Towards a Systematic Screening Tool for Quality Assurance and Semiautomatic Fraud Detection for Images in the Life Sciences

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.

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.

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.

Image manipulation in scholarly publications: are there ways to an automated solution?

Purpose

This paper provides an introduction to research in the field of image forensics and asks whether advances in the field of algorithm development and digital forensics will facilitate the examination of images in the scientific publication process in the near future.

Design/methodology/approach

This study looks at the status quo of image analysis in the peer review process and evaluates selected articles from the field of Digital Image and Signal Processing that have addressed the discovery of copy-move, cut-paste and erase-fill manipulations.

Findings

The article focuses on forensic research and shows that, despite numerous efforts, there is still no applicable tool for the automated detection of image manipulation. Nonetheless, the status quo for examining images in scientific publications remains visual inspection and will likely remain so for the foreseeable future. This study summarizes aspects that make automated detection of image manipulation difficult from a forensic research perspective.

Research limitations/implications

Results of this study underscore the need for a conceptual reconsideration of the problems involving image manipulation with a view toward the need for interdisciplinary collaboration in conjunction with library and information science (LIS) expertise on information integrity.

Practical implications

This study not only identifies a number of conceptual challenges but also suggests areas of action that the scientific community can address in the future.

Originality/value

Image manipulation is often discussed in isolation as a technical challenge. This study takes a more holistic view of the topic and demonstrates the necessity for a multidisciplinary approach.

Methods to assess research misconduct in health-related research: A scoping review

OBJECTIVE

To give an overview of the available methods to investigate research misconduct in health-related research.

STUDY DESIGN AND SETTING

In this scoping review, we conducted a literature search in MEDLINE, Embase, The Cochrane CENTRAL Register of Studies Online (CRSO), and The Virtual Health Library portal up to July 2020. We included papers that mentioned and/or described methods for screening or assessing research misconduct in health-related research. We categorized identified methods into the following four groups according to their scopes: overall concern, textual concern, image concern, and data concern.

RESULTS

We included 57 papers reporting on 27 methods: two on overall concern, four on textual concern, three on image concern, and 18 on data concern. Apart from the methods to locate textual plagiarism and image manipulation, all other methods, be it theoretical or empirical, are based on examples, are not standardized, and lack formal validation.

CONCLUSION

Existing methods cover a wide range of issues regarding research misconduct. Although measures to counteract textual plagiarism are well implemented, tools to investigate other forms of research misconduct are rudimentary and labour-intensive. To cope with the rising challenge of research misconduct, further development of automatic tools and routine validation of these methods is needed.

TRIAL REGISTRATION NUMBER

Center for Open Science (OSF) (https://osf.io/mq89w).

The Possibility of Systematic Research Fraud Targeting Under-Studied Human Genes: Causes, Consequences, and Potential Solutions

A major reason for biomarker failure is the selection of candidate biomarkers based on inaccurate or incorrect published results. Incorrect research results leading to the selection of unproductive biomarker candidates are largely considered to stem from unintentional research errors. The additional possibility that biomarker research may be actively misdirected by research fraud has been given comparatively little consideration. This review discusses what we believe to be a new threat to biomarker research, namely, the possible systematic production of fraudulent gene knockdown studies that target under-studied human genes. We describe how fraudulent papers may be produced in series by paper mills using what we have described as a 'theme and variations' model, which could also be considered a form of salami slicing. We describe features of these single-gene knockdown publications that may allow them to evade detection by journal editors, peer reviewers, and readers. We then propose a number of approaches to facilitate their detection, including improved awareness of the features of publications constructed in series, broader requirements to post submitted manuscripts to preprint servers, and the use of semi-automated literature screening tools. These approaches may collectively improve the detection of fraudulent studies that might otherwise impede future biomarker research.