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--- wiki:1_road-to-fair-strategy:start [2026/08/06 10:54] – esoeding
+++ wiki:1_road-to-fair-strategy:start [2026/08/19 12:42] (current) – dkottmeier
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-====== The Helmholtz Road-to-FAIR Strategy ======
+====== The Road-to-FAIR Strategy ======
-The Helmholtz Metadata Collaboration pursues the vision of a harmonized FAIR research data space. However, the FAIR Guiding Principles define the desired properties of research data and metadata without prescribing how these properties should be realized in practice. They do not determine which standards, technologies, responsibilities, or organizational processes should be adopted. A complementary implementation strategy is therefore required to translate the general objectives of FAIR into coordinated and actionable measures.
+The Road-to-FAIR Strategy provides the implementation framework for realizing the HMC vision described above. As discussed in the [[:preface|Preface]], the FAIR Guiding Principles define desired properties but do not prescribe the standards, technologies, responsibilities, or organizational processes through which these properties should be achieved.
-The **Road-to-FAIR Strategy** provides such a framework. It establishes a common narrative through which technical, semantic, organizational, and procedural activities can be related to one another, prioritized, and evaluated. This is particularly important because research data management encompasses a broad range of interdependent topics, including metadata quality, semantic interoperability, institutional responsibilities, technical infrastructures, and readiness for emerging forms of data-intensive research.
+The Road-to-FAIR Strategy translates these general objectives into coordinated and actionable measures. It provides a common structure through which technical, semantic, organizational, and procedural activities can be related to one another, prioritized, and evaluated. This is particularly important because research data management encompasses a broad range of interdependent topics, including metadata quality, semantic interoperability, institutional responsibilities, technical infrastructures, and readiness for emerging forms of data-intensive research.
 The strategy addresses four closely connected areas. It:
-  * defines and prioritizes implementation objectives through a common set of **Road-to-FAIR building blocks**;
+  * defines and prioritizes implementation objectives through a common set of **FAIR building blocks
-  * identifies the stakeholder groups involved in research data management and clarifies their respective responsibilities;
+  * identifies the stakeholder groups involved in research data management and clarifies their respective responsibilities
-  * establishes workflows through which relevant information can be created, maintained, and transferred between responsible actors and systems; and
+  * establishes workflows through which relevant information can be created, maintained, and transferred between responsible actors and systems, and
   * specifies the technical data ecosystem required to support these responsibilities and workflows.
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 ==== 1. Defining the FAIR Building Blocks ====
-The FAIR building blocks define the principal implementation objectives of the Road-to-FAIR Strategy. They provide a structured means of assessing which elements of FAIR are already supported and where further coordinated action is required.
+The [[..:2_fair-building-blocks:|FAIR building blocks]] define the principal implementation objectives of the Road-to-FAIR Strategy. They provide a structured means of assessing which elements of FAIR are already supported and where further coordinated action is required.
 Within the Helmholtz Research Field Earth and Environment, Findability and Accessibility are comparatively well supported for data deposited in established institutional or disciplinary repositories. Repository registries and discovery services, including re3data, FAIRsharing, and OpenAIRE, assist users in identifying appropriate repositories and locating relevant datasets. Open-science policies further support access to research data, while the provision of metadata for restricted datasets enables their discovery and communicates the conditions under which access may be obtained.
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 The principal remaining challenges concern the **Interoperability** and **Reusability** of heterogeneous research data. Repositories, projects, research programmes, disciplines, and observational networks frequently apply different standards and procedures when describing their data. As a result, information may be difficult to combine across repositories and, in some cases, even within individual infrastructures. Projects that integrate heterogeneous datasets must therefore devote substantial effort to data cleaning, semantic harmonization, metadata enrichment, and the reconciliation of incompatible structures.
-The Road-to-FAIR building blocks define practical measures intended to reduce these barriers. Measures supporting Interoperability include:
+The Road-to-FAIR [[..:2_fair-building-blocks:|building blocks]] define practical measures intended to reduce these barriers. Measures supporting Interoperability include:
-  * the consistent use of persistent identifiers for recurring and systematically described entities;
+  * Common and agreed procedures for referencing systematically described entities through the use of persistent identifiers (PIDs)
-  * the coordinated application of agreed semantic concepts and vocabularies;
+  * A shared understanding across systems and disciplines through the coordinated use of semantic artefacts
-  * the adoption of standardized interfaces, protocols, schemas, and exchange formats; and
+  * Common methods for structuring and exchanging data and metadata through the application of interfaces, protocols, exchange formats, and schemas
-  * the provision of data and metadata through uniform, machine-actionable representations.
+  * Consistent approaches to data dissemination through self-describing data packages, such as FAIR Digital Objects and RO-Crates.
-Measures supporting Reusability include the systematic documentation of provenance, licences, access conditions, data quality, and other contextual information required to assess whether data are suitable for a particular purpose.
+Measures supporting Reusability include the systematic ddocumentation of provenance, data quality, access and use constraints, and other contextual information required to assess whether data are suitable for a particular purpose.
 The building blocks do not constitute a single technical specification. Rather, they define areas in which institutions, communities, and infrastructures must establish and implement shared agreements.
-== 2. Defining Stakeholders and Responsibilities ==
+==== 2. Defining Stakeholders and Responsibilities ====
 The Road-to-FAIR Strategy regards research data management as a distributed institutional responsibility. It cannot be assigned exclusively to individual researchers, data managers, or repositories.
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 The information required to describe a research dataset is created and maintained by different stakeholder groups. These may include researchers, technicians, laboratory and field personnel, administrative units, libraries, data stewards, repository operators, infrastructure providers, and organizational management. No single stakeholder normally possesses all information required to produce a complete and accurate description of a digital research object.
-Responsibilities should therefore be assigned to those actors or systems that are best positioned to create, verify, and maintain the relevant information. Institutions must communicate these expectations clearly and, where appropriate, formalize them through policies, role descriptions, and agreed procedures. The identification of stakeholder groups and their responsibilities thus provides the organizational foundation for subsequent implementation.
+Responsibilities should therefore be assigned to those actors or systems best positioned to create, verify, and maintain the relevant information. Institutions must communicate these expectations clearly and, where appropriate, formalize them through policies, role descriptions, and agreed procedures. The identification of stakeholder groups and their responsibilities thus provides the organizational foundation for subsequent implementation.
-== 3. Establishing Coordinated Workflows ==
+==== 3. Establishing Coordinated Workflows ====
 Distributed responsibilities require coordinated workflows. The Road-to-FAIR Strategy therefore defines procedures through which information is captured close to its point of origin, maintained by an appropriate authoritative source, and transferred to downstream information systems.
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 Their implementation requires both institutional support and technical services capable of exchanging, validating, aggregating, and enriching metadata. Workflows must also define how information is updated, who is responsible for correcting errors, and how changes are propagated across connected systems.
-== 4. Providing an Interoperable Data Ecosystem ==
+==== 4. Providing an Interoperable Data Ecosystem ====
 The final component of the Road-to-FAIR Strategy is the definition and provision of a coordinated technical data ecosystem. This ecosystem comprises the tools, services, registries, interfaces, and infrastructures required to support the responsibilities and workflows established in the preceding steps.
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 The data ecosystem provides the operational foundation through which assigned responsibilities and agreed workflows are translated into sustainable practice.
-== Operationalizing FAIR ==
+==== Operationalizing FAIR ====
-The Road-to-FAIR Strategy complements the FAIR Guiding Principles by translating their high-level objectives into a coordinated implementation framework. FAIR defines the properties that digital research objects should exhibit, whereas the Road-to-FAIR Strategy addresses the standards, responsibilities, processes, and technical services required to realize those properties.
+The Road-to-FAIR Strategy provides the implementation framework through which the FAIR objectives described in the Preface are translated into coordinated practice. Its distinctive contribution is the integration of FAIR building blocks, stakeholder responsibilities, coordinated workflows, and an interoperable data ecosystem into a common implementation approach.
-A central component of the strategy is the development of shared community agreements. Persistent identifiers, metadata schemas, semantic vocabularies, exchange protocols, provenance models, licences, and quality procedures must be selected and applied consistently across infrastructures. FAIR implementation is therefore treated as a collaborative and incremental process rather than as a binary state. Institutions and communities can identify comparatively mature areas, prioritize remaining gaps, and progressively improve interoperability, reusability, and machine actionability.
+Implementation should be understood as incremental rather than binary. Rich metadata alone is insufficient: FAIRness also depends on semantic alignment, interoperable structures, and clearly documented conditions for access and reuse. As emphasized in the Preface, FAIR does not imply unrestricted open access; data may remain subject to legal, ethical, contractual, or institutional access controls when their metadata, access conditions, and procedures are adequately documented.
-The strategy also recognizes that the provision of rich metadata alone is insufficient. Metadata must employ harmonized semantics, explicit relationships, and interoperable structures if information is to be reliably interpreted and combined across disciplinary and organizational boundaries. At the same time, FAIRness is distinguished from unrestricted openness: data may remain subject to legal, ethical, contractual, or institutional access controls, provided that their metadata, access conditions, and access procedures are clearly documented.
+Taken together, these elements establish the socio-technical conditions required to operationalize FAIR. By connecting strategic objectives with community agreements, institutional responsibilities, coordinated workflows, and an interoperable data ecosystem, the strategy provides a structured pathway from a general commitment to FAIR towards a progressively harmonized and reusable Helmholtz research data space.
-The Road-to-FAIR Strategy thus establishes the socio-technical conditions required to operationalize FAIR. By connecting strategic objectives with community agreements, institutional responsibilities, coordinated workflows, and an interoperable data ecosystem, it provides a structured pathway from a general commitment to FAIR towards a progressively harmonized and reusable Helmholtz research data space.
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