法医学杂志 ›› 2022, Vol. 38 ›› Issue (5): 584-588.DOI: 10.12116/j.issn.1004-5619.2020.400902

• 论著 • 上一篇    下一篇

CNKI数据库收录1990—2020年死亡时间推断研究的文献信息可视化分析

林凌晓1(), 辛国斌2, 孔江炜1, 翟创彦1,2,3()   

  1. 1.南方医科大学法医学院,广东 广州 510515
    2.法庭毒物分析公安部重点实验室,北京 100192
    3.中国刑事警察学院法律教研部,辽宁 沈阳 110854
  • 收稿日期:2020-09-12 发布日期:2022-10-25 出版日期:2022-10-28
  • 通讯作者: 翟创彦
  • 作者简介:翟创彦,男,副研究员,博士后,主要从事法医学教学、科研和鉴定;E-mail:zhaichuangyan@163.com
    林凌晓(1999—),女,主要从事法医学研究;E-mail:572296929@qq.com
  • 基金资助:
    国家自然科学基金资助项目(22077061);广东省自然科学基金-博士启动纵向协同资助项目(2017A030310526);法庭毒物分析公安部重点实验室开放课题资助项目(2020FTDWFX01);广东省医学科研基金资助项目(A2018144)

Visualization of Literature Information on Postmortem Interval Estimation Indexed by CNKI Database from 1990 to 2020

Ling-xiao LIN1(), Guo-bin XIN2, Jiang-wei KONG1, Chuang-yan ZHAI1,2,3()   

  1. 1.School of Forensic Medicine,Southern Medical University,Guangzhou 510515,China
    2.Key Laboratory of Forensic Toxicology,Ministry of Public Security,People’s Republic of China,Beijing 100192,China
    3.Department of Law,Criminal Investigation Police University of China,Shenyang 110854,China
  • Received:2020-09-12 Online:2022-10-25 Published:2022-10-28
  • Contact: Chuang-yan ZHAI

摘要:

目的 通过对中国知识基础设施工程(China National Knowledge Infrastructure,CNKI)数据库收录的死亡时间推断研究的文献信息可视化分析,探究1990年1月—2020年8月我国死亡时间推断研究的发展过程、不同时期的研究热点、作者及机构间合作情况,为更好地开展死亡时间推断研究提供借鉴。 方法 利用信息可视化分析软件CiteSpace 5.7.R1对CNKI收录的1990年1月—2020年8月死亡时间推断研究文献中的突现热点、高频关键词、作者、机构等情况进行大数据分析。 结果 死亡时间推断研究的文献发表高峰期在2006—2010年,共114篇。关键词共现网络中,有效热点词汇为法医昆虫学、DNA含量分析,同时出现人工智能、大数据等新兴词汇。机构合作网络中,高频发文机构为科研院校;作者合作网络呈共聚、多合作态势。 结论 随着科技进步,基于传统方法的死亡时间推断研究日渐成熟,新的研究热点涌现,基于大数据、人工智能的研究为死亡时间推断提供了新方向。

关键词: 法医病理学, 文献计量学, 死亡时间推断, 可视化分析, CiteSpace, 中国知识基础设施工程

Abstract:

Objective To explore the development process of the postmortem interval (PMI) research in China from January 1990 to August 2020, research hotspots in different periods, authors and cooperation between institutions, and to provide a reference for the better development of PMI inference research through the visualization of the literature information of the PMI estimation research indexed in China National Knowledge Infrastructure (CNKI). Methods The information visualization analysis software CiteSpace 5.7.R1 was used to carry out big data analysis on hotspots, high-frequency keywords, authors, institutions and other information in the research literature on PMI inference from January 1990 to August 2020 indexed in CNKI. Results The peak time of publication of PMI was from 2006 to 2010 with 114 articles. In keyword co-occurrence network, the effective hot words were forensic entomology, DNA content analysis and some emerging words such as artificial intelligence and big data. In the cooperation network of institutions, the high-frequency institutions were mainly the scientific research institutions. The author cooperation network showed a trend of co-aggregation and multi-cooperation. Conclusion With the development of science and technology, the research on PMI estimation based on traditional methods is mature and novel strategies are emerging. Big data and artificial intelligence combined with forensic science provide new research directions on PMI estimation.

Key words: forensic pathology, bibliometrics, postmortem interval estimation, visualization analysis, CiteSpace, China National Knowledge Infrastructure

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