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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 ({'datetime', 'stationname', 'stationcode', 'value', 'municipality_id', 'sensordescription', 'measureunit'}) and 7 missing columns ({'date_event', 'place_id', 'taxonomy_id', 'registered_by', 'elevation_m', 'code_record', 'common_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/juanpac96/urban_tree_census_data/climate.csv (at revision f87ba58bace16cbd9f4a48273f8a0728df6053a1)
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 1871, in _prepare_split_single
writer.write_table(table)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/arrow_writer.py", line 623, 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 2293, in table_cast
return cast_table_to_schema(table, schema)
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/table.py", line 2241, in cast_table_to_schema
raise CastError(
datasets.table.CastError: Couldn't cast
municipality_id: int64
stationcode: int64
stationname: string
datetime: string
latitude: double
longitude: double
sensordescription: string
measureunit: string
value: double
-- schema metadata --
pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 1350
to
{'code_record': Value(dtype='int64', id=None), 'common_name': Value(dtype='string', id=None), 'latitude': Value(dtype='float64', id=None), 'longitude': Value(dtype='float64', id=None), 'elevation_m': Value(dtype='float64', id=None), 'registered_by': Value(dtype='string', id=None), 'date_event': Value(dtype='string', id=None), 'place_id': Value(dtype='int64', id=None), 'taxonomy_id': Value(dtype='int64', 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 1438, 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 1050, in convert_to_parquet
builder.download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 925, in download_and_prepare
self._download_and_prepare(
File "/src/services/worker/.venv/lib/python3.9/site-packages/datasets/builder.py", line 1001, 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 1742, 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 1873, 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 ({'datetime', 'stationname', 'stationcode', 'value', 'municipality_id', 'sensordescription', 'measureunit'}) and 7 missing columns ({'date_event', 'place_id', 'taxonomy_id', 'registered_by', 'elevation_m', 'code_record', 'common_name'}).
This happened while the csv dataset builder was generating data using
hf://datasets/juanpac96/urban_tree_census_data/climate.csv (at revision f87ba58bace16cbd9f4a48273f8a0728df6053a1)
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.
code_record
int64 | common_name
string | latitude
float64 | longitude
float64 | elevation_m
float64 | registered_by
string | date_event
string | place_id
int64 | taxonomy_id
int64 |
|---|---|---|---|---|---|---|---|---|
1
|
Gmelina melina
| 4.407358
| -75.143061
| 939
|
Cortolima
|
2017-09-30 08:58:15
| 495
| 197
|
2
|
Gmelina melina
| 4.407582
| -75.14304
| 939
|
Cortolima
|
2017-09-30 08:54:56
| 495
| 197
|
3
|
Gmelina melina
| 4.407822
| -75.142962
| 939
|
Cortolima
|
2017-09-30 08:51:43
| 495
| 197
|
4
|
Gmelina melina
| 4.407983
| -75.142962
| 937
|
Cortolima
|
2017-09-30 08:49:51
| 495
| 197
|
5
|
Gmelina melina
| 4.408368
| -75.142898
| 937
|
Cortolima
|
2017-09-30 08:48:01
| 495
| 197
|
6
|
Gmelina melina
| 4.408599
| -75.142869
| 937
|
Cortolima
|
2017-09-30 08:44:46
| 495
| 197
|
7
|
Gmelina melina
| 4.408738
| -75.142816
| 937
|
Cortolima
|
2017-09-30 08:41:28
| 495
| 197
|
8
|
Gmelina melina
| 4.408872
| -75.14284
| 937
|
Cortolima
|
2017-09-30 08:37:49
| 495
| 197
|
9
|
Gmelina melina
| 4.409477
| -75.142604
| 934
|
Cortolima
|
2017-09-30 08:34:29
| 495
| 197
|
10
|
Gmelina melina
| 4.409829
| -75.142551
| 934
|
Cortolima
|
2017-09-30 08:30:29
| 495
| 197
|
11
|
Gmelina melina
| 4.410075
| -75.142449
| 934
|
Cortolima
|
2017-09-30 08:27:46
| 235
| 197
|
12
|
Ocobo
| 4.40978
| -75.146175
| 942
|
Cortolima
|
2017-09-30 07:50:09
| 427
| 399
|
13
|
Ocobo
| 4.409618
| -75.146547
| 942
|
Cortolima
|
2017-09-30 07:44:29
| 427
| 399
|
14
|
Tulipan africano
| 4.409752
| -75.146766
| 942
|
Cortolima
|
2017-09-30 07:41:22
| 427
| 387
|
15
|
Ocobo
| 4.409678
| -75.146869
| 942
|
Cortolima
|
2017-09-30 07:37:38
| 427
| 399
|
16
|
Tulipan africano
| 4.409698
| -75.146806
| 942
|
Cortolima
|
2017-09-30 07:34:29
| 427
| 387
|
17
|
Ocobo
| 4.409741
| -75.146846
| 942
|
Cortolima
|
2017-09-30 07:33:30
| 21
| 399
|
18
|
Ocobo
| 4.409816
| -75.146937
| 942
|
Cortolima
|
2017-09-30 07:21:44
| 427
| 399
|
19
|
Ocobo
| 4.409771
| -75.146972
| 944
|
Cortolima
|
2017-09-30 07:18:31
| 427
| 399
|
20
|
Ocobo
| 4.409723
| -75.146991
| 944
|
Cortolima
|
2017-09-30 07:15:34
| 427
| 399
|
21
|
Caucho matapalo
| 4.409549
| -75.147259
| 944
|
Cortolima
|
2017-09-30 07:06:41
| 427
| 181
|
22
|
Matarraton
| 4.409442
| -75.147316
| 946
|
Cortolima
|
2017-09-30 07:01:23
| 427
| 196
|
23
|
Limon
| 4.407188
| -75.145363
| 945
|
Cortolima
|
2017-09-29 13:18:53
| 427
| 108
|
24
|
Tulipan africano
| 4.407031
| -75.145365
| 945
|
Cortolima
|
2017-09-29 13:16:25
| 427
| 387
|
25
|
Tulipan africano
| 4.406994
| -75.145446
| 945
|
Cortolima
|
2017-09-29 13:14:21
| 427
| 387
|
26
|
Almendro
| 4.407007
| -75.145545
| 945
|
Cortolima
|
2017-09-29 13:10:58
| 427
| 408
|
27
|
Tulipan africano
| 4.407058
| -75.145604
| 945
|
Cortolima
|
2017-09-29 13:08:34
| 427
| 387
|
28
|
Tulipan africano
| 4.407313
| -75.145532
| 945
|
Cortolima
|
2017-09-29 13:05:58
| 427
| 387
|
29
|
Millon croto
| 4.407419
| -75.145919
| 945
|
Cortolima
|
2017-09-29 13:03:06
| 427
| 333
|
30
|
Payande
| 4.408384
| -75.145836
| 942
|
Cortolima
|
2017-09-29 12:56:04
| 427
| 321
|
31
|
Palo cruz
| 4.408325
| -75.145873
| 942
|
Cortolima
|
2017-09-29 12:52:44
| 427
| 52
|
32
|
Carbonero
| 4.408285
| -75.1459
| 942
|
Cortolima
|
2017-09-29 12:50:06
| 427
| 72
|
33
|
Ocobo
| 4.408301
| -75.145927
| 942
|
Cortolima
|
2017-09-29 12:47:46
| 427
| 399
|
34
|
Habano laurel de judea
| 4.408241
| -75.146456
| 946
|
Cortolima
|
2017-09-29 12:41:37
| 427
| 286
|
35
|
Guanabano
| 4.40834
| -75.146499
| 946
|
Cortolima
|
2017-09-29 12:39:05
| 427
| 27
|
36
|
Limon
| 4.407994
| -75.146565
| 946
|
Cortolima
|
2017-09-29 11:33:54
| 427
| 108
|
37
|
Ocobo
| 4.408027
| -75.146479
| 946
|
Cortolima
|
2017-09-29 11:30:12
| 427
| 399
|
38
|
Ocobo
| 4.408137
| -75.146461
| 946
|
Cortolima
|
2017-09-29 11:26:23
| 427
| 399
|
39
|
Mirto
| 4.408015
| -75.14638
| 946
|
Cortolima
|
2017-09-29 11:20:22
| 427
| 278
|
40
|
Pera de malaca
| 4.40797
| -75.146362
| 946
|
Cortolima
|
2017-09-29 11:17:23
| 427
| 396
|
41
|
Cardo
| 4.407872
| -75.146336
| 946
|
Cortolima
|
2017-09-29 11:15:07
| 427
| 96
|
42
|
Nacedero
| 4.407765
| -75.146281
| 947
|
Cortolima
|
2017-09-29 11:12:07
| 427
| 418
|
43
|
Nevado
| 4.407752
| -75.146286
| 947
|
Cortolima
|
2017-09-29 11:09:31
| 427
| 217
|
44
|
Pino libro
| 4.407647
| -75.146236
| 947
|
Cortolima
|
2017-09-29 10:01:47
| 427
| 323
|
45
|
Pera de malaca
| 4.407688
| -75.146496
| 947
|
Cortolima
|
2017-09-29 09:56:04
| 427
| 396
|
46
|
Nevado
| 4.407729
| -75.146516
| 947
|
Cortolima
|
2017-09-29 09:53:02
| 427
| 217
|
47
|
Mirto
| 4.407751
| -75.146528
| 947
|
Cortolima
|
2017-09-29 09:49:21
| 427
| 278
|
48
|
Monaca
| 4.407789
| -75.146539
| 947
|
Cortolima
|
2017-09-29 09:45:46
| 427
| 55
|
49
|
Ebano arboreo costenno
| 4.407853
| -75.146571
| 947
|
Cortolima
|
2017-09-29 09:42:11
| 427
| 62
|
50
|
Arbol de la felicidad
| 4.407981
| -75.146618
| 946
|
Cortolima
|
2017-09-29 09:39:15
| 427
| 149
|
51
|
Araza
| 4.408025
| -75.146794
| 946
|
Cortolima
|
2017-09-29 09:34:29
| 427
| 171
|
52
|
Limon
| 4.407855
| -75.147262
| 948
|
Cortolima
|
2017-09-29 09:23:28
| 427
| 108
|
53
|
Acacio amarillo
| 4.407808
| -75.14722
| 948
|
Cortolima
|
2017-09-29 09:21:11
| 427
| 376
|
54
|
Casco de vaca pate buey
| 4.40783
| -75.147228
| 948
|
Cortolima
|
2017-09-29 09:18:27
| 427
| 44
|
55
|
Saman
| 4.40787
| -75.147281
| 948
|
Cortolima
|
2017-09-29 09:14:29
| 427
| 359
|
56
|
Noni
| 4.407819
| -75.1474
| 948
|
Cortolima
|
2017-09-29 09:11:40
| 427
| 273
|
57
|
Ocobo
| 4.407884
| -75.147416
| 948
|
Cortolima
|
2017-09-29 09:08:25
| 427
| 399
|
58
|
Ocobo
| 4.407956
| -75.147456
| 948
|
Cortolima
|
2017-09-29 09:05:49
| 427
| 399
|
59
|
Ocobo
| 4.407999
| -75.147477
| 948
|
Cortolima
|
2017-09-29 09:02:34
| 427
| 399
|
60
|
Noni
| 4.408017
| -75.147453
| 948
|
Cortolima
|
2017-09-29 08:59:39
| 427
| 273
|
61
|
Saman
| 4.408031
| -75.147496
| 948
|
Cortolima
|
2017-09-29 08:53:50
| 427
| 359
|
62
|
Limon
| 4.408128
| -75.147509
| 948
|
Cortolima
|
2017-09-29 08:40:49
| 427
| 108
|
63
|
Mango
| 4.408165
| -75.147531
| 948
|
Cortolima
|
2017-09-29 08:37:44
| 427
| 261
|
64
|
Almendro
| 4.408224
| -75.147574
| 948
|
Cortolima
|
2017-09-29 08:35:05
| 427
| 408
|
65
|
Saman
| 4.408299
| -75.147641
| 948
|
Cortolima
|
2017-09-29 08:30:44
| 427
| 359
|
66
|
Saman
| 4.408409
| -75.147732
| 948
|
Cortolima
|
2017-09-29 08:28:25
| 427
| 359
|
67
|
Gualanday
| 4.408259
| -75.147906
| 949
|
Cortolima
|
2017-09-29 08:24:25
| 427
| 227
|
68
|
Chirlobirlo
| 4.408155
| -75.147879
| 949
|
Cortolima
|
2017-09-29 08:21:41
| 427
| 405
|
69
|
Saman
| 4.408184
| -75.147984
| 949
|
Cortolima
|
2017-09-29 08:17:47
| 427
| 359
|
70
|
Acacio rojo
| 4.408012
| -75.14785
| 949
|
Cortolima
|
2017-09-29 08:12:12
| 427
| 146
|
71
|
Ocobo
| 4.408036
| -75.147905
| 949
|
Cortolima
|
2017-09-29 08:09:40
| 427
| 399
|
72
|
Palma areca
| 4.407845
| -75.147289
| 948
|
Cortolima
|
2017-09-29 08:04:44
| 427
| 153
|
73
|
Noni
| 4.408124
| -75.147205
| 948
|
Cortolima
|
2017-09-29 08:01:15
| 427
| 273
|
74
|
Pera de malaca
| 4.408183
| -75.147225
| 948
|
Cortolima
|
2017-09-29 07:58:27
| 427
| 396
|
75
|
Totumo
| 4.408358
| -75.147288
| 948
|
Cortolima
|
2017-09-29 07:55:52
| 427
| 136
|
76
|
Ocobo
| 4.408551
| -75.147219
| 948
|
Cortolima
|
2017-09-29 07:48:33
| 427
| 399
|
77
|
Ocobo
| 4.408567
| -75.147229
| 948
|
Cortolima
|
2017-09-29 07:41:55
| 427
| 399
|
78
|
Arbol de la felicidad
| 4.408503
| -75.14732
| 948
|
Cortolima
|
2017-09-29 07:34:40
| 427
| 149
|
79
|
Chirlobirlo
| 4.408503
| -75.147339
| 948
|
Cortolima
|
2017-09-29 07:31:31
| 427
| 405
|
80
|
Ocobo
| 4.408476
| -75.147379
| 948
|
Cortolima
|
2017-09-29 07:28:12
| 427
| 399
|
81
|
Papayuelo espinaco
| 4.408454
| -75.147449
| 948
|
Cortolima
|
2017-09-29 07:24:25
| 427
| 120
|
82
|
Palma areca
| 4.408435
| -75.147512
| 948
|
Cortolima
|
2017-09-29 07:20:20
| 427
| 153
|
83
|
Igua
| 4.408478
| -75.14757
| 948
|
Cortolima
|
2017-09-29 07:16:45
| 427
| 343
|
84
|
Payande
| 4.408665
| -75.147685
| 946
|
Cortolima
|
2017-09-29 07:08:22
| 427
| 321
|
85
|
Limon
| 4.406684
| -75.146053
| 945
|
Cortolima
|
2017-09-27 12:05:34
| 256
| 108
|
86
|
Aguacate
| 4.407421
| -75.147058
| 948
|
Cortolima
|
2017-09-27 11:55:24
| 256
| 306
|
87
|
Aguacate
| 4.407022
| -75.146159
| 949
|
Cortolima
|
2017-09-27 11:51:36
| 256
| 306
|
88
|
Cobalonga
| 4.406942
| -75.14599
| 945
|
Cortolima
|
2017-09-27 11:47:01
| 256
| 413
|
89
|
Almendro
| 4.407037
| -75.1472
| 951
|
Cortolima
|
2017-09-27 10:37:39
| 256
| 408
|
90
|
Oiti
| 4.407147
| -75.147299
| 948
|
Cortolima
|
2017-09-27 10:33:34
| 256
| 247
|
91
|
Oiti
| 4.407133
| -75.147267
| 948
|
Cortolima
|
2017-09-27 10:31:11
| 256
| 247
|
92
|
Tulipan africano
| 4.407332
| -75.147204
| 948
|
Cortolima
|
2017-09-27 10:27:20
| 256
| 387
|
93
|
Pera de malaca
| 4.407274
| -75.147207
| 948
|
Cortolima
|
2017-09-27 10:24:31
| 256
| 396
|
94
|
Marannon
| 4.407496
| -75.147741
| 948
|
Cortolima
|
2017-09-27 10:20:51
| 256
| 24
|
95
|
Oiti
| 4.407506
| -75.147782
| 951
|
Cortolima
|
2017-09-27 10:16:34
| 256
| 247
|
96
|
Munneco
| 4.407581
| -75.147965
| 951
|
Cortolima
|
2017-09-27 10:13:50
| 256
| 130
|
97
|
Pino libro
| 4.407626
| -75.148101
| 951
|
Cortolima
|
2017-09-27 10:10:57
| 256
| 323
|
98
|
Guanabano
| 4.408489
| -75.145839
| 942
|
Cortolima
|
2017-09-29 12:54:09
| 427
| 27
|
99
|
Pera de malaca
| 4.408879
| -75.146185
| 944
|
Cortolima
|
2017-09-29 12:49:46
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100
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Pera de malaca
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| 941
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Cortolima
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2017-09-29 12:46:29
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Urban Tree Census Data
This dataset was collected as part of the Urban Tree Observatory Project in Ibagué, Colombia.
It includes georeferenced records of urban trees, taxonomic details, physical measurements, and climate and observational data.
The main goal is to build and populate a structured PostgreSQL database using Django and SQLAlchemy.
Contents
biodiversity.csv: Main biodiversity registry per observed tree.climate.csv: Historical climate data associated with monitoring stations in Ibagué.measurements.csv: Physical measurements per tree (total height, DBH, volume, crown diameter, etc.).observations.csv: Information health condition, observations on field etc.places.csv: Geographic information where trees are located (name, neighborhood, municipality).taxonomy.csv: Taxonomic classification per unit (family, genus, species, common name).traits.csv: Functional traits per species such as maximum height, carbon sequestration potential etc.
Objective
To build a comprehensive database that enables ecological and functional analysis of urban trees, supporting applications like urban observatories, decision-making tools, and monitoring web platforms.
Data Source
The original dataset was downloaded from the official open data portal of Colombia:
Urban Tree Census in Ibagué - Secretaría de Ambiente y Gestión del Riesgo
The data was then cleaned, curated, and transformed by the local team of Omdena — GIBDET Colombia Chapter.
This process included designing a relational SQL schema, normalizing and enriching the dataset, and building a Geodatabase in PostgreSQL + PostGIS to support geospatial analysis and web-based applications.
License
MIT License — Free to use, modify, and redistribute with attribution.
Citation
Citation
Please cite this dataset as:
Juan Pablo Cuevas Gonzalez (2025). Omdena GIBDET Colombia Chapter. Urban Tree Census Data - Ibagué [Dataset]. Hugging Face. https://huggingface.co/datasets/juanpac96/urban_tree_census_data
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