It’s the question on every undergrad’s mind: how can I possibly fuck up my experiment when I can’t even be in the lab?
I find myself asking this question more and more as I sit across the country from my research lab, lamenting the lack of pipettes to break or precious cells to dump in the bleach. I’ve tried to make do with what I have at home. Some days I spill some water and pretend it’s the most expensive media in the lab, just to feel something, oh god, anything.
But I just can’t content myself with these simulacra anymore, these poor imitations of a more fulfilling life. I can pretend I’m spilling media all I want. But if it’s not going to make my PI at least a little disappointed in me, if it’s not going to make some lab manager’s life at least a little more difficult, then what’s the point?
Someone smart – Plato? who knows – once said that necessity is the mother of invention. And in these trying times, I know I’m not the only undergrad in a similar situation. So to all my undergrads out there: you’re not alone, and I have found ways to work around the distance. You, too, can fuck up your research – even when there’s not a pipette in sight.
Most labs have been transitioning to computational work, and in doing so I have found a wealth of ways to throw a wrench into things. R actively helps you do this – want to force your code into inefficacy? R will conveniently forget it ever read your dataset at all. I’ve found you can waste a fair amount of your PI’s time troubleshooting with this method. A rogue quotation mark also does wonders to gum up the works in your code. Other stray characters will do, but I like using quotation marks to mess things up because they’re small and easy to miss. This is a great way to waste more of your PI’s time when they’ve caught on to how R doesn’t remember your dataset. No matter your choice for sowing discontent, remember that the easier the fix, the more satisfying every moment of wasted time tends to be.
If you’ve grown tired of R, there are still ways to scratch that research-soiling itch, even if you do have to think outside the box. I’ve had some decent luck with misinterpreting my data, forcing my PI to reteach me the central dogma so I’ll understand why I’m probably wrong about this or that signaling pathway. Deleting – or “losing” – datasets is another tried-and-true method, especially with the added pressure of not being able to repeat the experiment until your lab is up and running. (This can be foiled pretty easily by having a backup though, so watch out for those.)
Like most researchers, I dream of one day returning to my lab to find the incubator just as putrid as I remember it, my lab notebooks just as disorganized, my lab manager just as tired. And while this cannot be, at least for now, there are ways to still get that rush of failing a task successfully. You may have to get creative. But any sense of normalcy in increasingly uncertain times can do wonders for the psyche, and for us undergrads, I struggle to find anything more normal than being a headache in the lab.
Just remember that in these pandemic times, your experiment will probably never fail as impressively as the American experiment currently is. So don’t get comfortable – there are always new bars for how bad you can mess something up.
God I wish that were me via Pixabay