One of the things you notice as a visitor from the UK in South Africa is how clean the toilets are. In restaurants, at the University, in public places. Sometimes a bit worn down but always clean. And then you start to notice how clear and clean the pavements are and your first response, well at least my first response, is that this is a sign of things going right. One element of the whole is working well. But of course one of the main reasons for this is that …

The software code that is written to support and manage research sits at a critical intersection of our developing practice of shared, reproducible, and re-useble research in the 21st century. Code is amongst the easiest things to usefully share, being both made up of easily transferable bits and bytes but also critically carrying its context with it in a way that digital data doesn’t do. Code at its best is highly reproducible but how do we get from “at its best” to its best being common practice? How hard should we be pushing on standards?

The Research Data Management movement is moving on apace. Tools are working and adoption is growing. Policy development is starting to back up the use of those tools and there are some big ambitious goals set out for the next few years. But has the RDM movement taken the vision of data intensive research to its heart? Does the collection, sharing, and analysis of data about research data management meet our own standards? And is policy development based on and assessed against that data? Can we be credible if it is not?

Michael Nielsen’s talk at Science Online was a real eye opener for many of us who have been advocating for change in research practice. He framed the whole challenge of change as an example of a well known problem, that of collective action. So how do we take this view and use it to effect the changes we want to see in research practice? And are we prepared to address our own collective action problem and place the overall goals above our own projects and approaches?

While there has been a lot of talk about data repositories and data publication there remains a real lack of good tools that are truly attractive to research scientists and also provide a route to more general and effective data sharing. Here I explore how a data repository system might be immediately attractive to researchers and what that means about design and driving uptake.

Science Online London ran late last week and into the weekend and I was very pleased to be asked to run a panel, broadly speaking focused on evaluation and incentives. Now I had thought that the panel went pretty well but I’d be fibbing if I said I wasn’t a bit disappointed with the audience reaction. Here I want to try and explain why the session was about incentives and what can really be done to make online science more valued. Because what I heard really excited me in terms of the opportunities for making that case and changing the conduct of science in general.