.. sidebar::

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

       * - **Domain**
         - Materials Science and Engineering
       * - **Category**
         - Materials Science
       * - **Current Version**
         - 1.0
       * - **Last Updated**
         - 17.08.2023
       * - **Creator**
         - Ahmad Zainul Ihsan
       * - **License**
         - Creative Commons Attribution 3.0 Unported (CC BY 3.0)
       * - **Format**
         - owl
       * - **Download**
         - `Download Dislocation Simulation and Model Ontology (DSIM) <https://github.com/OCDO/DSIM>`_

Dislocation Simulation and Model Ontology (DSIM)
========================================================================================================

The Dislocation Simulation and Model Ontology (DSIM) is a domain ontology developed to model concepts and relationships in the field of discrete dislocation dynamics and microscopy techniques used in dislocation research. DSIM provides a structured vocabulary for representing numerical representations of dislocations in simulations, as well as pictorial concepts such as pixels in experimental images (e.g., TEM, SEM, FIM). The ontology enables semantic annotation of simulation workflows, experimental setups, and image analysis procedures, supporting data integration and reproducibility in materials science. DSIM is designed for extensibility, allowing researchers to describe new simulation methods, image processing techniques, and dislocation phenomena. By providing a standardized framework, DSIM facilitates cross-study comparison, advanced analytics, and knowledge sharing in dislocation research. The ontology is actively maintained and extended to incorporate new concepts and requirements from the materials science community.

**Example Usage**:
Annotate a dislocation dynamics simulation with DSIM terms to specify the simulation method, dislocation types, image analysis workflow, and experimental conditions, enabling semantic search and integration with microscopy data.

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

.. tab:: Graph


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

        * - **Total Nodes**
          - 313
        * - **Total Edges**
          - 673
        * - **Root Nodes**
          - 19
        * - **Leaf Nodes**
          - 119
    ::


.. tab:: Coverage


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

        * - **Classes**
          - 47
        * - **Individuals**
          - 0
        * - **Properties**
          - 78

    ::

.. tab:: Hierarchy


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

        * - **Maximum Depth**
          - 7
        * - **Minimum Depth**
          - 0
        * - **Average Depth**
          - 2.05
        * - **Depth Variance**
          - 3.85
    ::


.. tab:: Breadth


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

        * - **Maximum Breadth**
          - 19
        * - **Minimum Breadth**
          - 1
        * - **Average Breadth**
          - 7.62
        * - **Breadth Variance**
          - 29.98
    ::

.. tab:: LLMs4OL


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

        * - **Term Types**
          - 0
        * - **Taxonomic Relations**
          - 51
        * - **Non-taxonomic Relations**
          - 6
        * - **Average Terms per Type**
          - 0.00
    ::

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

.. code-block:: python

    from ontolearner.ontology import DSIM

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