Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects

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Digital History workshop: Crowdsourcing in the Humanities and cultural heritage sector. Victoria University of Wellington 23 April 2013
Session: Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects
Presenter: Lynne Siemens
http://wtap.vuw.ac.nz/wordpress/digital-history/events/crowdsourcing-workshop/presenters/

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Crowd in the Cloud: Collaborative Frameworks for Virtual DH Projects

  1. 1. Crowd  in  the  Cloud:    Collaborative  Frameworks  for  Virtual  DH  Projects  Lynne  Siemens  siemensl@uvic.ca  Wellington,  April  2013  
  2. 2. A  glorious  endeavour….  h"p://collec*on.cooperhewi".org/objects/18422089/,  Drawing,  "Group  of  Angels  on  a  Cloud  Bank  (study  for  a  ceiling  decora*on)",  1630–50,  Smithsonian  Cooper-­‐Hewi",  Na*onal  Design  Museum  
  3. 3. Perhaps,  more  the  reality…  Devils,  from  the  Last  Judgement,  Luca  Signorelli,  hBp://www.1st-­‐art-­‐gallery.com/Luca-­‐Signorelli/Devils,-­‐From-­‐The-­‐Last-­‐Judgement.html  
  4. 4. Appropriate  middle  ground?  •  Crowdsourcing  offers  potenKal  to  academic  projects,  especially  for  those  with  large  amounts  of  data  to  process  and  relaKvely  small  budgets  •  But  how  best  to  organize  the  work  to  ensure  that  this  crowd’s  contribuKon  is  delivered  within  an  academic  project’s  schedule,  budget  and  other  resources  and  to  the  required  quality  standard?  •  ConsideraKon  of  more  than  the  moKvaKon  of  volunteers  •   Where  are  the  points  of  collaboraKon?  
  5. 5. Collaboration  Points      
  6. 6. Decision  points  for  collaboration  in  the  cloud  •  These  include:  •  the  type  of  experKse,  qualificaKon  and/or  knowledge  required  •  the  presence  of  contributors  •  the  mechanisms  by  which  they  will  parKcipate  and  contribute  •  project  remuneraKon  •  moKvators  to  keep  parKcipants  engaged  •  quality  control  mechanisms    
  7. 7. Frameworks:  Where/how/what/who/when  to  collaborate?  Simple  tasks   Moderate  tasks   Complex  tasks  •  Task:  Low  complexity  •  Outcomes  and  quality:  Easy  to  evaluate  •  “Any  Individual”  could  undertake  with  minimal  training,  skill,  and  special  experKse  •  PotenKal  for  gamificaKon  •  Example:  OCR  correcKon  and  tagging  •  Task:  Medium  complexity  •  Outcomes  and  quality:  More  difficult  to  evaluate  •  “Most  people”  could  undertake  with  training,  skill,  and  special  experKse  •  Example:  TranscripKon    •  Task:  High  complexity  •  Outcomes  and  quality:  Difficult  to  evaluate  •  “Expert”  needed  with  special  knowledge  and  skills  •  Example:  AnnotaKon  and  problem  solving  Australian  Historic  Newspapers  Transcribe  Bentham   Pynchonwiki  
  8. 8. Questions/discussion  

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