论文标题
细分和十字路口：在数据存档的引文网络中识别隐藏的社区结构
Subdivisions and Crossroads: Identifying Hidden Community Structures in a Data Archive's Citation Network
论文作者
论文摘要
数据档案是许多领域的高质量数据的重要来源，使其成为研究数据重用的理想站点。通过通过引用网络研究数据，我们可以学习隐藏的研究社区（使用相同科学数据集的隐藏研究社区）如何组织。本文分析了学术出版物中引用的数据集的权威网络的社区结构，该网络已由大型的社会科学数据档案库收集：政治和社会研究联盟（ICPSR）。通过网络分析，我们确定了通过共享数据使用相关的社会科学数据集和研究领域的社区。我们认为，排他性数据重用的社区形式的细分包含有价值的纪律资源，而“十字路口”的数据集广泛地连接了研究社区。我们的研究揭示了数据重用的隐藏结构，并证明了跨学科研究社区如何将数据集组织为共享的科学投入。这些发现贡献了描述科学社区的新方法，以了解研究数据的影响。
Data archives are an important source of high quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities - those that use the same scientific datasets - are organized. This paper analyzes the community structure of an authoritative network of datasets cited in academic publications, which have been collected by a large, social science data archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through network analysis, we identified communities of social science datasets and fields of research connected through shared data use. We argue that communities of exclusive data reuse form subdivisions that contain valuable disciplinary resources, while datasets at a "crossroads" broadly connect research communities. Our research reveals the hidden structure of data reuse and demonstrates how interdisciplinary research communities organize around datasets as shared scientific inputs. These findings contribute new ways of describing scientific communities in order to understand the impacts of research data reuse.
