No, we don’t have a list—not yet—but we’d like to. What short, readable papers or articles do you think every scientist doing computational work should read at some point in their career (preferably early in their career)?  Paul Dubois’ 2005 article in Computing in Science and Engineering on maintaining correctness in scientific programs is a favorite of mine; so is Evan Robinson’s summary of research on the effects of overwork on productivity. (Actually, I think everyone should read Robinson’s article, not just computational scientists…) What else should be on the list? To qualify, entries must be short (up to a few pages long), well written, of broad general interest, and have something important to say that’s relevant to our target audience. Suggestions in the comments, please…

Mark Guzdial recently posted some interesting (and for us, slightly depressing) statistics about MIT Open Courseware. Long story short, it looks like that flagship effort isn’t as widely used as many of us had hoped or believed.  I can’t find equivalent stats for the Khan Academy (a scrappy “agile” alternative to MOC that has been getting a lot of attention in the geek community), but even if I could, those numbers probably wouldn’t answer my real question: who is this reaching, and what impact is it having? As Guzdial points out, asking users to self-report is subject to large sampling bias, as is googling for links back to lectures. Our sponsors want to know who’s using our stuff, and whether it’s helping them: how do we answer those questions?