.. sidebar::

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

       * - **Domain**
         - Medicine
       * - **Category**
         - Biomaterials
       * - **Current Version**
         - 06/2021
       * - **Last Updated**
         - Jun 2, 2021
       * - **Creator**
         - Osnat Hakimi
       * - **License**
         - GPL-3.0
       * - **Format**
         - owl
       * - **Download**
         - `Download Devices, Experimental scaffolds and Biomaterials Ontology (DEB) <https://github.com/ProjectDebbie/Ontology_DEB>`_

Devices, Experimental scaffolds and Biomaterials Ontology (DEB)
========================================================================================================

The Devices, Experimental scaffolds and Biomaterials Ontology (DEB) is an open, community-driven ontology for organizing information about biomaterials, their design, manufacture, and biological testing. DEB provides a structured vocabulary for describing biomaterial types, experimental scaffolds, fabrication methods, and the biological assays used to evaluate them. The ontology was developed using text analysis of a biomaterials gold standard corpus and systematically curated to represent the domain's lexicon, with validation by biomaterials research experts. DEB supports semantic annotation of biomaterials research data, enabling interoperability, data integration, and advanced queries across experimental studies and databases. By providing a standardized framework, DEB facilitates reproducibility, knowledge sharing, and meta-analysis in biomaterials science and tissue engineering. The ontology is actively maintained and extended to incorporate new materials, experimental techniques, and biological endpoints as the field evolves.

**Example Usage**:
Annotate a biomaterials experiment with DEB terms to specify the scaffold material (e.g., "collagen hydrogel"), fabrication method (e.g., "electrospinning"), and biological assay (e.g., "cell viability test"), enabling cross-study comparison and data integration.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 1081
        * - **Total Edges**
          - 2354
        * - **Root Nodes**
          - 533
        * - **Leaf Nodes**
          - 278
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 601
        * - **Individuals**
          - 0
        * - **Properties**
          - 120

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 4
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 0.67
        * - **Depth Variance**
          - 0.59
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 533
        * - **Minimum Breadth**
          - 2
        * - **Average Breadth**
          - 213.80
        * - **Breadth Variance**
          - 43756.96
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 672
        * - **Non-taxonomic Relations**
          - 8
        * - **Average Terms per Type**
          - 0.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import DEB

    ontology = DEB()
    ontology.load("path/to/DEB-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