Acceleration of the arms race between fraudsters and honest researchers

I was recently asked to review a paper that piqued my curiosity. From the abstract it sounded as if it was a secondary analysis of a dataset that I had deposited in a repository. But the described data didn't quite match what was in the repository. As I usually do, I said I'd only review it if data and code were available. The authors complied: I was pointed at the repository where my data were stored, and when the editor requested it on my behalf, a script in Python code arrived. As I had suspected, the whole thing was fabricated. The analysis would have required linking data from two separate repositories - but the linkage information was not publicly available. No mention was made of the repository where the bulk of the supposedly analysed data were held. And the python script didn't clarify how the data were processed, and didn't even run with dummy code. So far, so depressingly predictable. But, despite all these issues, I was shocked at how plausible the paper appeared to be. In…