Data visualization heuristics for the physical sciences. (5th October 2019)

• Record Type: Journal Article
• Authors: Parish, Chad M.; Edmondson, Philip D.
• Is Part Of: Materials & design. Volume 179(2019)
• Publication Date: 2019-10-05
• Subjects: Visualization -- Data presentation -- Scientific publication -- Microscopy -- Data analytics; Materials -- Periodicals; Engineering design -- Periodicals; Matériaux -- Périodiques; Conception technique -- Périodiques; Electronic journals; 620.11
• DOI: 10.1016/j.matdes.2019.107868
• Languages: English
• ISSNs: 0264-1275

Abstract

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. Graphical abstract: Unlabelled Image Highlights: Data visualization is central to the scientific enterprise, but published papers' visualizations are often unclear. We suggest six heuristic rules for improving visualization in the physical, and particularly materials, sciences. We provide discussions of past work in the bio-, psycho-, and earth sciences literature. We suggest some best practices for visualization.