The disciplinary research landscape of data science reflected in data science journals
Information Discovery and Delivery
ISSN: 2398-6247
Article publication date: 5 November 2020
Issue publication date: 19 November 2021
Abstract
Purpose
This paper aims to selects 59 journals that focus on data science research in 14 disciplines from the Ulrichsweb online repository. This paper analyzes the aim and scope statement using both quantitative and qualitative methods to identify the research types and the scope of research promoted by these journals.
Design/methodology/approach
Multiple disciplines are involved in data science research and publishing, but there lacks an overview of what those disciplines are and how they relate to data science. In this study, this paper aims to understand the disciplinary characteristics of data science research. Two research questions are answered: What is the population of journals that focus on data science? What disciplinary landscape of data science is revealed in the aim and scope statements of these journals?
Findings
Theoretical research is mainly included in journals that belong to statistics, engineering and sciences. Almost all data science journals include applied research papers. Keywords analysis shows that data science research in computers, statistics, engineering and sciences appear to share characteristics. While in other disciplines such as biology, business and education, the keywords are indicative of the types of data to be used and the special problems in these disciplines.
Originality/value
This is the first study to use journals as the unit of analysis to identify the disciplines involved in data science research. The results provide an overview of how researchers and educators from different disciplinary backgrounds understand data science research.
Keywords
Citation
Hong, L., Moen, W., Yu, X. and Chen, J. (2021), "The disciplinary research landscape of data science reflected in data science journals", Information Discovery and Delivery, Vol. 49 No. 4, pp. 287-297. https://doi.org/10.1108/IDD-06-2020-0071
Publisher
:Emerald Publishing Limited
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