Incidence of Data Duplications in a Randomly Selected Pool of Life Science Publications

How to fight fake papers: a review on important information sources and steps towards solution of the problem

Scientific fake papers, containing manipulated or completely fabricated data, are a problem that has reached dramatic dimensions. Companies known as paper mills (or more bluntly as "criminal science publishing gangs") produce and sell such fake papers on a large scale. The main drivers of the fake paper flood are the pressure in academic systems and (monetary) incentives to publish in respected scientific journals and sometimes the personal desire for increased "prestige." Published fake papers cause substantial scientific, economic, and social damage. There are numerous information sources that deal with this topic from different points of view. This review aims to provide an overview of these information sources until June 2024. Much more original research with larger datasets is needed, for example on the extent and impact of the fake paper problem and especially on how to detect them, as many findings are based more on small datasets, anecdotal evidence, and assumptions. A long-term solution would be to overcome the mantra of publication metrics for evaluating scientists in academia.

Decatastrophizing research irreproducibility

The reported inability to replicate research findings from the published literature precipitated extensive efforts to identify and correct perceived deficiencies in the execution and reporting of biomedical research. Despite these efforts, quantification of the magnitude of irreproducible research or the effectiveness of associated remediation initiatives, across diverse biomedical disciplines, has made little progress over the last decade. The idea that science is self-correcting has been further challenged in recent years by the proliferation of unverified or fraudulent scientific content generated by predatory journals, paper mills, pre-print server postings, and the inappropriate use of artificial intelligence technologies. The degree to which the field of pharmacology has been negatively impacted by these evolving pressures is unknown. Regardless of these ambiguities, pharmacology societies and their associated journals have championed best practices to enhance the experimental rigor and reporting of pharmacological research. The value of transparent and independent validation of raw data generation and its analysis in basic and clinical research is exemplified by the discovery, development, and approval of Highly Effective Modulator Therapy (HEMT) for Cystic Fibrosis (CF) patients. This provides a didactic counterpoint to concerns regarding the current state of biomedical research. Key features of this important therapeutic advance include objective construction of basic and translational research hypotheses, associated experimental designs, and validation of experimental effect sizes with quantitative alignment to meaningful clinical endpoints with input from the FDA, which enhanced scientific rigor and transparency with real world deliverables for patients in need.

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.

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.

Perceptions of Chinese Biomedical Researchers Towards Academic Misconduct: A Comparison Between 2015 and 2010

Publications by Chinese researchers in scientific journals have dramatically increased over the past decade; however, academic misconduct also becomes more prevalent in the country. The aim of this prospective study was to understand the perceptions of Chinese biomedical researchers towards academic misconduct and the trend from 2010 to 2015. A questionnaire comprising 10 questions was designed and then validated by ten biomedical researchers in China. In the years 2010 and 2015, respectively, the questionnaire was sent as a survey to biomedical researchers at teaching hospitals, universities, and medical institutes in mainland China. Data were analyzed by the Chi squared test, one-way analysis of variance with the Tukey post hoc test, or Spearman's rank correlation method, where appropriate. The overall response rates in 2010 and 2015 were 4.5% (446/9986) and 5.5% (832/15,127), respectively. Data from 15 participants in 2010 were invalid, and analysis was thus performed for 1263 participants. Among the participants, 54.7% thought that academic misconduct was serious-to-extremely serious, and 71.2% believed that the Chinese authorities paid no or little attention to the academic misconduct. Moreover, 70.2 and 65.2% of participants considered that the punishment for academic misconduct at the authority and institution levels, respectively, was not appropriate or severe enough. Inappropriate authorship and plagiarism were the most common forms of academic misconduct. The most important factor underlying academic misconduct was the academic assessment system, as judged by 50.7% of the participants. Participants estimated that 40.1% (39.8 ± 23.5% in 2010; 40.2 ± 24.5% in 2015) of published scientific articles were associated with some form of academic misconduct. Their perceptions towards academic misconduct had not significantly changed over the 5 years. Reform of the academic assessment system should be the fundamental approach to tackling this problem in China.

Automatic detection of image manipulations in the biomedical literature

Images in scientific papers are used to support the experimental description and the discussion of the findings since several centuries. In the field of biomedical sciences, in particular, the use of images to depict laboratory results is widely diffused, at such a level that one would not err in saying that there is barely any experimental paper devoid of images to document the attained results. With the advent of software for digital image manipulation, however, even photographic reproductions of experimental results may be easily altered by researchers, leading to an increasingly high rate of scientific papers containing unreliable images. In this paper I introduce a software pipeline to detect some of the most diffuse misbehaviours, running two independent tests on a random set of papers and on the full publishing record of a single journal. The results obtained by these two tests support the feasibility of the software approach and imply an alarming level of image manipulation in the published record.

Scientific Misconduct and Social Media: Role of Twitter in the Stimulus Triggered Acquisition of Pluripotency Cells Scandal

Background

The academic scandal on a study on stimulus‑triggered acquisition of pluripotency (STAP) cells in Japan in 2014 involved suspicions of scientific misconduct by the lead author of the study after the paper had been reviewed on a peer‑review website. This study investigated the discussions on STAP cells on Twitter and content of newspaper articles in an attempt to assess the role of social compared with traditional media in scientific peer review.

Objective

This study examined Twitter utilization in scientific peer review on STAP cells misconduct.

Methods

Searches for tweets and newspaper articles containing the term “STAP cells” were carried out through Twitter’s search engine and Nikkei Telecom database, respectively. The search period was from January 1 to July 1, 2014. The nouns appearing in the “top tweets” and newspaper articles were extracted through a morphological analysis, and their frequency of appearance and changes over time were investigated.

Results

The total numbers of top tweets and newspaper articles containing the term were 134,958 and 1646, respectively. Negative words concerning STAP cells began to appear on Twitter by February 9-15, 2014, or 3 weeks after Obokata presented a paper on STAP cells. The number of negative words in newspaper articles gradually increased beginning in the week of March 12-18, 2014. A total of 1000 tweets were randomly selected, and they were found to contain STAP-related opinions (43.3%, 433/1000), links to news sites and other sources (41.4%, 414/1000), false scientific or medical claims (8.9%, 89/1000), and topics unrelated to STAP (6.4%, 64/1000).

Conclusions

The discussion on scientific misconduct during the STAP cells scandal took place at an earlier stage on Twitter than in newspapers, a traditional medium.

The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications

Inaccurate data in scientific papers can result from honest error or intentional falsification. This study attempted to determine the percentage of published papers that contain inappropriate image duplication, a specific type of inaccurate data. The images from a total of 20,621 papers published in 40 scientific journals from 1995 to 2014 were visually screened. Overall, 3.8% of published papers contained problematic figures, with at least half exhibiting features suggestive of deliberate manipulation. The prevalence of papers with problematic images has risen markedly during the past decade. Additional papers written by authors of papers with problematic images had an increased likelihood of containing problematic images as well. As this analysis focused only on one type of data, it is likely that the actual prevalence of inaccurate data in the published literature is higher. The marked variation in the frequency of problematic images among journals suggests that journal practices, such as prepublication image screening, influence the quality of the scientific literature.

The Prevalence of Inappropriate Image Duplication in Biomedical Research Publications

Inaccurate data in scientific papers can result from honest error or intentional falsification. This study attempted to determine the percentage of published papers that contain inappropriate image duplication, a specific type of inaccurate data. The images from a total of 20,621 papers published in 40 scientific journals from 1995 to 2014 were visually screened. Overall, 3.8% of published papers contained problematic figures, with at least half exhibiting features suggestive of deliberate manipulation. The prevalence of papers with problematic images has risen markedly during the past decade. Additional papers written by authors of papers with problematic images had an increased likelihood of containing problematic images as well. As this analysis focused only on one type of data, it is likely that the actual prevalence of inaccurate data in the published literature is higher. The marked variation in the frequency of problematic images among journals suggests that journal practices, such as prepublication image screening, influence the quality of the scientific literature.