Metroline Step: Define FAIRification objectives

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Short description

FAIRification enhances data Findability, Accessibility, Interoperability, and Reusability (FAIR) by improving documentation, metadata, and standardisation. It applies to both new and existing data. Defining FAIRification objectives is the first step in this process and can be guided by models such as FAIRopoly, A Generic Workflow for the Data FAIRification Process, and the FAIR in Action Framework.

These objectives may be set by institutes, funders, or journals to promote data reuse (see FAIRsharing). As a researcher, aligning with FAIRification efforts can increase visibility, recognition, and opportunities for collaboration. Spend some time considering your FAIRification objectives. What is your FAIR driving force?

Why is this step important

Setting clear FAIRification objectives is essential for several reasons, outlined below.

• Clarify purpose. Provides direction for FAIRification in studies and projects, ensuring clear roles and responsibilities.

• Enhance collaboration. Facilitates communication among team members and stakeholders, ensuring shared understanding of FAIR goals.

• Measure progress. Establishes measurable milestones, supporting tracking and continuous improvement.

FAIR objectives help identify which steps in the FAIR Metroline are relevant to your FAIRification journey, ensuring a structured and efficient approach.

How to

Step 1 - Familiarise yourself with the FAIR principles

Get acquainted with the FAIR principles, for instance via the GO FAIR Foundation (GFF) website and checking with your institute’s RDM-department. These form the basis for defining FAIRification objectives.

Step 2 - Check FAIRification objective prerequisites

When defining your FAIRification objectives, check if any of the following applies.

• What are the FAIR requirements set by the funder of your project, the consortium you are partaking in 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 catalogue.

• 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 DMP Online and the Data Stewardship Wizard (DSW), help to make your study or project more FAIR. A DMP-specific page will be written in the future.

Step 3

For onboarding metadata in the Dutch National Health Data Catalogue, the following FAIRification 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 regional node 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).

Step 4 - Consider your own FAIRification objectives

You may have your own requirements to formulate FAIRification objectives, for example:

• 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.

Step 5 - Define the target FAIR level

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?

See Metroline Step Pre-FAIR assessment for more information.

Step 6 - Identify required resources

Identify the resources (team of experts, time, tools, etc.) you need to complete your FAIRification process and whether they are available. For instance:

• is there someone in your team that has run a FAIRification project before?

• is there expertise/central support available in your institute? 

• do you need to hire specific expertise?

• do you need specific software or additional hardware, that allow you to meet your FAIRification objectives?

See Metroline Step: Build the Team for more information on team requirements.

Step 7 - Assess the impact of your FAIRification efforts

Consider the impact of your FAIRification efforts. For instance:

• Benefits for the researcher. Your FAIRification efforts can lead to more visibility, recognition and reuse of the work, potentially leading to new collaboration opportunities, more publications, more citations, etc.

• 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?

Step 8 - Organise and outline the steps and resources necessary

Organise and describe the steps and resources identified in step 6 necessary 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 FAIRification 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.

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.

• Researchers with domain knowledge. Provide context to the FAIRification efforts from the perspective of a domain.

• FAIR data stewards. Help defining FAIRification objectives to meet the project’s, funder’s, journal’s and/or institute’s requirements.

• Semantic experts. Help specifying the metadata/modelling aspects of FAIRification objectives.

• ELSI experts. Help identifying the legal compliance and ethical aspects of FAIRification objectives.

Practical examples from the community 

Below you can find several examples of projects with the FAIR objectives they set.

European Joint Programme on Rare Diseases (EJP RD)
Programme aiming to create an effective rare diseases research ecosystem.

FAIRification objectives include, for example (see for more information the FAIRopoly website):

• an interoperable registry, requiring standardised and machine-readable data;

• identification of patient cohorts for clinical trials, requiring to collect standardised and reusable data;

• a Rare Diseases research ecosystem, requiring data to be completely FAIR so that information can be queried at the source.

Metachromatic leukodystrophy initiative (MLDi)
The MLDi is an international patient registry for MLD and an academic collaborative network

FAIRification objectives include (see also the MLDi demonstrator project page):

• 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.

Training

Relevant training will be added soon.

Suggestions

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