Big Data in the Arts and Humanities

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Part off presentation to the Arts and Humanities Research Council, April 2014

Part off presentation to the Arts and Humanities Research Council, April 2014

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  • 1. • It has been calculated that the books in the Library of Congress represent 235 terabytes of data • In 1994, at a conference on data management at ULCC, I heard the word ‘petabyte’ for first time: ‘the problems which confront the meteorologist today will worry the arts and humanities in ten years time’ • A petabyte equals approximately four Library of Congresses • The Large Hadron Collider generates one petabyte of data every second (which would cost $1 trillion a year to store) • The Square Kilometre Array will generate 4 petabytes of data a second • A new word for me in 2013: zettabyte (equivalent of 250 billion DVDs) • By 2020, digital universe will be 35 zettabytes (44 times as big as in 2009)
  • 2. Is there big data in the arts and humanities? Letter of Gladstone to Disraeli, 1878: British Library, Add. MS. 44457, f. 166 The political and literary papers of Gladstone preserved in the British Library comprise 762 volumes containing approx. 160,000 documents.
  • 3. George W. Bush Presidential Library: 200 million e-mails 4 million photographs
  • 4. • FamilySearch  Genealogy Information from around the world  2.8 billion names; 300 million added every year  560 million digital images  10 million hits per day; 7,000 transactions / minute  Digitising microfilm to JPEG2000  Digitised in Utah, preserved in Maryland  Initial storage 7Pb  Ingest rate : 20Tb per day, rising to 50Tb per day
  • 5. Anglo-American Legal Tradition (http://aalt.law.uh.edu) contains over 8,500,000 images of the major series of legal records for medieval and early modern England, but how do we navigate, annotate and share these materials?
  • 6. Credit: Mark Basham, Diamond, Raw images (2,600 X 4,000 px each image/projection) (Total: 6,000 images/projections) Sinograms from the projections (Total: 4,000 sinograms Each sinogram: 2,600 x 4,000 x 1 px) A sinogram Reconstructed slices in a 3d volume (Total: 4,000 slices) 2,600 px 4,000 px 6, 000 projections A slice Tomographic Reconstruction ~100Gb per 3D image - ~40 mins on 16 GPU cluster ~10 TB per experiment” - ~3 days on site ~ 1PB per year (per beamline) Working on using the Emerald (376 GPUs)
  • 7. Use of Diamond Light Source to image fragile manuscripts Use of Diamond Light Source to investigate silver degradation in altarpieces
  • 8. Laser Scans of St Kilda and Rosslyn Chapel, produced by Digital Design Studio, Glasgow School of Art, as part of the ‘Scottish Ten’ project. Scans such as these produce data sets containing billions of points, amounting to many terabytes. Such data can have great cultural and economic value.
  • 9. http://worldservice.prototyping.bbc.co.uk/
  • 10. Big Data in the Humanities • Frequently (but not always) heterogeneous, comprising different layers of information: comparable to climate and environmental data • Extremely varied in format, from linguistic corpora to images and video. Navigation and processing major issue, even when in digital format • Nevertheless strong common characteristics with big data issues in other disciplines and scope for shared dialogue • One characteristic of ‘big data’ hype is use of predictive analytics. Can predictive analytics work with some arts and humanities data (eg sound?) • But data driven research (as opposed to hypothesis driven research) is potentially important in arts and humanities • Arts and humanities can potentially help in dealing with big data by new interpretative approaches
  • 11. Lise Autogena and Joshua Portway, Most Blue Skies, 2010: http://www.autogena.org/mbs.html
  • 12. Fabio Lattanzi Antinori,The Obelisk (2012): Open Data Institute Scanning online news websites in real-time, the artwork changes from opaque to transparent, according to the quantity of references to the four main crimes against peace (genocide, crimes against humanity, crimes of aggression and crimes of war)
  • 13. Available at: http://mith.umd.edu/sharedhorizons/resources/
  • 14. ParametricModeling Quantitatively MapsSingle Cell Protein Levelsto Individual Qualitative Components