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.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.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.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.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.