Last week I was invited to give a two hour workshop/presentation on research data management at the University of Nottingham (UoN) Academic Librarians’ Forum (ALF). This forum meets regularly to discuss wider LIS issues and topics relevant to their role in supporting the researchers’ at UoN.
An integral part of the ADMIRe project is to identify the RDM training needs of both our research community and those that will be providing services offering research data management support. A key aspect of raising RDM awareness at UoN is the delivery and organisation of RDM training, advocacy and outreach. This was a great opportunity to gather some initial thoughts and views on how the academic librarians’ saw the future of a sustainable RDM service, and in particular the skills that they may already have on managing information, as well as finding information.
The title of the event was ‘What is research data management?’ and the event organiser provided me with a series of RDM topics to cover during the session. The aim of the event was to raise awareness of research data management (RDM) and identify some of the key skills required for the delivery of a research data management service. The event and user feedback from the event will inform and enhance the development of the RDM service at the University of Nottingham.
We had 12 attendees and had two interesting break-out activities, one was around the RDM skills matrix ADMIRe has been working on and the other was reviewing the recently published: ‘Ten recommendations for libraries to get started with research data management’, published in August 2012 by the LIBER (Ligue des Bibliothèques Européennes de Recherche – Association of European Research Libraries).
Activity one – RDM skills matrix
The RDM skills matrix includes several key elements of the research lifecycle and attendees where asked to identify where they think library staff could provide support on a variety of RDM issues. The majority agreed that they already had the skills in the following areas:
- Open Access and Repositories
- Data discovery and data re-use
- Compliance with funding policies and requirements
- Data classification
Some of the areas where they felt they needed further training included:
- Data types
- Data storage
- Data preservation
- Data archiving
- Data Management Plans