ERMrest:具有细粒度访问控制的协作数据目录。

Karl Czajkowski, Carl Kesselman, Robert Schuler
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引用次数: 4

摘要

创建和维护数据资产和资产之间关系的准确描述是使数据可查找、可访问、可互操作和可重用(FAIR)的关键方面。通常,管理员在数据目录中创建和维护这些元数据,作为数据发布的一部分。但是,允许数据生产者在数据生成时创建和维护元数据,而不是等待发布,在生产力和可重复性方面具有显著的优势。元数据管理的责任不需要落在任何一个人身上,而是可以委托给协作的适当成员,使参与者能够编辑或维护特定的属性,描述数据元素之间的关系,或纠正错误。为了支持这种协作性数据编辑,我们创建了ERMrest,这是一种用于Web的关系数据服务,它支持创建、演变和导航用于描述和构建各种文件或关系数据对象的复杂模型。ERMrest的一个关键功能是它能够控制操作直至单个数据元素的级别,即细粒度访问控制,因此可以支持许多不同的面向数据的协作模式。在本文中,我们介绍了ERMrest,并描述了其支持协作编辑的细粒度访问控制功能。ERMrest在许多数据驱动的协作中被日常使用,我们描述了一个基于通用生物定位模式的示例策略。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
ERMrest: A Collaborative Data Catalog with Fine Grain Access Control.

Creating and maintaining an accurate description of data assets and the relationships between assets is a critical aspect of making data findable, accessible, interoperable, and reusable (FAIR). Typically, such metadata are created and maintained in a data catalog by a curator as part of data publication. However, allowing metadata to be created and maintained by data producers as the data is generated rather then waiting for publication can have significant advantages in terms of productivity and repeatability. The responsibilities for metadata management need not fall on any one individual, but rather may be delegated to appropriate members of a collaboration, enabling participants to edit or maintain specific attributes, to describe relationships between data elements, or to correct errors. To support such collaborative data editing, we have created ERMrest, a relational data service for the Web that enables the creation, evolution and navigation of complex models used to describe and structure diverse file or relational data objects. A key capability of ERMrest is its ability to control operations down to the level of individual data elements, i.e. fine-grained access control, so that many different modes of data-oriented collaboration can be supported. In this paper we introduce ERMrest and describe its fine-grained access control capabilities that support collaborative editing. ERMrest is in daily use in many data driven collaborations and we describe a sample policy that is based on a common biocuration pattern.

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