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9e3bb54 8605c69 2252aab 8605c69 2252aab 8605c69 2252aab 8605c69 9e3bb54 8605c69 9e3bb54 390a772 8605c69 2252aab 8605c69 2252aab 8605c69 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 | # MNB Benchmark Observatory Ledger
**Public experiment records from the Minimal Navigable Benchmark Observatory.**
## Overview
The MNB Benchmark Ledger contains **200,000 verified experiment records** documenting the evolution of memory-based systems under controlled conditions. Each entry includes cryptographic proof of execution.
## Dataset Statistics
| Metric | Value |
|--------|-------|
| Total Experiments | 200,000 |
| Score Range | 0.3000 - 0.9900 |
| Population Range | 100 - 9,999 |
| Avg Population | 5,053 |
| Verification | 100% |
## Data Schema
| Field | Type | Description |
|-------|------|-------------|
| `experiment_id` | string | Unique experiment ID (mnb_exp_XXXXXX) |
| `timestamp` | ISO 8601 | Experiment timestamp |
| `population_size` | integer | Number of MNBs in simulation |
| `global_coherence_psi` | float | System-wide coherence metric (0-1) |
| `entropy_h` | float | Information entropy |
| `mean_value_density_rho` | float | Average value density |
| `benchmark_score` | float | Composite performance score |
| `verified` | boolean | Cryptographic verification status |
| `cryptographic_proof` | JSON | SHA3-256 Merkle proof |
## Cryptographic Verification
All entries include SHA3-256 Merkle proofs for reproducibility verification. Each experiment generates a deterministic proof chain enabling independent verification.
```python
from huggingface_hub import hf_hub_download
path = hf_hub_download(repo_id="MatverseHub/mnb-benchmark-ledger", filename="mnb_ledger_200k.csv")
```
## Usage
```python
import pandas as pd
# Load dataset
df = pd.read_csv("mnb_ledger_200k.csv")
# Filter high-performing experiments
high_score = df[df["benchmark_score"] > 0.8]
# Analyze coherence trends
coherence_trend = df.groupby("population_size")["global_coherence_psi"].mean()
```
## Research Applications
- Memory system performance benchmarking
- Coherence evolution analysis
- Population dynamics modeling
- Cryptographic proof validation
## Citation
```
@dataset{matverse_mnb_benchmark_ledger,
title={MNB Benchmark Observatory Ledger},
author={MatVerse Research Program},
year={2026},
publisher={MatverseHub}
}
```
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