Data Selection & Triage
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This document discusses data selection and triage, outlining challenges in deciding what research data is worth keeping and how to organize its handover. It provides an outline of the topic including what guidelines exist, criteria for judging data reuse value, and challenges such as uncertain research processes, engaging originators, and affordable preservation. The document suggests prioritizing data based on reuse value and affordability, with a tiered approach to deploying resources. Selection presumes adequate metadata description to assess value and make data affordable to preserve.
Data Selection & Triage
- 1. Data Selection & Triage JISC/DCC Progress Workshop Managing Research Data & Institutional Engagement Nottingham 25 October 2012 This work is licensed under a Creative Commons Attribution 2.5 UK: Scotland License
- 2. Introduction How can researchers and support staff effectively decide what data is worth holding on to, agree what to do with it, and arrange for its handover? What challenges does this represent How to address them?
- 3. Outline • What guidelines are there and why do we need more?Angus Whyte DCC and Marie Therese Gramstadt - KAPTUR • UK Data Archive's Data Review Process - Veerle van Eynden UKDA • Applying NERC's Data Value Checklist - Sam Pepler, British Atmospheric Data Centre • Discussion
- 4. Guidelines clarify expectations …adapted by Archaeology Data Service NERC KAPTUR University of Leicester What criteria will be used to judge what’s handed over?
- 5. Basic model 1. Define a policy i.e. criteria and range of decisions All 2. Archive manager applies data criteria, involving researchers 3. Select the significant, dispose of the rest 10 % For records records yes, but researchdata? 90%
- 6. Characterising research data… • Research process more uncertain and open-ended than admin processes • Research data purpose may change before complete • More effort to make reusable - complex inter- relationships, and richer contexts to document • Originators should be engaged but may not have capacity e.g. if project funding has ceased • Others may need to be involved with broader view of potential in other disciplines • More than keep/dispose choice –need to prioritise attention and effort to make data fit for reuse
- 7. Triage analogy First Deposit location characterise research data Institutional Data Prioritise Repository Criteria High reuse value + Data Centre needs attention Duty of care affordable Subject Repository etc. Reuse value Other permutations Tiered approach to Quality and deploying resources More permutations condition Discoverability Accessibility Low reuse value, Unaffordable Access management Costs associated Storage performance Potential to automate ? Preservation actions
- 8. Clarify expectations What kinds of “data” are wanted For what kinds of reuse
- 9. e.g.Data Centre Collection Policies “The ADS expects to collect all of the following archaeological data types…” http://archaeologydataservice.ac.uk/advice/collectionsPolicy 9
- 10. Costs should persuade us IDC Digital Universe Study- Increasing volumes outpace declining storage hardware costs According to: John Gantz and David Reinsel 2011 Extracting Value from Chaos http://www.emc.com/digital_universe. 10
- 11. We can’t afford it all “Keeping 2018’s data in S3 would cost the entire global GDP” http://blog.dshr.org/2012/05/lets-just-keep-everything-forever-in.html 11
- 12. Selection presumes description • You can’t value what you don’t know about! • Researchers can’t afford NOT to spend effort on minimal metadata description and organisation, because costs of retention will be much higher if they don’t • Description makes data affordable – is citation potential a concrete enough reward? 12
- 13. Challenges • Identify what datasets are created and where they are • Differentiate those that are of high value from those where most uncertainty or least reusability • Be able to justify ‘natural’ wastage of low priority data as much as deliberate selection of high value
- 14. Questions • What has worked/is working • What lessons have you learned and how generalisable • What challenges remain • How may they be approached and what do you intend to do • What DCC / MRD activity do you think may help make the challenge more tractable.