Collaborative Research: AOC: Monitoring, Modeling and Memory: Dynamics of Data and Knowledge in Scientific Cyberinfrastructures

合作研究：AOC：监控、建模和记忆：科学网络基础设施中数据和知识的动态

• 批准号: 0827316
• 负责人: Paul Edwards
• 金额: $ 69.23万
• 依托单位: Regents of the University of Michigan - Ann Arbor
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2008
• 资助国家: 美国
• 起止时间: 2008-10-01 至 2014-03-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0827316&HistoricalAwards=false
• 关键词: Collaborative; Research; AOC; Monitoring; Modeling

Scientists and supporters of science have long worked to improve the sharing, reuse, storage, and retrieval of scientific data. Today, efforts focus on advanced "cyberinfrastructure": using networked computers, databases, and organizations to bridge divides among diverse scientific disciplines. Cyberinfrastructure divides into three main activities. First, large numbers of automatic sensors monitor subjects of interest, such as ecosystems and Earth's climate, producing massive volumes of digitized data. Second, in many fields computer models have replaced laboratory experiments as the principal means of data collection, prediction, and decision-making. Third, increasingly vast data resources (scientific memory) are now available, but are often distributed across thousands of research sites and institutions, in numerous incompatible formats. For cyberinfrastructure-enabled science to deliver on its transformative potential, cyberinfrastructure designers need better ways of understanding how scientists actually create and share data in practice, and how they use it to create new knowledge. To investigate data practices empirically, this project examines four large cyberinfrastructure efforts: the Long Term Ecological Research Network, the Center for Embedded Network Sensing, the WATer and Environmental Research Systems Network, and the Earth System Modeling Framework. In each case, the investigators will study how cyberinfrastructure is used in monitoring, modeling, and memory. These projects spread across many disciplines addressing three important domains, all related to climate change concerns: ecology and environment; hydrology and water management; and earth system science. In conducting the project, the investigators will develop innovative methods of distributed ethnography, collaborative history, and multimodal network analysis. In addition to published findings, the project will produce a 'best practices' handbook meant to guide data practices and coordination in cyberinfrastructure. The project will partner with organizations working to enhance the role of women in computing; to build database systems for American Indian communities; and to engage other groups often ignored in information infrastructure development. The project will thus lay groundwork for an inclusive, theoretically rich, and practically engaged social science of cyberinfrastructure.

长期以来，科学的科学家和支持者一直致力于改善科学数据的共享，重用，存储和检索。 如今，努力着眼于先进的“网络基础设施”：使用网络计算机，数据库和组织来弥合各种科学学科之间的划分。 网络基础设施分为三个主要活动。首先，大量自动传感器监视感兴趣的主题，例如生态系统和地球的气候，从而产生大量数字化数据。 其次，在许多领域，计算机模型已经取代了实验室实验，作为数据收集，预测和决策的主要手段。 第三，现在可以使用越来越广泛的数据资源（科学记忆），但通常以许多不兼容的格式分布在数千个研究地点和机构中。 为了使网络基础设施的科学发挥其变革性的潜力，网络基础设施设计师需要更好地理解科学家如何在实践中实际创建和共享数据，以及他们如何使用它来创建新知识。 为了凭经验研究数据实践，该项目研究了四个大型网络基础设施工作：长期生态研究网络，嵌入式网络传感中心，水和环境研究系统网络以及地球系统建模框架。 在每种情况下，研究人员都会研究如何将网络基础结构用于监视，建模和记忆。 这些项目分布在许多有关三个重要领域的学科中，所有这些都与气候变化有关：生态和环境；水文和水管理；和地球系统科学。 在进行该项目时，研究人员将开发分布式民族志，协作历史和多模式网络分析的创新方法。 除了已发表的发现外，该项目还将制作一本“最佳实践”手册，旨在指导网络基础设施中的数据实践和协调。 该项目将与致力于增强妇女在计算中的作用的组织合作；为美洲印第安社区建立数据库系统；并让其他群体在信息基础架构开发中经常忽略。 因此，该项目将为包容性，理论上富有且实际参与的网络基础结构的社会科学奠定基础。