THULab/nurburgring_gp_lap_data

Nürburgring Sesto Elemento Telemetry Dataset (TsFile)

Apache TsFile version of Nasim435/Nurburgring-GP-Lap-data.

Overview

High-frequency sim-racing telemetry recorded from Assetto Corsa through shared-memory extraction: hypercar vehicle dynamics, tyre telemetry, suspension behaviour, braking data and driver inputs across a 10-lap stint at the Nürburgring in a Lamborghini Sesto Elemento. The source Tableau/CSV export is republished here as one Parquet file with 86 channels sampled at roughly 100 Hz.

• Tables: telemetry
• Rows: 91,490 across 1 TsFile table(s).
  • telemetry: 91,490 rows — Time is the source wall_time second counter on the simulation clock, converted to milliseconds.

Schema (TsFile structure)

telemetry.tsfile

• Time (INT64, milliseconds) — the source wall_time second counter on the simulation clock, converted to milliseconds.
• TAG: none — the table is a single device / single series.
• FIELD (85): timestamp (double), lap_progress (double), completed_laps (int64), speed_kmh (double), rpms (int64), gear (int64), throttle (double), brake (double), clutch (double), steer_angle (double), g_lat (double), g_vert (double), g_lon (double), vel_x (double), vel_y (double), vel_z (double), slip_angle (double), yaw_rate (double), pitch_rate (double), roll_rate (double), heading (double), pitch (double), roll (double), susp_fl (double), susp_fr (double), susp_rl (double), susp_rr (double), load_fl (double), load_fr (double), load_rl (double), load_rr (double), slip_fl (double), slip_fr (double), slip_rl (double), slip_rr (double), psi_fl (double), psi_fr (double), psi_rl (double), psi_rr (double), tyre_core_fl (double), tyre_core_fr (double), tyre_core_rl (double), tyre_core_rr (double), temp_i_fl (double), temp_i_fr (double), temp_i_rl (double), temp_i_rr (double), temp_m_fl (double), temp_m_fr (double), temp_m_rl (double), temp_m_rr (double), temp_o_fl (double), temp_o_fr (double), temp_o_rl (double), temp_o_rr (double), wear_fl (double), wear_fr (double), wear_rl (double), wear_rr (double), brake_temp_fl (double), brake_temp_fr (double), brake_temp_rl (double), brake_temp_rr (double), wheel_speed_fl (double), wheel_speed_fr (double), wheel_speed_rl (double), wheel_speed_rr (double), camber_fl (double), camber_fr (double), camber_rl (double), camber_rr (double), ride_height_f (double), ride_height_r (double), cg_height (double), turbo_boost (double), drs (double), tc (double), abs (double), brake_bias (double), air_temp (double), road_temp (double), water_temp (double), fuel (double), pos_x (double), pos_z (double)

Conversion notes

• One TsFile (telemetry.tsfile) with the full Nürburgring GP telemetry log; single car, no TAG.
• Time is the absolute unix wall_time (seconds, ~64 Hz sampling) converted to INT64 milliseconds. The source timestamp column (seconds since session start) and lap_progress / completed_laps are kept as FIELDs.
• All 86 source columns are kept; no column is dropped.
• Not converted: session_info.json — session metadata (one JSON record, no time series); mirrored to the repository as reference metadata instead of a TsFile table.
• Not converted: car.jpg / map.png — images, not time series; not downloaded.

Usage

Install the Apache TsFile Python SDK (pip install tsfile) and read a converted file:

from pathlib import Path
from tsfile import TsFileReader

path = Path("telemetry.tsfile")
with TsFileReader(str(path)) as reader:
    schemas = reader.get_all_table_schemas()
    print("tables:", list(schemas))
    table_name = next(iter(schemas))
    table = schemas[table_name]
    columns = [column.get_column_name() for column in table.get_columns()]
    print("columns:", columns)
    field_names = [
        column.get_column_name()
        for column in table.get_columns()
        if column.get_column_name() not in {"Time", "time"}
    ]
    if field_names:
        with reader.query_table(table_name, field_names[:3], batch_size=1024) as result:
            batch = result.read_arrow_batch()
            if batch is not None:
                print(batch.to_pandas().head())

Source & license

• Original dataset: Nasim435/Nurburgring-GP-Lap-data
• Source card title / publisher: Nürburgring Sesto Elemento Telemetry Dataset
• License: mit (as declared by the original dataset).