Computational biologists are among the most expensive biotech employees. But many of them spend less than half their time on high value work. They spend the other half tracking down data, cleaning it and dealing with other technical obstructions. It's work they don't want to be doing. It's work that leaves them burnt out and ready to look for a new job. And yet it often goes unnoticed by leadership. Why? These employees just want to get the work done. They don't want to be a squeaky wheel. They don't want to put a burden on their wet lab colleagues who are creating the messy data. So instead they quietly work extra hours on the low-value work that allows them to do the high value work. Imagine what they'd get done if they could spend more time on the work they were hired for.
Mark Schreiber
7mo
Managers should be mandating that data is fit for purpose. This won’t mean integration and wrangling won’t be required for secondary data mining but if the primary analysis is impeded someone should be escalating that issue. A good way to put it in terms that have strong meaning for senior management is that when any company is looking to acquire a biotech or one of their drug candidates they will likely want to perform some due diligence on the data. If it can’t be found or is messy inconsistent or incomplete then confidence goes down and so does the offer price.
Yes absolutely - I would add that at a times you need explain the reasoning behind doing that low-value work specially if it comes from any omics platform/experiment. Perhaps a communication issue?
Paul Agapow
7mo
I suspect that "These employees just want to get the work done" is part of the problem - there's no one out there lobbying for and promoting their work. I wrote this some years ago and there problem is still very much there: https://agapow.net/science/computational-biology/no-respect/
Alison O'Mahony
7mo
Importance of FAIR data principles.
Jan Kotrs
7mo
I agree Jesse Johnson. I would also argue that many roles experience getting low-value work out before they hit their ideal high-value output. Sometimes good tools can help, sometimes other roles can share the burden. But I guess it comes down to whether or not this pre-work is being recognized (and accounted for) by the leadership right?
Daniel Thomas
7mo
Absolutely agree Jesse - technology is available to digitise a significant proportion of experimental processes which, when deployed using automated systems, is capable of providing much higher quality data for computational biologists to use. The fact that it has taken as long as it has to convince the industry to embrace these technologies is so frustrating given the transformational benefits realised by so many other industries.
Steve M.
7mo
I would say it’s not just comp bio folks but I’ve seen similar things with ML folks as well 😀 I think it is also important to show how the sausage is made.
Founder/CEO of CellVoyant and Professor at University of Bristol
7moJesse Johnson I agree with your general premise and the pain point you are trying to address however there’s no reason to be so apparently disrespectful : be nice to experimentalists, they’re the ones giving computational biologists the precious data without which what they would do is just regular coding! “wet lab colleagues who are creating the messy data” (disdainful!) “[computational biologists] quietly work extra hours on the low-value work that allows them to do the high value work” : are you trying to imply that experiments are simple turn key operations? Experimentalists also have to do lots of apparent low value work (to set up protocols, deal with reagent variations, do overnight experiments, fix the centrifuge, redo the PCR, ….) to be able to do their high value work of producing the data that gives meaning to the computational biologists work! Both experimental and computational biology are full of apparent low value work necessary to be able to do the high value work, empathy and understanding are the ways forward