FAIR Data Maturity Model: specification and guidelines

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This document specifies indicators for assessing adherence to the FAIR principles. These indicators are designed for re-use in evaluation approaches and are accompanied by guidelines for their use. The guidelines are intended to assist evaluators to implement the indicators in the evaluation approach or tool they manage. The exact way to evaluate data based on the core criteria is up to the owners of the evaluation approaches, taking into account the requirements of their community. The objective is to make sure that the indicators, the maturity levels and the prioritisation are understood in the same way. The maturity model is not meant as a “how to”, but instead as a way to normalise assessment. No part of this document is to be considered ‘normative’; it intends to provide guidelines to inform assessment approaches but leaves the way it is implemented to the evaluator. FAIR Data Maturity Model Working Group (2020). FAIR Data Maturity Model: specification and guidelines. Research Data Alliance. DOI: 10.15497/RDA00050

License: Creative Commons Attribution 4.0 International (CC BY 4.0)

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Rubric Assessments (3)


Assessment Metrics
Target Project   Metadata is identified by a persistent identifier (Essential) Data is identified by a persistent identifier (Essential) Metadata is identified by a globally unique identifier (Essential) Data is identified by a globally unique identifier (Essential) Rich metadata is provided to allow discovery (Essential) Metadata includes the identifier for the data (Essential) Metadata is offered in such a way that it can be harvested and indexed (Essential) Metadata contains information to enable the user to get access to the data (Important) Metadata can be accessed manually (i.e. with human intervention) (Essential) Data can be accessed manually (i.e. with human intervention) (Essential) Metadata identifier resolves to a metadata record (Essential) Data identifier resolves to a digital object (Essential) Metadata is accessed through standardised protocol (Essential) Data is accessible through standardised protocol (Essential) Data can be accessed automatically (i.e. by a computer program) (Important) Metadata is accessible through a free access protocol (Essential) Data is accessible through a free access protocol (Important) Data is accessible through an access protocol that supports authentication and authorisation (Useful) Metadata is guaranteed to remain available after data is no longer available (Essential) Metadata uses knowledge representation expressed in standardised format (Important) Data uses knowledge representation expressed in standardised format (Important) Metadata uses machine-understandable knowledge representation (Important) Data uses machine-understandable knowledge representation (Important) Metadata uses FAIR-compliant vocabularies (Important) Data uses FAIR-compliant vocabularies (Useful) Metadata includes references to other metadata (Important) Data includes references to other data (Useful) Metadata includes references to other data (Useful) Data includes qualified references to other data (Useful) Metadata includes qualified references to other metadata (Important) Metadata include qualified references to other data (Useful) Plurality of accurate and relevant attributes are provided to allow reuse (Essential) Metadata includes information about the licence under which the data can be reused (Essential) Metadata refers to a standard reuse licence (Important) Metadata refers to a machine-understandable reuse licence (Important) Metadata includes provenance information according to community-specific standards (Important) Metadata includes provenance information according to a cross-community language (Useful) Metadata complies with a community standard (Essential) Data complies with a community standard (Essential) Metadata is expressed in compliance with a machine-understandable community standard (Essential) Data is expressed in compliance with a machine-understandable community standard (Important)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding FAIR4ML - Bewertungsergebnisse
                                                                                 
Pangaea Test for functionality
yes (1.00) yes (1.00) nobut (0.25) yes (1.00) yes (1.00) yes (1.00) yes (1.00) yes (1.00) no (0.00)   no (0.00) yes (1.00)   nobut (0.25)   yesbut (0.75)   yes (1.00)                                              
Test geospatial dataset Test FAIR geospatial dataset