I’ve been following Titus Brown’s blog for a little while now, and this blog is part of an effort to emulate his (relatively conservative) brand of open science. However, if it wasn’t obvious from my lack of new content over the past few months, I’ve been in a bit of a rut. What gives?
Well, for one thing I don’t “own” the research I produce. Or, at the very least, I don’t completely own it. There are my collaborators to think about, not to mention my PI, whose ultimate permission I must obtain to openly discuss the things we are hammering on. And in a competitive field, there is the real fear (valid? I’m not sure) of being scooped.
So, what to do? I think the landscape science operates in is changing, and the way science is done must change in order to cope. Funding is becoming harder to get. Reproducing scientific work, even those dependent on software alone, is difficult. And these days obtaining data is becoming less of a problem; instead, in many fields the emerging problem is how to cope with data glut, which itself requires a new skill set.
The open science movement seeks in part to take the lessons from the open source software movement and apply them to science. This is particularly salient for someone in my field, since in simulation-based work raw and distilled data is both generated by software. Open science finds little contradiction here.
But the more competitive a field is, the more perceived incentive there is to keep work bottled up until the big reveal in the form of a journal publication. Rarely this publication includes the full data set (in my field, this would mean raw simulation trajectories and the input parameters) and all code (mainly analysis code, though not all molecular dynamics codes are open source). What’s more, the main input to our simulations are experimental structures, and the crystallography community is known to be very competitive. We are thus constrained by our experimentalist collaborators’ needs.
If anything, I’ll be using this space to share what I can about my work, especially ideas with regard to software and scalable analysis (with a bent toward applications to molecular dynamics problems). Change in science is slow, but I’ll try to do my part here.