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This dimension deals with types and leadership levels required to implement FAIR principles in a life science organization. Ultimately, leadership "owns" the vision of FAIR implementation, or the “why”. Leadership roles are also necessary to ensure that strategies can be defined, enabled, implemented, and executed. Leadership ensures the resources (financial, time, priority) are set and available. Ultimately, leadership, at various levels in and outside the boundaries of the organizations, is accountable for implementing FAIR data principles. This requires a sufficiently deep level of understanding of the FAIR principles, of the costs (time, financial, opportunity) associated with data practices that are not FAIR and the skills needed to implement FAIR data principles.
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FAIR strategy
Strategies are frameworks for making decisions related to FAIR implementation, from business case to capability-building to running operations. After setting the "why" of FAIR implementation journeys and what "will success look like,” "how and with which priorities will the organization evolve given the current status?” This dimension is also concerned with deciding what not to do (e.g., only some data may need to be FAIR, “FAIR Enough”), identifying metrics, organizational sectors (beyond R&D) involved and the cultural change required.
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FAIR roles
We address the roles required in an organization to implement FAIR principles in this dimension. What are the jobs to do? Who would ensure that happens? Who are the people responsible for FAIR implementation? These roles will ensure project execution to build capabilities and operational roles maintaining FAIR processes.
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Processes for FAIR
FAIR data principles implementation requires underlying processes connecting the necessary metadata, data, tools, roles and knowledge. Some of these processes may be implicit in the early stages of FAIR data implementation. Still, they will become more explicit and so ubiquitous that they will become transparent once we achieve the highest maturity levels.
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FAIR knowledge
The FAIR Knowledge dimension concerns the factual, conceptual, procedural knowledge required for FAIR implementation at the various stages. Knowledge is often associated with human expert roles and connects with the “FAIR roles” dimension.
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FAIR tools and infrastructures
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Interoperability The tool or infrastructure component contributes, enhances or enriches Interoperability.
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FAIR maturity matrix: maturity levels
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Additionally, a significant deficit in tools and infrastructure for FAIR data management contributes to unstructured data capture. A missing inventory of licenses and access policies further complicates FAIR compliance efforts. Overall, the organization has yet to start and possibly resists the initiation of a FAIR data implementation journey. https://pistoiaalliance.atlassian.net/wiki/x/AQChy
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Level 1: "Started the FAIR journey"
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At this stage, data is siloed and may reside in a shared data platform. Data has heterogeneous characteristics, requiring specialized and specific technical knowledge for access and interpretation. Leadership awareness of FAIR emerges, fostering visionary proposals for FAIR implementation and some pilots. Some strategic plans begin to take shape. Roles such as curators and semantic experts surface, with external experts engaged for training. The organization recognizes the potential business value of FAIR data, prompting considerations for retrospective implementation and specific initiatives of retrospective “FAIRification”. Discussions on metadata centralization and reference data alignment emerge, indicating a shift toward systematic approaches. Awareness among stakeholders expands through workshops. Initial plans for tooling and infrastructure emerge. Pragmatic progress involves enhancing existing IT practices. The organization focuses on centralizing metadata and implementing findability measures, marking an initial, if localized, commitment to embedding FAIR principles in some organizational processes. https://pistoiaalliance.atlassian.net/wiki/x/CACey
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Level 2: "Getting FAIR"
Level | Nickname | Marketplace metaphor | Features | Picture |
2 | "Getting FAIR" | "Street Market" | FAIR Pilots for implementation are in place |
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The organization initiates data conforming processes to local models in a shared data platform and progresses to system-level controls. These processes include Tools for metadata, controlled vocabularies, and persistent identifiers. Data is more “findable” thanks to unique identifiers. Emerging metadata and controlled vocabularies make accessing data with less specific knowledge requirements possible. Leadership awareness grows, initiating initial FAIR projects and forming champions within the company. Vision, strategy, and role development follow, integrating FAIR as a key element in the broader data strategy. Designated roles emerge, fostering prototypes and showcasing value. Formal training frameworks are structured, and pilot projects and governance considerations accompany the initiation of culture change processes. Informal communities of practice begin to form, facilitating knowledge exchange. We establish Infrastructure for Proof of Concepts (POCs) d, leading to organization-wide plans, RFPs, and evaluations of FAIR tools and profiles. At this journey stage, an increasing commitment to FAIR data principles, encompassing leadership engagement, role development, and systematic infrastructure implementation, is present. https://pistoiaalliance.atlassian.net/wiki/x/BIDFy
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Level 3: "Pretty FAIR"
Level | Nickname | Marketplace metaphor | Features | Picture |
3 | "Pretty FAIR" | “Specialized Local Markets” | FAIR Transition to good and best practice |
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FAIR data sets, adhering to domain-level models with controls on data access, increasingly appear. Machine interpretation begins to be demonstrated locally (e.g., in a department). Leadership still plays a crucial role in setting expectations for FAIR in project budgets and establishing organizational metrics related to FAIR. A refined vision and strategy exist, with an organization-wide supported plan for FAIR implementation. Key roles, such as data standard experts and curators, emerge. A cultural shift towards a data-driven approach begins to appear. Integrating Formal training into organizational practices and FAIR practices becomes ingrained in workflows, at least in some functions or departments. The organization fosters broader communities of knowledge and practice practitioners and establishes domain knowledge expertise within each key department, establishing Processes for FAIR data management. FAIR data generation and interaction mechanisms are conceptually defined. Budget and human-resources capacity is allocated for organization-wide FAIR delivery, utilising COTS (commercial, off-the-shelf) -, or “Standard” tools when possible. At this stage of the journey, the commitment to FAIR data principles shows increasing outcomes and impact, at least at the local level in the organization. https://pistoiaalliance.atlassian.net/wiki/x/BADDy
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Level 4:"Really FAIR"
Level | Nickname | Marketplace metaphor | Features | Picture |
4 | "Really FAIR " | "Hyper Market" | FAIR Operational, best practice known at the time of writing. Internal organizational focus. Emerging cross-company focus. |
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FAIR principles are pervasive across departments. Data, metadata, and identifiers conform to cross-domain standards, enabling enterprise-level interoperability and consistently implementing Globally unique, persistent identifiers (GUPRIs). Leadership mandates include FAIR budgets in all data projects and actively engage in the broader FAIR community. A comprehensive FAIR data strategy encompasses centralized and federated data backed by metrics and integrated into governance processes. Key roles, such as data standard experts and Citizen Data Scientists, play pivotal roles. Formalized training programs cover diverse roles, fostering a culture of proficiency. The organization demonstrates impact areas, engages in external leadership, and uses open-source tools. FAIR practices are embedded in workflows, emphasizing continuous improvement, impact measurement, and adherence to standards. We see the establishment of Cross-community collaboration and community of practice, highlighting shared learnings and real-world experiences. Business benefits from previous FAIR data implementation pilots are recognized, providing a qualitative framework and evaluation metrics for further initiatives. The organization utilizes automated tools, defined interaction mechanisms, and a registry of FAIRification tools, showcasing a commitment to advanced FAIR data management practices. https://pistoiaalliance.atlassian.net/wiki/x/EoDAy
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Level 5: "FAIRest of them all"
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On the other hand, for most data citizens, FAIR is transparent and FAIR embeds in daily practice. Benefits are visible for key stakeholders, and a pervasive data-centric culture resists adopting application-centric solutions. The organization acts as a community leader, encouraging organization-wide adoption of FAIR data practices and platforms. At this stage, the role of complementary organizations in the ecosystem provides time, cost and qualitative benefits resulting from interoperability and data reuse. https://pistoiaalliance.atlassian.net/wiki/x/AgDEy
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https://pistoiaalliance.atlassian.net/wiki/x/DoCZy
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