Close reading for visualization evaluation

Annie Bares, Daniel F. Keefe, Francesca Samsel

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Visualizations produced by collaborations between artists, scientists, and visualization experts lay claim to being not only more effective in delivering information but also more effective in their abilities to elicit qualities like human connection. However, as prior work in the visualization community has demonstrated, it is difficult to evaluate these claims because characteristics associated with human connection are not easily measured quantitatively. In this Visualization Viewpoints piece, we address this problem in the context of our work to develop methods of evaluating visualizations created by Sculpting Visualization, a multidisciplinary project that incorporates art and design theory and practice into the process of scientific visualization. We present the design and results of a study in which we used close reading, a formal methodology used by humanities scholars, as a way to test reactions and analyses from evaluation participants related to an image created using Sculpting Visualization. In addition to specific suggestions about how to improve future iterations of the visualization, we discuss key findings of the evaluation related to contextual information, visual perspective, and associations that individual viewers brought to bear on their experience with the visualization.

Original languageEnglish (US)
Article number9117084
Pages (from-to)84-95
Number of pages12
JournalIEEE Computer Graphics and Applications
Issue number4
StatePublished - Jul 1 2020

Bibliographical note

Funding Information:
The CRIT method reproduced on the guided close reading worksheet was developed in the Department of English at The University of Texas at Austin by Professors Phillip Barrish, Evan Carton, Coleman Hutchison, and Frank Whigham, and Ph.D. students Sydney Bufkin, Jessica Gou-deau, and Jennifer Sapio. CRIT is a product of a Course Transformation Grant generously funded by the Office of the Executive Vice President and Provost. CRIT is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

Funding Information:
This work was supported in part by the National Science Foundation (IIS-1704604 IIS-1704904). Brain microstructure applications were supported in part by Dr. Christophe Lenglet at the University of Minnesota and by the National Institutes of Health (P41 EB015894, P30 NS076408). MPAS-O simulations were conducted by Mathew E. Maltrud and Riley X. Brady as part of the Energy Exascale Earth System Model (E3SM) project, funded by the U.S. Department of Energy (DOE), Office of Science, BER with analyses conducted by Phillip Wolfram, MEM, and RXB under ARPA-E Funding Opportunity No. DE-FOA-0001726, MARINER Award 17/CJ000/09/01, Pacific Northwest National Laboratory, prime recipient.

Publisher Copyright:
© 1981-2012 IEEE.


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