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Cannot load the dataset split (in streaming mode) to extract the first rows.
Error code: StreamingRowsError
Exception: TypeError
Message: Couldn't cast array of type
struct<robotics-embodied: struct<label: string, industry: string, sub_niche: string>, ai-drug-discovery: struct<label: string, industry: string, sub_niche: string>, coding-agents: struct<label: string, industry: string, sub_niche: string>, inference-serving: struct<label: string, industry: string, sub_niche: string>, agent-frameworks: struct<label: string, industry: string, sub_niche: string>, post-training-alignment: struct<label: string, industry: string, sub_niche: string>, media-generation: struct<label: string, industry: string, sub_niche: string>, multimodal-foundation-models: struct<label: string, industry: string, sub_niche: string>>
to
{'robotics-embodied': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}, 'ai-drug-discovery': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}, 'ai-coding-agents': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}}
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 149, 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 129, 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 489, 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 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, 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 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2303, in cast_table_to_schema
cast_array_to_feature(
~~~~~~~~~~~~~~~~~~~~~^
table[name] if name in table_column_names else pa.array([None] * len(table), type=schema.field(name).type),
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
feature,
^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 1852, in wrapper
return pa.chunked_array([func(chunk, *args, **kwargs) for chunk in array.chunks])
~~~~^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2149, in cast_array_to_feature
raise TypeError(f"Couldn't cast array of type\n{_short_str(array.type)}\nto\n{_short_str(feature)}")
TypeError: Couldn't cast array of type
struct<robotics-embodied: struct<label: string, industry: string, sub_niche: string>, ai-drug-discovery: struct<label: string, industry: string, sub_niche: string>, coding-agents: struct<label: string, industry: string, sub_niche: string>, inference-serving: struct<label: string, industry: string, sub_niche: string>, agent-frameworks: struct<label: string, industry: string, sub_niche: string>, post-training-alignment: struct<label: string, industry: string, sub_niche: string>, media-generation: struct<label: string, industry: string, sub_niche: string>, multimodal-foundation-models: struct<label: string, industry: string, sub_niche: string>>
to
{'robotics-embodied': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}, 'ai-drug-discovery': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}, 'ai-coding-agents': {'label': Value('string'), 'industry': Value('string'), 'sub_niche': Value('string')}}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.
Evidaxis Momentum Snapshots
Weekly, hash-pinned, point-in-time measurements of rising open-source and research-native AI systems, scored by "momentum" (the rate of change of public signals) on a transparent, versioned public methodology. A system is recognized as rising only when two independent axes converge (development velocity + citation momentum). The archive is append-only: published snapshots are never rewritten.
- Canonical source: https://evidaxis.org (llms.txt: https://evidaxis.org/llms.txt)
- Methodology (versioned): https://evidaxis.org/methodology/current/
- Concept DOI: 10.5281/zenodo.21076012
- License: CC0-1.0 — reuse, redistribute, train on, cite freely.
- Cite a system:
urn:evidaxis:claim:{entity_id}:{methodology}:{snapshot_date}(per-record URNs are listed in each snapshot and on evidaxis.org).
Files
Per snapshot date YYYY-MM-DD/:
snapshot.json— full snapshot (entities, axes, statuses, counts) — person-free publication projection.entities.csv— flat convenience table (entity_id, name, cohort, status, momentum, axes z-scores, claim_urn).manifest.json,provenance.json— capture provenance.
Integrity
Every snapshot is content-addressed (snapshot_id = payload hash), anchored via
OpenTimestamps in the source repository, and byte-reproducible from public inputs.
The canonical bytes live in the public git archive; this dataset mirrors the
publication projection for convenient training/retrieval use.
Positive-only, person-free
The observatory measures systems, never people; it publishes no negative signals and no rankings of winners — measurements only, the reader sorts.
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