====== FAIR Building Blocks ======

==== 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:

  * 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.
  * 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: /*Vorschlag: FAIR building blocks zu Key topics umbenennen (?) und die unteren Themen als eigene Seite mit eigenem Titel (z.B. other importants topics oder was besseres mit Einführung (basierend auf Text unten) aufziehen. Die entsprechenden Unterseiten als Aufklappfunktion:*/
  * Inclusion of **provenance information**, capturing data origin, transformation steps, and responsible agents, to enable assessment of data reliability and reproducibility.
  * Clear **license information**, using standardized, machine-readable licenses, to define conditions of use and promote legal clarity.
  * Documented procedures for assessing and communicating **data quality**, including uncertainty, validation, completeness, and versioning, to ensure data are fit for purpose.
  * **Valuating research data management** (RDM) engagement, through citation of datasets with DOIs, inclusion of author contributions, and formal acknowledgment of data curation efforts.
  /* Um weitere Themen erweitern, wie "AI", "Wo Daten veröffentlichen", "Visualisierung des Kontents von Metadaten - z.B. als Autor auch den Autoren zeigen" */

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.

==== 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