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Datasets:
COINjecture
/
RichEmissions

Tags:
blockchain
proof-of-work
np-hard
mining
emissions
energy-consumption
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The dataset viewer is not available for this split.
Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ArrowInvalid
Message:      Schema at index 1 was different: 
block_hash: string
block_height: int64
block_reward: string
block_size_bytes: int64
bounty: string
cpu_cores: int64
cpu_model: string
cpu_threads: int64
data_version: string
difficulty_target: int64
energy_asymmetry: double
energy_efficiency: double
energy_measurement_method: string
energy_per_operation: double
hash_rate_estimate: double
measurement_confidence: string
metrics_source: string
mining_attempts: int64
node_version: string
nonce: int64
os_info: string
prev_block_hash: string
problem_complexity: double
problem_data: struct<cities: int64, distances: list<item: list<item: int64>>>
problem_id: string
problem_type: string
ram_total_bytes: int64
solution_data: struct<tour: list<item: int64>>
solution_quality: double
solve_energy_joules: double
solve_time_us: int64
solver: string
space_asymmetry: double
status: string
submission_mode: string
submitter: string
time_asymmetry: double
timestamp: int64
total_energy_joules: double
total_fees: string
transaction_count: int64
verify_energy_joules: double
verify_time_us: int64
work_score: double
vs
block_hash: string
block_height: int64
block_reward: string
block_size_bytes: int64
bounty: string
cpu_cores: int64
cpu_model: string
cpu_threads: int64
data_version: string
difficulty_target: int64
energy_asymmetry: double
energy_efficiency: double
energy_measurement_method: string
energy_per_operation: double
hash_rate_estimate: double
measurement_confidence: string
metrics_source: string
mining_attempts: int64
node_version: string
nonce: int64
os_info: string
prev_block_hash: string
problem_complexity: double
problem_data: struct<clauses: list<item: struct<literals: list<item: int64>>>, variables: int64, numbers: list<item: int64>, target: int64>
problem_id: string
problem_type: string
ram_total_bytes: int64
solution_data: struct<assignments: list<item: bool>, indices: list<item: int64>>
solution_quality: double
solve_energy_joules: double
solve_time_us: int64
space_asymmetry: double
status: string
submission_mode: string
submitter: string
time_asymmetry: double
timestamp: int64
total_energy_joules: double
total_fees: string
transaction_count: int64
verify_energy_joules: double
verify_time_us: int64
work_score: double
solver: string
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 243, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 3496, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2257, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2461, in iter
                  for key, example in iterator:
                                      ^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1952, in __iter__
                  for key, pa_table in self._iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 1974, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 547, in _iter_arrow
                  yield new_key, pa.Table.from_batches(chunks_buffer)
                                 ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "pyarrow/table.pxi", line 5039, in pyarrow.lib.Table.from_batches
                File "pyarrow/error.pxi", line 155, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 92, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: Schema at index 1 was different: 
              block_hash: string
              block_height: int64
              block_reward: string
              block_size_bytes: int64
              bounty: string
              cpu_cores: int64
              cpu_model: string
              cpu_threads: int64
              data_version: string
              difficulty_target: int64
              energy_asymmetry: double
              energy_efficiency: double
              energy_measurement_method: string
              energy_per_operation: double
              hash_rate_estimate: double
              measurement_confidence: string
              metrics_source: string
              mining_attempts: int64
              node_version: string
              nonce: int64
              os_info: string
              prev_block_hash: string
              problem_complexity: double
              problem_data: struct<cities: int64, distances: list<item: list<item: int64>>>
              problem_id: string
              problem_type: string
              ram_total_bytes: int64
              solution_data: struct<tour: list<item: int64>>
              solution_quality: double
              solve_energy_joules: double
              solve_time_us: int64
              solver: string
              space_asymmetry: double
              status: string
              submission_mode: string
              submitter: string
              time_asymmetry: double
              timestamp: int64
              total_energy_joules: double
              total_fees: string
              transaction_count: int64
              verify_energy_joules: double
              verify_time_us: int64
              work_score: double
              vs
              block_hash: string
              block_height: int64
              block_reward: string
              block_size_bytes: int64
              bounty: string
              cpu_cores: int64
              cpu_model: string
              cpu_threads: int64
              data_version: string
              difficulty_target: int64
              energy_asymmetry: double
              energy_efficiency: double
              energy_measurement_method: string
              energy_per_operation: double
              hash_rate_estimate: double
              measurement_confidence: string
              metrics_source: string
              mining_attempts: int64
              node_version: string
              nonce: int64
              os_info: string
              prev_block_hash: string
              problem_complexity: double
              problem_data: struct<clauses: list<item: struct<literals: list<item: int64>>>, variables: int64, numbers: list<item: int64>, target: int64>
              problem_id: string
              problem_type: string
              ram_total_bytes: int64
              solution_data: struct<assignments: list<item: bool>, indices: list<item: int64>>
              solution_quality: double
              solve_energy_joules: double
              solve_time_us: int64
              space_asymmetry: double
              status: string
              submission_mode: string
              submitter: string
              time_asymmetry: double
              timestamp: int64
              total_energy_joules: double
              total_fees: string
              transaction_count: int64
              verify_energy_joules: double
              verify_time_us: int64
              work_score: double
              solver: string

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COINjecture Rich Emissions Dataset

Overview

This dataset contains comprehensive emissions and energy consumption data from the COINjecture blockchain network. It captures rich, detailed metrics for every mined block, including:

  • Energy Consumption: Detailed energy measurements (solve, verify, total)
  • Problem Complexity: Work scores, complexity weights, and difficulty metrics
  • Time Metrics: Solve time, verify time, and time asymmetry ratios
  • Block Metadata: Height, hash, miner address, timestamps
  • Problem Types: SubsetSum, SAT, TSP, and Custom problem instances
  • Solution Quality: Solution quality scores and verification results
  • Aggregation Strategies: Bounty aggregation strategy information

Dataset Structure

Each record contains comprehensive emissions data including:

  • block_height: Blockchain height where the block was mined
  • block_hash: Unique block identifier
  • problem_type: Type of NP-hard problem solved (SubsetSum, SAT, TSP, Custom)
  • problem_data: Complete problem specification
  • solution_data: Solution details and verification results
  • energy_metrics:
    • solve_energy_joules: Energy consumed during problem solving
    • verify_energy_joules: Energy consumed during verification
    • total_energy_joules: Total energy consumption
    • energy_asymmetry: Ratio of solve to verify energy
  • time_metrics:
    • solve_time_us: Problem solving time in microseconds
    • verify_time_us: Verification time in microseconds
    • time_asymmetry_ratio: Ratio of solve to verify time
  • complexity_metrics:
    • work_score: Computed work score
    • complexity_weight: Problem complexity weight
    • solution_quality: Quality score of the solution
  • mining_metadata:
    • miner_address: Address of the miner who solved the problem
    • nonce: Mining nonce value
    • timestamp: Unix timestamp of block creation
  • bounty_info:
    • bounty_amount: Token reward amount
    • aggregation_strategy: Strategy used for bounty aggregation (Any, Best, Multiple, Statistical)

Use Cases

This dataset is designed for:

  1. Energy Research: Study energy consumption patterns in blockchain mining
  2. Algorithm Analysis: Compare performance across different NP-hard problem types
  3. Sustainability Studies: Analyze the environmental impact of proof-of-work systems
  4. Performance Optimization: Identify optimization opportunities in mining algorithms
  5. Academic Research: Support research in distributed systems, cryptography, and computational complexity

Data Collection

Data is automatically collected and uploaded by COINjecture network nodes running version 4.11.28+. Each node measures energy consumption using configurable methods (estimation or direct measurement) and uploads data in batches to ensure data integrity.

Citation

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

@dataset{coinjecture_rich_emissions_2026,
  title={COINjecture Rich Emissions Dataset},
  author={COINjecture Network},
  year={2026},
  url={https://huggingface.co/datasets/COINjecture/RichEmissions}
}

License

This dataset is released under the MIT License.

Contact

For questions or issues, please visit: https://github.com/beanapologist/coinjecture

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