Data visualization heuristics for the physical sciences

Parish, Chad; Edmondson, Philip

doi:10.1016/j.matdes.2019.107868

Title: Data visualization heuristics for the physical sciences

Journal Article · 02 May 2021 · Materials & Design

DOI: https://doi.org/10.1016/j.matdes.2019.107868 · OSTI ID:1532417

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Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

Data visualization – that is, the graphical representation of numerical information – is foundational to the scientific enterprise. A broad literature base is available providing rules, guidelines, and heuristics for authors of scientific literature to assist in the production of scientific graphics that are readable and intuitive. However, most of the available recent publications are in the bio-, psycho-, or climate sciences literature. In this paper, we address this deficiency and provide data visualization heuristics tuned to the specific needs of the physical sciences, and particularly materials sciences, community. We enumerate six general rules and provide examples of bad and improved data graphics, and provide source code to illustrate the generation of the improved figures. The six rules we enumerate are: (1) Generate figures programmatically; (2) Multivariate data calls for multivariate representation; (3) Showing the data beats mean ± standard deviation; (4) Choose colormaps that match the nature of the data; (5) Use small multiples; and (6) Don't use vendor exports naïvely.

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Research Organization:: Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States)

Sponsoring Organization:: USDOE Office of Science (SC)

Grant/Contract Number:: AC05-00OR22725

OSTI ID:: 1532417

Alternate ID(s):: OSTI ID: 1777817

Journal Information:: Materials & Design, Vol. 179, Issue n/a; ISSN 0264-1275

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

Citation Metrics:

Cited by: 9 works

Citation information provided by
Web of Science

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