[CODATA-international] RDA FAIR Data Maturity Model specification and guidelines - Public review

Kostas Repanas krepanas at gmail.com
Thu Apr 30 08:46:05 EDT 2020

Dear all,

your feedback is greatly appreciated and apologies if you have seen this

best regards,

*Konstantinos Repanas, MRes PhD*

Policy Officer – Open Science & European open science cloud

*European Commission*

DG Research & Innovation

Unit G.4 – Open Science

ORBN 07/99, Square Frere Orban, 8

1049 Brussels/Belgium

+32 229-83397

The RDA FAIR Data Maturity Model Working Group
is pleased to announce the public comment period for the FAIR Data Maturity
Model specification and guidelines, as part of the process to propose an
RDA Recommendation in mid-2020. The document
is available for public review until 13 May 2020.

The work of the FAIR Data Maturity Model Working Group started in early
2019. Its objective is to bring together stakeholders from different
scientific and research disciplines, the industry and public sector, who
are active and/or interested in the FAIR principles and in particular in
assessment criteria and methodologies for evaluating their real-life uptake
and implementation level of the FAIR principles.

It was noted that there were several evaluation approaches and assessment
frameworks, often using questionnaires that asked different questions due
to their own interpretation of the FAIR principles. Because of these
differences, it was difficult to compare the results of those approaches.

As part of the FAIR Data Maturity Model, the working group defined a set of
indicators for various aspects of the FAIR principles, priorities for those
indicators and a mechanism for assigning maturity levels to the evaluation
of FAIRness based on the indicators. These components of the FAIR Data
Maturity Model are described in the document that is now open for public
comment as a proposed RDA Recommendation.

The indicators have been identified through an extensive period of
consultation and testing with the working group members and beyond.

Rather than being yet another evaluation approach, the FAIR Data Maturity
Model aims to establish common ground for evaluation approaches so that
their results can be made comparable. It is not meant as a “how to”, but
instead as a way to normalise assessment.

The model proposes the set of indicators, priorities and maturity levels
for the evaluation of FAIRness on a general level. In practical application
of the model, thematic communities can adapt the model to their specific
needs and their expectations about FAIRness of the data resources they
produce and manage.

The model may be used during the development of Research Data Management
Plans *before* any data resources have been produced to specify the level
of FAIRness that the resources are expected to achieve. It can also be used
*after* the production of data resources to test what the achieved level of
the resources is. Data producers, i.e. researchers, and data publishers can
use the model to determine where their practices could be improved to
achieve a higher level of FAIRness, while project managers and funding
agencies can use the model to determine whether the data resources achieve
a pre-defined, expected level of FAIRness.

Application of the model in assessment approaches can then lead to
increased coherence and interoperability of existing or emerging FAIR
assessment frameworks, ensuring the combination and compatibility of their
results in a meaningful way.

To continue the work on the model and its implementation after the
publication of the RDA Recommendation, the working group is investigating
the option to establish an RDA maintenance working group.

For further information, please contact our editors: Makx Dekkers (
mail at makxdekkers.com) or Christophe Bahim (christophe.bahim at pwc.com).
-------------- next part --------------
An HTML attachment was scrubbed...
URL: <http://lists.codata.org/pipermail/codata-international_lists.codata.org/attachments/20200430/ae7633c4/attachment.html>

More information about the CODATA-international mailing list