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Cannot extract the features (columns) for the split 'train' of the config 'default' of the dataset.
Error code:   FeaturesError
Exception:    ValueError
Message:      Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/ivanpodd/S-OH@5ab8f51576bdcb51d1093070427971b66dd339e1/S-OH_data_dict.json.
Traceback:    Traceback (most recent call last):
                File "/src/services/worker/src/worker/job_runners/split/first_rows.py", line 247, in compute_first_rows_from_streaming_response
                  iterable_dataset = iterable_dataset._resolve_features()
                                     ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 4376, in _resolve_features
                  features = _infer_features_from_batch(self.with_format(None)._head())
                                                        ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2658, in _head
                  return next(iter(self.iter(batch_size=n)))
                         ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2836, in iter
                  for key, pa_table in ex_iterable.iter_arrow():
                                       ^^^^^^^^^^^^^^^^^^^^^^^^
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 2374, in _iter_arrow
                  yield from self.ex_iterable._iter_arrow()
                File "/usr/local/lib/python3.12/site-packages/datasets/iterable_dataset.py", line 536, in _iter_arrow
                  for key, pa_table in iterator:
                                       ^^^^^^^^
                File "/usr/local/lib/python3.12/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/python3.12/site-packages/datasets/packaged_modules/json/json.py", line 303, in _generate_tables
                  raise ValueError(
              ValueError: Failed to convert pandas DataFrame to Arrow Table from file hf://datasets/ivanpodd/S-OH@5ab8f51576bdcb51d1093070427971b66dd339e1/S-OH_data_dict.json.

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Scent of Health (S-OH) Dataset

The Scent of Health (S-OH) dataset is the largest public clinical electronic nose (eNose) collection for non-invasive disease screening via exhaled breath analysis. It comprises 1,234 patients across nine diagnostic groups (healthy controls and eight diseases), each providing a 17-channel multivariate time series of breath measurements.

Property Value
Patients 1,234
Diagnostic groups 9 (healthy + 8 diseases)
Time series channels 17 (eNose sensors) + auxiliary sensors
Sampling rate 0.4 Hz
Duration per sample 895 seconds (~15 minutes)
Collection period 13 consecutive weeks
Clinical sites 2

Diagnostic Groups

ICD-10 Code Diagnosis Count
Z00 Healthy controls 256
E11 Diabetes mellitus type II 128
K29 Gastritis and duodenitis 138
K76 Non-alcoholic fatty liver disease 128
B18 Hepatitis B/C 138
C34 Lung cancer 100
N18 Chronic renal failure 128
J44 COPD 100
A15 Respiratory tuberculosis 118
Total 1,234

Demographics:

  • Age range: 18–89 years (mean 53.9, std 13.4)
  • Gender: 690 female (55.9%), 544 male (44.1%)

Dataset Structure

File 1: S-OH_metadata.csv

CSV file containing patient metadata with the following columns:

Column Description
Patient_id Unique patient identifier
Patient_age Age in years
Patient_gender Gender (male or female)
Diagnosis ICD-10 diagnosis code
D_class Disease class (0–8)
D_bin_class Binary class for one-vs-rest classification
Datetime Collection timestamp
Week Collection week (1–13)
Site Clinical site (MONIKI or CRIT)

File 2: S-OH_data_dict.json

JSON dictionary where each key is a patient ID (as string) with the following structure:

{
    "patient_id": int,
    "patient_diag_class": int,
    "startDateTime": "ISO timestamp",
    "startTimeGases": int,
    "endTimeGases": int,
    "durationSec": int,
    "sensors": [
        {
            "id": "enose",
            "sampleRate": 0.4,
            "channels": [
                {"id": "R1", "samples": [float, ...]},
                {"id": "R2", "samples": [float, ...]},
                ...
                {"id": "R17", "samples": [float, ...]},
                {"id": "humidity", "samples": [float, ...]},
                {"id": "temperature", "samples": [float, ...]}
            ]
        },
        {
            "id": "ze03",
            "sampleRate": 0.4,
            "channels": [{"id": "0", "samples": [float, ...]}]
        },
        {
            "id": "mhz14",
            "sampleRate": 0.4,
            "channels": [{"id": "0", "samples": [float, ...]}]
        },
        {
            "id": "ze08",
            "sampleRate": 0.4,
            "channels": [{"id": "0", "samples": [float, ...]}]
        },
        {
            "id": "bme280",
            "sampleRate": 0.4,
            "channels": [
                {"id": "pressure", "samples": [float, ...]},
                {"id": "temperature", "samples": [float, ...]},
                {"id": "humidity", "samples": [float, ...]}
            ]
        }
    ]
}

eNose Channels (17 channels)

The eNose sensor array consists of 17 channels printed on a single chip:

Channel ID Material
R1–R17 ZnO and metal-doped ZnO (In-ZnO, Ag-ZnO, Ce-ZnO, Ni-ZnO)

Auxiliary Sensors

Sensor ID Measurements
ze03 Ozone (O₃)
mhz14 Carbon dioxide (CO₂)
ze08 Carbon monoxide (CO)
bme280 Pressure, temperature, humidity

Temporal Train/Test Splits

The dataset includes explicit temporal splits to enable drift-aware evaluation. For each disease, test weeks were selected to be temporally separated from training weeks, simulating real-world deployment conditions.

Ethics

  • The study protocol was approved by the Local Ethics Committee at Moscow Regional Research and Clinical Institute (MONIKI) and Central Research Institute of Tuberculosis (CRIT).

  • All participants provided written informed consent.

Citation

If you use this dataset in your research, please cite:


License

This dataset is released under the MIT License. You are free to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the dataset, subject to the following conditions:

Permission is hereby granted, free of charge, to any person obtaining a copy of this dataset and associated documentation files (the "Dataset"), to deal in the Dataset without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Dataset, and to permit persons to whom the Dataset is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Dataset.

THE DATASET IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE DATASET OR THE USE OR OTHER DEALINGS IN THE DATASET.

Contact

For questions or issues, please open an issue on this repository or contact the authors (see paper for details).

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