Department of Emergency Medicine, Chi-Mei Medical Center, Tainan, Taiwan.
Department of Medical Research, Chi-Mei Medical Center, Tainan, Taiwan.
Medicine (Baltimore). 2023 Aug 11;102(32):e34578. doi: 10.1097/MD.0000000000034578.
The appearance of a topic in a document stream is signaled by a burst of activity, with certain features rising sharply in frequency as the topic emerges. Although temporal bar graph (TBG) is frequently applied to present the burst spot in the bibliographical study, none of the research has combined the inflection point (IP) to interpret the burst spot feature. The aims of this study are to improve the traditional TBG and apply the TBG to understand better the evolution of a topic (e.g., publications and citations for a given author).
The EISTL model, including entity, indicator, selection of a few vital ones (named attributes) with higher values in quantity (e.g., the citation data of the top 10 entities), TBG and line-chart plots to verify the trend of interest, was proposed to demonstrate the TBG as a whole. The IP locations compared to the median point in data along with the heap map and line-chart trend were identified. The burst strength was computed. A dashboard on Google Maps was designed and launched for bibliometric analysis. Four authors in MDPI (Multidisciplinary Digital Publishing Institute) journals named to be Citation Laureates 2021 were recruited to compare their research achievements shown on the TBG, particularly displaying the burst spots and the recent developments and stages (e.g., increasing, ready to increase, slowdown, or decreasing).
We observed that the highest burst strengths in publication and citations are earned by Barry Halliwell (8.99) and Jean-Pierre Changeux (18.01). The breakthrough of TBG using the EISTL model to display the influence of authors in academics was made with 2 parts of the primary IP point and the trend feature in the data.
The dashboard-type TBG shown on Google Maps is unique and innovative and able to provide deeper insights to readers, not merely limited to the publications and citations for a given author as we did in this study.
主题在文献流中的出现是由一段活动爆发所标志的，随着主题的出现，某些特征的频率会急剧上升。尽管时间条形图（TBG）常用于呈现文献研究中的爆发点，但没有一项研究将拐点（IP）结合起来解释爆发点特征。本研究旨在改进传统的 TBG，并应用 TBG 来更好地理解主题的演变（例如，给定作者的出版物和引文）。
EISTL 模型包括实体、指标、选择数量较高的少数重要指标（命名为属性）（例如，排名前 10 位实体的引文数据）、TBG 和折线图来验证感兴趣的趋势，提出了整个 TBG。比较数据中位数的 IP 位置以及堆图和折线图趋势来确定 IP 位置。计算了爆发强度。设计并推出了一个基于 Google 地图的仪表板进行文献计量分析。招募了 MDPI（多学科数字出版机构）期刊的四位被提名为 2021 年引文桂冠奖的作者，比较他们在 TBG 上展示的研究成果，特别是显示爆发点以及最新发展和阶段（例如，增加、准备增加、放缓或减少）。
我们观察到，在出版物和引文方面，最高的爆发强度是由 Barry Halliwell（8.99）和 Jean-Pierre Changeux（18.01）获得的。使用 EISTL 模型显示作者在学术界影响力的 TBG 突破了两个主要 IP 点和数据中的趋势特征。
在 Google 地图上显示的仪表板类型 TBG 是独特且创新的，能够为读者提供更深入的见解，而不仅仅局限于本研究中我们对给定作者的出版物和引文的分析。
