Myth 1: Partial reproducibility is hard

Some people seem wary of the mountain of work that any true attempt at reproducible computational science must entail, and so they don't want any part of it.

Bah!

Start small; it's easy. Let me tell you: anything you do -- providing the raw data, posting any small scripts, detailing the versions of programs you used together with their parameters -- will be tremendously welcome to anyone trying to validate or build off your paper. This includes you yourself, in 2 years.

Myth 2: Incomplete reproducibility is useless

20% or 50% or 60% or 80% is all better than 0%. Sure, 100% is better than 80%, but it might be a lot more work to get to 100% rather than 80%, so why not start small and target something that seems achievable first? (It's OK, I give you permission!)

In practice, anything you do will be useful. (See #1.)

Myth 3: We need new platforms for reproducible computational science

Engineers like building stuff. It sure is easier (and hence more fun, at least in the short term) than doing science. But what we need right now is scientists actually using stuff that already exists, not engineers building new stuff that no one will ever use.

Every year at PyCon there's a fresh, new batch of eager young programmers who are interested in Open Science. Awesome! But they seem to spend a lot of time talking about how new software is needed. My message to them? However glamorous it may seem at first to build something new, you're wasting your time. Go help some scientists use existing software, instead.

Also: to a first approximation, IPython Notebook and knitr have won. Sure, they may not be everything you want or feel is needed, but they've hardly got any market penetration in science yet. I submit that you are more likely to make a difference by actually _using_ something imperfect than you are by trying to build something perfect. If, after you publish a paper or two and have some use cases that aren't met by existing tools, I will enthusiastically endorse you writing a new tool :).

Myth 4: Virtual machines solve the reproducibility problem

Maybe they do, but not usefully so.

Look, VMs are just giant black boxes. Your VM could be a giant lookup table that doesn't implement any actual methods but just regurgitates already calculated results. No one would be able to tell -- you're just providing an interface to something, right? But is that really useful in helping people understand what it is you did, replicating it on their own, or reusing and remixing it? No, not at all.

And yes, I've made this argument before. Bill even says that he sorta agrees with me now.

Myth 5: GUIs are the way to go, because scientists might actually use easy-to-use software.

Bioinformaticians like the idea of Galaxy (just to pick on one that I actually kinda like ;) because they think that it will make it easy for biologists to do computation. And hey, look, you get reproducibility for free!

Unfortunately, at least in the area of bioinformatics I work in, things change so rapidly that the GUIs don't keep up. By the time it's in a GUI, it's rarely still very cutting edge, which doesn't fit well with most research that I see being done.

But there's a bigger problem: almost all data analysis steps take place in a larger pipeline. The GUI has to wrap the entire pipeline, or else be scriptable in order to fit in; otherwise, it actually presents an obstacle to reproducibility, because you have to capture its parameters somewhere else.

Galaxy is a decent compromise: it is a GUI, that provides access to cloud resources; it wraps command lines, so you can pretty quickly integrate the latest thing; and reproducibility comes "for free". But (like all the other GUIs I've seen) it's pretty limiting in terms of what you can wrap with it.

Less talk, more work

At the end of the day, my lab's experience is this: you don't need much in the way of "magic sauce" to get started doing things reproducibly. You need a little bit of experience or training (cue Software Carpentry), and a little bit of elbow grease the first time around, but it's just not that hard to get started and make your research life better.

Fundamentally, my lab now does this stuff 'cause it makes everything easier. Automation reduces our maintenance burden; explicit workflows built for extensibility make paper revisions much easier; explicit instructions are good for training students. At this point I feel like we're moving clearly into the "virtuous cycle" stage, where the positive feedback of doing everything like this turns us into unstoppable borg-like computational scientists. We'll let you know how that goes.

--titus