使用文章引文网络对科学数据集进行认证
Assigning credit to scientific datasets using article citation networks
摘要：
引文是一种行之有效的连接科学作品的机制。引文网络用于引文分析有多种原因，突出的是对科学家工作的肯定。然而，由于目前的引用实践，科学家倾向于只引用出版物，而忽略其他类型的工件，如数据集。数据集没有得到适当的信任，即使它们越来越多地被重用和试验。我们开发了一个网络流量测量，称为DataRank，旨在解决这个差距。DataRank根据引文如何流经图表，为网络中的每个节点分配一个相对值，区分出版物和数据集流量。我们通过估计DataRank在预测真实数据集使用方面的准确性来评估其质量：对GenBank的网络访问和Figshare数据集的下载。我们表明，DataRank是更好地预测这种使用相比，替代品，同时提供额外的可解释的结果。我们讨论了对引用行为和算法的改进，以正确地跟踪和分配信贷数据集。
英文摘要：
A citation is a well-established mechanism for connecting scientific artifacts. Citation networks are used by citation analysis for a variety of reasons, prominently to give credit to scientists' work. However, because of current citation practices, scientists tend to cite only publications, leaving out other types of artifacts such as datasets. Datasets then do not get appropriate credit even though they are increasingly reused and experimented with. We develop a network flow measure, called DataRank, aimed at solving this gap. DataRank assigns a relative value to each node in the network based on how citations flow through the graph, differentiating publication and dataset flow rates. We evaluate the quality of DataRank by estimating its accuracy at predicting the usage of real datasets: web visits to GenBank and downloads of Figshare datasets. We show that DataRank is better at predicting this usage compared to alternatives while offering additional interpretable outcomes. We discuss improvements to citation behavior and algorithms to properly track and assign credit to datasets.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
