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Research Data in the Arts and Humanities: A Few Tricky Questions

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Presentation given at University of Warsaw Library, 14 December 2015

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Research Data in the Arts and Humanities: A Few Tricky Questions

  1. 1. ìResearch  Data  in  the  Arts  and   Humanities a few tricky questions Tom  Phillips,  A  Humument(1970,  1986,  1998,  2004,  2012…) Martin  Donnelly,  Digital  Curation  Centre,  University  of  Edinburgh  (and  the  FOSTER  project) OCEAN  launch  event,  University  of  Warsaw,  14  December  2015
  2. 2. About  the                              DCC ì The  UK’s  centre of  expertise  in  digital  preservation  and  data   management,  established  in  2004 ì Provide  guidance,  training,  tools  and  other  services  on  all  aspects  of   research  data  management ì Organise national  and  international  events  and  webinars  (International   Digital  Curation  Conference,   Research  Data  Management  Forum) ì 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) ì Involved  in  various  European  projects  and  initiatives,  including  FOSTER,   OpenAIRE and  EUDAT ì Now  offering  tailored  consultancy/training  services
  3. 3. Overview  of  talk 1. What  do  we  mean   by  “research  data   (management)”? 2. Why  is  it  different   in  the  arts  and   humanities? 3. What  can  we  do  to   make  things   better?
  4. 4. What  is  research  data  management? “the  active   management  and   appraisal  of  data   over  the  lifecycle  of   scholarly  and   scientific  interest”
  5. 5. 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
  6. 6. The  new  way  of  doing  research  (science) Plan Collect Assure Describe Preserve Discover Integrate Analyze DEPOSIT …and   RE-­‐USE The  DataONE   lifecycle  model
  7. 7. N.B.  other  models  are  available… Ellyn Montgomery, US Geological Survey
  8. 8. 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
  9. 9. Reminder:  key  drivers  and  benefits  of  RDM ì TRANSPARENCY:   The  evidence  that  underpins  research  can  be   made  open  for  anyone  to  scrutinise,  and  attempt  to  replicate   the  findings of  others. ì EFFICIENCY/VfM:  Data  collection  can  be  funded  once,  and  used   many  times  for  a  variety  of  purposes. ì SPEED:  Data  can  be  accessed  more  quickly.  In  some  disciplines,   such  as  climate  science,  this  is  vital. ì RISK  MANAGEMENT:  A  pro-­‐active  approach  to  data   management  reduces  the  risk  of  inappropriate  disclosure  of   sensitive  data,  whether  commercial  or  personal. ì PRESERVATION:  Lots  of  data  is  unique,  and  can  only  be   captured  once.  If  lost,  it  can’t  be  replaced.  
  10. 10. ì Definitions  vary  from  discipline  to  discipline,  and  from  funder  to  funder… ì Here’s  a  science-­‐centric   definition:   ì “The  recorded  factualmaterial  commonly  accepted  in  the  scientific  community  as   necessary  to  validate research  findings.”  (US  Office  of  Management  and  Budget,   Circular  110) ì [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)] ì And  another  from  the  visual  arts:   ì “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?
  11. 11. Scientific  and  other  methods… ì The scientific method is a body of techniques for investigating phenomena, acquiring new knowledge, or correcting and integrating previous knowledge. ì To be termed scientific, a method of inquiry must be based on empirical and measurable evidence subject to specific principles of reasoning. ì 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.” ì Source: http://en.wikipedia.org/wiki/Scientific_m ethod 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_methodo logy
  12. 12. ì “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 ì Paraphrased  by  Auden  as  “A  work  of  art  is  never  completed,  only   abandoned” ì “You  could  not  step twice into the same river”  – Heraclitus,  as   reported  by  Plato  (via  Socrates) ì “In  science,  one  man’s noise is  another  man’s signal”  – Edward  Ng ì ‘Truth?’  said  Pilate.  ‘What  is  that?’ – John  18:38 ì “What  is  truth? said  jesting  Pilate,  and  would  not  stay for  an   answer”  – Sir  Francis  Bacon A  few  tricky  quotations
  13. 13. ì 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… ì Think  Shakespeare’s  plots,  Kristeva’s intertextuality,  Barthes’  “galaxy  of   signifiers”,  found  sounds/objects/poems   (e.g.  Duchamp,  Morgan),   variations  on  a  theme,  collage  and  intermedia  art,  T.S.  Eliot,  DJ  culture   (sampling/breakbeat),  etc  etc   ì 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 ì 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  re.  data  in  the  Arts  and   Humanities  (I)
  14. 14. ì Digital  ‘data’  emerging  in  the  Arts  is  as  likely  to  be  an  outcome of  the  creative   research  process  as  an  input to  a  workflow  (e.g.  the  UK  AHRC  policy) ì 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 ì 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… ì This  variance  in  emphasis  tends  to  be  why  the  reason  why  Open  Access   embargoes  are  often  longer  in  the  Arts  and  Humanities  than  in  other  areas ì 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! ì What  do  Arts  and  Science  data  have  in  common?  Both  may  be  financially   valuable  and/or  precious  to  their  creators Strengths  and  weaknesses  re.  data  in  the  Arts  and   Humanities  (II)
  15. 15. ì Are  the  goals  – or  indeed  the  concepts  – of  evidence,  facts,  validation,  replication   still  central  in  disciplines   which  tend  towards  subjectivity,  interpretation,  argument   and  quality  of  expression? ì How  do  we  identify,  preserve  and  share  ephemera,  emotions,  the  unconscious…?   How  do  we  protect  rights  around  creative  data?  What  are  the  financial/ownership   issues  accompanying  creative/Arts  research? ì Is  it  clear  where  creative  research  begins  and  ends?  How  can  we  draw  a  line   between  funded  research  and  unfunded  personal  work? ì What  complexities  are  introduced  by  practice-­‐based  research? ì To  what  extent  is  non-­‐digital  material  a  problem?  Can  we  share  approaches  to  this   with  other  subject  areas  (e.g.  biology,  geology),  remembering  that  “the  map  is  not   the  land”?  (Korzybski) ì What  other  characteristics  do  Arts  and  Humanities  data  have  in  common with   those  of  the  Sciences?  Which  other  disciplines   share  these  issues  more  generally? ì Is  the  perfect  the  enemy  of  the  good? A  few  tricky  questions  around  data  in  the  Arts  and   Humanities
  16. 16. ì Business  case (“could  anyone  die  or  go  to  jail?”) 1. The  law:  data  protection 2. Policy:  retention  and  embargo  periods 3. Financial/cultural  benefit ì Commercial  considerations  and  IPR…  personal  data? ì Access  arrangements/digitisation.  Demand  for  digitisation/archiving  may  outstrip   capacity/budgets… ì Metadata  creation  (NISO  types):  descriptive  (for  discovery),  administrative  (for  reuse),  structural   (for  inter-­‐relating  objects)  – obviously  producing  metadata  also  costs  money/effort ì Multiplicity  of  (file)  formats  and  creation/storage  media ì Linking  analogue  and  digital,  structuring  collections ì Most  disciplinary  repositories  support  a  limited  set  of  recommended  file  formats/object  types ì Scope ì Respect  des  fonds?  Ownership/IP  issues  may  make  this  tricky ì Scale Archiving  issues  around  Arts  and  Humanities  data
  17. 17. ì Need  – what  do  we  need to  archive?  Is  it  always  evidence  without  which  the   research  outcomes  are  in  doubt? ì Want  – do  we  want to  archive  materials  for  other  reasons?  Does  preserving   early/developmental  work  provide  a  richer  experience/understanding  of  the   creative  work  and  process?  How  do  we  make  a  business  case  for  this? ì Liminality ì Many  creative  researchers  are  on  fractional  contracts,  and  there  is  not  always  a  clear   delineation  between  professional  work  and  personal  practice.  Where  and  how  do  we   locate  the  line? ì More  practically,  the  same  notebook  or  sketchbook  may  be  used  for  both   professional  and  personal  purposes.  Its  contents  may  be  messy,  personal,  confusing… ì Is  a  work  ever  finished,  or  just  abandoned?  (Valéry)  How  do  we  know?  Sometimes   early  versions  are  equally  (or  more)  valuable…  (Munch’s  “Scream”,  Blondie’s  “Out  In   The  Streets”) ì How  much  time/effort  does  (potentially)  sensitive  creative  ‘data’  require  in  order   to  be  prepared  for  archiving?  How  do  we  know  when  it’s  worth  it? Possible  discussion  points
  18. 18. Reprise:  key  drivers  and  benefits  of  RDM ì TRANSPARENCY:   The  evidence  that  underpins  research   can  be  made  open  for  anyone  to  scrutinise,  and  attempt  to   replicate  the  findings of  others. ì EFFICIENCY/VfM:  Data  collection  can  be  funded  once,  and   used  many  times  for  a  variety  of  purposes. ì SPEED:  Data  can  be  accessed  more  quickly.  In  some   disciplines,  such  as  climate  science,  this  is  vital. ì RISK  MANAGEMENT:  A  pro-­‐active  approach  to  data   management  reduces  the  risk  of  inappropriate  disclosure   of  sensitive  data,  whether  commercial  or  personal. ì PRESERVATION:  Lots  of  data  is  unique,  and  can  only  be   captured  once.  If  lost,  it  can’t  be  replaced.   1 2 5 3 4
  19. 19. ì Be  careful  with  our  terminology ì “Data”  – be  clear  that  this  is  not  the  dictionary  definition,  but   rather  shorthand  for  a  variety  of  scholarly  products/biproducts (see  www.researchobject.org for  examples) ì Don’t  use  “science”  and  “research”  interchangeably.  Challenge   those  who  do…  (c.f.  Jan’s  Wissenschaft example) ì Be  mindful  of  the  sometimes  blurred  lines  between   professional  investigation  and  personal  expression ì Talk  to  researchers:  understand  their  working  methods,  discover   their  needs,  assuage  their  fears ì Build  bridges  before they’re  needed ì Accept  that  not  everything  needs  to  be  archived  – prioritise! What  can  we  do?
  20. 20. ì 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 ì Projects: ì KAPTUR  (2011-­‐13)  URL:  http://www.vads.ac.uk/kaptur/ ì 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/ ì 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 ì 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 ì Blog  posts:   ì Marieke Guy  (2013)  “RDM  in  the  Performing  Arts”  URL:  http://www.dcc.ac.uk/blog/rdm-­‐performing-­‐arts ì 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 ì 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
  21. 21. Thank  you  /  Dziękuję ì For  information  about  the  DCC: ì Website:  www.dcc.ac.uk ì Director:  Kevin  Ashley  (kevin.ashley@dcc.ac.uk) ì General  enquiries:  info@dcc.ac.uk ì Twitter:  @digitalcuration ì For  information  about  the  FOSTER  project: ì Website:  www.fosteropenscience.eu ì Principal  investigator:  Eloy Rodrigues   (eloy@sdum.uminho.pt) ì General  enquiries:  Gwen  Franck   (gwen.franck@eifl.net)   ì Twitter:  @fosterscience ì My  contact  details: ì Email:  martin.donnelly@ed.ac.uk ì Twitter:  @mkdDCC ì Slideshare:  www.slideshare.net/martindonnelly This work is licensed under the Creative Commons Attribution 2.5 UK: Scotland License.Slide  3  image  credits:  score,  linked  open  data,  rhizomatic  network

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