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Title: Data visualization heuristics for the physical sciences

Abstract

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.

Authors:
ORCiD logo; ORCiD logo
Publication Date:
Research Org.:
Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1532417
Alternate Identifier(s):
OSTI ID: 1777817
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Published Article
Journal Name:
Materials & Design
Additional Journal Information:
Journal Name: Materials & Design Journal Volume: 179 Journal Issue: C; Journal ID: ISSN 0264-1275
Publisher:
Elsevier
Country of Publication:
United Kingdom
Language:
English

Citation Formats

Parish, Chad M., and Edmondson, Philip D. Data visualization heuristics for the physical sciences. United Kingdom: N. p., 2019. Web. doi:10.1016/j.matdes.2019.107868.
Parish, Chad M., & Edmondson, Philip D. Data visualization heuristics for the physical sciences. United Kingdom. doi:https://doi.org/10.1016/j.matdes.2019.107868
Parish, Chad M., and Edmondson, Philip D. Tue . "Data visualization heuristics for the physical sciences". United Kingdom. doi:https://doi.org/10.1016/j.matdes.2019.107868.
@article{osti_1532417,
title = {Data visualization heuristics for the physical sciences},
author = {Parish, Chad M. and Edmondson, Philip D.},
abstractNote = {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.},
doi = {10.1016/j.matdes.2019.107868},
journal = {Materials & Design},
number = C,
volume = 179,
place = {United Kingdom},
year = {2019},
month = {10}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record
DOI: https://doi.org/10.1016/j.matdes.2019.107868

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