Data stations als hoeksteen
The Five Layers of the FAIR Hourglass
The model is broken down into five distinct layers that guide data from its raw state to a reusable asset.
FAIRification (Top)
Layer 1: Raw Data Creation [cite_start]This top layer represents the creation and capture of raw data and metadata[cite: 73, 77]. [cite_start]At this stage, researchers and domain experts have maximum freedom, using established community practices and their preferred tools, from handwritten notes to specialized software[cite: 69, 119].
Layer 2: Harmonization In this layer, the raw data begins its journey toward standardization. [cite_start]It is a "funneling" process where tools are used to harmonize the data and metadata, converting them into more structured formats using machine-readable vocabularies and schemas[cite: 89, 122].
Center of the Hourglass
Layer 3: FAIR-Ready Data (The "Waist") [cite_start]This is the narrowest, most critical part of the model, acting as the bridge between the two phases[cite: 67]. [cite_start]Here, data and metadata are rendered as machine-actionable information according to a minimal, open, and technology-independent standard[cite: 81, 82, 90]. [cite_start]This standardized format, or "spanning layer," ensures that data from any source can be understood and processed by various automated services[cite: 250]. [cite_start]In the context of health data, a standard like FHIR is well-suited to function as this spanning layer[cite: 255].
FAIR Orchestration (Bottom)
Layer 4: Automated Services [cite_start]Once data is FAIR-ready, it is exposed to the internet through gateways like FAIR Data Points or APIs[cite: 91, 127]. [cite_start]This allows automated services to perform operations like indexing, searching, semantic resolution, and data integration[cite: 92, 128]. [cite_start]This layer puts the FAIR data into action[cite: 57].
Layer 5: Data Reuse and Analytics [cite_start]At the bottom of the hourglass, the "freedom to operate" is restored[cite: 69, 88]. [cite_start]End-users can now leverage high-level applications to perform complex data analytics, conduct data landscape surveys, or run distributed learning models on the integrated, interoperable data, leading to new insights[cite: 93, 129].