Datasets:

dwiest
/

axobench

Error code:   StreamingRowsError
Exception:    CastError
Message:      Couldn't cast
backend: string
coreneuron_gpu: bool
duration_ms: int64
elapsed_seconds: double
input_routing: struct<all_traces_identical: bool, defensibility_note: string, duplicated_source_sites_per_sign: str (... 446 chars omitted)
  child 0, all_traces_identical: bool
  child 1, defensibility_note: string
  child 2, duplicated_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 3, morphology_segments: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 4, n_traces: int64
  child 5, policy: string
  child 6, source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 7, status: string
  child 8, unique_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 9, unused_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
  child 10, used_sites_per_sign: struct<max: int64, mean: double, min: int64>
      child 0, max: int64
      child 1, mean: double
      child 2, min: int64
kind: string
morphology_id: string
morphology_path: string
samples: int64
shard_index: int64
shards: list<item: struct<count: int64, file: string, sample_ids: list<item: string>>>
  child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>>
      child 0, count: int64
      child 1, file: string
      child 2, sample_ids: list<item: string>
          child 0, item: string
version: int64
to
{'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string'))}), 'version': Value('int64')}
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 478, 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 2297, in cast_table_to_schema
                  raise CastError(
                  ...<3 lines>...
                  )
              datasets.table.CastError: Couldn't cast
              backend: string
              coreneuron_gpu: bool
              duration_ms: int64
              elapsed_seconds: double
              input_routing: struct<all_traces_identical: bool, defensibility_note: string, duplicated_source_sites_per_sign: str (... 446 chars omitted)
                child 0, all_traces_identical: bool
                child 1, defensibility_note: string
                child 2, duplicated_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 3, morphology_segments: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 4, n_traces: int64
                child 5, policy: string
                child 6, source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 7, status: string
                child 8, unique_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 9, unused_source_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
                child 10, used_sites_per_sign: struct<max: int64, mean: double, min: int64>
                    child 0, max: int64
                    child 1, mean: double
                    child 2, min: int64
              kind: string
              morphology_id: string
              morphology_path: string
              samples: int64
              shard_index: int64
              shards: list<item: struct<count: int64, file: string, sample_ids: list<item: string>>>
                child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>>
                    child 0, count: int64
                    child 1, file: string
                    child 2, sample_ids: list<item: string>
                        child 0, item: string
              version: int64
              to
              {'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string'))}), 'version': Value('int64')}
              because column names don't match

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AxoBench

This public working release contains AxoBench, the DendroBench v1 neuronio-like dataset generated with the CoreNEURON GPU path. It is intended for internal benchmark validation, diagnostic development, and paper preparation before a public release decision.

Layout

train/: 785 compressed NPZ shards, 100,000 traces.
val/: 100 compressed NPZ shards, 12,500 traces.
test/: 100 compressed NPZ shards, 12,500 traces.
private-test/: 100 compressed NPZ shards, 12,500 traces.
interventions/<name>/: paired baseline/intervention NPZ shards for event_dropout, exc_dropout, inh_dropout, site_silence, and temporal_jitter; each intervention contains 2,500 paired traces.

The ordinary trace shards use .npz files with arrays such as inputs, targets, and sample_ids. Intervention shards use paired arrays such as baseline_inputs, intervention_inputs, baseline_targets, and intervention_targets.

Compression

The payload is stored as shard-wise deflated NPZ files, matching the practical NeuronIO-style packaging while preserving partial downloads and resumable uploads. Do not wrap this folder in one monolithic zip or tar archive for the primary Hugging Face distribution: the shards are already compressed and an outer archive would make subset access worse.

See compression_report.json for measured compression ratios and checksums.sha256 for file integrity checks.

Metadata

dataset_manifest_template.json: intended dataset structure and benchmark framing.
generation_summary.json: generation backend, morphology set, sample counts, and runtime summary.
generation_completion_check.json: split/intervention completion summary.
dataset_package_manifest.json: upload/package-level summary.
compression_report.json: compression audit.
checksums.sha256: SHA-256 checksums for all packaged files except the checksum file itself.

Access State

This upload is public so collaborators and reviewers can access the packaged shards directly. The dataset card, license, private-test policy, and paper-facing framing should still be reviewed before a paper-facing release tag or DOI mirror.

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Total file size:

35 GB