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Summary Profile: Level 5 “FAIRest of them all.”
Level 5 - Capabilities (input needed)
input needed
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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FAIR data are a key strategic asset and advantage of the company. Based on expected business value, critical data assets comply with the FAIR data principle, and new data generated is "FAIR by design.
(Back to the FAIR Matrix).
Level 5 - FAIR leadership
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Leadership transcends mere virtue signalling, opting for thought leadership by exemplifying FAIR principles and breaking down data silos. The organization enjoys international recognition, attracting collaborations leveraging FAIR data principle applications for mutual benefits.
(Back to the FAIR Matrix).
Level 5 - FAIR strategy
A company-wide vision and plan is supported and known and supported by all employees. FAIR is the norm, and everyone understands why FAIR data is needed. The company is also recognized for its FAIR data work and is a data-savvy company.
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Outreach efforts to diverse stakeholders, including pharmaceutical companies, CROs, regulatory bodies, and academia, are also evident when relevant: strategy embodiments are externally visible.
(Back to the FAIR Matrix).
Level 5 - FAIR roles
FAIR is the norm, and everyone understands why FAIR data is needed. Everyone who needs to have adequate skills has them. Everyone new also gets FAIR data training. FAIR data is a competence of all employees. There is recognition for the FAIR data work.
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Developing a structured and well-conceived role framework is emphasized, guided by experts versed in FAIR principles, such as a " FAIR" inter-organization architect or a bridge between the Chief Technology Officer and Chief Strategy Officer. The passage also underscores the need for strategic roles in promoting cultural change and maintaining FAIR practices while nurturing newcomers and recognizing the champions who initiated the FAIR agenda. Other companies ask FAIR Champions to share their best practices.
(Back to the FAIR Matrix).
Level 5 - Processes for FAIR
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Regulatory agencies (FDA, EMEA, etc.) are incentivizing the ecosystem with guidance concerning FAIR data in their regulatory process.
(Back to the FAIR Matrix).
Level 5 - FAIR knowledge
There is a company-wide clear understanding of FAIR data. The company has a "FAIR data academy" (or similar education program) with FAIR data materials: training, best practices, use cases, showcases, a calendar with events, and trainers.
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Regulatory agencies are part of the FAIR practice community and incentivise the ecosystem with guidance.
(Back to the FAIR Matrix).
Level 5 - FAIR tools and infrastructures
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Community Leader/Beacons: Additionally, the organization actively builds common and shareable FAIR data practices and platforms pre-competitive within the industry, encouraging other companies and vendors to follow suit. The platforms provide user-friendly tools for achieving comprehensive FAIR data management with robust documentation and subject matter expertise (SME) support. Aspirationally, an automated FAIRification tool, possibly utilising AI, is envisaged for all data types. Implementing FAIR API and protocols is considered for connecting with various data sources, such as HR and legal data, following cross-company standards. "Federation tools'' may enhance data sharing and collaboration across organizations.
(Back to the FAIR Matrix).
Findability:
Tool(s) or infrastructure component which contributes, enhances or enriches Findability:
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