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Units of Evidence for Analyzing Subdisciplinary Difference in Data Practice Studies

机译:数据实践研究中分析亚学科差异的证据单位

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Digital libraries (DLs) are adapting to accommodate research data and related services. The complexities of this new content spans the elements of DL development, and there are questions concerning data selection, service development, and how best to align these with local, institutional initiatives for cyberinfrastructure, data-intensive research, and data stewardship. Small science disciplines are of particular relevance due to the prevalence of this mode of research in the academy, and the anticipated magnitude of data production. To support data acquisition into DLs - and subsequent data reuse - there is a need for new knowledge on the range and complexities inherent in practice-data-curation arrangements for small science research. We present a flexible methodological approach crafted to generate data units to analyze these relationships and facilitate cross-disciplinary comparisons.
机译:数字图书馆(DL)正在适应容纳研究数据和相关服务的需求。这一新内容的复杂性涵盖了DL开发的各个要素,并且存在有关数据选择,服务开发以及如何最好地将其与本地基础设施,网络基础设施,数据密集型研究和数据管理相结合的问题。小型科学学科尤其具有相关性,这是由于该研究模式在学院中盛行,以及预期的数据产生量很大。为了支持将数据采集到DL中并随后进行数据重用,需要有关小型科学研究的实践数据管理安排中固有的范围和复杂性的新知识。我们提出一种灵活的方法论方法,旨在生成数据单元来分析这些关系并促进跨学科的比较。

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