Evidence Based Library and Information Practice 杂志-刊鹿论文编译

Multidisciplinary Databases Outperform Specialized and Comprehensive Databases for Agricultural Literature Coverage

Abstract:

A Review of: \nRitchie, S. M., Young, L. M., & Sigman, J. (2018). A comparison of selected bibliographic database subject overlap for agricultural information. Issues in Science and Technology Librarianship, 89. http://doi.org/10.5062/F49Z9340 \nAbstract \nObjective – To determine the most comprehensive database(s) for agricultural literature searching. \nDesign – Data collection and analysis was conducted using a modified version of the bibliography method, overlap analysis, chi square tests, and data visualization methods. \nSetting – An academic library in the U.S. \nSubjects – Eight commonly used bibliographic databases, including comprehensive agricultural indexes (AGRICOLA, AGRIS, and CAB Abstracts), specialized databases (BIOSIS Previews and FSTA), and multidisciplinary databases (Google Scholar, Scopus, and Web of Science). \nMethods – The researchers selected three review articles that represented sub-topics within the field of agriculture. Sources listed in the bibliographies of the three review articles were used to build a bibliographic citation set for analysis. \nUsing a modified version of the bibliography method, 90 citations were randomly selected from the above-mentioned citation set. Researchers then turned to the 8 selected databases and searched for all 90 citations in each platform. Search queries were crafted in two ways: unique title strings in quotation marks and combinations of terms entered into the “title”, “keyword”, “journal source”, and “author” fields. Citations were considered to be covered in a database if the full bibliographic record was located using the above-mentioned search strategy. \nNext, chi square tests were used to evaluate if the expected number of citations from the sample group were found in each database or if the frequency differed between the eight databases. The overlap analysis method provided numerical representation of the degree of similarity and difference across the eight databases. Finally, data visualizations created in Excel and Gephi enhanced comparisons between the eight databases and highlighted differences that were not obvious based solely on the analysis of numerical data. \nMain Results – Researchers found that comprehensive databases (AGRICOLA, AGRIS, and CAB Abstracts) were not in fact comprehensive in their coverage of agricultural literature. However, the results suggested that CAB Abstracts was more comprehensive than AGRICOLA or AGRIS, particularly in regard to its coverage of the sub-topics “agronomy” and “meat sciences”. However, coverage of the sub-topic “sustainable diets” lagged behind multidisciplinary databases, which may be explained by the fact that the topic is interdisciplinary in nature. The superior coverage of CAB Abstracts over other comprehensive databases is consistent with findings reported by Kawasaki (2004). \nThe analysis of specialized databases (BIOSIS Previews and FSTA) suggested that citations within the scope of the database were covered very well, while those out of scope were not. For instance, the sub-topics “sustainable diets” and “meat science” are out of scope of the biological sciences and thus, were not well covered in BIOSIS. \n\xa0The multidisciplinary databases (Google Scholar, Scopus and Web of Science) provided the most comprehensive coverage agricultural literature. All three databases covered most citations included in the data set. However, researchers noted that all three databases provided weak coverage of trade published items, books, or older journals. \nConclusion – The study found that multidisciplinary databases provide close to full coverage of agricultural literature. In addition, they provide the best access to content that is interdisciplinary in nature. Specialized and comprehensive databases are recommended when research topics are within the scope of the database. Also, they best support in-depth projects such as bibliographies or comprehensive review articles.

Author Listing： Melissa Goertzen

Volume： 14

Pages： 140-142

DOI： 10.18438/EBLIP29561

Language： English

Journal： Evidence Based Library and Information Practice

Evidence Based Library and Information Practice

EVID BASED LIB INF P

影响因子：0.4

是否综述期刊：否

是否OA：是

是否预警：不在预警名单内

发行时间：-

ISSN：1715-720X

发刊频率：-

收录数据库：ESCI/Scopus收录/DOAJ开放期刊

出版国家/地区：Canada

出版社：University of Alberta

期刊介绍

年发文量	19
国人发稿量	-
国人发文占比	0%
自引率	0.0%
平均录取率	-
平均审稿周期	12 Weeks
版面费	-
偏重研究方向	INFORMATION SCIENCE & LIBRARY SCIENCE-
期刊官网	https://journals.library.ualberta.ca/eblip/index.php/EBLIP
投稿链接	-

质量指标占比

研究类文章占比	OA被引用占比	撤稿占比	出版后修正文章占比
100.00%	100.00%	-	-

预警情况查看说明

时间	预警情况
2024年02月发布的2024版	不在预警名单中
2023年01月发布的2023版	不在预警名单中
2021年12月发布的2021版	不在预警名单中
2020年12月发布的2020版	不在预警名单中

JCR分区 WOS分区等级：Q4区

版本	按学科	分区
WOS期刊SCI分区 WOS期刊SCI分区是指SCI官方（Web of Science）为每个学科内的期刊按照IF数值排序，将期刊按照四等分的方法划分的Q1-Q4等级，Q1代表质量最高，即常说的1区期刊。（2021-2022年最新版）
	INFORMATION SCIENCE & LIBRARY SCIENCE	Q4

关于2019年中科院分区升级版（试行）

分区表升级版（试行）旨在解决期刊学科体系划分与学科发展以及融合趋势的不相容问题。由于学科交叉在当代科研活动的趋势愈发显著，学科体系构建容易引发争议。为了打破学科体系给期刊评价带来的桎梏，“升级版方案”首先构建了论文层级的主题体系，然后分别计算每篇论文在所属主题的影响力，最后汇总各期刊每篇论文分值，得到“期刊超越指数”，作为分区依据。

分区表升级版（试行）的优势：一是论文层级的主题体系既能体现学科交叉特点，又可以精准揭示期刊载文的多学科性；二是采用“期刊超越指数”替代影响因子指标，解决了影响因子数学性质缺陷对评价结果的干扰。整体而言，分区表升级版（试行）突破了期刊评价中学科体系构建、评价指标选择等瓶颈问题，能够更为全面地揭示学术期刊的影响力，为科研评价“去四唯”提供解决思路。相关研究成果经过国际同行的认可，已经发表在科学计量学领域国际重要期刊。

《2019年中国科学院文献情报中心期刊分区表升级版（试行）》首次将社会科学引文数据库（SSCI）期刊纳入到分区评估中。升级版分区表（试行）设置了包括自然科学和社会科学在内的18个大类学科。基础版和升级版（试行）将过渡共存三年时间，推测在此期间各大高校和科研院所仍可能会以基础版为考核参考标准。提示：中科院分区官方微信公众号“fenqubiao”仅提供基础版数据查询，暂无升级版数据，请注意区分。

中科院分区查看说明

版本	大类学科	小类学科	Top期刊	综述期刊
暂无数据