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中国农学通报 ›› 2021, Vol. 37 ›› Issue (1): 147-157.doi: 10.11924/j.issn.1000-6850.casb2020-0202

• 农业信息·科技教育 • 上一篇    下一篇

文献计量分析在快速检索文献中的应用——以土壤氨挥发为例

吴汉卿1(), 张宝贵1, 王学霞2,3, 曹兵2,3, 陈立娟4, 刘杰4, 陈延华2,3()   

  1. 1中国农业大学土地科学与技术学院,北京 100193
    2北京市农林科学院植物营养与资源研究所,北京 100097
    3北京市缓控释肥料工程技术研究中心,北京 100097
    4禹城市农业农村局,山东禹城 251200
  • 收稿日期:2020-06-23 修回日期:2020-11-09 出版日期:2021-01-05 发布日期:2020-12-25
  • 通讯作者: 陈延华
  • 作者简介:吴汉卿,男,1992年出生,安徽枞阳人,博士研究生,研究方向为土壤氮素转化研究。通信地址:100193 北京市海淀区圆明园西路2号 中国农业大学土地科学与技术学院,E-mail:hqwu@cau.edu.cn
  • 基金资助:
    国家重点研发计划(2017YFD0200705);国家重点研发计划“农业面源污染和重金属污染监测技术与监管平台研发”(2016YFD0800906);北京市农林科学院青年基金(QNJJ201907)

The Method of Literature Retrieval via Biliometric Analysis:Taking Soil Ammonia Volatilization as an Example

Wu Hanqing1(), Zhang Baogui1, Wang Xuexia2,3, Cao Bing2,3, Chen Lijuan4, Liu Jie4, Chen Yanhua2,3()   

  1. 1College of Land Science and Technology, China Agricultural University, Beijing 100193
    2Institute of Plant Nutrition and Resources, Beijing Academy of Agricultural and Forestry Sciences, Beijing 100097
    3Beijing Engineering Technology Research Center for Slow/ Controlled-Release Fertilizer, Beijing 100097
    4Agricultural and Rural Bureau of Yucheng, Yucheng Shandong 251200
  • Received:2020-06-23 Revised:2020-11-09 Online:2021-01-05 Published:2020-12-25
  • Contact: Chen Yanhua

摘要:

为解决关注的研究领域中检索关键文献效率低的问题,本研究利用R语言bibliometrix包,以土壤氨(NH3)挥发为例,进行文献计量分析(包括关键词共现分析、共词分析及高被引论文分析),探索文献计量分析在Web of Science核心数据库中精确快速检索文献的方法。研究发现,第一次文献检索时,输入少量简单关键词(土壤氨挥发),检索出来的文献数量达到3573篇,且其中的高被引论文多数与主题词关系不大。对第一次检索结果进行文献计量分析,根据关键词聚类、共词分析结果,增加了关键词进一步检索文献,进行上述过程2次后,检索的文献数目已降至160篇,其中的Top 10高被引论文与检索主题(土壤NH3挥发)紧密相关。此时,假设需进一步精确查找关于土壤NH3挥发与水分、温度、管理方面研究文献,再次分别增加关键词进一步检索,分别检索出26、20和28篇相关文献,且关键词及高被引论文分析结果也再次验证了方法的准确性。本研究中利用文献计量分析快速检索权威、关键文献的方法切实可行。在大数据背景下,文献计量结合R-bibliometrix工具,有助于快速、精确地检索关键文献、获取科研思路及解决方法。

关键词: 文献计量分析, 共现分析, 共词分析, 文献检索, 方法, 土壤氨挥发, R语言

Abstract:

Based on the Web of Science core collection database, the study used the R-bibliometrix package for literature biliometric analysis such as keyword co-occurrence analysis, co-word analysis and high cited publications analysis taking soil ammonia volatilization as an example, and explored a practical method of literature retrieval by using literature biliometric analysis so as to quickly find key literatures that are closely related to research topics in a huge literature database. The results showed the number of retrieved literatures was 3573 (a large number) and the majority of the Top 10 highly cited publications had little to do with soil ammonia volatilization after first literature retrieval via entering a few easy-to-think keywords like soil ammonia volatilization. Then the first retrieved literature data was biliometric analyzed to get keyword co-occurrence network map, keywords conceptual structure map and Top 10 highly cited publications. According to the above-mentioned results, we screened and eliminated the inconsequential keywords, and then refined search for the second literature retrieval. After performing the process twice, the number of retrieved literatures had dropped to 160, among which the Top 10 highly cited publications were closely related to the research topic. And then we hypothesized that the key literatures about the relationships between soil NH3 volatilization and water, temperature, management needed to be accurately retrieved. The results by using the above-mentioned method demonstrated that only 26, 20, and 28 related literatures were retrieved, respectively. Therefore, the feasibility of the method was verified. Consequently, the method of literature retrieval by using literature biliometric analysis is feasible to accurately retrieve key literatures. Furthermore, the method of literature retrieval by using R-biliometrix tool based on big data could help researchers retrieve key literatures, find scientific gaps, determine scientific problems, and obtain scientific research ideas and solutions.

Key words: biliometric analysis, co-occurrence analysis, co-word analysis, literature retrieval, method, soil ammonia volatilization, R programming language

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