Successfully reported this slideshow.
We use your LinkedIn profile and activity data to personalize ads and to show you more relevant ads. You can change your ad preferences anytime.
Research Data Management in the
Arts and Humanities a few difficulties
TomPhillips,AHumument(1970,1986,1998,2004,2012…)
M...
About the DCC
 The UK’s centre of expertise in digital preservation and data
management, established in 2004
 Provide gu...
Context and overview
 Policy-driven expectations to archive, link and share the data
(evidence) underpinning scholarly pu...
Structure of the hour
1. Talk (“Defining our terms”)
2. Exercise (“Sketching the problem”)
3. Discussion (“What can we ...
What is research data management?
“the active
management and
appraisal of data
over the lifecycle of
scholarly and
scienti...
The old way of doing research (science)
1. Researcher collects data (information)
2. Researcher interprets/synthesises dat...
The new way of doing research (science)
Plan
Collect
Assure
Describe
Preserve
Discover
Integrate
Analyze
DEPOSIT
…and
RE-U...
N.B. other models are available…
Ellyn Montgomery, US Geological Survey
What’s “normal” is shifting…
Data management is a part of good research practice.
- RCUK Policy and Code of Conduct on the...
Why do RDM?
In a word,
so we and others
can re-use data
in the future
RDM key drivers
 TRANSPARENCY: The evidence that underpins research can be
made open for anyone to scrutinise, and attemp...
And also (persuasively)….
Because
funders
mandate it
 Definitions vary from discipline to discipline, and from funder to funder…
 Here’s a science-centric definition:
 “The...
Scientific and other methods…
 The scientific method is a body of
techniques for investigating phenomena,
acquiring new k...
 “A work is never completed except by some accident such as
weariness, satisfaction, the need to deliver, or death: for, ...
 There’s nothing new about data re-use in the Arts and
Humanities; it’s an integral part of the culture, and
always has b...
 Digital ‘data’ emerging in the Arts is as likely to be an outcome of the creative
research process as an input to a work...
Archiving issues
 Business case (“could anyone die or go to jail?”)
 The law: data protection
 Policy: retention and em...
Exercise
 Make a quick list of the policies and laws
which govern the management of research
data in your institution. Wh...
Suggested ranking for the Sciences
 TRANSPARENCY: The evidence that underpins research
can be made open for anyone to scr...
Suggested ranking for the Arts & Humanities
 TRANSPARENCY: The evidence that underpins research
can be made open for anyo...
 Need – what do we need to archive? Is it always evidence without which the
research outcomes are in doubt?
 Want – do w...
 Be careful with our terminology
 “Data” – be clear that this is not the dictionary definition, but rather
shorthand for...
 Paper: Marieke Guy, Martin Donnelly, Laura Molloy (2013) “Pinning It Down: Towards a Practical Definition of
‘Research D...
And if all else fails?
 Eat your data
 Put your data in the cloud
 Abandon your data at a grocery store
 Put your data...
Thank you / Diolch
 For information about the DCC:
 Website: www.dcc.ac.uk
 Director: Kevin Ashley
(kevin.ashley@dcc.ac...
Upcoming SlideShare
Loading in …5
×

Research Data in the Arts and Humanities: A Few Difficulties

1,100 views

Published on

Session given at SCONUL Summer Conference, Cardiff, June 2016

Published in: Education
  • Be the first to comment

Research Data in the Arts and Humanities: A Few Difficulties

  1. 1. Research Data Management in the Arts and Humanities a few difficulties TomPhillips,AHumument(1970,1986,1998,2004,2012…) Martin Donnelly, Digital Curation Centre, University of Edinburgh SCONUL Summer Conference, Cardiff, 23 & 24 June 2016
  2. 2. About the DCC  The UK’s centre of expertise in digital preservation and data management, established in 2004  Provide guidance, training, tools and other services on all aspects of research data management  Organise national and international events and webinars (International Digital Curation Conference, Research Data Management Forum)  Our primary audience has been the UK higher education sector, but we increasingly work further afield (Europe, North America, Australia, South Africa) and in new sectors (government, commercial, etc)  Involved in various European projects and initiatives, including FOSTER, OpenAIRE and EUDAT  Now offering tailored consultancy and training services
  3. 3. Context and overview  Policy-driven expectations to archive, link and share the data (evidence) underpinning scholarly publications are increasingly becoming ‘the new normal’  The drivers behind this shift tend to be quite science-centric, to the extent that in some circles the terms ‘research’ and ‘science’ are used almost interchangeably. This, alongside other terminological problems such as the use of ‘data’ as shorthand for a broad range of quantitative and non-quantitative research objects, can serve to alienate those working in the Arts and Humanities  These sessions will explore some of the problems inherent in treating all research observations, records and products as ‘data’ and will include an opportunity for attendees to reflect on the policy and support environment within which their own institutions operate…
  4. 4. Structure of the hour 1. Talk (“Defining our terms”) 2. Exercise (“Sketching the problem”) 3. Discussion (“What can we do to make things better?”)
  5. 5. What is research data management? “the active management and appraisal of data over the lifecycle of scholarly and scientific interest”
  6. 6. The old way of doing research (science) 1. Researcher collects data (information) 2. Researcher interprets/synthesises data 3. Researcher writes paper based on data 4. Paper is published (and preserved) 5. Data is left to benign neglect, and eventually ceases to be accessible
  7. 7. The new way of doing research (science) Plan Collect Assure Describe Preserve Discover Integrate Analyze DEPOSIT …and RE-USE The DataONE lifecycle model
  8. 8. N.B. other models are available… Ellyn Montgomery, US Geological Survey
  9. 9. What’s “normal” is shifting… Data management is a part of good research practice. - RCUK Policy and Code of Conduct on the Governance of Good Research Conduct
  10. 10. Why do RDM? In a word, so we and others can re-use data in the future
  11. 11. RDM key drivers  TRANSPARENCY: The evidence that underpins research can be made open for anyone to scrutinise, and attempt to replicate the findings of others.  EFFICIENCY/VfM: Data collection can be funded once, and used many times for a variety of purposes.  SPEED: Data can be distributed and accessed more quickly. In some disciplines, such as climate science, this is vital.  RISK MANAGEMENT: A pro-active approach to data management reduces the risk of inappropriate disclosure of sensitive data, whether commercial or personal.  PRESERVATION: Lots of data is unique, and can only be captured once. If lost, it can’t be replaced.
  12. 12. And also (persuasively)…. Because funders mandate it
  13. 13.  Definitions vary from discipline to discipline, and from funder to funder…  Here’s a science-centric definition:  “The recorded factual material commonly accepted in the scientific community as necessary to validate research findings.” (US Office of Management and Budget, Circular 110)  [Addendum: This policy applies to scientific collections, known in some disciplines as institutional collections, permanent collections, archival collections, museum collections, or voucher collections, which are assets with long-term scientific value. (US Office of Science and Technology Policy, Memorandum, 20 March 2014)]  And another from the visual arts:  “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.” (Leigh Garrett, KAPTUR project: see http://kaptur.wordpress.com/ 2013/01/23/what-is-visual-arts-research-data-revisited/) So what is ‘data’ exactly?
  14. 14. Scientific and other methods…  The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge.  To be termed scientific, a method of inquiry must be based on empirical and measurable evidence subject to specific principles of reasoning.  The Oxford English Dictionary defines the scientific method as: “a method or procedure that has characterized natural science since the 17th century, consisting in systematic observation, measurement, and experiment, and the formulation, testing, and modification of hypotheses.”  Source: http://en.wikipedia.org/wiki/Scientific_method An art methodology differs from a science methodology, perhaps mainly insofar as the artist is not always after the same goal as the scientist. In art it is not necessarily all about establishing the exact truth so much as making the most effective form (painting, drawing, poem, novel, performance, sculpture, video, etc.) through which ideas, feelings, perceptions can be communicated to a public. With this purpose in mind, some artists will exhibit preliminary sketches and notes which were part of the process leading to the creation of a work. Sometimes, in Conceptual art, the preliminary process is the only part of the work which is exhibited, with no visible end result displayed. In such a case the "journey" is being presented as more important than the destination. Source: http://en.wikipedia.org/wiki/Art_methodology
  15. 15.  “A work is never completed except by some accident such as weariness, satisfaction, the need to deliver, or death: for, in relation to who or what is making it, it can only be one stage in a series of inner transformations” – Paul Valéry  Paraphrased by Auden as “A work of art is never completed, only abandoned”  “You could not step twice into the same river” – Heraclitus, as reported by Plato (via Socrates)  “In science, one man’s noise is another man’s signal” – Edward Ng  ‘Truth?’ said Pilate. ‘What is that?’ – John 18:38 A few useful (?) quotations
  16. 16.  There’s nothing new about data re-use in the Arts and Humanities; it’s an integral part of the culture, and always has been…  Think Shakespeare’s plots, found sounds/ objects/ poems (e.g. Duchamp, Morgan), variations on a theme, collage and intermedia art, T.S. Eliot’s allusions, DJ culture (sampling/breakbeat), etc.  However, it’s often more fraught than data re-use in other areas (e.g. the Physical Sciences, if not the Social Sciences). Some characteristics of Arts and Humanities data are likely to require a different kind of handling from that afforded to other disciplines  For starters, people do not always think of their sources/ influences/ outputs as ‘data’, and the value and referencing systems (and norms) may be quite different… Strengths and weaknesses (I) opinions? affect? beliefs? perceptions?
  17. 17.  Digital ‘data’ emerging in the Arts is as likely to be an outcome of the creative research process as an input to a workflow  Furthermore, practice/praxis based research is more or less the sole preserve of the Humanities, and research/production methods are not always rigorously methodical or linear. This is at odds with the scientific approach, and the way in which most RDM resources are described/defined/oriented  Arts ‘data’ is often personal, and creative data in particular may not be factual in nature. What matters most may not be the content itself, but rather the presentation, the arrangement, the quality of expression…  Creative researchers also care a great deal about the way in which their work is presented, or ‘showcased’: standard repository installations don’t cut it!  What do Arts and Science data have in common? Both may be financially valuable and/or precious to their creators Strengths and weaknesses (II)
  18. 18. Archiving issues  Business case (“could anyone die or go to jail?”)  The law: data protection  Policy: retention and embargo periods  Financial/cultural benefit  Commercial considerations and IPR… personal data?  Multiplicity of (file) formats and creation/storage media (N.B. other disciplines report this as well, e.g. Dryad 2015 Stats Roundup)  Most disciplinary repositories support only a limited set of recommended file formats/object types  Linking analogue and digital, structuring collections  Scope. Respect des fonds? Ownership/IP issues may make this tricky  Non-digital material. Access arrangements/digitisation. Demand for digitisation/archiving may outstrip capacity/budgets…  Scale. Imaging data is comparatively large in terms of disk space
  19. 19. Exercise  Make a quick list of the policies and laws which govern the management of research data in your institution. Who is responsible for creating and monitoring compliance with these?  Rank the five key drivers and benefits of RDM for (a) the STEM subject areas, and (b) the Arts and Humanities  Is it more appropriate to have a single institutional RDM policy, or should these be devolved to School or Department level?  Or is it better to devolve guidance and procedures as opposed to high level policy? 1. TRANSPARENCY: The evidence that underpins research can be made open for anyone to scrutinise, and attempt to replicate the findings of others. 2. EFFICIENCY/VfM: Data collection can be funded once, and used many times for a variety of purposes. 3. SPEED: Data can be accessed more quickly. In some disciplines, such as climate science, this is vital. 4. RISK MANAGEMENT: A pro-active approach to data management reduces the risk of inappropriate disclosure of sensitive data, whether commercial or personal. 5. PRESERVATION: Lots of data is unique, and can only be captured once. If lost, it can’t be replaced. KEY DRIVERS
  20. 20. Suggested ranking for the Sciences  TRANSPARENCY: The evidence that underpins research can be made open for anyone to scrutinise, and attempt to replicate the findings of others.  EFFICIENCY/VfM: Data collection can be funded once, and used many times for a variety of purposes.  SPEED: Data can be accessed more quickly. In some disciplines, such as climate science, this is vital.  RISK MANAGEMENT: A pro-active approach to data management reduces the risk of inappropriate disclosure of sensitive data, whether commercial or personal.  PRESERVATION: Lots of data is unique, and can only be captured once. If lost, it can’t be replaced. 2 4 3 5 1
  21. 21. Suggested ranking for the Arts & Humanities  TRANSPARENCY: The evidence that underpins research can be made open for anyone to scrutinise, and attempt to replicate the findings of others.  EFFICIENCY/VfM: Data collection can be funded once, and used many times for a variety of purposes.  SPEED: Data can be accessed more quickly. In some disciplines, such as climate science, this is vital.  RISK MANAGEMENT: A pro-active approach to data management reduces the risk of inappropriate disclosure of sensitive data, whether commercial or personal.  PRESERVATION: Lots of data is unique, and can only be captured once. If lost, it can’t be replaced. 1 2 5 3 4
  22. 22.  Need – what do we need to archive? Is it always evidence without which the research outcomes are in doubt?  Want – do we want to archive materials for other reasons? Does preserving early/developmental work (such as artists’ sketch books) provide a richer experience/understanding of the creative work and process? How do we make a business case for this? Are datasets really “the special collections of the future”?  Liminality  Many creative researcher/practitioners are on fractional contracts, and there is not always a clear delineation between professional work and personal practice. Where and how do we draw the line?  More practically, the same notebook or sketchbook may be used for both professional and personal purposes. Its contents may be messy, personal, confusing…  How much time/effort does potentially sensitive Arts ‘data’ require in order to be prepared for archiving? Again, how do we know when it’s worth it? Possible discussion points
  23. 23.  Be careful with our terminology  “Data” – be clear that this is not the dictionary definition, but rather shorthand for a variety of scholarly products/by-products (see www.researchobject.org for examples)  Don’t use “science” and “research” interchangeably. Challenge those who do.  Ensure that institutional policies fit the full range of scholarly disciplines  Be mindful of the sometimes blurred lines between professional investigation and personal expression  Talk to researchers: understand their working methods, discover their needs, assuage their fears  Build bridges before they’re needed  Accept that not everything needs to be archived – prioritise! What can we do?
  24. 24.  Paper: Marieke Guy, Martin Donnelly, Laura Molloy (2013) “Pinning It Down: Towards a Practical Definition of ‘Research Data’ for Creative Arts Institutions”, International Journal of Digital Curation, Vol. 8, No. 2, pp. 99-110. URL: doi:10.2218/ijdc.v8i2.275  Projects:  KAPTUR (2011-13) URL: http://www.vads.ac.uk/kaptur/  A consortial approach to building an integrated RDM system (2014-16) (Partners: CREST, University for the Creative Arts, ULCC, Leeds Trinity University, Arkivum). URL: http://www.crest.ac.uk/welcome-to-the-crest-rdms-project-blog/  Event: “Research Data Management Forum #10: RDM in the Arts and Humanities”, September 2013, St Anne's College, University of Oxford. URL: http://www.dcc.ac.uk/events/research-data-management-forum-rdmf/rdmf10- research-data-management-arts-and-humanities  Case study: Jonathan Rans (2013) “Planning for the future: developing and preserving information resources in the Arts and Humanities” URL: http://www.dcc.ac.uk/resources/developing-rdm-services/dmps-arts-and-humanities  Blog posts:  Marieke Guy (2013) “RDM in the Performing Arts” URL: http://www.dcc.ac.uk/blog/rdm-performing-arts  Elizabeth Hull (2016) Dryad 2015 Stats Roundup. URL: https://blog.datadryad.org/2016/04/18/2015-stats-roundup/  Laura Molloy (2015) “Digital Preservation for the Arts, Social Sciences and Humanities - benefits for everyone” URL: http://www.dcc.ac.uk/blog/digital-preservation-arts-social-sciences-and-humanities-benefits-everyone  Slides: Martin Donnelly (2013) “‘Found’ and ‘after’ - a short history of data reuse in the Arts” URL: http://www.slideshare.net/martindonnelly/data-reuse-in-the-arts Further reading and links
  25. 25. And if all else fails?  Eat your data  Put your data in the cloud  Abandon your data at a grocery store  Put your data in an old tube sock  Put a fake mustache on your data  Teach your data how to protect itself  Poison your data  Bury your data  Don’t make any data Ahmed Amer – Ways to Protect Your Data, McSweeney’s, November 2015
  26. 26. Thank you / Diolch  For information about the DCC:  Website: www.dcc.ac.uk  Director: Kevin Ashley (kevin.ashley@dcc.ac.uk)  General enquiries: info@dcc.ac.uk  Twitter: @digitalcuration  My contact details:  Email: martin.donnelly@ed.ac.uk  Twitter: @mkdDCC  Slideshare: www.slideshare.net/martindonnelly This work is licensed under the Creative Commons Attribution 2.5 UK: Scotland License.

×