SciCurious has a poll up asking readers how many papers they read per week, and whether they think they read enough (so far, most respondents don’t think they do). Which prompted this rather peeved reaction from DrugMonkey, about how the number of papers one reads is meaningless, and certainly not something one should brag about. Reading is a means to end, and one reads very differently depending on the purpose for which one is reading. That often means not reading full papers, but perhaps just skimming the figures.
As a grad student, I read a lot, by which I mean I read the full text (often while making marginal notes) of every paper that interested me in every leading journal (and I have pretty broad interests). I also read the full text of many, many older papers. I really took to heart Steve Stearns’ “modest advice” to read and think exhaustively if I wanted to make a success of my graduate program. I eventually became my lab’s walking, talking EndNote database; anyone who was trying to remember a citation would come ask me. I tried my best to continue the habit as a postdoc and later as a faculty member, but it’s gradually been crowded out by other demands on my time, even though I can’t quite bring myself to admit it (I have a growing folder of unread pdf’s on my hard drive called “To Read”). And yes, reading that much was something that I was proud of, probably in part for the wrong reasons (the sort of reasons that got DrugMonkey annoyed). But then again, as I’ve remarked elsewhere on this blog, I definitely think I’m a better scientist for having read that much, that broadly, and in that much detail. So even if my reading habits did in part (but only in part) reflect a rather silly desire to just read lots of stuff for its own sake, as if whoever dies having read the most words wins, well, all that reading still had beneficial effects. Put it this way: had my reading been solely motivated by a ruthless calculation as to how much and what sort of reading would best develop my “scientific chops” (as DrugMonkey puts it), I’d have chosen to read the same way.
Which is not to say that’s the right way for everybody. Brad Anholt, for instance, once told me that he reads the Introductions, and just the Introductions, of every paper published in every leading ecology and evolution journal. He said that that’s how he keeps up with current thinking in the field–what are the big questions, what do we know about the answers, and what are people doing right now to increase that knowledge? I was impressed with this, both because it sounded like a really good approach given the goals of his reading, and because I couldn’t imagine myself finding the time to do it, even as a grad student!
These days, I read a fair number of abstracts (basically, as many abstracts as I once would’ve read full papers). And I read–or plan to read!–the full text of a much smaller number of papers that look really interesting or are directly relevant to my own work. This lets me keep up with the field (not as well as Brad does!), and ensures that I’m very familiar with the stuff I really need to be familiar with.
I encourage you to click through and check out the linked posts, especially the one by DrugMonkey. DrugMonkey is right that your reading should be tailored to its purpose, and that faculty give students the impression of erudition simply because they’ve had more cumulative time to read a lot of stuff (so don’t feel like you need to have read everything your supervisor has read by the time you graduate). Not sure I entirely agree that Discussion sections should just be skipped, though. DrugMonkey’s post is titled “I don’t give a flying fig about your interpretation of your data”, because what he cares about is his own interpretation of your data. I can see where he’s coming from–I certainly read critically and don’t just take the author’s word for what the data mean. But I do read Discussion sections, for various reasons. For instance, they sometimes make the paper much easier to understand. It’s particularly helpful to be walked through difficult math by an author who’s a gifted explainer, like Steve Ellner or Robin Snyder. And even if I don’t agree with an author’s interpretation of his or her own data, I often want to know what his or her interpretation is, so that I’m aware that I disagree. Such disagreements can be good fodder for one’s own research (and one’s own blog!) Work that convincingly undermines a prevailing view can be very important.
So how, and how much, do you read?