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

1. data / visual analysis
& digital humanities
zoe borovsky
zoe@ats.ucla.edu

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. data/ visual analysis
MONK: metadata offers new knowledge
• traditional text-
analysis tools
feature prominent
visualization tools
http://www.monkproject.org/

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. data/ visual analysis
incorporating
results directly
into
publications

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. • runs in web-browser
• interactive displays
• users have access to the
underlying data
• visualizations can be
embedded or linked

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. 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. data/ visual analysis
macfadyen: meter & rhyme, repetition
a quick, overall view

11. data/ visual analysis
almila: overview of a discipline, citation network
spreadsheets are your new best-friend

12. data/ visual analysis

14. data/ visual analysis
• other examples
• Gedankenraum: semaspace
•

15. 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.)

16. examples: data/ visual analysis
• Cave Art:
“Lascaux” (2005)
the order of
superimposed
images: horse,
aurochs-stag

17. examples: data/ visual analysis
• manuscripts

18. applications to watch
• Simile: http://simile.mit.edu
• Swivel: http://www.swivel.com
• Google visualization and spreadsheets:
e.g. Motion Chart

19. 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