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A biophysicist teaches himself how to code

Although he blogs almost as rarely as I do, John Wilbanks (VP of Science Commons) tends to inspire me with many of the things he writes.

Back at the end of 2009, he had a few posts on why the Open Source metaphor doesn’t work well when talking about science. While he’s speaking in this case more generally about science as a whole, his comments reflect directly on my post from yesterday on data management. I wanted to summarize a few of his key points and my thoughts on them.

Before I do so, however, I’ll put in another plug for the Science Commons Symposium, taking place on February 20th in Seattle. John Wilbanks will be there, along with a host of other strong voices interested in knowledge sharing. It should be a great event. If you can’t make it in person, it will be streamed live at

If you’re interested in reading his posts in their entirety, you can find them in parts 1, 2, & 3. In order to stick to a more continuous story, I’ll just be pulling quotes at random out of all three of John’s posts.

Several of the comments here yesterday pointed out some specific LIMS projects that have been started. I can see why (given how tightly I focus on a LIMS at the end of my post) people would latch onto this idea, but what I really had in mind was something more like the following:

We need the biological equivalent of the C compiler, of Emacs […] These tools need to be democratized to bring the beginning of distributed knowledge creation into labs, with the efficiencies we know from eBay and Amazon

Because of the complex and variable nature of “DATA” being generated in science labs, I think making one LIMS to rule them all would be nearly impossible. What I’d rather see are some tools that are accessible to the average bench scientist which can be easily modified and expanded upon by the technically gifted scientist. These tools would (if they are to be truly useful) automate some annotation/tagging/parsing of the data as a precursor to deposition in shared repositories such as:

[OpenWetWare and the Registry of Standard Biological Parts] are resources and toolchains that absolutely support distribution of capability and increase capacity, which are fundamental to early-stage distributed innovation.

Above the meat-space layer where the science is actually being done and data is being collected, we need decentralized places to store and share the “functional information units” – i.e. the data that other scientists can use. Unfortunately:

science is like writing code in the 1950s – if you didn’t work at a research institution then, you probably couldn’t write code, and if you did, you were stuck with punch cards. Science is in the punch cards stage, and punch cards aren’t so easy to turn into GNU/Linux.

I think John stretches the metaphor a bit here, but I see where he is going. The punch card above has more to do with the controlling influence of the institution than it has to do with the day-to-day practice of science. The key point is that there are interests who will put up a resistance to a more free distribution of scientific knowledge, for a variety of reasons.

He goes on to summarize his argument:

I propose that the point of this isn’t to replicate “open source” as we know it in software. The point is to create the essential foundations for distributed science so that it can emerge in a form that is locally relevant and globally impactful


it’s not something that’s enabled by an open source license, a code version repository, and other hallmarks of open source software. It’s users saying, “screw this, I can do better” – and doing it. It’s users who know the problem best and design the best solutions.

I couldn’t agree more, and I think this is what we’re seeing from the blog posts and conversations that are taking place. There are a subset of people who are doing science or who are avidly interested in aiding the practice of science who feel like they can do better than the current system. These people (probably most people reading this blog, especially if you’ve gotten this far) are the ones who have to effect change. It will take more than writing and talking about it, although these are important as well. I’d like to also see a nascent, community-driven project which we can point to and say “it will be like this, but better”.

One final word from John:

Data and databases are another place where the underlying property regimes don’t work as well for open source as in software. But that’s difficult enough to merit its own post. Suffice to say if Open Data had a facebook page, its relationship status with the law would be “It’s Complicated.”


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