关于科学网、Scopus 和期刊网站中综述文章文件类型分配的探索性研究

IF 1.5 3区管理学 Q2 INFORMATION SCIENCE & LIBRARY SCIENCE

Journal of Data and Information Science Pub Date : 2024-02-19 DOI:10.2478/jdis-2024-0003

Manman Zhu, Xinyue Lu, Fuyou Chen, Liying Yang, Zhesi Shen

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引用次数: 0

摘要

目的在科学网（WoS）和斯科普斯（Scopus）等引文索引数据库中准确分配评论文章的文献类型非常重要。本研究旨在大规模调查 Web of Science、Scopus 和出版商网站中综述文章的文献类型分配情况。设计/方法/手段对 SCI 收录的 10 个评论期刊系列 160 种期刊中的 27616 篇论文进行了分析。检索并比较这些论文在期刊网站上标注的文献类型以及 WoS 和 Scopus 分配的文献类型，以确定分配的准确性并找出错误分配的可能原因。对于网站上标注的文献类型，我们根据网站是否直接标注为综述进一步区分为显性综述和隐性综述。研究结果总体而言，WoS 和 Scopus 的表现类似，平均精确度约为 99%，召回率约为 80%。不过，在不同的期刊系列和同一期刊系列中，WoS 和 Scopus 之间存在一些差异。WoS 和 Scopus 对隐性评论的指定准确率明显下降，尤其是 Scopus。研究局限我们作为金标准的文献类型是基于期刊网站的标注，而这些标注没有经过人工逐一验证。我们只研究了2017-2018年间发表在评论期刊上的评论文章的标注性能。这一结论能否推广到非综述期刊上发表的综述文章以及目前的大多数情况还不是很清楚。实践意义本研究为 WoS 和 Scopus 中综述文章文献类型赋值的准确性提供了参考，所发现的隐性综述赋值模式可能有助于网站、WoS 和 Scopus 更好地进行标注。原创性/价值本研究调查了评论文章文档类型分配的准确性，发现了一些错误的分配模式。

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An explorative study on document type assignment of review articles in Web of Science, Scopus and journals’ websites

Purpose Accurately assigning the document type of review articles in citation index databases like Web of Science(WoS) and Scopus is important. This study aims to investigate the document type assignation of review articles in Web of Science, Scopus and Publisher’s websites on a large scale. Design/methodology/approach 27,616 papers from 160 journals from 10 review journal series indexed in SCI are analyzed. The document types of these papers labeled on journals’ websites, and assigned by WoS and Scopus are retrieved and compared to determine the assigning accuracy and identify the possible reasons for wrongly assigning. For the document type labeled on the website, we further differentiate them into explicit review and implicit review based on whether the website directly indicates it is a review or not. Findings Overall, WoS and Scopus performed similarly, with an average precision of about 99% and recall of about 80%. However, there were some differences between WoS and Scopus across different journal series and within the same journal series. The assigning accuracy of WoS and Scopus for implicit reviews dropped significantly, especially for Scopus. Research limitations The document types we used as the gold standard were based on the journal websites’ labeling which were not manually validated one by one. We only studied the labeling performance for review articles published during 2017-2018 in review journals. Whether this conclusion can be extended to review articles published in non-review journals and most current situation is not very clear. Practical implications This study provides a reference for the accuracy of document type assigning of review articles in WoS and Scopus, and the identified pattern for assigning implicit reviews may be helpful to better labeling on websites, WoS and Scopus. Originality/value This study investigated the assigning accuracy of document type of reviews and identified the some patterns of wrong assignments.

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来源期刊

Journal of Data and Information Science INFORMATION SCIENCE & LIBRARY SCIENCE-

CiteScore

3.50

自引率

6.70%

发文量

495

期刊介绍： JDIS devotes itself to the study and application of the theories, methods, techniques, services, infrastructural facilities using big data to support knowledge discovery for decision & policy making. The basic emphasis is big data-based, analytics centered, knowledge discovery driven, and decision making supporting. The special effort is on the knowledge discovery to detect and predict structures, trends, behaviors, relations, evolutions and disruptions in research, innovation, business, politics, security, media and communications, and social development, where the big data may include metadata or full content data, text or non-textural data, structured or non-structural data, domain specific or cross-domain data, and dynamic or interactive data. The main areas of interest are: (1) New theories, methods, and techniques of big data based data mining, knowledge discovery, and informatics, including but not limited to scientometrics, communication analysis, social network analysis, tech & industry analysis, competitive intelligence, knowledge mapping, evidence based policy analysis, and predictive analysis. (2) New methods, architectures, and facilities to develop or improve knowledge infrastructure capable to support knowledge organization and sophisticated analytics, including but not limited to ontology construction, knowledge organization, semantic linked data, knowledge integration and fusion, semantic retrieval, domain specific knowledge infrastructure, and semantic sciences. (3) New mechanisms, methods, and tools to embed knowledge analytics and knowledge discovery into actual operation, service, or managerial processes, including but not limited to knowledge assisted scientific discovery, data mining driven intelligent workflows in learning, communications, and management. Specific topic areas may include: Knowledge organization Knowledge discovery and data mining Knowledge integration and fusion Semantic Web metrics Scientometrics Analytic and diagnostic informetrics Competitive intelligence Predictive analysis Social network analysis and metrics Semantic and interactively analytic retrieval Evidence-based policy analysis Intelligent knowledge production Knowledge-driven workflow management and decision-making Knowledge-driven collaboration and its management Domain knowledge infrastructure with knowledge fusion and analytics Development of data and information services