CI-TEAM Demonstration Project: Developing Scientific Visualization Literacy for Cybterinfrastructure Training

基于网络的科学调查涉及来自分布式源的大量多方面数据的复杂问题，包括计算模拟，高维传感/数据收集和元数据分析。示例包括：在宇宙学、机械工程、土木工程、气候研究、海洋学、地质学和航空学中使用的大规模科学计算模拟;地质勘探;生物系统分析（生物信息学）;安全信息分析;和多媒体分析（视频、文本、图像处理）。可视化和基于计算机的交互与分析推理相结合是使我们能够解决这些巨大的大规模问题的关键组成部分。然而，人们如何访问和解释当前的可视化技术知之甚少。随着商品PC的图形能力变得越来越强大，新手用户越来越多地使用更复杂的技术。任何人都可以下载程序来查看3D MRI/CT或查看卫星地图和其他观测数据。目前，除了简单的图形和图表之外，还没有教授“科学可视化素养”的一般科学课程，并且存在一种隐含的期望，即学生可以自己内在地理解这些图形/图像。然而，在各种科学领域中已经观察到，一部分学生难以理解用于解释这些领域中的概念的表示。该项目的目标是研究如何培养学生的可视化素养，基于一类通常用于许多科学学科的科学可视化来理解3D空间填充图形，即切片或剖视图。该项目涉及确定这些类型的可视化的一般特征，了解需要获得解决涉及这些数字的问题的基本策略，并创建一个结构化的培训计划，教学生如何解决涉及这些可视化的问题。为了评估通用培训如何促进获取基于特定学科的科学可视化，然后将在入门级地质学课程中介绍和测试培训。地质学是一个自然的试验台的选择，因为它吸引了学生的不同代表，它已经利用许多三维可视化，例如，了解地下地质。然而，这项研究的结果可以用来培养学生在所有领域利用空间填充可视化。

Cyber-based scientific investigations involve complex problems with massive multifaceted data from distributed sources including computational simulations, high dimensional sensing/data collections, and meta-data analysis. Examples include: large scale scientific computational simulations used in cosmology, mechanical engineering, civil engineering, climate studies, oceanography, geology, and aeronautics; geological exploration; biological system analysis (bioinformatics); security information analysis; and multimedia analysis (video, text, image processing). Visualization and computer-based interaction combined with analytical reasoning are crucial components enabling us to tackle these immense large scale problems. Little is known, however, about how people access and interpret current visualization techniques. As the graphics capabilities of commodity PC's become more powerful, more sophisticated techniques are increasingly available to the novice user. Anyone can download programs to view 3D MRI/CT or view satellite maps and other observation data. Currently, there is no general scientific curriculum teaching "scientific visualization literacy" for more than simple graphs and plots, and there is an implicit expectation that students can inherently understand such figures/images on their own. However, it has been observed in a variety of scientific fields that a subset of students has difficulty in understanding the representations used to explain concepts in these fields. The goal of this project is to investigate how to train students in visualization literacy, based on one class of scientific visualizations commonly used in many scientific disciplines to understand 3D space-filling figures, namely, slice or cutaway views. The project involves determining the generic characteristics of these types of visualizations, understanding the underlying strategies that need to be acquired to solve problems involving these figures, and creating a structured training program to teach students how to solve problems involving these visualizations. To evaluate how well the generic training facilitates the acquisition of a specific discipline-based scientific visualization, the training will then be introduced and tested in an introductory level geology course. Geology is a natural test-bed choice, as it attracts a diverse representation of students, and it already utilizes many 3D visualizations, for example, to understand subsurface geology. However, the results of this research can be used to train students in all fields utilizing space-filling visualization.