Datasets:

Axym-Labs
/

axobench-v1

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>, sha256: string>>
  child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>, sha256: string>
      child 0, count: int64
      child 1, file: string
      child 2, sample_ids: list<item: string>
          child 0, item: string
      child 3, sha256: string
schema: struct<context_index: string, inputs: string, native_timestep_ms: int64, soma_mv: string, target_is_ (... 57 chars omitted)
  child 0, context_index: string
  child 1, inputs: string
  child 2, native_timestep_ms: int64
  child 3, soma_mv: string
  child 4, target_is_inhibitory: string
  child 5, target_role: string
  child 6, targets: string
version: int64
to
{'schema': {'context_index': Value('string'), 'inputs': Value('string'), 'native_timestep_ms': Value('int64'), 'soma_mv': Value('string'), 'target_is_inhibitory': Value('string'), 'target_role': Value('string'), 'targets': Value('string')}, 'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string')), 'sha256': 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 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
              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>, sha256: string>>
                child 0, item: struct<count: int64, file: string, sample_ids: list<item: string>, sha256: string>
                    child 0, count: int64
                    child 1, file: string
                    child 2, sample_ids: list<item: string>
                        child 0, item: string
                    child 3, sha256: string
              schema: struct<context_index: string, inputs: string, native_timestep_ms: int64, soma_mv: string, target_is_ (... 57 chars omitted)
                child 0, context_index: string
                child 1, inputs: string
                child 2, native_timestep_ms: int64
                child 3, soma_mv: string
                child 4, target_is_inhibitory: string
                child 5, target_role: string
                child 6, targets: string
              version: int64
              to
              {'schema': {'context_index': Value('string'), 'inputs': Value('string'), 'native_timestep_ms': Value('int64'), 'soma_mv': Value('string'), 'target_is_inhibitory': Value('string'), 'target_role': Value('string'), 'targets': Value('string')}, 'shards': List({'count': Value('int64'), 'file': Value('string'), 'sample_ids': List(Value('string')), 'sha256': Value('string')}), 'version': Value('int64')}
              because column names don't match

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AxoBench

Deprecated. This is AxoBench v1, kept for reproducibility of earlier results. Use the current release, Axym-Labs/axobench (AxoBench v2).

AxoBench is a large-scale CoreNEURON corpus for training and evaluating data-driven models of layer-5 pyramidal neurons. It contains 125,000 six-second traces across multiple reconstructed morphologies, with event inputs and paired somatic-voltage/spike targets.

Two companion datasets live in subfolders of this repository:

population/ contains population-compatible shards and connectivity context (formerly Axym-Labs/axobench-v1-population).
interventions/ contains paired causal interventions (formerly Axym-Labs/axobench-v1-interventions).

Each subfolder has its own README.md, manifest and checksums.sha256.

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.
population/: the v1 population dataset.
interventions/: the v1 intervention dataset.

Each compressed NPZ shard contains arrays including inputs, targets, and sample_ids; the adjacent JSON file records shard-level provenance and metadata.

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 checksums.sha256 for file integrity checks.

Metadata

dataset_package_manifest.json: split counts, sizes, and packaging details.
checksums.sha256: SHA-256 checksums for all trace and shard-metadata files.

Access State

The public test split supports reproducible comparison. An unreleased confirmation set is held separately and is not part of this Hub repository.

Downloads last month: 475

Total file size:

34 GB