Multi-Scaled Data in Ecology: Scale Dependent Patterns in the Environment

生态学中的多尺度数据：环境中的尺度依赖模式

• 批准号: 0542868
• 负责人: Steven Kelling
• 金额: $ 119.68万
• 依托单位: Cornell Univ - State: AWDS MADE PRIOR MAY 2010
• 依托单位国家: 美国
• 项目类别: Continuing Grant
• 财政年份: 2006
• 资助国家: 美国
• 起止时间: 2006-08-15 至 2009-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=0542868&HistoricalAwards=false
• 关键词: Multi; Scaled; Data; Ecology; Scale

Cornell University is awarded a grant to bring new information on birds online and disseminate these to a wide and diverse audience. The goal is to continue to expand the resources and analysis tools available at the Avian Knowledge Network (AKN) (http://www.avianknowledge.net). The data resources of the Constant Effort Capture Method (CECM) community will be added. These data, which have been gathered over a 20-year period by more than 1000 field stations across North America, provide detailed avian demographic information. Bringing this data resource to the AKN will create the most comprehensive source of environmental monitoring and conservation biology available. The data will be integrated into the AKN via a bird banding data exchange schema. This schema is based on the Darwin Core standard and is compatible with AKN's observational data schema. The goal is to expand the existing bird monitoring data exchange schema to include bird demographic data, and, specifically, to modify the schema to handle information gathered (age, sex, size, weight) on individual birds. The constant effort capture data will be federated using the DiGIR protocol. A data warehouse structure will archive these data and make them generally available. Finally, an Internet-based multi-scale analysis application will be developed and publicly released to a broad spectrum of users. The interdisciplinary approach will bring statistical and computational specialists together with population biologists to develop new and broadly applicable technologies to create new methodologies that track long-term environmental patterns at a range of scales. To allow rapid browsing of these data over the Internet, new strategies in data visualizations will be explored. This project will expose new data resources and advances in statistical analysis to vast new audiences: from biologists, conservation agencies, and land-use planners to school classrooms and thousands of citizens who participate in environmental monitoring. The project will open new scientific, conservation, and educational opportunities, and by actively disseminating data visualization and exploration applications it will enable researchers as well as literally millions of people across North America who watch and appreciate wild birds to access a vast resource focused on bird populations.

康奈尔大学获得了一笔赠款，用于将有关鸟类的新信息带到网上，并将这些信息传播给广泛和不同的受众。目标是继续扩大禽类知识网络(http://www.avianknowledge.net).)可用的资源和分析工具将增加恒定努力捕获方法(CECM)社区的数据资源。这些数据是由北美1000多个野外监测站在20年的时间里收集的，提供了详细的鸟类人口信息。将这一数据资源带到AKN将创建可用的最全面的环境监测和保护生物学来源。这些数据将通过鸟类环志数据交换方案整合到AKN中。该模式基于达尔文核心标准，并与AKN的观测数据模式兼容。目标是扩展现有的鸟类监测数据交换模式，以包括鸟类人口统计数据，特别是修改该模式，以处理收集的关于单个鸟类的信息(年龄、性别、大小、体重)。恒定努力捕获数据将使用DiGIR协议进行联合。数据仓库结构将对这些数据进行存档，并使它们普遍可用。最后，将开发一个基于互联网的多尺度分析应用程序，并向广大用户公开发布。这种跨学科的方法将把统计和计算专家与种群生物学家聚集在一起，开发新的、广泛适用的技术，以创建在一系列尺度上跟踪长期环境模式的新方法。为了能够在互联网上快速浏览这些数据，将探索数据可视化的新策略。该项目将向广大新的受众展示新的数据资源和统计分析方面的进展：从生物学家、保护机构和土地利用规划者到学校教室和数千名参与环境监测的公民。该项目将带来新的科学、保护和教育机会，通过积极传播数据可视化和探索应用程序，它将使研究人员以及北美数百万观察和欣赏野生鸟类的人能够访问专注于鸟类种群的巨大资源。