PLANNING.md

# 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 = hour` at 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.csv` at `/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)

1. **Spine as canonical schema.** Every source adapts into one fixed record shape.
   Source-specific extras go in `extra` JSON blob.
2. **Append-only, provenance immutable.** `source`, `source_record_id`, `original_text`,
   `fetched_at` locked at write time. No overwrites. Reports are separate evidentiary
   artifacts, not views of one truth.
3. **Uncertainty as first-class fields.** Every fuzzy field gets a companion:
   `time_precision`, `geocode_method`, `geocode_confidence`, `location_uncertainty_km`.
4. **Environmental context = on-demand join.** Weather, RF, flight, seismic, geomagnetic
   NOT backfilled into records. Joined at query time against event_time + lat/lon.
5. **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-spine` was 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_identity` table 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.*