My post about wet and dry PIs in biology and the fact that one can (should) be able to be a “switch hitter” / “damp” PI fro either side sparked some good twitter comments - more things this morning
First a number of people have noted that reviews seem particularly harsh on female joint wet/dry - I have 2 anecdotes of this myself - (other successes though) and I’d encourage funding agencies to run some solid stats on this
Potential hypothesis : reviewers find wet/dry proposals higher risk and less expected ; they have an unconscious bias (likely : both gender of reviewer) to be less able to imagine success here for female vs male candidates
Interesting aspect of this is that it’s potentially a categorical variable (setting up a joint wet/dry lab) which can be tracked / compared between funding agencies and years - ie in might be a useful “natural experiment” in understanding presence and features of bias
(To stress - our collective goal should be to removed such bias in all decisions - i’m just a v data driven person)
The second point is my own experience as a "dry" PI (who - waaaay back - did train "wet") - for a long time I hankered about having my own specific lab, but one of the real beauties of computational biology is that the toolkit/mindset is so portable
It's not just that (say) you can do genomics / genetics on nearly any species, it is also that things like data cleaning, batch correction, statistical modelling is often portable between different measurement techniques
This means it's far easier as a computational biologist to "move sub fields" - I'm definitely onto my third research "epoch" (started on genome analysis, then went to epigenomics/ENCODE for ~10 years, now quantitative genetics)
"Wet" laboratories have more inertia - there's both the kit and resource committments that build up, but just the timelines on some things are necessarily long. This means once one has a lab on X, there ends up being momentum (alternatively: sunk costs)
So - mainly I access "wet" experimental work or clinical work through deep collaborations with experimental and clinical colleagues - who have that expertise and momentum, and that's really worked for me.
That said, I can really see the value of people with wet/dry set ups (for example, @pedrobeltrao who has his dry lab @emblebi and his wet lab is hosted in @embl Heidelberg); Another good example is @danjgaffney at the Sanger Institute
I can see this development all around - for example, @HKilpinen, @e_petsalaki, @afilimon and younger up and coming people such as @HannahVMeyer - we need to embrace this wet/dry world, and not think of it as any more of a risk compared to all the other complexities in science
