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Summary Profile: Level 5 “FAIRest of them all.”
Level 5 - Capabilities (input needed)
input neededAdditionally 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 dimensions
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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