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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 7 new columns ({'trans_x', 'trans_y', 'framewise_displacement', 'rot_x', 'rot_z', 'trans_z', 'rot_y'}) and 5 missing columns ({'gender', 'birth_age', 'birth_weight', 'singleton', 'participant_id'}).

This happened while the csv dataset builder was generating data using

hf://datasets/RichardErkhov/dhcp_fmri_pipeline/sub-CC00058XX09/ses-11300/func/sub-CC00058XX09_ses-11300_motion.tsv (at revision 0fb675d609ffa5f971145a358e5a7a3a2ae9e4a5)

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 1870, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 622, 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
              trans_x: double
              trans_y: double
              trans_z: double
              rot_x: double
              rot_y: double
              rot_z: double
              framewise_displacement: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1085
              to
              {'participant_id': Value(dtype='string', id=None), 'gender': Value(dtype='string', id=None), 'birth_age': Value(dtype='float64', id=None), 'birth_weight': Value(dtype='float64', id=None), 'singleton': 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 1412, 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 988, in stream_convert_to_parquet
                  builder._prepare_split(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1741, 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 1872, 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 7 new columns ({'trans_x', 'trans_y', 'framewise_displacement', 'rot_x', 'rot_z', 'trans_z', 'rot_y'}) and 5 missing columns ({'gender', 'birth_age', 'birth_weight', 'singleton', 'participant_id'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/RichardErkhov/dhcp_fmri_pipeline/sub-CC00058XX09/ses-11300/func/sub-CC00058XX09_ses-11300_motion.tsv (at revision 0fb675d609ffa5f971145a358e5a7a3a2ae9e4a5)
              
              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)

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participant_id
string
gender
string
birth_age
float64
birth_weight
float64
singleton
string
CC00549XX22
Female
42
3.685
Single
CC00576XX16
Male
28.857143
0.85
Single
CC00202XX04
Male
38.571429
2.63
Single
CC00720XX11
Female
40.428571
3.95
Single
CC00388XX18
Female
34.428571
2.8
Single
CC00122XX07
Male
37.428571
3.5
Single
CC00492BN15
Female
35.142857
2.27
Multiple
CC00672BN13
Female
28.714286
0.695
Multiple
CC00407BN11
Male
35.142857
2.41
Multiple
CC00071XX06
Female
39.857143
3.32
Single
CC00119XX12
Male
40.428571
3.37
Single
CC00207XX09
Female
39
3.07
Single
CC00465XX12
Male
36.857143
3.16
Single
CC00484XX15
Female
41.142857
2.78
Single
CC00590XX14
Male
39.285714
3.25
Single
CC00586XX18
Female
40.142857
3.095
Single
CC00723XX14
Male
30.142857
1.6
Single
CC00467XX14
Male
40.142857
4.025
Single
CC00161XX05
Male
29.857143
1.39
Single
CC00593XX17
Male
39.428571
3.46
Single
CC00442XX14
Male
41.285714
3.47
Single
CC00344XX15
Male
39.142857
3.33
Single
CC00098BN17
Male
36.571429
2.57
Multiple
CC00409XX13
Male
40.714286
3.735
Single
CC00168XX12
Male
40.142857
3.92
Single
CC00907XX16
Male
34.428571
2.1
Single
CC00514XX11
Female
40
3.88
Single
CC00618XX16
Female
27.428571
0.76
Single
CC00158XX10
Female
39.571429
3.69
Single
CC00136AN13
Female
31
0.9
Multiple
CC00613XX11
Male
40.428571
2.9
Single
CC00731XX14
Female
40.428571
2.8
Single
CC00198XX18
Male
40.714286
2.86
Single
CC00063AN06
Female
35.142857
1.95
Multiple
CC00226XX12
Male
40.714286
3.51
Single
CC00664XX13
Female
38.285714
3
Single
CC00397XX19
Male
41.571429
3.82
Single
CC00270XX07
Male
41.571429
4.09
Single
CC00252XX05
Male
40.142857
3.8
Single
CC00787XX21
Female
39.714286
3.41
Single
CC00223XX09
Female
40
3.2
Single
CC00703XX10
Male
29.857143
1.23
Single
CC00429XX17
Female
41.142857
4.15
Single
CC00367XX13
Male
39.571429
2.65
Single
CC00183XX11
Male
41.142857
3.85
Single
CC00478XX17
Male
38.285714
2.92
Single
CC00540XX13
Female
40.714286
3.56
Single
CC00512XX09
Male
38.857143
3.61
Single
CC00476XX15
Female
40.428571
3.4
Single
CC00120XX05
Male
39.857143
2.71
Single
CC00712XX11
Male
34.571429
3.05
Single
CC00164XX08
Male
38.714286
3.47
Single
CC00562XX10
Female
38.571429
2.8
Single
CC00336XX15
Male
40.571429
4.7
Single
CC00261XX06
Female
38.142857
3.015
Single
CC00194XX14
Male
38.857143
2.932
Single
CC00383XX13
Female
40
3.95
Single
CC00440XX12
Female
38.714286
2.73
Single
CC00153XX05
Male
39.142857
3.15
Single
CC00482XX13
Male
39
3.84
Single
CC00170XX06
Male
37.857143
3.405
Single
CC00595XX19
Female
39.142857
3.62
Single
CC00744XX19
Female
38.857143
4.245
Single
CC00135AN12
Male
34.142857
2.09
Multiple
CC00398XX20
Female
38.857143
3.29
Single
CC00248XX18
Female
30.285714
1.6
Single
CC00350XX04
Female
32.714286
1.81
Single
CC00547XX20
Female
38.714286
3.2
Single
CC00466BN13
Female
37.142857
2.13
Multiple
CC00258XX11
Male
39.142857
2.92
Single
CC00196XX16
Female
39.571429
2.83
Single
CC00839XX23
Male
42
3.78
Single
CC00203XX05
Male
40.714286
3.46
Single
CC00500XX05
Male
41
4.095
Single
CC00654XX11
Male
39.428571
3.365
Single
CC00754BN12
Male
31.285714
1.535
Multiple
CC00073XX08
Male
40.714286
3.345
Single
CC00118XX11
Male
40.571429
3.66
Single
CC00563XX11
Female
34.714286
1.75
Single
CC00115XX08
Male
40
2.58
Single
CC00754AN12
Male
31.285714
1.64
Multiple
CC00070XX05
Female
37.428571
2.2
Single
CC00422XX10
Female
36.428571
4.1
Single
CC00840XX16
Male
40.571429
4.13
Single
CC00411XX07
Male
40
3.47
Single
CC00293AN14
Male
31.285714
1.41
Multiple
CC00184XX12
Female
41.285714
3.7
Single
CC00205XX07
Female
39.428571
3.7
Single
CC00216BN10
Male
36.142857
2.515
Multiple
CC00302XX05
Male
39.714286
3.45
Single
CC00187XX15
Male
39.142857
3.29
Single
CC00456XX11
Male
40.714286
3.58
Single
CC00337XX16
Male
37.428571
3.06
Single
CC00121XX06
Male
28.142857
0.915
Single
CC00313XX08
Female
38.857143
2.92
Single
CC00107XX08
Female
39.142857
3
Single
CC00144XX13
Male
39.714286
3.485
Single
CC00804XX12
Female
34.142857
1.66
Single
CC00845BN21
Male
34.857143
1.75
Multiple
CC00585XX17
Male
39.571429
4.18
Single
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