COINjecture/NP_Solutions_v2

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🔬 COINjecture NP Solutions Dataset v2

Institutional-Grade Blockchain Research Data

  

A comprehensive, real-time dataset of NP-complete problem solutions generated through Proof-of-Useful-Work (PoUW) blockchain consensus

Overview • Data Schema • Metrics Categories • Usage • Citation

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📋 Overview

This dataset contains institutional-grade metrics from the COINjecture Network B blockchain, which implements a novel Proof-of-Useful-Work (PoUW) consensus mechanism. Unlike traditional Proof-of-Work systems that compute arbitrary hashes, COINjecture miners solve genuine NP-complete computational problems, producing verifiable solutions with real-world applicability.

Key Characteristics

Research Applications

• —Computational Complexity: Empirical analysis of NP-complete problem hardness
• —Algorithm Performance: Solve/verify time distributions across problem types
• —Distributed Systems: Consensus metrics and network propagation analysis
• —Energy Research: Computational efficiency and resource utilization studies
• —Cryptographic Analysis: Hash function behavior and difficulty adjustment

📊 Data Schema

Each record represents a block in the COINjecture blockchain containing a solved NP-complete problem instance.

Core Fields

Problem Instance Fields

📈 Metrics Categories

⏱️ Timing Metrics (Microsecond Precision)

High-resolution timing data for performance analysis:

💾 Memory Metrics

Resource utilization during computation:

🌐 Network Metrics

Distributed system behavior:

⛏️ Mining Metrics

Consensus and difficulty data:

🔗 Chain Metrics

Blockchain state information:

💰 Economic Metrics

Token economics data:

🖥️ Hardware Context

Node environment information for reproducibility:

🏷️ Provenance Metadata

Data lineage and quality indicators:

🔬 Problem Types

SAT (Boolean Satisfiability)

The canonical NP-complete problem. Given a Boolean formula in CNF, find a satisfying assignment or prove none exists.

{
  "problem_type": "SAT",
  "problem_instance": {
    "num_variables": 50,
    "num_clauses": 215,
    "clauses": [[1, -3, 5], [-2, 4], ...]
  },
  "solution": {
    "satisfiable": true,
    "assignment": [true, false, true, ...]
  }
}

SubsetSum

Given a set of integers and a target sum, find a subset that sums to the target.

{
  "problem_type": "SubsetSum",
  "problem_instance": {
    "set": [3, 7, 1, 8, -2, 4],
    "target": 12
  },
  "solution": {
    "subset_indices": [1, 3, 5]
  }
}

TSP (Traveling Salesman Problem)

Find the shortest Hamiltonian cycle through all vertices in a weighted graph.

{
  "problem_type": "TSP",
  "problem_instance": {
    "num_cities": 20,
    "distances": [[0, 10, 15], [10, 0, 20], ...]
  },
  "solution": {
    "tour": [0, 3, 1, 4, 2, 0],
    "total_distance": 97
  }
}

📖 Usage

Loading with Hugging Face Datasets

from datasets import load_dataset

# Load the complete dataset
dataset = load_dataset("COINjecture/NP_Solutions_v2")

# Access records
for record in dataset["train"]:
    print(f"Block {record['block_height']}: {record['problem_type']}")
    print(f"  Solve time: {record['solve_time_us']}μs")
    print(f"  CPU: {record['cpu_model']}")

Loading Raw JSONL

import json
from pathlib import Path

records = []
for jsonl_file in Path("data").glob("*.jsonl"):
    with open(jsonl_file) as f:
        for line in f:
            records.append(json.loads(line))

print(f"Loaded {len(records)} records")

Filtering by Problem Type

sat_problems = dataset["train"].filter(
    lambda x: x["problem_type"] == "SAT"
)
print(f"SAT problems: {len(sat_problems)}")

Performance Analysis Example

import pandas as pd

# Convert to DataFrame for analysis
df = pd.DataFrame(dataset["train"])

# Analyze solve times by problem type
stats = df.groupby("problem_type")["solve_time_us"].agg(["mean", "std", "min", "max"])
print(stats)

# Hardware comparison
hardware_stats = df.groupby("cpu_model")["solve_time_us"].mean()
print(hardware_stats)

📊 Data Quality

Verification Standards

All data in this dataset meets the following quality criteria:

Data Versioning

🔄 Update Frequency

This dataset receives real-time updates approximately every 10 blocks (~10 seconds of blockchain time). New JSONL files are appended as blocks are mined on the COINjecture Network B.

Data Pipeline Architecture

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⛓️ CONSENSUS LAYER

     🌱 Genesis (Block 0)
            │
    ┌───────┼───────┐
    ▼       ▼       ▼
  🧮SAT   📊Sum   🗺️TSP
    │       │       │
    └───────┴───────┘
            │
            ▼

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🌐 P2P NETWORK

  📡 Node 1 ◄────► 📡 Node 2
     │                 │
     └────────┬────────┘
              ▼
       💬 Gossipsub
              │
              ▼

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📈 METRICS ENGINE

  ⏱️Timing  💾Memory  🖥️Hardware  🌐Network
      │         │          │          │
      └─────────┴──────────┴──────────┘
                     │
            54+ metrics/block
                     │
                     ▼

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🎯 DATA OUTPUT

         📦 Buffer (10 blocks)
                │
         every ~10 seconds
                │
                ▼
         🤗 HuggingFace v2
                │
                ▼
         🔌 Datasets API

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🔬 RESEARCH APPLICATIONS

Data flows from NP-complete problem solving → metrics collection → real-time research availability

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📜 Citation

If you use this dataset in your research, please cite:

@dataset{coinjecture_np_solutions_v2,
  title={COINjecture NP Solutions Dataset v2},
  author={{COINjecture Network Contributors}},
  year={2024},
  publisher={Hugging Face},
  url={https://huggingface.co/datasets/COINjecture/NP_Solutions_v2},
  note={Institutional-grade blockchain research data from Proof-of-Useful-Work consensus}
}

📄 License

This dataset is released under the MIT License. You are free to use, modify, and distribute this data for any purpose, including commercial applications.

🔗 Related Resources

🤝 Contributing

We welcome contributions to improve data quality and documentation. Please open an issue or pull request on our GitHub repository.

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Built with 💎 by the COINjecture Network

Transforming computational waste into useful work

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