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Digital Scholarship Intersection Scale Social Machines

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Digital Scholarship Intersection Scale Social Machines

  1. 1. David De Roure @dder Digital scholarship: Intersection, Scale, and Social Machines DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE Centre for Digital Scholarship
  2. 2. Porter, Bernard. 1939. Being a Map of Physics. Courtesy of Maine State Library and Mark Melnicove. In "10th Iteration (2014): The Future of Science Mapping," Places & Spaces: Mapping Science, edited by Katy Börner and Samuel Mills. http://scimaps.org
  3. 3. http://oxfordschoolofphotography.wordpress.com/tag/tilt-shift-photography/
  4. 4.                                                                                 Digital Humanities                                                                                 Social Machines Engineering   Cyber   Linguis.cs   English   Oxford   Mar.n   School   Saïd   Colleges   ARC   IT  Services   ECI  Geography   SKA   CUDA   Physics   Computer   Science   Maths   History   Oxford   Internet   Ins.tute   Music   Pharma   Archaeology   Classics   Zoology   DDeR 2015-04-25 Museums  
  5. 5. Segolene’s  slide  
  6. 6. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
  7. 7. The Big Picture(s)
  8. 8. Four  Quadrant  Diagram   Digital Scholarship
  9. 9. ChristineBorgman
  10. 10. RCUK Big Data – 21st century raw material Energy Efficient Computing Infrastructure (STFC) De-identified admin (including health) data Business data Open data (public sector) Social media data Research data Longitudinal survey data Open data Securely held data Environment data Business and LG Data Research Centres (ESRC) Admin Data Research Centres (ESRC) High Performance Data Environment (NERC) Clinical data Medical Bioinformatics (MRC) Understanding Populations (ESRC) Clinical Practice Datalink (MHRA, NIHR) 100,000 Genome Project NHS) Research Data Facility (EPSRC) European Bioinformatics Institute (EMBL) Bioscience E-Infrastructure (BBSRC) Square Kilometre Array (STFC) Digital Transformations (AHRC) Archive data Open Data Institute Commercial Research Understanding Populations (ESRC) http://www.rcuk.ac.uk/research/infrastructure/big-data/
  11. 11. theODI.org
  12. 12. F i r s t
  13. 13. Data Detect Store AnalyticsFilter Analysts
  14. 14. Citizens
  15. 15. Social  Machines  Defini.on  TBL  
  16. 16. www.zooniverse.org
  17. 17. Scientists Talk Forum Image Classification data reduction Citizen Scientists
  18. 18. Big Data Network
  19. 19. http://www.parliament.uk/business/committees/committees-a-z/commons-select/science-and-technology-committee/news/report-responsible-use-of-data/
  20. 20. New Forms of Data CDT ▶ Much of the value of ‘new forms of data’ lie in the potential for them to be analysed in near real-time, which presents opportunities for revealing phenomena as they unfold, enabling timely response with immediate influence. Such analysis brings distinct new computational requirements, requires new skills, and makes new demands on the ease of use and capability of the national e-Infrastructure. http://www.esrc.ac.uk/funding-and-guidance/postgraduates/dtc/dtc-policy/commissioning-of-centres-for-doctoral-training.aspx
  21. 21. Multi/Inter/Trans/Post Disciplinarity
  22. 22. Community   SoOware   Supercomputer   Digital  Music   Collec.ons   Student-­‐sourced   ground  truth   Community   SoOware   Linked  Data   Repositories   Supercomputer   23,000 hours of recorded music Music Information Retrieval Community SALAMI
  23. 23. Sequence alignment http://en.wikipedia.org/wiki/Sequence_alignment#/media/File:Histone_Alignment.png
  24. 24. Dan Edelstein, Robert Morrissey, and Glenn Roe, To Quote or not to Quote: Citation Strategies in the Encyclopédie. Journal of the History of Ideas , Volume 74, Number 2, April 2013 . pp. 213-236. 10.1353/jhi.2013.0012
  25. 25. 3,610 Shared Passages Montesquieu - 681 passages •  De l'esprit des lois (1746) - 477 passages •  Considérations sur les Romains… (1734) - 173 passages Voltaire - 528 passages •  Essai sur l'histoire générale… (1756) - 415 passages Jean-Baptiste Dubos - 229 passages •  Réflexions critiques sur la poésie et sur la peinture (1719) - 227 passages René Aubert de Vertot - 122 passages •  Histoire des révolutions arrivées dans le gouvernement romain (1727) - 122 passages Antoine Arnauld & Pierre Nicole - 107 passages •  La logique, or l'art de penser (1662) - 107 passages Charles Rollin - 100 passages •  Histoire ancienne des Égyptiens (1738) - 94 passages Montaigne - 91 passages •  Les Essais (1595) - 91 passages Condillac - 91 passages •  Essai sur l'origine des connaissances humaines (1746) - 91 passages Aligned passages in the over 900 texts that predate the publication of the Encyclopédie in the ARTFL-Frantext collection, from Russell Horton, Mark Olsen, and Glenn Roe, Something Borrowed: Sequence Alignment and the Identification of Similar Passages in Large Text Collections, Digital Studies - Le Champ numérique 2 (1)
  26. 26. Psychology and digital technology are being combined to understand music in new ways. In the run-up to the Being Human festival, a group of students in the audience for Wagner’s epic ‘Ring Cycle’, conducted by Valery Gergiev (Birmingham Hippodrome) will take part in an intriguing experiment to monitor the sensations produced over the 16-hour cycle of four operas. How do we really experience Wagner’s music? http://beinghumanfestival.org/event/hearing-wagner/
  27. 27. !
  28. 28. The Process of Scholarship
  29. 29. The  R  Dimensions   Research  Objects  facilitate  research  that  is   reproducible,  repeatable,  replicable,  reusable,   referenceable,  retrievable,  reviewable,   replayable,  re-­‐interpretable,  reprocessable,   recomposable,  reconstructable,  repurposable,   reliable,  respecUul,  reputable,  revealable,   recoverable,  restorable,  reparable,  refreshable?”   @dder 14 April 2014 sci  method   access   understand   new  use   social   cura.on   Research   Object   Principles  
  30. 30. Richard O’Bierne
  31. 31. First  Folio  Social  Machines   Metadata Story of the

Editor's Notes

  • Developing data landscape and boom in ‘big data’ – which includes electronic data not designed for research but with potential research value which records transactions, communications, physical movements (e.g. customer databases, service delivery records, internet search activity, etc.). This diagram describes the vision at the time when RCUK was setting the Big Data Agenda to secure investment. The ESRC has moved since then to consolidating Business/LG and ADRN into the ESRC Big Data network, for which a diagram will be presented shortly explaining the different stages and where the present call sits.
  • 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 2 is 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.

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