README.md

# 🟣 Crovia — CEP Capsules (v1)
**Crypto Evidence Packages for the AI Act Era**  
Portable, offline-verifiable provenance capsules for the world’s most-used open datasets.

> **If a dataset shaped modern AI, it deserves a receipt.**  
> These are the first publicly verifiable evidence capsules of their kind.

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# 🚀 What Are CEP Capsules?
A **CEP.v1 capsule** (*Crypto Evidence Package*) is a compact, self-contained file that proves:

- **where attribution signals came from**  
- **how payouts would be computed**  
- **which trust bundle verified the run**  
- **what the hashchain root is**  
- **that everything inside is immutable**

Each capsule is:

- **📦 portable** (just a JSON file)  
- **🔍 independently verifiable** (3 lines of Python)  
- **🛡 AI Act–aligned** (trust bundle, receipts, payouts)  
- **⛓ backed by a hashchain** (tamper-evident)  

Think of this not as a dataset —  
**but as the evidence layer underneath datasets.**

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# 📘 Included Capsules (Dec 2025 Preview)

| Dataset Slice | Capsule |
|---------------|---------|
| C4 | [CEP-C4-2025-12.json](./CEP-C4-2025-12.json) |
| LAION 5B (sample) | [CEP-LAION-2025-12.json](./CEP-LAION-2025-12.json) |
| Wikipedia | [CEP-WIKIPEDIA-2025-12.json](./CEP-WIKIPEDIA-2025-12.json) |
| Wikitext | [CEP-WIKITEXT-2025-12.json](./CEP-WIKITEXT-2025-12.json) |
| FineWeb | [CEP-FINEWEB-2025-12.json](./CEP-FINEWEB-2025-12.json) |
| The Pile | [CEP-PILE-2025-12.json](./CEP-PILE-2025-12.json) |
| ArXiv abstracts | [CEP-ARXIV-2025-12.json](./CEP-ARXIV-2025-12.json) |
| OpenSubtitles | [CEP-OPENSUB-2025-12.json](./CEP-OPENSUB-2025-12.json) |
| Stack / Code | [CEP-STACK-2025-12.json](./CEP-STACK-2025-12.json) |
| BookCorpus | [CEP-BOOKCORPUS-2025-12.json](./CEP-BOOKCORPUS-2025-12.json) |

> These capsules do **not** contain dataset content.  
> They contain **evidence about how attribution signals flow through Crovia’s open engine.**

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# 🧪 Verify Any Capsule in 3 Lines

Save a capsule (e.g. `CEP-C4-2025-12.json`) locally and run:

    import json, hashlib

    with open("CEP-C4-2025-12.json") as f:
        cep = json.load(f)

    root = cep["hashchain"]["root"]
    print("Root:", root)
    print("Valid:", root == hashlib.sha256(cep["payouts"].encode()).hexdigest())

Everything is verifiable **offline**, with **no token**, **no API**, **no Crovia server**.

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# 🧠 Why This Matters
Modern AI models are trained on massive public datasets…  
…but nobody can **prove**:

- what signals came from where  
- how much each source contributed  
- how payouts would flow  
- what trust criteria were applied  
- whether logs were tampered with  

**Crovia introduces the evidence layer that the ecosystem was missing.**  
A standard way to ship **real, inspectable provenance** with AI training pipelines.

These capsules:

- help researchers audit models  
- help companies comply with the AI Act  
- help dataset creators receive attribution visibility  
- help the community trust what models are built on  

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# 👇 Want to Explore or Collaborate?
Crovia is entirely community-driven.  
We're looking for:

- dataset maintainers  
- compliance researchers  
- cryptography engineers  
- model evaluators  
- people who care about transparent AI  

If you want to contribute, explore, or join early pilots:

- **Open an issue on this dataset**  
- **Star the dataset to follow updates**  
- **Mention @Crovia on LinkedIn or X — we respond to everyone**

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# 🧩 Roadmap (Public Layer)
- CEP.v2 — multi-run lineage  
- DSSE Open Integration (semantic signal explorer)  
- Verified Dataset Manifests  
- Training Pipeline Attestations  

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# 🙌 Credits
Crovia is an independent initiative committed to  
**transparent, evidence-based AI attribution.**

This preview is released under an open license to accelerate adoption  
and give researchers the tools missing from the ecosystem.

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# ⭐ If you find this useful…

**Please star the dataset.**  
It helps more researchers discover the project — and it signals that this space matters.

Dataset home:  
https://huggingface.co/datasets/Crovia/cep-capsules