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Scholarship in the Digital World

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Seminar at CSAIL, MIT, Cambridge, Mass. Date: Friday October 30, 2015. Time: 4:00 pm - 5:00 pm, Location: D463 (Star)

Abstract:
Today we are witnessing several shifts in scholarly practice, in and across multiple disciplines, as researchers embrace digital techniques to tackle established research questions in new ways and new questions afforded by digital and digitized collections, approaches, and technologies. Pervasive adoption of technology, coupled with the co-creation of new social processes, has created a new and complex space for scholarship where citizens both generate and analyse data as they interact at the intersection of the physical and digital. Drawing on a background in distributed computing, and adopting the lens of Social Machines, this talk discusses current activity in digital scholarship, framing it in its interdisciplinary settings.

Bio:
David De Roure is Professor of e-Research at University of Oxford, Director of the Oxford e-Research Centre, and chairs Oxford’s Digital Humanities research programme. He previously directed the Digital Social Research programme for the UK Economic and Social Research Council, and serves as a strategic advisor in new forms of data and realtime analytics. Trained in electronics and computer science, his career has involved interdisciplinary collaborations in chemistry, astrophysics, bioinformatics, social computing, digital libraries, and sensor networks. His personal research is in Computational Musicology, Web Science, and Internet of Things. He is a frequent speaker and writer on digital research and the future of scholarly communications. URL: http://www.oerc.ox.ac.uk/people/dder

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Scholarship in the Digital World

  1. 1. David De Roure @dder Intersection, Scale, and Social Machines: Scholarship in the digital world DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE
  2. 2. Today we are witnessing several shifts in scholarly practice, in and across multiple disciplines, as researchers embrace digital techniques to tackle established research questions in new ways and new questions afforded by digital and digitized collections, approaches, and technologies. Pervasive adoption of technology, coupled with the co- creation of new social processes, has created a new and complex space for scholarship where citizens both generate and analyze data as they interact at the intersection of the physical and digital. Drawing on a background in distributed computing, and adopting the lens of Social Machines, this talk discusses current activity in digital scholarship, framing it in its interdisciplinary settings. data-intensive social processes social machines
  3. 3. The Big Picture(s) Challenging Assumptions
  4. 4. ChristineBorgman
  5. 5. 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
  6. 6. The  Big  Picture   More people Moremachines Big Data Big Compute Conventional Computation “Big Social” Social Networks e-infrastructure Online R&D (Science 2.0) Social Machines @dder
  7. 7. theODI.org
  8. 8. Data Detect Store AnalyticsFilter Analysts @dder
  9. 9. 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
  10. 10. F i r s t
  11. 11. New Research Questions ▶ Social media data offers the possibility of studying social processes as they unfold at the level of populations, as an alternative to traditional surveys or interviews. ▶ The data from social media is described as "qualitative data on a quantitative scale" and requires innovative analysis techniques. Social media data and real time analytics
  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  oSen  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. Scientists Talk Forum Image Classification data reduction Citizen Scientists
  19. 19. “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
  20. 20. Musical Social Machines Social Machines of Scholarship
  21. 21. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT. ê The  Problem   signal understanding
  22. 22. 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
  23. 23. Digital  Music   Collec6ons   Student-­‐sourced   ground  truth   Community   SoSware   Linked  Data   Repositories   Supercomputer   23,000 hours of recorded music Music Information Retrieval Community SALAMI
  24. 24. Ashley Burgoyne
  25. 25. class structure Ontology models properties from musicological domain •  Independent of Music Information Retrieval research and signal processing foundations •  Maintains an accurate and complete description of relationships that link them Segment  Ontology   Ben Fields, Kevin Page, David De Roure and Tim Crawford (2011) "The Segment Ontology: Bridging Music-Generic and Domain-Specific" in 3rd International Workshop on Advances in Music Information Research (AdMIRe 2011) held in conjunction with IEEE International Conference on Multimedia and Expo (ICME), Barcelona, July 2011
  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. seasr.org/meandre  Meandre  
  28. 28. Stephen  Downie  
  29. 29. http://chordify.net/
  30. 30. Studying Social Machines Scholarship of Social Machines
  31. 31. 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
  32. 32. time Social Machine instances @dder
  33. 33. 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
  34. 34. 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.
  35. 35. 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.
  36. 36. By Ségolène Tarte, David De Roure and Pip Willcox Working out the Plot The Role of Stories in Social Machines 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
  37. 37. 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
  38. 38. Pip Willcox
  39. 39. 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
  40. 40. Automation Dystopias
  41. 41. https://www.gartner.com/technology/research/digital-marketing/transit-map.jsp
  42. 42. Notifications and automatic re-runs Machines are users too Autonomic Curation Self-repair New research?
  43. 43. The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sense-making network of expertise, data, software, models, and narratives. Big data elephant versus sense-making network?
  44. 44. The  R  Dimensions   Research  Objects  facilitate  research  that  is   reproducible,  repeatable,  replicable,  reusable,   referenceable,  retrievable,  reviewable,   replayable,  re-­‐interpretable,  reprocessable,   recomposable,  reconstructable,  repurposable,   reliable,  respec`ul,  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
  45. 45. Closing thoughts Reflections
  46. 46. Principles of Robotics 1.  Robots are multi-use tools. Robots should not be designed solely or primarily to kill or harm humans, except in the interests of national security. 2.  Humans, not robots, are responsible agents. Robots should be designed; operated as far as is practicable to comply with existing laws & fundamental rights & freedoms, including privacy. 3.  Robots are products. They should be designed using processes which assure their safety and security. 4.  Robots are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent. 5.  The person with legal responsibility for a robot should be attributed. https://www.epsrc.ac.uk/research/ourportfolio/themes/engineering/activities/principlesofrobotics/ AlanWinfield
  47. 47. Principles of Robotics Social Machines? 1.  Social Machines are multi-use tools. Social Machines should not be designed solely or primarily to kill or harm humans, except in the interests of national security. 2.  Humans, not Social Machines, are responsible agents. Social Machines should be designed; operated as far as is practicable to comply with existing laws & fundamental rights & freedoms, including privacy. 3.  Social Machines are products. They should be designed using processes which assure their safety and security. 4.  Social Machines are manufactured artefacts. They should not be designed in a deceptive way to exploit vulnerable users; instead their machine nature should be transparent. 5.  The person with legal responsibility for a Social Machine should be attributed.
  48. 48. http://groups.csail.mit.edu/mac/projects/amorphous/6.966/
  49. 49. Normal Science – computer science is a puzzle-solving activity under our current paradigm, inspired by great achievements. Kuhn cycle We are in the period of crisis, where the failure of established methods permits us to experiment with new methods to crack the anomaly. We experiment with social machines as an underpinning model. If successful, social machines become the new paradigm and scientific revolution has occurred. This is evidenced by the papers and books that train the next generation. De Roure, D. 2014. The Emerging Paradigm of Social Machines, Digital Enlightenment Yearbook 2014 227 K. O’Hara et al. (Eds.) IOS Press, 2014. pp 227-234. Successful social machines, like Wikipedia, are the anomaly. They do not yield to standard techniques despite attempts to extend those techniques and fit social machines in as machines. cf Newtonian mechanics.
  50. 50. Data-Intensive + Social Processes co-created real-time at-scale = Social Machines
  51. 51. PipWillcox
  52. 52. david.deroure@oerc.ox.ac.uk @dder Thanks to Stephen Downie, Ich Fujinaga, Chris Lintott, Grant Miller, Kevin Page, Ségolène Tarte, Pip Willcox, Project SOCIAM and Project MAC. http://www.slideshare.net/davidderoure/scholarship-in-the-digital-world Supported by SOCIAM: The Theory and Practice of Social Machines, funded by the UK Engineering and Physical Sciences Research Council (EPSRC), under grant number EP/J017728/1, also FAST EP/L019981/1, and Smart Society: Hybrid and Diversity-Aware Collective Adaptive Systems: When People Meet Machines to Build a Smarter Society, funded under the European Commission FP7-ICT FET Proactive Initiative: Fundamentals of Collective Adaptive Systems (FOCAS), Project Reference 600854.
  53. 53. www.oerc.ox.ac.uk david.deroure@oerc.ox.ac.uk @dder

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