The dataset viewer is not available for this split.
Error code: StreamingRowsError
Exception: CastError
Message: Couldn't cast
display_name: string
type: string
submitter: string
subtrack: string
fallback_rate: double
n_tasks: int64
n_boot: int64
fairness_attributes: list<item: string>
child 0, item: string
seed: int64
baseline: string
methods: list<item: string>
child 0, item: string
to
{'n_boot': Value('int64'), 'seed': Value('int64'), 'baseline': Value('string'), 'methods': List(Value('string')), 'n_tasks': Value('int64'), 'fairness_attributes': List(Value('string'))}
because column names don't match
Traceback: Traceback (most recent call last):
File "/src/services/worker/src/worker/utils.py", line 147, in get_rows_or_raise
return get_rows(
dataset=dataset,
...<4 lines>...
column_names=column_names,
)
File "/src/libs/libcommon/src/libcommon/utils.py", line 272, in decorator
return func(*args, **kwargs)
File "/src/services/worker/src/worker/utils.py", line 127, in get_rows
rows_plus_one = list(itertools.islice(safe_iter(ds, dataset=dataset), rows_max_number + 1))
File "/src/services/worker/src/worker/utils.py", line 478, in safe_iter
yield from ds.decode(False) if ds.features else ds
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2818, in __iter__
for key, example in ex_iterable:
^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2355, in __iter__
for key, pa_table in self._iter_arrow():
~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 2380, in _iter_arrow
for key, pa_table in self.ex_iterable._iter_arrow():
~~~~~~~~~~~~~~~~~~~~~~~~~~~~^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
for key, pa_table in iterator:
^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/iterable_dataset.py", line 419, in _iter_arrow
for key, pa_table in self.generate_tables_fn(**gen_kwags):
~~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 343, in _generate_tables
self._cast_table(pa_table, json_field_paths=json_field_paths),
~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.14/site-packages/datasets/packaged_modules/json/json.py", line 132, in _cast_table
pa_table = table_cast(pa_table, self.info.features.arrow_schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2369, in table_cast
return cast_table_to_schema(table, schema)
File "/usr/local/lib/python3.14/site-packages/datasets/table.py", line 2297, in cast_table_to_schema
raise CastError(
...<3 lines>...
)
datasets.table.CastError: Couldn't cast
display_name: string
type: string
submitter: string
subtrack: string
fallback_rate: double
n_tasks: int64
n_boot: int64
fairness_attributes: list<item: string>
child 0, item: string
seed: int64
baseline: string
methods: list<item: string>
child 0, item: string
to
{'n_boot': Value('int64'), 'seed': Value('int64'), 'baseline': Value('string'), 'methods': List(Value('string')), 'n_tasks': Value('int64'), 'fairness_attributes': List(Value('string'))}
because column names don't matchNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
OpenMHC Leaderboard Data
Per-user error substrate behind the OpenMHC wearable-health benchmark leaderboard. Each file is one method's reduced per-user, per-task errors for one track; the leaderboard recompute consumes these to produce paired skill scores, cross-method ranks, and fairness skill scores.
This repo holds reduced metrics keyed by pseudonymous participant id — not raw sensor data and not model predictions.
Layout
<track>/<method>.parquet e.g. imputation/locf.parquet, forecasting/seasonal_naive.parquet
<track>/bootstrap/draws.parquet per-draw bootstrap reference for the CIs
Tracks: imputation and forecasting (live); downstream added later.
Schema
See SCHEMA.md for the full column spec per track. In brief: each
row is a per-user value for one task cell, evaluated on the canonical
sharable_users_seed42_2026 test split. The two tracks differ slightly:
- Track 2 (imputation) stores the per-user error
E_per_user(MAE /1 − AUC). - Track 3 (forecasting) stores the raw per-user
metric_value(so one file serves skill, rank, and fairness — each reducer converts/uses it on load).
Why per-user (not aggregate)
The skill score is a paired per-user geometric mean against the track baseline
(locf for imputation, seasonal_naive for forecasting), and the rank is a
per-user rank across all methods — so both require each method's per-user
values, paired on user_id, not per-task aggregates.
Usage
import glob, os
import pandas as pd
from huggingface_hub import snapshot_download
root = snapshot_download("MyHeartCounts/OpenMHC-leaderboard-data", repo_type="dataset")
# one track at a time (schemas differ per track)
frames = [pd.read_parquet(p) for p in glob.glob(os.path.join(root, "forecasting", "*.parquet"))]
df = pd.concat(frames, ignore_index=True)
Methods are uploaded with tools/upload_leaderboard_substrate.py; the per-track
bootstrap references with tools/upload_leaderboard_bootstrap.py, both in the
code repo.
Provenance
Generated by the OpenMHC evaluation harness. Baselines: locf (Track 2),
seasonal_naive (Track 3).
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