DataCite Ontology (DataCite)

The DataCite Ontology is an RDF-based representation of the DataCite Metadata Schema, providing standardized vocabulary and semantic structure for describing research data and digital objects with comprehensive citation and identification metadata. It enables formal representation of essential dataset properties including creators, titles, publication dates, contributors, funding information, and relationships to other scholarly resources. DataCite provides machine-readable definitions of metadata properties for accurate and consistent identification, retrieval, and citation of diverse digital resources including datasets, software, and research outputs. The ontology supports FAIR data principles by enabling standardized, interoperable representation of dataset metadata in linked data formats (RDF, JSON-LD). DataCite facilitates data discovery, citation tracking, and research impact assessment by providing standardized semantic metadata structures.

Example Usage: Represent a published research dataset with DataCite ontology terms including persistent identifier (DOI), creators and contributors (with ORCID), publication date, subject areas, funding information, and related publications to enable proper citation and discovery across research repositories.

Metrics & Statistics

Graph Statistics

Total Nodes

260

Total Edges

519

Root Nodes

14

Leaf Nodes

120
Knowledge Coverage Statistics

Classes

19

Individuals

70

Properties

10
Hierarchical Metrics

Maximum Depth

8

Minimum Depth

0

Average Depth

3.21

Depth Variance

5.93
Breadth Metrics

Maximum Breadth

14

Minimum Breadth

3

Average Breadth

7.56

Breadth Variance

9.80
LLMs4OL Dataset Statistics

Term Types

71

Taxonomic Relations

27

Non-taxonomic Relations

2

Average Terms per Type

8.88

Usage Example

Use the following code to import this ontology programmatically:

from ontolearner.ontology import DataCite

ontology = DataCite()
ontology.load("path/to/DataCite-ontology.rdf")

# 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