Data Beyond Vision

experimental physical representations of humanities data

Data visualization is frequently used in Digital Humanities for exploration, analysis, making an argument, or grappling with large-scale data. Increasing access to off-the-shelf data visualization tools is beneficial to the field, but it can lead to homogenized visualizations.

Data physicalization has potential to defamiliarize and refresh the insight that data visualizations initially brought to DH. Proliferation in 3D modeling software and relatively affordable 3D printing technology makes iterative, computer-generated data physicalization more feasible. Working in three dimensions gives additional affordances: parallel data series can be seen next to each other, rather than color-coded, overlapped, or staggered; and physical objects can be viewed from multiple angles, allowing for changing perspective.

This project explores new ways of engaging with a dataset and the arguments and narratives behind it, in order to challenge the dominant paradigms of conventional screen-based data visualization. The project currently comprises:

  • 3D printing a model of library member activity over time from the Shakespeare and Company Project juxtaposing documented activities from two sets of archival materials
  • Folding paper forms of borrowing activity from the Shakespeare and Company Project surfacing the activity of women and and non-famous members
  • Weaving representing intertextuality based on references in Jacques Derrida’s de la Grammatologie from Derrida’s Margins

The project was exhibited as a poster at DH2019 Utrecht and as an installation at ACH2019 Pittsburgh.


Presentations

Koeser, Rebecca Sutton, Nick Budak, Gissoo Doroudian, and Xinyi Li. “Data Beyond Vision.” Poster presented at DH2019, Utrecht, July 11, 2019. http://doi.org/10.5281/zenodo.3261531

Koeser, Rebecca Sutton, Nick Budak, Gissoo Doroudian, and Xinyi Li. “Data Beyond Vision.” Installation presented at ACH2019, Pittsburgh, July 25, 2019. 

CDH Grant History

  • 2018– Staff R&D