datasets:
- kaggle
language:
- en
task_categories:
- text-classification
- text-retrieval
- time-series-forecasting
pretty_name: UFO Sightings Unified Semantic Dataset
size_categories:
- 100K<n<1M
license: mit
tags:
- ufo
- ufology
- anomalous-phenomena
- embeddings
- hdbscan
- semantic-search
- cluster-analysis
- dataset
- geospatial
- moon-illumination
- aviation
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.
📦 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 “sidecar” fields were added based on timestamp + coordinates:
moon_illum— moon illumination fractionmoon_alt_deg— moon altitude in degreesnearest_airport_code— closest airport (ICAO)nearest_airport_km— distance to that airport in kmwx_bucket— 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— numeric labelprob— membership confidence
These reflect text similarity, not verified categories or event types. They are included because they were already part of the cleaned file.
📝 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.
📄 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.