Data citation and the citation graph

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE

Quantitative Science Studies Pub Date : 2021-11-05 DOI:10.1162/qss_a_00166

P. Buneman, Dennis Dosso, Matteo Lissandrini, G. Silvello

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引用次数: 10

Abstract

Abstract The citation graph is a computational artifact that is widely used to represent the domain of published literature. It represents connections between published works, such as citations and authorship. Among other things, the graph supports the computation of bibliometric measures such as h-indexes and impact factors. There is now an increasing demand that we should treat the publication of data in the same way that we treat conventional publications. In particular, we should cite data for the same reasons that we cite other publications. In this paper we discuss what is needed for the citation graph to represent data citation. We identify two challenges: to model the evolution of credit appropriately (through references) over time and to model data citation not only to a data set treated as a single object but also to parts of it. We describe an extension of the current citation graph model that addresses these challenges. It is built on two central concepts: citable units and reference subsumption. We discuss how this extension would enable data citation to be represented within the citation graph and how it allows for improvements in current practices for bibliometric computations, both for scientific publications and for data.

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《我是特朗普政府内部抵抗力量的一部分》，《纽约时报》，2018年9月5日。

摘要引文图是一种计算人工制品，广泛用于表示已发表文献的领域。它代表了已发表作品之间的联系，如引用和作者身份。除其他外，该图支持文献计量指标的计算，如h指数和影响因素。现在有越来越多的要求，我们应该像对待传统出版物一样对待数据的发布。特别是，我们引用数据的理由与引用其他出版物的理由相同。在本文中，我们讨论了引用图表示数据引用所需要的东西。我们确定了两个挑战：对信用随时间的演变进行适当的建模（通过参考文献），以及不仅对作为单个对象处理的数据集，而且对其部分进行数据引用建模。我们描述了当前引文图模型的扩展，以应对这些挑战。它建立在两个核心概念之上：可引用单位和引用包容。我们讨论了这种扩展将如何使数据引用能够在引用图中表示，以及它如何改进当前科学出版物和数据的文献计量计算实践。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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