Connecting spectral research, FAIR best practices, and data policy in cultural heritage science
TRUST-inspiring and FAIR-enabling digital repositories: an implementation story
by Ioana Maria Cortea — Reading time: 11 min
Posted June 27, 2025 in FAIR data management & FAIR adoption stories
» Metadata provides essential context, making data discoverable and usable.
» Machine-readable formats enable automated discovery and reuse.
» Standards and vocabularies ensure interoperability across platforms.
» Repository-level metadata fosters transparency and builds trust.
» Community support for FAIR adoption drives sustainable data practices.
Turning FAIR principles into practice: a starting guide for researchers
by Ioana Maria Cortea — Reading time: 12 min
Posted June 20, 2025 in FAIR data management
» FAIR is a flexible framework, not a rigid standard.
» Data FAIRification transforms unstructured data into reusable, interoperable research assets.
» Metadata, identifiers, and licensing are the foundation of reusable research data.
» FAIR ≠ Open: Accessibility can include restrictions—as long as they’re transparent.
» Trustworthy repositories and data stewardship are essential to making FAIR sustainable.
» A rich ecosystem of tools, standards, and resources are available to support every step of the FAIR data journey.
Keywords
data curation data FARification data interoperability data management plan DCAT digital repository EOSC guidelines F-UJI tool FAIR-IMPACT prototype FAIR assessment FAIR by design FAIR data FAIR data principles FAIR implementation machine-readable metadata metadata standards RDA recommendations repository trustworthiness research data management starting guide TRUST principles
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