UAP Consolidated Dataset — Planning Phase
What we're building
A single canonical UAP/anomaly dataset on HuggingFace, built from scratch. Not a mirror of existing datasets — a ground-up assembly with provenance discipline, uncertainty modeling, and adapter isolation as first-class concerns.
What's being abandoned
The old uap-spine project (SQLite + HTML viz on Spark). Its five architecture principles transfer
cleanly. Its execution path (HF mirror → SQLite → static HTML) doesn't. This is a HuggingFace
dataset, not a local database with a web frontend.
Context inventory (what's now in memory)
1. Existing processed data (tau/Ufo_data_clustered)
- 327,009 records, JSONL, 142MB
- Sourced from Kaggle NUFORC-derived CSVs
- Fields: uid, t_utc, lat, lon, text, src, city, state, country, cluster_id, prob, moon_illum, moon_alt_deg, nearest_airport_km, nearest_airport_code, wx_bucket, reports_z
- Pipeline: Cleaning → BGE-large-en-v1.5 embeddings → UMAP-15 → HDBSCAN → sidecar enrichment
- Limitations:
- Almost entirely NUFORC-lineage. No Blue Book, no AARO
- Local→UTC timestamp conversion unverified (
time_precision = hourat best) - Cluster labels are text-similarity only, not event categories
- Likely duplicates across Kaggle mirror lineages
- Shape and duration fields dropped during cleaning
- Astronomical sidecars (moon_*) depend on unverified timestamps — do not trust
2. Raw source CSVs (from tau source/)
- nuforc_reports.csv (116MB), scrubbed.csv (14MB), ufo_sightings_scrubbed.csv (14MB), ufo_sighting_data.csv (14MB), complete.csv (15MB), data.csv (43MB), weatherHistory.csv (16MB), airports.csv (8.7MB), runways.csv (3.0MB), airport-frequencies.csv (1.2MB), definitions_and_sources.csv (57KB)
- All Kaggle-derived
3. Raw CSV from downloads
ufo_sightings_1900_2024.csvat/mnt/shared/MyFiles/Downloads/ufo-dataset-downloads/— alternate source, needs inspection for overlap/redundancy
4. Global dataset landscape (from overview document)
- Civilian: NUFORC, MUFON, Kaggle mirrors
- US Government: AARO (imagery, case resolutions), PURSUE (rolling declassification tranches, unredacted kinematics), NARA RG 615/341 (Blue Book, Condon, Roswell, Presidential libraries), FBI/CIA vaults
- International: GEIPAN (France, weirdness/consistency scoring), SEFAA (Chile, aviation focus), UK National Archives, LAC (Canada), NAA (Australia)
- Academic: Galileo Project (Harvard, custom calibrated observatories), SCU on Zenodo (morphological analysis, operational presence, Aguadilla), NASA Independent Study
- ML hazard: acronym collisions with "UAP" (Universal Adversarial Perturbations) and "UFO" (Urban Flood Observations, Unified Fine-grained Perception)
5. Architecture principles (from spine project — still correct)
- Spine as canonical schema. Every source adapts into one fixed record shape.
Source-specific extras go in
extraJSON blob. - Append-only, provenance immutable.
source,source_record_id,original_text,fetched_atlocked at write time. No overwrites. Reports are separate evidentiary artifacts, not views of one truth. - Uncertainty as first-class fields. Every fuzzy field gets a companion:
time_precision,geocode_method,geocode_confidence,location_uncertainty_km. - Environmental context = on-demand join. Weather, RF, flight, seismic, geomagnetic NOT backfilled into records. Joined at query time against event_time + lat/lon.
- Adapter isolation. Each adapter reads one source. Does not dedupe. Does not cross-reference. Resolution is downstream.
6. Canonical spine schema (from spine project — the frozen contract)
| field | type | nullable | notes |
|---|---|---|---|
id |
text (uuid4) | no | assigned at ingestion |
event_time |
text (ISO8601 UTC) | yes | best-known event time |
time_precision |
text | no | second / minute / hour / day / month / year / unknown |
latitude |
real | yes | |
longitude |
real | yes | |
location_uncertainty_km |
real | yes | |
geocode_method |
text | no | source_provided / mirror_geocoded / city_centroid / unknown |
geocode_confidence |
text | no | high / medium / low / unknown |
source |
text | no | our source tag |
source_record_id |
text | no | id in source's own namespace |
original_text |
text | no | verbatim narrative from source |
fetched_at |
text (ISO8601 UTC) | no | when we pulled it |
extra |
text (JSON) | yes | source-specific extras |
Constraints: PK on id, indexes on event_time, source, (latitude, longitude).
No unique constraint on (source, source_record_id) — dedup is downstream.
7. Source ingestion plan (from spine project — adapted for HF)
| Order | Source | Tag | What it contributes |
|---|---|---|---|
| 1 | NUFORC-derived Kaggle data | hf_mirror_cjc0013 |
327k records, fastest path to working pipeline |
| 2 | NUFORC raw | nuforc_raw |
Shape/duration fields, authentic submission metadata |
| 3 | Project Blue Book | bluebook |
Disposition field (official USAF classifications) |
| 4 | GEIPAN (France) | geipan |
Scientifically vetted, weirdness/consistency scored |
| 5+ | AARO, SEFAA, Galileo, SCU | tbd | Document-centric / sensor-validated / academic |
8. Old spine project's open forks (still relevant)
- Repo name:
uap-spinewas placeholder. Needs a real name. - Source order: HF mirror first (fastest path), then whichever stresses untested architecture. NUFORC-raw stresses geocoding/timezone. Blue Book stresses source-specific field handling.
- disposition field: stays in
extra, not promoted to spine (avoids schema pressure from future sources with their own labels) - AARO adapter shape: document corpus → OCR/extraction → intermediate events → spine. Two-stage pipeline, not one monolithic adapter.
- Cross-source identity resolution: downstream queryable relation, not a mutation
on the spine.
event_identitytable mapping spine record clusters to canonical event ids. - Storage: HF datasets as primary distribution. Parquet format. Local SQLite for development/testing.
What needs decisions (planning phase)
A. Dataset name
uap-spine was placeholder. Considerations: short, doesn't overpromise, doesn't bind to a
single source. Open.
B. HuggingFace format
Parquet is standard for HF datasets. Could also ship JSONL for line-by-line access. Default: Parquet as primary, JSONL as convenience mirror.
C. Adapter implementation language
Python with datasets library. Each adapter is a script that reads source → writes
Parquet shards to data/ directory → datasets.load_dataset() reads them.
D. First adapter scope
Option A: Clean re-ingestion of the HF mirror (adapt existing JSONL → spine schema with uncertainty fields populated honestly, drop the untrusted sidecars). Option B: NUFORC-raw first (get shape/duration fields, do geocoding properly). Option C: Both in sequence, starting with the mirror for speed.
E. What to do with the existing clustered data
The cjc0013/Ufo_data_clustered dataset already exists on HF. This project is a
separate dataset with a different contract (spine schema + provenance). The existing
data is a source, not the destination. We map it in, we don't extend it.
F. The 1900-2024 CSV
The ufo_sightings_1900_2024.csv at /mnt/shared/MyFiles/Downloads/ufo-dataset-downloads/
is unexplored. Need to determine: source lineage, overlap with other sources, field
completeness, whether it adds anything NUFORC-derived sources don't.
Immediate next step
The planning deliverable is answers to A–F above from Sid (name, format, first-adapter scope, posture on existing data). Then: adapter #1 build.
Context consolidated 2026-07-30. Sources: tau/Ufo_data_clustered, old spine docs, UFO Datasets Overview and Links.md, freshly built ufo-consolidated repo.