The 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:

• 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.

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.

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