A while ago I started a series on writing that I’ve been meaning to come back to. I’d been on sort of an extended hiatus from reviewing papers, just because I had a bunch of grant deadlines in the spring- but the few papers I’ve reviewed recently have made me think about paper writing organization of data and figures, how to decide when you are ready to write, the content of the different sections, what order I write things in, and so on… and so on. An endless topic for blog posts.
Anyway. The process of writing a paper begins long before you actually sit down to type the words into the document itself, at least for me. This process should begin in your head while you are collecting the data, and carefully recording protocols, data, conclusions, and next steps in your notebook. It would be nice if it could start even before that- with planning of the project itself. But I think that in reality it doesn’t really work like that, and projects take unpredictable twists and turns, and sometimes you end up on interesting (and fruitful) tangents that are impossible to plan for. So- when you are doing experiments and collecting the data- you should be thinking about figures that include all the relevant controls (and if you don’t know whether or not you got them all, try bringing that one up in lab meeting because I’m sure you’ll get some input). And, you should be thinking about beautiful publication quality figures with all the right statistical tests, properly applied. It is just horrible to have a bunch of data on figures that are messy, ugly, and worst of all – incomplete. And reviewers hate messy looking incomplete data, and they can use one spot of weakness to take you down!!
A note about statistics, because it is always shocking to me the number of papers that I review that come to me without ANY statistics. I mean error bars alone, do not statistically significant data make. After you figure the standard error, you have to apply a statistical test (you’ll have to figure out which is the appropriate statistical test for your particular situation), and then determine the probability that the results you got could have occurred by chance alone. I’m always amazed by how many authors forget this critical step. Or they say they do the statistical test, the put asterisks all over the figure, and they forget to tell you what statistical cutoff they used …P< ????, you say. It is ok if you are not a statistical wizard, and most of the time you don’t really need to be-… but if you should find yourself under-educated in this area- get the help of someone that knows what he/she is doing!
While you are doing the experiments- you should also be thinking ahead about what set of figures it will take to make a complete set of data. You are going to have to try to think of all the pieces, and of how the reviewer will think when they look at your data set. This will probably take some practice- so if your mentor is a genius at this- try to pay attention and learn all that you can. Learning to do this will save you having to do actual experiments to answer reviewer criticisms, although you will probably still have to write long point-by-point rebuttals of reviews.
Once I get a set of data, I fastidiously make all the figures basically just as I would want them to appear in the paper. I lay them all out as I’m going along, and figure out where the holes in the story are- if there are any left at that point. Then we try to do those very last, clean experiments. I don’t know why- but that last experiment that you need, always seems to take nearly forever to get just exactly right … that last western always has more nonspecific background than it should, and just by Murphy’s law these take what feels like an eon to troubleshoot.
While I’m waiting for that last perfect figure, I just start writing… and I always do the Materials and Methods first..and actually I can even start that earlier while I’m making the figures themselves… because it breaks up the monotony of using all the same font and formatting figures.
So, next up- Materials & Methods.