![]() ![]() ![]() The Image Problem: Honest Mistake or Falsification? As a case-in-point, let’s take a closer look at the specific concern of image falsification, the specific data fabrication in the paper discussed last week, as an example of how to characterize a particular problem and how technology and publishing policies might be used to minimize it. However, there are still small, short-term changes – such as fraud-detection practices and data/methodology sharing – that are relatively easy to implement and can yield meaningful improvements in research integrity. Unfortunately, scientific publishing is riddled with myriad problems, many of which likely can’t be solved without completely rethinking current processes and the underlying research culture. So can anything be done to prevent these problems in the future? And how it can, understandably, undermine public trust in science. The flagrant misconduct evident in that case has naturally left many asking the same question: “how does this happen?” Though the actual percentage of retracted papers is only about 0.04% of all scientific research, the Alzheimer’s case shows us how even a fairly small number of fraudulent works can potentially result in significant costs in misdirecting future research and funding. In last week’s newsletter, I highlighted some of the implications of the recent exposé of fabricated data in a heavily cited paper in the field of Alzheimer’s disease research. ![]()
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