Data science as an interdiscipline: Historical parallels from information science

Considerable debate exists today on almost every facet of what data science entails. Almost all commentators agree, however, that data science must be characterized as having an interdisciplinary or metadisciplinary nature. There is interest from many stakeholders in formalizing the emerging discipline of data science by defining boundaries and core concepts for the field. This paper presents a comparison between the data science of today and the development and evolution of information science over the past century. Data science and information science present a number of similarities: diverse participants and institutions, contested disciplinary boundaries, and diffuse core concepts. This comparison is used to discuss three questions about data science going forward: (1) What will be the focal points around which data science and its stakeholders coalesce? (2) Can data science stakeholders use the lack of disciplinary clarity as a strength? (3) Can data science feed into an "empowering profession"? The historical comparison to information science suggests that the boundaries of data science will be a source of contestation and debate for the foreseeable future. Stakeholders face many questions as data science evolves with the inevitable societal and technological changes of the next few decades.

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Author Mayernik, Matthew
Publisher UCAR/NCAR - Library
Publication Date 2023-06-14T00:00:00
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Topic Category geoscientificInformation
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Metadata Date 2025-07-11T15:17:06.531952
Metadata Record Identifier edu.ucar.opensky::articles:26619
Metadata Language eng; USA
Suggested Citation Mayernik, Matthew. (2023). Data science as an interdiscipline: Historical parallels from information science. UCAR/NCAR - Library. https://n2t.org/ark:/85065/d7n01bk5. Accessed 04 August 2025.

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