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juanpac96
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urban_tree_census_data

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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	427	396
100	Pera de malaca	4.408696	-75.146106	941	Cortolima	2017-09-29 12:46:29	427	396

End of preview.

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.

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

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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Urban Tree Census Data

Contents

Objective

Data Source

License

Citation

Citation