Driving institutional research data policy
Institutional data policy is necessary as one of the drivers of changing practices towards research data across the institution. The role of data policy generally, according to Neylon, is to drive data availability, data management, and data archiving while stressing the importance of data as a core output of public research.
In our first post we identified DataPool’s three-pronged approach – system, policy, training – that we hope will enable us to develop and support a rich collection of research data emerging from the University of Southampton. Here we report on how the proposed research data policy is shaping at Southampton, and on progress piloting it through the research and senior management channels towards adoption.
Where we stand now with the policy at Southampton is it was recently given a final-stage presentation to the university’s Research and Enterprise Advisory Group (REAG), which directed the policy to the University Executive Group (UEG). Ultimately, UEG can forward it to the university’s highest policy-making body, the Senate, perhaps by March 2012 if it goes well.
This rate of progress is due in part to the work of our predecessor Institutional Data Management Blueprint project, but credit is also due to the sterling work of Wendy White and Mark Brown at the head of the DataPool Project in piloting the draft policy through the advisory group and policy-making networks. Wendy and Mark are veterans of the university’s Open Access Policy, so they know their way around the networks of influence concerning the development of institutional repositories.
The policy includes the policy document supported by a series of user guides to smooth implementation. It would be premature to describe the specifics of the policy here, although broadly it covers a researcher’s responsibilities, IPR, storage, retention, disposal and access, as well as setting out contextual issues such as purpose, objectives, and definitions. My viewpoint on reading the draft policy is to anticipate how a researcher might respond to it in terms of clarity of actions, options and consequences. In this respect it is noticeable how much the policy has improved through review and iterations. Admittedly it may not attract the same level of excited publicity as, say, an open data policy, but the scope is wider and the purpose more pragmatic.
We do not expect the policy to be without issues when it comes to implementation, clearly, for an initiative of this scale, but the policy will give the DataPool Project the basis to investigate and resolve the issues, in terms of actions and answers. On current schedule, there should be a year for the project to work with this.
There is little prior art on institutional data policy, and one of the reasons JISC has funded DataPool is not just to help produce a data policy, but to inform other institutions on implementation. Logged on the DCC page of UK Institutional data policies are currently just four examples, one of which is a ‘commitment’ rather than a policy, while others are in the early stages of implementation. Policy implementation, monitoring and ability to adapt are the real testing ground for this latest phase of research data management projects.
More, and somewhat better established, data policies can be found among the UK’s research funders, again as logged by DCC. These policies can be seen as context rather than competition for institutional data policies. One of the reasons managers of institutions might commit to research data policy are the requirements on their researchers that are embedded in the funder policies. For the institutions there is a need to support their researchers in complying with the policies, for no doubt there will in future be implications for research assessment processes. There is also the incentive of competition between institutions, and the scent of a leading edge in exploiting innovation driven by the profound changes in digital research data management. As Neylon says: “In the longer term, those who adopt more effective and efficient approaches will simply out compete those who do not or can not.” We will look in more detail at the funder policies and their implications for institutions in a later post.
One of the points of contention in emerging data policy is to define the term ‘research data’. How can policy on this be effectively implemented unless everyone has the same understanding? This may be a semantic argument, but it must also be rooted in current practice by researchers, and also in how that practice is already being shaped by current policy, notably from the research funders. My simplistic take here is that researchers are finding their own preferred approaches to storing and managing early-stage research data, that is, data some way from publication. We might call this the Dropbox approach. Meanwhile funder policy, on the other hand, tends to apply more to data that underpins publication, that is, is concerned with the quality and reproducibility of results, the bedrock of scientific testability. If simple and unrepresentative, this view on the different motivations and practices for capturing both early and late-stage research data nevertheless seems to mirror the framework of our companion JISC DataFlow Project at the University of Oxford, as represented in its DataStage (a secure personalized ‘local’ file management environment for use at the research group level) and DataBank (an institutional-level research data repository allowing researchers to store, reference, manage and discover datasets) processes, respectively.
Seeing the Southampton policy develop through engagement with research, policy and legal experts on advisory groups it is easy to anticipate this prospectively as a worthy policy exemplar for research data. It won’t be the last institutional research data management policy:
> @simonhodson99 By March 2013 all these #jiscmrd projects will develop research data management policies for their institutions http://t.co/gqzYf4pC #idcc11, 6 December 2011
Timing is key, and our aim is to bring forward policy ratification early in 2012 rather than by March 2013, the project end. It’s important to allow enough time to test the policy in practice. Given the scope of its intended coverage and the range of open questions posed by research data, it is possible the policy might contain unexpected holes or omissions that could limit uptake by both willing and unwilling researchers. Even when adopted – perhaps even more so when adopted – we have to be proactive and vigilant in monitoring how researchers respond to the institutional research data policy.
[…] DataPool Project has published an interesting blog post about that project’s progress on taking forward a research data management policy. DataPool’s three pronged approach (addressing system, policy and training) was described […]
Hi Steve – great to hear where this work is heading. There’s been a bit of work in Australia in this area, including a policy summit for research organisations held in June this year. Presentations and resources (such as policy docs from a number of universities, including my institution Monash University) are available on the summit website – http://sydney.edu.au/research/data_policy/programme.shtml.
At Monash University, we took a slow and steady approach. Guidelines aimed at researchers were available on our website first, and then various draft versions of policy and procedures docs circulated for a very long time (more than two years) through various forums and committees. We weren’t too worried about pushing it through quickly and treated the policy consultation process itself as a set of opportunities for advocacy and awareness raising. By the time the policy framework finally made it to Academic Board in December 2010, it went through without a hitch, possibly because people had gotten used to the concepts over a fairly long period.
Responding more directly to the question of how to define ‘research data’ we went with a broad and fairly functional definition at Monash. It seemed to us more important to be inclusive than to be (at this early stage anyway) pinning things down. Other institutions have taken a different approach to definitions and to their policies – I think it’s very much about finding the right way to do it for your institution.
Good luck!
Sam
Sam, Thanks for sharing your experience with the Monash research data policy. How would you sum up implementation of the policy in the year since it was ratified? Can you quantify this? What issues have emerged?
Hi Steve – the process of implementing the policy has been multi-faceted, involving both top-down and bottom-up activities. Monash is a large multi-campus university, and responsibilities for data management are very distributed, so things can take some time to get going.
On the top-down front, the data management policy framework is only one of a suite of new or revised research policies that represent Monash’s response to the requirements of the Australian Code for the Responsible Conduct of Research (2007). These have been developed at different times in the past few years and a top-down communication/promulgation process about the new/revised policies is expected to take place this year.
In the meantime, staff from the Library, the Monash e-Research Centre and eSolutions (formerly ITS) have been working closely with our Faculty of Pharmacy and Pharmaceutical Sciences to explore a more bottom-up implementation. This work is described in the Monash keynote at the policy summit that I mentioned (including a very positive assessment from the Associate Dean of Research of the Faculty). The learnings from this project will hopefully feed into a toolkit of some kind for faculties / units that we will make available when we launch our redeveloped data management website in mid-2012.
During 2011, extensive work was done on a data management strategic plan for 2012-2015. In the first half of 2012, we will also be consulting wth the Monash community about this document, which outlines objectives not just for policy framework adoption, but also for technical infrastructure, skills development, improving research impact through data dissemination, and consolidating the university’s approach to leadership and collaboration in this area. Unfortunately it will be some time before this strategy is publicly available, but people are welcome to get in touch if they’d like to find out more.
Overall, I would say that implementation of the policy is only one part of what we are doing. It’s an important part, but unless it is undertaken as part of a much broader program of work, the sustainable cultural change that is really needed would be difficult to achieve.
Sam