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Humanities in the Digital World

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Humanities in the Digital World

  1. 1. David De Roure @dder Intersection, Scale, and Social Machines: The Humanities in the Digital World DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE
  2. 2. Data-intensive research Human-intensive research Music Scholarly Communication
  3. 3. The Big Picture(s) Challenging Assumptions
  4. 4. ChristineBorgman
  5. 5. 13,785,659  total  volumes   6,871,154  book  6tles   364,473  serial  6tles   4,824,980,650  pages   618  terabytes   163  miles   11,201  tons   5,372,477  public  domain  volumes   10,000,000,000,000,000 bytes archived!
  6. 6. New Forms of Data ▶ Internet data, derived from social media and other online interactions (including data gathered by connected people and devices, eg mobile devices, wearable technology, Internet of Things) ▶ Tracking data, monitoring the movement of people and objects (including GPS/geolocation data, traffic and other transport sensor data, CCTV images etc) ▶ Satellite and aerial imagery (eg Google Earth, Landsat, infrared, radar mapping etc) http://www.oecd.org/sti/sci-tech/new-data-for- understanding-the-human-condition.htm
  7. 7. The  Big  Picture   More people Moremachines Big Data Big Compute Conventional Computation “Big Social” Social Networks e-infrastructure Online R&D (Science 2.0) Digital Scholarship @dder
  8. 8. theODI.org
  9. 9. Data Detect Store AnalyticsFilter Analysts @dder
  10. 10. There is no such thing as the Internet of Things There is no such thing as a closed system Humans are creative and subversive The Rise of the Bots A Swarm of Drones Accidents happen (in the lab, bin) Holding machines to account Software vulnerability Where are the throttle points? @dder
  11. 11. F i r s t
  12. 12. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
  13. 13. Social Machines Empowered Citizens
  14. 14. Social  Machines  Defini6on  TBL   Pip Willcox
  15. 15. https://twitter.com/CR_UK/status/446223117841494016/ Some people's smartphones had autocorrected the word "BEAT" to instead read "BEAR". "Thank you for choosing an adorable polar bear," the reply from the WWF said. "We will call you today to set up your adoption." http://www.bbc.com/news/technology-26723457
  16. 16. SOCIAM: The Theory and Practice of Social Machines is funded by the UK Engineering and Physical Sciences Research Council (EPSRC) under grant number EPJ017728/1 and comprises the Universities of Southampton, Oxford and Edinburgh. See sociam.org
  17. 17. “Yet  Wikipedia  and  its  stated  ambi6on  to  “compile  the  sum  of  all   human  knowledge”  are  in  trouble.  The  volunteer  workforce  that   built  the  project’s  flagship,  the  English-­‐language  Wikipedia—and   must  defend  it  against  vandalism,  hoaxes,  and  manipula6on— has  shrunk  by  more  than  a  third  since  2007  and  is  s6ll  shrinking…     The  main  source  of  those  problems  is  not  mysterious.  The  loose   collec6ve  running  the  site  today,  es6mated  to  be  90  percent   male,  operates  a  crushing  bureaucracy  with  an  oYen  abrasive   atmosphere  that  deters  newcomers  who  might  increase   par6cipa6on  in  Wikipedia  and  broaden  its  coverage…”    http://www.technologyreview.com/featuredstory/520446/the-decline-of-wikipedia/
  18. 18. “Panoptes has been designed so that it’s easier for us to update and maintain, and to allow more powerful tools for project builders. It’s also open source from the start, and if you find bugs or have suggestions about the new site you can note them on Github (or, if you’re so inclined, contribute to the codebase yourself).” "   http://blog.zooniverse.org/2015/06/29/a-whole-new-zooniverse/ http://monsterspedia.wikia.com/wiki/File:Argus-Panoptes.jpg Panoptes
  19. 19. Musical Social Machines Social Machines of Scholarship
  20. 20. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT. ê The  Problem   signal understanding Ichiro Fujinaga
  21. 21. salami.music.mcgill.ca Jordan B. L. Smith, J. Ashley Burgoyne, Ichiro Fujinaga, David De Roure, and J. Stephen Downie. 2011. Design and creation of a large-scale database of structural annotations. In Proceedings of the International Society for Music Information Retrieval Conference, Miami, FL, 555–60
  22. 22. Sequence alignment http://en.wikipedia.org/wiki/Sequence_alignment#/media/File:Histone_Alignment.png
  23. 23. Dan Edelstein, Robert Morrissey, and Glenn Roe, To Quote or not to Quote: Citation Strategies in the Encyclopédie. Journal of the History of Ideas , Volume 74, Number 2, April 2013 . pp. 213-236. 10.1353/jhi.2013.0012 Glenn Roe
  24. 24. Digital  Music   Collec6ons   Grad-­‐sourced   ground  truth   Community   SoYware   Linked  Data   Repositories   Supercomputer   23,000 hours of recorded music Music Information Retrieval Community SALAMI
  25. 25. Ashley Burgoyne
  26. 26. www.music-ir.org/mirex Music Information Retrieval Evaluation eXchange Audio Onset Detection Audio Beat Tracking Audio Key Detection Audio Downbeat Detection Real-time Audio to Score Alignment(a.k.a Score Following) Audio Cover Song Identification Discovery of Repeated Themes & Sections Audio Melody Extraction Query by Singing/Humming Audio Chord Estimation Singing Voice Separation Audio Fingerprinting Music/Speech Classification/Detection Audio Offset Detection Downie, J. Stephen, Andreas F. Ehmann, Mert Bay and M. Cameron Jones. (2010). The Music Information Retrieval Evaluation eXchange: Some Observations and Insights. Advances in Music Information Retrieval Vol. 274, pp. 93-115
  27. 27. Stephen  Downie  
  28. 28. http://chordify.net/
  29. 29. Digital  Material   Pip Willcox
  30. 30. Kevin Page David Weigl Interfaces, for computer and human !
  31. 31. Sonifying  the  Variants   •  From  Play  to  Sonifica6on   •  Using  First  Folio  and  Quartos  data   •  Parsing  the  TEI  XML,  conver6ng  it  with  rule  set  into  numbers,   sonifying  the  data  to  produce  sounds   34 Sonification   Iain Emsley
  32. 32. Studying Social Machines Scholarship of Social Machines
  33. 33. Ecosystem Perspective •  We see a community of living, hybrid organisms, rather than a set of machines which happen to have humans amongst their components •  Their successes and failures inform the design and construction of their offspring and successors
  34. 34. time Social Machine instances @dder
  35. 35. Observer of one social machine Observers using third party observatory Observer of multiple social machines Human participants in Social Machine Human participants in multiple Social Machines Observer of Social Machine infrastructure 1   4   2   3   5   6   SM SM SM Social Machine Observing Social Machines 7   @dder De Roure, D., Hooper, C., Page, K., Tarte, S., and Willcox, P. 2015. Observing Social Machines Part 2: How to Observe? ACM Web Science
  36. 36. The Web Observatory Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N. and Hendler, J. 2013. The Web Science Observatory, IEEE Intelligent Systems 28(2) pp 100–104. ThanassisTiropanis
  37. 37. Simpson, R., Page, K.R. and De Roure, D. 2014. Zooniverse: observing the world's largest citizen science platform. In Proceedings of the companion publication of the 23rd international conference on World Wide Web, 1049-1054. Kevin Page
  38. 38. STORYTELLING AS A STETHOSCOPE FOR SOCIAL MACHINES 1.  Sociality through storytelling potential and realization 2.  Sustainability through reactivity and interactivity 3.  Emergence through collaborative authorship and mixed authority Zooniverse  is  a  highly   storified  Social  Machine   Facebook  doesn’t  allow   for  improvisa6on   Wikipedia  assigns   authority  rights  rigidly   http://ora.ox.ac.uk/objects/ora:8033 Tarte, S.M., De Roure, D. and Willcox, P. 2014. Working out the Plot: the Role of Stories in Social Machines. SOCM2014: The Theory and Practice of Social Machines, Seoul, Korea, International World Wide Web Conferences pp. 909–914
  39. 39. Pip Willcox
  40. 40. Tarte, S. Willcox, P., Glaser, H. and De Roure, D. 2015. Archetypal Narratives in Social Machines: Approaching Sociality through Prosopography. ACM Web Science 2015. SégolèneTarte
  41. 41. Scholarly Communication Preface
  42. 42. Elizabeth Williamson
  43. 43. Richard O’Bierne
  44. 44. A computationally-enabled sense-making network of expertise, data, software, models and narratives Big Data, in a Big Data Centre
  45. 45. Pip Willcox and Kevin Page
  46. 46.     consume     produce     compose   perform   capture     distribute                 Mark  Sandler   Curate            Preserve   !
  47. 47. Notifications and automatic re-runs Machines are users too Autonomic Curation Self-repair New research?
  48. 48. The  R  Dimensions   Research  Objects  facilitate  research  that  is   reproducible,  repeatable,  replicable,  reusable,   referenceable,  retrievable,  reviewable,   replayable,  re-­‐interpretable,  reprocessable,   recomposable,  reconstructable,  repurposable,   reliable,  respecful,  reputable,  revealable,   recoverable,  restorable,  reparable,  refreshable?”   @dder 14 April 2014 sci  method   access   understand   new  use   social   cura6on   Research   Object   Principles   De Roure, D. 2014. The future of scholarly communications. Insights: the UKSG journal, 27, (3), 233-238. DOI 10.1629/2048-7754.171
  49. 49. https://www.gartner.com/technology/research/digital-marketing/transit-map.jsp
  50. 50. Intersection, Scale, and Social Machines: The Humanities in the Digital World
  51. 51. First  Folio  Social  Machines   Metadata Story of the

Editor's Notes


  • ----- Meeting Notes (25/10/15 20:38) -----
    Pipeline to transform the XML into numbers according to a simple set of rules. These numbers are then transformed into sound in the black box.

    Mention the Hinman collator here and stereoscopy.

    Used the First Folio Hamlet and the Quartos variants as the test data.

    One stream
    Two steams to create an audio version of a steroscopic illusion.

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