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Modeling community standards for metadata as templates makes data FAIR

Computer Science

Modeling community standards for metadata as templates makes data FAIR

M. A. Musen, M. J. O'connor, et al.

This paper explores a template-based approach to determine the FAIRness of datasets, emphasizing rich metadata and community standards. Conducted by Mark A. Musen, Martin J. O'Connor, Erik Schultes, Marcos Martínez-Romero, Josef Hardi, and John Graybeal, it showcases how CEDAR and FAIRware Workbenches can transform data management and sharing.

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Playback language: English
Abstract
Determining whether datasets are FAIR (findable, accessible, interoperable, and reusable) is challenging due to idiosyncratic metadata criteria. The FAIR principles require rich metadata adhering to domain-relevant community standards. This paper explores a template-based approach using the CEDAR and FAIRware Workbenches. CEDAR assists investigators in authoring metadata, while FAIRware evaluates archived datasets for adherence to community standards. Representing metadata standards as machine-readable templates benefits data management and sharing by serving as a community reference for FAIR data and enabling distribution among various software applications.
Publisher
Scientific Data
Published On
Nov 12, 2022
Authors
Mark A. Musen, Martin J. O'Connor, Erik Schultes, Marcos Martínez-Romero, Josef Hardi, John Graybeal
Tags
FAIR data
metadata
community standards
CEDAR
FAIRware
data management
template-based approach

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