Datasetpublic

ajto/GoldenCheetah

GoldenCheetah Data from the GoldenCheetah OpenData Project in Parquet format. Table Contents measurements Source CSV filename, time (s), distance (km), power (W), heart rate (bpm), cadence (rpm), altitude (m) activities Original activity fields, with METRICS and XDATA stored as JSON athletes Athlete ID, gender, birth year, and source version athlete_id identifies the athlete in each table. source_file identifies each CSV trace. Activity dates and CSV… See the full description on the dataset page: https://huggingface.co/datasets/ajto/GoldenCheetah.

sourceHugging Faceupdated 9d agoView on Hugging Face

0likes125downloads

Dataset Card

GoldenCheetah

Data from the GoldenCheetah OpenData Project in Parquet format.

Table	Contents
`measurements`	Source CSV filename, time (s), distance (km), power (W), heart rate (bpm), cadence (rpm), altitude (m)
`activities`	Original activity fields, with `METRICS` and `XDATA` stored as JSON
`athletes`	Athlete ID, gender, birth year, and source version

athlete_id identifies the athlete in each table. source_file identifies each CSV trace. Activity dates and CSV filenames use the source timestamps.

Use

Download the Python scripts and requirements.txt, then install:

python3 -m venv .venv
.venv/bin/python -m pip install -r requirements.txt

Convert ZIP files:

.venv/bin/python prepare_dataset.py /path/to/zips/*.zip --output data

Query the public data:

.venv/bin/python query_dataset.py --remote --sql "SELECT sport, COUNT(*) FROM activities GROUP BY sport"

Query a source metric:

sql

SELECT date, TRY_CAST(METRICS::JSON ->> '$.average_power[0]' AS DOUBLE) AS power
FROM activities;