Subdivisions and crossroads: Identifying hidden community structures in a data archive’s citation network

• Published in: Quantitative science studies Vol. 3; no. 3; pp. 694 - 714
• Main Authors: Lafia, Sara, Fan, Lizhou, Thomer, Andrea, Hemphill, Libby
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 01.11.2022; MIT Press Journals, The
• Subjects: archival science; Archives & records; community detection; Community structure; data citation; data reuse; Datasets; Interdisciplinary studies; Network analysis; Social organization; Social sciences; Subdivisions
• ISSN: 2641-3337; 2641-3337
• DOI: 10.1162/qss_a_00209

Data archives are an important source of high-quality data in many fields, making them ideal sites to study data reuse. By studying data reuse through citation networks, we are able to learn how hidden research communities—those that use the same scientific data sets—are organized. This paper analyzes the community structure of an authoritative network of data sets cited in academic publications, which have been collected by a large, social science data archive: the Interuniversity Consortium for Political and Social Research (ICPSR). Through network analysis, we identified communities of social science data sets and fields of research connected through shared data use. We argue that communities of exclusive data reuse form “subdivisions” that contain valuable disciplinary resources, while data sets at a “crossroads” broadly connect research communities. Our research reveals the hidden structure of data reuse and demonstrates how interdisciplinary research communities organize around data sets as shared scientific inputs. These findings contribute new ways of describing scientific communities to understand the impacts of research data reuse.