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Level | Nickname | Marketplace metaphor | Features | Picture |
3 | "Pretty FAIR" | "Specialized Local Markets | Transition to good and best practice |
Table level 3 summarySummary Profile: Level 3 “Pretty FAIR”
Level 3 - Capabilities
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Capabilities
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”
Level 3 - Business value
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.
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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 company's organization’s departments, divisions or functions.
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Data is “conformed” using a domain-level data model. It is integrated, processed and audited to support specific consumption patterns.
While we achieve Findability “Findability” and there is evidence of re-use at the local level, the "IInteroperability" and "RReusability" principles are still a challenge in the broader scope of the organization.
There are controls in place on Access at the data level.
Creation The creation of analytics-ready 'Martsdata-marts' enabling self-service analytics .Role: Citizen Data Scientistis under way.
Machine interpretation is possible at the department level.
Initial FAIR data sets are in production and proving value. There is a company-wide supported plan to make all (relevant) data FAIR. More data sets are bornFAIR while the company's budget and competencies grow. There is evidence of local reusability and, to some extent, interoperability.
(Back to the FAIR Matrix).
Level 3 - FAIR leadership
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For example, leadership starts engaging outside the organization's boundaries to enable convergence standards.
(Back to the FAIR Matrix).
Level 3 - FAIR strategy
A refined version of the vision, strategy and plans for implementation exist.
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The data strategy accommodates federated domain-specific FAIR data, and there is an operating model, set of procedures, and defined models to harmonize, align, and connect functional domain data to expand and scale FAIR data beyond single or simple domain-specific use cases.
(Back to the FAIR Matrix).
Level 3 - FAIR roles
Key Roles are data standard expert, data curator, semantic web expert, knowledge engineer, data steward, and data strategist.
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For example, IT and business functions collaborate in creating Knowledge Graphs, scientific domain experts and data architects for data mesh
(Back to the FAIR Matrix).
Level 3 -
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FAIR processes
The organization initiates formal training for FAIR roles while a majority have embraced FAIR principles for major resources, backed by well-trained personnel capable of evaluating and implementing FAIR standards. At least one business unit actively employs FAIR processes in production, supported by documentation and training, with other FAIR initiatives underway and coordinated efforts recognized.
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A "FAIR implementation plan" to enact the strategy is in place.
(Back to the FAIR Matrix).
Level 3 - FAIR knowledge
FAIR data is part of the training curriculum, with some materials and experts available company-wide.
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A community of knowledgeable FAIR practitioners have met and have started sharing experiences in making data FAIR, at least at the department level.
(Back to the FAIR Matrix).
Level 3 - FAIR tools and infrastructures
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It becomes easier to make data FAIR company-wide, using the well-supported off-the-shelf tools than in the previous maturity level.
(Back to the FAIR Matrix).
Findability:
Tool(s) or infrastructure component which contributes, enhances or enriches Findability:
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