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MACROSCOPES
AND DISTANT
READING
IMPLICATIONS FOR INFRASTRUCTURES TO
SUPPORT COMPUTATIONAL HUMANITIES
SCHOLARSHIP
Joel de Rosney, 1979, The Macroscope
DISTANT READING
"Where distance is not an
obstacle, but a specific form of
knowledge: fewer elements,
hence a sharper sens...
HYPERMEDIA
FLATTENED
HIERARCHIES
EXPLORE
ORDER IN
AGGREGATES:
COLLECTIONS,
ARCHIVES, ETC.
THREE MODELS IN THE WILD
1.Data Dumps & Export Buttons
2.Sandboxes & Platforms
3.Analysis as a Service & Onsite
Facilities
EXPORT
BUTTONS
& DATA DUMPS
THREE CHALLENGES
1.Rights: Can you broadly provide bulk
access to works?
2.Scale: Can your infrastructure deliver bulk
exp...
SANDBOXES & PLATFORMS
ANALYSIS
AS A SERVICE
& ONSITE
FACILITIES
UNPACKING IMPLICATIONS
1.Whenever possible, move toward
providing bulk access to data.
2.Consider deriving intermediary or...
Catspyjamasnz, The Network by @nancywhite,
https://www.flickr.com/photos/catspyjamasnz/7169043832
CC-BY-NC-ND
onegoodbumbl...
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Macroscopes and Distant Reading: Implications for Infrastructures to Support Computational Humanities Scholarship

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A talk exploring the implications for digital library infrastructures in the face of developments in how humanities scholars are engaging in computational research of library collections.

Published in: Science
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Macroscopes and Distant Reading: Implications for Infrastructures to Support Computational Humanities Scholarship

  1. 1. MACROSCOPES AND DISTANT READING IMPLICATIONS FOR INFRASTRUCTURES TO SUPPORT COMPUTATIONAL HUMANITIES SCHOLARSHIP
  2. 2. Joel de Rosney, 1979, The Macroscope
  3. 3. DISTANT READING "Where distance is not an obstacle, but a specific form of knowledge: fewer elements, hence a sharper sense of their overall interconnection. Shapes, relations, structures. Forms. Models." Moretti, Graphs, Maps and Trees: Abstract Models for Literary History.
  4. 4. HYPERMEDIA FLATTENED HIERARCHIES
  5. 5. EXPLORE ORDER IN AGGREGATES: COLLECTIONS, ARCHIVES, ETC.
  6. 6. THREE MODELS IN THE WILD 1.Data Dumps & Export Buttons 2.Sandboxes & Platforms 3.Analysis as a Service & Onsite Facilities
  7. 7. EXPORT BUTTONS & DATA DUMPS
  8. 8. THREE CHALLENGES 1.Rights: Can you broadly provide bulk access to works? 2.Scale: Can your infrastructure deliver bulk exports? Is the material too large for researchers to work with in their environments? 3.Skills: Do your users have the skills to work with data at the command line?
  9. 9. SANDBOXES & PLATFORMS
  10. 10. ANALYSIS AS A SERVICE & ONSITE FACILITIES
  11. 11. UNPACKING IMPLICATIONS 1.Whenever possible, move toward providing bulk access to data. 2.Consider deriving intermediary or transformative data products, like n- grams 3. If no go on 1 & 2 explore possibilities for analytic services
  12. 12. Catspyjamasnz, The Network by @nancywhite, https://www.flickr.com/photos/catspyjamasnz/7169043832 CC-BY-NC-ND onegoodbumblebee Pez. https://www.flickr.com/photos/onegoodbumblebee/141388029 4 CC-BY-NA-ND Dstrelau, Toys https://www.flickr.com/photos/dstrelau/5861814214 CC-BY

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