ivanpodd/S-OH

Scent of Health (S-O-H) Dataset

Overview

The Scent of Health (S-O-H) 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 eight diagnostic groups (healthy controls and seven 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

Repository Structure

S-OH/
├── README.md                       # This file
├── LICENSE.txt                     # MIT License
├── metadata.csv                    # Patient metadata (demographics, diagnosis, site, week)
├── S-OH_data_dict.json             # Legacy - Combined data (time series) of all patient files
├── data/
│ ├── manifest.json                 # Index of all patient files
│ ├── Z00/                          # Healthy controls
│ │ ├── patient_1.json
│ │ ├── patient_2.json
│ │ └── ...
│ ├── B18/                          # Hepatitis B/C
│ │ ├── patient_10.json
│ │ └── ...
│ ├── K29/                          # Gastritis and duodenitis
│ ├── K76/                          # Non-alcoholic fatty liver disease
│ ├── E11/                          # Diabetes mellitus type II
│ ├── N18/                          # Chronic renal failure
│ ├── J44/                          # COPD
│ ├── C34/                          # Lung cancer
│ └── A15/                          # Respiratory tuberculosis
└── scripts/
  ├── quick_start.py                # Load metadata and patient JSONs
  ├── validate_metadata.py          # Metadata check
  ├── validate_temporal_splits.py   # Temporal splits check
  ├── baseline_lstm_lung_cancer.py  # Example: ML/AI Use Case (LSTM baseline for C34)
  ├── baseline_cnn_z00.py           # Temporal splits check
  └── baseline_resnet18_z00.py      # Example: ML/AI Use Case (LSTM baseline for C34)

Dataset Structure

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 (0 = female, 1 = male)
Diagnosis	ICD-10 diagnosis code
D_class	Disease class (0–7)
D_bin_class	Binary class for one-vs-rest classification
Datetime	Collection timestamp
Week	Collection week (1–13)
Site	Clinical site (SiteA or CiteB)

S-OH_data_dict.json

Legacy - Combined data (time series) of all patient json files in data/. Each patient is stored as a separate key-value pair in JSON format, the key is the Patient_id and the value is a dictionary with a structure exactly as in the next section.

data/ - Per-Patient JSON Files

Each patient is stored as a separate JSON file in a subdirectory named after their ICD-10 diagnosis code. The file name format is patient_{Patient_id}.json. Example path: data/Z00/patient_1.json.

Each patient JSON file has the following structure:

{
    "patient_id": 1,
    "patient_diag_class": 0,
    "startDateTime": "2025-09-01T08:05:47.676148Z",
    "startTimeGases": 20,
    "endTimeGases": 450,
    "durationSec": 895,
    "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, ...]}
            ]
        }
    ]
}

data/manifest.json

Index file mapping patient IDs to their JSON file paths:

{
    "total_patients": 1234,
    "files": [
        {"patient_id": 1, "diagnosis": "Z00", "file": "Z00/patient_1.json"},
        {"patient_id": 2, "diagnosis": "Z00", "file": "Z00/patient_2.json"},
        ...
    ]
}

scripts/

Utility scripts for loading and processing the dataset.

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

Quick Start

import json
import pandas as pd

# 1. Load metadata
metadata = pd.read_csv('../metadata.csv')

# 2. Load patient data via manifest
with open('../data/manifest.json', 'r') as f:
    manifest = json.load(f)

# 3. Load a specific patient
patient_id, icd = '1', 'Z00'
entry = next(e for e in manifest['files'] if e['patient_id'] == patient_id)
with open(f"../data/{icd}/{entry['file']}", 'r') as f:
    patient_data = json.load(f)

# 4. Extract eNose signals
for sensor in patient_data['sensors']:
    if sensor['id'] == 'enose':
        for channel in sensor['channels']:
            print(f"{channel['id']}: {len(channel['samples'])} samples")

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 Anonymized Clinical Institute (SiteA) and Anonymized Research Institute (SiteB).

• All participants provided written informed consent.

Citation

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

@article{melba:2026:042:poddiakov,
    title = "Scent of Health (S-OH): Olfactory Multivariate Time Series Dataset for Non-Invasive Disease Screening",
    author = "Poddiakov, Ivan and Kruzhilov, Ivan and Zubkova, Galina and Erofeeva, Svetlana and Savchenko, Andrey and Blinov, Pavel",
    journal = "Machine Learning for Biomedical Imaging",
    volume = "2026",
    issue = "Special Issue on MICCAI Open Data 2026",
    year = "2026",
    pages = "813--820",
    issn = "2766-905X",
    doi = "https://doi.org/10.59275/j.melba.2026-7d42",
    url = "https://melba-journal.org/2026:042"
}

License

This dataset is released under the MIT License. See LICENSE.txt for full terms. You are free to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the dataset, subject to the condition that the copyright notice and permission notice are included in all copies or substantial portions.

Contact

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