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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 12 new columns ({'Hypocotyl length (mm)', 'Unnamed: 12', 'Epicotyl length (mm)', 'Shoot DW (g)', 'Shoot length (mm)', '#leaf', 'Stem diameter (mm)', 'Leaf area (without cotyledons) (cm2)', 'block', 'Leaf area including cotyledons (cm2)', 'Shoot FW (g)', 'treatment'}) and 11 missing columns ({'WFL (oC)', 'Time', 'Unnamed: 2', 'RH (%)', 'UV (oC)', 'Unnamed: 1', 'CO2 (ppm)', 'LED1 (oC)', 'LED3 (oC)', 'Air (oC)', 'Day'}).

This happened while the csv dataset builder was generating data using

hf://datasets/koidra/VF_Tomato/VF_Tomato/UAzIntUVBDataKubota/IntUVBPlantDataKubotaEguchi/Growth_data.csv (at revision b8988f9f01ab4249b9a6245b2c4087036e200b4e)

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 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 644, 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 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              treatment: string
              block: int64
              ID: int64
              Hypocotyl length (mm): double
              Epicotyl length (mm): double
              Shoot length (mm): double
              #leaf: int64
              Stem diameter (mm): double
              Leaf area including cotyledons (cm2): double
              Leaf area (without cotyledons) (cm2): double
              Shoot FW (g): double
              Shoot DW (g): double
              Unnamed: 12: double
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1968
              to
              {'ID': Value('int64'), 'Unnamed: 1': Value('int64'), 'Unnamed: 2': Value('int64'), 'Day': Value('string'), 'Time': Value('int64'), 'LED1 (oC)': Value('float64'), 'LED3 (oC)': Value('float64'), 'WFL (oC)': Value('float64'), 'UV (oC)': Value('float64'), 'Air (oC)': Value('float64'), 'RH (%)': Value('float64'), 'CO2 (ppm)': Value('float64')}
              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 1456, in compute_config_parquet_and_info_response
                  parquet_operations = convert_to_parquet(builder)
                File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1055, in convert_to_parquet
                  builder.download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1702, 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 1833, 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 12 new columns ({'Hypocotyl length (mm)', 'Unnamed: 12', 'Epicotyl length (mm)', 'Shoot DW (g)', 'Shoot length (mm)', '#leaf', 'Stem diameter (mm)', 'Leaf area (without cotyledons) (cm2)', 'block', 'Leaf area including cotyledons (cm2)', 'Shoot FW (g)', 'treatment'}) and 11 missing columns ({'WFL (oC)', 'Time', 'Unnamed: 2', 'RH (%)', 'UV (oC)', 'Unnamed: 1', 'CO2 (ppm)', 'LED1 (oC)', 'LED3 (oC)', 'Air (oC)', 'Day'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/koidra/VF_Tomato/VF_Tomato/UAzIntUVBDataKubota/IntUVBPlantDataKubotaEguchi/Growth_data.csv (at revision b8988f9f01ab4249b9a6245b2c4087036e200b4e)
              
              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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ID
int64
Unnamed: 1
int64
Unnamed: 2
int64
Day
string
Time
int64
LED1 (oC)
float64
LED3 (oC)
float64
WFL (oC)
float64
UV (oC)
float64
Air (oC)
float64
RH (%)
float64
CO2 (ppm)
float64
1
2,016
177
6/25
0
23.01
23.17
26.18
23.38
23.26
61.62
393.4
2
2,016
177
6/25
15
24.09
24.07
26.57
24.07
23.94
63.33
391.6
3
2,016
177
6/25
30
22.29
22.49
25.55
22.66
22.27
64.09
389
4
2,016
177
6/25
45
24.41
24.41
27
24.5
24.67
61.19
390.2
5
2,016
177
6/25
100
22.21
22.37
25.33
22.46
21.97
65.2
387
6
2,016
177
6/25
115
23.54
23.68
26.56
23.86
24.02
60.73
388.7
7
2,016
177
6/25
130
23.41
23.43
26.01
23.42
22.99
65.2
386
8
2,016
177
6/25
145
22.6
22.79
25.81
22.97
22.7
63.08
386.2
9
2,016
177
6/25
200
24.27
24.23
26.7
24.24
24.23
62.78
386.8
10
2,016
177
6/25
215
22.17
22.38
25.42
22.53
22.13
64.45
385.3
11
2,016
177
6/25
230
24.47
24.45
26.97
24.51
24.64
61.5
387.6
12
2,016
177
6/25
245
22.25
22.41
25.35
22.51
22.08
65.1
385.7
13
2,016
177
6/25
300
23.49
23.62
26.53
23.81
23.96
60.72
388.5
14
2,016
177
6/25
315
23.31
23.33
25.92
23.32
22.88
65.41
387.3
15
2,016
177
6/25
330
22.77
22.96
25.95
23.14
22.94
62.53
388.9
16
2,016
177
6/25
345
24.12
24.08
26.54
24.07
24
63.46
389.8
17
2,016
177
6/25
400
22.42
22.61
25.62
22.76
22.4
64.22
389.5
18
2,016
177
6/25
415
24.49
24.44
26.94
24.52
24.62
61.68
391.6
19
2,016
177
6/25
430
22.23
22.41
25.36
22.49
22.08
65.22
389.2
20
2,016
177
6/25
445
23.73
23.85
26.65
24.02
24.21
60.82
390.7
21
2,016
177
6/25
500
23.11
23.17
25.77
23.13
22.65
65.62
387.3
22
2,016
177
6/25
515
22.88
23.07
26.04
23.24
23.08
62.29
387.1
23
2,016
177
6/25
530
24
23.99
26.43
23.95
23.83
64.04
386.4
24
2,016
177
6/25
545
22.41
22.62
25.63
22.77
22.42
63.98
384.7
25
2,016
177
6/25
600
24.39
24.35
26.77
24.34
24.4
62.44
384.9
26
2,016
177
6/25
615
22.29
22.49
25.46
22.62
22.21
64.89
382.5
27
2,016
177
6/25
630
24.28
24.32
26.89
24.42
24.61
61.29
384.5
28
2,016
177
6/25
645
22.5
22.63
25.41
22.65
22.16
65.65
382.1
29
2,016
177
6/25
700
23.4
23.54
26.41
23.71
23.8
61.29
384
30
2,016
177
6/25
715
23.58
23.58
26.07
23.55
23.19
65.6
382.4
31
2,016
177
6/25
730
22.81
23.02
25.94
23.16
22.9
63.6
381.1
32
2,016
177
6/25
745
24.41
24.37
26.76
24.35
24.34
63.18
380.2
33
2,016
177
6/25
800
22.3
22.52
25.44
22.62
22.23
65.28
377.4
34
2,016
177
6/25
815
24.21
24.26
26.87
24.37
24.56
61.91
378.7
35
2,016
177
6/25
830
22.73
22.83
25.54
22.83
22.34
66.14
376.3
36
2,016
177
6/25
845
23.1
23.29
26.19
23.44
23.37
62.52
378
37
2,016
177
6/25
900
23.94
23.93
26.37
23.88
23.66
65.6
378.2
38
2,016
177
6/25
915
22.58
22.77
25.72
22.9
22.57
64.95
378.2
39
2,016
177
6/25
930
24.17
24.19
26.79
24.27
24.41
63.28
380.2
40
2,016
177
6/25
945
17.56
17.96
21.82
18.24
17.43
60.64
375
41
2,016
177
6/25
1,000
14.25
14.59
18.06
14.56
13.57
58.74
371.5
42
2,016
177
6/25
1,015
13.27
13.5
16.5
13.31
12.3
62.37
369.9
43
2,016
177
6/25
1,030
13.59
13.83
16.79
13.82
13.2
69.61
371.6
44
2,016
177
6/25
1,045
13.62
13.85
16.7
13.86
13.33
72.4
373.9
45
2,016
177
6/25
1,100
13.69
13.89
16.71
13.91
13.5
74.4
375.6
46
2,016
177
6/25
1,115
13.93
14.11
16.82
14.13
13.68
76.6
376.9
47
2,016
177
6/25
1,130
13.96
14.09
16.71
14.09
13.58
77.8
378
48
2,016
177
6/25
1,145
13.61
13.74
16.41
13.71
13.13
77.4
378.3
49
2,016
177
6/25
1,200
13.31
13.48
16.3
13.5
13.11
77.2
380.1
50
2,016
177
6/25
1,215
13.34
13.5
16.36
13.54
13.2
77.5
382
51
2,016
177
6/25
1,230
13.61
13.75
16.57
13.81
13.56
78.5
383.9
52
2,016
177
6/25
1,245
13.83
13.95
16.71
14.02
13.77
79.5
385.3
53
2,016
177
6/25
1,300
13.98
14.07
16.7
14.07
13.65
80.5
385.6
54
2,016
177
6/25
1,315
13.54
13.64
16.36
13.63
13.15
79.5
385.6
55
2,016
177
6/25
1,330
13.35
13.48
16.32
13.53
13.2
78.9
387.2
56
2,016
177
6/25
1,345
13.46
13.59
16.43
13.63
13.38
79.1
388.8
57
2,016
177
6/25
1,400
13.64
13.75
16.57
13.81
13.62
79.6
390.9
58
2,016
177
6/25
1,415
13.98
14.07
16.79
14.13
13.9
80.9
391.8
59
2,016
177
6/25
1,430
13.95
14.02
16.69
14.03
13.66
80.8
392.3
60
2,016
177
6/25
1,445
13.63
13.72
16.43
13.7
13.3
79.9
392.8
61
2,016
177
6/25
1,500
13.43
13.55
16.36
13.56
13.28
79.5
394.4
62
2,016
177
6/25
1,515
13.49
13.61
16.43
13.63
13.39
79.5
396.5
63
2,016
177
6/25
1,530
13.74
13.85
16.62
13.87
13.64
80.4
398.6
64
2,016
177
6/25
1,545
15.62
15.67
17.96
15.52
15.31
83.8
402.4
65
2,016
177
6/25
1,600
17.45
17.42
19.77
17.32
17.43
82.8
406.7
66
2,016
177
6/25
1,615
18.83
18.77
21.16
18.65
18.88
80.1
408.4
67
2,016
177
6/25
1,630
20.01
19.93
22.36
19.83
20.1
77.2
407.6
68
2,016
177
6/25
1,645
21.13
21.05
23.5
20.93
21.22
74.5
404.2
69
2,016
177
6/25
1,700
22.18
22.09
24.57
21.99
22.32
71.4
390.5
70
2,016
177
6/25
1,715
23.06
22.98
25.49
22.91
23.25
69.62
379
71
2,016
177
6/25
1,730
23.89
23.81
26.37
23.79
24.12
68.24
370.2
72
2,016
177
6/25
1,745
22.72
22.84
25.84
22.95
22.98
68.9
363.9
73
2,016
177
6/25
1,800
23.58
23.54
26.12
23.48
23.46
73.9
360.3
74
2,016
177
6/25
1,815
22.92
23.04
26.06
23.12
23.18
70.1
360.3
75
2,016
177
6/25
1,830
23.54
23.55
26.13
23.47
23.33
75.6
359.9
76
2,016
177
6/25
1,845
22.93
23.1
26.07
23.14
23.12
71.4
361.3
77
2,016
177
6/25
1,900
23.77
23.79
26.34
23.7
23.58
76.1
362.5
78
2,016
177
6/25
1,915
22.74
22.94
25.91
22.97
22.81
72.8
363.8
79
2,016
177
6/25
1,930
24.18
24.17
26.68
24.07
24.09
75.4
365.8
80
2,016
177
6/25
1,945
22.35
22.59
25.59
22.63
22.32
73.6
366.2
81
2,016
177
6/25
2,000
24.42
24.42
26.89
24.3
24.41
74.4
368.5
82
2,016
177
6/25
2,015
22.28
22.54
25.51
22.56
22.2
74.2
367.9
83
2,016
177
6/25
2,030
24.35
24.4
26.97
24.34
24.57
72.9
370.7
84
2,016
177
6/25
2,045
22.56
22.75
25.56
22.7
22.23
75.1
369.7
85
2,016
177
6/25
2,100
23.49
23.63
26.48
23.66
23.79
71.1
371.3
86
2,016
177
6/25
2,115
23.55
23.62
26.19
23.52
23.17
75.6
369.1
87
2,016
177
6/25
2,130
22.79
23.01
25.98
23.04
22.87
71.1
367.2
88
2,016
177
6/25
2,145
24.06
24.09
26.56
23.97
23.9
74.2
365.6
89
2,016
177
6/25
2,200
22.63
22.86
25.84
22.91
22.61
72
363.3
90
2,016
177
6/25
2,215
24.51
24.52
26.98
24.42
24.52
72
363.2
91
2,016
177
6/25
2,230
22.13
22.38
25.35
22.4
21.96
73
361.5
92
2,016
177
6/25
2,245
24.15
24.23
26.92
24.24
24.49
70.2
363.9
93
2,016
177
6/25
2,300
22.78
22.93
25.69
22.87
22.37
73.8
363.1
94
2,016
177
6/25
2,315
23.2
23.37
26.31
23.43
23.45
69.19
365.5
95
2,016
177
6/25
2,330
23.56
23.62
26.15
23.51
23.2
74.1
366
96
2,016
177
6/25
2,345
22.84
23.05
26.02
23.12
22.95
69.58
367.8
97
2,016
178
6/26
0
23.95
23.97
26.45
23.86
23.72
73.2
369.9
98
2,016
178
6/26
15
22.54
22.79
25.79
22.86
22.58
70.3
371.5
99
2,016
178
6/26
30
24.26
24.27
26.74
24.16
24.18
71.7
373.5
100
2,016
178
6/26
45
22.44
22.68
25.65
22.75
22.38
71.4
374
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