Digital Scholarship Intersection Scale Social Machines
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Open Lecture at Centre for Digital Scholarship, Bodleian Libraries, 28 May 2015
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Digital Scholarship Intersection Scale Social Machines
- 1. David De Roure @dder Digital scholarship: Intersection, Scale, and Social Machines DIRECTOR, UNIVERSITY OF OXFORD E-RESEARCH CENTRE Centre for Digital Scholarship
- 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
- 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
- 7. Edwards, P. N., et al. (2013) Knowledge Infrastructures: Intellectual Frameworks and Research Challenges. Ann Arbor: Deep Blue. http://hdl.handle.net/2027.42/97552
- 9. Four Quadrant Diagram Digital Scholarship
- 10. ChristineBorgman
- 11. 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/
- 12. theODI.org
- 13. F i r s t
- 14. Data Detect Store AnalyticsFilter Analysts
- 15. Citizens
- 16. Social Machines Defini.on TBL
- 19. Big Data Network
- 21. 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
- 23. 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
- 24. Sequence alignment http://en.wikipedia.org/wiki/Sequence_alignment#/media/File:Histone_Alignment.png
- 25. 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
- 26. 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)
- 28. 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/
- 29. !
- 30. The Process of Scholarship
- 32. 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
- 33. Richard O’Bierne
- 34. First Folio Social Machines Metadata Story of the First Folio Social Machines Annotation David De Roure and Pip Willcox ‘“Coniunction, with the participation of Society”: Citizens, Scale, and Scholarly Social Machines’ Beyond the PDF: Born-Digital Humanities, Boston, 27–28 April 2015
- 35. First Folio Social Machines
- 36. Digital Material
- 37. By Ségolène Tarte, David De Roure and Pip Willcox Working out the Plot The Role of Stories in Social Machines
- 39. A computationally-enabled sense-making network of expertise, data, software, models and narratives Big Data, in a Big Data Centre
- 40. Scholars Digital Methods Team Research Projects Digital Methods Team Scholars Community Representa.on, communica.on and making of knowledge 2.TRANSFORM methods model enhanced resources Partners networks 3. SHARE 1. ENGAGE
- 41. Digital Scholarship @ Oxford We have seen the affordances of digital in scholarship: digitize, democratize, discover, access, analyze, automate, create, cite, curate, link, scale, share, re-use. We can answer old questions in new ways, and new questions. We see the future university leading in digital scholarship. How do we get there? 1. Digital strategy to guide coherent investment 2. Innovation with new digital technology and methods 3. Co-creating capability with scholars and content 4. Ongoing support for research using digital infrastructure
- 42. David De Roure david.deroure@oerc.ox.ac.uk Centre for Digital Scholarship Thanks to Christine Borgman, Chris Lintott, Richard O’Bierne, Glenn Roe, Ségolène Tarte, Pip Willcox; CofK, FAST, FORCE11, SOCIAM, Transforming Musicology; AHRC, EPSRC, ESRC, JISC, Andrew W. Mellon Foundation. http://www.slideshare.net/davidderoure/digital-scholarship-intersection-scale-social-machines
Notes de l'éditeur
- 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.