The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 81, in _split_generators
first_examples = list(islice(pipeline, self.NUM_EXAMPLES_FOR_FEATURES_INFERENCE))
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 32, in _get_pipeline_from_tar
fs: fsspec.AbstractFileSystem = fsspec.filesystem("memory")
~~~~~~~~~~~~~~~~~^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 302, in filesystem
cls = get_filesystem_class(protocol)
File "/usr/local/lib/python3.14/site-packages/fsspec/registry.py", line 239, in get_filesystem_class
raise ValueError(f"Protocol not known: {protocol}")
ValueError: Protocol not known: memory
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/split_names.py", line 71, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.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.
EEG-fMRI ds006040 standardized derivatives
This public development dataset is the persistent, versioned data layer for an
external NeuroBOLT replication on OpenNeuro ds006040.
The repository does not mirror the complete OpenNeuro dataset. OpenNeuro is the authoritative raw-data source. This repository records immutable source identifiers and checksums and will contain only standardized run-level EEG, DiFuMo BOLD features, synchronization evidence, QC records and preprocessing provenance.
Current status
The faithful v0.2 multistate pilot and its paired 15 Hz artifact-guard v0.3
branch are complete: 48 deterministic packages represent 24 runs from four
participants and six conditions. The frozen split
contains 12 train runs (sub-001, sub-005), six validation runs (sub-010)
and six test runs (sub-019). Together they provide 4,502 eligible BOLD target
volumes. Each faithful run has one guard companion with identical BOLD targets,
timebase and target indices. These are preprocessing-validation samples, not a
model-performance or clinical result.
All 24 paired audits passed. Relative to faithful EEG, the guard branch changed 1-10 Hz power by only -0.0220 to -0.0153 dB while attenuating 20-90 Hz power by 130.41 to 142.65 dB. This confirms construction of the sensitivity branch; it does not yet show whether NeuroBOLT performance depends on those frequencies.
The original v0.1 checkerboard manifest is preserved under
manifests/legacy_v0-1/; its old shard and index remain for audit but are not
training-eligible. The canonical checkerboard manifest now describes v0.2.
Each run package contains 200 Hz scalp EEG, causal 16 s EEG window indices, DiFuMo-64 BOLD targets with and without global-signal regression, a NeuroBOLT-faithful target, atlas labels, events, synchronization evidence, source hashes and complete preprocessing provenance.
Version 0.2 and v0.3 store original target-volume indices, hash all nine BIDS
sources including the EEGLAB .fdt payload, excludes explicitly marked
non-steady-state targets without motion scrubbing, and drops rather than pads
targets that lack a complete 16-second causal EEG history. All 48 packages were
validated locally and then anonymously re-downloaded at their upload revision.
Intended schema
index/*.parquet: faithful run indices;index/artifact_guard_15hz/runs/*.parquet: paired guard indices;shards/{train,validation,test}/*.tar: faithful shards;shards/artifact_guard_15hz/*/*.tar: paired guard shards;manifests/openneuro/*.json: versioned raw-source paths and hashes;manifests/validation/*.json: modality and synchronization QC;docs/storage_contract.md: lifecycle, versioning and privacy rules.docs/preprocessing_and_sync_protocol.md: frozen signal and alignment rules.
Overlapping EEG contexts are constructed by the training loader after complete participant/run splits are assigned. They are not stored as duplicated samples.
Source and license
- Source dataset: OpenNeuro
ds006040, snapshot1.0.1 - Pinned source commit:
fcba697fd4225ffbd735d593d1b7946663e178b4 - Source license: CC0
Future clinical or controlled-access data are outside this license and must not be uploaded without explicit consent, IRB and data-use authorization.
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