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wildfire-final-size

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The dataset viewer is not available for this split.

Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.

Error code:   FeaturesError
Exception:    ArrowTypeError
Message:      ("Expected bytes, got a 'float' object", 'Conversion failed for column FIRE_CODE with type object')
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 244, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 4523, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2768, in _head
                  return next(iter(self.iter(batch_size=n)))
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2972, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ~~~~~~~~~~~~~~~~~~~~~~^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2483, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 430, in _iter_arrow
                  for key, pa_table in self.generate_tables_fn(**gen_kwags):
                                       ~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
                File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/csv/csv.py", line 199, in _generate_tables
                  pa_table = pa.Table.from_pandas(df)
                File "pyarrow/table.pxi", line 4768, in pyarrow.lib.Table.from_pandas
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 651, in dataframe_to_arrays
                  arrays = [convert_column(c, f)
                            ~~~~~~~~~~~~~~^^^^^^
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 639, in convert_column
                  raise e
                File "/usr/local/lib/python3.14/site-packages/pyarrow/pandas_compat.py", line 633, in convert_column
                  result = pa.array(col, type=type_, from_pandas=True, safe=safe)
                File "pyarrow/array.pxi", line 390, in pyarrow.lib.array
                File "pyarrow/array.pxi", line 91, in pyarrow.lib._ndarray_to_array
                  check_status(NdarrayToArrow(pool, values, mask, from_pandas,
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
                  raise convert_status(status)
              pyarrow.lib.ArrowTypeError: ("Expected bytes, got a 'float' object", 'Conversion failed for column FIRE_CODE with type object')

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FPA FOD-Attributes: yearly files 1992-2020 (file mirror)

This repository is a verbatim mirror for reproducible builds: it holds exact copies (byte for byte) of selected files of the published original listed below; SHA256SUMS gives the SHA-256 of each file here. Please cite and credit the original authors.

The 29 yearly files of the FPA FOD-Attributes Zenodo record (version 1.0), under their original names: 1992_FPA_FOD_cons.csv to 2020_FPA_FOD_cons.csv, one per fire year. The record's combined file FPA_FOD_Plus.csv is not included.

Total: 5.03 GB (5,025,049,182 bytes). Check a download with sha256sum -c SHA256SUMS; the MD5 of each file is the one the Zenodo record publishes.

file	bytes	sha256	md5 (as published on Zenodo)
`1992_FPA_FOD_cons.csv`	139,775,538	`b217bc91a7b90e112f6ce3470bda832a01d14ac76c2459f7fb918044c07f22a1`	`ae5d8f188b50fe440b0c01eb0e15a77f`
`1993_FPA_FOD_cons.csv`	127,781,137	`149d9469d192e0921bb640bdc398e551b6d037c5f28ea5532c50694f1d9cd6cd`	`f33b2ccb20fa46743df6f53caaa871bd`
`1994_FPA_FOD_cons.csv`	156,211,252	`dfb35efd6109cf628187a7507edad38ac61f665e770e5e38c75b0dc73bbe85da`	`0a330b1fe14df638c2bc016981e2dd08`
`1995_FPA_FOD_cons.csv`	146,779,628	`615874b153ca19772e8aca0354e318d112d916776a0b6fbc8028a2d20c7845cf`	`9146b715a38bf4c1ea98d3fbaf7aaf23`
`1996_FPA_FOD_cons.csv`	155,633,665	`0c421a652bead34822239d5254c584e6cd8486afa3ca6a75ba0ab8dec3bf00ec`	`3bc556e29b7d7ce73046893d3cd064d7`
`1997_FPA_FOD_cons.csv`	125,509,597	`26aeb957db41abc513a71b8d75af01eb203a00a97e6c5652738c9f176e07fc08`	`a4c1d53d54c6e6fb0a8b2e7c622688df`
`1998_FPA_FOD_cons.csv`	139,159,621	`13a39e4eade55d197bffe51bf9799173b30499d0480f594c49b6ec1fbc722b75`	`cbd2deec089c91c6cd0637fd37d4fc34`
`1999_FPA_FOD_cons.csv`	182,035,358	`68209aeeeb405825a648e50b8a0a437f3ac5dd971bcd2c2343dbf5f7ee9ccf19`	`48af5ccd1e19d59b6b1b59f7cfc66ab9`
`2000_FPA_FOD_cons.csv`	200,783,859	`5f310c4d96b8f67c0b5acc0ebb7ada40545db2552ad71b82e95d64ae917d1247`	`6d15a018cc87e9173badee99477ce113`
`2001_FPA_FOD_cons.csv`	189,755,975	`5a99b7d9aae450a7034ebd09c54b6182fe9fe6e24f381290b55a91282f3dfe12`	`4b85ca68e8217f8c7c17dc45edff3d12`
`2002_FPA_FOD_cons.csv`	166,231,685	`cd91f9317dc7778156b62a50645d1dbdbe5710bdfc31d29ebb56bad56cf681d1`	`7e4af3f7cd6b36d15004fa119833b8c4`
`2003_FPA_FOD_cons.csv`	146,847,742	`09f5025555afb3db6d5a3034d7b4a1aa2372a0952e0c11b6f61049285ab82d40`	`9ad17507e2e0b467d5bac62b0cb896f1`
`2004_FPA_FOD_cons.csv`	150,762,985	`d6c754ec0e1c45b240b29dc92aef2e388da5036bd01ed645e65f3279c0a89534`	`927427dbf9844680aca61e3b26c4e94b`
`2005_FPA_FOD_cons.csv`	201,748,041	`89b2ff3ec28c93251b4767dfe9b49b4fb4723e85845a806f020898f701085c7c`	`18f7835fe755403dac2d24350fcdefe6`
`2006_FPA_FOD_cons.csv`	258,291,429	`d1aa6b5c813990c3a6f45e6f8d3442cc8e68384b2e2f2913232fd9c84bdc60ae`	`80b45fba7cd2016fba7a870a970626df`
`2007_FPA_FOD_cons.csv`	217,948,682	`d76213daf65d192aba67ee3c2146dd6c053a83e465801fc8c8163d6019a3a780`	`820d1df7017343e0b6e4d1f4e0310890`
`2008_FPA_FOD_cons.csv`	197,820,889	`0ec64f5f4cc96a500df730bded7a3c5bee3975619c61f7cb1af34638de8f7ed5`	`ff2294359bac5969f51f4def7d6c2f6b`
`2009_FPA_FOD_cons.csv`	181,845,466	`945c32b1ee5e45358ba3bd7c7470178a29d2a2b14d772653727b9dbde0f5ef4a`	`9cefadf3611ee1071144e8fb5d11acaf`
`2010_FPA_FOD_cons.csv`	190,672,745	`c7761457f15f065e4c89ac2bf19eee7bc5ee0cc8c769c9c70c4e7184f7020269`	`e1e0f6b8c056fcbd3a291bbd147f28aa`
`2011_FPA_FOD_cons.csv`	222,000,668	`5d962375785b0addeb69d2a1d50890236b3d8c2f67dc5db1258bacfcf0e1afbd`	`c226eb2cb1e3e7955a45167ff181024b`
`2012_FPA_FOD_cons.csv`	166,891,051	`7614f896129c8a8103162672a14812112b185d4b617c939d594d27991db6c27d`	`22aca1c2f06e35df1fdbe2baee82ab78`
`2013_FPA_FOD_cons.csv`	150,155,219	`0dcf2cd88791aceb91d7fe249f57be55a29579f665228ab720cb8dc77af43fb6`	`e3f2ef3d26884d545267057c15791b3c`
`2014_FPA_FOD_cons.csv`	160,447,054	`69dcfdd455a8515475d92ca337ec16a96baccf146721766e9875de954426acf7`	`dfc3a3bc7dd7385ab5109eb7100b821b`
`2015_FPA_FOD_cons.csv`	175,425,318	`d813563b44ff4055a3e7de31070babd468708619515a43aae82dd89b93aa7b20`	`8e0bee4f19fb5047be807581fb776554`
`2016_FPA_FOD_cons.csv`	188,108,189	`4145c8e8b2ba9a58a9c287ae6e0eb8ec652cc49a5d83a7b352b480a9ac223006`	`2a37db809b882746e253ecc8893f9038`
`2017_FPA_FOD_cons.csv`	188,877,690	`52f5025f958d9c0e7672d907aebfac2e5addb17256bb4a892bc77e3a0a17848e`	`91322e895946fb22d570b9e6f247eda0`
`2018_FPA_FOD_cons.csv`	186,160,898	`f1267c0e8f3a4d081e36b6e8fced0c5534ad9b2fd6824d5a6fa1a5814c845270`	`e6a2a00e8990fe38e31e70d7f0d8110b`
`2019_FPA_FOD_cons.csv`	143,602,358	`217c9b808b7a90271090aa2912386969daff22daab0305d9ccfd1fe3e9797372`	`a48e542e51ad1c0394caf78744616484`
`2020_FPA_FOD_cons.csv`	167,785,443	`79e0cbc27dad54b4ca0af66884f794de21d15223ffe3e2b329bae92c457bdec1`	`113cfd9fe9f85541f9b573b8c694ebfe`

Source

Y. Pourmohamad, J. Abatzoglou, E. Belval, K. Short, E. Fleishman, M. Reeves, N. Nauslar, P. Higuera, E. Henderson, S. Ball, A. AghaKouchak, J. Prestemon, J. Olszewski and M. Sadegh, "Physical, Social, and Biological Attributes for Improved Understanding and Prediction of Wildfires: FPA FOD-Attributes Dataset", version 1.0, Zenodo (2023), record 8381129, https://doi.org/10.5281/zenodo.8381129 (https://zenodo.org/records/8381129).
Described in Y. Pourmohamad et al., "Physical, social, and biological attributes for improved understanding and prediction of wildfires: FPA FOD-Attributes dataset", Earth System Science Data 16, 3045-3060 (2024), https://doi.org/10.5194/essd-16-3045-2024.
The dataset augments the Fire Program Analysis fire-occurrence database, 6th edition (K. C. Short, "Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]", USDA Forest Service Research Data Archive, 2022, https://doi.org/10.2737/RDS-2013-0009.6), with attributes from the public sources listed in the paper.

Licence

The Zenodo record releases the files under Creative Commons Attribution 4.0 International (CC BY 4.0, https://creativecommons.org/licenses/by/4.0/). The files here are unmodified.

Attribution: FPA FOD-Attributes by Y. Pourmohamad, J. Abatzoglou, E. Belval et al., Zenodo record 8381129, version 1.0 (CC BY 4.0); unmodified files.

No endorsement by the original authors is implied.

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FPA FOD-Attributes: yearly files 1992-2020 (file mirror)

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