.png){kind=icon}
返回
Data visualization heuristics for the physical sciences
.png){kind=icon}
OA
AI
DOI：10.1016/j.matdes.2019.107868.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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 naively. (c) 2019 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Keyword:
Visualization
Data presentation
Scientific publication
Microscopy
Data analytics
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
M
IF:
7.9
论文数:
1.9W
被引数:
9.8W
机构
引用论文
Bioreductive deposition of palladium (0) nanoparticles onShewanella oneidensiswith catalytic activity towards reductive dechlorination of polychlorinated biphenyls钯 (0) 纳米颗粒在 Shewanella oneidensis 上的生物还原沉积，对多氯联苯的还原脱氯具有催化活性
PREAI
分享收藏Big-deep-smart data in imaging for guiding materials design大-深-智能成像数据指导材料设计.png)
NATURE MATERIALSOAAI
NATURE MATERIALSIF38.5
OAAI分享收藏分享收藏
查看更多内容