subpop_50k

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.

This dataset contains genotypes from chromosome chr22 for a subset of samples from the 1000 Genomes Project and HGDP. For this specific dataset, 50k SNPs at random positions were selected from the original sequences and missing values where introduced with a probability of 0%. The task is to predict the specific subpopulation of each sample based on the genotypes.

Domain	Task	Samples	Features	Classes
Genomics	classification	2,501	32,339	26

Splits. Fixed train/val/test split (1751/375/375 rows).

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).

from datasets import load_dataset
ds = load_dataset("TabBioMed/subpop_50k")

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

Source and license

Dataset license: CC0-1.0.

• gnomAD v3.1.2 HGDP + 1000 Genomes callset and harmonized metadata. gnomAD Consortium, 1000 Genomes Project and Human Genome Diversity Project. Cite the source release and relevant consortium publications. Source license: CC0-1.0. The primary gnomAD data are CC0. Users agree not to attempt participant reidentification. Acknowledge gnomAD, 1000 Genomes and HGDP. Do not extend this grant to separately licensed annotations.

See NOTICE.md for attribution and modifications and LICENSES/ for license texts.

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}
}