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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.
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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.
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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.
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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.
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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.
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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.
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