In the wake of this post that touched on recently released documents detailing investigations into Bengü Sezen’s scientific misconduct, and that noted that a C & E News article described Sezen as a “master of deception”, I had an interesting chat on the Twitters:
@UnstableIsotope (website) tweeted:
@geernst @docfreeride I scoff at the idea that Sezen was a master at deception. She lied a lot but plenty of opportunities to get caught.
@geernst (website) tweeted back:
@UnstableIsotope Maybe evasion is a more accurate word.
@geernst I’d agree she was a master of evasion. But she was caught be other group members but sounds like advisor didn’t want to believe it.
@docfreeride (that’s me!):
@UnstableIsotope @geernst Possible that she was master of deception only in environment where people didn’t guard against being deceived?
@docfreeride @geernst I agree ppl didn’t expect deception, my read suggests she was caught by group members but protected by advisor.
@docfreeride @geernst The advisor certainly didn’t expect deception and didn’t encourage but didn’t want to believe evidence
@UnstableIsotope @geernst Not wanting to believe the evidence strikes me as a bad fit with “being a scientist”.
@docfreeride @geernst Yes, but it is human. Not wanting to believe your amazing results are not amazing seems like a normal response to me.
@docfreeride @UnstableIsotope I agree. Difficult to separate scientific objectivity from personal feelings in those circumstances.
@geernst @UnstableIsotope But isn’t this exactly the argument for not taking scrutiny of your results, data, methods personally?
@docfreeride @geernst Definitely YES. I look forward to people repeating my experiments. I’m nervous if I have the only result.
@docfreeride @UnstableIsotope Couldn’t agree more.
This conversation prompted a question I’d like to ask the PIs. (Trainees: I’m going to pose the complementary question to you in the very next post!)
In your capacity as PI, your scientific credibility (and likely your name) is tied to all the results that come out of your research group — whether they are experimental measurements, analyses of measurements, modeling results, or whatever else it is that scientists of your stripe regard as results. What do you do to ensure that the results generated by your trainees are reliable?
Now, it may be the case that what you see as the appropriate level of involvement/quality control/”let me get up in your grill while you repeat that measurement for me” would still not have been enough to deter — or to detect — a brazen liar. If you want to talk about that in the comments, feel free.
Commenting note: You may feel more comfortable commenting with a pseudonym for this particular discussion, and that’s completely fine with me. However, please pick a unique ‘nym and keep it for the duration of this discussion, so we’re not in the position of trying to sort out which “Anonymous” is which. Also, if you’re a regular commenter who wants to go pseudonymous for this discussion, you’ll probably want to enter something other than your regular email address in the commenting form — otherwise, your Gravatar may give your other identity away!
Published non-reproducible results affect the entire lab, especially those whose names are on the paper. It is not just the PI who suffers. The peer review process should begin in the lab, and a good PI will foster the kind of environment where everyone (including undergrads) is encouraged to ask questions and point out inconsistencies.
In my last lab we had weekly meetings, with progress reports and group trouble-shooting sessions. In this kind of environment only a very clever and determined individual could fake results and get away with it. Openness is the key. No one likes to be suspected of incompetence or fraud, but if framed right most of us appreciate the safety checks peer support can offer.
For chemical characterisation data no work will be published unless I have copies of the original electronic files generated by the instrument in question with dates that align with laboratory notebook records. This has to be good archival practice if nothing else. Other types of data are harder such as drug release studies – while I can have copies of the analytical data, I have to verify the means by which the samples were taken by looking at the students protocols and querying anything that seems odd. If results do not withstand my scrutiny they will not be published.
This sounds quite harsh when written out but I’d look at the original data during manuscript preparation to verify that there were no careless errors made during interpretation or typing out. Protocols are usually best when someone else casts an eye over them to check for appropriate controls. It doesn’t take as much effort as it sounds.
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