Training dates
for the
Implementer Module
FAIR Awareness
training
5 * 2 hour online sessions
Sep 22, 2025
09.00 - 11.00 CEST​
Sep 24, 2025
09.00 - 11.00 CEST​
Sep 29, 2025
09.00 - 11.00 CEST​
Oct 1, 2025
09.00 - 11.00 CEST​
Oct 8, 2025
09.00 - 11.00 CEST​
Please note: For the FIP training and the M4M Vocab training, the FAIR Awareness training is prerequisite
FAIR Implementation Profile training
10 * 2 hour sessions
Oct 13, 2025
09.00 - 11.00 CEST​
Oct 15, 2025
09.00 - 11.00 CEST​
Oct 20, 2025
09.00 - 11.00 CEST​
Oct 22, 2025
09.00 - 11.00 CEST​
Oct 27, 2025
09.00 - 11.00 CET​
Oct 29, 2025
09.00 - 11.00 CET​
Nov 10, 2025
09.00 - 11.00 CET​
Nov 12, 2025
09.00 - 11.00 CET​
Nov 24, 2025
09.00 - 11.00 CET​
Nov 26, 2025
09.00 - 11.00 CET​
Metadata for Machines Vocab training
8 * 2 hour sessions
Sep 11, 2025
09.00 - 11.00 CEST​
Sep 18, 2025
09.00 - 11.00 CEST​
Sep 25, 2025
09.00 - 11.00 CEST​
Oct 2, 2025
09.00 - 11.00 CEST​
Oct 16, 2025
09.00 - 11.00 CEST​
Oct 23, 2025
09.00 - 11.00 CEST​
Oct 30, 2025
09.00 - 11.00 CET​
Nov 6, 2025
09.00 - 11.00 CET​
Extra information​
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All training sessions will be held online and will be recorded.
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The recordings will be made available shortly after the training date. It is no problem if you are unable to join all sessions.
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The total time investment including self-study time will be:
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ca. 20 hours for the FAIR Awareness training
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ca. 40 hours for the FAIR Implementation Profile training
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ca. 25 hours for the Metadata for Machines Vocab training
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For the FIP training and the M4M Vocab training, the FAIR Awareness training is prerequisite.
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The training will be followed by an exam.
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Passing the exam after the FIP training makes you eligible for GO FAIR Foundation's Qualification for FIP Implementer. More information about our training modules
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Training fees:
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The fee for the FAIR Awareness training is €1250,- excl. VAT
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The fee for the FAIR Implementation Profile training is €2750,- excl. VAT
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The fee for the M4M Vocab training is €1500,- excl. VAT
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The total training fee (FA, FIP and M4M Vocab) is €5500,- excl. VAT
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Location - online
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Period - September 22 ~ October 8, 2025
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Duration - 5 times 2 hours (9:00-11:00 CEST)
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Time investment - 10 hours teaching, 10 hours self-study
Description
The FAIR Principles call for findable, accessible, interoperable, and reusable data for humans and for machines, but they don’t provide detailed instructions on how to achieve these goals. This course provides a rigorous understanding of the FAIR Principles and ideas on how to roadmap your FAIR implementation ambitions using the Three-Point-FAIRification Framework.
Learning objectives
After completion of the course the trainees will be knowledgeable about the origins and history of FAIR, the problems that FAIR solves (Why do we need FAIR?), the costs/benefits of implementing FAIR, be aware of good implementation examples and of “Fake FAIR”, be aware of qualitative and quantitative FAIR assessment tools, be knowledgeable on how FAIR fits into data management and data stewardship, and understand how to prioritize FAIR implementations in project proposals and roadmapping.
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FAIR Implementation Profiles (FIP) training
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Location - online
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Period - October 13 ~ November 26, 2025
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Duration - 10 times 2 hours (9:00-11:00 CEST/CET)
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Time investment - 20 hours teaching, 20 hours self-study
Description
The FAIR Implementation Profile course leverages the understanding gained in the FAIR Awareness training based on the Three-Point-FAIRification Framework. The course augments theoretical understanding with practical skills for constructing a FAIR Implementation Profile, how to apply this knowledge toward the rigorous FAIR assessment of a given resource, and how to foster convergence on FAIR technologies across communities. After completing the course, participants can qualify as FIP implementers under the GO FAIR Foundation’s FAIR Capacity Building Programme. Qualified FIP implementers will have the knowledge, skills and confidence to raise FAIR awareness in their organizations and provide advice on how best to implement FAIR fit to their purposes.
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Learning objectives
After completion of the course the trainees will be able to give a concise definition of the FIP and understand its role in multiple applications such as education, implementation and FAIR assessment. The trainee will be able to explain the FIP Ontology, will learn skills for working with nanopublications making use of the FIP Wizard and Nanodash. Trainees will know how to curate FAIR Supporting Resources as part of a global community of FIP implementers. Finally, trainees will understand the different levels of convergence, be able to build and explain the FAIR Convergence Matrix and do analysis on the results of the Matrix.
Metadata for Machines Vocab (M4M) training
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Location - online
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Period - September 11 ~ November 6, 2025
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Duration - 8 times 2 hours (9:00-11:00 CEST/CET)
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Time investment - 16 hours teaching, 9 hours self-study
Description
For metadata to be FAIR, it should use concepts from controlled vocabularies. The Metadata for Machines course helps you to understand how to find, select and reuse vocabularies. Sometimes, however, the appropriate vocabulary is missing, requiring the extension of existing resources or even the creation of new vocabularies. The course provides a step-by-step guide on how to make a vocabulary FAIR. It also explains how to set up a namespace and vocabulary infrastructure, and how to maintain the vocabulary..
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Learning objectives
After completion of the course the trainees will be able to explain semantic artifacts and the semantic ladder, and be knowledgeable about domain-relevant community standards as it relates to semantics. They will be able to set up a vocabulary, learn about mapping techniques and governance models.
Disclaimer
GFF makes every effort to ensure the accuracy of the information provided, including dates and amounts. However, we cannot guarantee that all details are free from errors or omissions. GFF shall not be held responsible for any inaccuracies, discrepancies, or misinterpretations arising from the use of this information. Users are encouraged to verify all details independently before making any decisions based on the provided data.