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Reasons to select research data and where to start

Reasons to select research data and where to start

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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

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

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Reasons to select research data and where to start

  1. 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. 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. 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. 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. 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. 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. 6
  7. 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 7
  8. 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 8
  9. 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 9
  10. 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. 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. 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. 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. 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. 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. 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. 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. 18. Clarify expectations What kinds of “data” are wanted For what kinds of reuse
  19. 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 19
  20. 20. e.g. High Energy Physics community
  21. 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. 22. e.g. Archaeology Data Service “The ADS expects to collect all of the following archaeological data types…” http://archaeologydataservice.ac.uk/advice/collectionsPolicy 22
  23. 23. A triage process What levels of care & ground rules to decide
  24. 24. Clarify expectations What ground rules will you use to prioritise care?
  25. 25. What kinds of data? Conceptualise Performances Sketchbooks Disseminate Data? Create or Collect Prototypes A/V collections Assemble and Interpret 25

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