Tackling visual arts as research data

20 September, 2012

KAPTUR, a JISC MRD project following on from the Keepit and Kultivate projects, will discover, create and pilot a sectoral model of best practice in the management of research data in the visual arts. It is led by the Visual Arts Data Service (VADS) and undertaken in collaboration with four institutional partners: Glasgow School of ArtGoldsmiths, University of London; University for the Creative Arts; and University of the Arts London.

The project is attempting to answer questions like 'what is so different about research data in the visual arts?' and 'where are there overlaps with research activities in other disciplines?'. It is also looking at research data managment policy development.

The DCC is carrying out an institutional engagement with UAL and hopes to support the delivery of an institutional policy on management of research data in the near future.

Last week’s KAPTUR workshop, entitled Managing the Material: Tackling Visual Arts as Research Data, was inspired by the DCC and ANDS 'how to guide' on Appraise & Select Research Data for Curation by Angus Whyte (DCC) and Andrew Wilson (ANDS).

Leigh Garrett, KAPTUR Project Director began the day by explaining that most people involved in the project had known very little about research data in the visual arts this time last year.

None of the institutional partners had policies in this area and there was no agreement on what research data in the visual arts actually was.

Although we’ve yet to arrive at consensus the discussions have begun and the Kaptur environmental assessment report and EVA paper both offer a good base from which to start looking at what research data a researcher might have and how they chose what to keep.

Where's the data? What's the use?

The first session of the day on was facilitated by DCC staff. Laura Molloy gave an overview of other JISC MRD projects in the arts field.

She explained how the Incremental project identified the importance of discipline-specific terminology when carrying out research data management training. 

Angus Whyte reiterated the need to be selective and explained that despite the differences between visual arts and other disciplines there are at least seven areas of agreement when it comes to research data.

  • Digital material is becoming more pervasive
  • Research Councils want more transparency in use of public funding, planning for digital resources , ongoing access to ‘significant electronic resources or datasets’
  • Artists, researchers, audiences influence what is ‘significant’
  • We can track what’s significant online, for example by tracking access and citation
  • Digital material is at risk e.g. from tech obsolescence or loss of knowledge;  researchers need advice on how to mitigate risks and tools are already available to help organisations support this, e.g. the DRAMBORA risk assessment tool
  • Characterising ‘research data’ in the visual arts can help get materials our institution has a ‘duty of care’ towards into the hands of those who can help care for it.
  • The key point in establishing guidelines to encourage deposit is not to be overly specific or dogmatic about what researchers must deposit, but to offer clear examples of different kinds of 'data' alongside their potential value for reuse - whether in learning about how work was created or reusing elements of it in new creations. In other words the 'producers' need to know what there is a demand for, and the final key point, how they can earn credit by sharing through the repository.

Clarifying expectations (of your funders, of your institution etc.) will help researchers know what they need to keep. Keeping everything just isn’t possible. Cost is one of the main reasons for this - at least if recent market projections are reliable.

Marieke Guy then took a look at some current definitions of research data and asked the delegates to consider how these might work within visual arts.

A hands-on session had delegates writing examples of research data within visual arts on post-it notes and considering if they fitted the given definitions.

Angus, Laura and  Marieke’s slides are available from Slideshare.

Collecting and interpreting Visual Arts Materials

The next session was presentations by two arts researchers.

As part of his PhD on Peirce's Semeiotic and the Implications for Aesthetics in the Visual Arts: a study of the sketchbook and its positions in the hierarchies of making, collecting and exhibiting, Paul Ryan, University of the Arts London has created a tagging tool (called Triadic Analytic Guide - TAG) for research data. TAG allows researchers to semeiotically analyse objects through 9 different emotional, material and conceptual categories. It asks 'what is your object?',  'What is your position of interpretation?'. Such a process could be hugely beneficial in enabling arts researchers to identify and assess their research data.

Amanda Couch, a Lecturer in Fine Art at the University for the Creative Arts talked about the research process she had been through during her PGCE. She explained that she hadn’t realised how much the research process would change her and mould her artistic identity. Her talk on framing research within practice allowed her to show some of her research data, from huge handwritten transcripts to notebooks and sketchpads. Amanda, like many others through the day, commented on the importance of the material presence of the work. Within the arts digital objects often don’t live up to their physical counterparts, research data is often originally physical.

Visual Arts Materials in Practice

Louise Corti, Associate Director of the UK Data Archive gave a whirlwind overview of ‘the journey of a data set’.

The UK Data Archive is the only national qualitative archive. It currently holds over 5,000 digital data collections which can be accessed for research and teaching.

The archive are keen to keep rich data that are well-documented and explained that bringing new data sets into the collection requires considerable work.

Applicants must fill in a brief form for each data set. Details will be given on the format, usability and condition of material. Some funders (e.g. the ESRC) may require data to be deposited with the archive and the archive are not in a position to make value judgments on the quality of the data. With interview data all transcripts are read, annonymised, contextualised, and DOIs created. Everything is zipped up and XML added, the procedures are very detailed. There are issues with contextualising data, for example, historians are good at using old data, it’s par for the course, but sociologists still find doing this problematic. There is an 'I wasn’t there, I can’t use it' attitude to data. The more conceptual details available, the more usable data is.

Applying the DCC and ANDS guide to visual arts research data 

The final sessions of the day were given by the KAPTUR team. Marie-Therese Gramstadt, the KAPTUR Project Manager, facilitated a session helping us work through some of the themes of the day: What is research data in the visual arts? How can visual arts research data be managed appropriately? Her excellent resource is available in a Prezi format and includes embedded videos and questions.

Leigh Garrett, KAPTUR Project Director, pulled together discussion threads by asking whether it would be possible to arrive at a definition for research data in the visual arts.

The following working definition from the EVA paper is an approach towards a definition for the KAPTUR, but at the same time the project team are aware of the complexities of the nature of visual arts data and have raised concerns about a definition which may limit the scope of data curation: “Research data can be described as data which arises out of, and evidences, research. This can be classified as observational, including: sensor data; experimental; simulation; derived or compiled data for example databases and 3D models; or reference or canonical for example, a collection of smaller datasets gathered together (University of Edinburgh 2011a). Examples of visual arts research data may include sketchbooks, log books, sets of images, video recordings, trials, prototypes, ceramic glaze recipes, found objects, and correspondence." During the plenary session Leigh suggested that maybe it was anything which is used or created to generate new knowledge or interpretations. If this was the case then where did intentionality fit in, and what about research outputs?

A blog post from KAPTUR will continue the discussions. The Kaptur project runs until the end of March 2013, a few months still to work out the answers!