INTEROP: A Community-Driven Scientific Observations Network to Achieve Interoperability of Environmental and Ecological Data

该项目将建立一个“科学观测网络”-这是一个多学科、社区驱动的努力，目的是界定和开发统一的观测数据模型，加强地球和生命科学领域的数据共享、合并和再利用。这项工作将协调来自生态学、水文学、海洋学、地球科学、地理空间界和生命科学等多个学科的专家群体的工作，与计算机科学家和信息管理人员密切合作，制定必要的规格和技术，以促进对观测数据的智能解释和无缝整合。环境科学和生态学的进步越来越依赖于来自多个学科的信息，以解决有关自然世界的广泛而复杂的问题。然而，研究人员在有效定位、解释和整合可能与这些调查相关的数据方面面临着极大的挑战。这是由于科学家收集的数据在结构和内容上存在极大的差异，该项目将支持地球科学和生命科学对在观测和测量层面描述数据的可能性的日益增长的兴趣，而不是传统上在数据集层面的关注，以实现更强的数据发现和互操作性。科学观测网络将致力于开发兼容的、开放源代码的、基于标准的方法，对观测数据进行语义建模。一个关键目标将是开发一个核心概念数据模型，用于代表科学观测。这一核心观测模型将为开发、扩展和应用详细描述环境研究相关数据所需的高度专业化科学术语提供共同基础。专家小组将参与扩展核心数据模型，以包括代表性学科收集的广泛的具体测量数据，一系列示范项目将说明这些方法的能力，以联合数据，在更广泛和意外的情况下重新使用。科学观测网络将有助于确保科学数据一旦收集，就能为最广泛的用户群体提供尽可能多的使用。

This project will build a "Scientific Observations Network"--as a multi-disciplinary, community-driven effort to define and develop a unified model for observational data, to enhance data sharing, merging and reuse in the earth and life sciences. This effort will coordinate work of a community of experts drawn from numerous disciplines, including ecology, hydrology, oceanography, geo-sciences, the geospatial community, and life sciences, working closely with computer scientists and information managers, to develop necessary specifications and technologies to facilitate intelligent interpretation and seamless integration of observational data. Advances in environmental science and ecology increasingly depend on information from multiple disciplines to address broad, complex questions about the natural world. Researchers are extremely challenged, however, in effectively locating, interpreting, and integrating data that might be relevant for these investigations. This is due to extreme variability in the structure and contents of the data that scientists collect. This project will support the growing interest in the earth and life sciences in the possibilities of describing data at the level of observation and measurement, rather than the traditional focus at the level of the data set, in order to achieve stronger data discovery and interoperability. The Scientific Observations Network will work to develop compatible, open-source, standards-based approaches to the semantic modeling of observational data. A key goal will be the development of a core conceptual data model for representing scientific observations. This core observations model will provide a common basis for developing, extending, and applying highly specialized scientific terminologies required for detailed descriptions of data relevant for environmental research. Subgroups of experts will engage in extending the core data model to include a broad range of specific measurements collected by the representative disciplines, and a series of demonstration projects will illustrate the capabilities of these approaches to confederate data for reuse in broader and unanticipated contexts. The scientific Observations Network will help to insure that scientific data, once collected, is put to the greatest possible use by the broadest group of users.