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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 summary
Summary Profile: Level 5 “FAIRest of them all.”
Level 5 - Capabilities (input needed)
Level 5 - 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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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 -
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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.
The organization actively shares its learnings internally and externally, among others, through communities of practices. Processes are in a position to align data harmonisation efforts beyond the organization, such as feedback loops with standard-setting bodies. Mechanisms exist to sustain metadata across organizations, including update and feedback processes between data vendors and consumers.
organizational Organizational practices embed FAIR data principles compliance, with a commitment to maintaining this compliance in the future. This holistic approach ensures the embedding of FAIR principles in every facet of data management and exchange within and outside the organization.
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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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