GV: Small: Collaborative Research: Supporting Knowledge Discovery through a Scientific Visualization Language

这项合作研究汇集了来自马里兰州巴尔的摩县大学（UMBC）和布朗大学的计算机科学家以及来自密西西比大学医学中心（UMMC）的神经科学家，以研究科学可视化语言（SVL）的设计。尽管已经设计了许多可视化方法，但可视化仍然是一门艺术而不是科学。基于以人为中心的计算，机器学习和认知心理学的理论和方法，这项工作是开发和评估科学可视化语言（SVL），以提供一种原则性的方法来帮助科学家了解可视化是如何工作的。该项目开发的工具和理论可以有效地发现知识，帮助神经科学家使用弥散张量磁共振成像（DTI）研究大脑。这项工作有以下具体目标和成果：（1）与科学家密切合作，以发现、提炼和验证符号空间，（2）描述符号之间关系的语义空间，（3）为神经科学家实现SVL以组成可视化的测试平台，（4）在南密西西比大学和布朗大学开发新的和增强的课程，以及（5）通过开源软件，实验数据，开放实验室，出版物广泛传播研究成果，该项目预计将产生广泛的影响。它可能会导致显着更好的方法来人类知识的发现和决策在许多学科中，可视化已经发现成功的应用，包括神经科学，生物医学，生物信息学，生物学，化学，地球科学，商业，经济学和教育。本科生和研究生预计将通过我们的课程参与研究，并计划在USM和布朗之间进行学生交流。K-12学生可以在项目进行期间访问USM实验室。软件和成果将通过项目网站（https：//sites.google.com/site/simplevisualizationlanguage）传播。

This collaborative research brings together computer scientists from University of Maryland Baltimore County (UMBC) and Brown University and neuroscientists from the University of Mississippi Medical Center (UMMC) to study the design of a scientific visualization language (SVL). Despite the numerous visualization approaches already devised, visualization remains more of an art than a science. Grounded in theories and methods from human-centered computing, machine learning, and cognitive psychology, this work is to develop and evaluate a scientific visualization language (SVL) to provide a principled way to help scientists understand how and why visualizations work. Tools and theories developed in this project can lead to efficient knowledge discovery to help neuroscientists study brains using diffusion tensor magnetic resonance imaging (DTI).This work has the following specific objectives and outcomes: (1) close collaboration with scientists to discover, refine, and verify a symbol space, (2) a semantic space that describes the relationship among symbols, (3) a testbed that implements SVL for neuroscientists to compose visualizations, (4) development of new and enhanced courses at University of Southern Mississippi and Brown University, and (5) wide dissemination of the research outcomes through open-source software, experimental data, open labs, publications, and presentations.This project is expected to have broad impact. It may lead to significantly better approaches to human knowledge discovery and decision making in many disciplines where visualizations have found successful application, including neuroscience, biomedicine, bioinformatics, biology, chemistry, geosciences, business, economics, and education. Undergraduate and graduate students are expected to participate in the research through our courses, and student exchanges are planned between USM and Brown. K-12 students can visit the USM lab while the project is in progress. Software and results will be disseminated via the project Web site (https://sites.google.com/site/simplevisualizationlanguage).