Content Comparison

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Short description

Building upon the findings from on the pre-FAIR assessment, this step provides defines a structured approach to addressing identified gaps in FAIR compliance. A well-defined solution plan ensures that all necessary It transforms assessment insights into an actionable solution plan, ensuring technical, organisational, and procedural aspects are systematically considered, leading to an effective and sustainable transition towards FAIR data. By transforming assessment insights into actionable strategies, this step provides a clear roadmap for implementationconsidered. It helps your team allocate resources efficiently and prepare for upcoming challenges. The outcome is a clear roadmap that enhances data management and prepares for improved FAIRification.

Why is this step important 

Following the The pre-FAIR assessment , where identifies key gaps and improvement areas are identified, this challenges in FAIR compliance, such as incomplete metadata, lack of a data storage strategy, or missing documentation practices. If left unaddressed, these issues can hinder data quality, interoperability, and reuse. This step ensures that those assessment insights are systematically addressed. Without a well-defined plan, FAIRification efforts can become translated into a structured plan, preventing fragmented and ineffective . This step transforms assessment findings into actionable solutions, helping to:

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FAIRification efforts. A well-defined solution plan helps to:

• provide a structured approach to tackling complex data issues and aligning stakeholders on a shared approach,strategy;

• Optimise optimise the use of available resources and infrastructure,Define define success criteria to measure track progress,

• Facilitate long-term sustainability and interoperability of FAIR data.

The How to section should:

• be split into easy to follow steps;

  • Step 1 - Title of the step

  • Step 2 - Title of the step

  • etc.

• help the reader to complete the step;

• aspire to be readable for everyone, but, depending on the topic, may require specialised knowledge;

• be a general, widely applicable approach;

• if possible / applicable, add (links to) the solution necessary for onboarding in the Health-RI National Catalogue;

• aim to be practical and simple, while keeping in mind: if I would come to this page looking for a solution to this problem, would this How-to actually help me solve this problem;

• contain references to solutions such as those provided by FAIR Cookbook, RMDkit, Turing way and FAIR Sharing;

• contain custom recipes/best-practices written by/together with experts from the field if necessary.

Expertise requirements for this step 

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• ;

• enhance data quality, ensuring better insights, improved compliance with FAIR principles, and greater efficiency in research processes.

How to 

Step 1 - Identify and prioritise key requirements

Using the pre-FAIR assessment findings, identify and rank key issues based on urgency, impact, and feasibility. Prioritisation ensures that critical gaps, such as incomplete metadata, lack of persistent identifiers, or missing documentation, are addressed first.

Example: If metadata incompleteness is blocking data reuse, improving metadata standards should take precedence over less critical issues.

Step 2 - Define clear objectives

For each prioritised issue, define SMART objectives (Specific, Measurable, Achievable, Relevant, Time-bound). These objectives will guide implementation and measure progress.

Example: By [date], update metadata for 90% of datasets to comply with [standard], ensuring all required fields are completed.

Step 3 - Assess feasibility and identify constraints

Evaluate the current infrastructure, policies, and governance environment. Identify constraints such as legal barriers, technical limitations, or resource availability. Develop mitigation strategies to address them.

Example: If data governance policies prevent external repository use, consider alternative institutional solutions.

Step 4 - Select appropriate tools and methodologies

Choose tools, frameworks, and methodologies that best address identified gaps. Resources such as FAIRsharing and FAIR Implementation Profiles (FIPs) ensure alignment with best practices.

Examples:

• Metadata improvement: Use controlled vocabularies and standards.

• Persistent identifiers: Implement DOIs or Handles for datasets.

Step 5 - Engage stakeholders and align objectives

Involve key stakeholders from the pre-FAIR assessment phase, including data stewards, domain experts, and IT teams. Ensure alignment between technical and organisational priorities to create a feasible solution plan.

Tip: Regular check-ins help maintain engagement and prevent misalignment.

Step 6 - Develop a structured FAIRification roadmap

Create an implementation roadmap outlining key actions, responsibilities, timelines, and resources. Define key performance indicators (KPIs) to track progress and allow for iterative adjustments.

Step 7 - Conduct risk assessment and refine the plan

Identify potential risks (e.g., delays, lack of resources) and define mitigation strategies. Review the solution plan with stakeholders and refine it based on feedback.

Example: Risk: Limited personnel availability. Mitigation: Cross-train team members to ensure continuity.

Expertise requirements for this step 

Designing a solution plan is typically a collaborative effort by a range of experts, as described in Metroline Step: Build the Team.

• FAIR experts.Provide guidance on best practices, standards, and methodologies.

• Domain experts. Define data semantics, quality standards, and use cases.

• Data stewards. Ensure data and metadata comply with FAIR principles.

• IT specialists. Support infrastructure needs, interoperability, and tool integration.

• Project managers. Oversee planning, stakeholder coordination, and implementation tracking.

Practical examples from the community 

This section should show the step applied in a real project. Links to demonstrator projects. 

Training

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• FAIRification Tools and Templates – ELIXIR-UK Fellowship

• FAIRification Workflow – FAIR Toolkit

• FAIR Implementation Profile

Suggestions

This page is under construction. Learn more about the contributors here and explore the development process here. If you have any suggestions, visit our How to contribute page to get in touch.

When released, the Editorial board will change this to:

This page has been written and reviewed by field experts through a rigorous process and has reached the “released” status. Learn more about the contributors here and explore the development process here. If you have any suggestions, visit our How to contribute page to get in touch.

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One approach is to have multiple iterative FAIRification cycles, with each cycle focussing on one FAIRification objective. Each cycle can have its own workplan, guiding the implementation [FAIRInAction].  

How to 

[Sander]

Perhaps: https://rdmkit.elixir-europe.org/dm_coordination

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