1. Big Data for the Social Sciences:
The Landscape forWeb Observatories
David De Roure, Strategic Adviser for Data Resources @dder

2. Overview
1. Big Data for research (UK perspective)
2. Social Media Data is distinctive
3. A series of shifts in how scholarship is conducted
4. And hence the context for Web Observatories

3. Big Data doesn’t respect
disciplinary boundaries
Digital Social Research

4. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research
Challenges.Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552

5. theODI.org

9. Mandy Chessell

10. The Big Picture
More people
Moremachines
Big Data
Big Compute
Conventional
Computation
“Big Social”
Social Networks
e-infrastructure
online
R&D
Big Data
Production
& Analytics
deeply
about
society

11. RCUK and Big Data
▶ ‘Big data is a term for a collection of datasets so
large and complex that it is beyond the ability
of typical database software tools to capture,
store, manage, and analyse them. ‘Big’ is not defined
as being larger than a certain number of ‘bytes’
because as technology advances over time, the size
of datasets that qualify as big data will also increase’
(RCUK)

13. Research benefits of new data
▶ Undertaking research on pressing policy-related issues
without the need for new data collection
• Food consumption, social background and obesity
• Energy consumption, housing type and climatic conditions
• Rural location, private/public transport alternatives and
incomes
• School attainment, higher education participation, subject
choices, student debt and later incomes
▶ New data such as social media enable us to ask big questions,
about big populations, and in real time – this is
transformative

14. Big Data Network

15. Phase 1 and 2

16. Research questions
– Social and political
movements
– Political participation and
trust
– Individual,
group/community and
national identities
– Personal, local, national and
global security (including
crime, law enforcement and
defence)
– Rural development and
‘UrbanTransformations’
– Crisis prevention,
preparedness, response,
management and
recovery
– Education
– Health and wellbeing
(including ageing)
– Environment and
sustainability
– Economic growth and
financial markets
(including employment
and the labour market)

19. E-infrastructureLeadershipCouncil

21. Mandy Chessell

23. F i r s t

24. Interdisciplinary and “in the wild” *
* “in it” versus “on it”

25. Nigel Shadbolt et al

26. 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)
The Order of Social Machines

27. SOCIAM:TheTheory 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 sociam.org

28. A revolutionary idea…
Open Science!

29. Join the W3C Community Group www.w3.org/community/rosc
Jun Zhao
www.researchobject.org

30. Web as
lens
Web as
artefact
Web Observatories
http://www.w3.org/community/webobservatory/

31. 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 sense-making
network of expertise, data, models, visualisations and narratives
Iain Buchan

32. PipWillcoxPipWillcox

33. PipWillcox

34. The Observatory Context
▶ New forms of data enable us answer old questions in
new ways and to address entirely new questions
– Especially about (new) social processes
▶ There are multiple shifts occurring:
– Academia and business
– Volumes and velocity of data
– Realtime analytics
– Computational infrastructure
– Dataflows vs datasets (and curation infrastructure)
– Correlation vs causation
– Increasing automation and ethical implications
– Machine-to-Machine in Internet of Things

35. david.deroure@oerc.ox.ac.uk
www.oerc.ox.ac.uk/people/dder
@dder
Slide and image credits: Fiona Armstrong, Christine Borgman, Iain
Buchan, Mandy Chessell, Neil Chue Hong, Cat De Roure, Kevin
Page, Nigel Shadbolt, Pip Willcox, Jun Zhao, Guardian newspaper

36. www.oerc.ox.ac.uk
david.deroure@oerc.ox.ac.uk
@dder