Microscopy Ontology (MO)

The Microscopy Ontology (MO) is a domain ontology developed to provide a structured framework for describing microscopy and microanalysis experiments, data, and equipment. It extends the PMD Core Ontology (PMDco) and was developed within the Platform MaterialDigital ecosystem to support semantic integration and interoperability of microscopy data [2] [1]. The ontology covers microscopy-specific concepts and relationships needed to describe processes, equipment, and parameters in microscopy and microanalysis workflows [1] [3]. MO is intended to improve the semantic representation of microscopy knowledge and support better query results and logical linking among related terms and data objects [1] [2]. By providing a standardized vocabulary grounded in PMDco, the ontology supports interoperable data description and integration across materials-science microscopy datasets and related digital research infrastructures [3] [4].

Example Usage: Annotate a microscopy dataset with MO terms to specify the imaging modality (for example scanning electron microscopy or transmission electron microscopy), relevant equipment and parameters, sample-related descriptors, and analysis-related concepts, enabling semantic search, interoperable data integration, and improved querying across microscopy data sources [1] [2].

Metrics & Statistics

Graph Statistics

Total Nodes

931

Total Edges

1776

Root Nodes

10

Leaf Nodes

693

Knowledge Coverage Statistics

Classes

217

Individuals

0

Properties

3

Hierarchical Metrics

Maximum Depth

1

Minimum Depth

0

Average Depth

0.09

Depth Variance

0.08

Breadth Metrics

Maximum Breadth

10

Minimum Breadth

1

Average Breadth

5.50

Breadth Variance

20.25

LLMs4OL Dataset Statistics

Term Types

0

Taxonomic Relations

130

Non-taxonomic Relations

0

Average Terms per Type

0.00

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import MO

ontology = MO()
ontology.load("path/to/MO-ontology.ttl")

# 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

References