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Advancing Reproducibility Through Shared Data: Bridging Archival and Library Practice

机译:通过共享数据推进再现性:桥接档案和图书馆练习

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At the Children's Hospital of Philadelphia (CHOP), a team of librarians and archivists is implementing a new biomedical research data archives and a data discovery catalog as part of Arcus, a multi-year strategic initiative of the CHOP Research Institute dedicated to making research data more broadly available within the institution. Arcus presents the Library Science team with the opportunity to develop new methods and solutions for addressing a number of important issues. This paper serves to highlight three areas of focus. First, archival appraisal methods will be used for CHOP's large-scale biomedical research data, which presents issues for quality data management and preservation. The Archives will employ expanded appraisal workflows to encompass strong selection and prioritization criteria before a collection is ingested, as opposed to current archival research data frameworks, which situate appraisal after a collection is acquired. Second, CHOP research data efforts will be organized as archival collections because they offer a framework for encapsulating research and mapping the relationships between a dataset, software, protocols and other contextual information critical to data reproducibility. Third, a custom descriptive metadata schema is being developed because archival item-level descriptive metadata doesn't address the complex discovery needs of diverse biomedical data. This will require addressing tension between archival arrangement and the item-level descriptive metadata required for discovery.
机译:在费城(Chec)的儿童医院(Choc),一个图书馆员和档案馆团队正在实施一个新的生物医学研究数据档案和数据发现目录,作为arcus的一部分,这是致力于进行研究数据的印章研究所的多年战略倡议在机构内更广泛地提供。 artus展示了图书馆学团队,有机会开发新的方法和解决方案,以解决一些重要问题。本文有助于突出三个焦点领域。首先,档案评估方法将用于斩波的大规模生物医学研究数据,这提出了质量数据管理和保存的问题。该档案将采用扩展的评估工作流程来包含强烈的选择和优先级标准,而在收集收集后的当前档案研究数据框架,而不是当前的档案研究数据框架。其次,Chop Research数据努力将组织为归档集合,因为它们提供了一种用于封装研究和映射数据集,软件,协议和其他对数据再现性的其他上下文信息之间的关系的框架。第三,正在开发自定义描述性元数据模式,因为档案项目级描述性元数据不会解决各种生物医学数据的复杂发现需求。这需要解决归档排列之间的张力和发现所需的项目级描述性元数据。

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