Earlier this week, I and my co-facilitator had four wide-ranging and thought-provoking discussions across two days with the JISCMRD02 projects who attended the programme workshop on institutional research data management policy development and implementation at Weetwood Hall in Leeds.  Conducted under the Chatham House rule, we hoped projects and interested Fellow Travellers would feel able to share their challenges, successes, questions and institutional quirks openly, and I’d like to thank the participants for their time and energy in doing so!

It has been indicated to me that some preliminary notes of themes arising from our discussion would be useful, in advance of more detailed reporting.  I’d like to share some of the main themes that emerged from our group, with the provisos that:

• these only represent one of the several discussion groups – main themes from the others may vary (and you can read Bill Worthington’s useful account from another group here); and
• these are presented here for interest and discussion – please don’t interpret any of them as the official position of or advice from the MRD02 programme, the DCC or JISC – they’re simply ideas that bubbled up from our group conversations and were contributed by twelve individuals representing ten very diverse institutions, as well as the thoughts of our facilitator.

That said, we hope the lessons they’ve learned from their work so far in RDM policy development will be useful to others travelling the same path.

Themes and observations:

– At this point (March 2012), institutions are still all at different stages with their research data management policies.  However, as far as  they’re funded by the major funding councils, research councils and associated bodies, institutions are all subject to a common set of requirements, mandates and expectations from those funders, in addition to UK and EU legislation. In other words, the responsibility to have these expectations and requirements clarified and complied with is already there. It’s now up to institutions to decide their approach to an appropriate and realistic response.

– The idea of having an institutional research data management policy in place at your institution can be reassuring.  However, having a policy in place without any real buy-in from staff can be more harmful over time – by breeding complacency – than having no policy yet in place. So it’s best to take a little longer and get it right than rush through a policy in which researchers, research support staff or senior management have no investment or of which they have little awareness.

– A useful approach may be to craft an aspirational, high-level document which outlines principles as opposed to specific attributions of responsible persons, workflows, budgets and so on.  This high-level statement is often more easily understood by senior management and so can be the most effective way to get the policy through university senior committees and into institutional regulations.  This high-level policy should then be accompanied by, and executed by way of, working documents which translate the principles into specific tasks allocated to specific roles.  It should be anticipated that the high-level policy will not need frequent changes; it should allow enough room for, for example, new funder requirements, whereas the working documents should be regularly updated and seen as much more volatile documents.  This is, however, only one type of approach to institutional RDM policy development.  See also the JISCMRD02 Open Exeter project’s blog on the value of aspirational policy here.

– Policy and infrastructure need to evolve in correlation.  Some policies have been well-written but have foundered at the point of senior approval because they have specified responsibilities and workflows which the institution didn’t yet have the infrastructure to deliver.  At the same time, a well-organised policy can help to make the case to senior management for the investment in the necessary infrastructure.  This is another argument in favour of the high-level principles-based approach to the main policy, which can then be used to justify moving towards a more detailed position over time, via the working documents, whilst avoiding the danger of being rejected because of the lack of infrastructure.  It’s also an argument in favour of carrying out some surveying of the current state of infrastructure at your institution – including the ‘soft’ infrastructure elements of training provision, current skills levels in relevant staff groups, staff awareness of the requirements under which they’re currently working, etc.

– Consider the other policies – both internal and external – with which your new research data management policy should work in concert.  It’s obviously better to identify and iron out any potential wrinkles between these before you start plugging the new policy to senior management.  Examples of internal documents may include institutional policies on digital preservation, IT equipment use, open data, response to Freedom of Information requests, data protection, research ethics, intellectual property and academic integrity.  External documents to consider may include the Data Protection Act, Freedom of Information legislation, INSPIRE regulations, environmental data legislation, expectations and requirements of your funding council, expectations and requirements of your research funders, the Research Integrity Office’s research code of conduct, the RCUK code of research practice and relevant legislation relating to use of government data, intellectual property and copyright.

– Retain awareness of the different roles and legislation for research data and administrative data.  Whilst anyone drafting a research data management policy would benefit from knowledge of how the institution handles administrative data, and there may be some crossover in relevant legislation (particularly UK and EU legislation for some aspects of both), it’s important to remember these two categories of data have different purposes, different stakeholders, and attract different expectations by funders, and so should be dealt with by discrete policies, clearly pitched to the relevant audience for each.

– Try to avoid taking the view that researchers will automatically resist implementation of a research data management policy.  Some may be suspicious of it, some will be enthusiastic – and the difference is often down to the approach used.  In institutions where the development and implementation of such a policy is presented as a way to help researchers (e.g. ‘We’ll look after it so you don’t have to’, promotion of the benefits to the researcher, etc.), as opposed to being a new rule or requirement imposed by the central administration, researchers have generally responded enthusiastically.

– Whilst recent research (e.g. the JISC/RIN/DCC DaMSSI project) found that researchers respond well to data management training when it is presented as just one of many aspects of excellence in research practice, there is a tension between embedding RDM training as just another part of routine business and highlighting it sufficiently to attract attendance at training and to ensure researchers pay attention to good RDM practice.  Motivation can be helped by underlining the benefits of good RDM practice to the researcher’s career and profile, their enhanced ability to find their own work in the future, increased impact and a more efficient way of working.

Do any of these points chime with your experience?  Or contradict it?  Let us know in the comments!