Digital curation for postgraduate students

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Slides from a DC101 workshop run at the University of Northumbria on behalf of the JISC-funded DATUM for Health project.


  • 1. Digital Curation for PGR Students
    Joy Davidson and Sarah Jones
    HATII and the Digital Curation Centre
    DATUM for Health
  • 2. Session aims and objectives
    Aimed at PGRs, well use the context of starting a PhD to:
    introduce the curation lifecycle model as a means of contextualizing the roles and activities required to maintain access to data over time
    highlight some of the curation tools and approaches available and provide pointers to further information and support
    help participants prepare the curation aspects of their PhD study
    We hope you leave able to explain why data curation is important and
    what roles PGR students / researchers play
  • 3. What is data curation?
    the active management and appraisal of data
    over the lifecycle of scholarly and scientific interest
    Data have importance as the evidential base
    of scholarly conclusions
    Curation is part of good research practice
  • 4. Why curate: requirements
    Code of good research conduct
    data should be preserved and accessible for 10 years +
    data are a public good and should be openly available
    Funders data policies
    Common principles on data policy DataPolicy.aspx
  • 5. Why curate: rewards
    Prevent data loss
    More citations: 69%
    (Piwowar, 2007 in PLoS)
    Validation of results
    New research opportunities and collaborations
    Easier to do your research
  • 6. DCC curation lifecycle model
  • 7. Conceptualise: planning what to do
    • define a research question and design your methodology
    • 8. bid for funding (incl. data management and sharing plans)
    • 9. plan data creation (capture methods, standards, formats)
    PGR student, supervisory team, sponsors / funding bodies, IT, research governance, ethics panel
    Decisions made now have an impact on every other stage of the lifecycle, so it is worth getting things right from the start!
  • 10. Specific issues to consider
    • Defining your method access to software, equipment, skills
    • 11. What storage needs do you anticipate - enough capacity?
    • 12. What are your universitys / sponsors requirements?
    • 13. What ethical approval do you require?
    • 14. What agreements do you have to establish at the outset?
    • 15. Will you make use of any existing data licences needed?
    • 16. Can the data be shared?
    • 17. Are there any legal or ethical restrictions?
    • 18. Are there likely to be any embargoes on data publication?