Seeing the Forest, or The Importance of Writing

I'm now well into my 6th year of grad school, and partly because of that, I've been doing a lot more writing than in years past.  I'm writing job applications, I'm writing manuscript drafts, I'm writing (occasionally) here on this blog.  And I'll be honest, writing is not always as easy as programming or field work.  In fact, it's almost never that easy.  I haven't mentioned it much on the blog, but most of the research I do is actually quantitative and programatic in nature.  I spend little time in the field compared to how much time I spend working on R code.  That's not a complaint - just a fact.  As the amount of writing I do increases, especially for job apps, it strikes me that this process is very important to help me focus on the bigger picture of my research goals.  

If asked what type of research I do, I'll often say, "I develop quantitative methods to answer questions in conservation biology".  Where do those questions come from? For the past five and half years, they have mostly come from sitting around with my colleagues, discussing some paper, and thinking, "Yeah, but what about this thing they don't talk about ..." or "That seems good, but what would you do to make it even better ...".  Or from talking with other Ecologists and Evolutionary Biologists (particularly my committee members) about some pattern that just hasn't been explained that well.  In a large part, that's how my research project on non-native species lag-phase dynamics has come about.  Let me delve into my own thesis project a bit more - I took this question about lag-phase dynamics (i.e. what's going on during that lag-phase) and said, "ok, given some data, how might I go about understanding the processes going on during that time? I can build a model, simulate the most likely processes, and see if the simulation matches reality." (There you go folks, a description of my PhD in two sentences.) I decided on the "most likely processes", chose the types of models I would need for simulation, and got started.  This is a good project management technique - everything got broken down into little pieces.  Each piece was further subdivided.  The subdivisions get translated into programming projects. The subdivision continues from there.   So where I am now is at a point of wrapping up multiple projects, most of which involve finishing of pieces of code here and there. And it takes a conscious effort to step back and look at the project as a whole. (It doesn't help that the integration of each part of the project is the last step!)  And that's where the writing comes in.  I'm forced to step away from my coding for a bit, and describe the research I do.  It's not writing code - it's posing questions relavent to conservation, then developing ways to answer those questions.  And while I always know this, hours debugging an R script will pretty much clear my mind of everything not related to that script.  So for now, I welcome that time dedicated to writing (like right now), and appreciate the focus it give me, so I'm not missing the forest for the trees.