Metroline Step: Define FAIRification objectives

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Short description

FAIRification is the process of improving a dataset’s alignment with the FAIR (Findable, Accessible, Interoperable, and Reusable) principles. This can apply to both new data, where FAIR principles are integrated from the start, and to existing data, where efforts are made to enhance its findability, accessibility, interoperability, and reusability through modifications such as better documentation, metadata, and standardisation.

Defining FAIRification objectives is the starting point of your FAIRification journey. It is a fundamental step in making data FAIR, according to FAIRifcation models, such as FAIRopoly, A Generic Workflow for the Data FAIRification Process, FAIR in Action Framework by FAIRplus, A goal-oriented method for FAIRification planning and EJPRD FAIRification objectives. These FAIRification objectives may be set by your local institute, funders, journals, (research) organisations (see FAIRsharing) , who encourage data FAIRness to stimulate reuse of data. As a researcher, you can benefit from FAIRification efforts, due to increased visibility, recognition and reuse of your work, potentially leading to new collaboration opportunities, more publications, more citations, etc.

FAIRification objectives can focus on (see FAIR Cookbook):

• a single aspect of FAIR, such as increasing the Findability of a dataset by making it available in Zenodo, dataverseNL, a Domain-specific catalogue or 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).

Spend some time considering your FAIRification objectives and the reasons behind them. What is your FAIR driving force?

Why is this step important

Setting clear FAIRification goals is essential for a number of reasons, 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 FAIRification 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 FAIRification 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.

Importantly, clear FAIRification objectives help identifying which steps of the FAIR Metroline are relevant to your FAIRification journey.

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 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.