One odd thing about the scientific process itself: unlike industrialization, scientific productivity doesn’t really lend itself to the sort of efficiencies that drive many business texts. Which is not to say that convergent technological advances haven’t been hugely important in driving recent progress in science–particularly in science of the trans-disciplinary variety that we do at Krasnow. Rather that a central part of science success comes directly out of contemplative thinking about information/clues from the very edge of human knowledge. To be perfectly clear, a successful scientist needs to allot significant periods of time for quiet thinking.

For myself, one of my own successes in neuroscience came directly out of the realization from my thesis work that imaging metabolic rates in brains wasn’t going to be very useful for imaging learning and memory because those metabolic rates had very high between- and within-variabilities. I needed instead to image the activation of a molecule that was central to mnemonic function (think close to rate-determining) so that the experimental signal-to-noise could be improved upon. That quiet thinking led me to consider protein kinase C less than a decade after it had been discovered within the context of cancer.

But there are roles for efficiencies in science. The advent of new general purpose simulators for computational neuroscience combined with Moore’s law, make for in silico experiments that take minutes rather than weeks.

Robotics allows genome-wide analysis for a small fraction of the cost of Craig Venter’s initial success with his own DNA more than a decade ago.

There are also roles for efficiencies in the administrative support that we provide to scientists. One important consideration is the ever-increasing burden of regulations that, if not checked, can literally eat away at the time a scientist can devote to creating new knowledge. There is of course an important balance between compliance work done to protect society from scientific mistakes (of many types) and the scientific process itself (work of directed creativity). At this institute, we do our level best as administrators to shield our scientists from as much as possible of the regulational burden, by taking it on ourselves–but there are certain areas where that is not possible (such as certifying that a project has no conflict-of-interest).

A part of the life of the very best scientists is close to dreaming. New seemingly random pieces of data (and knowledge) are fitted up against conventional ideas to create novel “idea combinations” (hypotheses) which then can be tested at the bench. Dreaming has never been a good metaphor for efficiency, but it may well be pretty good for describing what it is to scientifically break open a new paradigm.