Scientific visualizations are the foundation for communicating results and findings to a variety of audiences. As the creation of novel and large environmental datasets has grown, this has necessitated new schemes and recommendations for creating effective visualizations. In this overview, we review the foundations of scientific visualization and considerations for visualization of large datasets within the context of the four Vs of big data (volume, variety, veracity, and velocity). Using big datasets requires making decisions as to whether to aggregate or preserve details, approaches for grouping to enable comparisons, and considering how best to show complex data in many-dimensional space. To enable more effective visualizations, we provide several considerations regarding common decisions faced during the visualization process. These recommendations are accompanied by examples applied to existing large datasets. While our recommendations are just that, they encourage intentionality and awareness of the choices faced when visualizing scientific datasets.

科学可视化是将结果和发现传达给各种受众的基础。随着新的大型环境数据集的创建不断增长，这需要新的方案和建议来创建有效的可视化。在本概述中，我们回顾了科学可视化的基础以及在大数据的四个 V（数量、多样性、真实性和速度）的背景下可视化大型数据集的考虑因素。使用大数据集需要决定是聚合还是保留细节、分组方法以进行比较，并考虑如何最好地在多维空间中显示复杂数据。为了实现更有效的可视化，我们提供了一些关于可视化过程中面临的常见决策的注意事项。这些建议附有应用于现有大型数据集的示例。虽然我们的建议仅此而已，但它们鼓励对可视化科学数据集时所面临的选择的意识和意识。