datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
ai-writing-evolutionary-dynamics
Evolutionary Dynamics of AI-Mediated Scientific Writing
Complete experimental logs and reproduction package.
Author: Arif Mohamed Khan Rabi AhamadAffiliation: School of Information Studies, Syracuse UniversityContact: arabiaha@syr.edu | ORCID: 0009-0001-0986-7570
Contents
Directory
Files
Description
logs/
0
Complete stdout from all experiments
figures/
91
All paper figures (PDF + PNG)
data/
17
Derived datasets (QTE matrices, Price components)… See the full description on the dataset page: https://huggingface.co/datasets/arifmohamedkhan/ai-writing-evolutionary-dynamics.synthetic-gsm8k-evolutionary-405b
gretelai/synthetic-gsm8k-evolutionary-405b
This dataset is a synthetically generated version inspired by the GSM8K dataset, created entirely using Gretel Navigator with meta-llama/Meta-Llama-3.1-405B as the agent LLM. It contains Grade School-level reasoning tasks with step-by-step solutions, focusing on multi-step reasoning problems.
Key Features:
Synthetically Generated: Built using Gretel Navigator, leveraging evolutionary approach for diversity to create both the… See the full description on the dataset page: https://huggingface.co/datasets/gretelai/synthetic-gsm8k-evolutionary-405b.FishCaduceus-Evolutionary-Constraint-Benchmark
FishCaduceus Evolutionary Constraint Benchmark
Dataset description
This dataset contains sequence-based benchmarks for evaluating whether FishCaduceus representations capture evolutionary constraint in fish genomes.
Constraint labels were derived from a 26-fish whole-genome alignment generated with Progressive Cactus. Grass carp (Ctenopharyngodon idella) was used as the primary reference genome for defining aligned and conserved positions. The resulting labeled… See the full description on the dataset page: https://huggingface.co/datasets/FishCaduceus/FishCaduceus-Evolutionary-Constraint-Benchmark.evolutionary-origin-ontology
Licensing
The source text El hijo de José states that it is licensed under Creative Commons Attribution-NonCommercial(CC BY-NC). Commercial use of source-derived material requires explicit written permission from the rights holder.
Dataset Card for Evolutionary Origin Ontology
A high-density instruction-tuning corpus for mapping systemic human contradictions to ontological resolutions through the framework of Inversion, correct naming, captured life-energy… See the full description on the dataset page: https://huggingface.co/datasets/elhijodeJose/evolutionary-origin-ontology.X-Teaming_Evolutionary_M2S
X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak Templates
Paper: X-Teaming Evolutionary M2S: Automated Discovery of Multi-turn to Single-turn Jailbreak TemplatesarXiv: 2509.08729 [cs.CL]Accepted at: NeurIPS 2025 Workshop on LockLLMGitHub: M2S-x-teaming-pipeline-final
Dataset Description
This dataset contains the complete experimental results from our M2S (Multi-turn to Single-turn) template evolution pipeline, which uses… See the full description on the dataset page: https://huggingface.co/datasets/hyunjun1121/X-Teaming_Evolutionary_M2S.Evolutionary
Evolutionary MSA Data
This repository contains precomputed evolutionary sequence-alignment data in an archive format that is practical to host and download from the Hub. The original file paths are preserved inside the tar shard, while metadata.csv gives a searchable index of every file.
The dataset is meant for workflows that need ready-to-use MSA/cache files without rebuilding them from sequence databases.
Contents
Component
Files
Size
msa_cache/
134,898… See the full description on the dataset page: https://huggingface.co/datasets/LiteFold/Evolutionary.clean-synthetic-gsm8k-evolutionary-405bevolutionary-constraint
Evolutionary constraint prediction
Reproducing this dataset
import pandas as pd
CENTER_POSITION = 255
NUCLEOTIDES = list("ACGT")
df = pd.read_csv("hf://datasets/kuleshov-group/cross-species-single-nucleotide-annotation/Evolutionary_constraint/valid.tsv", sep="\t")
df = df.rename(columns={"sequences": "seq"})
df["chrom_pos"] = df.pos
df["pos"] = CENTER_POSITION
df["ref"] = df.seq.str[CENTER_POSITION]
def subsample(df, n, seed):
return (… See the full description on the dataset page: https://huggingface.co/datasets/plantcad/evolutionary-constraint.clean-synthetic-gsm8k-evolutionary-405b-1kCombining-evolutionary-and-assay-labelled-dats