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Level | Nickname | Marketplace metaphor | Features | Picture |
2 | "Getting FAIR" | "Street Market" | Pilots for FAIR implementation are in place |
Table level 2 summary.Summary Profile: Level 2 “Getting FAIR”
Level 2 - Capabilities
The organization ensures findability of its own data through unique identifiers, standardized metadata, and data registries. Unique identifiers facilitate specific dataset searches, while standardized metadata schemas provide comprehensive descriptions. Data registries organize datasets with searchable interfaces based on criteria like keywords, enhancing discoverability. Data and metadata are retrieved by their identities using standardized resolution protocols.
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Level 2 - Questions to ask
How is data being cataloged within the organization, and what role does metadata play in this process?
Do metadata documents contain their own identifiers and other ones for the data?
Are the protocols for resolution of data and metadata identifiers universally implementable? Are they open? Are they free?
Is Are there a license document documents for both the data and its associated metadata which is are retrievable by humans?(WIP input needed): questions that speak to other dimensions?
What role do champions play in the early stages of FAIR data implementation, and how are teams forming under their leadership?
Which efforts are being made to align FAIR implementation with existing data strategies and models within the organization?
What efforts are being made to designate and recognize FAIR-related roles within the company?
How is the organization conducting needs assessments to structure formal training frameworks for FAIR data practices?
In what ways are efforts being made to standardize FAIR data processes and coordinate proof-of-concept projects?
How are designated roles shaping the understanding and formalization of FAIR knowledge within the company?
What internal and external resources are being utilized to develop FAIR training tailored to the organization's needs?
What tools have been introduced to capture and publish metadata within the organization?
How is the organization seeking or creating reference tools, such as controlled vocabularies, ontologies, and data standards, to support FAIR data principles implementation?
Level 2 - FAIR data
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Data, conformed to a local model, is cataloged (i.e. a metadata record of existence is created), and data resides in a data lake.
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Requires average level technical and subject matter knowledge to use. ( cf. the role : of data scientist).
(Back to the FAIR Matrix).
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More "buy-in" is happening: leadership-funded initiatives, commitment to enabling key steps, and Memberships to enabling organizations (e.g. for PIDs ).
(Back to the FAIR Matrix).
Level 2 - FAIR strategy
The first version of the vision, strategy and plan has approval.
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The realization that technical choices, architectures and partnerships have strategic implications, e.g. on the policy and concrete implementation of PIDs, URI, and GUPRIs.
(Back to the FAIR Matrix).
Level 2 - FAIR roles
Key Role: Data Scientist.
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The first champions and teams of various roles mentioned above develop Minimal Viable Products or services showcasing what prototypes can do, prove value and get internal recognition.
(Back to the FAIR Matrix).
Level 2 -
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FAIR processes
The organization conducts needs assessments to structure formal training frameworks while continuing ad hoc training initiatives. At least one pilot project showcases successful FAIR implementation, emphasizing impact and value.
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Governance processes may have other names. They focus on conflict resolution, reconciliation, and community building.
(Back to the FAIR Matrix).
Level 2 - FAIR knowledge
Designated roles start to take shape in the company.
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"Vendors" (e.g. publishers) and "service providers" (e.g. CROs) curate, distribute, and generate data and play a role in the FAIR knowledge ecosystem.
(Back to the FAIR Matrix).
Level 2 - FAIR tools and infrastructures
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Configure existing systems to implement FAIR, e.g., assigning PIDs to key documents, persons in organizational structures dealing with Human Resource, grant management, and publishing (authoring).
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The organization may look for reference "tools", e.g. controlled vocabularies, ontologies, data standards.
(Back to the FAIR Matrix).
Findability:
Tool(s) or infrastructure component which contributes, enhances or enriches Findability:
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