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Datasets:
Axym-Labs
/
axobench

Tasks:
Time Series Forecasting
Tabular Regression
Tags:
neuroscience
computational-neuroscience
neuron-simulation
single-cell
benchmark
dendrites
License:
Dataset card Files
xet
 Community
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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
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 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 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

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 cover distinct evaluation settings:

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.

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.
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