Publish CC-BY-4.0 License and Use section per Terms §13

README.md

@@ -1,115 +1,44 @@
 ---
-license: mit
-task_categories:
-  - question-answering
-  - text-generation
+license: cc-by-4.0
 language:
-  - en
+- en
+pretty_name: World State Daily
 tags:
-  - prediction-markets
-  - world-model
-  - real-time-data
-  - ai-agents
-  - geopolitics
-  - economics
-  - calibrated-probabilities
-  - kalshi
-  - polymarket
-size_categories:
-  - n<1K
-pretty_name: World State Daily Snapshots
+- prediction-markets
+- kalshi
+- polymarket
+- finance
+- world-state
+- time-series
+source_datasets:
+- original
 ---
 
-# World State Daily Snapshots
+# World State Daily
 
-Daily snapshots of the world's state as measured by prediction markets. Each snapshot contains calibrated probabilities for geopolitics, economy, energy, elections, crypto, and tech — derived from 9,706 contracts on Kalshi (CFTC-regulated) and Polymarket.
+Daily end-of-day world state snapshots from Kalshi + Polymarket. Each JSON file captures consensus probabilities across tens of thousands of prediction markets, the SF Index (disagreement, geo-risk, breadth, activity), top edges, divergences, and a markdown summary.
 
-## Why This Dataset Exists
+## License and Use
 
-LLMs have a knowledge cutoff. This dataset provides ground truth for what the world looked like on any given day — not from news (narratives) or surveys (opinions), but from prediction markets where participants risk real money. Get it wrong, lose money.
+This dataset is released under Creative Commons Attribution 4.0 International
+(CC-BY-4.0; https://creativecommons.org/licenses/by/4.0/). You may use it
+freely for personal, research, educational, and commercial purposes — including
+training, evaluating, and fine-tuning machine-learning models. Attribution is
+required when the dataset is redistributed in substantially its original form
+or cited in published work; credit as "SimpleFunctions (simplefunctions.dev)".
 
-Use cases:
-- **Agent evaluation**: Test whether your agent can accurately answer "what's the recession probability?" on a given date
-- **Training data**: Fine-tune models with calibrated world state data
-- **Research**: Analyze how prediction market probabilities evolve over time
-- **Backtesting**: Validate agent decisions against historical world state
+Additional terms apply: you may not re-host this dataset, in whole or in
+substantial part, as an API or service that functionally substitutes for a
+SimpleFunctions endpoint. See Terms §13.2 at https://simplefunctions.dev/terms.
 
-## Schema
+Provenance, update cadence, and schema are documented below.
 
-Each daily JSON file contains:
+## Update cadence
 
-```json
-{
-  "date": "2026-04-02",
-  "timestamp": "2026-04-02T20:56:00Z",
-  "index": {
-    "uncertainty": 22,
-    "geopolitical": 62,
-    "momentum": 0.06
-  },
-  "traditional": [
-    {"symbol": "SPY", "price": 655.13, "changePct": 0.81}
-  ],
-  "topics": [
-    {
-      "name": "Geopolitical",
-      "movers": [
-        {"title": "Iran invasion probability", "price": 53, "delta": 5, "volume": 225000, "venue": "kalshi"}
-      ]
-    }
-  ],
-  "topEdges": [
-    {"title": "Market X", "edge": 15, "direction": "yes", "price": 35}
-  ],
-  "divergences": [
-    {"description": "Stocks and gold both up — unusual risk-on + haven bid"}
-  ],
-  "markdown": "# World State — ...",
-  "markdownTokenEstimate": 667
-}
-```
+Daily at 23:50 UTC.
 
-## Fields
+## Provenance
 
-| Field | Description |
-|-------|-------------|
-| `index.uncertainty` | Market uncertainty (0-100), derived from orderbook spreads |
-| `index.geopolitical` | Geopolitical risk (0-100), from geo-related market velocity |
-| `index.momentum` | Directional market bias (-1 to +1) |
-| `traditional` | SPY, VIX, TLT, GLD, USO prices and daily change |
-| `topics` | 6 categories with anchor contracts and significant movers |
-| `topEdges` | Largest mispricings detected by thesis models |
-| `divergences` | Cross-market anomalies (e.g., stocks and gold both rising) |
-| `markdown` | Raw markdown output (~800 tokens, ready for LLM injection) |
-
-## Data Source
-
-[SimpleFunctions](https://simplefunctions.dev/world) — aggregates 9,706 prediction market contracts from Kalshi (CFTC-regulated) and Polymarket. Updated every 15 minutes. The daily snapshot captures the final state of each day.
-
-## Live API
-
-For real-time data (not just daily snapshots):
-
-```python
-pip install simplefunctions-ai
-
-from simplefunctions import world, delta
-print(world())           # current state (~800 tokens)
-print(delta(since="1h")) # what changed (~30-50 tokens)
-```
-
-## Citation
-
-```bibtex
-@dataset{simplefunctions_world_state_2026,
-  title={World State Daily Snapshots: Calibrated Probabilities from Prediction Markets},
-  author={SimpleFunctions},
-  year={2026},
-  url={https://huggingface.co/datasets/SimpleFunctions/world-state-daily},
-  note={Daily snapshots from 9,706 prediction markets on Kalshi and Polymarket}
-}
-```
-
-## License
-
-MIT
+Source: https://simplefunctions.dev
+Generator: SimpleFunctions public data pipeline
+Contact: patrick@simplefunctions.dev