After my last post on the frustrating inefficiencies of experimental failure, I recieved an interesting comment:
I discovered in the late stages of graduate school that my extremely long hours (upwards of 80/week) were extraordinarily unproductive. I was doing cell culture and electrophysiology and while I had reams of data, it wasn’t going anywhere. Only when I switched to a lab doing slice electrophysiology, where the length of the day is limited by the survival of the slice (~6hrs after cutting, making for a 8-9 hr day), did I discover that I could get more work done in less time by increasing my productivity. I’d been fooling myself that my long hours were necessary, when they were really a hinderance (I was so drained that my productivity was poor).
As far as the cheap labor arguments go (and technicians are nearly as expensive as many postdocs), it’s all supply and demand. Postdocs w/demonstrated productivity can command much higher wages if they’re smart (fund yourself w/an NRSA and demand extra from the boss), while others are left out in the cold… And the dearth of PI jobs will continue indefinitely..
Is this a general phenomenon in experimental science? My own experience in the lab suggests that it is. Sleep deprived post-docs are the ones who forget to add the antibody, or add the wrong buffer, or can’t think through the experiment. Scientists certainly work longer hours than they used to, and yet it takes longer than ever to get published. From the perspective of an economist, scientific productivity (at least when measured in terms of papers per hour) is stagnant at best.
But are the numbers missing some important variable? After all, you’d think that with all the technological improvements in lab techniques in the last 15 years (PCR’s, mini-prep kits, etc.), scientific productivity would have gone through the roof. What happened? Have our scientific questions gotten that much more complicated? And how should we measure scientific productivity anyways?
P.S. I’ll venture an tentative answer to the last question. Instead of simply counting the number of papers published as a measure of productivity, we should focus on the number of citations those papers subsequently produce. This way scientists won’t be forced to only tackle tiny questions for fear of not getting published.