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--- wiki:1_fair-building-blocks:start [2026/01/15 11:07] – dkottmeier
+++ wiki:1_fair-building-blocks:start [2026/03/19 11:09] (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. They include:
+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.
-  * Common and agreed procedures to refer to shared entities and metadata values, particularly through the use of **persistent identifiers** (PIDs), to reduce redundancy and improve consistency in referencing key entities such as people, organizations, instruments, and datasets.
+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 interoperability and reusability.
-  * Harmonized use of **semantic resources** to standardize metadata element names and their meanings, reduce ambiguity, and ensure consistent interpretation across disciplines and systems.
-  * Common **metadata schema** such as DataCite, DCAT, or ISO 19115/19119, to ensure consistent structuring and exchange of metadata across systems, including associated interfaces and exchange formats.
-  * Common data **exchange containers** such as FAIR Digital Objects or DataCrates, to enable machine-actionable reuse and portability.
-**Other important topics** where harmonization procedures are important are:
+Building blocks that mainly address **Interoperability** are:
-  * Inclusion of **provenance information**, capturing data origin, transformation steps, and responsible agents, to enable assessment of data reliability and reproducibility (**P**).
+  * Common and agreed procedures to refer to systematically described entities through the use of **persistent identifiers** (PIDs), to reduce redundancy, enrich context and improve consistency in referencing instances such as people, organizations, instruments, and datasets.
-  * Clear **license information**, using standardized, machine-readable licenses, to define conditions of use and promote legal clarity (**L**).
+  * Common understanding across systems and disciplines through the use of **semantic resources** to standardize metadata element names and reduce ambiguity.
-  * Documented procedures for assessing and communicating **data quality**, including uncertainty, validation, completeness, and versioning, to ensure data are fit for purpose (**Q**).
+  * Common methods to structure data through the application of **interfaces, protocols, exchange formats, and schemas**, to ensure consistent structuring and exchange of metadata across systems.
-  * Clear definition of **stakeholder roles** and responsibilities, including who is accountable for metadata provision, data stewardship, infrastructure maintenance, and policy implementation (**R**).
+  * Common handling of data dissimination through **exchange containers** such as FAIR Digital Objects or DataCrates, to enable machine-actionable reuse and portability.
-  * **Valuating research data management** (RDM) engagement, through citation of datasets with DOIs, inclusion of author contributions, and formal acknowledgment of data curation efforts (**V**).
+The following building blocks mainly address **Reusability**:
+  * 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 */
 These elements form the structural foundation for the detailed recommendations presented in this wiki.
-A key precondition for the implementation of these recommendations is the availability of skilled personnel, such as data stewards, data curators, and developers, who support data management and the technical and semantic infrastructure required to implement FAIR.
+A key precondition for the implementation of these recommendations is the availability of skilled personnel, such as data stewards, data curators, and developers, who support data management and the technical and semantic infrastructure required to implement FAIR. /* **Valuating research data management** (RDM) engagement, through citation of datasets with DOIs, inclusion of author contributions, and formal acknowledgment of data curation efforts.*/
+/*   ==== Suggested Disciplinary Metadata Fields: ====
+Instrument used for the measurement
+  * method of the measurement
+  * measured attribute
+  * measured parameter
+  * measured unit
+  * measured Object / Medium
+  * sampleID
+  * region where the sample was obtained
+*/