EAGER: Framework for Exposure of Government and Scientific Data

PI提出的工作将从考虑政府数据开始，但将通过研究数据联盟（RDA）进行扩展，从而为政府和科学数据提供一套共同的数据模型、api和注册表的前景。考虑到RDA的全球连接，拟议的工作提供了推进通用数据基础设施的潜力，甚至超出了美国的范围。约翰霍普金斯大学将开发一套独立于实现的数据模型和应用程序编程接口（API）规范，以支持语义上有用的共享和机器对从异构数据源聚合的元数据的操作。将通过与这些类型相关联的类型和属性（字段、概念等）系统提供对这些数据中的公共概念进行对齐和推理的支持。初始规范将包括一个类型（和相关属性），该类型定义了一组核心元数据，参与的数据提供者可以将每个注册的数据集映射到这些元数据。允许多种类型将支持扩展这些核心属性，并允许每个元数据记录枚举它所符合的类型集。属性可以描述数据集的高级属性和更详细的特征。PI和他的团队将开发和澄清核心元数据的初始集，并开发扩展类型的范例集。智力优势：跨时间、跨地域、跨科学领域无缝、轻松地转移政府和研究数据是一个难题，这个小而公认有风险的项目只能希望提供一块拼图，但成功的潜在回报是巨大的，非常值得追求。更广泛的影响：拟议的工作将处理来自政府和科学来源的一系列不同类型的数据，使它们在没有阻碍的情况下普遍可供采用。通过研究数据联盟协调外联和采用工作，将把这一提议的成果扩大到广泛的社区、数据生产者和数据消费者。此外，由于提案团队在研究图书馆内工作，因此有一个自然的场所可以向社区提供额外的扩展，采用和可持续性。最后，值得注意的是，团队成员之一（迪劳罗）是非裔美国人。

The PI is proposing work that will begin with a consideration of government data but will be extended through the Research Data Alliance (RDA) thereby bolstering the prospects of a common set of data models, APIs and registries for both government and scientific data. Given the global connections of RDA,the proposed work offers the potential to advance common data infrastructure even beyond the US landscape. Johns Hopkins University will develop a set of implementation-independent data models and application programming interface (API) specifications to support semantically useful sharing and machine action over metadata aggregated from heterogeneous data sources. Support for aligning and reasoning over common concepts within these data will be provided through a system of types and properties (fields, concepts, etc.) associated with these types. The initial specification will include a type (and associated properties) that defines a set of core metadata to which participating data providers can map each registered dataset. Allowing multiple types will support extending these core properties and permitting each metadata record to enumerate the set of types to which it conforms. Properties may describe high-level attributes of a dataset and more detailed features. The PI and his team will develop and clarify the initial set of core metadata and to develop an exemplar set of extension types. Intellectual Merit : Seamless and effortless transfer of government and research data across time, geographies, and scientific domains is a difficult problem and this small and admittedly risky project can only hope to provide one piece of the puzzle, but the potential rewards of success are significant and well worth pursuing. Broader Impacts: The proposed work would address a range of diverse data types from government and scientific sources, making them generally available for adoption without encumbrance. The coordination of outreach and adoption through the Research Data Alliance would amplify the results of this proposed to a wide range of communities, data producers and data consumers. Additionally, since the proposal team works within a research library, there is a natural venue for outreach to a community that provides capacity for additional outreach, adoption and sustainability. Finally, it is worth noting that one of the team members (DiLauro) is African-American.