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.