Co-citation Analysis

The data in this version of the dataset are being actively annotated and supplemented. Please feel free to send email to the corresponding author for the Bradley et al. (2019) article, if you have questions. Access to the raw data used requires a Web of Science subscription that must be negotiated with Clarivate Analytics.

Figure 1: Effect of Research Discipline, Background Network, and Citation Count on Conventionality and Novelty. Data are shown for the applied physics (18,305), immunology (21,917), metabolism (97,405) and WoS (476,288) networks for 1995; numbers in paren- theses are the count of publications in each network. Subfigures (a) and (b): the x-axis show publications classified into percentile groups based on citation counts (e.g., Top 1 in- dicates those publications in the top 1%) and the y-axis shows the percent of publications in each set that are HC or HN. Based on the selected background network, z-scores are computed for each disciplinary network; thus, imm denotes the immunology network with immunology z-scores and imm wos denotes the immunology network with z-scores from WoS z-scores.

本版本数据集的数据正处于主动标注与补充阶段。若您有相关疑问，可致信Bradley等人2019年发表论文的通讯作者。获取本数据集使用的原始数据需订阅Web of Science（WoS），相关订阅事宜需与科睿唯安（Clarivate Analytics）协商办理。
图1：研究学科、背景网络与被引频次对研究成果常规性与创新性的影响。本图展示了1995年应用物理学（18305篇）、免疫学（21917篇）、代谢学（97405篇）及Web of Science（WoS，476288篇）四个背景网络的数据；括号内数字为各网络的论文总篇数。子图(a)与(b)的横轴为按被引频次划分的百分位组别（例如，Top 1指代被引频次位列前1%的论文），纵轴为各组内高常规性（HC, Highly Conventional）或高创新性（HN, High Novelty）论文的占比。根据选定的背景网络，计算各学科网络的z分数（z-scores）：其中`imm`代表以免疫学自身网络为基准计算z分数的免疫学数据集网络，`imm wos`代表以Web of Science整体网络为基准计算z分数的免疫学数据集网络。