datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
latent-dna-diffusion100k_Tdk_zurriyet_dna_v6.jsonl
🌟 DESTEK & TOPLULUK ÇAĞRISI (SUPPORT & LIKE):Açık kaynak ve ücretsiz olarak sunduğum bu devasa çalışmayı faydalı bulduysanız, projenin sürdürülebilirliğine ve açık kaynak ekosisteminin görünürlüğüne katkı sağlamak için lütfen sayfanın sağ üstündeki Like (❤️ Beğeni) butonuna basarak destek olmayı unutmayın!(If you find this open-source dataset valuable for your research or models, please consider leaving a ❤️ Like at the top-right to support future updates and maintenance).
🇹🇷… See the full description on the dataset page: https://huggingface.co/datasets/bysismo/100k_Tdk_zurriyet_dna_v6.jsonl.Table-Instructs
📚 Table-Instructs
Bundled instruction-tuning corpora used to train the table LLMs in:
Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang,
Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng.
What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects.
Findings of EACL 2026. https://aclanthology.org/2026.findings-eacl.195/
This dataset re-packages the four training corpora used in the paper as a single HF dataset so… See the full description on the dataset page: https://huggingface.co/datasets/dnaihao/Table-Instructs.omni-dna-sad-mutation-dataset
Omni-DNA SAD Mutation Dataset
Synthetic and real DNA mutation pairs for training cross-domain HGT mutation prediction models.
Files
File
Pairs
Source
synthetic_expanded.jsonl
8,112
ICI dual-model generation (Omni + HyenaDNA consensus)
train.jsonl
3,317
Real NCBI sequences
test.jsonl
826
Real NCBI sequences (held-out)
Format
Each line is a JSON object:
{"parent": "ATGGCT...", "child": "ATAGCT..."}
Generation Method (Synthetic… See the full description on the dataset page: https://huggingface.co/datasets/Nhoodie/omni-dna-sad-mutation-dataset.omnigene4-sft-data
OmniGene-4 SFT corpus
Supervised fine-tuning data for the OmniGene-4 / OmniGene-4-MM family.
See https://github.com/maris205/omnigene4 for the training scripts that
consume these files.
Files
File
Rows
Used by
Description
bio_sft_v2_train.jsonl
~179K
Bio-SFT v2
Eight task families: protein homology (BioPAWS), DNA, structure (3Di/DSSP), cell biology, molecules, mutation, structure prediction, general bio QA
distill_seed.jsonl
~6K
seed-only
Initial… See the full description on the dataset page: https://huggingface.co/datasets/dnagpt/omnigene4-sft-data.dna-tiny-world
DNA-World-Tiny
Benchmark for DNA foundational models using real MPRA data from MPRAbase.
Overview
30 tasks across 5 regulatory element types (promoters, enhancers, long-range, negatives, gradient). All targets are real wet-lab MPRA measurements.
Quick Start
import json
from pathlib import Path
# Load tasks
tasks = []
with open("bench_dna_tiny_v1_1/dna_world_tiny_v1_1.jsonl") as f:
for line in f:
tasks.append(json.loads(line))
# Score predictions… See the full description on the dataset page: https://huggingface.co/datasets/sethmorton/dna-tiny-world.omnigene4-mm-corpus
OmniGene-4-MM unified corpus
Multi-modal training corpus used for the OmniGene-4-MM Stages 1–3
(see https://github.com/maris205/omnigene4 ).
Each row is a JSON object with messages (chat-format), images
(list of relative image paths), and modality field. Vision rows
reference images that live in the source datasets:
Vis-CheBI20 (PharMolix/Vis-CheBI20)
PubMedVision (FreedomIntelligence/PubMedVision)
HPA10M (Human Protein Atlas microscopy)
ChartQA (HuggingFaceM4/ChartQA)… See the full description on the dataset page: https://huggingface.co/datasets/dnagpt/omnigene4-mm-corpus.omnigene4-cpt-corpus
OmniGene-4 CPT corpus
Continued-pre-training (CPT) corpus for OmniGene-4 (see
https://github.com/maris205/omnigene4 ). Total ~96 GB across DNA,
protein, structure, and English-text replay splits.
Files
File
Size
Source / Description
dna_32g.txt
31 GB
DNA sequences sampled from public genomes
protein_uni_16.txt
16 GB
UniRef-derived protein sequences
protein_lucaone_15g.txt
15 GB
Protein sequences from the LucaOne pretraining pool
openwebtext.txt
37… See the full description on the dataset page: https://huggingface.co/datasets/dnagpt/omnigene4-cpt-corpus.table-sft-eval-predictions
💾 Raw Predictions for "What Really Matters for Table LLMs?"
This dataset contains the raw model outputs from the experiments in:
Naihao Deng, Sheng Zhang, Henghui Zhu, Shuaichen Chang, Jiani Zhang,
Alexander Hanbo Li, Chung-Wei Hang, Hideo Kobayashi, Yiqun Hu, Patrick Ng.
What Really Matters for Table LLMs? A Meta-Evaluation of Model and Data Effects.
Findings of EACL 2026. https://aclanthology.org/2026.findings-eacl.195/
🗂️ Layout… See the full description on the dataset page: https://huggingface.co/datasets/dnaihao/table-sft-eval-predictions.