Web Observatories and e-Research

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Web Observatories, e-Research and the Importance of Collaboration. WST 2014 Webinar series, 20th March 2014
See Web Science Trust http://webscience.org/

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  • EPSRC: Under ‘Big Data’ we are considering both very large and also complex data, including dynamic and heterogenous data from all the various sources including sensors, social media, industry etc.
  • ESRC was allocated 64m and much of this is being used to set up the ESRC Big Data Network. The ESRC’s Big Data Network will support the development of a network of innovative investments which will strengthen the UK’s competitive advantage in Big Data for the social sciences. The core aim of this network is to facilitate access to different types of data and thereby stimulate innovative research and develop new methods to undertake that research. Although you should note that diagram it is only illustrative in terms of how the UKDS and ADS will work across – that is still under discussion; and only illustrative in the number of Business and Local Government Data Research.This network has been divided into three phases. In Phase 1 of the Big Data Network the ESRC has invested in the development of the Administrative Data Research Network (ADRN) which will provide access to de-identified administrative data collected by government departments for research use – focus of this meeting and all your grants.A few words about Phase 2 and 3 before we pass to Vanessa to talk about the ADRN some more. Phase 2is currently bring commissioned and will deal primarily with business data and/ or local government data. Phase 3, further details of which will be released in the last autumn / winter and will focus primarily on third sector data and social media data. It is expected that there will be opportunities for interaction across all elements of the ESRC Big Data Network and that they will all work together around the wider objectives of facilitating access to different forms of data and of ensuring maximum impact is generated from the use of that data for the mutual benefit of data owners and researchers, and through the research facilitated by the Network, benefit society and the economy more generally.
  • Thanks to Simon Hettrick for additional input to this slide.
  • ESRC Cities Expert Group
  • Web Observatories and e-Research

    1. 1. Web Observatories, e-Research and the Importance of Collaboration David De Roure e-Research Centre, University of Oxford ESRC Strategic Adviser for Data Resources @dder
    2. 2. Overview 1. Big Data for research (UK perspective) 2. Social Media Data is distinctive 3. Several shifts in how scholarship is conducted 4. And hence the context for Web Observatories
    3. 3. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue.
    4. 4. Innovative Technology Transforming Research
    5. 5. Big Data doesn‟t respect disciplinary boundaries Digital Social Research
    6. 6. theODI.org
    7. 7. Mandy Chessell
    8. 8. 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
    9. 9. 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)
    10. 10. 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
    11. 11. Big Data Network
    12. 12. Phase 1 and 2
    13. 13. 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 „Urban Transformations‟ – 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)
    14. 14. http://www.theguardian.com/uk/series/reading-the-riots
    15. 15. E-infrastructureLeadership
    16. 16. NeilChueHong
    17. 17. Mandy Chessell
    18. 18. F i r s t
    19. 19. Interdisciplinary and “in the wild” * * “in it” versus “on it”
    20. 20. Nigel Shadbolt et al
    21. 21. 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
    22. 22. SOCIAM: The Theory 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
    23. 23. A revolutionary idea… Open Science! rstl.royalsocietypublishing.org
    24. 24. Join the W3C Community Group www.w3.org/community/rosc Jun Zhao www.researchobject.org
    25. 25. Web as lens Web as artefact Web Observatories http://www.w3.org/community/webobservatory/
    26. 26. 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
    27. 27. PipPip From data to signal to understanding
    28. 28. Pip Willcox @marstonbikepath Datasets or dataflows?
    29. 29. 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
    30. 30. Towards a socio- technical system of observatories Technicalandbusinessinterface
    31. 31. Knowledge Infrastructure Knowledge Objects Descriptive layer Observatories
    32. 32. WOW2014 Web Observatory Workshop at WWW2014 Keynote Professor Dame Wendy Hall The Web Observatory: A Web Science Perspective Huanbo Luan and Tat-Seng Chua, The Design of a Live Social Observatory System Matthew Weber, Observing the Web by Understanding the Past: Archival Internet Research Mizuki Oka, Yasuhiro Hashimoto and Takashi Ikegami, Fluctuation and Burst Response in Social Media Gareth Beeston, Manuel Leon, Caroline Halcrow, Xianni Xiao, Lu Liu, Jinchuan Wang, Jinho Jay Kim and Kunwoo Park,Humour Reactions in Crisis: A Proximal analysis of Chinese posts on Sina Weibo in Reaction to the Salt Panic of March 2011 Robert Simpson, Kevin Page and David De Roure, Zooniverse: Observing the World‟s Largest Citizen Science Platform Paul Booth,Visualising Data in Web Observatories: A Proposal for Visual Analytics Development & Evaluation Marie Joan Kristine Gloria, John S. Erickson, Joanne S. Luciano, Deborah McGuinness and Dominic Difranzo, Legal and Ethical Considerations: Step 1b in Building a Health Web Observatory Ian Brown, Wendy Hall and Lisa Harris, Towards a Taxonomy for Web Observatories Posters: Reuben Binns, Observation without Surveillance: Web Observatories and Privacy Besnik Fetahu, Stefan Dietze, and Wolfgang Nejdl, What's all the Data about? - Creating Structured Profiles of Linked Data on the Web Caroline Halcrow, Jinchuan Wang, Xianni Xiao, Lu Liu, Scaling and geo-locating commonly used humour tags in Weibo Shuangjie Li, Zhigang Wang and Juanzi Li, Observation on Heterogeneous Online Wikis of Different Languages Panel: Web Observatory interoperability and standards moderator David De Roure Panellists: Wendy Hall (Web Science Trust), Jim Hendler (RPI), Thanassis Tiropanis (University ofwow.oerc.ox.ac.uk
    33. 33. 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, Nigel Shadbolt, Pip Willcox, Jun Zhao, The http://www.w3.org/community/webobservato ry/
    34. 34. www.oerc.ox.ac.uk david.deroure@oerc.ox.ac.uk @dder

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