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“A ‘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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FAIRification objectives can focus on (see FAIR Cookbook):
A a single aspect of FAIR, such as increasing Findability of a dataset by making it available in the Dutch National Health Data Catalogue.;
Multiple 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).
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Based on the aforementioned models, setting clear FAIRification goals is essential for a number of reasons, including: some of which are described below.
Clarity of purpose:. It provides a clear purpose and direction for FAIRificaton efforts in (clinical) studies and projects. It helps understanding who is responsible for what and why it matters.
Collaboration and communication:. Team members and others can communicate more easily when FAIR objectives are clearly defined. It guarantees that all parties involved are aware of the FAIR goals and are able to collaborate to meet them.
Measurable progress:. Clearly defined FAIR objectives act as measurable milestones and guarantee that FAIRification efforts have the biggest possible effect. They support monitoring the procedure and identifying areas for improvement.
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When defining your FAIR objectives, check if any of the following applies:.
What are the FAIR requirements set by the funder of your project or the journal you aim to publish in? FAIRsharing provides an overview of policies.
What are the FAIR requirements set by your institute? For instance, uploading your (meta)data to a certain repository or catalog.
What are the FAIR requirements set by your project? These are usually written down in your project’s Data Management Plan (DMP). DMP tooling, such as Data Stewardship Wizard (DSW), help to make your study or project more FAIR.
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For onboarding metadata in the Dutch National Health Data Catalogue, the following FAIR objectives are relevant:.
The (meta)data should be findable - by humans and computers - in the Catalogue (Findability).
Information should be provided on how the data can be accessed. Note: accessible does not imply Open. Data should be “As open as possible, as closed as necessary” (Open Science in Horizon 2020).(Accessibility).
Provide your metadata via a FAIR Data Point to the Catalogue. For this the metadata must be mapped to the DCAT-AP based Catalogue’s core metadata schema (Interoperability).
The data should be provided with a machine-readable license and provenance information on how the data was formed (Reusability).
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I would like to add more metadata to improve the reusability of my dataset.;
I’m setting up a new data collection and my data must be collected in a standardised, interoperable way.;
I want to transform my existing data into an interoperable dataset.;
I have standard, core data which needs to be collected and made FAIR in real time.
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Identify the resources (team of experts, time, tools, etc.) you need to complete your FAIRification process and whether they are available. For instance:
Is is there someone in your team that has run a FAIRification project before?
Do do you need to hire specific expertise?
Do do you need specific software or additional hardware, that allow you to meet your FAIR objectives?
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Consider the impact of your FAIRification efforts. For instance:
Cost-benefit ratio: . Does the end result outweigh the efforts you are putting in it?
Reuse of data: . In what way do potential reusers of your data (e.g., researchers, other users of the data) benefit most from the results of your FAIRification efforts?
Stakeholder benefits: . What other stakeholders (e.g., patients, general population, clinicians) would benefit from making your data more FAIR?
Research question: . How does the driving research question impact your FAIRification objectives?
Which stakeholders would benefit from making your data more FAIR (patients, general population, clinicians, researchers, etc.)?
Step 8
Organise and describe the steps and resources needed for reaching your FAIRification objective(s). This can be a separate plan (see Metroline Step: Design Solution Plan - in development) or part of your DMP. Make sure to formulate your FAIR objectives as specific as possible, and where possible, incorporate research questions. For example:
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.
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Defining FAIRification objectives is typically a collaborative effort by a range of experts, as described in Metroline Step: Build the Team:
Domain experts; . provides context to the FAIRification efforts from the perspective of a domain.
Data stewards; . helps defining FAIR objectives to meet the project’s, funder’s, journal’s and/or institute’s requirements.
FAIR experts,such as metadata/semantics specialists; helps . Help specifying the metadata/modelling aspects of FAIR objectives.
ELSI experts; help . Help identifying the legal compliance and ethical aspects of your FAIR objectives.
Practical
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examples from the
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community
European Joint Programme on Rare Diseases (EJP RD)
Programme aiming to create an effective rare diseases research ecosystem.
FAIR objectives include, for example (see for more information the FAIRopoly website):
A an interoperable registry, requiring standardised and machine-readable data.;
Identification identification of patient cohorts for clinical trials, requiring to collect standardised and reusable data.;
A a Rare Diseases research ecosystem, requiring data to be completely FAIR so that information can be queried at the source.
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FAIR objectives include (see also the MLDi demonstrator project page):
Sustainable sustainable MLDi registry, requiring interoperability with future and existing (international) MLD databases.;
Reusable reusable MLDi data, by creating a web-based semantic model to improve interoperability.;
FAIR as team effort, requiring people with the right expertise.
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Relevant training will be added in the future.
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