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

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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.

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Weighing ‘best practice’ against real world concerns for conservation of resources and meeting expectations…

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Reflections on: Peer, Limor, Ann Green and Elizabeth Stephenson, “Committing to Data Quality Review” (2014).

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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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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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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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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)