
This blog article introduces key tools that support the assessment and implementation of the FAIR data principles, helping researchers and institutions evaluate the FAIRness of their datasets, ontologies, and software. It highlights widely used tools such as FAIR-Aware, F-UJI, and O’FAIRe, offers guidance on choosing the right one, and presents a summary of the F-UJI evaluation conducted on the INFRA-ART Spectral Library.
FAIR data, FAIR metrics, FAIR principles, FAIR-Aware, Research, FAIR implementation catalogue, FAIR implementation, FAIR assessment tools, EOSC, Databases, INFRA-ART blog, FAIRsFAIR, data service, open science, data stewardship, F-UJI, research data management, digital repository
FAIR data, FAIR metrics, FAIR principles, FAIR-Aware, Research, FAIR implementation catalogue, FAIR implementation, FAIR assessment tools, EOSC, Databases, INFRA-ART blog, FAIRsFAIR, data service, open science, data stewardship, F-UJI, research data management, digital repository
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
