FAIR for Machine Learning Model: Principles and Assessment Metrics

FAIR4ML Model specifies the adoption of the FAIR principles for ML models, including a set of metrics to conduct its assessment. This work is based on existing initiatives; specifically, the FAIR principles for ML models are mainly based on FAIR Principles for Research Software (v1.0), whereas the evaluation metrics are based on RDA’s FAIR Data Maturity Model and FAIRsFAIR’s Data Object Assessment Metrics initiatives.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-11-21
DOI
https://doi.org/10.5281/zenodo.17673536
Primary Topic
Research Data Management Practices
Type
article
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article

FAIR for Machine Learning Model: Principles and Assessment Metrics

Zenodo (CERN European Organization for Nuclear Research)
Research Data Management Practices
article

FAIR for Machine Learning Model: Principles and Assessment Metrics

article en

Abstract

FAIR4ML Model specifies the adoption of the FAIR principles for ML models, including a set of metrics to conduct its assessment. This work is based on existing initiatives; specifically, the FAIR principles for ML models are mainly based on FAIR Principles for Research Software (v1.0), whereas the evaluation metrics are based on RDA’s FAIR Data Maturity Model and FAIRsFAIR’s Data Object Assessment Metrics initiatives.

Zenodo (CERN European Organization for Nuclear Research)
ZBW – Leibniz-Informationszentrum Wirtschaft (DE)
Partnerships for the goals
Openalex Percentile: Top 30%
Research Data Management Practices
DOIOpen Access PDFOpenAlex
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FAIR for Machine Learning Model: Principles and Assessment Metrics · Zenodo (CERN European Organization for Nuclear Research) (2025) | TGRS Research Map | TGRS