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e-Research and the Demise of the Scholarly Article

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Innovations 2013 - e-Science, we-Science and the latest evolutions in e-publishing. STM International Association of Scientific, Technical & Medical Publishers. 4th December 2013, Congress Centre, Great Russell Street, London, UK.

Publié dans : Technologie, Formation
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e-Research and the Demise of the Scholarly Article

  1. 1. e-Research and the Demise of the Scholarly Article David De Roure
  2. 2. A revolutionary idea… Open Science!
  3. 3. This is a Fourth Quadrant Talk More machines e-infrastructure Big Data Big Compute The Fourth The Future! Conventional Computation Social Networking Quadrant More people David De Roure online R&D
  4. 4. Christine Borgman
  5. 5. First
  6. 6. Research Councils UK
  7. 7. Notifications and automatic re-runs Autonomic Curation Self-repair New research? Machines are users too
  8. 8. Image Classification Talk Forum Citizen Scientists data reduction Scientists
  9. 9. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
  10. 10. http://www.scilogs.com/eresearch/pages-of-history/ David De Roure
  11. 11. http://www.scilogs.com/eresearch/pages-of-history/ David De Roure
  12. 12. 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…”
  13. 13. 2. It was no longer possible to reconstruct a scientific experiment based on a paper alone
  14. 14. 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
  15. 15. 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.
  16. 16. 5. Single authorship gave way to casts of thousands
  17. 17. 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?
  18. 18. 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!)
  19. 19. 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!
  20. 20. Big data elephant versus sense-making network? The challenge is to foster the co-constituted socio-technical system on the right i.e. a computationally-enabled sensemaking network of humans and machines sharing social objects… not just papers but data, models, software, narratives – new digital artefacts we call research objects.
  21. 21. method script protocol program workflow model data …
  22. 22. Neil Chue Hong
  23. 23. http://www.myexperiment.org/
  24. 24. Evolving the myExperiment Social Machine OAI ORE Workflows Packs Research Objects W3C PROV Computational Research Objects
  25. 25. The R dimensions Reusable. The key tenet of Research Replayable. Studies might involve Objects is to support the sharing and single investigations that happen in milliseconds or protracted processes reuse of data, methods and that take years. processes. Referenceable. If research objects Repurposeable. Reuse may also are to augment or replace traditional involve the reuse of constituent publication methods, then they must parts of the Research Object. be referenceable or citeable. 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 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 start with the same inputs and methods and see if a prior result can of the provenance, lineage and flow of intellectual property. be confirmed. http://www.scilogs.com/eresearch/replacing-the-paper-the-twelve-rs-of-the-e-research-record/
  26. 26. Jun Zhao www.researchobject.org
  27. 27. The Order of 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. Berners-Lee, Weaving the Web, 1999
  28. 28. Some Social Machines
  29. 29. Scholarly Machines Ecosystem
  30. 30. Discussion points 1. Citation in tomorrow’s sense-making network of humans and machines: – What are the artefacts / social objects? – How and why are they cited? 2. Think about an ecosystem of interacting Scholarly Social Machines 3. Science as a Social Machine?
  31. 31. Thanks to Christine Borgman, Iain Buchan, Neil Chue Hong, Jun Zhao, Carole Goble, FORCE11, myExperiment, Software Sustainability Institute, wf4ever and SOCIAM david.deroure@oerc.ox.ac.uk www.oerc.ox.ac.uk/people/dder http://www.scilogs.com/eresearch @dder www.oerc.ox.ac.uk www.force11.org www.researchobject.org www.software.ac.uk sociam.org