractions, 5 expressions of concern, 7 author corrections which included one resolved expression of concern) as well as 6 stated decisions to take no action. Variations in published responses were noted both between journals and by 4 journals that published different responses to at least 2 papers. A subset of published responses revealed conflicting explanations for the wrongly identified control reagent, despite 30/31 papers obtaining their gene knockdown reagents from the same external supplier. Viewed collectively, different journal responses to human gene knockdown publications with a common reagent error type suggest that editorial staff require more support to interpret post-publication notifications of incorrect nucleotide sequence reagents. We propose a draft template to facilitate the communication, interpretation and investigation of published errors, including errors affecting research reagents.

Reproducible image handling and analysis

Image data are universal in life sciences research. Their proper handling is not. A significant proportion of image data in research papers show signs of mishandling that undermine their interpretation. We propose that a precise description of the image processing and analysis applied is required to address this problem. A new norm for reporting reproducible image analyses will diminish mishandling, as it will alert co-authors, referees, and journals to aberrant image data processing or, if published nonetheless, it will document it to the reader. To promote this norm, we discuss the effectiveness of this approach and give some step-by-step instructions for publishing reproducible image data processing and analysis workflows.

Effective image visualization for publications - a workflow using open access tools and concepts

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization.  Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible "cheat sheet"-style format, enabling wide distribution, use, and adoption to more specific needs.

Effective image visualization for publications - a workflow using open access tools and concepts

Today, 25% of figures in biomedical publications contain images of various types, e.g. photos, light or electron microscopy images, x-rays, or even sketches or drawings. Despite being widely used, published images may be ineffective or illegible since details are not visible, information is missing or they have been inappropriately processed. The vast majority of such imperfect images can be attributed to the lack of experience of the authors as undergraduate and graduate curricula lack courses on image acquisition, ethical processing, and visualization.  Here we present a step-by-step image processing workflow for effective and ethical image presentation. The workflow is aimed to allow novice users with little or no prior experience in image processing to implement the essential steps towards publishing images. The workflow is based on the open source software Fiji, but its principles can be applied with other software packages. All image processing steps discussed here, and complementary suggestions for image presentation, are shown in an accessible "cheat sheet"-style format, enabling wide distribution, use, and adoption to more specific needs.

Psychometric properties of Persian version of the research misconduct questionnaire (PRMQ)

Assessment of scientific misconduct is considered to be an increasingly important topic in medical sciences. Providing a definition for scientific research misconduct and proposing practical methods for evaluating and measuring it in various fields of medicine discipline are required. This study aimed at assessing the psychometric properties of Scientific Research Misconduct-Revised (SMQ-R) and Publication Pressure Questionnaires (PPQ). After translation and merging of these two questionnaires, the validity of the translated draft was evaluated by 11-member expert panel using Content Validity Index (CVI) and Content Validity Ratio (CVR). Reliability of the final questionnaire, completed by 100 participants randomly chosen from medical academic members, was assessed by calculating Cronbach’s alpha coefficient. The final version was named Persian Research Misconduct Questionnaire (PRMQ) and consisted of 63 question items. The item-level content validity indices of 61 questions were above 0.79, and reliability assessment showed that 6 out of 7 subscales had alpha values higher than 0.6. Hence, PRMQ can be considered an acceptable, valid and reliable tool to measure research misconduct in biomedical sciences researches in Iran.