Visual Analysis and Digital Humanities

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a presentation on using visualization in the process of modeling and analyzing digital materials.

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Visual Analysis and Digital Humanities

  1. 1. data / visual analysis & digital humanities zoe borovsky zoe@ats.ucla.edu
  2. 2. drucker (& nowviskie), 2004, speculative computing • embodiment should be a dynamic and subjective process • our tools should engage us in a “dynamic, generative, iterative” process • model as an interpretive expression of a particular dataset
  3. 3. data/ visual analysis MONK: metadata offers new knowledge • traditional text- analysis tools feature prominent visualization tools http://www.monkproject.org/
  4. 4. data/ visual analysis TAPoR: text analysis portal for research • runs in web-browser • interactive displays • upload your own texts http://portal.tapor.ca/
  5. 5. data/ visual analysis incorporating results directly into publications
  6. 6. visualization applications become text-friendly • “Many Eyes is a bet on the power of human visual intelligence to find patterns.” • “Our goal is to ‘democratize’ visualization and to enable a new social kind of data analysis.” http://services.alphaworks.ibm.com/manyeyes/home
  7. 7. • runs in web-browser • interactive displays • users have access to the underlying data • visualizations can be embedded or linked
  8. 8. data/ visual analysis • visualization tools are more accessible to the “lone scholar” • more data is available in machine-readable format • are these useful tools for humanities research? can they engage us in a “dynamic, generative, iterative” analysis?
  9. 9. data/ visual analysis an approach (works in progress) • model your data/metadata • interpret • re-present • the modeling process may be more important than any one model
  10. 10. data/ visual analysis macfadyen: meter & rhyme, repetition a quick, overall view
  11. 11. data/ visual analysis almila: overview of a discipline, citation network spreadsheets are your new best-friend
  12. 12. data/ visual analysis
  13. 13. data/ visual analysis • other examples • Gedankenraum: semaspace •
  14. 14. data/ visual analysis authors who cite articles published in Leonardo mostly art journals Record mostly Leonardo Subject Area % of 1689 Count Record ART 770 45.5891% Source Title % of 1689 Count LEONARDO 659 39.0172% PSYCHOLOGY, EXPERIMENTAL 154 9.1178% PERCEPTION 39 2.3091% PSYCHOLOGY 103 6.0983% PERCEPTION & 23 1.3618% PSYCHOPHYSICS HUMANITIES, MULTIDISCIPLINARY 77 4.5589% DIGITAL CREATIVITY 18 1.0657% MUSIC 68 4.0261% LEONARDO MUSIC 18 1.0657% JOURNAL PSYCHOLOGY, MULTIDISCIPLINARY 58 3.4340% COMPUTER MUSIC 13 0.7697% JOURNAL COMPUTER SCIENCE, SOFTWARE 52 3.0787% BRITISH JOURNAL OF ENGINEERING 11 0.6513% AESTHETICS COMPUTER SCIENCE, THEORY & JOURNAL OF AESTHETICS 47 2.7827% 11 0.6513% METHODS AND ART CRITICISM COMPUTER SCIENCE, INTERFACE-JOURNAL OF 42 2.4867% 10 0.5921% INTERDISCIPLINARY APPLICATIONS NEW MUSIC RESEARCH PHILOSOPHY 35 2.0722% BELFAGOR 9 0.5329% (140 Subject Area value(s) outside (529 Source Title value(s) outside display options.) display options.)
  15. 15. examples: data/ visual analysis • Cave Art: “Lascaux” (2005) the order of superimposed images: horse, aurochs-stag
  16. 16. examples: data/ visual analysis • manuscripts
  17. 17. applications to watch • Simile: http://simile.mit.edu • Swivel: http://www.swivel.com • Google visualization and spreadsheets: e.g. Motion Chart
  18. 18. will digital humanities provide new knowledge? • or just “better”/different artifacts, communication & arguments? • weigh the benefits and risks of an opportunity • greater benefits if: • viewed as a process (rather than product) • integrated into research as well as instruction • as much processing in the hands of researchers as practical • scholars and developers work together

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