SOCIAM all-hands meeting, September, University of Oxford

1. Data sharing
in the Age of the SOCIAL MACHINE
Thanassis Tiropanis
University of Southampton
t.tiropanis@southampton.ac.uk

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. 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. Data for Social Machines
• It is beyond a system, we need an infrastructure
• Which, in turn, is a Social Machine

5. Web Observatory
People
Datasets
Streams Apps
Thanassis Tiropanis – University of Southampton

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. webobservatory.soton.ac.uk

8. webobservatory.soton.ac.uk

9. webobservatory.soton.ac.uk

10. webobservatory.soton.ac.uk

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. 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. ..and complex relationships,
and, varied stakeholder
expectations
WO Stakeholders

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. 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. Ongoing discussion

17. Thank you!
www.petrashub.org

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. webobservatory.org
Follow us: @wo_team
Contact us: hello@webobservatory.org