Presented to "Managing the Material: Tackling Visual Arts as Research Data" workshop, organised by Visual Arts Data Service (VADS) in conjunction with the Digital Curation Centre (DCC), through the JISC-funded KAPTUR project. London, 14 September 2012

1. What's the data? Where’s the (re)use?
Reasons to select and where to start
Angus Whyte
Visual Arts
Data Service
(VADS) DCC
and KAPTUR
project
Managing the Pablo Picasso
Material: Bottle of Vieux
Tackling Visual Marc, Glass, Guit
Arts as
Research Data ar and
London Newspaper 1913
Friday, 14
September
2012
This work is licensed under a Creative Commons Attribution 2.5 UK: Scotland License

2. The Digital Curation Centre
• Consortium of 3 units in Universities of Bath (UKOLN),
Edinburgh (DCC Centre) and Glasgow (HATII)
• Funded by JISC, plus HEFCE funding from 2011
• challenges in digital curation
• across institutions or disciplines
• support to JISC e.g. MRD
• targeted institutional development
• Including University of the Arts London

3. DCC Mission
“Helping to build
capacity, capability and skills in
data management and curation
across the UK’s higher education
research community”
DCC Phase 3
Business Plan

4. Aims today
• Help gather thoughts on the need to be selective
• Suggest 7 things on which we might agree
• Focus on practical implications of scoping “research data”
• Consider kinds of data for reuse
• Triage – levels of care and how to decide

5. Selection Strategies
1. Keep everything, dispose by natural wastage
Practitioners
2. Select the significant, dispose of the rest
Traditional records mgmt
3. Select and prioritise effort, review cost benefits, dispose as
last resort
Practical?

6. Why not keep it all?
Increasing volumes outpacing declining storage hardware costs
Increasing care costs
According to: John Gantz and David Reinsel 2011 Extracting Value from Chaos
http://www.emc.com/digital_universe.
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7. 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
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8. We can’t share it all
Steven Harnad “Open Access Evangelism”
“ Researchers' unwillingness to make their
laboriously gathered data immediately OA is not
just out of fear of misuse and misappropriation.
It is much closer to the reason that a sculptor
does not do the hard work of mining rock for a
sculpture only in order to put the raw rock on
craigslist for anyone to buy and sculpt for
themselves, let alone putting it on the street
corner for anyone to take home and sculpt for
themselves. That just isn't what sculpture is
about. And the same is true of research …
http://openaccess.eprints.org/index.php?/archives/2010/05.html
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9. But…a better example?
bus routes data sculpture
• “a 3D data sculpture of the Sunday Minneapolis / St. Paul
public transit system, where the horizontal axes represent
directional movement and the vertical represents time.
the piece titled "bus structure 2am-2pm" is constructed
of 47 horizontal layers, each forming a map of the bus
routes that run during a given interval of time. looking
down from the top, one sees the Sunday bus map of the
Twin Cities, while looking from the side, the times
appears as strata building upwards. within each
layer, every transit route that operates at that time is
Reusingpublicdata to create an object represented by wood balls placed at its scheduled
stops, connected by the horizontal copper rods. each
with reuse value? route moves through time and space differently, carving
out its own trail that may or may not meet conveniently
with other routes.
• in total 42 routes, 47 intervals of time & 296 bus stops
are depicted by about a half-mile of copper rod & 6,000
wood balls, suspended in the air by hundreds of blue
threads
http://infosthetics.com/archives/2008/05/bus_routes_data_sculpture.html
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10. Things we might agree on?
1. Digital material becoming more pervasive
2. Research Councils want more transparency in use of public
funding, planning for digital resources , ongoing access to
‘significant electronic resources or datasets’
3. Artists, researchers, audiences influence what is
‘significant’
4. We can track what’s significant online, as will they

11. Things we might agree on?
1. Digital material becoming more pervasive
2. Research Councils want more transparency in use of public
funding, planning for digital resources , ongoing access to
‘significant electronic resources or datasets’
3. Artists, researchers, audiences influence what is
‘significant’
4. We can track what’s significant online, as will they

12. Things we might agree on?
4. Digital material is at risk e.g. from tech obsolescence or
loss of knowledge; researchers need advice on how to
mitigate risks, which they already get …

13. Things we might agree on?
5. Digital material is at risk e.g. from tech obsolescence or
loss of knowledge; researchers need advice on how to
mitigate risks, which they already get …

14. Things we might agree on?
5. Digital material is at risk e.g. from tech obsolescence or
loss of knowledge; researchers need advice on how to
mitigate risks, which they already get …

15. Things we might agree on?
5. Digital material is at risk e.g. from tech obsolescence or
loss of knowledge; researchers need advice on how to
mitigate risks, which they already get …

16. Things we might agree on?
6. Characterising ‘research data’ in the visual arts can help
get materials our institution has a ‘duty of care’ towards
(E.g. it arises out of and evidences any research or practice for which it
shares responsibility)
….into the hands of those who can help care for it
(wherever they are)
7. If their producers know there is a demand and earn credit
(e.g. citations, impact case studies)
…and everyone has clear expectations and examples

17. Things we might agree on?
6. Characterising ‘research data’ in the visual arts can help
get materials our institution has a ‘duty of care’ towards
(E.g. it arises out of and evidences any research or practice for which it
shares responsibility)
….into the hands of those who can help care for it
(wherever they are)
7. If their producers know there is a demand and earn credit
(e.g. citations, impact case studies)
…and everyone has clear expectations and examples
Then a definition does not need to do much more!
“Example moves the world more than doctrine” Henry Miller

18. Clarify expectations
What kinds of
“data” are
wanted
For what kinds
of reuse

19. Examples of what?
Institutions can follow research communities and data
centres’ lead in establishing collections policies and
preservation models through consultation
• What kinds of material
• What kinds of reuse
• What do we have ‘duty of care’ for
• What levels of preservation
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20. e.g. High Energy Physics community

21. e.g. High Energy Physics community
Levels of data to preserve Use case
1) Additional documentation Publication-related information search
(e.g. wikis, news forums)
2) Data in a simplified format Outreach, simple training analyses
3) Analysis level software and the Full scientific analysis based on
data format existing
reconstruction
4) Reconstruction and simulation Full potential of the experimental data
software and basic level data
Adapted from: DPHEP Study Group: Towards a Global Effort for Sustainable Data
Preservation in High Energy Physics, May 2012 . http://arxiv.org/abs/1205.4667

22. e.g. Archaeology Data Service
“The ADS expects to
collect all of the
following
archaeological data
types…”
http://archaeologydataservice.ac.uk/advice/collectionsPolicy
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23. A triage process
What levels of care
& ground rules
to decide

24. Clarify expectations
What ground rules
will you use to
prioritise care?

25. What kinds of data?
Conceptualise
Performances Sketchbooks
Disseminate Data? Create or
Collect
Prototypes A/V collections
Assemble and
Interpret
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