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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 16 new columns ({'gps_code', 'home_link', 'wikipedia_link', 'iata_code', 'name', 'scheduled_service', 'iso_country', 'local_code', 'latitude_deg', 'ident', 'elevation_ft', 'continent', 'keywords', 'iso_region', 'municipality', 'longitude_deg'}) and 4 missing columns ({'frequency_mhz', 'airport_ident', 'description', 'airport_ref'}).

This happened while the csv dataset builder was generating data using

hf://datasets/cjc0013/Ufo_data_clustered/source/airports.csv (at revision a9bd94bfd224fad09c4f2d34f65da802d3d7e399)

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 "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1831, in _prepare_split_single
                  writer.write_table(table)
                File "/usr/local/lib/python3.12/site-packages/datasets/arrow_writer.py", line 714, in write_table
                  pa_table = table_cast(pa_table, self._schema)
                             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2272, in table_cast
                  return cast_table_to_schema(table, schema)
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/table.py", line 2218, in cast_table_to_schema
                  raise CastError(
              datasets.table.CastError: Couldn't cast
              id: int64
              ident: string
              type: string
              name: string
              latitude_deg: double
              longitude_deg: double
              elevation_ft: double
              continent: string
              iso_country: string
              iso_region: string
              municipality: string
              scheduled_service: string
              gps_code: string
              iata_code: string
              local_code: string
              home_link: string
              wikipedia_link: string
              keywords: string
              -- schema metadata --
              pandas: '{"index_columns": [{"kind": "range", "name": null, "start": 0, "' + 2401
              to
              {'id': Value('int64'), 'airport_ref': Value('int64'), 'airport_ident': Value('string'), 'type': Value('string'), 'description': Value('string'), 'frequency_mhz': 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 1455, 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 1054, in convert_to_parquet
                  builder.download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 894, in download_and_prepare
                  self._download_and_prepare(
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 970, in _download_and_prepare
                  self._prepare_split(split_generator, **prepare_split_kwargs)
                File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1702, in _prepare_split
                  for job_id, done, content in self._prepare_split_single(
                                               ^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/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 16 new columns ({'gps_code', 'home_link', 'wikipedia_link', 'iata_code', 'name', 'scheduled_service', 'iso_country', 'local_code', 'latitude_deg', 'ident', 'elevation_ft', 'continent', 'keywords', 'iso_region', 'municipality', 'longitude_deg'}) and 4 missing columns ({'frequency_mhz', 'airport_ident', 'description', 'airport_ref'}).
              
              This happened while the csv dataset builder was generating data using
              
              hf://datasets/cjc0013/Ufo_data_clustered/source/airports.csv (at revision a9bd94bfd224fad09c4f2d34f65da802d3d7e399)
              
              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.

id
int64
airport_ref
int64
airport_ident
string
type
string
description
string
frequency_mhz
float64
70,518
6,528
00CA
CTAF
CTAF
122.9
307,581
6,589
01FL
ARCAL
null
122.9
75,239
6,589
01FL
CTAF
CEDAR KNOLL TRAFFIC
122.8
60,191
6,756
04CA
CTAF
CTAF
122.9
59,287
6,779
04MS
UNIC
UNICOM
122.8
60,682
6,784
04NV
UNIC
UNICOM
123
60,091
6,812
05CL
CTAF
CTAF
122.9
63,835
6,853
05UT
UNIC
UNICOM
122.8
70,676
6,868
06FA
APP
PALM BEACH APP
124.6
70,677
6,868
06FA
GND
GND
121.65
70,678
6,868
06FA
TWR
TWR
120.4
65,868
6,887
06MO
CTAF
CTAF
122.9
71,176
6,924
07FA
UNIC
UNICOM
122.7
66,637
6,943
07MT
CNTR
SALT LAKE CITY CNTR
126.85
69,383
6,954
07TE
CNTR
HOUSTON CNTR
126.625
61,673
7,008
08TE
UNIC
UNICOM
122.8
61,013
7,019
09CL
CTAF
CTAF
122.9
59,566
7,021
09FA
UNIC
UNICOM
122.8
68,176
7,054
09TS
CNTR
ALBUQUERQUE CNTR
132.65
59,735
7,094
0AZ2
UNIC
UNICOM
122.95
328,741
7,102
0C2
CTAF
null
122.9
62,825
7,116
0CA9
CTAF
CTAF
122.9
59,296
7,213
0ID2
ARTC
SALT LAKE CITY CNTR
128.35
60,367
7,542
0TE4
UNIC
CTAF/UNICOM
122.8
59,580
7,543
0TE5
CNTR
HOUSTON CNTR
126.625
59,437
7,545
0TE7
UNIC
UNICOM
122.8
67,677
7,569
0TX1
UNIC
UNICOM
123.05
59,357
7,712
11II
CNTR
INDIANAPOLIS CNTR
134.85
59,358
7,712
11II
CTAF
CTAF
126.2
59,360
7,712
11II
MISC
RNG CON
38.9
59,359
7,712
11II
MISC
ANG RNG CON
138.25
71,354
7,781
12NC
APP
CHERRY POINT APP
119.75
69,316
7,789
12OK
UNIC
UNICOM
123
70,478
7,838
13NC
APP
CHERRY POINT APP
119.35
333,203
7,838
13NC
CTAF
null
322.1
70,367
7,898
14NC
A/D
CHERRY POINT APP
119.35
66,604
7,917
14TS
CNTR
HOUSTON CNTR
126.625
59,174
7,928
15AR
UNIC
RDO CTL
122.725
68,311
7,934
15FL
UNIC
UNICOM
123
60,025
8,028
16X
CNTR
FORT WORTH CNTR
127.45
70,821
8,082
18AZ
UNIC
UNICOM
122.975
67,241
8,117
18TA
CNTR
HOUSTON CNTR
126.625
62,339
8,118
18TE
UNIC
UNICOM
122.8
61,636
8,205
1AZ0
UNIC
UNICOM
122.725
67,423
8,222
1CA1
CTAF
CTAF
122.9
59,235
8,233
1CD2
APP
DENVER APP
119.3
59,171
8,297
1GA0
UNIC
UNICOM
122.725
70,225
8,486
1MS8
APP
MERIDIAN APP
31.48
298,625
8,732
1WA6
CTAF
Fall City Traffic
122.9
59,128
8,769
1XS8
CNTR
HOUSTON CNTR
126.625
70,996
8,787
20GA
MULT
MULT
122.9
63,036
8,924
22XS
A/G
TWR
143.35
63,037
8,924
22XS
ATIS
ATIS
118.8
63,038
8,924
22XS
INFO
RNG CTL
30.45
63,039
8,924
22XS
MISC
FORT HOOD FLT FLW
38.75
63,040
8,924
22XS
OPS
GRAY OPS
38.7
63,041
8,924
22XS
PMSV
PMSV GRAY METRO
41.2
63,042
8,924
22XS
TWR
GRAY TWR
120.75
60,377
8,982
23XS
A/G
LONGHORN AUX TWR
143.35
60,378
8,982
23XS
ATIS
ATIS
118.8
60,379
8,982
23XS
INFO
RNG CTL
30.45
60,380
8,982
23XS
MISC
FORT HOOD FLT FLW
38.75
60,381
8,982
23XS
OPS
GRAY OPS
38.7
60,382
8,982
23XS
PMSV
PMSV GRAY METRO
41.2
60,383
8,982
23XS
TWR
GRAY TWR
120.75
67,720
8,990
24CL
CTAF
CTAF
122.9
60,694
9,068
25NC
MULT
MULTICOM
122.9
60,360
9,078
25TA
CNTR
HOUSTON CNTR
126.625
64,363
9,080
25TS
CNTR
ALBUQUERQUE CNTR
132.65
61,802
9,138
27AZ
CTAF
CTAF
122.9
59,707
9,191
28CL
CTAF
CTAF
122.8
63,365
9,223
28TA
CNTR
HOUSTON CNTR
126.625
63,366
9,223
28TA
UNIC
UNICOM
122.8
69,599
9,236
29AZ
UNIC
UNICOM
122.8
64,791
9,277
29TX
CNTR
FORT WORTH CNTR
127.45
62,214
9,354
2CL9
CTAF
CTAF
122.9
71,454
14,060
2CN4
CTAF
CTAF
122.9
333,743
9,368
2D7
APP/DEP
CLEVELAND APP/DEP
125.5
333,742
9,368
2D7
CTAF
CTAF/UNICOM
122.8
62,263
20,770
2IG4
UNIC
UNICOM
122.8
60,042
9,696
2OR1
UNIC
UNICOM
122.8
67,219
9,772
2TA6
CNTR
FORT WORTH CNTR
127.45
63,137
9,774
2TA8
CNTR
HOUSTON CNTR
126.625
71,475
9,784
2TE8
CNTR
HOUSTON CNTR
126.625
70,329
9,809
2TX3
CNTR
HOUSTON CNTR
126.625
60,402
9,810
2TX4
CNTR
HOUSTON CNTR
126.625
69,995
9,835
2VG2
UNIC
UNICOM
122.725
60,635
9,880
2XA0
CNTR
FORT WORTH CNTR
127.45
60,636
9,880
2XA0
CTAF
CTAF
122.9
70,047
9,891
2XS3
CNTR
FORT WORTH CNTR
127.45
67,073
9,893
2XS5
CNTR
HOUSTON CNTR
126.625
61,432
9,985
31VA
UNIC
UNICOM
122.75
68,592
10,080
34AZ
UNIC
UNICOM
122.8
67,703
10,175
36CA
CTAF
CTAF
122.9
71,005
10,177
36CN
CTAF
CTAF
122.9
61,623
10,214
36WI
CTAF
CTAF
122.9
61,624
10,214
36WI
MISC
LIGHT
122.6
71,572
10,265
38AZ
AWOS
AWOS 2
119.225
71,573
10,265
38AZ
CTAF
CTAF
122.8
58,969
10,268
38CA
UNIC
UNICOM
122.8
End of preview.

UFO Sightings – Cleaned & Unified Dataset (~327k rows)

This dataset merges several publicly available UFO sighting datasets from Kaggle into one cleaned, standardized, and enriched file. The goal is simply to provide a consolidated dataset instead of many fragmented sources with inconsistent formatting.

This release contains a single JSONL file with approximately 327,000 records.

No private or identifying information was present in the original data.


πŸ“¦ Source

All entries originate from publicly available UFO sighting datasets on Kaggle. Each row corresponds to a single reported sighting. Source Files located in source folder

🧹 Cleaning / Normalization Performed

All rows in this unified file were standardized using the same basic rules:

  • timestamps parsed and converted into a consistent t_utc (ISO-8601, UTC)
  • city/state/country fields harmonized where possible
  • latitude/longitude coerced to floats
  • basic HTML/unicode cleanup in free-text descriptions (text)
  • invalid or fully unparseable rows removed
  • source field preserved as src

No interpretation or filtering based on content was performed.


✨ Added Contextual Fields

A small number of lightweight β€œsidecar” fields were added based on timestamp + coordinates:

  • moon_illum β€” moon illumination fraction
  • moon_alt_deg β€” moon altitude in degrees
  • nearest_airport_code β€” closest airport (ICAO)
  • nearest_airport_km β€” distance to that airport in km
  • wx_bucket β€” rough weather bucket (coarse category)

These values are approximate and should be treated as exploratory metadata only.


🧩 Clustering Fields (Included in the File)

The dataset includes two fields that come from text-similarity grouping:

  • cluster_id β€” numeric label
  • prob β€” membership confidence

These reflect text similarity, not verified categories or event types. They are included because they were already part of the cleaned file.


πŸ“ Field Reference

Each row has the following structure (example):

{
  "uid": "scrubbed/row327047",
  "t_utc": "2013-09-09T09:51:00.000Z",
  "lat": 32.7152778,
  "lon": -117.1563889,
  "text": "2 white lights zig-zag over Qualcomm Stadium...",
  "src": "scrubbed",
  "city": "san diego",
  "state": "ca",
  "country": "US",

  "cluster_id": 725,
  "prob": 1.0,

  "moon_illum": 0.163603127,
  "moon_alt_deg": -67.0003509521,
  "nearest_airport_km": 3.7174715996,
  "nearest_airport_code": "KSAN",
  "reports_z": null,
  "wx_bucket": "unknown"
}

Field descriptions

Field Type Notes
uid string Stable row identifier
t_utc string Event timestamp, ISO-8601 UTC
lat, lon float Approximate coordinates
city, state, country string Cleaned location fields (best-effort)
text string Free-text sighting description
src string Original Kaggle dataset source
cluster_id int Text-similarity cluster (for research use only)
prob float Cluster membership probability
moon_illum float Moon illumination (0–1)
moon_alt_deg float Moon altitude in degrees
nearest_airport_km float Distance to nearest airport
nearest_airport_code string ICAO code
wx_bucket string Approximate weather category
reports_z float/null Unused placeholder field (kept for completeness)

⚠️ Notes & Limitations

  • Accuracy of timestamps and locations depends entirely on original reporting.
  • Weather buckets are coarse (not NOAA-grade).
  • Airport distances are approximate nearest-neighbor lookups.
  • Cluster labels are based solely on text similarity and do not reflect event reality.
  • No claims are made about the nature or validity of any sighting.

πŸ“„ License

Source data was public on Kaggle. This cleaned, merged, and lightly enriched version is released for research and educational use. Users should follow the original dataset licensing terms.

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