追踪数据:一项调查数据引用的学科差异的调查

IF 4.1 Q1 INFORMATION SCIENCE & LIBRARY SCIENCE
Kathleen Gregory, Anton Ninkov, Chantal Ripp, Emma Roblin, Isabella Peters, Stefanie Haustein
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引用次数: 3

摘要

摘要数据引用,或参考文献列表中对数据的引用,越来越被视为跟踪数据重用和激励数据共享的重要手段。虽然数据引用实践的学科差异已经通过科学计量学方法得到了很好的记录,但我们还不知道这些实践在学科内的代表性如何。我们也不知道研究人员在学术工作中引用或不引用数据的动机。在这里,我们展示了已知最大的调查结果(n = 2,492),以明确调查数据引用实践,偏好和动机,使用学科的学术作者的代表性样本,如Web of Science (WoS)所示。我们提出了研究人员目前重复使用和引用数据的做法和动机,并研究了他们希望自己的数据如何被引用的偏好。最后,我们讨论了两大集群中的学科模式,重点关注社会科学和人文科学的模式,并考虑了我们的结果对跟踪和奖励数据共享和重用的影响。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Tracing data: A survey investigating disciplinary differences in data citation
Abstract Data citations, or citations in reference lists to data, are increasingly seen as an important means to trace data reuse and incentivize data sharing. Although disciplinary differences in data citation practices have been well documented via scientometric approaches, we do not yet know how representative these practices are within disciplines. Nor do we yet have insight into researchers’ motivations for citing—or not citing—data in their academic work. Here, we present the results of the largest known survey (n = 2,492) to explicitly investigate data citation practices, preferences, and motivations, using a representative sample of academic authors by discipline, as represented in the Web of Science (WoS). We present findings about researchers’ current practices and motivations for reusing and citing data and also examine their preferences for how they would like their own data to be cited. We conclude by discussing disciplinary patterns in two broad clusters, focusing on patterns in the social sciences and humanities, and consider the implications of our results for tracing and rewarding data sharing and reuse.
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来源期刊
Quantitative Science Studies
Quantitative Science Studies INFORMATION SCIENCE & LIBRARY SCIENCE-
CiteScore
12.10
自引率
12.50%
发文量
46
审稿时长
22 weeks
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