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Short description
‘A FAIRification objective is the end goal that the owners of a resource [e.g. dataset, database, protocols, analysis workflow] are looking to achieve with the process of FAIRification. It needs to be based on the research question, the data available in the resource, the expertise of the FAIRification team, available FAIR solutions and the availability of budget and appropriate infrastructure.’ (EJP RD FAIRification Guidelines)
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a single aspect of FAIR, such as increasing Findability of a dataset by making it available in the Dutch National Health Data Catalogue;
multiple aspects of FAIR, such as use of terminologies and vocabularies to represent the data (Interoperability) combined with making the dataset available in the before mentioned National Catalogue (Findability).
Why is this step important
Based on the aforementioned models, setting clear FAIRification goals is essential for a number of reasons, some of which are described below.
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Importantly, clear FAIR objectives help identifying which steps of the FAIR Metroline are relevant to your FAIRification journey.
How to
Step 1
Get acquainted with the FAIR principles, for instance via the GO FAIR Foundation (GFF) website. These form the basis for defining FAIR objectives.
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Decide on the desired FAIR level for your project or study, using resources such as the RDA FAIR Data Maturity Model, FAIRplus Dataset Maturity (DSM) Model or GFF's FIP mini-questionnaire. Does a lightweight FAIR layer suffice, i.e. , improving from non-structured to structured metadata? Or should it be more extensive, i.e. , transforming a relational database to fully ontologised linked data?
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I require Interoperable data, because I need to connect two different datasets to answer question X;
I want to make my data more Findable for researchers in my field by publishing it in repository Z.
Expertise requirements for this step
Defining FAIRification objectives is typically a collaborative effort by a range of experts, as described in Metroline Step: Build the Team.
Domain experts. Provide context to the FAIRification efforts from the perspective of a domain.
Data stewards. Help defining FAIR objectives to meet the project’s, funder’s, journal’s and/or institute’s requirements.
FAIR experts,such as metadata/semantics specialists. Help specifying the metadata/modelling aspects of FAIR objectives.
ELSI experts. Help identifying the legal compliance and ethical aspects of your FAIR objectives.
Practical examples from the community
European Joint Programme on Rare Diseases (EJP RD)
Programme aiming to create an effective rare diseases research ecosystem.
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sustainable MLDi registry, requiring interoperability with future and existing (international) MLD databases;
reusable MLDi data, by creating a web-based semantic model to improve interoperability;
FAIR as team effort, requiring people with the right expertise.
Tools and resources on this page
Training
Relevant training will be added in the future if available.
Suggestions
Visit our How to contribute page for information on how to get in touch if you have any suggestions about this page.