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Social Machines of Scholarly Collaboration

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Social Machines of Scholarly Collaboration

  1. 1. The Social Machines of Scholarly Collaboration David De Roure
  2. 2. A revolutionary idea… Open Science!
  3. 3. Overview 1. Shifts in scholarship 2. End of the article 3. Future of the article 4. Scholarly Social Machines
  4. 4. The Big Picture More people More machines Big Data Big Compute Conventional Computation “Big Social” Social Networks e-infrastructure Online R&D (Science 2.0) Social Machines @dder
  5. 5. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue.
  6. 6. Christine Borgman
  7. 7.
  8. 8. F i r s t
  9. 9. New Social Process
  10. 10.
  11. 11. Talk Forum Scientists Image Classification Citizen Scientists data reduction
  12. 12. Digital Music Collections Student-sourced ground truth Community Software Supercomputer Linked Data Repositories 23,000 hours of recorded music Music Information Retrieval Community SALAMI
  13. 13. Pip Willcox Annotated Books Online — Archive of Early Medieval English — The Bodin Project — Crowdmap the Crusades — The Devonshire Manuscript: A Social Edition —
  14. 14. Pip Willcox
  15. 15. Interdisciplinary and “in the wild” In it not on it Pull not Push
  16. 16. Overview 1. Shifts in scholarship 2. End of the article 3. Future of the article 4. Scholarly Social Machines
  17. 17. David De Roure
  18. 18. David De Roure
  19. 19. 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…”
  20. 20. 2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
  21. 21. 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
  22. 22. 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.
  23. 23. 5. Single authorship gave way to casts of thousands
  24. 24. 6. Quality control models scaled poorly with the increasing volume Filter, Publish, Filter, Publish, … Like big data, publishing has increasing volume, variety and velocity But what about veracity?
  25. 25. 7. Alternative reporting necessary for compliance with regulations One piece of research may have multiple reports and multiple narratives for multiple readerships, in multiple formats and languages (Computer are readers too!)
  26. 26. 8. Research funders frustrated by inefficiencies in scholarly communication An investment is only worthwhile if • Outputs are discoverable • Outputs are reusable …and preferably outputs accrue value through use Using an obsolete scholarly communication system impedes innovation and hence return on investment What are we doing about it? Trying to fix it using an obsolete scholarly communication system!
  27. 27. Overview 1. Shifts in scholarship 2. End of the article 3. Future of the article 4. Scholarly Social Machines
  28. 28. method data script protocol program workflow model …
  29. 29. Neil Chue Hong Open Source Software as a model for Open Scholarship?
  30. 30. The Journal of Open Research Software (JORS) features peer reviewed Software Metapapers describing research software with high reuse potential. We are working with a number of specialist and institutional repositories to ensure that the associated software is professionally archived, preserved, and is openly available. Equally importantly, the software and the papers will be citable, and reuse will be trhattcp:k//
  31. 31. The Evolution of myExperiment Research Objects Workflows Computational Research Objects Packs OAI ORE W3C PROV Social Objects
  32. 32. Notifications and automatic re-runs Autonomic Curation Self-repair New research? Machines are users too
  33. 33. Executable Documents
  34. 34. • Will digital libraries provide the infrastructure to execute documents, or will people deploy them on alternative infrastructures? What are the implications for discovery, curation, and its automation? • Who gains credit and owns the intellectual property generated when a document runs automatically? Who is liable for damage that arises? What are the implications of unintended or accidental assembly of research methods and outcomes? • What are the implications of research that occurs at very high speed, possibly speculatively, without human intervention? Where is the (critical, creative, subversive) human in the loop? Are we ‘burning’ research methods into an automated research platform? • How do executable documents sit in the social websites of discovery, authoring, publishing and sharing; i.e. the ecosystem of scholarly social machines? De Roure, D, Executable Music Documents, Digital Libraries for Musicology (DLfM '14), 2014, London, UK, ACM.
  35. 35. The R Dimensions Research Objects facilitate research that is reproducible, repeatable, replicable, reusable, referenceable, retrievable, reviewable, replayable, re-interpretable, reprocessable, recomposable, reconstructable, repurposable, reliable, respectful, reputable, revealable, recoverable, restorable, reparable, refreshable?” @dder 14 April 2014 sci method access understand new use social curation Research Object Principles
  36. 36. Overview 1. Shifts in scholarship 2. End of the article 3. Future of the article 4. Scholarly Social Machines
  37. 37. Social Machines 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. The ability to create new forms of social process would be given to the world at large, and development would be rapid. Berners-Lee, Weaving the Web, 1999 (pp. 172–175)
  38. 38. 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
  39. 39. “Yet Wikipedia and its stated ambition 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 manipulation— has shrunk by more than a third since 2007 and is still shrinking… The main source of those problems is not mysterious. The loose collective running the site today, estimated to be 90 percent male, operates a crushing bureaucracy with an often abrasive atmosphere that deters newcomers who might increase participation in Wikipedia and broaden its coverage…”
  40. 40. Scholarly Machines David De Roure, JCDL 2013 Ecosystem
  41. 41. Richard O’Bierne
  42. 42. A computationally-enabled sense-making network of expertise, data, software, models and narratives Iain Buchan
  43. 43. Fusing Audio and Semantic Technologies for Intelligent Music Production and Consumption consume produce compose perform capture distribute Sandler, Benford, De Roure Future of Research Communication and e-Scholarship curate preserve
  44. 44. Scholarly practice is changing profoundly as we embrace new methods of digital research and engage society. Our centuries-old research communication practices that underpin scholarship are to be celebrated — but are they still fit for their purpose?
  45. 45. 1. Shifts in scholarship – Machines are users too 2. End of the article – Don’t retrofit digital, think post-digital 3. Future of the article – Social Objects in a sensemaking network of humans and machines – Evolution or the other side of the road? 4. Social Machines – Humans in the loop, empowered, creative, subversive – You are designers of scholarly social machines – Library as social machines knowledge infrastructure
  46. 46. Pip Willcox
  47. 47. Thanks to Richard O’Bierne, Christine Borgman, Iain Buchan, Neil Chue Hong, Carole Goble, Chris Lintott, Nigel Shadbolt, Pip Willcox, Jun Zhao; FORCE11, myExperiment, Software Sustainability Institute, wf4ever, SOCIAM; Andrew W. Mellon Foundation, JISC, EPSRC, ESRC, AHRC. @dder
  48. 48. @dder