Towards a Shared Framework: A Classificatory Matrix for Teaching Data Standards

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Date

2023-12

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Lamar Soutter Library, UMass Chan Medical School

Research Projects

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Abstract

Standards for research data can be a mystifying topic for both researchers and data professionals. A common source of confusion is that they are multipurpose: standards can (and should) be applied to both primary data and metadata, enabling a wide range of functions from the search features in a repository to the integration of disparate data sources. This paper reviews examples of classificatory approaches used by both librarians and researchers to describe data standards. This literature is synthesized into a classificatory matrix that can be used to map different types of standards. The matrix is constructed around two organizing principles: purpose (finding or using data) and type of information controlled (meaning or syntax). The objective of this classificatory exercise is to encourage further discussion about the misunderstandings between researchers and data support professionals and to spur further development of the educational resources needed to improve understanding and use of data standards.

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This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International License (CC-BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Keywords

standards, metadata, data management, knowledge organization, instruction

Citation

Badger, Kelsey. 2023. "Towards a Shared Framework: A Classificatory Matrix for Teaching Data Standards." Journal of eScience Librarianship 12(3): e758. https://doi.org/10.7191/jeslib.758