All examples reference the published DPROD JSON-LD context and use prefixed
terms (dprod:outputPort, dct:title) together with the JSON-LD keywords
@id and @type. The context defines no bare-term aliases and no @vocab, so
DPROD JSON can be combined with other JSON-LD contexts and an undefined term
stays visibly undefined instead of being silently coined in the DPROD
namespace. See issues #93 and #246.
Every example here — the standalone files, the JSON-LD and Turtle snippets in
each README.md, and the worked examples in the specification itself — is
validated by tests/test_examples.py on every build: it must parse, expand
against the generated context without dropping a term, and produce triples in
which IRI-valued properties are resources rather than literals.
- SBA Pool Rates - Mortgage-backed securities rates served through three ports: database query, API, and Kafka topic.
- Equity Trade - Equity trades data product providing datasets for London Stock Exchange and Euronext.
- Data Lineage - Tracing lineage between data products via input/output ports and at the dataset level using PROV.
- Data Rights - Describing rights and entitlements on data products and datasets using ODRL policies.
- Data Quality - Measuring dataset quality using the W3C Data Quality Vocabulary (DQV).
- Data Schema - Describing dataset schemas using SHACL node shapes and property shapes.
- Observability Ports - Exposing monitoring and diagnostic data through a dedicated observability port.
- Core Data Product Extensions - Extending a data product with additional metadata such as FIBO-based agreements.
- Apply DCAT
- Gleif
- OpenData file