The utilization of multi-layer network structures now enables the explanation of complex systems in nature from multiple perspectives. Multi-layer academic networks capture diverse relationships among academic entities, facilitating the study of academic development and the prediction of future directions. However, there are currently few academic network datasets that simultaneously consider multi-layer academic networks; often, they only include a single layer. In this study, we provide a large-scale multi-layer academic network dataset, namely, LMANStat, which includes collaboration, co-institution, citation, co-citation, journal citation, author citation, author-paper and keyword co-occurrence networks. Furthermore, each layer of the multi-layer academic network is dynamic. Additionally, we expand the attributes of nodes, such as authors' research interests, productivity, region and institution. Supported by this dataset, it is possible to study the development and evolution of statistical disciplines from multiple perspectives. This dataset also provides fertile ground for studying complex systems with multi-layer structures.

翻译：多层网络结构现已成为从多重视角阐释自然复杂系统的重要工具。多层学术网络通过刻画学术实体间的多元关系，为研究学术发展态势和预测未来方向提供了支撑。然而，当前同时考虑多层结构的学术网络数据集仍较为罕见，多数研究仅涉及单一网络层面。本研究构建了一组大规模多层学术网络数据集LMANStat，包含合作网络、合著机构网络、引用网络、共被引网络、期刊引用网络、作者引用网络、作者-论文二分网络及关键词共现网络等八个维度。该多层学术网络的每个层面均具备动态演化特性。此外，我们拓展了节点属性信息，涵盖作者研究兴趣、科研产出力、地域分布及所属机构等。基于该数据集，可从多视角系统研究统计学科的发展轨迹与演化规律，同时为具有多层结构的复杂系统研究提供了优质实验平台。