So why is research a good career?  The years of training are long.  There’s the 4 year undergrad, the 5-6 year PhD, the 2-3 year post-doctoral fellowship and finally, finally, if all has gone according to plan and you’ve been lucky, you are ready to look for a job.  Oh, but what a job!

 

What hooked me on a research career was twofold: the freedom, and the surprises.  As an academic, I am paid to study something I personally find fascinating.  I follow the leads I think are most promising, I organize my day and I tell the stories I want to tell.  Could it get any better than that?  I’m fundamentally curious about biology, surrounded by colleagues who are too, as well as by energetic young students who are just discovering how much fun learning new stuff is.  So all around me are people who are trying to understand basic aspects of biology, surprising themselves (and me) on a regular basis because what we don’t know is huge.   While pop music suffers from sameness (ABBA is back???) and the new fashion simply reinvents the styles of the 60s, 70s and 80s (arghh!  leg warmers are back????), biology sparkles with its ability to surprise.  Just when we thought we understood something, we find that we didn’t after all.  It turns out to be much more interesting.  Cool.

 

 I have a friend who is a TV producer, and she was telling me that there are only about 8 stories, and the job of directors/producers is to tell these stories over and over again and make then seem new.  Wow.  There’s a study in contrast for you.  What is prized in science is the new.  The surprise is what it is all about.  We thought we knew how it worked, we did the experiment, and lo and behold, it way more interesting than we thought!  That’s why it gets into a famous journal--not because the science is fundamentally better (although sometimes it is) but because of the surprise factor.  And I dearly love to be surprised.  I even like to be wrong (at the bench anyway).  This is really hard for many young students to grasp.  They start out with a biological question, a nice juicy one ripe with promise, devise a few models for how it could work, and then plan some careful experiments to test it.  Now at any given time, it’s  hard not to favor one model over the others, especially when it is elegantly simple and well, beautiful to look at.  The student falls in love with a model, or worse, succumbs to the fate of believing a model irrespective of the data.  This is happening more and more as cartoons are being published many many times:  people start to believe that the model is right and don’t bother going back to read the primary literature.  A charismatic spokesman championing a specific model will necessarily give it more weight, but it is essential to remember that models are imaginary constructions, and disposable ones at that. One must be willing to abandon a favorite model when the data points in another direction.  Period.  This is often quite hard for lovestruck young students (although established scientists can also be seduced).   Difficult or not, it must be done.  Savour the joy of surprise (hmmm--that model just can’t be right), and imagine a NEW favorite model that might be.  Move on.