Datasets:

Axym-Labs
/

axobench-interventions

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
pair_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
{'pair_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
              pair_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
              {'pair_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 Interventions

AxoBench Interventions is the paired perturbation part of AxoBench, released separately so intervention studies do not need to download the ordinary single-neuron benchmark corpus. Every example contains matched baseline and intervention inputs and teacher outputs.

Conditions

Condition	Paired traces	NPZ shards
`event_dropout`	2,500	40
`exc_dropout`	2,500	40
`inh_dropout`	2,500	40
`site_silence`	2,500	40
`temporal_jitter`	2,500	40
total	12,500	200

Each condition directory also contains 40 JSON sidecars and one manifest.

Shard schema

Compressed NPZ shards contain paired arrays including baseline_inputs, intervention_inputs, baseline_targets, and intervention_targets. The condition manifests provide sample IDs and shard counts; checksums.sha256 covers the complete release package.

Loading

The Hugging Face Dataset Viewer does not natively expand these high-dimensional NPZ tensors. Download a condition or the full repository and load shards with NumPy:

from pathlib import Path

import numpy as np

shard = next(Path("event_dropout").glob("*.npz"))
with np.load(shard, allow_pickle=False) as data:
    baseline = data["baseline_inputs"]
    intervention = data["intervention_inputs"]

Related datasets

Axym-Labs/axobench: ordinary isolated-neuron benchmark traces.
Axym-Labs/axobench-population: connected population-context traces.

The files are provided for research use. Users are responsible for checking the terms of the upstream simulator, morphology, and teacher-model resources used in their application.

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