1.
‘Good, better, best’?
Examining the range and rationales of
institutional data curation practices
ROBIN RICE (PANEL CHAIR)
IPRES, UNIVERSITY OF NORTH CAROLINA, CHAPEL HILL, 3 NOV. 2015

2.
What is this panel about?
Many academic institutions are grappling with managing
local research data assets. Resources and approaches
vary. This panel will explore the range of curation
procedures at four data repositories in institutions.
And:
Weighing ‘best practice’ against real world concerns for
conservation of resources and meeting expectations…
And:
Reflections on: Peer, Limor, Ann Green and Elizabeth
Stephenson, “Committing to Data Quality Review” (2014).
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3.
Factors in levels of resourcing curation
 Pre-existing or new data service
 Expertise of staff
 Perceived importance of Research Data Management (RDM) by senior managers / policymakers
 Degree of commitment to long-term preservation and re-use of the data
 Level of funding brought in by research activity
 Extent of in-house support provided by libraries and IT centres
 Technologies available
 Relative size of institution
 Scope of disciplines
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4.
Context of Higher Education Institutions
 Academic libraries not traditionally involved in data curation (upstream vs downstream)
 Libraries are dealing with diversity, not only of disciplines but content types
 What are the institutional responsibilities for research outputs such as data?
 Vis-à-vis funders?
 Publishers?
 Disciplinary communities (domain data archives)?
 Open source communities?
 Commercial cloud-based services?
 Research institutes?
 Individuals?
 Other memory institutions?
 Nonetheless, institutions are creating value through engagement, partnering with researchers
 One size fits all, by necessity
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5.
Context of data repositories
 Some commonality with published information (discovery, citation), but much is different
 Data are research inputs as well as outputs – who are ‘users’? (Creators/depositors or re-users)
 Peculiar properties of research data
 Dynamism
 Huge variation in size
 Multiplicity of formats; closely coupled with software / code / models
 NOT self-explanatory, requires documentation
 Raw, processed, summarised, visualised, etcetera
 Concerns about sensitivity, confidentiality
 Value difficult to ascertain
 Ownership can be opaque
 Yet data rescue is a useful undertaking: doing something is still better than doing nothing!
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6.
This morning’s panel members -
 Each will talk about curation at a data
repository based in an institution
 Microcosm of diversity of IPRES delegation:
 Two countries represented
 Two institutional repositories: one specialising
in datasets, one IR that accepts data
 Two social science research institutes
 Different job titles, responsibilities
 Data repositories with different missions
 But first: Let’s find out more about YOU!
(Action poll)
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