文章基本信息

标题：Testing Google Scholar bibliographic data: Estimating error rates for Google Scholar citation parsing
本地全文：下载
作者：David Zeitlyn ; Megan Beardmore Herd
期刊名称：First Monday
电子版ISSN：1396-0466
出版年度：2018
卷号：23
期号：11
页码：1-9
DOI：10.5210/fm.v23i11.8658
语种：English
出版社：University of Illinois at Chicago Library
摘要：We present some systematic tests of the quality of bibliographic data exports available from Google Scholar. While data quality is good for journal articles and conference proceedings, books and edited collections are often wrongly described or have incomplete data. We identify a particular problem with material from online repositories.