Datasets:

TabBioMed
/

Noc_s

license: cc-by-4.0
pretty_name: Noc_s
task_categories:
  - tabular-regression
tags:
  - tabular
  - biomedical
  - benchmark
  - TabBioMed
  - metabolomics
  - regression
size_categories:
  - n<1K
configs:
  - config_name: default
    data_files:
      - split: fold_0_train
        path: data/fold_0/train.parquet
      - split: fold_0_validation
        path: data/fold_0/val.parquet
      - split: fold_0_test
        path: data/fold_0/test.parquet
      - split: fold_1_train
        path: data/fold_1/train.parquet
      - split: fold_1_validation
        path: data/fold_1/val.parquet
      - split: fold_1_test
        path: data/fold_1/test.parquet
      - split: fold_2_train
        path: data/fold_2/train.parquet
      - split: fold_2_validation
        path: data/fold_2/val.parquet
      - split: fold_2_test
        path: data/fold_2/test.parquet
      - split: fold_3_train
        path: data/fold_3/train.parquet
      - split: fold_3_validation
        path: data/fold_3/val.parquet
      - split: fold_3_test
        path: data/fold_3/test.parquet
      - split: fold_4_train
        path: data/fold_4/train.parquet
      - split: fold_4_validation
        path: data/fold_4/val.parquet
      - split: fold_4_test
        path: data/fold_4/test.parquet

Noc_s

Part of TabBioMed (TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning), a benchmark of 94 curated biomedical tabular datasets. Code, preprocessing and documentation: https://github.com/AI-sandbox/TabBioMed.

PTR-ToF-MS analysis of hazelnut paste (Corylus avellana L.) from different geographical origins, roasted at varying times and temperatures (72 samples, 380 peaks, 24 batches of technical replicates). Regression: predicted value is the roasting time. S/P=0.19. Experimental details in Mazzucotelli et al. Source PTR-ToF-MS peak values are area-normalized to a row sum of one and are retained without an additional logarithmic transformation. Feature scaling is fitted within each training partition. Five grouped outer folds test every sample exactly once. All samples sharing a source ID stay together in outer and inner CV and in the final-fit validation holdout; source IDs are split metadata, not features.

Domain	Task	Samples	Features	Classes
Metabolomics	regression	72	380	-

Splits. 5 stored outer cross-validation folds (data/fold_<i>/), each with train/val/test files (fold 0: 48/9/15 rows). Every row appears in exactly one outer test fold. HPO in the paper uses inner folds drawn from each outer non-test pool.

Files

data/**.parquet: one row per sample. row_idx is the row index in the original TabBioMed array; f_<i>[_<name>] are the features (in order); target (or target_<j> for multi-target tasks) is the label. Categorical feature indices (0-based, among the features): []. Missing values are stored as nulls.
metadata.json: TabBioMed task metadata (task type, categorical indices, source, description).
split_groups.json: groups[row_idx] gives the sample group (e.g. patient, donor or strain) that must stay together in any re-split. Split metadata only, never a feature.

from datasets import load_dataset
ds = load_dataset("TabBioMed/Noc_s")

A Croissant (1.1) description with Responsible-AI fields is distributed with the TabBioMed release.

Source and license

Source: On the use of TabPFN on mass spectrometry analysis of volatile organic compounds. Scientific Reports (2025). GitHub: https://github.com/CIFASIS/TabPFN-VOCS

This derived dataset is distributed under CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/). Please also cite and respect the terms of the original source.

Citation

@inproceedings{tabbiomed2026,
  title     = {TabBioMed: A Large-Scale Benchmark for Biomedical Tabular Learning},
  author    = {Mateo Bernad{\'o}, Pau and Polapragada, Saivenkata Nagavyjayanthi and Bonet, David and Arbiol Rakuljic, Pol and Mogas Pladevall, Laia and Comajoan Cara, Mar{\c{c}}al and Gonzalez Ferrer, Jesus and Abante, Jordi and Mas Montserrat, Daniel and Haussler, David and Ioannidis, Alexander G.},
  booktitle = {Advances in Neural Information Processing Systems (NeurIPS), Evaluations and Datasets Track},
  year      = {2026}
}