20130222 kaptur training_goldsmiths

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Goldsmiths, University of London, RDM training session on 22nd February 2013.

Goldsmiths, University of London, RDM training session on 22nd February 2013.

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  • 1. 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– DMPOnline2
  • 2. 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 Policy3
  • 3. Kaptur– Model of best practice– Environmental assessment– Evaluate management systems from user perspective– Deliver RDM policy– Sustainability and business plan– DMPOnline– Dissemination4
  • 4. 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 Report5
  • 5. Findings– Difficult to define– Multiple roles– Awareness– Collaboration– Outside the institution– Need for assistance– Archiving– Storage– Re-use of material6
  • 6. 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, VADS7
  • 7. 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 CAiRO8
  • 8. 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, correspondence9 with venue/curators.
  • 9. 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 Service10
  • 10. 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 return11
  • 11. Funder requirements12
  • 12. 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 repository13
  • 13. 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 Records14
  • 14. DigitalCuration ConceptResearch DataLifecycle1. Select2. Organise Finalise/Prese Develop nt Proposal3. Preserve4. Present Plan/Perform Research
  • 15. DCC Curation Lifecycle Model16
  • 16. 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.17
  • 17. 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.18
  • 18. 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.19
  • 19. File formatsContent Type Ideal Format Acceptable formatDocuments Rich text format Docx, open document formatImage Tiff Png, Raw Jpeg 2000 (uncompressed)Audio Aiff Mp3 Wav FlacAudio/Video Mpeg2 Mpeg420
  • 20. 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 sorting21
  • 21. break22
  • 22. Group exercise From the research output example1. Identify the different possible types of research data2. 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?23
  • 23. 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/glossary24
  • 24. 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 Ward25
  • 25. Thanks!26