The dataset viewer is not available for this subset.
Exception: SplitsNotFoundError
Message: The split names could not be parsed from the dataset config.
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 286, in get_dataset_config_info
for split_generator in builder._split_generators(
~~~~~~~~~~~~~~~~~~~~~~~~~^
StreamingDownloadManager(base_path=builder.base_path, download_config=download_config)
^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/webdataset/webdataset.py", line 80, in _split_generators
raise ValueError(
...<2 lines>...
)
ValueError: The TAR archives of the dataset should be in WebDataset format, but the files in the archive don't share the same prefix or the same types.
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/split_names.py", line 68, in compute_split_names_from_streaming_response
for split in get_dataset_split_names(
~~~~~~~~~~~~~~~~~~~~~~~^
path=dataset,
^^^^^^^^^^^^^
config_name=config,
^^^^^^^^^^^^^^^^^^^
token=hf_token,
^^^^^^^^^^^^^^^
)
^
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 340, in get_dataset_split_names
info = get_dataset_config_info(
path,
...<6 lines>...
**config_kwargs,
)
File "/usr/local/lib/python3.14/site-packages/datasets/inspect.py", line 291, in get_dataset_config_info
raise SplitsNotFoundError("The split names could not be parsed from the dataset config.") from err
datasets.inspect.SplitsNotFoundError: The split names could not be parsed from the dataset config.Need help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
Formula 1 research archive, 2014–2025
A versioned assembly of historical Formula 1 data from Jolpica and FastF1, collected September 10–11, 2026. This is an independent research archive, not an official Formula 1 product. The files preserve broad inputs before modeling or lap filtering. Collection and analysis code are maintained in a separate private team repository.
Downloads
- analysis.tar.gz: Parquet tables, session metadata, original collection receipts, coverage reports, and Italy 2018 recovery evidence.
- reproduction.tar.gz: raw source responses, FastF1 caches, and historical collection status. Add this to the analysis archive for the full archive.
- manifest.json: SHA-256 checksums, byte sizes, file counts, collection coverage, and the packaging source commit.
Each archive extracts directly into a data root: tables/, receipts/, etc.
Use a specific repository commit as the revision for reproducible downloads.
No account is required to download. Archives preserve heterogeneous table schemas;
they are not a single Hugging Face load_dataset() table, and the dataset viewer
is not configured.
uv tool install huggingface-hub
hf download AndreyKopanev/f1-data-mining analysis.tar.gz manifest.json \
--repo-type dataset --revision COMMIT_SHA --local-dir download
Verify the archive SHA-256 against manifest.json before extracting into a new
empty directory. The private team's f1-data download command automates pinned
downloads, checksum checks, and safe extraction. The manifest's code commit
identifies the packaging implementation, not the original collector launch.
Sources and coverage
- Jolpica: 2014–2025 calendars, circuit information, drivers, constructors, race and qualifying results, sprints, pit stops, and 2014–2017 race laps.
- FastF1, version 3.8.3: 2018–2025 practice, qualifying, sprint and race sessions, including lap/sector timing, tyres, weather, results, track/session status, and race-control messages.
858 running sessions are collected; five source-confirmed cancellations account
for all 863 scheduled sessions in the detailed period. See coverage.json and
coverage.md. The full uncompressed archive is approximately 3.9 GiB; most of it
is raw responses and caches. High-frequency car/position telemetry, preseason
testing and the incomplete 2026 season are excluded.
Organization and interpretation
tables/jolpica/season=YYYY/ contains flattened Parquet tables; nested source
fields use dotted column names. tables/fastf1/season=YYYY/round=RR/session_name/
contains separate table families plus session metadata. Use season and round for
event joins, retain source driver IDs, and treat driver numbers as session-scoped.
Do not concatenate unrelated table families or count their combined rows as
independent observations.
Lap tables retain invalid/deleted/interrupted/pit laps and quality flags. Classifications may reflect later penalties; they are not point-in-time forecasts. FastF1 reconstructs some values and uses sources including Jolpica, so the two sources are not independent. Timing durations are usually relative to the timing stream origin, not necessarily the scheduled session start.
Italy 2018 race recovery leaves Ricciardo tyre fields missing and marks
TyreDataUncertain=True; it does not invent tyre histories. Fuel loads, setup,
damage, upgrades and actual tyre condition cannot be reconstructed here.
Original receipts retain collector-machine paths for provenance. Their tables/
suffix identifies the corresponding output in a downloaded archive. Archived
worker status describes the old collection, not a current process.
Attribution and caches
Underlying data rights remain with the respective sources and rights holders; no new permissive license is asserted over the assembled source data. The source software licenses do not themselves license all upstream data.
The optional FastF1 caches contain Python pickles. Only load caches from a trusted publisher after verification. Ordinary analysis can read the Parquet files without loading these caches.
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