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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 769 new columns ({'ds004743', 'ds004406', 'ds002000', 'ds004024', 'ds001232', 'ds002116', 'ds004920', 'ds000220', 'ds004715', 'ds004109', 'ds000216', 'ds004624', 'ds000221', 'ds004837', 'ds003103', 'ds003454', 'ds004910', 'ds004007', 'ds003508', 'ds001345', 'ds003688', 'ds005027', 'ds001534', 'ds002620', 'ds003798', 'ds005357', 'ds000008', 'ds003848', 'ds004648', 'ds001775', 'ds002154', 'ds003763', 'ds004725', 'ds003382', 'ds004730', 'ds005088', 'ds004144', 'ds002411', 'ds002169', 'ds002750', 'ds000105', 'ds004797', 'ds004450', 'ds003438', 'ds003823', 'ds001297', 'ds003965', 'ds004650', 'ds003545', 'ds004632', 'ds002293', 'ds005479', 'ds005230', 'ds005038', 'ds000206', 'ds002940', 'ds001796', 'ds004589', 'ds003357', 'ds001145', 'ds000011', 'ds002513', 'ds004332', 'ds005375', 'ds003927', 'ds004101', 'ds003714', 'ds003758', 'ds002551', 'ds005239', 'ds003007', 'ds002366', 'ds005009', 'ds001751', 'ds004564', 'ds005492', 'ds003345', 'ds003717', 'ds004484', 'ds003338', 'ds000171', 'ds002001', 'ds004562', 'ds002550', 'ds002549', 'ds002790', 'ds004086', 'ds003642', 'ds004776', 'ds002813', 'ds002614', 'ds003434', 'ds002748', 'ds000170', 'ds002814', 'ds003550', 'ds003059', 'ds002105', 'ds001635', 'ds004783', 'ds001607', 'ds002382', 'ds002898', 'ds005143', 'ds000017', 'ds003145', 'ds002156', 'ds004616', 'ds002345', 'ds003711', 'ds004693', 'ds001705', 'ds004647', 'ds005050', 'ds000214', 'ds001747', 'ds005072', 'ds002715', 'ds002878', 'ds005123', 'ds004645', 'ds003633', 'ds000052', 'ds004513', 'ds002338',
...
'ds004775', 'ds001894', 'ds004471', 'ds000117', 'ds002608', 'ds000254', 'ds000255', 'ds004230', 'ds003089', 'ds003466', 'ds000053', 'ds000164', 'ds000149', 'ds004346', 'ds002835', 'ds002886', 'ds005134', 'ds001499', 'ds002013', 'ds000236', 'ds000231', 'ds004597', 'ds004718', 'ds002155', 'ds004814', 'ds002785', 'ds004698', 'ds001554', 'ds003216', 'ds003082', 'ds003076', 'ds003465', 'ds004386', 'ds005047', 'ds005365', 'ds005233', 'ds004516', 'ds005139', 'ds003495', 'ds004631', 'ds002016', 'ds005496', 'ds004656', 'ds001386', 'ds003988', 'ds003612', 'ds003392', 'ds001725', 'ds003778', 'ds003078', 'ds002522', 'ds003417', 'ds004283', 'ds004786', 'ds003126', 'ds005003', 'ds000240', 'ds004720', 'ds002385', 'ds002168', 'ds001486', 'ds004815', 'ds003464', 'ds004393', 'ds004958', 'ds004765', 'ds003524', 'ds004021', 'ds004644', 'ds003877', 'ds001417', 'ds003481', 'ds004456', 'ds004746', 'ds003972', 'ds005427', 'ds004636', 'ds004212', 'ds002675', 'ds004489', 'ds004889', 'ds002938', 'ds004475', 'ds002328', 'ds004215', 'ds003701', 'ds003791', 'ds004440', 'ds003440', 'ds004285', 'ds004455', 'ds005299', 'ds005464', 'ds003949', 'ds004894', 'ds005412', 'ds002739', 'ds003090', 'ds002702', 'ds003836', 'ds004141', 'ds004956', 'ds003574', 'ds004791', 'ds000203', 'ds004169', 'ds002606', 'ds005069', 'ds004808', 'ds003799', 'ds005266', 'ds004533', 'ds004663', 'ds002672', 'ds004134', 'ds004009', 'ds003043', 'ds003104', 'ds004542', 'ds005127', 'ds004065', 'ds001595', 'ds004946', 'ds000005', 'ds002896'}) and 6 missing columns ({'metadata', 'id', 'stars', 'name', 'followers', 'public'}).
This happened while the json dataset builder was generating data using
hf://datasets/nev/all-brains-online/all_openneuros/openneuro_mri_dataset_file_list.json (at revision 13a881bca0e28fac32793b7ef1e39f65a9811e77)
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 "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1869, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 580, in write_table
pa_table = table_cast(pa_table, self._schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2292, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2240, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
ds000001: struct<latestSnapshot: struct<files: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>, downloadFiles: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>, readme: string, hexsha: string, id: string, issues: list<item: struct<severity: string, reason: string, key: string, helpUrl: string, additionalFileCount: int64, code: int64>>, tag: string>>
child 0, latestSnapshot: struct<files: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>, downloadFiles: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>, readme: string, hexsha: string, id: string, issues: list<item: struct<severity: string, reason: string, key: string, helpUrl: string, additionalFileCount: int64, code: int64>>, tag: string>
child 0, files: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>
child 0, item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>
child 0, urls: list<item: string>
child 0, item: string
child 1, size: int64
child 2, key: string
...
child 1, size: int64
child 2, key: string
child 3, id: string
child 4, filename: string
child 5, directory: bool
child 6, annexed: bool
child 1, downloadFiles: list<item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>>
child 0, item: struct<urls: list<item: string>, size: int64, key: string, id: string, filename: string, directory: bool, annexed: bool>
child 0, urls: list<item: string>
child 0, item: string
child 1, size: int64
child 2, key: string
child 3, id: string
child 4, filename: string
child 5, directory: bool
child 6, annexed: bool
child 2, readme: string
child 3, hexsha: string
child 4, id: string
child 5, issues: list<item: struct<severity: string, reason: string, key: string, helpUrl: string, additionalFileCount: int64, code: int64>>
child 0, item: struct<severity: string, reason: string, key: string, helpUrl: string, additionalFileCount: int64, code: int64>
child 0, severity: string
child 1, reason: string
child 2, key: string
child 3, helpUrl: string
child 4, additionalFileCount: int64
child 5, code: int64
child 6, tag: string
ds005497: null
ds005503: null
to
{'name': Value(dtype='string', id=None), 'metadata': {'affirmedConsent': Value(dtype='bool', id=None), 'affirmedDefaced': Value(dtype='bool', id=None), 'dxStatus': Value(dtype='string', id=None)}, 'id': Value(dtype='string', id=None), 'public': Value(dtype='bool', id=None), 'stars': [{'userId': Value(dtype='string', id=None)}], 'followers': [{'userId': Value(dtype='string', id=None)}]}
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 1387, in compute_config_parquet_and_info_response
parquet_operations, partial, estimated_dataset_info = stream_convert_to_parquet(
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in stream_convert_to_parquet
builder._prepare_split(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1740, in _prepare_split
for job_id, done, content in self._prepare_split_single(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1871, in _prepare_split_single
raise DatasetGenerationCastError.from_cast_error(
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 769 new columns ({'ds004743', 'ds004406', 'ds002000', 'ds004024', 'ds001232', 'ds002116', 'ds004920', 'ds000220', 'ds004715', 'ds004109', 'ds000216', 'ds004624', 'ds000221', 'ds004837', 'ds003103', 'ds003454', 'ds004910', 'ds004007', 'ds003508', 'ds001345', 'ds003688', 'ds005027', 'ds001534', 'ds002620', 'ds003798', 'ds005357', 'ds000008', 'ds003848', 'ds004648', 'ds001775', 'ds002154', 'ds003763', 'ds004725', 'ds003382', 'ds004730', 'ds005088', 'ds004144', 'ds002411', 'ds002169', 'ds002750', 'ds000105', 'ds004797', 'ds004450', 'ds003438', 'ds003823', 'ds001297', 'ds003965', 'ds004650', 'ds003545', 'ds004632', 'ds002293', 'ds005479', 'ds005230', 'ds005038', 'ds000206', 'ds002940', 'ds001796', 'ds004589', 'ds003357', 'ds001145', 'ds000011', 'ds002513', 'ds004332', 'ds005375', 'ds003927', 'ds004101', 'ds003714', 'ds003758', 'ds002551', 'ds005239', 'ds003007', 'ds002366', 'ds005009', 'ds001751', 'ds004564', 'ds005492', 'ds003345', 'ds003717', 'ds004484', 'ds003338', 'ds000171', 'ds002001', 'ds004562', 'ds002550', 'ds002549', 'ds002790', 'ds004086', 'ds003642', 'ds004776', 'ds002813', 'ds002614', 'ds003434', 'ds002748', 'ds000170', 'ds002814', 'ds003550', 'ds003059', 'ds002105', 'ds001635', 'ds004783', 'ds001607', 'ds002382', 'ds002898', 'ds005143', 'ds000017', 'ds003145', 'ds002156', 'ds004616', 'ds002345', 'ds003711', 'ds004693', 'ds001705', 'ds004647', 'ds005050', 'ds000214', 'ds001747', 'ds005072', 'ds002715', 'ds002878', 'ds005123', 'ds004645', 'ds003633', 'ds000052', 'ds004513', 'ds002338',
...
'ds004775', 'ds001894', 'ds004471', 'ds000117', 'ds002608', 'ds000254', 'ds000255', 'ds004230', 'ds003089', 'ds003466', 'ds000053', 'ds000164', 'ds000149', 'ds004346', 'ds002835', 'ds002886', 'ds005134', 'ds001499', 'ds002013', 'ds000236', 'ds000231', 'ds004597', 'ds004718', 'ds002155', 'ds004814', 'ds002785', 'ds004698', 'ds001554', 'ds003216', 'ds003082', 'ds003076', 'ds003465', 'ds004386', 'ds005047', 'ds005365', 'ds005233', 'ds004516', 'ds005139', 'ds003495', 'ds004631', 'ds002016', 'ds005496', 'ds004656', 'ds001386', 'ds003988', 'ds003612', 'ds003392', 'ds001725', 'ds003778', 'ds003078', 'ds002522', 'ds003417', 'ds004283', 'ds004786', 'ds003126', 'ds005003', 'ds000240', 'ds004720', 'ds002385', 'ds002168', 'ds001486', 'ds004815', 'ds003464', 'ds004393', 'ds004958', 'ds004765', 'ds003524', 'ds004021', 'ds004644', 'ds003877', 'ds001417', 'ds003481', 'ds004456', 'ds004746', 'ds003972', 'ds005427', 'ds004636', 'ds004212', 'ds002675', 'ds004489', 'ds004889', 'ds002938', 'ds004475', 'ds002328', 'ds004215', 'ds003701', 'ds003791', 'ds004440', 'ds003440', 'ds004285', 'ds004455', 'ds005299', 'ds005464', 'ds003949', 'ds004894', 'ds005412', 'ds002739', 'ds003090', 'ds002702', 'ds003836', 'ds004141', 'ds004956', 'ds003574', 'ds004791', 'ds000203', 'ds004169', 'ds002606', 'ds005069', 'ds004808', 'ds003799', 'ds005266', 'ds004533', 'ds004663', 'ds002672', 'ds004134', 'ds004009', 'ds003043', 'ds003104', 'ds004542', 'ds005127', 'ds004065', 'ds001595', 'ds004946', 'ds000005', 'ds002896'}) and 6 missing columns ({'metadata', 'id', 'stars', 'name', 'followers', 'public'}).
This happened while the json dataset builder was generating data using
hf://datasets/nev/all-brains-online/all_openneuros/openneuro_mri_dataset_file_list.json (at revision 13a881bca0e28fac32793b7ef1e39f65a9811e77)
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.
name
string | metadata
dict | id
string | public
bool | stars
list | followers
list |
|---|---|---|---|---|---|
ds001 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000001 | true | [
{
"userId": "e5523b9a-0169-4fd2-9199-8cfea0df0068"
},
{
"userId": "783d18f5-5835-43d9-abd1-8c5c3e72876f"
},
{
"userId": "11c28ab2-beff-46c8-9c45-425314e3a408"
},
{
"userId": "3418e57a-5b80-4f18-94f1-3c140bfe9790"
},
{
"userId": "5afd75a6-9852-4e0c-8587-d26042b4b22b"
},
{
"userId": "6f253bb6-3b73-4351-804e-d6f74656e244"
}
] | [
{
"userId": "e5523b9a-0169-4fd2-9199-8cfea0df0068"
},
{
"userId": "d6e7650f-9691-4553-bc4e-db5aeee87862"
},
{
"userId": "5afd75a6-9852-4e0c-8587-d26042b4b22b"
}
] |
Classification learning | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000002 | true | [
{
"userId": "8eddf65d-6e3f-46b7-99e9-d7e5be9fa2ca"
},
{
"userId": "a8e9a1de-c18e-4ee0-9622-30e0a1bdbc64"
},
{
"userId": "679f9c2c-c02d-47a2-a374-62b065e6bd84"
}
] | [
{
"userId": "a8e9a1de-c18e-4ee0-9622-30e0a1bdbc64"
},
{
"userId": "4a0f1cbf-c2b8-4326-9cee-1f77e84a7c3f"
}
] |
ds000005 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000005 | true | [
{
"userId": "763f18bc-e8ee-4bee-abcb-1f2f7098e7f8"
},
{
"userId": "5afd75a6-9852-4e0c-8587-d26042b4b22b"
}
] | [
{
"userId": "763f18bc-e8ee-4bee-abcb-1f2f7098e7f8"
},
{
"userId": "d6e7650f-9691-4553-bc4e-db5aeee87862"
},
{
"userId": "5afd75a6-9852-4e0c-8587-d26042b4b22b"
},
{
"userId": "6e889967-468b-4a8b-b8d0-3e9c249c155c"
}
] |
ds000006 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000006 | true | [] | [] |
ds000008 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000008 | true | [
{
"userId": "2b7f938a-db18-4f49-95ff-a4d48ac134b1"
},
{
"userId": "4808d6fb-12f8-4ee3-b59a-be1f6eb0fafc"
}
] | [
{
"userId": "4808d6fb-12f8-4ee3-b59a-be1f6eb0fafc"
}
] |
Classification learning and tone-counting | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000011 | true | [
{
"userId": "733a3b96-339d-4dd3-ae53-05581d23ff13"
}
] | [] |
Cross-language repetition priming | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000051 | true | [
{
"userId": "01a23077-b9a3-4858-b073-6918d96a85c9"
}
] | [] |
ds000052 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000052 | true | [
{
"userId": "338ac9df-e77b-4454-8b2f-6ed4ae4ed692"
},
{
"userId": "94535c9c-89c1-45dd-b5ba-07bb39b7dd69"
}
] | [
{
"userId": "338ac9df-e77b-4454-8b2f-6ed4ae4ed692"
}
] |
ds000101 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000101 | true | [
{
"userId": "5d665388-5bca-4862-a1a4-1b3a73623c53"
},
{
"userId": "c4274ca8-40e3-4ebf-81c5-ad974c35c905"
},
{
"userId": "6f253bb6-3b73-4351-804e-d6f74656e244"
}
] | [
{
"userId": "5d665388-5bca-4862-a1a4-1b3a73623c53"
},
{
"userId": "c4274ca8-40e3-4ebf-81c5-ad974c35c905"
},
{
"userId": "6f253bb6-3b73-4351-804e-d6f74656e244"
}
] |
Visual object recognition | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000105 | true | [
{
"userId": "7932d39a-9f2a-4858-b972-b1cb75f914c8"
},
{
"userId": "2e1fec8d-fc30-487a-ac91-d07b4df1f86e"
},
{
"userId": "cb1dbdc0-1990-417b-bdf1-60736b758b0b"
},
{
"userId": "01a23077-b9a3-4858-b073-6918d96a85c9"
},
{
"userId": "5d24eb01-b06d-4062-ae79-e21fe7458fd8"
}
] | [
{
"userId": "7932d39a-9f2a-4858-b972-b1cb75f914c8"
}
] |
Word and object processing | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000107 | true | [
{
"userId": "7932d39a-9f2a-4858-b972-b1cb75f914c8"
},
{
"userId": "eecea653-c2b1-4ba9-b896-12f30a14e5f6"
},
{
"userId": "338ac9df-e77b-4454-8b2f-6ed4ae4ed692"
},
{
"userId": "945edaee-8258-4906-a09e-12448e3e6e27"
},
{
"userId": "4a139fc7-570c-48cb-b924-78b1ac7b8c10"
}
] | [
{
"userId": "7932d39a-9f2a-4858-b972-b1cb75f914c8"
}
] |
Social cognitive tasks (animate movement task, moral judgment task, false belief task) | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000109 | true | [
{
"userId": "c37404e3-21e1-42ff-8a61-01380b8264e3"
},
{
"userId": "478534c0-ad60-4bb3-9157-31d40cd6af4d"
},
{
"userId": "a45d2f23-7a8e-4194-826a-461cdd69f262"
}
] | [] |
ds000017 | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds000017 | true | [] | [] |
NKI-sample | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds001021 | true | [
{
"userId": "70a99e10-4d2e-4209-ad65-d2948c566354"
},
{
"userId": "c837eca6-4812-465d-b38a-aad6b6f41f83"
}
] | [] |
The brain of Chris | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds001037 | true | [] | [] |
Social predictive coding | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds001105 | true | [] | [] |
Sherlock_Merlin | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds001110 | true | [
{
"userId": "2b7f938a-db18-4f49-95ff-a4d48ac134b1"
},
{
"userId": "9dc997b3-78ed-4411-be06-32670c03b437"
},
{
"userId": "37036d56-b24e-45b9-8360-d3c0b2ea8704"
},
{
"userId": "f729dfc3-3cab-48b8-8fd3-053ee1b56ede"
},
{
"userId": "c243bc4d-0dc0-48b2-9bf7-e0a90d52e17b"
},
{
"userId": "cd002308-4b79-4493-a3c9-e0e3a1dc209c"
}
] | [
{
"userId": "7c2ffc11-d665-4bf1-850a-2ae06a02dd1f"
},
{
"userId": "9dc997b3-78ed-4411-be06-32670c03b437"
},
{
"userId": "cd002308-4b79-4493-a3c9-e0e3a1dc209c"
}
] |
sherlock | {
"affirmedConsent": null,
"affirmedDefaced": null,
"dxStatus": null
} | ds001132 | true | [
{
"userId": "2b7f938a-db18-4f49-95ff-a4d48ac134b1"
},
{
"userId": "647c03ca-7650-460c-b42a-f80714bb9e32"
},
{
"userId": "1d499796-0481-4dea-8969-ad3a42a16228"
},
{
"userId": "b7010ed2-dc24-4f3d-9d8f-c1fe813a973a"
},
{
"userId": "9dc997b3-78ed-4411-be06-32670c03b437"
},
{
"userId": "790fac31-c573-4219-a065-a24390c39b98"
},
{
"userId": "37036d56-b24e-45b9-8360-d3c0b2ea8704"
}
] | [
{
"userId": "647c03ca-7650-460c-b42a-f80714bb9e32"
},
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Twilight Zone Movie Watching Dataset | {
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State representation pictures | {
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BTC_preop | {
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Decoding intentions of self and others | {
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Competition between items in working memory leads to forgetting | {
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singleFingerRSA | {
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Human-Object Interaction | {
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Generic Object Decoding | {
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Personally familiar faces | {
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Inverse Base Rate | {
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ds000208_R1.0.0 | {
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ds000217_R1.0.1 | {
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ds000248 | {
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ds000138_R2.0.0 | {
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Adjudicating between face-coding models with individual-face fMRI responses | {
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ds000256_R1.0.0 | {
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ds000236 | {
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NameFacePriming | {
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Scene Perception | {
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Distinct brain systems mediate the effects of nociceptive input and self-regulation on pain | {
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Early signs of disrupted rule learning in adolescents at clinical high risk for psychosis | {
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SCA2 Diffusion Tensor Imaging | {
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Loci case study | {
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Subject Data | {
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multibandACQtests | {
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