EAGER: Collaborative Research: Connecting Communities Through Data, Visualizations, and Decisions

陆地和水生系统可视化项目帮助环境科学家为他们自己的研究制作可视化图像，并向包括决策者在内的其他科学家和利益攸关方展示。迄今为止工作的一个关键发现是，科学家不仅使用可视化以新的方式探索数据和展示结果，而且还与利益攸关方合作，共同产生可在决策过程中使用的信息。科学可视化在共同生产知识中的作用尚未得到检验，尽管这种参与在创造可接受的解决方案、信息或技术方面可能是至关重要的。这项提议重塑了共同制作可视化工具的前景，即探索用户之间的谈判？需求和技术能力决定了所使用的可视化类型和实施的工具，改变或修改了科学家提出的研究问题，并影响了如何解释结果，以便社区能够应对关键的生态挑战，包括气候变化。这是社会科学家、计算机科学家和环境科学家与非科学家利益相关者之间的一次独特的实验和合作，以共同产生用于决策的数据可视化。将使用社会科学方法探索知识联合生产，并结合技术创新，导致社区决策，以解决气候变化适应问题。将确定需要在多大程度上区分科学家和非科学家的科学可视化，并将与项目合作者共同开发独特的可视化。目标是确定可视化对科学家和利益攸关方在与气候适应有关的关键决策中共同产生知识的影响。这个项目涉及计算机科学家和社会科学家。计算机科学：Vistas是一款C++科学可视化应用程序，具有重要的GPU处理能力，可以帮助环境科学家生成图像，使他们能够看到？地形对生态现象的影响。对于这一奖项，将开发新的可视化技术，进行可视化和视觉分析研究，使决策者能够有效地展示，并将为环境和社会科学家提供技术支持。如果时间和资金允许对当前软件进行扩展，使其更便于主要和次要用户使用，并使主要用户更易于维护和直接扩展，将提供：Vistas工程师将继续执行从C++迁移到Python的较长期战略，这将使用户界面开发更加有效和灵活，数据或可视化插件的最终用户编程，以及使用新兴的和现有的Python库和R库进行可视化分析。社会科学调查将有助于确定联合制作如何使可用的软件能够满足环境科学家和决策者的需求，环境科学家生成大量难以解释的数据集，决策者在做出重要选择时必须平衡多种需求。将对三名合作者进行案例研究，因为他们与利益攸关方合作共同开发有用的信息；这些研究通过前后比较的测试设计进行，分为三个阶段，以探索参与者在参与可视化开发前后如何看待和交流科学成果的变化。在基线阶段，社会科学家将与参与者合作，记录他们目前对数据的理解、对可视化和分析产品的期望，以及用于向包括非科学家在内的其他人交流科学的能力和工具。在开发阶段，案例参与者将被观察，因为他们一起创建可视化和分析产品。评估后阶段寻求确定参与可视化开发后对数据的理解和传播科学的能力的变化。还将探讨不同类型的可视化和分析工具的可用性，以确定有助于或分散有用性的特征。信息将主要通过与参与者(合作者和利益攸关方)的半结构化访谈来收集。将使用现有的量表，衡量决策中的环境态度和对科学的偏好，以及对科学的一般态度，以便与更大的国内和国际样本进行比较。此外，还将观察范围划分和开发会议，以确定如何建立对用户需求的共同理解，然后将其框定为可视化问题。

The Visualization for Terrestrial and Aquatic Systems project helps environmental scientists produce visualizations for their own research and for presentation to other scientists and stakeholders including decision makers. A critical finding of work to date is the extent to which scientists use visualizations not only to explore data in new ways and present results, but also to work with stakeholders to jointly produce information that can be used during decision-making processes. The role of scientific visualization in the co-production of knowledge is as yet untested, even though this involvement could be critical in creating acceptable solutions, information, or technology. This proposal recasts VISTAS to co-produce visualization tools, i.e., exploring how negotiations between the users? needs and technological capacity shape the type of visualizations used and tools implemented, change or modify the research questions posed by scientists, and impact how results are interpreted so communities can respond to critical ecological challenges, including climate change. This is a unique experiment and collaboration among social-, computer-, and environmental scientists, with non-scientist stakeholders, to co-produce data visualizations for use in decision making. Social science methods will be used to explore knowledge co-production coupled with technology innovations that lead to community decision making to solve problems of climate change adaptation. The extent to which distinctions between scientific visualization for scientists and non-scientists need to be made will be determined, and unique visualizations will be developed jointly with project collaborators. The goal is to determine the influence of visualization on the co-production of knowledge among scientists and stakeholders on critical decisions related to climate adaptation. This project involves both computer scientists and social scientists. Computer science: VISTAS, a C++ scientific visualization application with significant GPU processing, helps environmental scientists produce images that allow them to ?see? the effects of topography on ecological phenomena. For this award, new visualization techniques will be developed, visualization and visual analytics research that enables effective presentations to decision makers will be conducted, and technical support for environmental- and social scientists will be provided. If time and funds permit extensions to the current software that render it both more usable by primary and secondary users, and more maintainable and extensible directly by primary users will be provided: VISTAS engineers will proceed with a longer term strategy of migrating from C++ to Python, which will enable more effective and flexible user interface development, end user programming of data or visualization plug-ins, and use of emerging and existing Python and R libraries for visual analytics. The social science inquiry will help determine how the co-production enables usable software that answers the needs of both environmental scientists who generate large difficult to interpret data sets as well as decision-makers who must balance multiple demands as they make important choices. Case studies with three collaborators will be conducted as they work with stakeholders to co-develop usable information; these are structured through a comparative pre/post-test design with three phases to explore changes in how participants view and communicate scientific results before and after involvement in visualization development. In the baseline phase VISTAS social scientists will work with participants to document their current understanding of their data, expectations for the visualization and analytic products, and ability and tools used to communicate science to others including non-scientists. During the development phase case participants will be observed as they work together to create the visualization and analytic products. The post-assessment phase seeks to determine changes in understanding of data and ability to communicate science as a result of participation in visualization development. The usability of different types of visualizations and analytic tools, identifying the characteristics that contribute to or distract from usefulness, will also be explored. Information will be collected primarily through semi-structured interviews with participants (collaborators and stakeholders). Existing scales measuring environmental attitudes and preferences for science in decision-making and general attitudes toward science will be used so comparisons with larger national and international samples can be made. In addition, scoping and development meetings will be observed to determine how shared understanding of user needs is developed and then framed as a visualization problem.