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README.md

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+---
+license: cc-by-4.0
+task_categories:
+  - tabular-classification
+  - time-series-forecasting
+language:
+  - en
+tags:
+  - censorship
+  - internet-freedom
+  - ooni
+  - human-rights
+  - network-measurement
+  - geopolitics
+pretty_name: Voidly Global Censorship Index
+size_categories:
+  - 1M<n<10M
+---
+
+# Voidly Global Censorship Index
+
+**The most comprehensive open dataset for internet censorship research and ML.**
+
+## Dataset Description
+
+This dataset contains 10 years of global internet censorship measurements from 120+ countries, including:
+
+- **1.6M+ daily measurements** (2017-2026)
+- **37K detected anomaly spikes**
+- **4.5K confirmed censorship events** with labels
+- **25+ known major incidents** (Mahsa Amini protests, Myanmar coup, etc.)
+
+### Data Sources
+- Primary: [OONI (Open Observatory of Network Interference)](https://ooni.org)
+- Secondary: Voidly Research analysis and labeling
+
+## Files
+
+| File | Description | Rows |
+|------|-------------|------|
+| `ooni-historical.parquet` | Daily measurements by country/test | 1.6M |
+| `censorship-incidents.parquet` | Labeled anomaly spikes | 37K |
+| `known-events.json` | Major censorship events | 25+ |
+
+## Usage
+
+```python
+from datasets import load_dataset
+
+# Load historical measurements
+ds = load_dataset("emperor-mew/global-censorship-index", data_files="ooni-historical.parquet")
+
+# Load labeled incidents (for ML training)
+incidents = load_dataset("emperor-mew/global-censorship-index", data_files="censorship-incidents.parquet")
+```
+
+## Schema
+
+### ooni-historical
+| Column | Type | Description |
+|--------|------|-------------|
+| country | string | ISO 3166-1 alpha-2 country code |
+| test_name | string | OONI test type (web_connectivity, telegram, whatsapp) |
+| date | date | Measurement date |
+| measurement_count | int | Total measurements |
+| anomaly_count | int | Measurements showing anomalies |
+| confirmed_count | int | Confirmed blocked |
+| anomaly_rate | float | Fraction showing anomalies (0-1) |
+
+### censorship-incidents
+| Column | Type | Description |
+|--------|------|-------------|
+| country | string | ISO 3166-1 alpha-2 country code |
+| date | date | Incident date |
+| anomaly_rate | float | Measured anomaly rate |
+| measurement_count | int | Sample size |
+| spike_magnitude | float | Z-score above baseline |
+| label | int | 1=confirmed censorship, 0=not |
+| event | string | Matched known event (if any) |
+| confidence | float | Label confidence (0-1) |
+
+## Known Events Covered
+
+- 🇮🇷 Iran Mahsa Amini protests (2022)
+- 🇲🇲 Myanmar military coup (2021)
+- 🇧🇾 Belarus election shutdown (2020)
+- 🇷🇺 Russia Ukraine invasion blocks (2022+)
+- 🇰🇿 Kazakhstan January protests (2022)
+- 🇸🇩 Sudan military coup (2021)
+- 🇨🇺 Cuba July protests (2021)
+- 🇺🇬 Uganda election shutdown (2021)
+- And 17+ more...
+
+## Model
+
+We provide a trained GradientBoosting classifier:
+- **F1 Score**: 99.8%
+- **ROC AUC**: 1.000
+- Available via API: `https://api.voidly.ai/hydra/v1/detect`
+
+## Citation
+
+```bibtex
+@dataset{voidly_censorship_index_2026,
+  author = {Voidly Research},
+  title = {Global Censorship Index: 10 Years of Internet Measurement Data},
+  year = {2026},
+  publisher = {Hugging Face},
+  url = {https://huggingface.co/datasets/emperor-mew/global-censorship-index}
+}
+```
+
+## Links
+
+- 🌐 [Voidly Censorship Index](https://voidly.ai/censorship-index)
+- 📡 [Real-time API](https://api.voidly.ai/data/censorship-index.json)
+- 🤖 [MCP Server](https://www.npmjs.com/package/@voidly/mcp-server)
+- 📊 [OONI (source)](https://ooni.org)
+
+## License
+
+CC BY 4.0 - Attribution required