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来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。例子包括：用于宇宙学、机械工程、土木工程、气候研究、海洋学、地质学和航空学的大规模科学计算模拟；地质勘查;生物系统分析（生物信息学）；安全信息分析；以及多媒体分析（视频、文字、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，人们如何使用和解释当前的可视化技术却知之甚少。随着商用PC的图形功能变得越来越强大，越来越多的新用户可以使用更复杂的技术。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，没有一般的科学课程教授“科学可视化素养”，而不仅仅是简单的图形和图表，并且有一种隐含的期望，即学生能够天生地理解这些图形/图像。然而，在各种科学领域中观察到，一部分学生在理解用于解释这些领域概念的表征方面存在困难。该项目的目标是研究如何培养学生的可视化素养，基于许多科学学科中常用的一类科学可视化来理解三维空间填充图形，即切片视图或剖面图。该项目包括确定这些类型的可视化的一般特征，理解需要获得的潜在策略来解决涉及这些图形的问题，并创建一个结构化的培训计划来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得基于特定学科的科学可视化，培训将在入门级地质学课程中进行介绍和测试。地质学是一个自然的测试平台，因为它吸引了不同学生的代表，并且它已经利用了许多3D可视化，例如，了解地下地质。然而，这项研究的结果可以用来训练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.