Dataset Viewer
Duplicate
The dataset viewer is not available for this split.
Cannot load the dataset split (in streaming mode) to extract the first rows.
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 match

Need 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.

RDFLIB

licence offline-first audit checks

Governed Anticloud packaging of the upstream project RDFLIB in 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

Build Status Documentation Status Coveralls branch

GitHub stars Downloads PyPI PyPI DOI

Contribute with Gitpod Gitter Matrix

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

  • main branch in this project is the current major release development branch for backwards-compatible fixes and features
  • next branch in this project is the next major release development branch
  • 7.6.0 GraphDB Client, and many improvements and fixes. See changelog for details
  • 7.5.0 RDF4J Store integration, RDF4J Client, and other improvements. See changelog for details
  • 7.4.0 a few small fixes, add test matrix for active python versions, and move v7 documentation to MkDocs
  • 7.3.0 many fixes and usability improvements, particularly for the Dataset class. See changelog for details
  • 7.2.1 tiny clean up release, relaxes Python version requirement
  • 7.2.0 general fixes and usability improvements, see changelog for details
  • 7.1.4 tidy-up release, possibly last 7.x release
  • 7.1.3 current stable release, small improvements to 7.1.1
  • 7.1.2 previously deleted release
  • 7.1.1 previous stable release
  • 7.0.0 previous stable release, supports Python 3.8.1+ only.
  • 6.x.y supports Python 3.7+ only. Many improvements over 5.0.0
  • 5.x.y supports 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.

Open in Gitpod Open in 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:


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

Coveralls branch

GitHub stars Downloads PyPI PyPI DOI

Contribute with Gitpod Gitter Matrix

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

Gitter Matrix

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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