FAIR Metadata

metric

ML-F4-01M: Metadata is offered in such a way that it can be retrieved by machines. Test: 1) Metadata is registered in major research data registries (DataCite). 2) Metadata is given in a way major search engines can ingest it for their catalogues (JSON-LD, Dublin Core, RDFa). *Note: Use automatic tool to assess this metric and enter the result here.*

Principle: F

Rationale: Metadata can be made available in different ways via different standards, protocols, embedded as structured data in Web pages, etc. This metric only refers to its representation aspect, ensuring that the ways through which the metadata is exposed or provided is based on a standard and machine-readable format.

FAIR Metrics: F4

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FAIR for Machine Learning Models

This rubric consists of assessment metrics that evaluate the FAIR maturity of ML models. The metrics...

FAIR machine learning model FAIR assessment NFDI4DataScience

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