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Error code: DatasetGenerationError
Exception: ValueError
Message: Expected object or value
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 290, in _generate_tables
pa_table = paj.read_json(
io.BytesIO(batch), read_options=paj.ReadOptions(block_size=block_size)
)
File "pyarrow/_json.pyx", line 342, in pyarrow._json.read_json
File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
return check_status(status)
File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
raise convert_status(status)
pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to string in row 0
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1816, in _prepare_split_single
for key, table in generator:
^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 613, in wrapped
for item in generator(*args, **kwargs):
~~~~~~~~~^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 304, in _generate_tables
batch = json_encode_fields_in_json_lines(original_batch, json_field_paths)
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 111, in json_encode_fields_in_json_lines
examples = [ujson_loads(line) for line in original_batch.splitlines()]
~~~~~~~~~~~^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/utils/json.py", line 20, in ujson_loads
return pd.io.json.ujson_loads(*args, **kwargs)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^
ValueError: Expected object or value
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1369, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
~~~~~~~~~~~~~~~~~~~~~~~~~^
builder, max_dataset_size_bytes=max_dataset_size_bytes
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 948, in stream_convert_to_parquet
builder._prepare_split(split_generator=splits_generators[split], file_format="parquet")
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1683, in _prepare_split
for job_id, done, content in self._prepare_split_single(
~~~~~~~~~~~~~~~~~~~~~~~~~~^
gen_kwargs=gen_kwargs, job_id=job_id, **_prepare_split_args
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
):
^
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
schema dict | shards list | version int64 |
|---|---|---|
{"context_index":"int16[trace]","inputs":"int8[trace,time,1278]","native_timestep_ms":1,"soma_mv":"f(...TRUNCATED) | [{"count":80,"file":"excitatory__specimen_479770916__i1000.npz","sample_ids":["specimen_479770916:po(...TRUNCATED) | 2 |
AxoBench Population
AxoBench Population contains population-context neuron traces for training and evaluating learned neuron surrogates under connected-network input distributions. Connected AxoSim rollouts supply branch-resolved input histories; role-correct biophysical teachers supply the target voltage and event traces.
Size and splits
| Split | Contexts | Traces | NPZ shards |
|---|---|---|---|
train |
80 | 40,000 | 480 |
val |
20 | 10,000 | 120 |
| total | 100 | 50,000 | 600 |
Contexts, rather than neighboring windows from one context, define the train/validation boundary. The corpus has exact 80/20 excitatory/inhibitory composition across six morphology conditions.
Shard schema
Each compressed NPZ shard contains:
inputs:int8[trace, time, 1278];targets:float32[trace, time, 2];soma_mv:float32[trace, time];context_index:int16[trace];target_is_inhibitory:bool[trace]; andtarget_role: excitatory or inhibitory metadata.
The native timestep is 1 ms. train/manifest.json and val/manifest.json
record shard counts, sample IDs, schema, and SHA-256 hashes.
Validation boundary
All 600 shards passed the release validator for schema, finite values, sample counts, role balance, manifest hashes, and disjoint context splits. This establishes package integrity and population-compatible provenance; it does not imply that one fixed training recipe will improve every connected population metric.
Loading
The Hugging Face Dataset Viewer does not natively expand these high-dimensional
NPZ tensors. Download the required shards with hf download or
huggingface_hub.snapshot_download, then load a shard with numpy.load.
from pathlib import Path
import numpy as np
shard = next(Path("train").glob("*.npz"))
with np.load(shard, allow_pickle=False) as data:
inputs = data["inputs"]
targets = data["targets"]
Related datasets
Axym-Labs/axobench: ordinary isolated-neuron benchmark traces.Axym-Labs/axobench-interventions: paired perturbation 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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