README.md

metadata

license: mit
language:
  - en
task_categories:
  - text-classification
  - time-series-forecasting
size_categories:
  - 100K<n<1M
pretty_name: Danger UFO Dataset
tags:
  - ufo
  - uap
  - anomalous-phenomena
  - nuforc
  - provenance
  - geospatial
configs:
  - config_name: default
    data_files: data/danger-ufo-dataset/*.parquet

Danger UFO Dataset

A canonical, provenance-disciplined dataset of 327,009 UFO/UAP sighting reports. Built from scratch with first-class uncertainty modeling, adapter isolation, and append-only provenance — not a mirror of existing datasets.

Design principles

1. Spine as canonical schema. Every source maps into one fixed record shape. Source-specific extras live in a JSON extra blob — the spine never changes to accommodate a new source.
2. Append-only, provenance immutable. source, source_record_id, original_text, and fetched_at are locked at write time. No overwrites. Reports are separate evidentiary artifacts, not views of one truth to reconcile.
3. Uncertainty as first-class fields. Every fuzzy field has a companion: time_precision, geocode_method, geocode_confidence, location_uncertainty_km. A latitude with no companion says nothing about whether it came from GPS or geocoding "somewhere near Denver."
4. Environmental context = on-demand join. Weather, RF, flight, seismic data are NOT backfilled into records. Joined at query time against event_time + lat/lon.
5. Adapter isolation. Each adapter reads exactly one source. No dedup, no cross-referencing. Resolution is downstream.

Quick start

from datasets import load_dataset

ds = load_dataset("sidkrishna/danger-ufo-dataset")
print(f"{len(ds['train']):,} records")

# Or read Parquet directly
import pandas as pd
df = pd.read_parquet("data/danger-ufo-dataset/")

Schema

Field	Type	Nullable	Description
id	string (uuid4)	no	Assigned at ingestion
event_time	string (ISO8601 UTC)	yes	Best-known event time
time_precision	string	no	One of: second, minute, hour, day, month, year, unknown
latitude	float64	yes	Decimal degrees
longitude	float64	yes	Decimal degrees
location_uncertainty_km	float64	yes	Radius of uncertainty
geocode_method	string	no	source_provided, mirror_geocoded, city_centroid, unknown
geocode_confidence	string	no	high, medium, low, unknown
source	string	no	Our source tag (e.g. hf_mirror_cjc0013)
source_record_id	string	no	ID in the source's own namespace
original_text	string	no	Verbatim sighting narrative
fetched_at	string (ISO8601 UTC)	no	When we pulled the source data
extra	string (JSON)	yes	Source-specific fields that don't fit the spine

Companion field semantics

• time_precision: How precisely we know the event time. Current source uses hour because the upstream local→UTC conversion is unverified.
• geocode_method: How lat/lon was derived. Current source uses mirror_geocoded because coordinates come from upstream Kaggle mirror geocoding, not original GPS.
• geocode_confidence: Coarse trust level. Current source uses medium — Kaggle mirrors have quality variation.
• location_uncertainty_km: Currently null for this source (the mirror didn't preserve this data). Will be populated for sources that provide it.

Current sources

Source tag	Description	Records
hf_mirror_cjc0013	NUFORC-derived Kaggle aggregations, cleaned and clustered (via cjc0013/Ufo_data_clustered)	327,009

Planned sources

Order	Source	What it adds
2	NUFORC raw	Shape/duration fields, authentic submission metadata, proper geocoding
3	Project Blue Book	USAF disposition classifications (1947–1969)
4	GEIPAN (France)	Scientifically vetted cases with weirdness/consistency scoring
5+	AARO, SEFAA, Galileo Project, SCU	Sensor-validated, document-centric, and academic data

What's in extra

For hf_mirror_cjc0013 records, the extra JSON blob contains all source fields that don't map to the spine: cluster_id, prob, moon_illum, moon_alt_deg, nearest_airport_km, nearest_airport_code, wx_bucket, city, state, country, and original_src (the Kaggle source tag). HDBSCAN cluster labels reflect text similarity only — not verified event categories.

Known limitations

• Timestamp precision: time_precision = hour for all current records. Upstream local→UTC conversion is unverified. Do not treat event_time as more precise than an hour boundary.
• Geographic bias: 97%+ of records are from US/Canada. This is a reporting bias, not a physical distribution.
• No deduplication: Records may appear multiple times across different Kaggle mirror lineages. Cross-source dedup is a downstream operation.
• No shape/duration: These fields were dropped during upstream cleaning. They exist in raw NUFORC but haven't been ingested yet (planned as source #2).
• Cluster labels are not event categories: cluster_id and prob in extra come from text-similarity grouping (BGE → UMAP → HDBSCAN). They surface linguistic themes, not verified event types.

Format

• Parquet (primary): 7 shards, 59 MB total, zstd compression
• JSONL (convenience): 1 file, 264 MB, for line-by-line access

License

Source data was public on Kaggle. This cleaned, schema-normalized version is released under MIT for research and educational use.

Build

python3 adapters/hf_mirror.py

Reads from data/raw/ufo_data_clustered.jsonl (the upstream HF mirror JSONL), writes Parquet shards to data/danger-ufo-dataset/ and JSONL to data/danger-ufo-dataset.jsonl.