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Science in the Open

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I recently made the most difficult decision I’ve had to take thus far as a journal editor. That decision was ultimately to accept the paper; that probably doesn’t sound like a difficult decision until I explain that I made this decision despite a referee saying I should reject the paper with no opportunity for resubmission not once, but twice.

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I had a pretty ropey day today. Failing for 45 minutes to do some simple algebra. Transformation overnight that didn’t work…again…and just generally being a bit down what with the whole imploding British science funding situation. But in the midst of this we did one very cool and simple experiment, one that worked, and one that actually has some potentially significant implications. The only things is…I can’t tell you about it.

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The rationale behind open approaches is the way it enables you to make unexpected connections and to find otherwise hidden shortcuts. People, data, code, and expertise can be more effectively connected when the information is out there and discoverable. Here I wanted to document a little collaboration that was sparked on twitter and carried through using an entirely open toolset.

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When we talk about open research practice, more efficient research communication, wider diversity of publication we always come up against the same problem. What’s in it for the jobbing scientist? This is so prevalent that it has been reformulated at “Singh’s Law” (by analogy with Godwin’s law) that any discussion of research practice will inevitably end when someone brings up career advancement or tenure.

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Image via Wikipedia Richard Stallman and Richard Grant, two people who I wouldn’t ever have expected to group together except based on their first name, have recently published articles that have made me think about what we mean when we talk about “Open” stuff. In many ways this is a return right to the beginning of this blog, which started with a post in which I tried to define my terms as I understood them at the time.

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Image via Wikipedia I had a bit of a rant at a Science Online London panel session on Saturday with Theo Bloom, Brian Derby, and Phil Lord which people seemed to like so it seemed worth repeating here. As usual when discussing scientific publishing the dreaded issue of the Journal Impact Factor came up. While everyone complains about metrics I’ve found that people in general seem remarkably passive when it comes to challenging their use.

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Image via Wikipedia I have been trying to draft a collaboration agreement to support a research project where the aspiration is to be as open as possible and indeed this was written into the grant. This necessitates trying to pin down exactly what the project will do to release publications, data, software, and other outputs into the wild.