Using data citation to define a knowledge domain: A case study of the Add-Health dataset

To date, most studies in scientometric map and track the main topics in a knowledge domain by measuring publications in core journals or keyword searches in databases. The present study instead proposes a novel metrics in which a knowledge domain is mapped and tracked via articles that cite the same openly accessible dataset. We retrieved 1,537 journal articles citing the National Longitudinal Study of Adolescent to Adult Health (Add-Health) as the basis for an investigation of the major research topics associated with this dataset and how they evolved over time. To identify the primary research interests associated with the dataset, co-word network modularity analysis was used. Another novel aspect of this study is that it juxtaposes the research topics identified by the co-word approach with those generated by topic modeling: an approach that complements network modularity analysis, and allows for cross-referencing between the results of these two methods. Keyness analysis was also performed to identify significant keywords in different time periods, which enables tracing of research interests in Add-Health as they evolve. The methodological implications of using data citation as the basis for delineating a knowledge domain and techniques for its mapping are also discussed.

Automated summary

This study proposes a new metric and method to map and track the main topics and research interests in a knowledge domain through citations of the same dataset. The study utilizes co-word network modularity analysis and topic modeling for analysis, and discusses the methodological implications of using data citation for delineating a knowledge domain and mapping.