Thought of the Day
May 16, 2014
We non-cheatfucks have to stick together and remind each other that not everyone gets ahead in science by faking data, abusing trainees and generally being the ass.
Some people are actually trying to do science right. Never forget that.
DrugMonkey 
May 16, 2014 at 10:23 am
I’m sorry, what did you say? I was busy “touching up” some figures in Photoshop…
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May 16, 2014 at 11:02 am
fuck you, these results are totally trending towards significance… just need to apply my “exclusion criteria” to a few “outliers.”
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May 16, 2014 at 11:11 am
You two are not helping.
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May 16, 2014 at 12:27 pm
Hey, so, do you PI people call out your colleagues one-on-one when they have reputations for any of these things? Or do you just let it slide for the sake of professional camaraderie and vent on the interwebs?
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May 16, 2014 at 12:54 pm
…..but Nature paper FTW!!!
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May 16, 2014 at 1:14 pm
JT – So far I am unaware of any people in my field with a reputation for cheating. There was the Miller guy who was busted but I didn’t previously hear anything about cheating or faking.
This lack of gossip in my fields of interest relative to some other fields may reflect head-in-sand optimism or a real difference in cheating rate. The correlation of cheatfuckery with JIF would suggest the latter.
I cannot know what I would do in any specific case but I hope I would say something to the person.
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May 16, 2014 at 2:03 pm
ouch, my previous comment needs a comma.
Maybe I am being naive, but I feel like the cheating is such a small minority. We just hear about it more now because, like people jumping in front of the subway, every time it happens, it’s reported all over.
I am wondering how my opinion of what constitutes trainee abuse will change when I have my own trainees. Right now, I primarily interact with students and postdocs, and everyone complains about their PI at some point.
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May 16, 2014 at 3:42 pm
I would imagine that whatever your baseline, you see fewer things as trainee abuse in the future.
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May 16, 2014 at 10:45 pm
DM: more than cheating, what bothers me is the so called “productivity”, that pushes people to churn out articles and beat the drum with the same beats again and again, without taking a break to think and work the problem. Too much noise and if you don’t play the game, you are out.
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May 17, 2014 at 3:54 am
The only hard truth about cheating is it’s rampant. If you claim not to see it in your field, then you’re not looking hard enough. The prevalence is at least 1% of PIs (per Nick Staneck and other leaders in documenting misconduct). So, go to a decent size meeting and try to figure out which 5 are the crooks.
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May 17, 2014 at 6:48 am
(1) Yeah, it’s totally all those other fields that are filled with cheaters, not yours, DoucheMonkey.
(2) In what semantic universe is 1% of anything “rampant”?????
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May 17, 2014 at 6:52 am
I’m glad you recognize the truth at last PP. Contingencies matter.
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May 19, 2014 at 8:52 am
@CPP. I take rampant to mean on the rise. There are no indications that this type of behavior is ebbing, and several studies suggesting it is indeed increasing as competition heats up in academia.
Percentages matter, even when small. 1% of $30bn (NIH budget) is $300m of wasted taxpayer money. Would you get on a plane if there was a 1% chance of it crashing in a fiery inferno? What if you found a drug that enabled 1% of the population to live an extra 10 years – does it not matter because it only affects a few people?
FYI, in addition to the 1% that is confirmed as the absolute worst kind of fraud, there’s another significant chunk that falls into the category of “not adhering to best practices”. Stuff like using the wrong kind of statistical test, calling something a loading control when you ran it on a separate gel, etc. Often not done intentionally, but likely contributes to the high proportion of research that can’t be repeated. The best estimates for this are 25-30% of all research.
So this is where the gray area comes in – at what point can someone legitimately claim “I’m sorry, I never knew it was wrong”, when accused of not doing things properly. That 1% becomes a whole lot bigger if you start including all the people who should know better. Throw in all those examples of “we inadvertently pasted in the wrong image during manuscript preparation”, many of which sound completely incredulous (especially when coming from serial offenders who should improve their data handling skills after the first infraction), and suddenly the real number starts looking closer to 5-10%.
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May 19, 2014 at 10:10 am
Jessica – IME the cheaty ones use *your* data and then accuse *you* of plagiarism.
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May 26, 2014 at 2:52 pm
Is double-billing (i.e. asking for grant money twice for the same project) ‘cheatfucking’?
If one can employ two or three people to do the same work, surely, there should be no need to ‘fake data, abuse trainees and be an ass’… one trainee survives, two get no papers. The grant-(S) are renewed. The grant-funding agencies are happy!
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May 26, 2014 at 3:03 pm
In the NIH system that is illegal so…yes.
Different agencies though? Just depends on their rules and transparency. And University accounting. Lot of times the thing that really matters is that you are not double paying for the exact same science. What you’ve proposed is less important.
Say you secure Aim I worth of money from a foundation and then the R01 hits. You just negotiate with the PO over the overlap and what *other* stuff you will do instead.
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