Differences

This shows you the differences between two versions of the page.

--- wiki:2_fair-building-blocks:start [2026/05/26 12:03] – jwurz
+++ wiki:2_fair-building-blocks:start [2026/08/19 12:28] (current) – dkottmeier
@@ Line 3: / Line 3: @@
 ==== Definition ====
-The [[..:0_vision-and-mission:start|Mission and vision]] of the HMC Hub Earth and Environment is realized by the **Road-to-FAIR-Strategy**. To do so, we suggest implementing a common set of **FAIR building blocks**. These building blocks are operational measures required to support the Findability, Accessibility, Interoperability, and Reusability of research data.
+The FAIR building blocks are the principal implementation elements of the Road-to-FAIR Strategy. They translate the strategy's objectives into concrete technical, semantic, organizational, and procedural measures that support the Findability, Accessibility, Interoperability, and Reusability of research data.
-In our experience, the aspects Findability and Accessibility are relatively well supported by existing technical solutions in Earth and Environment. Therefore, our building blocks focus particularly on the aspects Interoperability and Reusability.
+Within the Helmholtz Research Field Earth and Environment, existing infrastructures provide comparatively mature support for Findability and Accessibility. The main implementation challenges therefore concern Interoperability and Reusability. Registry services such as re3data.org, FAIRsharing.org, and OpenAIRE facilitate the discovery of disciplinary repositories and relevant research data resources. Their effectiveness, however, depends on the continuous maintenance of accurate, complete, and up-to-date information.
-Building blocks that mainly address **Interoperability** are:
+Open-access policies have further improved access to research data held in repositories. Where unrestricted access cannot be provided, for example because of legal, ethical, contractual, or security-related constraints, the publication of comprehensive metadata can nevertheless enable users to discover relevant data and determine the conditions under which access may be granted.
-  * Common and agreed procedures to refer to systematically described entities through the use of **[[.:0_pid:start|persistent identifiers (PIDs)]]**, to reduce redundancy, enrich context and improve consistency in referencing instances such as people, organizations, instruments, and datasets.
-  * Common understanding across systems and disciplines through the coordinated use of **[[.:1_semantic:start|semantic artefacts]]** to standardize metadata element names and reduce ambiguity.
-  * Common methods to structure (meta)data through the application of **[[.:2_structured_exchange:start|interfaces, protocols, exchange formats, and schemas]]**, to ensure a smooth exchange of metadata across systems.
-  * Common data dissemination through **[[.:3_data_packages:start|self-describing data packages]]** such as FAIR Digital Objects or DataCrates, to enable machine-actionable reuse and portability.
-The following building blocks mainly address **Reusability**:
+The following building blocks primarily support **Interoperability**:
-  * Inclusion of **provenance information**, capturing data origin, transformation steps, and responsible agents, to disclose lineage, and enable assessment of data reliability and reproducibility.
-  * Standardized procedures for documenting and assessing **data quality**, including uncertainty, validation, completeness, and versioning, to establish a basis for data trust and ensure data are fit for purpose.
-  * Clear definition of **access and use constraints** using standardized and machine-readable methods, e.g. common licenses, to promote legal clarity.
-/* Um weitere Themen erweitern, wie "AI", "Wo Daten veröffentlichen", "Visualisierung des Kontents von Metadaten - z.B. als Autor auch den Autoren zeigen/Anzeige der Zitationen", Empfehlungen zu Findability (Stichwort R3data, Schema.org) und Accesibility, AI readiness */
+* Common and agreed procedures for referencing systematically described entities through the use of **[[.:0_pid:start|persistent identifiers (PIDs)]]**. PIDs reduce ambiguity and redundancy, enrich contextual information, and improve the consistent identification of entities such as people, organizations, instruments, samples, publications, and datasets.
-These building blocks form the structural foundation for the detailed recommendations presented in this wiki.
+* A shared understanding across systems and disciplines through the coordinated use of **[[.:1_semantic:start|semantic artefacts]]**. Controlled vocabularies, terminologies, taxonomies, and ontologies support the consistent description of metadata concepts and reduce semantic ambiguity.
-Furthermore, the wiki contains concrete recommendations for [[..:3_implementation:|implementing our recommendations]], combined with the documentation of positive use cases.
+* Common methods for structuring and exchanging data and metadata through the application of **[[.:2_structured_exchange:start|interfaces, protocols, exchange formats, and schemas]]**. Their coordinated implementation enables reliable metadata exchange and supports the integration of heterogeneous information systems.
-Finally, we present [[..:4_stakeholders:|guidance tailored to the various stakeholders]] who are or need to be involved in implementig FAIR.
+* Consistent approaches to data dissemination through **[[.:3_data_packages:start|self-describing data packages]]**, such as FAIR Digital Objects and RO-Crates. Such packages combine data, metadata, identifiers, and contextual information in portable structures that facilitate machine actionability and reuse across systems.
-/* **Valuating research data management** (RDM) engagement, through citation of datasets with DOIs, inclusion of author contributions, and formal acknowledgment of data curation efforts.*/
+The following building blocks primarily support **Reusability**:
-/*   ==== Suggested Disciplinary Metadata Fields: ====
+* The inclusion of **[[.:4_provenance:start|provenance information]]** describing the origin of data, the processes and transformations applied to them, and the agents responsible for their creation and management. Provenance information makes data lineage transparent and supports the assessment of reliability, authenticity, and reproducibility.
-Instrument used for the measurement
+* Standardized procedures for documenting and assessing **[[.:5_quality_assessment:start|data quality]]**. Relevant information may include uncertainty, validation procedures, completeness, accuracy, consistency, and versioning. Such information enables potential users to determine whether a data product is suitable for a particular purpose.
-  * method of the measurement
-  * measured attribute
-  * measured parameter
-  * measured unit
-  * measured Object / Medium
-  * sampleID
-  * region where the sample was obtained
-Messinstrument
+* The clear description of **[[.:6_access_use_constraints:start|access and use constraints]]** through standardized and, wherever possible, machine-readable methods. This includes the application of recognized licences and the explicit documentation of legal, ethical, contractual, or institutional restrictions, thereby improving transparency and legal certainty.
-		Methodik
-		gemessenes Attribut
-		gemessener Parameter
-		gemessene Einheit
-		gemessenes Objekt / Medium
-		SampleID
-*/
+/* Potential future topics include artificial-intelligence readiness, guidance on the selection of appropriate data repositories, visualization of metadata content, presentation of authorship and citation information, and further recommendations concerning Findability and Accessibility, including re3data and Schema.org. */
+Together, these building blocks provide the structural foundation for the detailed recommendations documented in this wiki, which are complemented by practical guidance for [[..:3_implementation:|implementing these recommendations]], examples of established practices, and successful use cases.
+The wiki also provides [[..:4_stakeholders:|guidance for the different stakeholder groups]] involved in, supporting, or responsible for implementating FAIR research data practices.
+/* A further building block may address the recognition and valuation of research data management activities, including the citation of datasets through DOIs, the documentation of contributor roles, and the formal acknowledgement of data curation efforts. */
+/* ==== Suggested Disciplinary Metadata Fields ====
+* Instrument used for the measurement
+* Measurement method
+* Measured attribute
+* Measured parameter
+* Unit of measurement
+* Measured object or medium
+* Sample identifier
+* Geographic region in which the sample was obtained
+  */