通过使用时间条形图,可视化MDPI期刊中四位被命名为Citation Laureates 2021的作者研究的突破点
Sam Yu-Chieh Ho1, Tsair-Wei Chien2, Willy Chou3,4
1Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine
|August 11, 2023
概括
本研究引入了一种增强的时间条形图 (TBG) 模型,其中包含了拐点,以更好地分析主题演变和研究影响. 创新的仪表板可视化作者出版和引文爆发,以获得更深入的文献计量洞察力.
科学领域:
- 图书识别和科学识别技术
- 信息可视化 信息可视化
- 学术研究分析学术研究的分析.
背景情况:
- 文件流中主题的出现以活动爆发为标志.
- 传统的时间条形图 (TBG) 显示了爆裂点,但缺乏详细的解释.
- 将拐点 (IP) 与TBG集成,可以增强爆点分析.
研究的目的:
- 为了改进传统的时间条形图 (TBG).
- 应用增强的TBG来分析主题演变,特别是作者出版物和引用.
- 纳入转折点 (IP) 分析,以更细致地了解研究爆发的细微差别.
主要方法:
- 提出了整合实体,指标和属性选择的EISTL模型.
- 使用了TBG和线图图形图形与IP识别和中位点比较.
- 计算突破强度,并开发了一个基于谷歌地图的图书识别分析仪表板.
- 招募了四位引用奖获得者,对他们的研究成果进行比较分析.
主要成果:
- 确定了出版物 (巴里·哈利韦尔,8.99) 和引用 (让·皮埃尔·切克斯,18.01) 的最高爆发强度.
- 使用EISTL模型在TBG中取得了突破,突出了作者通过IP和趋势特征的影响.
- 增强的TBG有效地显示学术影响,爆发点和研究阶段.
结论:
- 谷歌地图上的仪表板类型的TBG提供了一种独特而创新的图书识别分析方法.
- 这种方法提供了比简单的出版和引用数量更深入的见解.
- 这项研究成功地证明了增强的TBG与IP的实用性,以了解作者研究演变.
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