CGV: Medium: Collaborative Research: Visualizing Comparisons

比较是数据分析的重要组成部分，因此也是许多可视化任务的重要组成部分。虽然已发表的文献提供了丰富的可视化工具来查看单个对象（图形、体积、时间序列、基因序列、分子运动等），但迄今为止，对比较的支持考虑较少。pi认为比较任务最好由明确为此目的而设计的工具来支持。问题是，随着对象的数量、大小、对象的复杂性和/或它们之间关系的增加，视觉比较变得更具挑战性。我们收集和生成数据的能力迅速增长，这进一步加剧了这一困难。在先前的工作中，pi已经开发了一些令人鼓舞的比较工具的初步示例，但这些都是专门的成功，对未来的努力提供了很少的指导。因此，以我们目前有限的理解在更大范围内解决更广泛的比较问题在很大程度上仍然是一门需要相当努力的艺术。pi在这个项目中的目标是朝着视觉比较科学的方向发展。通过将视觉比较作为一个普遍问题来研究，他们将为该领域建立一个独立于领域的基础，从而促进未来工具的设计，从而创建更有效和可扩展的比较。为了达到这些目的，该团队将追求三个相互关联的研究线索。他们将定义基于视觉认知原理的理论。他们将探索案例研究（来自领域合作者提出的实际问题），挑战和扩展这些理论，为实证研究提供例子，并建议或使用一般概念。并且他们将确定通用的任务、设计和策略，从而能够开发通用的技术、指导方针和软件组件。这种方法独特地结合了实证研究、设计探索和软件开发，将视觉比较领域提升到一个新的水平，既根植于理论，又在实践中可行。更广泛的影响：由于视觉比较在不同的领域（包括基本上所有的科学、工程和医学）中起着关键作用，因此视觉比较工具的改进所带来的潜在好处是深远的。为了确保项目成果的最大适用性，pi直接与物理、生物、社会、教育和医学科学家以及人文学科的工程师和学者合作。该项目将为其他人的可视化开发生成可视化工具、软件组件和资源。视觉比较将作为一种机制，使各级学生了解数据理解中的问题。该项目还将为可视化专家提供培训，吸引非技术学生参与可视化，并探索可视化在公共宣传工作中的作用。

Comparison is an essential part of data analysis and, therefore, of many visualization tasks. While the published literature provides a wealth of visualization tools for looking at individual objects (graphs, volumes, time series, gene sequences, molecular motions, etc.), there has to date been less consideration of support for comparison. The PIs argue that comparison tasks are best supported by tools explicitly designed for that purpose. The problem is that visual comparison becomes more challenging as the number of objects, their size, and the complexity of the objects and/or of the relationships among them increases. The difficulty is further compounded by our rapidly growing ability to collect and generate data. In prior work the PIs have developed some encouraging initial examples of comparison tools, but these are specialized successes that offer little guidance for future endeavors. Addressing a wider range of comparison problems at greater scale with our present limited understanding thus largely remains an art that requires considerable effort. The PIs' goal in this project is to move towards a science of visual comparison. By studying visual comparison as a general problem, they will establish a domain-independent foundation for the field that facilitates the design of future tools which allow the creation of more effective and scalable comparisons. To these ends the team will pursue three interconnected research threads. They will define theories that are grounded upon principles of visual cognition. They will explore case studies (derived from real problems suggested by domain collaborators) that challenge and extend these theories, provide examples for empirical study, and suggest or use general concepts. And they will identify common tasks, designs, and strategies that enable development of generalized techniques, guidelines, and software components. This approach uniquely combines empirical studies, design explorations, and software development to take the field of visual comparisons to a new level that is both rooted in theory yet viable in practice. Broader Impacts: Because visual comparison plays a key role in diverse domains (including essentially all of the sciences, engineering, and medicine), the potential benefits from an improved science of visual comparison tools are far reaching. To ensure maximum applicability for project outcomes, the PIs are directly collaborating with physical, biological, social, educational, and medical scientists, as well as with engineers and scholars in the humanities. The project will generate visualization tools, software components, and resources for visualization development by others. Visual comparison will serve as a mechanism to expose students at all levels to issues in data understanding. This project will also provide training for visualization specialists, engage non-technical students in visualization, and explore the role of visualization in public outreach efforts.