.. sidebar::

    .. list-table:: **Ontology Card**
       :header-rows: 0

       * - **Domain**
         - Upper Ontology
       * - **Category**
         - Data, Metadata
       * - **Current Version**
         - None
       * - **Last Updated**
         - None
       * - **Creator**
         - None
       * - **License**
         - Creative Commons 4.0
       * - **Format**
         - owl
       * - **Download**
         - `Download FAIR Vocabulary (FAIR) <https://terminology.tib.eu/ts/ontologies/FAIR>`_

FAIR Vocabulary (FAIR)
========================================================================================================

The FAIR Vocabulary is a formal ontology that provides machine-readable definitions and semantic representation of the FAIR Data Principles (Findability, Accessibility, Interoperability, Reusability). It formalizes key concepts underlying FAIR data management including data discoverability mechanisms, access protocols, interoperability standards, and reusability conditions. The ontology enables automated assessment and verification of FAIR compliance, supporting the development of FAIR data management tools and services. FAIR vocabulary terms can be applied to dataset descriptions, data repositories, and digital object properties to formally declare their FAIR characteristics and compliance levels. The vocabulary facilitates communication of FAIR principles within research organizations, funding agencies, and data management communities by providing standardized semantic definitions.

**Example Usage**: Annotate a dataset in a repository with FAIR terms to indicate its Findability (via persistent identifiers like DOIs), Accessibility (through standard protocols), Interoperability (using standard formats and ontologies), and Reusability (via clear licensing and metadata).

Metrics & Statistics
--------------------------

.. tab:: Graph


    .. list-table:: Graph Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Total Nodes**
          - 92
        * - **Total Edges**
          - 180
        * - **Root Nodes**
          - 9
        * - **Leaf Nodes**
          - 37
    ::


.. tab:: Coverage


    .. list-table:: Knowledge Coverage Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Classes**
          - 7
        * - **Individuals**
          - 19
        * - **Properties**
          - 1

    ::

.. tab:: Hierarchy


    .. list-table:: Hierarchical Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Depth**
          - 1
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.18
        * - **Depth Variance**
          - 0.15
    ::


.. tab:: Breadth


    .. list-table:: Breadth Metrics
        :widths: 50 50
        :header-rows: 0

        * - **Maximum Breadth**
          - 9
        * - **Minimum Breadth**
          - 2
        * - **Average Breadth**
          - 5.50
        * - **Breadth Variance**
          - 12.25
    ::

.. tab:: LLMs4OL


    .. list-table:: LLMs4OL Dataset Statistics
        :widths: 50 50
        :header-rows: 0

        * - **Term Types**
          - 19
        * - **Taxonomic Relations**
          - 3
        * - **Non-taxonomic Relations**
          - 3
        * - **Average Terms per Type**
          - 9.50
    ::

Usage Example
----------------
Use the following code to import this ontology programmatically:

.. code-block:: python

    from ontolearner.ontology import FAIR

    ontology = FAIR()
    ontology.load("path/to/FAIR-ontology.owl")

    # Extract datasets
    data = ontology.extract()

    # Access specific relations
    term_types = data.term_typings
    taxonomic_relations = data.type_taxonomies
    non_taxonomic_relations = data.type_non_taxonomic_relations