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Level | Nickname | Marketplace metaphor | Features | Picture |
5 | "FAIRest of them all" | ""Digital Online Store" | It is aspirational and conceivable, but it still needs to be realized. Evidence exists of cross-organization standards and Interoperability. |
Table level 5 summarySummary Profile: Level 5 “FAIRest of them all.”
Level 5 -
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Capabilities
Additionally to level 4 capabilities FAIR data principles are implemented across all key functions and organization domains. Data creation remains FAIR throughout its lifecycle, facilitated by scalable tools. Data can be exchanged and is interoperable across organizations in the life-science ecosystem such as pharmaceutical companies, CROs, regulatory bodies, and scientific data providers.
Machine actionability and automated operations are possible, leveraging self-describing digital objects. Knowledge-enabled citizens can utilize data products and robust analytical services, including AI linking provenance to machine-interpretable data. FAIR processes are integral to the business value generation, sustaining value creation throughout the data lifecycle. The maintenance costs of FAIR tools and infrastructure can be minimized and shared in the ecosystem, through community-established standards for data, metadata, models, and FAIR implementation profiles.
Level 5 - Business value
Ability to exchange and augment data sets across organizations. Cross-organizational insight generation. Machine actionability. Increased cost-effectiveness of FAIR infrastructure investments (cost-sharing across organizations). Secondary reuse of data (e.g., clinical trials). Real-world evidence data-mining and improved patient outcomes. Enhanced drug development efficacy (e.g. drug repurposing) and reduced time-to-market.
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Does the data and metadata make strict use of ontologies and vocabularies that are themselves, FAIR
Does the metadata for the data contain links that resolve to different data sources?
Does the data and metadata meet the standards required by the community or a particular application?
What are the mechanisms to maintain cross-company references, for example controlled vocabularies?
(WIP input needed): questions that speak to other dimensionsCan you elaborate on how data is maintained as FAIR throughout its lifecycle, (from collection to consumption and re-use).
How does C-level leadership oversee and hold themselves accountable for FAIR data strategies?
Can you describe the links between FAIR data practices and tangible business outcomes within your organization?
Can you describe the range of functional roles defined, recognized, and empowered within your organization?
Can you describe the world-class training programs established by your organization for FAIR data?
How does your organization actively share its FAIR learnings internally and externally through
communities of practice?
Can you describe the collaborative effort among various roles to ensure data is machine-actionable and compliant with industry standards?
What efforts does your organization make to build common and shareable FAIR data practices and platforms within the industry?
Level 5 - FAIR data
All organization functions and units adhere to FAIR data principles as much as relevant, emphasizing data Findability and Accessibility. The need for data to be Interoperable and Reusable across internal and external data standards, potentially driven by regulatory compliance, is fulfilled.
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(Back to the FAIR Matrix).
Level 5 -
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FAIR processes
FAIR processes are integral across all data processes, ensuring proven value throughout the entire data lifecycle.
World-class training programs are established and disseminated through courses. organizations Organizations employ adaptable FAIR data frameworks that provide common standards, facilitating seamless FAIR data exchange among entities via fit-for-purpose FAIR-enabled APIs or tools.
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