datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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
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.BenchMAX_Science
Dataset Sources
Paper: BenchMAX: A Comprehensive Multilingual Evaluation Suite for Large Language Models
Link: https://huggingface.co/papers/2502.07346
Repository: https://github.com/CONE-MT/BenchMAX
Dataset Description
BenchMAX_Science is a dataset of BenchMAX, sourcing from GPQA, which evaluates the natural science reasoning capability in multilingual scenarios.
We extend the original English dataset to 16 non-English languages.
The data is first translated by Google… See the full description on the dataset page: https://huggingface.co/datasets/LLaMAX/BenchMAX_Science.hle_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.islamic-sciences
islamlab — The Islamic Sciences Corpus
The Islamic sciences other than Qur'an and hadith, as their authors wrote
them: 4,022 works by scholars who died between the
0st and the 14th Hijri century, cut along their own chapter
and biographical-entry boundaries into 1,864,389 units
(3.41 billion characters of Arabic), each carrying the volume and
page it sits on so a quotation can be cited rather than merely produced.
Scope is Ahl al-Sunnah wa'l-Jamāʿah, and the gate is the author… See the full description on the dataset page: https://huggingface.co/datasets/islamlab/islamic-sciences.xlam-function-calling-60k-raw-augmented
XLAM Function Calling 60k Raw Augmented Dataset
This dataset includes augmented train and test splits derived from product-science/xlam-function-calling-60k-raw.
Train split size: Original size plus augmented data
Test split size: Original size plus augmented data
Augmentation Details
This dataset has been augmented by modifying function names in the original data. Randomly selected function names have underscores replaced with periods at random positions… See the full description on the dataset page: https://huggingface.co/datasets/product-science/xlam-function-calling-60k-raw-augmented.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.science_reasoning
science_reasoning
Mistral-7B의 과학 지식·추론 능력 향상을 위해 6개 공개 과학 객관식 QA 데이터셋을 통일 포맷으로 변환하고, ARC-Challenge test와의 오염을 제거한 데이터셋입니다.
원본 데이터셋
allenai/sciq
allenai/openbookqa (main)
allenai/qasc
allenai/quartz
allenai/ai2_arc (ARC-Easy / ARC-Challenge)
nguyen-brat/worldtree
전처리
포맷 통일: 각 데이터셋의 서로 다른 스키마를 unique_id, orig_id, source, question, choices, answer, support 필드로 변환. support는 근거 문단/문장으로, 데이터셋별 원본 필드(support/fact/para/cot)에서 구성하거나 없으면 빈 문자열.… See the full description on the dataset page: https://huggingface.co/datasets/seonjeongh/science_reasoning.Science-QnA
Science-QnA
The Science-QnA is a large-scale, high-quality science-focused dataset (~5.63M rows) curated using synthetic data generation through distillation techniques and select open-source resources. Designed to train and evaluate reasoning-capable models in science domains with emphasis on conceptual understanding, numerical problem-solving, and exam-style Q&A patterns across Physics, Chemistry, Biology, and Mathematics.
Summary
• Domain: Science, Physics… See the full description on the dataset page: https://huggingface.co/datasets/169Pi/Science-QnA.hle_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/stonelight/hle_material_science.10001-Science-Facts
10,001 Science Facts
10,000+ obscure, surprising, and verifiable science facts
The kind that make you go "wait, really?"
🔗 GitHub Repository •
📁 Download by Category
🤔 What is this?
A curated dataset of 10,003 science facts across 32 categories — from quantum physics to parasites to the history of food.
Every fact is:
Sourced — from Wikipedia, Wikidata, academic sources
Verifiable — no LLM hallucinations
Surprising — passes the "dinner party test"… See the full description on the dataset page: https://huggingface.co/datasets/Royal-lobster/10001-Science-Facts.science-qa-sft-100k
Science QA SFT (100K)
100,000 science Q&A examples with step-by-step explanations for SFT fine-tuning. Covers physics, chemistry, biology, astronomy, and earth science at beginner through advanced difficulty.
Motivation
Models trained on general text often give superficially plausible but mechanistically wrong answers to science questions — stating the right conclusion without understanding the underlying reasoning. This dataset trains models to explain why an… See the full description on the dataset page: https://huggingface.co/datasets/stindardlogic/science-qa-sft-100k.ScienceQA
Dataset Card for "ScienceQA"
Dataset Summary
ScienceQA is collected from elementary and high school science curricula, and contains 21,208 multimodal multiple-choice science questions. Out of the questions in ScienceQA, 10,332 (48.7%) have an image context, 10,220 (48.2%) have a text context, and 6,532 (30.8%) have both. Most questions are annotated with grounded lectures (83.9%) and detailed explanations (90.5%). The lecture and explanation provide general external… See the full description on the dataset page: https://huggingface.co/datasets/TheMrguiller/ScienceQA.GLM-5.2-Science
GLM-5.2 · Science-50000x
50,000x traces distilled from GLM-5.2 on High reasoning
Physics · Chemistry · Biology
Token Count: 160M
Theres prompt overlap with my Kimi K2.5 dataset science subset, which I think those prompts are getting used in alot of places now
You can use this dataset for any purpose and you dont need to credit me, preferably dont claim it as your own.
hi - ianncity
PhysLogic
PhysLogic
English | 简体中文
PhysLogic is a physics reasoning benchmark for evaluating both final-answer
accuracy and the logicality of model reasoning processes.
It accompanies the paper Scientific Logicality Enriched Methodology for LLM
Reasoning: A Practice in Physics.
🎉 This work has been accepted to ICML 2026.
Highlights
Process-aware evaluation: PhysLogic evaluates not only whether a model
reaches the right answer, but also… See the full description on the dataset page: https://huggingface.co/datasets/ScienceOne-AI/PhysLogic.science-on-a-sphere-prompt-completions
Dataset Card for Science On a Sphere QA Dataset
Dataset Details
Dataset Description
This dataset comprises question-and-answer (QA) pairs generated from NOAA's Science On a Sphere (SOS) website, including support documentation and the dataset catalog. Each entry contains a prompt and a corresponding completion, designed to support educational and research use cases in Earth science.
This dataset includes a custom dataset_script.py and a consolidated file… See the full description on the dataset page: https://huggingface.co/datasets/HacksHaven/science-on-a-sphere-prompt-completions.VietEmbed-RAG-Science
VietEmbed-RAG Science
VietEmbed-RAG Science is a Vietnamese retrieval dataset containing 68,567 query-document examples across seven scientific and technical domains.
Each record consists of:
A Vietnamese query (anchor)
A relevant passage (positive)
A semantically related but non-answering passage (hard_negative)
Topic and domain metadata
The dataset is designed for training and domain adaptation of Vietnamese text embedding, semantic retrieval, and Retrieval-Augmented… See the full description on the dataset page: https://huggingface.co/datasets/nhminh107/VietEmbed-RAG-Science.nasa-science-repos-sme-benchmark
NASA Science Repos SME Benchmark
A benchmark dataset for evaluating retrieval systems on NASA science repository discovery tasks. This dataset contains expert queries, a corpus of NASA science GitHub repositories, and relevance judgments.
Dataset Structure
Files
├── corpus.jsonl # 5,264 repositories with full metadata
├── queries.jsonl # 219 expert queries
└── qrels/
├── earth.tsv # Earth Science relevance judgments (162)
├──… See the full description on the dataset page: https://huggingface.co/datasets/nasa-impact/nasa-science-repos-sme-benchmark.Cannabis_Science_Data
Cannabis Science Literature QA Dataset
This dataset contains 161,170 high-quality question-answer pairs derived from over 400 peer-reviewed cannabis science research papers and textbooks. Created to advance AI research in cannabis science and medical applications, it provides a comprehensive resource for training language models on cannabis-related scientific knowledge.
Dataset Details
Dataset Description
This dataset was systematically generated from a curated… See the full description on the dataset page: https://huggingface.co/datasets/KellanF89/Cannabis_Science_Data.food-science-llm-protocol
Food Science LLM Text-Mining Protocol
Pipeline and derived data accompanying:
Guo X, Fu W. Data Mining and Text Mining Using Large Language Models.
In: Li Y, Zhang D, Guo Z (eds), AI in Food Science: Methods and Protocols.
Methods and Protocols in Food Science. Springer.
The chapter prints one protocol as 26 numbered steps with abbreviated code
listings. This repository is the executable form of that protocol. Every step
has a corresponding function here, and every number in… See the full description on the dataset page: https://huggingface.co/datasets/KSU-HW-SEC/food-science-llm-protocol.Global-Ocean-Science-Corpus
🌊 Global-Ocean-Science-Corpus (v2.0 Curated & Cleaned)
A Highly Curated, Large-Scale Pre-Training & RAG Corpus for Deep Ocean Sciences, Marine Biology, and Oceanography
Language Note: This dataset is a 100% English-language scientific corpus (language: "en") aggregating peer-reviewed literature, deep-sea exploration dossiers, and technical oceanographic reports from leading global marine institutes.
Global-Ocean-Science-Corpus, derin okyanus bilimleri, deniz biyolojisi… See the full description on the dataset page: https://huggingface.co/datasets/tilikumotp/Global-Ocean-Science-Corpus.islamic-sciences-training
islamlab — Islamic Sciences Training Sets
Training data derived from
islamlab/islamic-sciences:
text for domain adaptation, retrieval pairs with hard negatives, and citation
questions whose answers are read out of the corpus rather than written by a
model.
Nothing here is generated. Questions come from a fixed set of templates
and every answer is a field already present in the corpus. That buys a
narrow dataset in exchange for one that cannot teach a model a fact the
sources do… See the full description on the dataset page: https://huggingface.co/datasets/islamlab/islamic-sciences-training.10001-Science-Facts
10,001 Science Facts
10,000+ obscure, surprising, and verifiable science facts
The kind that make you go "wait, really?"
🔗 GitHub Repository •
📁 Download by Category
🤔 What is this?
A curated dataset of 10,003 science facts across 32 categories — from quantum physics to parasites to the history of food.
Every fact is:
Sourced — from Wikipedia, Wikidata, academic sources
Verifiable — no LLM hallucinations
Surprising — passes the "dinner party test"… See the full description on the dataset page: https://huggingface.co/datasets/percepteyeAI/10001-Science-Facts.Dense-Information-Science-Physics-Dataset
Dense Information With Multiple Fine-tuned Variations
This dataaset has multiple for each input to learn how to express the same answer in different ways
Dataset Structure
The dataset contains two columns:
Column
Description
input
A science or quantum-physics question
output
A conversational answer to the question
Example:
{
"input": "What is quantum entanglement?",
"output": "Quantum entanglement is when two quantum systems share one… See the full description on the dataset page: https://huggingface.co/datasets/StarpowerTechnology/Dense-Information-Science-Physics-Dataset.science-mcqa-training-pool
Science multiple-choice training pool
Public multiple-choice science questions from three datasets, read at the pinned revisions named
below and laid out twice. Train on either layer or on both.
pool.jsonl
Every source rewritten into one shape, 182035 rows, one JSON object per line, with these fields.
Field
What it holds
id
a row identifier unique within this file
question
the question text, as its source publishes it
options
the answer options, as… See the full description on the dataset page: https://huggingface.co/datasets/Emulated-Inc/science-mcqa-training-pool.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/Gisiyuan/ScienceQA.nemotron-terminal-data_science
nemotron-terminal-data_science
Per-source partition of nvidia/Nemotron-Terminal-Corpus,
filtered to source == "data_science". The difficulty column preserves the original
easy / medium / mixed split (na for the dataset_adapters/* files, which
did not carry a difficulty label).
Partitioning scheme:
adapters_{code,math,swe} — rows from dataset_adapters/{code,math,swe}.parquet
{skill} (e.g. debugging, security, …) — rows from
synthetic_tasks/skill_based/{easy,medium… See the full description on the dataset page: https://huggingface.co/datasets/laion/nemotron-terminal-data_science.verisci-verified-science-math-code
VeriSci Verified Science Math Code
Verifier-grounded dataset for the Adaption AutoScientist Challenge Part 2, targeting the Science category with secondary Math and Code coverage.
Summary
VeriSci trains models to solve scientific computations, finite-difference PDE updates, numerical ODE steps, unit-checked mechanics, thermodynamics, circuits, chemistry stoichiometry, molarity, unit conversion, vector decomposition, two-point linear modeling, small Python… See the full description on the dataset page: https://huggingface.co/datasets/TheJackBright/verisci-verified-science-math-code.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/aarav7397/ScienceQA.