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Scholarly Social Machines


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Public Lecture at Wolfson College, Oxford, as part of the Digital Humanities Summer School, 10 July 2013

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Scholarly Social Machines

  1. 1. David De Roure Scholarly Social Machines
  2. 2. 1. The End of the Article 2. How digital research is done today 3. Social Objects 4. Social Machines
  3. 3.
  4. 4. A revolutionary idea… Open Science!
  5. 5.
  6. 6. 1. It was no longer possible to include the evidence in the paper – container failure! “A PDF exploded today when a scientist tried to paste in the twitter firehose…”
  7. 7. 2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
  8. 8. 3. Writing for increasingly specialist audiences restricted essential multidisciplinary re-use Grand Challenge Areas: • Energy • Living with Environmental Change • Global Uncertainties • Lifelong Health and Wellbeing • Digital Economy • Nanoscience • Food Security • Connected Communities • Resilient Economy
  9. 9. 4. Research records needed to be readable by computer to support automation and curation A computationally-enabled sense-making network of expertise, data, models and narratives.
  10. 10. 5. Single authorship gave way to casts of thousands
  11. 11. 6. Quality control models scaled poorly with the increasing volume
  12. 12. 7. Alternative reporting necessary for compliance with regulations
  13. 13. 8. Research funders frustrated by inefficiencies in scholarly communication An investment is only worthwhile if • Outputs are discoverable • Outputs are reusable • Outputs accrue value
  14. 14. More people Moremachines This is a Fourth Quadrant Talk Big Data Big Compute Conventional Computation The Future! Social Networking e-infrastructure online R&D The Fourth Quadrant
  15. 15. F i r s t BioEssays,,26(1):99–105,January2004
  16. 16. INT. VERSE VERSE VERSE VERSEBRIDGEBRIDGE OUT.  The Problem signal understanding
  17. 17.
  18. 18. Digital Music Collections Student-sourced ground truth Community Software Linked Data Repositories Supercomputer 23,000 hours of recorded music Music Information Retrieval Community SALAMI
  19. 19. Ashley Burgoyne
  20. 20. 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
  21. 21. 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
  22. 22. MIREX TASKS Audio Artist Identification Audio Onset Detection Audio Beat Tracking Audio Tag Classification Audio Chord Detection Audio Tempo Extraction Audio Classical Composer ID Multiple F0 Estimation Audio Cover Song Identification Multiple F0 Note Detection Audio Drum Detection Query-by-Singing/Humming Audio Genre Classification Query-by-Tapping Audio Key Finding Score Following Audio Melody Extraction Symbolic Genre Classification Audio Mood Classification Symbolic Key Finding Audio Music Similarity Symbolic Melodic Similarity 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 Music Information Retrieval Evaluation eXchange
  23. 23.
  24. 24. Stephen Downie
  25. 25. Information Circuits Community Software Execution Digital Music Community annotation Linked Data Repositories Workflows Generate Paper Conference
  26. 26. Notifications and automatic re-runs Machines are users too Autonomic Curation Self-repair New research?
  27. 27. Scientists Talk Forum Image Classification data reduction Citizen Scientists
  28. 28. Web as lens Web as artefact Web Observatories
  29. 29. 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, models and narratives. This requires a “social machines” perspective from the outset as well as humanistic input. The Web, and with it Web Science, are an important exemplar. Big data elephant versus sense-making network?
  30. 30. data method
  31. 31. Knowledge InfrastructureKnowledge Objects Descriptive layer Observatories Annotation
  32. 32.
  33. 33. Research Objects Computational Research Objects Evolving the myExperiment Social Machine Workflows Packs OAI ORE W3CPROV
  34. 34. Reusable. The key tenet of Research Objects is to support the sharing and reuse of data, methods and processes. Repurposeable. Reuse may also involve the reuse of constituent parts of the Research Object. Repeatable. There should be sufficient information in a Research Object to be able to repeat the study, perhaps years later. Reproducible. A third party can start with the same inputs and methods and see if a prior result can be confirmed. Replayable. Studies might involve single investigations that happen in milliseconds or protracted processes that take years. Referenceable. If research objects are to augment or replace traditional publication methods, then they must be referenceable or citeable. Revealable. Third parties must be able to audit the steps performed in the research in order to be convinced of the validity of results. Respectful. Explicit representations of the provenance, lineage and flow of intellectual property. The R dimensions Replacing the Paper: The Twelve Rs of the e-Research Record” on
  35. 35. Join the W3C Community Group
  36. 36. Nigel Shadbolt et al
  37. 37. Real life is and must be full of all kinds of social constraint – the very processes from which society arises. Computers can help if we use them to create abstract social machines on the Web: processes in which the people do the creative work and the machine does the administration… The stage is set for an evolutionary growth of new social engines. Berners-Lee, Weaving the Web, 1999 The Order of Social Machines
  38. 38. Some Social Machines
  39. 39. myExperiment is a Social Machine protected by the reCAPTCHA Social Machine
  40. 40. SocialMachinesofSpam
  41. 41. Whither the Social Machine?
  42. 42. Whither the Social Machine?
  43. 43. Whither the Social Machine?
  44. 44. What to observe?  Logs  Analytics  Data findings e.g. Success rate of transcription  Social sciences  Qualitative study  Motivation Individual and group  Mixed methods  Differences in technique and scale  Unlikely to be an simple transferable metric
  45. 45. Trajectories... distinguished by purpose Trajectories through Social Machines
  46. 46. Cat De Roure
  47. 47.
  48. 48. Scholarly Machines Ecosystem
  49. 49. The Social Machines of MIR SoundCloud Mirex Machine eTree Internet Archive Annotation machine ISMIR Machine Peer review MusicBrainz recommenders
  50. 50. Physical World (people and devices) Building a Social Machine Design and Composition Participation and Data supply Model of social interaction Virtual World (Network of social interactions) Dave Robertson
  51. 51. Bush, Vannevar (July 1945). "As We May Think". The Atlantic.
  52. 52. Correspondence Chess
  53. 53. 1. The End of the Article – Necessary but not sufficient 2. How digital research is done today – Systems which don’t fit in papers 3. Social Objects – Why papers work, new artefacts emerging 4. Social Machines – Design one tonight!
  54. 54. @dder Slide credits: Christine Borgman, Iain Buchan, Ichiro Fujinaga, , Kevin Page, Stephen Downie, Nigel Shadbolt, Dave Robertson 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 Research also supported in part by Wf4Ever (FP7-ICT ICT-2009.4 project 270192), e- Research South (EPSRC EP/F05811X/1), Digital Social Research (ESRC RES-149-34-0001- A), Smart Society (FP7-ICT ICT-2011.9.10 project 600854).
  55. 55. • Theory and Practice of Social Machines (SOCIAM) • Future of Research Communication (FORCE11) • myExperiment project wiki • Workflow Forever project (Wf4Ever) Links