CIF21 DIBBs: PD: - Metadata Toolkits for Building Multi-Faceted Data - Relationship Models

科学研究面临着越来越大、越来越复杂的数据集的挑战，这些数据集以不同的形式存储在复杂的存储库中，使得发现有用的内容变得困难。其中一个原因是“导航”元数据的相对稀缺性——这种元数据明确地揭示了数据元素之间的大量关系。该项目开发了改进的数据管理工具，允许数据管理人员创建元数据模式，揭示数据元素之间存在的多种复杂关系。该团队与三个不同的研究团体直接合作开发这些工具：等离子体物理学（与麻省理工学院等离子体科学与融合中心），海洋监测和建模（与麻省理工学院地球科学系）和不确定性量化（与德克萨斯大学计算工程与科学研究所）。该项目提供的工具允许数据管理人员轻松地开发元数据模式，这些模式表示和公开存在于数据元素之间的多种复杂关系，这些关系在数据系统中通常无法很好地表示。这些数据元素包括数据源、来源、数据中表示的物理属性、数据版本、注释线程、数据字典、数据目录和数据形状（通常决定哪些应用程序可以使用或显示数据），以及更大的组织实体，如研究活动、实验提案和研究产品（例如出版物、演示文稿和公共数据库）。模式和数据通过具象状态传输-应用程序编程接口（RESTful API）进行操作。数据之间的关系表示为数学图结构，这些结构都建立在公共元模式之上。它强调使用RESTful API和粒度数据对象统一资源标识符（URI）模式记录完整的数据生命周期，以方便对复杂多变的工作流进行检测。在等离子体物理、海洋监测和建模以及不确定性量化的初步应用领域，建立了一个基于网络的现代勘探工具。通过将元数据和程序更普遍地视为图形的集合，其节点是数据文件或记录，该项目创建了一组可以探索这些图形的程序，并使系统更加通用和易于扩展。此外，通过允许用户创建任何特定级别的数据对象，可以使用数据所属的图来标记对象分组。这种表示数据关系的能力将对科学界的广大队伍有用，并可能对许多领域的科学事业有用。该奖项由高级网络基础设施部颁发，由美国国家科学基金会地球科学理事会和美国国家科学基金会数学与物理科学理事会（物理部）联合支持。

Scientific research is challenged by ever-larger, more complex data sets, stored in disparate form in complicated repositories, making it difficult to discover useful content. One reason is the relative scarcity of 'navigational' metadata - metadata that explicitly reveals the multitude of relationships between data elements. This project develops improved data management tools allowing data managers to create metadata schemas that reveal the multiple and complex relationships existing between data elements. The team develops these tools while collaborating directly with three different research communities: plasma physics (with the MIT Plasma Science and Fusion Center), ocean monitoring and modeling (with the MIT Department of Earth Sciences) and uncertainty quantification (with the University of Texas Institute for Computational Engineering and Sciences). The project provides tools that allow data managers to easily develop metadata schemas that represent and expose the multiple and complex relationships that exist between data elements and which are typically not well represented in data systems. Such data elements include data source, provenance, physical properties represented in the data, data versioning, annotation threads, data dictionaries, data catalogs and data shape (which typically determines which applications can consume or display the data), and larger organizational entities such as research campaigns, experimental proposals and research products (e.g., publications, presentations and public databases). Schemas and data are manipulated through a Representational State Transfer - Application Programming Interface (RESTful API). Relationships among the data are represented as mathematical graph structures that are all built upon a common meta-schema. There is an emphasis on recording the full data lifecycle using a RESTful API and granular data object uniform resource identifier (URI) schema that facilitate instrumenting complex and varied workflows. A modern web based exploration tool is built upon these technologies in the initial application areas of plasma physics, ocean monitoring and modeling, and uncertainty quantification. By viewing meta-data and programs more generally as a collection of graphs whose nodes are the data files or records, the project creates a set of programs which can explore these graphs and make the system much more general and easily extensible. Also, by allowing users to create data objects at any level of specificity, the graphs of which the data is a member can be used to label object groupings. This ability to represent data relationships would be of use to a broad contingent of the scientific community and could be useful to the scientific enterprise in many domains. This award by the Advanced Cyberinfrastructure Division is jointly supported by the NSF Directorate for Geosciences, and the NSF Directorate for Mathematical & Physical Sciences (Division of Physics).