This document discusses research data in the context of visual arts research. It defines research data, discusses its importance and challenges in the visual arts domain. Key points covered include the heterogeneous nature of visual arts data, principles of data curation and preservation, and the need for data management planning and assistance with archiving. Examples of types of visual arts research data are provided.

2. program
– What is research data
– Kaptur
– What is visual arts research
data
– Importance of research data
– Principles for data curation
and preservation
– Break
– Group exercise
– Data management planning
– DMPOnline
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3. What is research data?
‘data in the form of facts,
observations, images,
computer program results,
recordings, measurements
or experiences on which an
argument, theory, test or
hypothesis, or another
research output is based.
Data may be numerical,
descriptive, visual or
tactile. It may be raw,
cleaned or processed, and
may be held in any format
or media’ - Queensland University
of Technology Management Policy
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4. Kaptur
– Model of best practice
– Environmental assessment
– Evaluate management
systems from user
perspective
– Deliver RDM policy
– Sustainability and business
plan
– DMPOnline
– Dissemination
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5. Findings
There appears to be little
consensus in the visual
arts on what research data
is and what it consists of.
Variously described by the
interviewees as tangible,
intangible, digital, and
physical; this confirms the
view of the project team
that visual arts research
data is heterogeneous and
infinite, complex and
complicated. – Kaptur
Environmental Assessment Report
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6. Findings
– Difficult to define
– Multiple roles
– Awareness
– Collaboration
– Outside the institution
– Need for assistance
– Archiving
– Storage
– Re-use of material
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7. Kaptur definition
Evidence which is used or created to generate new
knowledge and interpretations. ‘Evidence’ may be
intersubjective or subjective; physical or emotional;
persistent or ephemeral; personal or public; explicit or tacit;
and is consciously or unconsciously referenced by the
researcher at some point during the course of their research.
As part of the research process, research data maybe
collated in a structured way to create a dataset to
substantiate a particular interpretation, analysis or
argument. A dataset may or may not lead to a research
output, which regardless of method of presentation, is a
planned public statement of new knowledge or
interpretation– Leigh Garrett, VADS
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8. Definition
“Within the creative arts
research data is evidence
of an identified research
activity… Research data
includes preparatory,
unfinished and
supportive work in
digital form in addition
to data relating to
completed works.” –
Project CAiRO
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9. Types of data
storyboards, mood boards,
sketch book pages, notes,
architectural models,
reflection journals,
recordings of
activities/conversations,
video/audio, digital
photographs, video
recordings, interviews,
computer algorithms ,
interactive physical art,
installation, exhibition
records, catalogues, preview
invitations, correspondence
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with venue/curators.

10. Why manage?
‘data drives a huge amount of what happens in our
lives…because someone takes the data and does
something with it’ -Tim Berners-Lee
‘The management of research data is recognised as one
of the most pressing challenges facing the higher
education and research sectors’ - JISC
‘It is a truth universally acknowledged that researchers
are interested in data of all kinds, regardless of origin
or type’ – Australian National Data Service
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11. Drivers
– Good practice
– Funder requirements
– Quantity of data in digital
form being produced
– New technologies and
practices
– Danger of obsolescence, loss
of data, integrity of the data
– Follow up projects
– Data can be of value long
after a research project
– Validation of research
– Full economic return
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12. Funder requirements
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13. Goldsmiths RDM Policy
http://www.gold.ac.uk/research-data/
– Agreed standards
– Throughout research data lifecycle
– Funding body requirements
– PI responsibility
– Capture, management, integrity, confidentiality, retention, sharing,
reuse, publication
– College will preserve access (up to 10 years)
– Deposit elsewhere should be registered
– FOI needs to be considered
– Data repository
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14. Curation
– Focusing on what is needed
for validation and re-use,
rather than on the intrinsic
attributes of research data,
is useful because it raises
important considerations
that might otherwise be
seen as external to the
dataset itself but impact
upon the value and future
use of the dataset: for
example, identifiers, file-
naming protocols,
metadata and
documentation University of
Melbourne draft policy on the Management of
Research Data and Records
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15. Digital
Curation
Concept
Research Data
Lifecycle
1. Select
2. Organise Finalise/Prese Develop
nt Proposal
3. Preserve
4. Present
Plan/Perform
Research

16. DCC Curation Lifecycle Model
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17. Digital Preservation
– Longevity: the data will be available for the period of time
their current and future users (the designated community)
requires.
– Integrity: the data are authentic – they have not been
manipulated, forged or substituted. Because digital
preservation techniques such as migration inevitably alter the
data, authenticity has to be demonstrated by paying attention
to characteristics of the data such as provenance and context
– Accessibility: we can locate and use the data in the future in
a way that is acceptable to its designated community.
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18. Techniques: Integrity
– Copying data to a reliable digital storage system
– Managing ongoing data protection in accordance with good IT
practices for data security, backups, error checking
– Refreshing (moving to a newer version of the same storage
media, or to different storage media, with no changes to the bit
stream), checking accuracy of the results and documenting the
process
– Maintaining multiple copies
– Ensuring you have the right to copy and apply preservation
processes, which may require negotiation with rights owners.
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19. Techniques: Accessiblity
– Assigning persistent identifiers to the data to ensure they can be found
– Adding sufficient representation information to data (for example,
information about file format, operating system, character encoding) so
that the bit stream is still meaningful and understandable in the future
– Producing data in open, well-supported standard formats
– Limiting the range of preservation formats to be managed
– Keeping track of developments (especially obsolescence) in hardware,
software, file formats and standards that might have high impact on
digital preservation
– Retaining and managing the original bit stream in case future
developments mean we can restore access to it.
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20. File formats
Content Type Ideal Format Acceptable format
Documents Rich text format Docx, open document
format
Image Tiff Png, Raw
Jpeg 2000
(uncompressed)
Audio Aiff Mp3
Wav
Flac
Audio/Video Mpeg2
Mpeg4
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21. File naming
– Consider the elements that will help you to organise and locate
content
– E.g. Participant ID, site of data collection, date of data collection
– Consider how data files and directories may be organised & sorted
– 001, 002, 003, 004, can be used for sequential files
– YYYY-MM-DD (2012-12-04) useful for organising by date (use year first)
– Identify different versions of content in filename (and in content)
– Creation date (YY-MM-DD)
– Version/draft number
– Consider how your filenames will look to others
– Avoid spaces - ‘My file.pdf’ becomes ‘My%20file.pdf’ on the web
– Avoid capitalisation - Alters file sorting
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22. break
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23. Group exercise
From the research output example
1. Identify the different possible types of research data
2. How would you ‘Kaptur’ this data? Hardware? Software?
Formats? Documentation?
3. Are there any issues concerning IPR, copyright, data protection,
ethics?
4. What would you need to do to ensure longevity, accessibility and
integrity of the data?
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24. links
– https://dmponline.dcc.ac.uk/
– http://kaptur.wordpress.com/
– http://www.dcc.ac.uk/
– http://www.jisc.ac.uk/whatwedo/programmes/mrd.aspx
– http://datalib.edina.ac.uk/mantra/
– http://www.dcc.ac.uk/resources/curation-lifecycle-model
– http://kapturmrd01.eventbrite.co.uk/
– http://www.projectcairo.org/
– http://www.vads.ac.uk/kaptur/
– http://vocab.bris.ac.uk/data/glossary
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25. images
– http://www.flickr.com/photos/articnomad/16153058/sizes/z/in/photostream/ - joshua Davis
Photography
– http://kirok-of-lstok.deviantart.com/art/Secrets-in-Unanswered-Questions-Title-Artwork-290260406
– Back them up! http://vads.ac.uk/flarge.php?uid=33946&sos=0
– Reckitt, Helena, Mullin, Diane and Scoates, Christopher. 0001.
– Paul Shambroom: Picturing Power.http://eprints.gold.ac.uk/7628/
– Born out of pleasure – Harrell Fletcher http://eprints.gold.ac.uk/7655/
– Other images Andrew Gra/Janice Ward
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26. Thanks!
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