This week, Cameron Neylon published a new study and set of recommendations that should inform how we think about making change happen in data sharing and data management through policy. As Cameron says,

"While there’s a steadily increasing number of funders requiring data management plans and data sharing there is less work asking whether they work. There’s a lot less work asking what might be an even harder question, what does it actually mean for them to work?"

You can read Cameron's summary on his blog and the study 'Building a Culture of Data Sharing: Policy Design and Implementation for Research Data Management in Development Research' in full in RIO (open access, naturally).

Cameron spent two years working with International Development Research Center of Canada (IDRC) to look at the implementation of data policy. IDRC's ultimate goal with their policy and policy implementation was culture change  "good data management and data sharing being the default for researchers", says Cameron. 

Cameron Neylon's thoughts on open access and open data are always worth listening to, in my opinion. I can't summarise better than he said it, so I'm just going to excerpt from his excellent post:

"When we looked at the projects we found any of the challenges you might expect for data sharing in places away traditional centers of research prestige. Lack of good network connectivity, limited capacity and resources. We saw serious issues arising out of the English-centric nature of not just research communication, but systems and infrastructures. And we saw that the funding of infrastructures was inconsistent, periodic and unstable.

But what we also saw were challenges that arose at the funder level.....Within our pilot project we had a group of highly engaged program officers that were learning about data management as they went. New policies required them to become instant experts able to advise, and where necessary push, funded projects towards new practice. There are a lot of policy initiatives within funders at the moment, and the load means that not all of them get the attention they need.

This turns out to be a problem because of the message it can send. Culture change really involves change within both researchers, and the funder. Each on their own can change practice, but embedding that practice so it becomes a culture, of “that’s just how things are done” requires mutual reinforcement.... I had a very engaged and sympathetic group of program officers and projects, but even for this group, data wasn’t always the highest priority. We had to plan to regularly reinforce, and provide support for, the message that data was important over the course of the pilot project. Across a whole funder the risk of balls being dropped is very high. This raises issues for policy design and implementation."

It would be great to hear your views on data policy implementation as a means of driving behaviour and culture change. Do you agree with Cameron's recommendations? What can you add based on your experience?