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> that they could catch sophisticated liars who manipulate raw instrument data There was no raw data manipulation, but fabrication of derived data. These kind
by flobosg 3y ago
> that they could catch sophisticated liars who manipulate raw instrument data
There was no raw data manipulation, but fabrication of derived data. These kind of too-good-to-be-true results are easily auditable, if anything by replicating the experiment. The PI’s blind enthusiasm prevented her to do her due diligence.
> would you apply the same standards to yourself?
While I was in academia I checked basically every kind of raw biological data I was getting. Routine QC allowed me, for instance, to find contaminants in some of the reagents the lab was using.
- achileas 3y agoFor many labs, there are no resources, time, or ability to rerun every bit of data collection that happens. When I was in academia, our lab had a hard enough time recruiting participants (we were a cog neuro lab) and our experiments were several hours long per participant with enough individual variation to be useless without several months of data collection and analysis to find any sort of signal in the noisy data. And that’s a single lab, with PIs overseeing several and collaborating with even more, an element of trust is going to be needed if anything is to ever get done.
- flobosg 3y agoIn the case of this article, the experiment entailed putting a sample in an instrument, do the measurement and check the values. And the checks I propose are nothing out of the ordinary. Many academic fraud postmortems tell that the fabricated data had repeated or perfectly rounded values, strange timestamps, numeric data in a different format than expected, etc. Take a look at the (raw or processed) data, there’s unfortunately no way around it.