The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
dataset: string
version: string
updated: timestamp[s]
purpose: string
status_values: list<item: string>
child 0, item: string
claims: list<item: struct<claim_id: string, entity_id: string, claim: string, source: string, source_ref: st (... 77 chars omitted)
child 0, item: struct<claim_id: string, entity_id: string, claim: string, source: string, source_ref: string, evide (... 65 chars omitted)
child 0, claim_id: string
child 1, entity_id: string
child 2, claim: string
child 3, source: string
child 4, source_ref: string
child 5, evidence_refs: list<item: string>
child 0, item: string
child 6, date: timestamp[s]
child 7, status: string
integrity_rule: string
baseline_date: timestamp[s]
measurement_fields: list<item: string>
child 0, item: string
status: string
rule: string
records: list<item: null>
child 0, item: null
entities: list<item: struct<entity_id: string, canonical_name: string>>
child 0, item: struct<entity_id: string, canonical_name: string>
child 0, entity_id: string
child 1, canonical_name: string
to
{'dataset': Value('string'), 'version': Value('string'), 'baseline_date': Value('timestamp[s]'), 'purpose': Value('string'), 'entities': List({'entity_id': Value('string'), 'canonical_name': Value('string')}), 'measurement_fields': List(Value('string')), 'records': List(Value('null')), 'status': Value('string'), 'rule': 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 2840, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2373, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2398, 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 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, 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
dataset: string
version: string
updated: timestamp[s]
purpose: string
status_values: list<item: string>
child 0, item: string
claims: list<item: struct<claim_id: string, entity_id: string, claim: string, source: string, source_ref: st (... 77 chars omitted)
child 0, item: struct<claim_id: string, entity_id: string, claim: string, source: string, source_ref: string, evide (... 65 chars omitted)
child 0, claim_id: string
child 1, entity_id: string
child 2, claim: string
child 3, source: string
child 4, source_ref: string
child 5, evidence_refs: list<item: string>
child 0, item: string
child 6, date: timestamp[s]
child 7, status: string
integrity_rule: string
baseline_date: timestamp[s]
measurement_fields: list<item: string>
child 0, item: string
status: string
rule: string
records: list<item: null>
child 0, item: null
entities: list<item: struct<entity_id: string, canonical_name: string>>
child 0, item: struct<entity_id: string, canonical_name: string>
child 0, entity_id: string
child 1, canonical_name: string
to
{'dataset': Value('string'), 'version': Value('string'), 'baseline_date': Value('timestamp[s]'), 'purpose': Value('string'), 'entities': List({'entity_id': Value('string'), 'canonical_name': Value('string')}), 'measurement_fields': List(Value('string')), 'records': List(Value('null')), 'status': Value('string'), 'rule': 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.
AIO CODE Entity Dataset
Overview
The AIO CODE Entity Dataset is a structured research dataset produced by the AIO CODE project. It records canonical entities, relationships, claims, observations, experiments, metrics, platform representations, and selected content-ecosystem records used to study how digital entities are represented and resolved across AI systems and search environments.
AIO CODE is a research project focused on the pipeline:
Indexation → Retrieval → Entity Resolution → Entity Representation → Citation → Recommendation
The dataset is designed as a structured downstream representation of selected machine-readable research artifacts. It is not a generative AI model, search engine, ranking system, or claim that any external system recognizes an entity correctly.
Source and synchronization
GitHub is the project's Source of Truth. This dataset is populated through a controlled export generated from the canonical AIO CODE repository.
GitHub Source of Truth
↓
Controlled Data Export
↓
Hugging Face Dataset
Only explicitly listed machine-readable sources are synchronized. The full research and documentation repository is not mirrored automatically.
Dataset contents
The controlled export currently includes records covering:
- canonical entity graph
- social/entity mapping
- claim ledger
- AI/social baseline records
- entity-resolution observations
- experiment definitions and records
- metric definitions
- entity content registries
- platform registries
- Spotify playlist registry for MC-001
Canonical entities currently represented include:
- MC-001 — Marii Cuadros — Person
- AIO-001 — AIO CODE — ResearchProject
- NUX-001 — NUX — DigitalCreativeEntity
Canonical entities remain distinct. Relationships connect entities but do not collapse their identities.
Evidence and research integrity
AIO CODE distinguishes between observed facts, interpretations, hypotheses, and verified findings. The dataset may contain records with different evidence states, including observed, corroborated, verified, hypothesized, and unknown.
An observation in this dataset should not be interpreted as proof of causality or as proof that an external AI system has established an entity identity.
Evidence strength determines claim strength.
Data governance
The export follows these principles:
- Preserve canonical entity IDs.
- Do not invent URLs or platform presence.
- Do not overwrite historical observations with later measurements.
- Do not treat planned records as observed facts.
- Do not treat platform presence as proof of AI recognition.
- Do not treat defined canonical claims as externally verified findings.
- Keep canonical entities distinct unless evidence supports a relationship.
- Keep historical observations separate from later measurements.
No secret, token, credential, or private data belongs in the export layer.
Intended use
This dataset is intended for research, documentation, structured analysis, entity-resolution experiments, and study of digital-entity representation across distributed information environments.
It should not be used as an authoritative source for claims about external search engines or AI systems without consulting the associated evidence and observation records.
Project architecture
AIO CODE uses GitHub as its versioned research Source of Truth and Hugging Face as a structured data distribution layer. Public documentation and other representations may exist in additional project components, but they are governed separately from this controlled export.
Versioning
The dataset is synchronized from the canonical GitHub repository through GitHub Actions. Changes to exported machine-readable sources can therefore produce new dataset revisions.
For methodology, provenance, schemas, and the complete research architecture, consult the AIO CODE Source of Truth repository.
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