ARGUS — UAP Intelligence Command

Not a UFO blog. An AI investigation system.

ARGUS is a full-stack data science platform that analyzes 79,621 declassified sightings from the National UFO Reporting Center (NUFORC), runs them through an NLP pipeline, and surfaces patterns humans miss at scale — bias corrections, per-capita normalization, behavioral clustering, and credibility scoring included.

Live Demo →  ·  LinkedIn Post →  ·  ▶ Watch Walkthrough (1m47s)

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Key Findings

Finding	Stat	Method
Night dominance	61% of sightings occur 8pm–2am	Computed from hourly breakdown across all 79,621 records
2012 spike is reporting bias	Flattens when normalized	Corrected using World Bank internet penetration data
True hotspot	Washington State #1 at 58.6/100k	US population normalized via 2010 Census
Hardest to explain	77 cases combine silent flight + instant acceleration	Rule-based NLP across full corpus
Shape shift	Triangle reports 3× more common since 1980s	Computed from decade-level shape breakdown
Physics language	207 reports use explicit violation terms	Keyword extraction from raw witness text

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What's Inside

Intelligence Pipeline

NUFORC Raw Data (79,621 reports)
        ↓
    COLLECT      — ingest from public NUFORC database
        ↓
    EXTRACT      — rule-based NLP: shape, speed, movement, duration,
                   military context, physics-violation language
        ↓
    ANALYZE      — bias correction, per-capita normalization,
                   temporal and geographic pattern detection
        ↓
    CLUSTER      — behavioral grouping: 6 clusters by signature
                   (silent+instant, high-altitude hover, formation,
                    military proximity, EM interference, trace evidence)
        ↓
    SCORE        — multi-factor credibility ranking: radar confirmation,
                   witness type, corroboration, physical traces
        ↓
    VISUALIZE    — interactive global map, research dashboard,
                   live anomaly feed, AI analyst

Pages

Page	What it shows
Mission Overview	Live command center — global map, anomaly feed, headline stats
Collect	Data sources: NUFORC, Pentagon files, pilot testimonies, radar logs
Extract	NLP pipeline output — extracted fields, 77 anomalous cases, witness cards
Analyze	Core research dashboard — year chart with bias toggle, per-capita state ranking, 8 key questions answered with data
Cluster	6 behavioral clusters with counts and confidence scores
Score	Top-ranked incidents by multi-factor credibility model
Visualize	Interactive global heat map — filterable by shape, year, severity
Insights	6 key stats computed from raw data, nothing fabricated
Live Feed	Real-time anomaly stream — filter by severity, shape, behavior tag
AI Analyst	Bring your own OpenAI key — query the dataset in plain English

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Data Science Methodology

Bias Correction

Raw NUFORC counts are heavily inflated post-2010 due to smartphone adoption. We normalize using World Bank internet penetration data:

correction_factor = baseline_internet_pct / year_internet_pct
corrected_count   = raw_count * correction_factor

This deflates the 2012 spike from ~8,000 reports to a much flatter trend — more representative of actual sighting frequency.

Per-Capita Normalization

State-level counts are normalized using 2010 US Census population data. California drops significantly; Washington State emerges as the true #1 at 58.6 sightings per 100,000 people.

NLP Pipeline

Rule-based regex matching across all 79,621 raw report texts — no external API, no LLM hallucinations, fully auditable:

• Shape extraction: light, triangle, disc, fireball, sphere, cylinder
• Behavior flags: silent flight, instant acceleration, hovering, formation
• Context flags: military mention, radar reference, multiple witnesses, physical trace
• Physics-violation language: 207 reports flagged

Known Limitations

• NUFORC is self-reported — no independent verification of individual accounts
• 81% US-centric — international coverage is sparse and inconsistent
• Reporting bias increases with internet access — pre-1990 data is underrepresented
• NLP is rule-based — complex descriptions may be missed or miscategorized

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Tech Stack

Layer	Technology
Frontend	Next.js 16, TypeScript, Recharts
Styling	Inline styles, dark military aesthetic
Data pipeline	Python 3 — Pandas, Pillow, regex NLP
Data format	Static JSON served from public/data/
AI Analyst	OpenAI API (user-supplied key, no server-side key required)
Deployment	Vercel

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Local Setup

git clone https://github.com/Mugeshgithub/Argus_UFO_AI_Data
cd Argus_UFO_AI_Data
npm install
npm run dev

No environment variables required. All data is pre-computed and shipped as static JSON.

The AI Analyst tab requires your own OpenAI API key — enter it at runtime via the sidebar. Nothing is stored server-side.

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Data Sources

Source	Records	Used For
NUFORC — National UFO Reporting Center	79,621 reports	Primary sighting database (1941–2014)
World Bank — Internet users (% of population)	Yearly 1990–2024	Bias correction for reporting inflation
US Census 2010 — State population estimates	50 states	Per-capita normalization
Wikipedia — Pentagon UAP disclosures	Reference	Context for high-credibility cases
FAA — Airspace & pilot report conventions	Reference	Military proximity and radar flag context

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License

MIT — open source, use freely, attribution appreciated.

Data sourced from NUFORC public database. This project makes no claims about the nature of reported phenomena.