Research data management can make a significant contribution to an institution’s research performance but needs solid user requirements research, an understanding of the researcher working space and a collaborative approach between researchers and support staff for infrastructure to be adopted, understood and sustained in the institution.  That was the message from this session on 11 July in Edinburgh at Open Repositories 2012 on research data management and infrastructure, from the perspectives of three particular institutions.

Unmanaged to managed

First we heard from Natasha Simons from Australia’s Griffith University.  Natasha made a clear connection between the university’s position in the top 10 research universities of Australia, and the existence of their Research Hub, which was developed with funding from the Australian National Data Service.  The Hub stores data and relationships between the data, exports to ANDS, and provides Griffiths researchers with their own profiles which allow better collaboration across the institution by allowing researchers to find others with similar research interests for collaboration and supervision.

Natasha outlined some challenges the Griffith team have met and are currently facing, but ultimately reported that they are successfully transforming institutional data in line with ANDS aims from unmanaged to managed; disconnected to connected; invisible to visible; and single-use to reusable.

Resourcing for RDM

Another institution which connects RDM with its prestigious position in the research league tables is Oxford; Sally Rumsey of the University’s Bodleian library took us through their vision for their institutional research data management infrastructure, encompassing current work on the Oxford DMP Online and the DaMaRO project; data creation and local management (DataStage, ViDASS); archival storage and curation (DataBank, software store); and data discovery and dissemination (document repository, Oxford DataFinder and Colwiz).

Sally argued that that data management doesn’t stop at digital objects:

“Paper in filing cabinets, specimens in jars: all could exist as data.”

She also reminded us that although emerging funder requirements, and particularly this year’s EPSRC roadmap requirement, were doing much to focus minds on RDM, there is also the challenge of unfunded research, a major component of research activity at Oxford.  This needs requirements and funding for management, too.

Sally was asked whether researchers were going to end up paying for RDM infrastructure.  She argued that there needs to be a budget line in research bids to cover these costs.  This prompted me to think about the fact that we talk about getting researchers trained from the start of their research activity, but to bring about the kind of awareness that will lead to researchers knowing to cost in data management in their bid, we need to engage with them before they start even writing the bid.  This is an argument for engagement at PhD level at the latest, and for a much wider and more consistent provision of RDM training in universities in order to bring about this kind of change in culture.  Clearly we also need simple, accessible costing tools to help non-specialists quantify explicit costs for data management and preservation, for inclusion in funding bids.

Adopt, adapt, develop

Anthony Beitz, manager of Australia’s Monash University eResearch Centre, also has nascent culture change in mind.  He described the availability of research data as having the potential to change research work:

“We’re going to see things we’ve never seen before.”

Anthony’s description of how the eResearch team works at Monash is based on a clear understanding of the characteristics of the research space and how that differs from the way in which IT services staff work.

• Researchers: focused on outcomes.  They work in an interpretive mode, using iterative processes.  The approach may be open-ended and thrives on ambiguity.  Requirements and goals may change over time.  May require an ICT capability for only a short period of time – don’t tend to care what happens to it after the end of the project.  Resourceful, driven, and loyal to their discipline more than the institution.
• IT services: broad service base.  Supporting administration, education and research.  Continuity of IT services is a priority.  Excel at selecting and deploying supporting institutional enterprise solutions.  IT works in analytical mode as opposed to the research space, which is in interpretive mode.

The volume of data is growing exponentially, but funding to manage it is certainly not.  In this context, a clear articulation of need between the researcher space and the IT services space is crucial.  Anthony argues that researchers need to participate actively in the deployment of an institution’s RDM infrastructure.  Media currently used is not good for reliability, security or sharing, but no single institutional RDM platform will fit all researchers’ needs.  RDM solutions must be a good cultural fit as researchers have stronger synergies with colleagues beyond the institution and are more likely to use solutions within their disciplines.  Anthony suggests that IT services should adopt existing solutions being used within disciplines, where possible, as building a new one breaks the collaboration cycle for researchers with colleagues from other institutions, asserting, “going into development should be a last resort.”

In this way, much of the RDM activity at Monash seems to be explicitly responding to current researcher behaviours.  Adoption of emerging solutions is encouraged by promoting a sense of ownership by the researchers; by delivering value early and often; and by supporting researchers in raising awareness of a RDM platform to their research community.  If users don’t feel they own a resource, they’ll look to the developers to sustain funding.  If they feel ownership, they’ll look for funding for it themselves, so buy-in is not only good for adoption but also for sustainability.