Problem
Research questions and friction points this paper is trying to address.
citation network
statistics
data science
scientific literature
bibliographic metadata
StatCite: A Large-scale Citation Network Dataset for Statistics and Data Science
🤖 AI Summary
This study addresses the notable scarcity of large-scale, structurally deep, and metadata-rich citation network datasets in statistics and data science. To bridge this gap, the authors construct a comprehensive citation network dataset comprising 189,101 papers published between 1981 and 2025, enriched with extensive metadata including titles, authors, abstracts, keywords, and references. For the first time in this domain, the work simultaneously integrates four complementary network types: paper citation, co-citation, bibliographic coupling, and journal citation networks. Through systematic data collection, network construction, and community detection methodologies, the dataset successfully reproduces canonical structural properties of citation networks and reveals multiple core research themes. This resource provides a high-quality foundation for advancing bibliometric analysis, knowledge graph development, and science of science research.
📝 Abstract
In this paper, we introduce StatCite, a large-scale citation network dataset covering publications in statistics and data science from 1981 to 2025. The dataset contains 189,101 research articles collected from 62 representative journals and provides bibliographic metadata, including title, author list, publisher, published year, abstract, keywords, and reference list. Based on the collected publications, we construct four complementary citation-based networks, namely the paper citation network, the co-citation network, the bibliographic coupling network, and the journal citation network. To illustrate the utility of the dataset, we present descriptive analyses of the constructed networks and investigate the community structure of the paper citation network. The results show that StatCite preserves key structural characteristics commonly observed in large-scale citation networks and captures several major research areas in statistics and data science. By integrating multiple network representations with rich textual metadata, StatCite provides a valuable resource for statistical analysis, knowledge discovery, and data-driven studies of scientific literature.
Innovation
Methods, ideas, or system contributions that make the work stand out.
citation network
StatCite
bibliographic metadata
co-citation
bibliographic coupling
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