The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
root: string
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
child 0, head: string
child 1, url: string
child 2, branch: string
child 3, sha_verified_from: string
all_passed: bool
passed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
child 1, max_lines: int64
child 2, min_lines: int64
child 1, code_lines: int64
child 2, command: string
child 3, counts_command: string
child 4, files_source: int64
child 5, files_tests: int64
child 6, files_total: int64
child 7, method: string
child 8, test_to_source_line_ratio: double
child 9, total_lines: int64
child 10, a_licences: list<ite
...
hild 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
platform: string
failed: int64
sbom_written: bool
version: string
python: string
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
total: int64
schema: string
failed_ids: list<item: null>
child 0, item: null
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 483, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2951, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2461, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2486, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2378, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2306, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
digest: string
kind: string
path: string
payload_sha3: string
prev: string
seq: int64
ts: timestamp[s]
root: string
source: struct<git: struct<head: string, url: string, branch: string, sha_verified_from: string>>
child 0, git: struct<head: string, url: string, branch: string, sha_verified_from: string>
child 0, head: string
child 1, url: string
child 2, branch: string
child 3, sha_verified_from: string
all_passed: bool
passed: int64
benchmarks: list<item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: i (... 3044 chars omitted)
child 0, item: struct<evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code (... 3032 chars omitted)
child 0, evidence: struct<ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>, code_lines: int64, co (... 2961 chars omitted)
child 0, ceilings: struct<max_files: int64, max_lines: int64, min_lines: int64>
child 0, max_files: int64
child 1, max_lines: int64
child 2, min_lines: int64
child 1, code_lines: int64
child 2, command: string
child 3, counts_command: string
child 4, files_source: int64
child 5, files_tests: int64
child 6, files_total: int64
child 7, method: string
child 8, test_to_source_line_ratio: double
child 9, total_lines: int64
child 10, a_licences: list<ite
...
hild 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 6, rerunnable_verification: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 7, versioning: struct<detail: string, evidence_path: string, present: bool>
child 0, detail: string
child 1, evidence_path: string
child 2, present: bool
child 62, criteria_missing: list<item: null>
child 0, item: null
child 63, criteria_satisfied: int64
child 64, criteria_total: int64
child 65, framework_controls_verified: int64
child 66, ladder: string
child 67, observed_level: int64
child 68, source: string
child 69, trL9_claimed: bool
child 70, trL9_reason: string
child 1, id: string
child 2, ok: bool
child 3, status: string
child 4, title: string
platform: string
failed: int64
sbom_written: bool
version: string
python: string
method: struct<aggregate_rule: string, excluded: string, reproduce: string, thresholds_note: string>
child 0, aggregate_rule: string
child 1, excluded: string
child 2, reproduce: string
child 3, thresholds_note: string
total: int64
schema: string
failed_ids: list<item: null>
child 0, item: null
to
{'all_passed': Value('bool'), 'benchmarks': List({'evidence': {'ceilings': {'max_files': Value('int64'), 'max_lines': Value('int64'), 'min_lines': Value('int64')}, 'code_lines': Value('int64'), 'command': Value('string'), 'counts_command': Value('string'), 'files_source': Value('int64'), 'files_tests': Value('int64'), 'files_total': Value('int64'), 'method': Value('string'), 'test_to_source_line_ratio': Value('float64'), 'total_lines': Value('int64'), 'a_licences': List(Value('string')), 'b_licences': List(Value('string')), 'default_for_unknown_is_C': Value('bool'), 'mismatches': {}, 'observed': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'only_class_A_may_relicense': Value('bool'), 'policy_probes': {'agpl3': Value('string'), 'apache2': Value('string'), 'bsd3': Value('string'), 'commons_clause': Value('string'), 'garbage': Value('string'), 'gpl3': Value('string'), 'isc': Value('string'), 'lgpl': Value('string'), 'mit': Value('string'), 'mpl2': Value('string'), 'noncommercial': Value('string'), 'proprietary': Value('string'), 'sspl': Value('string'), 'unknown_absent': Value('string')}, 'project_licence': {'LICENSE': Value('string'), '
...
Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'qualification_environment_pinned': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'reproducible_tests': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'rerunnable_verification': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}, 'versioning': {'detail': Value('string'), 'evidence_path': Value('string'), 'present': Value('bool')}}, 'criteria_missing': List(Value('null')), 'criteria_satisfied': Value('int64'), 'criteria_total': Value('int64'), 'framework_controls_verified': Value('int64'), 'ladder': Value('string'), 'observed_level': Value('int64'), 'source': Value('string'), 'trL9_claimed': Value('bool'), 'trL9_reason': Value('string')}, 'id': Value('string'), 'ok': Value('bool'), 'status': Value('string'), 'title': Value('string')}), 'failed': Value('int64'), 'failed_ids': List(Value('null')), 'method': {'aggregate_rule': Value('string'), 'excluded': Value('string'), 'reproduce': Value('string'), 'thresholds_note': Value('string')}, 'passed': Value('int64'), 'platform': Value('string'), 'python': Value('string'), 'root': Value('string'), 'sbom_written': Value('bool'), 'schema': Value('string'), 'total': Value('int64'), 'version': Value('string'), 'source': {'git': {'head': Value('string'), 'url': Value('string'), 'branch': Value('string'), 'sha_verified_from': Value('string')}}}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
- What This Project Does
- RDFlib Family of packages
- Versions & Releases
- Documentation
- Installation
- Getting Started
- Features
- Running tests
- Contributing
- Support & Contacts
- Installation
- Usage
- API
- Dependencies
- Configuration
- Contributing
- License
- Upstream
- Benchmarks
- Archives and Permanent Records
- Press and Independent Publication
RDFLIB
Governed Anticloud packaging of the upstream project
RDFLIBin category PHILOSOPHY_SEMANTICS. check results: see ISOLATED_LAB_RESULTS. Every number below traces to a named file + run stamp; nothing is borrowed from other projects.
Upstream: RDFLIB · Upstream pin: b3e60e79c9b0b85d760832fb7bd4d0da3392bb76 · Category: PHILOSOPHY_SEMANTICS · Vendor: Anticloud FZ LLE · Licence: BSD-3-Clause
What This Project Does
RDFLib
RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including:
- parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, Trig, JSON-LD and even HexTuples
- a Graph interface which can be backed by any one of a number of Store implementations
- Store implementations for in-memory, persistent on disk (Berkeley DB) and remote SPARQL endpoints
- additional Stores can be supplied via plugins
- a SPARQL 1.1 implementation - supporting SPARQL 1.1 Queries and Update statements
- SPARQL function extension mechanisms
RDFlib Family of packages
The RDFlib community maintains many RDF-related Python code projects with different purposes. For example:
- rdflib - the RDFLib core
- sparqlwrapper - a simple Python wrapper around a SPARQL service to remotely execute your queries
- pyLODE - An OWL ontology documentation tool using Python and templating, based on LODE.
- pyrdfa3 - RDFa 1.1 distiller/parser library: can extract RDFa 1.1/1.0 from (X)HTML, SVG, or XML in general.
- pymicrodata - A module to extract RDF from an HTML5 page annotated with microdata.
- pySHACL - A pure Python module which allows for the validation of RDF graphs against SHACL graphs.
- OWL-RL - A simple implementation of the OWL2 RL Profile which expands the graph with all possible triples that OWL RL defines.
Please see the list for all packages/projects here:
Help with maintenance of all of the RDFLib family of packages is always welcome and appreciated.
Versions & Releases
mainbranch in this project is the current major release development branch for backwards-compatible fixes and featuresnextbranch in this project is the next major release development branch7.6.0GraphDB Client, and many improvements and fixes. See changelog for details7.5.0RDF4J Store integration, RDF4J Client, and other improvements. See changelog for details7.4.0a few small fixes, add test matrix for active python versions, and move v7 documentation to MkDocs7.3.0many fixes and usability improvements, particularly for the Dataset class. See changelog for details7.2.1tiny clean up release, relaxes Python version requirement7.2.0general fixes and usability improvements, see changelog for details7.1.4tidy-up release, possibly last 7.x release7.1.3current stable release, small improvements to 7.1.17.1.2previously deleted release7.1.1previous stable release7.0.0previous stable release, supports Python 3.8.1+ only.- see Releases
6.x.ysupports Python 3.7+ only. Many improvements over 5.0.0- see Releases
5.x.ysupports Python 2.7 and 3.4+ and is mostly backwards compatible with 4.2.2.
See https://github.com/RDFLib/rdflib/releases/ for the release details.
Documentation
See https://rdflib.readthedocs.io for our documentation built from the code. Note that there are latest, stable and versioned builds, such as 5.0.0, matching releases.
Installation
The stable release of RDFLib may be installed with Python's package management tool pip:
$ pip install rdflib
Some features of RDFLib require optional dependencies which may be installed using pip extras:
$ pip install rdflib[berkeleydb,networkx,html,lxml,orjson]
Alternatively manually download the package from the Python Package Index (PyPI) at https://pypi.python.org/pypi/rdflib
Installation from the current development branch (for developers)
With pip you can also install rdflib from the git project with one of the following options:
$ pip install git+https://github.com/rdflib/rdflib@main
or
$ pip install -e git+https://github.com/rdflib/rdflib@main#egg=rdflib
or from your locally sourced project you can install it with one of the following options:
$ uv sync # installs into a uv-managed venv
or
$ pip install -e .
Getting Started
RDFLib aims to be a pythonic RDF API. RDFLib's main data object is a Graph which is a Python collection
of RDF Subject, Predicate, Object Triples:
To create graph and load it with RDF data from DBPedia then print the results:
from rdflib import Graph
g = Graph()
g.parse('http://dbpedia.org/resource/Semantic_Web')
for s, p, o in g:
print(s, p, o)
The components of the triples are URIs (resources) or Literals (values).
URIs are grouped together by namespace, common namespaces are included in RDFLib:
from rdflib.namespace import DC, DCTERMS, DOAP, FOAF, SKOS, OWL, RDF, RDFS, VOID, XMLNS, XSD
You can use them like this:
from rdflib import Graph, URIRef, Literal
from rdflib.namespace import RDFS, XSD
g = Graph()
semweb = URIRef('http://dbpedia.org/resource/Semantic_Web')
type = g.value(semweb, RDFS.label)
Where RDFS is the RDFS namespace, XSD the XML Schema Datatypes namespace and g.value returns an object of the triple-pattern given (or an arbitrary one if multiple exist).
Or like this, adding a triple to a graph g:
g.add((
URIRef("http://example.com/person/nick"),
FOAF.givenName,
Literal("Nick", datatype=XSD.string)
))
The triple (in n-triples notation) <http://example.com/person/nick> <http://xmlns.com/foaf/0.1/givenName> "Nick"^^<http://www.w3.org/2001/XMLSchema#string> .
is created where the property FOAF.givenName is the URI <http://xmlns.com/foaf/0.1/givenName> and XSD.string is the
URI <http://www.w3.org/2001/XMLSchema#string>.
You can bind namespaces to prefixes to shorten the URIs for RDF/XML, Turtle, N3, TriG, TriX & JSON-LD serializations:
g.bind("foaf", FOAF)
g.bind("xsd", XSD)
This will allow the n-triples triple above to be serialised like this:
print(g.serialize(format="turtle"))
With these results:
PREFIX foaf: <http://xmlns.com/foaf/0.1/>
PREFIX xsd: <http://www.w3.org/2001/XMLSchema#>
<http://example.com/person/nick> foaf:givenName "Nick"^^xsd:string .
New Namespaces can also be defined:
dbpedia = Namespace('http://dbpedia.org/ontology/')
abstracts = list(x for x in g.objects(semweb, dbpedia['abstract']) if x.language=='en')
See also ./examples
Features
The library contains parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, JSON-LD, RDFa and Microdata.
The library presents a Graph interface which can be backed by any one of a number of Store implementations.
This core RDFLib package includes store implementations for in-memory storage and persistent storage on top of the Berkeley DB.
A SPARQL 1.1 implementation is included - supporting SPARQL 1.1 Queries and Update statements.
RDFLib is open source and is maintained on GitHub. RDFLib releases, current and previous are listed on PyPI
Multiple other projects are contained within the RDFlib "family", see https://github.com/RDFLib/.
Running tests
Running the tests on the host
Run the test suite with pytest.
uv sync --group tests
uv run pytest
Running test coverage on the host with coverage report
Run the test suite and generate a HTML coverage report with pytest and pytest-cov.
uv run pytest --cov
Viewing test coverage
Once tests have produced HTML output of the coverage report, view it by running:
uv run pytest --cov --cov-report term --cov-report html
python -m http.server --directory=htmlcov
Contributing
RDFLib survives and grows via user contributions! Please read our contributing guide and developers guide to get started. Please consider lodging Pull Requests here:
To get a development environment consider using Gitpod or Google Cloud Shell.
You can also raise issues here:
Support & Contacts
For general "how do I..." queries, please use https://stackoverflow.com and tag your question with rdflib.
Existing questions:
If you want to contact the rdflib maintainers, please do so via:
- the rdflib-dev mailing list: https://groups.google.com/group/rdflib-dev
- the chat, which is available at gitter or via matrix #RDFLib_rdflib:gitter.im
Installation
The stable release of RDFLib may be installed with Python's package management tool pip:
$ pip install rdflib
Some features of RDFLib require optional dependencies which may be installed using pip extras:
$ pip install rdflib[berkeleydb,networkx,html,lxml,orjson]
Alternatively manually download the package from the Python Package Index (PyPI) at https://pypi.python.org/pypi/rdflib
Installation from the current development branch (for developers)
With pip you can also install rdflib from the git project with one of the following options:
$ pip install git+https://github.com/rdflib/rdflib@main
or
$ pip install -e git+https://github.com/rdflib/rdflib@main#egg=rdflib
or from your locally sourced project you can install it with one of the following options:
$ uv sync # installs into a uv-managed venv
or
$ pip install -e .
Usage
- rdflib - the RDFLib core
- sparqlwrapper - a simple Python wrapper around a SPARQL service to remotely execute your queries
- pyLODE - An OWL ontology documentation tool using Python and templating, based on LODE.
- pyrdfa3 - RDFa 1.1 distiller/parser library: can extract RDFa 1.1/1.0 from (X)HTML, SVG, or XML in general.
- pymicrodata - A module to extract RDF from an HTML5 page annotated with microdata.
- pySHACL - A pure Python module which allows for the validation of RDF graphs against SHACL graphs.
- OWL-RL - A simple implementation of the OWL2 RL Profile which expands the graph with all possible triples that OWL RL defines.
Please see the list for all packages/projects here:
Help with maintenance of all of the RDFLib family of packages is always welcome and appreciated.
API
RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including:
- parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, Trig, JSON-LD and even HexTuples
- a Graph interface which can be backed by any one of a number of Store implementations
- Store implementations for in-memory, persistent on disk (Berkeley DB) and remote SPARQL endpoints
- additional Stores can be supplied via plugins
- a SPARQL 1.1 implementation - supporting SPARQL 1.1 Queries and Update statements
- SPARQL function extension mechanisms
Dependencies
| Metric | Value |
|---|---|
| Files | unknown |
| Lines of Code | unknown |
| Dependencies | unknown |
| Upstream license (harvested) | BSD-3-Clause |
| Overlay license | Anticommons 0.1.0 |
Dependency manifests live in UPSTREAM_CLONE/; pinned lockfile in anticloud/ where applicable.
Configuration
$ pip install git+https://github.com/rdflib/rdflib@main
or
$ pip install -e git+https://github.com/rdflib/rdflib@main#egg=rdflib
or from your locally sourced project you can install it with one of the following options:
$ uv sync # installs into a uv-managed venv
or
$ pip install -e .
Contributing
RDFLib is a pure Python package for working with RDF. RDFLib contains most things you need to work with RDF, including:
- parsers and serializers for RDF/XML, N3, NTriples, N-Quads, Turtle, TriX, Trig, JSON-LD and even HexTuples
- a Graph interface which can be backed by any one of a number of Store implementations
- Store implementations for in-memory, persistent on disk (Berkeley DB) and remote SPARQL endpoints
- additional Stores can be supplied via plugins
- a SPARQL 1.1 implementation - supporting SPARQL 1.1 Queries and Update statements
- SPARQL function extension mechanisms
License
Upstream © its respective contributors under BSD-3-Clause (harvested MIT/Apache-2.0/BSD source; see UPSTREAM_CLONE/LICENSE). This packaging overlay is licensed under Anticommons 0.1.0.
Upstream
- project: RDFLIB
- Pinned SHA:
b3e60e79c9b0b85d760832fb7bd4d0da3392bb76 - source:
UPSTREAM_CLONE/(pinned at the SHA above) - Upstream README source:
UPSTREAM_CLONE/README.md
Benchmarks
Measured by the Anticloud assurance suite. Every value below is read from this
project's BENCH.json, produced by a real run — the SHA3-256 of that file is
d9553635258989f5bc81bbc26a263e288f439da4ba95cb6c25df414059ff147b.
| Framework | Controls | Evidence | Coverage | Result |
|---|---|---|---|---|
| OWASP Top 10 for LLM Applications | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| OWASP Top 10 (2021) | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| SOC 2 Type II readiness | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| NIST AI Risk Management Framework | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| NIST SP 800-53 Rev. 5 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| NIST Cybersecurity Framework 2.0 | 8 controls mapped | 8 with evidence | 100.0% | PASS |
| FedRAMP Rev. 5 | 10 controls mapped | 10 with evidence | 100.0% | PASS |
| PCI DSS v4.0.1 | 11 controls mapped | 11 with evidence | 100.0% | PASS |
| ISO/IEC 27001:2022 | 9 controls mapped | 9 with evidence | 100.0% | PASS |
| MITRE ATT&CK v16 | 12 controls mapped | 12 with evidence | 100.0% | PASS |
| ML Technology Readiness Level | TRL 8 | 8/8 criteria | PASS |
Overall: 16/16 checks passing.
See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.
Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the
evidence source bound to each control. This project does not claim an audit
opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are
issued by an independent assessor.
Archives and Permanent Records
| Platform | Identifier | Volume |
|---|---|---|
| Harvard Dataverse | DOI 10.7910/DVN/YMJKOG | 145 citable datasets |
| AIOSS verification kit | DOI 10.7910/DVN/OORKNJ | Offline hash verification |
| Zenodo (CERN) | — | 146 records, DOI-registered |
| OSF | — | 144 preregistered records |
| Figshare | author 20849885 | Research data and figures |
| Internet Archive | aioss-format, Anticode | Permanent binary specification |
| ORCID | 0009-0009-2233-6107 | Permanent researcher ID |
| Kaggle | pax-millennium-20 | Reproducible T4 benchmark run |
Press and Independent Publication
The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.
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