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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 12 new columns ({'significant', 'kind', 'p_perm', 'z', 'rsa', 'control', 'ceiling', 'frac_of_ceiling', 'p_holm', 'variant', 'null_mean', 'null_sd'}) and 6 missing columns ({'ceiling_lower', 'n_subjects_rdm', 'ceiling_lower_sem', 'ceiling_upper', 'ceiling_n', 'file'}).
This happened while the csv dataset builder was generating data using
hf://datasets/BrainAlign/brain-lm-alignment-ds006239/control/control_by_cell.csv (at revision c4b9198475ef76716a026459b15f74acca6ae155), ['hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/ceilings_ds006239.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/control_by_cell.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/control_summary.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/rdm_dimensionality.csv']
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 1848, in _prepare_split_single
writer.write_table(table)
~~~~~~~~~~~~~~~~~~^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/arrow_writer.py", line 765, 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 773, in _write_table
pa_table = table_cast(pa_table, self._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
variant: string
task: string
session: string
control: string
n_stim: int64
ceiling: double
frac_of_ceiling: double
within_run_normalized: bool
rsa: double
p_perm: double
null_mean: double
null_sd: double
z: double
kind: string
p_holm: double
significant: bool
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2114
to
{'task': Value('string'), 'session': Value('string'), 'file': Value('string'), 'within_run_normalized': Value('bool'), 'n_stim': Value('int64'), 'n_subjects_rdm': Value('int64'), 'ceiling_lower': Value('float64'), 'ceiling_upper': Value('float64'), 'ceiling_lower_sem': Value('float64'), 'ceiling_n': Value('int64')}
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 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 1694, 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 1850, 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 12 new columns ({'significant', 'kind', 'p_perm', 'z', 'rsa', 'control', 'ceiling', 'frac_of_ceiling', 'p_holm', 'variant', 'null_mean', 'null_sd'}) and 6 missing columns ({'ceiling_lower', 'n_subjects_rdm', 'ceiling_lower_sem', 'ceiling_upper', 'ceiling_n', 'file'}).
This happened while the csv dataset builder was generating data using
hf://datasets/BrainAlign/brain-lm-alignment-ds006239/control/control_by_cell.csv (at revision c4b9198475ef76716a026459b15f74acca6ae155), ['hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/ceilings_ds006239.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/control_by_cell.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/control_summary.csv', 'hf://datasets/BrainAlign/brain-lm-alignment-ds006239@c4b9198475ef76716a026459b15f74acca6ae155/control/rdm_dimensionality.csv']
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.
task string | session string | file string | within_run_normalized bool | n_stim int64 | n_subjects_rdm int64 | ceiling_lower float64 | ceiling_upper float64 | ceiling_lower_sem float64 | ceiling_n int64 |
|---|---|---|---|---|---|---|---|---|---|
Orth | ses-11+ | data/processed/fmri_wrn/ds006239/Orth/session_rdm_ses-11+.npz | true | 96 | 22 | 0.562195 | 0.61485 | 0.012346 | 22 |
Orth | ses-11 | data/processed/fmri_wrn/ds006239/Orth/session_rdm_ses-11.npz | true | 96 | 18 | 0.522525 | 0.589811 | 0.016597 | 18 |
Phon | ses-11+ | data/processed/fmri_wrn/ds006239/Phon/session_rdm_ses-11+.npz | true | 96 | 22 | 0.562195 | 0.61485 | 0.012346 | 22 |
Phon | ses-11 | data/processed/fmri_wrn/ds006239/Phon/session_rdm_ses-11.npz | true | 96 | 18 | 0.522525 | 0.589811 | 0.016597 | 18 |
Sem | ses-11+ | data/processed/fmri_wrn/ds006239/Sem/session_rdm_ses-11+.npz | true | 48 | 22 | 0.356634 | 0.438561 | 0.013141 | 22 |
Sem | ses-11 | data/processed/fmri_wrn/ds006239/Sem/session_rdm_ses-11.npz | true | 48 | 18 | 0.289344 | 0.394831 | 0.011896 | 18 |
SemLocal | ses-11+ | data/processed/fmri_wrn/ds006239/SemLocal/session_rdm_ses-11+.npz | true | 48 | 23 | 0.305571 | 0.38963 | 0.019058 | 23 |
SemLocal | ses-11 | data/processed/fmri_wrn/ds006239/SemLocal/session_rdm_ses-11.npz | true | 48 | 15 | 0.230569 | 0.364206 | 0.01661 | 15 |
Orth | ses-11 | null | true | 96 | null | null | null | null | null |
Orth | ses-11+ | null | true | 96 | null | null | null | null | null |
Phon | ses-11 | null | true | 96 | null | null | null | null | null |
Phon | ses-11+ | null | true | 96 | null | null | null | null | null |
Sem | ses-11 | null | true | 48 | null | null | null | null | null |
Sem | ses-11+ | null | true | 48 | null | null | null | null | null |
SemLocal | ses-11 | null | true | 48 | null | null | null | null | null |
SemLocal | ses-11+ | null | true | 48 | null | null | null | null | null |
null | null | null | null | null | null | null | null | null | null |
Orth | ses-11 | null | null | 96 | null | null | null | null | null |
Orth | ses-11+ | null | null | 96 | null | null | null | null | null |
Phon | ses-11 | null | null | 96 | null | null | null | null | null |
Phon | ses-11+ | null | null | 96 | null | null | null | null | null |
Sem | ses-11 | null | null | 48 | null | null | null | null | null |
Sem | ses-11+ | null | null | 48 | null | null | null | null | null |
SemLocal | ses-11 | null | null | 48 | null | null | null | null | null |
SemLocal | ses-11+ | null | null | 48 | null | null | null | null | null |
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Brain–language-model alignment: ds006239
Wang et al. 2025 — word-level phonological and semantic reading tasks in children and adolescents aged 10–17.
- Paper: https://www.sciencedirect.com/science/article/pii/S2352340925009692
- Data: https://openneuro.org/datasets/ds006239/versions/1.0.5
- Generated: 2026-08-28
- Pipeline: https://github.com/suchirsalhan/cdl-representations-brains-babylms
Read this first: does the measurement work?
Every alignment number in this dataset is only as meaningful as the brain RDMs it was computed against. So before any model result, the same pipeline is asked whether anything stimulus-driven correlates with those RDMs — stimulus duration, intensity, word length, frequency, phoneme and syllable counts, an acoustic model of the audio where the stimuli are audio, and the study's own condition contrast — each tested by a permutation test that shuffles stimulus identity.
GATE: FAILED. 0/8 stimulus tests are significant after Holm correction — not the acoustic model of the audio the children actually heard, not the study's own experimental contrast.
The alignment numbers below are therefore uninterpretable as evidence about language models. They measure a representational geometry that does not demonstrably encode the stimuli. They are published for completeness and for whoever fixes the estimator, not as a result. Do not cite them as evidence that models fail to align with the developing brain.
Measured cause, from control/:
- RDM effective rank: 52 of 72 stimuli
Note that this is NOT ds003604's failure mode. There, the RDM
effective rank was ~3 of 40-48 stimuli -- near-degenerate betas
that could not express stimulus-level structure at all. The rank
recorded above is a large fraction of the stimulus count, so these
RDMs do carry stimulus structure and the control failing here means
the specific controls tested did not reach significance, not that
the measurement is uninterpretable. Check control/ for which
controls ran: an acoustic or visual control needs the dataset's
stimulus files present, and reports zero features if they are not.
What was built
8 task × session cells, each an RDM over the stimuli shared by that cell's subjects, with voxel patterns z-scored within run before aggregation (without that, the RDM measures scanner drift rather than language) and an inter-subject noise ceiling.
| task | session | n_stim | ceiling_lower | ceiling_upper | ceiling_n |
|---|---|---|---|---|---|
| Orth | ses-11+ | 96 | 0.562195 | 0.61485 | 22 |
| Orth | ses-11 | 96 | 0.522525 | 0.589811 | 18 |
| Phon | ses-11+ | 96 | 0.562195 | 0.61485 | 22 |
| Phon | ses-11 | 96 | 0.522525 | 0.589811 | 18 |
| Sem | ses-11+ | 48 | 0.356634 | 0.438561 | 22 |
| Sem | ses-11 | 48 | 0.289344 | 0.394831 | 18 |
| SemLocal | ses-11+ | 48 | 0.305571 | 0.38963 | 23 |
| SemLocal | ses-11 | 48 | 0.230569 | 0.364206 | 15 |
Dataset-specific notes
Contains LocalSem, the only genuinely run/stimulus-CROSSED language cell across all four datasets in this project: its stimuli recur across runs, so run identity and stimulus identity are separable and the scanner-run confound that invalidated the first ds003604 analysis cannot arise. Per-subject age is NOT recoverable from the release — participants.tsv has birthdate but no scan date and there are no *_scans.tsv files — so this dataset is cohort-level only and cannot carry the developmental axis as published.
Files
| path | what |
|---|---|
alignment_by_checkpoint.csv |
every model × checkpoint × cell, with ceiling |
alignment_by_family.csv |
per family, with equivalence tests |
alignment_by_cell.csv |
per task × session |
ceilings_*.csv |
noise ceiling per cell |
control/ |
the positive control and RDM dimensionality — the gate |
scale_ladder.csv |
the Pythia 70M→1.4B scale test |
fig_*.pdf, fig_*.png |
figures |
Method
Representational similarity analysis. For each cell, a brain RDM over stimuli (correlation distance between per-stimulus GLM beta patterns, within-run z-scored, aggregated across subjects) is compared by Spearman correlation with a model RDM over the same stimuli, taken from each checkpoint's hidden states. Alignment is reported raw and as a fraction of the inter-subject noise ceiling, and judged against a null built from the PARC suite — 18 models differing only by random seed, which is what 'no effect' looks like on this measurement.
Null and fixation trials are excluded from the stimulus set. For paired designs the stimulus identity is the pair, not either word alone.
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