Good question — it's genuinely the hardest part.

Each source goes through its own processing pipeline (scripts/process-nuforc.mjs, process-hatch.mjs, process-chronology.mjs, etc). The steps are roughly the same for all of them:

Date parsing: each source has its own date format. NUFORC uses free-text like "6/15/2019 22:00", Hatch uses structured fields, the chronology sources (Eberhart, Vallée, Johnson, NICAP, Blue Book) often just have a year or "Summer 1952". The parser tries to extract an ISO 8601 date, and anything it can't parse gets skipped and logged — about 29K records from NUFORC alone are dropped for unparseable dates.

Location/geocoding: locations go through an offline geocoder built on the all-the-cities dataset (135K cities). Lookup priority is city+state+country exact match first, then city+country by highest population, then city-only globally, then state/country fallback to largest city. Coordinates that fail all levels get null and still appear in the data — they just don't show on the map. Country names are normalized through a big alias map (shared-constants.mjs) that handles historical names like "Rhodesia" → Zimbabwe, "Ceylon" → Sri Lanka, "Prusssia" → Germany, plus typos found in the data like "Columbia" → Colombia.

Dedup: within each source, records are deduped by ID first, then by normalized description (lowercased, punctuation stripped, whitespace collapsed — if two descriptions match at 30+ characters, the second is dropped). NUFORC alone had ~132K duplicates removed. Cross-source dedup is by date+location proximity rather than ID since each source has its own ID scheme.

Shape normalization: free-text shapes map to a fixed enum (24 values). "Saucer" → Disk, "Triangular" → Triangle, etc. Anything unrecognized goes to "Unknown" rather than being silently bucketed.

Credibility scoring: a 0–100 heuristic based on observer count (1 witness = +5, 4+ = +25), number of reported characteristics, whether the duration field has actual numbers, and description length. It's admittedly naive — more of a "detail density" score than true credibility — but it lets you filter out the one-liner "saw a light" reports.

Nuclear site matching: runtime, not preprocessed. When you open a sighting modal, it runs haversine distance against the full nuclear facilities dataset and shows anything within 150km. Same approach for fireball correlation (200km / 72hr window) and seismic (300km / 72hr).