食品科学 ›› 2020, Vol. 41 ›› Issue (13): 291-301.doi: 10.7506/spkx1002-6630-20190608-078

• 专题论述 • 上一篇    下一篇

我国食品安全与数据科学交叉研究的科学计量学分析

邵航,宋英华,李墨潇,邵伟,雷生姣,库任俊,夏亚琼   

  1. (1.武汉理工大学中国应急管理研究中心,湖北 武汉 430070;2.安全预警与应急联动技术湖北省协同创新中心,湖北 武汉 430070;3.武汉理工大学安全科学与应急管理学院,湖北 武汉 430070;4.三峡大学生物与制药学院,湖北 宜昌 443001)
  • 出版日期:2020-07-15 发布日期:2020-07-29
  • 基金资助:
    国家社会科学基金重大项目(15ZDB168);中央高校基本科研业务费专项资金项目(195261008); 湖北省自然科学基金项目(2019CFB340);宜昌市应用基础研究项目(A19-302-02); 武汉理工大学研究生优秀学位论文培育项目(2017-YS-022);武汉理工大学自主创新研究基金项目(2019IVA013); 武汉理工大学自主创新研究基金项目(2019V1031)

Scientometric Analysis of Cross-Disciplinary Studies on Food Safety and Data Science in China

SHAO Hang, SONG Yinghua, LI Moxiao, SHAO Wei, LEI Shengjiao, KU Renjun, XIA Yaqiong   

  1. (1. China Research Center for Emergency Management, Wuhan University of Technology, Wuhan 430070, China; 2. Hubei Collaborative Innovation Center for Early Warning and Emergency Response Technology, Wuhan 430070, China; 3. School of Safety Science and Emergency Management, Wuhan University of Technology, Wuhan 430070, China; 4. School of Biology and Pharmacy, China Three Gorges University, Yichang 443001, China)
  • Online:2020-07-15 Published:2020-07-29

摘要: 《“十三五”国家食品安全规划》提出要提升食品安全技术支撑能力,而各类食品安全数据的采集、储存与挖掘等技术是食品安全技术的重要组成部分。因此,分析我国食品安全与数据科学交叉研究已取得的学术成果,探寻其发展趋势,对数据驱动的食品安全技术的应用具有重要的实际参考意义。以中国知网为数据库,以“主题 = 食品安全 AND 数据”为检索式,以搜集到的1996—2019年共3 375 条文献数据作为数据集,主要使用科学计量学方法及软件Citespace对数据集进行科技文本挖掘,并以关键词聚类图谱、时间线图谱和突现度图谱等进行数据可视化。研究发现,在这23 年中,该领域文献数量呈指数式增长;研究主题形成了3大类11小类的主题聚类,发展历程被时间线聚类划分为1996—2006年、2007—2014年和2015—2019年3 个阶段。结果表明,我国食品安全与数据科学的交叉研究从产出、合作到研究主题都已初具规模,经过积累与发展,交叉研究正在新的学科增长点的引领下快速发展。

关键词: 食品安全, 数据科学, 科学计量学, Citespace, 数据可视化, 聚类分析, 研究热点

Abstract: China’s 13th Five-Year Plan for National Food Safety proposes to enhance the capacity of technical support for food safety. The collection, storage and mining of food safety data are a crucial part of food safety technology. Analyzing the achievements that have been made in cross-disciplinary studies on food safety and data science and exploring future trends in this area in China are of great practical significance for promoting the application of data-driven food safety technology. In this study, the China National Knowledge Infrastructure database was retrieved for “topic containing food safety AND data”, and totally 3 375 pieces of literature information published in 1996–2019 were collected and used as dataset for mining scientific and technological texts by scientometric method and Citespace software. Moreover, data visualization was carried out by using keywords clustering map, time line map and burstness map. It was found that the number of published papers in this field was increased exponentially in the past 23 years. Totally, 3 categories and 11 sub-categories of subject clustering were formed, and the development process involved 3 stages by time line clustering: 1996–2006, 2007–2014 and 2015–2019. Those studies showed that the cross-disciplinary study on food safety and data science in China has reached a certain scale in terms of output, cooperation and research topics, and is currently developing rapidly under the guidance of new disciplinary growth points.

Key words: food safety, data science, scientometrics, Citespace, data visualization, cluster analysis, research hotspots

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