Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014


Published on

The analysis of government data, data held by business, the web, social science survey data will support new research directions and findings. Big Data is one of David Willetts’ 8 great technologies, and in order to secure the UK’s competitive advantage new investments have been made by the Economic Social Science Research Council ( ESRC) in Big Data, for example the Business Datasafe and Understanding Populations investments. In this session the benefits of the use of Big Data in social science , and the ESRCs Big Data strategy will be explained by Professor David De Roure.of the Oxford e-Research Centre and advisor to the ESRC.

Published in: Education
  • Be the first to comment

No Downloads
Total views
On SlideShare
From Embeds
Number of Embeds
Embeds 0
No embeds

No notes for slide

Big Data for the Social Sciences - David De Roure - Jisc Digital Festival 2014

  1. 1. Big Data for the Social Sciences David De Roure, Strategic Adviser for Data Resources @dder
  2. 2. Big Data doesn’t respect disciplinary boundaries Digital Social Research
  3. 3.
  4. 4. Mandy Chessell
  5. 5. 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
  6. 6. 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) ▶ But why do we want it? New forms of data enable us to 1. Answer existing research questions in new ways 2. Ask entirely new research questions
  7. 7. NERC Big Data diverse as our science • From micro- to macro-scale • Many sources: • Monitoring campaigns • Field sites & sensors • State-of-the-art laboratories • Ships & aircraft • Remote Sensing & EO • Regulator networks • Volunteers/citizen science • Model output • Long-term and unique! 10µm
  8. 8. 100 TB Big data: time-based media including film, tv, cctv footage - retail data - geospatial data - email and social media - images and associated metadata - performance data including raw data of recordings, choreography, performance structure - open government data - music - large-scale digital scans - library, museum & gallery archives and metadata
  9. 9. 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
  10. 10. Big Data Network
  11. 11. Phase 1 and 2
  12. 12. E-infrastructureLeadershipCouncil
  13. 13. Mandy Chessell
  14. 14. F i r s t
  15. 15. Interdisciplinary and “in the wild” * “in it” versus “on it”
  16. 16. Nigel Shadbolt et al
  17. 17. 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
  18. 18. Some Social Machines 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
  19. 19. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges.Ann Arbor: Deep Blue.
  20. 20. Web as lens Web as artefact Web Observatories
  21. 21. 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 and narratives. Iain Buchan
  22. 22. Join the W3C Community Group Jun Zhao
  23. 23. PipWillcox
  24. 24. Take homes ▶ New forms of data enable us answer old questions in new ways and to answer entirely new questions ▶ There are multiple shifts occurring: – Volumes of data – Realtime analytics – Computational infrastructure – Dataflows vs datasets (and curation infrastructure) – Correlation vs causation – Increasing automation – Machine-to-Machine in Internet of Things
  25. 25. @dder Slide and image credits: Fiona Armstrong, Christine Borgman, Iain Buchan, Mandy Chessell, Neil Chue Hong, Nigel Shadbolt, Pip Willcox, Jun Zhao, Guardian newspaper
  26. 26. @dder