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Summary Profile: Level 3 “Pretty FAIR”
Level 3 - Capabilities
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Findability is largely achieved in the context the organization, access protocols and controls are in place. Data can be re-used at least at departmental levels. Machine interpretation is possible at the local (e.g. department) level. Processes for FAIR are formalized, including training, documentation, integration into workflow. The organization can support those processes financially and operationally. Data that increasingly comply to FAIR data principles can be generated from the onset and the organization can begin to reduce efforts required by retrospective “FAIRification”
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Department-level data reuse and insight generation; increased effectiveness and efficiency of data resources available in the organization. Reuse of data reduces procurement costs and the need for data generation.
Level 3 - Questions to ask
Are the identifiers for data and metadata persistent? Does the metadata contain “structured” elements?
Does the data and metadata identifier resolution support authentication and authorisation for access?
Does the metadata contain “structured” elements?
Is there a policy for persistence of metadata?
Does the metadata provide a pointer to disclose the owner or license to the data which is readable by machine?
How is FAIR data currently managed within different organizational departments, and across divisions, or functions?
How does leadership ensure that financing for FAIR implementation actions is included in the budget of data acquisition and related projects budgets?
How are FAIR strategies and guidelines embodied in organizational documents, and how are they communicated?
What key and emerging roles are identified for FAIR data management within the organization? Which roles are emerging?
How are metadata implementation and other FAIR practices standardized and integrated into existing processes?
What initiatives are underway to curate FAIR implementation tools and facilitate cross-functional FAIR initiatives?
How is FAIR data knowledge integrated into the organization's training curriculum and shared among business units?
What IT infrastructure mechanisms ensures that data is made FAIR prospectively rather than retrospectively in the organization?
What budget and capacity are allocated for delivering FAIR data at scale, and how are vendors selected for tooling and infrastructure implementation?
Is a knowledge representation language being used that has ontological machine-resolvable formats?(WIP input needed): questions that speak to other dimensions
Level 3 - FAIR data
One can find “FAIR” data in local environments such as a organization’s departments, divisions or functions.
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