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The dataset generation failed because of a cast error
Error code: DatasetGenerationCastError
Exception: DatasetGenerationCastError
Message: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'name', 'low_cutoff', 'status_description', 'units', 'type', 'high_cutoff', 'status', 'sampling_frequency', 'description'}) and 11 missing columns ({'site', 'ilae_score', 'ethnicity', 'date_follow_up', 'hand', 'years_follow_up', 'age', 'participant_id', 'outcome', 'engel_score', 'sex'}).
This happened while the csv dataset builder was generating data using
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'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-umf004/ses-presurgery/sub-umf004_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-umf005/ses-presurgery/ieeg/sub-umf005_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-umf005/ses-presurgery/ieeg/sub-umf005_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-umf005/ses-presurgery/sub-umf005_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/ieeg/sub-ummc001_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc001/ses-presurgery/sub-ummc001_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/ieeg/sub-ummc002_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc002/ses-presurgery/sub-ummc002_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/ieeg/sub-ummc003_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc003/ses-presurgery/sub-ummc003_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/ieeg/sub-ummc004_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc004/ses-presurgery/sub-ummc004_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc005/ses-presurgery/ieeg/sub-ummc005_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc005/ses-presurgery/ieeg/sub-ummc005_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc005/ses-presurgery/ieeg/sub-ummc005_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc005/ses-presurgery/ieeg/sub-ummc005_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc005/ses-presurgery/sub-ummc005_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/ieeg/sub-ummc006_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc006/ses-presurgery/sub-ummc006_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/ieeg/sub-ummc007_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc007/ses-presurgery/sub-ummc007_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/ieeg/sub-ummc008_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc008/ses-presurgery/sub-ummc008_ses-presurgery_scans.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/sub-ummc009_ses-presurgery_scans.tsv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/builder.py", line 1856, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 811, in write_table
self._write_table(pa_table, writer_batch_size=writer_batch_size)
~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 822, in _write_table
pa_table = table_cast(pa_table, self._schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2381, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2309, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
name: string
type: string
units: double
low_cutoff: double
high_cutoff: double
description: string
sampling_frequency: double
status: string
status_description: string
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1357
to
{'participant_id': Value('string'), 'age': Value('float64'), 'sex': Value('string'), 'hand': Value('string'), 'outcome': Value('string'), 'engel_score': Value('float64'), 'ilae_score': Value('float64'), 'date_follow_up': Value('string'), 'ethnicity': Value('float64'), 'years_follow_up': Value('float64'), 'site': Value('string')}
because column names don't match
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1379, 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 956, 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 1702, 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 1858, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
...<4 lines>...
)
datasets.exceptions.DatasetGenerationCastError: An error occurred while generating the dataset
All the data files must have the same columns, but at some point there are 9 new columns ({'name', 'low_cutoff', 'status_description', 'units', 'type', 'high_cutoff', 'status', 'sampling_frequency', 'description'}) and 11 missing columns ({'site', 'ilae_score', 'ethnicity', 'date_follow_up', 'hand', 'years_follow_up', 'age', 'participant_id', 'outcome', 'engel_score', 'sex'}).
This happened while the csv dataset builder was generating data using
hf://datasets/EEGDash/ds003029/sub-jh101/ses-presurgery/ieeg/sub-jh101_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv (at revision 13686c6f7a51d7822bbe377746f150278fd85caa), ['hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/participants.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-jh101/ses-presurgery/ieeg/sub-jh101_ses-presurgery_task-ictal_acq-ecog_run-01_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-jh101/ses-presurgery/ieeg/sub-jh101_ses-presurgery_task-ictal_acq-ecog_run-01_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-jh101/ses-presurgery/ieeg/sub-jh101_ses-presurgery_task-ictal_acq-ecog_run-02_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-jh101/ses-presurgery/ieeg/sub-jh101_ses-presurgery_task-ictal_acq-ecog_run-02_events.tsv', 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'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-03_channels.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/ieeg/sub-ummc009_ses-presurgery_task-ictal_acq-ecog_run-03_events.tsv', 'hf://datasets/EEGDash/ds003029@13686c6f7a51d7822bbe377746f150278fd85caa/sub-ummc009/ses-presurgery/sub-ummc009_ses-presurgery_scans.tsv']
Please either edit the data files to have matching columns, or separate them into different configurations (see docs at https://hf.co/docs/hub/datasets-manual-configuration#multiple-configurations)Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
participant_id string | age null | sex null | hand null | outcome string | engel_score float64 | ilae_score float64 | date_follow_up null | ethnicity null | years_follow_up float64 | site string |
|---|---|---|---|---|---|---|---|---|---|---|
sub-jh101 | null | null | null | F | 4 | 6 | null | null | 1 | JHH |
sub-jh102 | null | null | null | NR | -1 | -1 | null | null | 1 | JHH |
sub-jh103 | null | null | null | F | 4 | 6 | null | null | 1 | JHH |
sub-jh104 | null | null | null | NR | -1 | -1 | null | null | 1 | JHH |
sub-jh105 | null | null | null | S | 1 | 1 | null | null | 1 | JHH |
sub-jh106 | null | null | null | NR | -1 | -1 | null | null | 1 | JHH |
sub-jh108 | null | null | null | F | 4 | 6 | null | null | 1 | JHH |
sub-pt01 | null | null | null | S | 1 | 2 | null | null | 3 | NIH |
sub-pt10 | null | null | null | F | 2 | 3 | null | null | 1 | NIH |
sub-pt11 | null | null | null | S | 1 | 1 | null | null | 1 | NIH |
sub-pt12 | null | null | null | F | 2 | 4 | null | null | 5 | NIH |
sub-pt13 | null | null | null | S | 1 | 2 | null | null | 2 | NIH |
sub-pt14 | null | null | null | F | 4 | 4 | null | null | 1 | NIH |
sub-pt15 | null | null | null | S | 1 | 1 | null | null | 2 | NIH |
sub-pt16 | null | null | null | S | 1 | 1 | null | null | 2 | NIH |
sub-pt17 | null | null | null | S | 1 | 1 | null | null | 4 | NIH |
sub-pt2 | null | null | null | S | 1 | 1 | null | null | 3 | NIH |
sub-pt3 | null | null | null | S | 1 | 1 | null | null | 2 | NIH |
sub-pt6 | null | null | null | F | 2 | 5 | null | null | 3 | NIH |
sub-pt7 | null | null | null | F | 3 | 1 | null | null | 7 | NIH |
sub-pt8 | null | null | null | S | 1 | 1 | null | null | 2 | NIH |
sub-umf001 | null | null | null | S | 1 | 1 | null | null | 1 | UMF |
sub-umf002 | null | null | null | F | 2 | 1 | null | null | 1 | UMF |
sub-umf003 | null | null | null | F | 3 | 4 | null | null | 1 | UMF |
sub-umf004 | null | null | null | S | 1 | 1 | null | null | 1 | UMF |
sub-umf005 | null | null | null | S | 1 | 1 | null | null | 1 | UMF |
sub-ummc001 | null | null | null | NR | -1 | -1 | null | null | 7 | UMMC |
sub-ummc002 | null | null | null | S | 1 | 1 | null | null | 4 | UMMC |
sub-ummc003 | null | null | null | S | 1 | 1 | null | null | 1 | UMMC |
sub-ummc004 | null | null | null | S | 1 | 1 | null | null | 4 | UMMC |
sub-ummc005 | null | null | null | S | 1 | 1 | null | null | 1 | UMMC |
sub-ummc006 | null | null | null | S | 1 | 2 | null | null | 4 | UMMC |
sub-ummc007 | null | null | null | NR | -1 | -1 | null | null | 2 | UMMC |
sub-ummc008 | null | null | null | S | 1 | 1 | null | null | 1 | UMMC |
sub-ummc009 | null | null | null | S | 1 | 1 | null | null | 1 | UMMC |
null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null |
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null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null | null |
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Epilepsy-iEEG-Multicenter-Dataset
This repository hosts the complete scientific files of NEMAR on003029,
snapshot v1.0.0. It includes every subject and recording, the
BIDS sidecars, and the source's sourcedata/ and code/ files where present.
All source files were verified against NEMAR's manifest checksums before upload.
Only DataLad control files and source .gitattributes files are omitted because
Hugging Face manages repository storage. The original README is preserved as
SOURCE_README.md.
A multicenter collection of intracranial EEG (ECoG and SEEG) recordings from epilepsy patients at four clinical centers (NIH, JHH, UMMC, UMF), organized in BIDS. Recordings are snapshots around seizure events and include clinician annotations of seizure onset and offset, seizure onset zone, and resected or ablated contacts. The data support research on localizing the epileptogenic zone, including neural fragility analysis.
Load with the BIDS builder
These examples require datasets PR #9011 until its BIDS builder is released:
pip install "datasets[bids] @ git+https://github.com/bruAristimunha/datasets@feat/bids-biosignal"
from datasets import load_dataset
dataset = load_dataset("EEGDash/ds003029", split="train", subjects="jh101")
row = dataset[0]
recording = row["biosignal"]
print(recording)
samples = recording.get_data(start=0, stop=100)
print(row["metadata"])
There are 35 subjects and 106 recordings in the complete mirror. The example may select one subject; all subjects are stored here. Accessing a recording caches its subject/session/datatype folder and the repository’s JSON/TSV metadata. Other runs in that folder may also be downloaded. MNE then opens the cached recording lazily.
Source and attribution
- Source: https://nemar.org/dataset/on003029
- Snapshot manifest: https://data.nemar.org/on003029/v1.0.0/manifest.json
- Original authors, license and citations: source README and source metadata.
- Machine-readable checksums: nemar_manifest.json.
- Mirror version and omitted control files: mirror.json.
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