datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
terminal-bench-science
Terminal-Bench-Science
The primary source is hosted on GitHub, please open
issues and pull requests there, not here.
Terminal-Bench-Science is a benchmark of real-world computational research
workflows across the life, physical, earth, mathematical, and engineering sciences. Like Terminal-Bench, it's a
continuous benchmark: releases are published as tags on the source repo. This dataset mirrors that on the Hub:
one repo, tagged per version, instead of a separate repo per… See the full description on the dataset page: https://huggingface.co/datasets/harborframework/terminal-bench-science.ScienceQA
Dataset Card Creation Guide
Dataset Summary
Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering
Supported Tasks and Leaderboards
Multi-modal Multiple Choice
Languages
English
Dataset Structure
Data Instances
Explore more samples here.
{'image': Image,
'question': 'Which of these states is farthest north?',
'choices': ['West Virginia', 'Louisiana', 'Arizona', 'Oklahoma'],
'answer': 0… See the full description on the dataset page: https://huggingface.co/datasets/derek-thomas/ScienceQA.ScienceQA
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted version of derek-thomas/ScienceQA. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{lu2022learn,
title={Learn to Explain: Multimodal Reasoning via Thought… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab-encoder/ScienceQA.Nemotron-SFT-Science-v2
Dataset Description:
Nemotron-Science-v2 is a science reasoning dataset with synthetic (synthetic MCQ, RQA) and non-synthetic vendor problems and LLM-generated solutions. It comprises three domains (Physics, Biology, and Chemistry), two question formats (multiple-choice questions [MCQ] and open questions [OpenQ]), and three generation setups: chain-of-thought (CoT) reasoning without tools, Python tool usage, and search tools usage with the Tavily API.
The solutions were… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-SFT-Science-v2.Nemotron-Science-v1
Dataset Description:
Nemotron-Science-v1 is a synthetic science reasoning dataset with two subsets: an MCQA set that improves on the STEM portion of Nemotron-Post-Training-v1 using GPT-OSS-120B to generate GPQA-style questions and reasoning traces, and an RQA set of synthetic chemistry questions.
This dataset is ready for commercial use.
The Nemotron-Science-v1 dataset contains the following subsets:
MCQA
This subset is an improvement of the STEM subset in… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-Science-v1.ScienceAgentBench
ScienceAgentBench
Update 04/30/2026: To mitigate false negatives in evaluation, we have released a verified version of ScienceAgentBench. Please load our benchmark using the following code going forward and make sure you follow the latest instructions in our github repository:
from datasets import load_dataset
ds = load_dataset("osunlp/ScienceAgentBench", split="verified")
The advancements of language language models (LLMs) have piqued growing interest in developing… See the full description on the dataset page: https://huggingface.co/datasets/osunlp/ScienceAgentBench.Aneumo
Aneumo Datasets
AneumoDataset is a comprehensive multi-modal dataset containing 3D anatomical structures and simulated hemodynamic parameters for cerebral aneurysms, supporting both computational modeling and AI-based analysis.
xlam-function-calling-60k-raw
XLAM Function Calling 60k Raw Dataset
This dataset includes train and test splits derived from Salesforce/xlam-function-calling-60k.
Train split size: 95% of the original dataset
Test split size: 5% of the original dataset
mmu_manga
mmu_manga HATS Catalog Collection
This is the collection of HATS catalogs representing mmu_manga.
This dataset is part of the Multimodal Universe,
a large-scale collection of multimodal astronomical data. For full details, see the paper:
The Multimodal Universe: Enabling Large-Scale Machine Learning with 100TBs of Astronomical Scientific Data.
Access the catalog
We recommend the use of the LSDB Python framework to access HATS catalogs.
LSDB can be installed via… See the full description on the dataset page: https://huggingface.co/datasets/hugging-science/mmu_manga.S1-MMAlignS1-MMAlign
A Large-Scale Multi-Disciplinary Scientific Multimodal Dataset
S1-MMAlign is a large-scale, multi-disciplinary multimodal dataset comprising over 15.5 million high-quality image-text pairs derived from 2.5 million open-access scientific papers.
Multimodal learning has revolutionized general domain tasks, yet its application in scientific discovery is hindered by the profound semantic gap between complex scientific imagery and sparse textual descriptions. S1-MMAlign aims to… See the full description on the dataset page: https://huggingface.co/datasets/ScienceOne-AI/S1-MMAlign.SWE-bench-Science
SWE-bench Science
SWE-bench Science evaluates coding agents on software-engineering tasks drawn from scientific-computing repositories. The release contains 119 tasks across 20 scientific domains, with isolated environments and separate programmatic verifiers.
GitHub release repository: OpenMOSS/SWE-bench-Science
Runtime images: Docker Hub, pinned by immutable linux/amd64 digests
Evaluation framework: Pier, compatible with Harbor task format
Dataset Summary… See the full description on the dataset page: https://huggingface.co/datasets/OpenMOSS-Team/SWE-bench-Science.vidore_v3_computer_scienceViDoRe V3 : Computer Science
This dataset, Computer Science, is a corpus of textbooks from the openstacks website, intended for long-document understanding tasks. It is one of the 10 corpora comprising the ViDoRe v3 Benchmark.
About ViDoRe v3
ViDoRe V3 is our latest benchmark for RAG evaluation on visually-rich documents from real-world applications. It features 10 datasets with, in total, 26,000 pages and 3099 queries, translated into 6 languages. Each query comes with… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_computer_science.Medical-Sciences
🔭 Overview
R2MED: First Reasoning-Driven Medical Retrieval Benchmark
R2MED is a high-quality, high-resolution synthetic information retrieval (IR) dataset designed for medical scenarios. It contains 876 queries with three retrieval tasks, five medical scenarios, and twelve body systems.
Dataset
#Q
#D
Avg. Pos
Q-Len
D-Len
Biology
103
57359
3.6
115.2
83.6
Bioinformatics77
47473
2.9
273.8
150.5
Medical Sciences
88
34810
2.8
107.1
122.7
MedXpertQA-Exam
97… See the full description on the dataset page: https://huggingface.co/datasets/R2MED/Medical-Sciences.ScienceQA_text_only
Dataset Card for "scienceQA_text_only"
ScienceQA text-only examples (examples where no image was initially present, which means they should be doable with text-only models.)
@article{10.1007/s00799-022-00329-y,
author = {Saikh, Tanik and Ghosal, Tirthankar and Mittal, Amish and Ekbal, Asif and Bhattacharyya, Pushpak},
title = {ScienceQA: A Novel Resource for Question Answering on Scholarly Articles},
year = {2022},
journal = {Int. J. Digit. Libr.},
month = {sep}
}
judged_science_completionshle_material_science
HLE Material Science: A Specialized Benchmark for Materials Science
A Materials Science Subset of Humanity's Last Exam (HLE)
Overview
HLE Material Science is a carefully curated materials science subset derived from the Humanity's Last Exam (HLE) dataset, containing 106 high-quality expert-level questions covering 25+ materials science subfields, with 97% of questions rated as high confidence.
This dataset is designed to evaluate large language models'… See the full description on the dataset page: https://huggingface.co/datasets/TalentZHOU/hle_material_science.Science-T2I-Fullset
Science-T2I Fullset
Resources
Website
arXiv: Paper
GitHub: Code
Huggingface: SciScore
Huggingface: Science-T2I-S&C Benchmark
Data
The Science-T2I Fullset comprises a comprehensive collection of data for scientific T2I generation, including both training and test sets with a unified data structure. The test sets are split into 'test-S' and 'test-C,' corresponding to the Science-T2I-S and Science-T2I-C benchmarks, respectively.
Download Dataset… See the full description on the dataset page: https://huggingface.co/datasets/Jialuo21/Science-T2I-Fullset.ScienceQA-IMG
Large-scale Multi-modality Models Evaluation Suite
Accelerating the development of large-scale multi-modality models (LMMs) with lmms-eval
🏠 Homepage | 📚 Documentation | 🤗 Huggingface Datasets
This Dataset
This is a formatted and filtered version of derek-thomas/ScienceQA with only image instances. It is used in our lmms-eval pipeline to allow for one-click evaluations of large multi-modality models.
@inproceedings{lu2022learn,
title={Learn to Explain:… See the full description on the dataset page: https://huggingface.co/datasets/lmms-lab/ScienceQA-IMG.vidore_v3_computer_science_mteb_format
Vidore3ComputerScienceRetrieval
An MTEB dataset
Massive Text Embedding Benchmark
Retrieve associated pages according to questions.
Task category
t2i
Domains
Academic
Reference
https://huggingface.co/blog/QuentinJG/introducing-vidore-v3
Source datasets:
vidore/vidore_v3_computer_science
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task =… See the full description on the dataset page: https://huggingface.co/datasets/vidore/vidore_v3_computer_science_mteb_format.terminal-bench-science-trailFrench-Science-Commons
French Science Commons
French Science Commons (Commun numérique des sciences en français) rassemble des publications scientifiques d'origine française en accès ouvert, couvrant une période de vingt ans, de 2007 à 2026. Il comprend 1 248 860 documents scientifiques — 1 189 628 articles et 59 232 thèses — indexés à travers de multiples dépôts académiques en accès public, tels que HAL, OpenAlex, des revues scientifiques, des dépôts institutionnels, et d'autres.
Le corpus est conçu… See the full description on the dataset page: https://huggingface.co/datasets/PleIAs/French-Science-Commons.weact-native-science-checkpoint
Native WeAct science checkpoint
The fixed eight-question, seven-arm pilot has reached its human-review checkpoint. All 56 planned task records are in pilot_v4/. The frozen 3,000-question test has not been run. No accuracy score or retraining conclusion is claimed.
The independent Serper, Jina and E2B checks passed, and each backend passed native webpage extraction with the live auxiliary model. The corrected runtime uses original questions, native hard/soft routing, the… See the full description on the dataset page: https://huggingface.co/datasets/Corning/weact-native-science-checkpoint.S1-DeepResearch-15k
S1-DeepResearch-15k Dataset
Overview
The S1-DeepResearch dataset is a curated collection of approximately 15k samples designed to improve deep research capabilities of large language models.
The dataset includes two types of tasks:
Verifiable tasks (labeled as "Closed-ended Multi-hop Resolution")
Open-ended tasks (labeled as "Open-ended Exploration")
Dataset Composition
The dataset is organized into five core capability dimensions:
Long-chain complex… See the full description on the dataset page: https://huggingface.co/datasets/ScienceOne-AI/S1-DeepResearch-15k.math-code-science-deepseek-r1-en
R1 Dataset Collection
Aggregated high-quality English prompts and model-generated responses from DeepSeek R1 and DeepSeek R1-0528.
Dataset Summary
The R1 Dataset Collection combines multiple public DeepSeek-generated instruction-response corpora into a single, cleaned, English-only JSONL file. Each example consists of a <|user|> prompt and a <|assistant|> response in one "text" field. This release includes:
~21,000 examples from the DeepSeek-R1-0528 Distilled Custom… See the full description on the dataset page: https://huggingface.co/datasets/Hugodonotexit/math-code-science-deepseek-r1-en.chinese-materials-science-open-intelligence
🔬 Chinese Materials Science & Metallurgy Open Intelligence Dataset
Curated open intelligence dataset providing English research briefs, authoritative DOIs, executive summaries, and high-resolution micrographs of breakthrough Chinese scientific research in Materials Science, Metallurgy, Advanced Alloys, and Mining Engineering.
[!IMPORTANT]
Data Completeness & Research Authenticity Notice:
Included in this Hugging Face Open Dataset: English structured abstracts, core… See the full description on the dataset page: https://huggingface.co/datasets/simpleG2023/chinese-materials-science-open-intelligence.severity_ablation_sciencearc-science-cot-10k-shortopen-thoughts-scienceNemotron-RL-Science-v1
Dataset Description:
Nemotron-RL-Science-v1 is a reinforcement learning (RL) dataset for science reasoning. Each example provides a problem, a reference answer, and a verifiable RL environment configuration (the agent prompt, the agent/verifier reference, and the answer-extraction template) so that a policy model can be trained with verifiable rewards. It covers three domains (Physics, Biology, and Chemistry), the open-question (OpenQ) format, and two generation setups:… See the full description on the dataset page: https://huggingface.co/datasets/nvidia/Nemotron-RL-Science-v1.natural-science-reasoning
Natural Sciences Reasoning: the "smolest" reasoning dataset
A smol-scale open dataset for reasoning tasks using Hugging Face Inference Endpoints. While intentionally limited in scale, this resource prioritizes:
Reproducible pipeline for reasoning tasks using a variety of models (Deepseek V3, Deepsek-R1, Llama70B-Instruct, etc.)
Knowledge sharing for domains other than Math and Code reasoning
In this repo, you can find:
The prompts and the pipeline (see the config file).
The… See the full description on the dataset page: https://huggingface.co/datasets/dvilasuero/natural-science-reasoning.