DataCite metadata schema
E572613
The DataCite metadata schema is a widely used standard for describing research datasets and other scholarly outputs to support citation, discovery, and persistent identification.
All labels observed (1)
| Label | Occurrences |
|---|---|
| DataCite metadata schema canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T6175511 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: DataCite metadata schema Context triple: [Harvard Dataverse, supportsStandard, DataCite metadata schema]
-
A.
Dublin Core
Dublin Core is a widely used standard for describing digital resources through a simple, generic set of metadata elements to support discovery and interoperability across systems.
-
B.
S-100 metadata framework
The S-100 metadata framework is an IHO-developed standard that defines a flexible, interoperable structure for describing and managing geospatial and hydrographic data within the broader S-100 universal hydrographic data model.
-
C.
Dataverse
Dataverse is Microsoft's cloud-based data platform that securely stores, manages, and structures business data for use across Power Platform applications and services.
-
D.
OAI-PMH
OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) is a low-barrier protocol used to collect and share metadata from repositories to enable interoperable discovery of scholarly and other digital resources.
-
E.
ePIC Persistent Identifier Consortium
The ePIC Persistent Identifier Consortium is an international collaboration that provides and promotes persistent identifier services to support long-term access to digital research data and resources.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: DataCite metadata schema Target entity description: The DataCite metadata schema is a widely used standard for describing research datasets and other scholarly outputs to support citation, discovery, and persistent identification.
-
A.
Dublin Core
Dublin Core is a widely used standard for describing digital resources through a simple, generic set of metadata elements to support discovery and interoperability across systems.
-
B.
S-100 metadata framework
The S-100 metadata framework is an IHO-developed standard that defines a flexible, interoperable structure for describing and managing geospatial and hydrographic data within the broader S-100 universal hydrographic data model.
-
C.
Dataverse
Dataverse is Microsoft's cloud-based data platform that securely stores, manages, and structures business data for use across Power Platform applications and services.
-
D.
OAI-PMH
OAI-PMH (Open Archives Initiative Protocol for Metadata Harvesting) is a low-barrier protocol used to collect and share metadata from repositories to enable interoperable discovery of scholarly and other digital resources.
-
E.
ePIC Persistent Identifier Consortium
The ePIC Persistent Identifier Consortium is an international collaboration that provides and promotes persistent identifier services to support long-term access to digital research data and resources.
- F. None of above. chosen
Statements (71)
| Predicate | Object |
|---|---|
| instanceOf |
metadata schema
ⓘ
metadata standard ⓘ research data metadata schema ⓘ |
| appliesTo |
audiovisual materials
ⓘ
collections ⓘ datasets ⓘ images ⓘ physical objects ⓘ software ⓘ text publications ⓘ |
| domain |
digital preservation
ⓘ
research data management ⓘ scholarly communication ⓘ |
| governingBody | DataCite Metadata Working Group NERFINISHED ⓘ |
| hasComponent |
alternateIdentifier element
ⓘ
contributor element ⓘ creator element ⓘ date element ⓘ description element ⓘ format element ⓘ fundingReference element ⓘ geoLocation element ⓘ identifier element ⓘ language element ⓘ publicationYear element ⓘ publisher element ⓘ relatedIdentifier element ⓘ relatedItem element ⓘ resourceType element ⓘ rights element ⓘ size element ⓘ subject element ⓘ title element ⓘ version element ⓘ |
| maintainedBy | DataCite NERFINISHED ⓘ |
| primaryPurpose |
description of research datasets
ⓘ
support for data citation ⓘ support for discovery of research outputs ⓘ support for persistent identification of research outputs ⓘ |
| publisher | DataCite NERFINISHED ⓘ |
| serializationFormat |
JSON
ⓘ
XML ⓘ |
| supportsCitation | through persistent identifiers and relations ⓘ |
| supportsConcept |
FAIR data principles
ⓘ
data citation ⓘ interoperability ⓘ machine-readable metadata ⓘ persistent identifier ⓘ |
| supportsDiscovery | through standardized metadata ⓘ |
| supportsIdentifierType | DOI NERFINISHED ⓘ |
| supportsRelationType |
Cites
ⓘ
HasPart ⓘ IsCitedBy ⓘ IsDerivedFrom ⓘ IsIdenticalTo ⓘ IsNewVersionOf ⓘ IsPartOf ⓘ IsPreviousVersionOf ⓘ IsReferencedBy ⓘ IsSourceOf ⓘ IsSupplementTo ⓘ IsSupplementedBy ⓘ References ⓘ |
| usedBy |
data publishers
ⓘ
disciplinary repositories ⓘ institutional repositories ⓘ research data repositories ⓘ |
| usedFor |
data citation
ⓘ
metadata registration with DataCite ⓘ research datasets ⓘ scholarly outputs ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: DataCite metadata schema Description of subject: The DataCite metadata schema is a widely used standard for describing research datasets and other scholarly outputs to support citation, discovery, and persistent identification.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.