dataforge-labs/bitcoin-mining-pool-templates

Bitcoin mining pool templates

Timestamped Stratum job messages collected directly from Bitcoin mining pool endpoints. The data records changes in the work each endpoint sends to miners, including the previous block hash, coinbase data and clean-jobs flag.

Contents

Table	Record
bitcoin_mining_pool_jobs	A job received from a pool endpoint, with its observation time, nTime, coinbase, merkle branch count and clean-jobs flag

Using the data

Use pool to identify the endpoint and operator to group endpoints belonging to the same operator. Compare observed_ts across endpoints to study when new jobs arrived. The pool's nTime field uses its own clock and update policy, so it should not be used as an arrival timestamp.

The initial configuration covers eleven endpoints across ten operators. Endpoint availability varies between collection windows. Regional endpoints operated by the same pool may distribute different jobs.

Limitations

• Observations come from one collection location and include connection latency.
• Merkle branches are stored as a count and first entry. The dataset does not contain the full transaction list for each proposed block.
• The clean-jobs flag instructs miners to discard earlier jobs. Check the previous block hash when identifying a change of chain tip.
• Endpoint coverage is a sample of mining pool activity. It does not measure each operator's full mining capacity or global response time.

Files and access

Data is stored as Parquet files under table_name/YYYY/MM/, with partitions for collection windows. Each measurement table has a fixed 7-day sample beginning at its configured collection start date. The sample windows in this repository span 2026-08-28 to 2026-09-03. Availability within each window depends on successful collection. The public sample dates remain fixed as additional history accumulates privately. Contact DataForge through the discussions tab to enquire about additional history.

Load a table

Install datasets and pandas to run this example. The train split contains all observations in the selected table; it is not a predefined modelling split.

from datasets import load_dataset

data = load_dataset("dataforge-labs/bitcoin-mining-pool-templates",
                    "bitcoin_mining_pool_jobs", split="train")
df = data.to_pandas()

Coverage

collection_runs records collection windows, poll counts and failures. It is published in full and may cover dates beyond the fixed data sample. Collection gaps are not interpolated. Use this table together with measurement timestamps and error fields to assess coverage.

License and contact

The public sample is published under ODC-BY. Attribute it to "DataForge (dataforge-labs)". For questions about the data or access to additional history, open a discussion in this repository.

Earlier file paths

Each table is stored under a directory with the same descriptive name. The file contents and date partitions are unchanged. Scripts using an earlier directory name should use the corresponding table name below, or pin downloads to revision before-folder-rename-20260915 to access the original layout. Internal collector IDs are retained in the private archive and may appear in raw coverage records.

Current table and directory	Earlier directory
bitcoin_mining_pool_jobs	e20_stratum_jobs_direct/
collection_runs	e0_run_manifest/