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.
[maxbutton id=”4″ url=”https://doi.org/10.1162/dint_a_00025″ text=”Read more” linktitle=”Data Intelligence: Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR” ]Citation
Nick Juty, Sarala M. Wimalaratne, Stian Soiland-Reyes, John Kunze, Carole A. Goble, Tim Clark (2022):
Unique, Persistent, Resolvable: Identifiers as the Foundation of FAIR.
Data Intelligence 2(1-2)
https://doi.org/10.1162/dint_a_00025
About the author
Stian works in School of Computer Science, at the University of Manchester in Carole Goble‘s eScience Lab as a technical software architect and researcher.
In addition to BioExcel, Stian’s involvements include Open PHACTS (pharmacological data warehouse), Common Workflow Language (CWL), Apache Taverna (scientific workflow system), Linked Data and identifiers, research objects (open science) and digital preservation, myExperiment (sharing scientific workflows), provenance (where did things come from and who did it) and annotations (who said what).
orcid.org/0000-0001-9842-9718
