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Data sharing in the age of the Social Machine

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SOCIAM all-hands meeting, September, University of Oxford

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Data sharing in the age of the Social Machine

  1. 1. Data sharing in the Age of the SOCIAL MACHINE Thanassis Tiropanis University of Southampton t.tiropanis@southampton.ac.uk
  2. 2. Data and Social Machines • Social Machines generate data – E.g. Web, Zooniverse, Twitter, Wikipedia • Social Machines consume data – E.g. Web, Zooniverse, Twitter, Wikipedia • The human element in Social Machines for data generation, analysis, visualisation and consumption
  3. 3. The human element in data analysis • More than one interpretations of data • Different visualisations for different people • Discourse on data • Data and visualisation sharing • Ethics • Privacy • Algorithms • Marketplace
  4. 4. Data for Social Machines • It is beyond a system, we need an infrastructure • Which, in turn, is a Social Machine
  5. 5. Web Observatory People Datasets Streams Apps Thanassis Tiropanis – University of Southampton
  6. 6. A unifying concept Datastore People Datasets Streams Apps People Datasets Streams Apps People Datasets Streams Apps People Datasets Streams Apps Appstore Personal Dashboard Personal Datastore Thanassis Tiropanis – University of Southampton
  7. 7. webobservatory.soton.ac.uk
  8. 8. webobservatory.soton.ac.uk
  9. 9. webobservatory.soton.ac.uk
  10. 10. webobservatory.soton.ac.uk
  11. 11. WO Architectural Principles • Not all datasets or applications can be public. • Web Observatories list two main types of resources: datasets [or streams] and analytic applications, including visualisations. • Not all listed resources need to be locally hosted • Metadata describing the listed resources and projects are published. The Web Observatory: A Middle Layer for Broad Data. (2014). Tiropanis, T, Hall, W, Hendler, J A, De Larinaga, C. Big Data, 2(3).
  12. 12. The Web Observatory: A Middle Layer for Broad Data. (2014). Tiropanis, T, Hall, W, Hendler, J A, De Larinaga, C. Big Data, 2(3).
  13. 13. ..and complex relationships, and, varied stakeholder expectations WO Stakeholders
  14. 14. Opportunities • Virtual Research Data Repositories – Data sharing across repositories • Management of ‘Live’ Research Data – Beyond research data archival – Engagement with researchers across Universities, across disciplines • Secure Access Control and Attribution – How research data are used – Data publishers take back control
  15. 15. Challenges • Designing for generality • Ethical and legal challenges • Infrastructural challenges – Standards for metadata and security – Network and cloud infrastructures • Technological challenges – Fine-grain access control – Searching across personal datastores – Performance on lightweight computers
  16. 16. Ongoing discussion
  17. 17. Thank you! www.petrashub.org
  18. 18. The human element in data analysis • Artificial Intelligence thrives on data – but also thrives on people’s contribution • In the age of AI the human element is essential – Web Observatories support the human element
  19. 19. webobservatory.org Follow us: @wo_team Contact us: hello@webobservatory.org

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