Upload README.md

README.md

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----
-license: apache-2.0
----
+---
+language:
+- en
+- ar
+- zh
+- fr
+- ru
+- es
+license: cc0-1.0
+task_categories:
+- text-classification
+- token-classification
+- text-generation
+- question-answering
+pretty_name: UN Security Council Complete (UNSC-Complete)
+size_categories:
+- 1K<n<10K
+tags:
+- legal
+- international-relations
+- voting
+- united-nations
+- diplomacy
+- geopolitics
+- multilingual
+- vetoes
+---
+
+# UN Security Council Complete Dataset (UNSC-Complete)
+
+## 🌍 The Most Comprehensive UN Security Council Dataset Available
+
+### Dataset Summary
+
+**UNSC-Complete** is the first dataset to combine **ALL** UN Security Council activity:
+- ✅ **2,722 adopted resolutions** (1946-2024) with full texts and voting records
+- ❌ **271 vetoed draft resolutions** (1946-2025) that were blocked by P5 members
+- 📊 **2,993 total records** showing what passes AND what fails at the UN's most powerful body
+
+This unified dataset reveals the complete picture of Security Council decision-making, including the **90.9% passage rate** and the critical 9.1% of drafts that never see the light of day due to vetoes.
+
+### 🎯 Why This Dataset Matters
+
+Previous datasets only included adopted resolutions, missing the crucial story of what gets blocked. This dataset shows:
+- **271 vetoed drafts** - the "dark matter" of international diplomacy
+- **Russia/USSR: 161 vetoes**, **USA: 95 vetoes**, showing geopolitical fault lines
+- **Cold War vs Post-Cold War dynamics**: 200 vetoes (1946-89) vs 71 vetoes (1990-2025)
+- **Complete voting records**: See exactly how each country voted on every resolution
+
+## 📁 Dataset Structure
+
+### Unified Schema
+
+Every record (adopted or vetoed) contains:
+
+```json
+{
+  "unified_id": 1234,
+  "res_no": 242,  // Positive for adopted, negative for vetoed
+  "symbol": "S/RES/242(1967)",  // or draft number for vetoed
+  "date": "1967-11-22",
+  "status": "adopted",  // or "vetoed"
+  "is_adopted": true,   // boolean flag
+
+  "vote_yes": 15,
+  "vote_no": 0,
+  "vote_abstention": 0,
+  "voting_countries": [
+    {"country": "UNITED STATES", "vote": "yes"},
+    {"country": "USSR", "vote": "yes"},
+    ...
+  ],
+
+  "has_veto": false,
+  "vetoed_by": [],  // List of P5 members who vetoed
+
+  "english_text_best": "Full resolution text...",
+  "text_length": 1977,
+
+  "chapter7": false,  // Legal framework indicators
+  "enforcement_level": "none",  // none/threat/breach/aggression
+
+  "m49_region": "Asia",
+  "cited_resolutions": ["181", "234"],
+  ...
+}
+```
+
+### Key Features
+
+| Feature | Description |
+|---------|------------|
+| **Unified ID** | Sequential ID across all records (1-2993) |
+| **Status** | "adopted" or "vetoed" - know immediately what passed |
+| **Voting Details** | Complete country-by-country votes for adopted resolutions |
+| **Veto Information** | Which P5 member(s) blocked each draft |
+| **Legal Framework** | Chapter VI/VII/VIII, enforcement levels, human rights |
+| **Text Content** | Full resolution texts (for adopted) |
+| **Geographic Scope** | Countries and regions mentioned |
+| **Citation Network** | Links between resolutions |
+
+## 📊 Dataset Statistics
+
+### Overall
+- **Total records**: 2,993
+- **Success rate**: 90.9% adopted, 9.1% vetoed
+- **Date range**: 1946-2025 (79 years)
+- **Text length**: 189 to 343,887 characters
+
+### Adopted Resolutions (2,722)
+- **Unanimous**: 81.4%
+- **Chapter VII**: 32.9% (enforcement actions)
+- **With citations**: 94.5%
+- **Human rights**: 29.1%
+
+### Vetoed Drafts (271)
+- **Russia/USSR**: 161 vetoes (59.4%)
+- **United States**: 95 vetoes (35.1%)
+- **United Kingdom**: 32 vetoes (11.8%)
+- **China**: 21 vetoes (7.7%)
+- **France**: 18 vetoes (6.6%)
+- **Double/Triple vetoes**: 43 drafts
+
+### Top Vetoed Topics
+1. Admission of new Members (60)
+2. Middle East, including Palestinian question (22)
+3. Middle East (Syria) (18)
+4. Occupied Arab territories (16)
+5. Middle East (Lebanon) (10)
+
+## 🚀 Supported Tasks
+
+### 1. Veto Prediction (Binary Classification)
+- **Task**: Predict if a draft will be vetoed
+- **Baseline**: 9.1% positive rate (highly imbalanced)
+- **Challenge**: Understand geopolitical red lines
+
+### 2. Passage Prediction (Binary Classification)
+- **Task**: Given draft text/metadata, predict adoption
+- **Baseline**: 90.9% positive rate
+- **Value**: Understand what makes resolutions passable
+
+### 3. P5 Consensus Analysis
+- **Task**: Predict P5 voting alignment
+- **Features**: Historical patterns, topics, regional focus
+- **Insight**: When do great powers agree?
+
+### 4. Temporal Analysis
+- **Task**: Classify era (Cold War/Post-Cold War/War on Terror/Multipolar)
+- **Signal**: Language evolution, topics, voting patterns
+- **Text growth**: 170 words (1946) → 3,600+ words (2011)
+
+### 5. Legal Framework Detection
+- **Chapter VII**: 32.9% of adopted (enforcement)
+- **Threat hierarchy**: none → threat → breach → aggression
+- **Human rights**: Growing from rare to 29.1%
+
+### 6. Geographic Classification
+- **Multi-label**: Africa (33.6%), Asia (22.2%), Europe (6.2%)
+- **Challenge**: Multiple regions per resolution
+- **Vetoed drafts**: Heavy Middle East focus
+
+### 7. Citation Network Analysis
+- **Task**: Predict citation links
+- **Data**: 94.5% of adopted resolutions cite others
+- **Application**: Understanding precedent and evolution
+
+## 📥 Usage
+
+### Loading the Dataset
+
+```python
+from datasets import load_dataset
+
+# Load the unified dataset
+dataset = load_dataset("your-username/unsc-complete")
+
+# Separate adopted and vetoed
+adopted = dataset.filter(lambda x: x['is_adopted'])
+vetoed = dataset.filter(lambda x: not x['is_adopted'])
+```
+
+### Example: Analyzing Veto Patterns
+
+```python
+import pandas as pd
+
+# Load the unified dataset
+df = pd.read_json("unsc_unified_dataset.jsonl", lines=True)
+
+# Analyze veto patterns over time
+vetoed = df[df['status'] == 'vetoed']
+
+# P5 veto counts
+for member in ['United States', 'Russia/USSR', 'China', 'United Kingdom', 'France']:
+    count = vetoed['vetoed_by'].apply(lambda x: member in x if x else False).sum()
+    print(f"{member}: {count} vetoes")
+
+# Veto rate by decade
+veto_rate = df.groupby('decade').agg({
+    'is_adopted': lambda x: (1 - x.mean()) * 100
+}).round(1)
+print(f"Veto rate by decade:\n{veto_rate}")
+```
+
+### Example: Predicting Passage
+
+```python
+from sklearn.ensemble import RandomForestClassifier
+from sklearn.feature_extraction.text import TfidfVectorizer
+
+# Prepare features
+X_text = df['title'].fillna('') + ' ' + df['agenda_information'].fillna('')
+y = df['is_adopted'].astype(int)
+
+# Create features
+vectorizer = TfidfVectorizer(max_features=1000)
+X = vectorizer.fit_transform(X_text)
+
+# Train model
+model = RandomForestClassifier(class_weight='balanced')
+model.fit(X, y)
+
+# Feature importance shows what topics face vetoes
+important_features = vectorizer.get_feature_names_out()[model.feature_importances_.argsort()[-20:]]
+print(f"Topics associated with vetoes: {important_features}")
+```
+
+## 🗂️ Files
+
+- **`unsc_unified_dataset.csv`** - Complete unified dataset (28.9 MB)
+- **`unsc_unified_dataset.jsonl`** - JSONL format for streaming (30.8 MB)
+- **`unsc_master_data.csv`** - Adopted resolutions only with full details (28.8 MB)
+- **`unsc_vetoed_drafts.csv`** - Vetoed drafts only (50 KB)
+- **`unsc_voting_details.csv`** - Country-by-country voting records (1.9 MB)
+
+## 📚 Data Sources
+
+1. **Adopted Resolutions**: [CR-UNSC Academic Dataset](https://zenodo.org/doi/10.5281/zenodo.11212056) by Fobbe, Gasbarri, and Ridi
+2. **Vetoed Drafts**: [UN DPPA Security Council Vetoes Database](https://www.un.org/depts/dhl/resguide/scact_veto_table_en.htm)
+
+## ⚖️ License
+
+Released under **CC0 1.0 Universal (Public Domain)** in accordance with UN document policy.
+
+## 📖 Citation
+
+```bibtex
+@dataset{unsc_complete_2024,
+  title={UN Security Council Complete Dataset (UNSC-Complete)},
+  author={[Your Name]},
+  year={2024},
+  publisher={HuggingFace},
+  note={Combines adopted resolutions and vetoed drafts for complete UNSC coverage}
+}
+```
+
+## 🎯 Key Insights from the Data
+
+1. **Veto Power Shapes Everything**: 271 drafts never became resolutions due to P5 vetoes
+2. **Cold War Legacy**: 73.8% of all vetoes occurred during Cold War (1946-1989)
+3. **Middle East Dominance**: Most vetoed topic post-Cold War
+4. **Text Explosion**: Resolutions grew from ~170 to ~3,600 words, reflecting complexity
+5. **Consensus Building**: 81.4% of adopted resolutions are unanimous - but this hides the vetoed 9.1%
+
+## 🔬 Research Applications
+
+- **International Relations**: Quantify great power politics
+- **Conflict Studies**: What conflicts get UN attention vs ignored?
+- **Legal NLP**: Train models on diplomatic/legal language
+- **Temporal Analysis**: 79 years of language evolution
+- **Network Analysis**: Citation networks reveal precedent patterns
+- **Fairness Studies**: Geographic and political biases in UN action
+
+## 💡 What Makes This Dataset Unique
+
+This is the **FIRST** dataset to show both sides of Security Council action:
+- ✅ What passes (adopted resolutions)
+- ❌ What fails (vetoed drafts)
+
+Previous datasets only showed adopted resolutions, missing the critical story of power politics revealed by vetoes. With this complete picture, researchers can finally study:
+- True P5 disagreement rates
+- Topics that trigger vetoes
+- Evolution of international consensus
+- The "selection bias" in adopted resolutions
+
+---
+
+**Ready to explore 79 years of international diplomacy? Download the dataset and discover what shapes our world order!**