During the last few years I’ve noticed one topic coming up again and again over coffee/drinks with other researchers: our collective gradual shift from the bench to the desk.
Of course, none of us were expecting the wet lab to actually go off limits for six months!
During my PhD, most of the day would be spent hanging out in the lab, with two or three ‘wet’ experiments on the go at a time, and minimal time during incubations for analysis/writing. During my postdoc years, this balance began to shift for me, and I think this is the same for a lot of us. We had all noticed a massive increase in wet lab data being generated, with virtually every technique gradually being made obsolete by increasingly affordable multiplexed or genome-wide versions. With more and more data being generated quicker and quicker, we all had a bit more time to sit at the desk, and a lot more data to play with there.
This manifested itself quite clearly in the perpetual fight for space in academic departments shifting from fighting over bench spaces in the labs, to desk spaces in the offices!
With my generation of researchers not always having in depth bioinformatics or statistical knowledge as a given, there has been an element of trying to play catch-up at the desk. Most of us know one or two computer whizzes who we can ask for help in our departments, but they of course are swamped with ‘quick’ questions from everyone, and just can’t train everyone from first principles. So we’ve been collectively trying to self-learn large scale data analysis while still producing wet lab data at the same time. It’s been a lot.
The covid months:
So how has seven months at home affected this? Well for me, it’s safe to say I’m beginning to run out of data to analyse for the first time in a very long time. I didn’t anticipate ‘running out’ of my own wet lab data ever – so it’s quite an odd feeling. I’m simultaneously making the transition to life as a faculty member, taking over modules and preparing new ways of teaching online, so it probably took me a bit longer than the average researcher to run out of research data – I imagine many wet lab PhD students hit this stage a good few months before I did.
For others, from what I’ve seen and heard, there has been a lot of upskilling happening to fill that lab-gap, and not a moment too soon. Many have been learning R or Python for the first time, or brushing off old half-attempted databases. Many have been learning to conduct systematic reviews and meta-analyses for the first time too, with our Division’s online modules on these topics having recently been made available to staff to as well as students – and with an enthusiastic uptake.
On a wider scale, for the first time in what feels like a long time, my field is starting to catch up with itself. People are stepping back, taking a breath, and appreciating the enormous volume of data around us. What’s more, we’re taking the time to not only read more of each other’s papers, but critically analyse them, validate what we can from home, and publish these findings too. This is something we’ve all previously lamented at those coffee/drinks chats that we wish we had the time to do!
This is much-needed, and well overdue.
I can only hope we continue to take this approach to research, as we gradually transition back to life in the lab. I now fully believe that one or two days of the week at the bench, with three or four at home or in the office could honestly achieve more overall than my previous habit of 5 days minimum in the lab.
For this academic year, although our labs have partially reopened, I’ve designed four student research projects that are all fully desk based. This means that whether lockdowns happen or not, research can continue. If you’d asked me this time last year, I wouldn’t have thought I could supervise four non wet lab students, but the collective ‘we will figure this out’ attitude has rubbed off on me! If all four go to plan, they’ll really help to get my lab off the ground while I’m recruiting my new team, and I’m really glad that this is possible from home.
It’s hard to find silver linings from 2020, but I honestly think our collective shift in focus from creation of data to critical analysis of data could be transformative. Let’s hope we all learn from this and continue to improve our practice as time goes on!