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Northumbria University case study

  1. 1. Northumbria University: Towards a robust RDM solution Dr David Young Research Funding and Policy Manager, Research and Innovation Services, Northumbria University
  2. 2. Context: Research at Northumbria Metric Value Research Grants and Contracts Income (2015/16) £6M Active research projects (2017) 473 Total outputs in repository (2017) 22,438 Students (2015/16) 32,000 Academic staff FTE (2017) 1,200 Ranking in REF2014 “research power” tables 50th REF2014 GPA 2.71 Research information systems PURE, Agresso, Eprints, SAP, SITS
  3. 3. Timeline I: RDM at Northumbria …But no supporting infrastructure in place, and then no IT representative! (Summer 2015) RDM policy and guidance approved by RIC (April 2015) In-depth interviews with EPSRC award holders (Spring 2015) Initial survey of research data management practice (Early 2015) RDM Working Group reporting to Research and Innovation Committee (Late 2014)
  4. 4. Interaction with DCC • Jan 2016 – Workshop on RDM policy development and implementation Services that are being implemented across UK HEIs based on results of 2015 DCC survey which collected responses from 60 institutions Results of group exercise which indicates RIS, Library and IT Services seen as natural leads in delivering majority of RDM services
  5. 5. RISE Workshop I • May 2016 – RISE Workshop with RDM WG focusing specifically on options appraisal for data repository Level One Level Two Level Three Service primarily supports data deposit to third-party repositories, and holds datasets in-house when legal /regulatory compliance requires this Service defines criteria for in-house retention of datasets of long-term value to the institution Service defines criteria for developing datasets as special collections and ensures these meet specialist depositor and user needs Level One Level Two Level Three Service supports minimum external requirements for metadata and publicly accessible data Service supports community best practice standards for data access, citation and metadata exchange Service supports bespoke content discoverability, access and quality review needs for user groups or organisations
  6. 6. RISE Workshop II Area of Data Management Capability E-Prints ReCollect Figshare for Institutions PURE Ingest 2 2 1.5 Data access, publishing and discovery 2 2 1.5 Preservation 2.5 2 1.5 Management and reporting 2 2.5 2.5 Integration 3 2.5 2
  7. 7. How has the RISE model helped us? • Enabled us to have a clear view of our current capabilities and shortcomings around RDM support infrastructure • Provided a benchmark for senior managers • Formed part of evidence base for position paper on RDM • Helped secure budget allocation for RDM support system and staffing • Bridged the communication gap between research support services (RIS, Library) and other critical central services (IT, procurement) • Key input to formal University options appraisal and business requirements paper
  8. 8. Timeline II: RDM at Northumbria post-RISE Aim to pilot system and then roll out (from Jan 2018) Aim to go to procurement (Sept 2017) Business requirements and options appraisal (May-June 2017) Budget allocation for RDM system and staffing (Mar 2017) Further survey of likely heavy data users (Jan 2017) IT Services representation on RDM Working Group (late 2016)
  9. 9. Any Questions?

Editor's Notes

  • Sources for data:
    RGCI and student numbers:
    Active research projects: internal G&C database
    Academic staff FTE: internal HR database
    Repository data:
    REF2014 ranking/GPA:
  • RDM working group chaired by PVC Research, with academic representation from all faculties, and Library, Research & Innovation Services, IT, VCO/Legal, established to formulate an RDM policy for the University and support development of RDM good practice and technical infrastructure
    Research data management practice survey (Spring 2015): responses from 11/22 departments, showed a wide range of practices in relation to management of data during and post-project, including storage on memory sticks, personal hard drives, laptops, varied backup practice, low awareness of need for long term preservation
    In-depth EPSRC award holder interviews revealed similar lack of awareness of EPSRC’s requirements around RDM
    Policy and guidance:
    No RDM infrastructure: awareness of what was required, but no data repository, no staffing to support staff development
    IT representative left the group and was difficult to replace
  • Initial workshop with Joy Davidson and Angus Whyte (DCC) indicated aspects of good practice across areas of: policy and strategy (RDM part of good practice rather than just about funder mandates), data management planning, managing active data, but that longer term business planning, long term preservation and storage were areas of weakness.
    Also initial scoping of responsibility for RDM showed most people in working group felt RIS, Library and IT were natural leaders for this
  • The first part of the RISE workshop involved a self assessment on the University’s current level of capability to support RDM against the level we would like to achieve. In total we measured current performance and target according to RDM policy on 8 criteria:
    Data collection
    Security, legal and ethical risk assessment
    Preservation, planning and action
    Continuity support
    Monitoring locally produced datasets
    Data publishing mandate
    Level of data curation
    Metadata collection policy
    On most of these criteria we assessed current performance as being at level 1, with the aim of reaching level 2 by the end of the RDM WG lifecycle. The examples given on this slide relate to data collection policy – where the aim is to move from existing practice of deposit on third party datasets towards in-house retention – and data publishing – where the ambition is to move from minimum support to best practice standards for data access, citation and metadata exchange
  • The next part of the workshop defined a platform longlist and shortlist. The shortlist was assessed by DCC against the ReCap model (an extension of RISE) and forms an initial options appraisal to be used to inform further work by the University.
    Each of the broad areas listed above was broken down into specific criteria in the full report. The table above shows the (rounded) average score for each system in each area of data management capability (each area had a number of criteria which were rated between 1 and 3.
    The only system which didn’t meet Northumbria requirements in some areas – in the opinion of the report author – was PURE.
    This was supplemented by a narrative report discussing the pros and cons of each longlisted system, including the three in the shortlist above.