How to avoid honest mistakes in academic publishing

Accuracy is key to the credibility of scientific research, so when researchers notice inaccuracies, they may report them. Websites such as PubPeer and Retraction Watch document ongoing investigations into allegedly fraudulent content and consequential retractions. However, when we look deeper into the issue, retractions due to fraud are actually very rare, often it is a simple case of an honest mistake. Dr. Dror Kolodkin Gal, Founder of automated image integrity software developer Proofiger, explains how image integrity mistakes can happen and offers tips on how to prevent them. 

Retractions play an important role in protecting the integrity of the scientific literature, by removing incorrect data that could negatively impact future research. The numbers are on the rise — according to Ivan Oransky, co-founder of Retraction Watch, rates have increased from around 45 per month in 2010, to 300 a month in 2021. Oransky also estimates that one in 50 papers today would meet at least one of the criteria for retraction from the Committee on Publication Ethics. 

Micrsocopy images with issues highlighted

Scientific authors are rationally fearful of being accused of fraud, as investigations and retractions can be severely damaging to their reputation. However, when we look closer into the investigations, retractions due to fraud are actually very rare. This was evidenced during a trial that ran from January 2021 to May 2022, where the AACR used Proofig to screen 1,367 papers accepted for publication. Of those, 208 papers required author contact to clear up issues such as mistaken duplications, and only four papers were withdrawn. In almost all cases (204 cases), there was no evidence of intentional image manipulation. It was simply an honest mistake. 

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This reframes the conversation entirely. Instead of being conscious post-publication that accusations might be made about the validity of their research, researchers can focus on eliminating human error pre-publication. For example, checking for unintentional image integrity issues before submitting for peer review. So, what are the most common errors, and how can they be avoided? 

Avoiding duplications 

Duplications are the most common form of unintentional image issue, and they are often introduced during experimentation. For example, when looking at a sample under the microscope, researchers may want an image of the entire specimen to use in research. Depending on the magnification the researcher must move the microscope from left to right and up and down to document every section of the slide. Unfortunately, the microscope will not tell the researcher if there are overlaps when they capture images. Additionally, when the researcher changes the magnification, they may accidentally capture the same part of the sample twice. 

Organising data 

Researchers often use images to convey scientific results in a compelling way, so during experiments, they may capture thousands of images before choosing the best images to include in the manuscript. To keep track of their images, researchers will often label the files with descriptions of the sample. For example, if a researcher were conducting the efficacy of a treatment on a pancreas they may label a file with the name of the organ, magnification, the date, slide number, and if this sample is before or after treatment.  

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Filling in this data every time an image is captured can be time-consuming, so researchers may opt to copy the last name entry and just change a few of the details. While this enables researchers to file their images more quickly, it can lead to unintentional mistakes. Forgetting to change something or inputting an incorrect number could lead to file name duplications or errors. When the researcher comes to include images in their paper, they might not be aware of the error and unintentionally include these duplications. 

Avoiding mistakes 

We all know that mistakes happen — the important thing is that researchers resolve these mistakes before publication. They may be unintentional, but if peers in the scientific community report these types of image integrity issues in published content, the journal will investigate.

After this investigation, a researcher may need to resolve the issue, but the investigation itself can cause lasting reputational damage, regardless of the outcome. 

Researchers can reduce the risk of image integrity issues by proactively checking their images before submitting them for publication. Automated software, such as Proofig, uses AI to detect all the sub-images in a paper. It compares the images against one another, producing a report about different forms of duplication in minutes. Researchers then have the opportunity to resolve any anomalies before submitting their paper. 

Picture illustrating a duplication

Reports on investigations suggest that retractions are on the rise, but when we look more closely at the outcomes of those investigations, many of these issues are honest mistakes. By increasing awareness of how these mistakes can happen, as well as providing tools that researchers can use to proactively detect and resolve image integrity issues, the scientific research community can reduce the risk of investigations that result in costs to both their finances and their reputation.  

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