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John Kunze
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Journal Articles
Publisher: Journals Gateway
Data Intelligence (2023) 5 (1): 242–260.
Published: 08 March 2023
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Abstract
View articletitled, Building Community Consensus for Scientific Metadata with YAMZ
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for article titled, Building Community Consensus for Scientific Metadata with YAMZ
ABSTRACT This paper reports on a demonstration of YAMZ (Yet Another Metadata Zoo) as a mechanism for building community consensus around metadata terms. The demonstration is motivated by the complexity of the metadata standards environment and the need for more user-friendly approaches for researchers to achieve vocabulary consensus. The paper reviews a series of metadata standardization challenges, explores crowdsourcing factors that offer possible solutions, and introduces the YAMZ system. A YAMZ demonstration is presented with members of the Toberer materials science laboratory at the Colorado School of Mines, where there is a need to confirm and maintain a shared understanding for the vocabulary supporting research documentation, data management, and their larger metadata infrastructure. The demonstration involves three key steps: 1) Sampling terms for the demonstration, 2) Engaging graduate student researchers in the demonstration, and 3) Reflecting on the demonstration. The results of these steps, including examples of the dialog provenance among lab members and voting, show the ease with YAMZ can facilitate building metadata vocabulary consensus. The conclusion discusses implications and highlights next steps.
Journal Articles
Publisher: Journals Gateway
Data Intelligence (2020) 2 (1-2): 30–39.
Published: 01 January 2020
Abstract
View articletitled, Unique, Persistent, Resolvable: Identifiers as the Foundation of
FAIR
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for article titled, Unique, Persistent, Resolvable: Identifiers as the Foundation of
FAIR
The FAIR principles describe characteristics intended to support access to and reuse of digital artifacts in the scientific research ecosystem. Persistent, globally unique identifiers, resolvable on the Web, and associated with a set of additional descriptive metadata, are foundational to FAIR data. Here we describe some basic principles and exemplars for their design, use and orchestration with other system elements to achieve FAIRness for digital research objects.