新兴科学引文索引数据库的比较研究  

A Comparative Study of Emerging Science Citation Index Databases

作　　者：李洁[1] 孟烨 金佳丽 姚翔宇 黄晨[1] Li Jie;Meng Ye;Jin Jiali;Yao Xiangyu;Huang Chen

机构地区：[1]浙江大学图书馆,浙江杭州310027

出　　处：《大学图书馆学报》2021年第6期48-55,77,共9页Journal of Academic Libraries

基　　金：浙江省教育厅一般科研项目(人文社科类)“新兴科学引文索引数据库调研”(编号:Y202146118)的研究成果之一。

摘　　要：随着网络爬虫、基于人工智能的文本语义分析技术的更新和研究范式的发展,越来越多的新兴科学引文索引数据库问世,不仅为科研工作者搜索和筛选学术文献资源提供了新的体验,也为科研评价提供了更多的选择。本文选取了7个科学引文索引数据库(Microsoft Academic、Dimensions、Semantic Scholar、Lens、Scinapse、Scilit、Scite)进行调研比较和案例分析,以期挖掘各数据库的收录范围、指标、人工智能技术、可视化分析、插件及导出等特色,供图书馆员做科研评价分析时参考和选择。通过调研和案例分析发现,对于机构和学者的评价,综合考虑文献收录范围和查准率,Microsoft Academic和Dimensions是最优的选择。With the development of web crawler technology,artificial intelligence-based text semantic analysis technology,and newer research paradigms,more emerging scientific citation index databases have come out.These databases provide a new experience for researchers to search and filter academic literature resources and create more scientific research evaluation options.Seven science citation index databases-Microsoft Academic,Dimensions,Semantic Scholar,Lens,Scinapse,Scilit,and Scite-were chosen to conduct comparative research and case analysis.This paper evaluated and explored collection coverage,indicators,artificial intelligence technology,visualization analysis,plug-ins,and export features of each database.These functions allow librarians to have more references and choices when doing scientific research evaluation.Based on investigations and case studies,it can be concluded that Microsoft Academic and Dimensions are the best choices for evaluating institutions and scholars when taking an enormous amount of papers coverage and optimal accuracy into account.

关 键 词：科学引文索引 引文分析 语义引文 学术影响力评价 

分 类 号：G255.75[文化科学—图书馆学]