datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.Nemotron-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.llama-nemotron-science-reasoning-on-canonical-think-full
Llama-Nemotron science reasoning — Delphi canonical-think (COMPLETE, no length filter)
The complete reasoning:on science split of
nvidia/Llama-Nemotron-Post-Training-Dataset, converted once into the canonical
Delphi chat-template thinking format. 708,920 rows.
Unlike the cold-start warmup slice
open-athena/llama-nemotron-science-reasoning-on-le3000tok-100k
(and its -canonical-think variant), this build applies no length cap and no subsample — every
long-CoT science example is… See the full description on the dataset page: https://huggingface.co/datasets/laion/llama-nemotron-science-reasoning-on-canonical-think-full.openthoughts4-science-26041-prompts-qwen3-30b-a3B-thinking-2507-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-30B-A3B-Thinking-2507 (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-30B-A3B-Thinking-2507
on the Marin OpenThoughts-4 science SDG prompt
set.
Each prompt is sampled n=8 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-30b-a3B-thinking-2507-n8-flattened-logprobs-k16.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.openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-32B (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-32B
on the Marin OpenThoughts-4 science SDG prompt
set.
Each prompt is sampled n=8 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field
Value
Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-32b-n8-flattened-logprobs-k16.10001-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.data-science-en-id
Data Science EN-ID Parallel Corpus (Scientific Domain)
Dataset Description
This dataset is a curated English-Indonesian (EN-ID) parallel corpus specifically designed for the Scientific and Data Science domains. It was developed to support the training of Machine Translation (NMT) models and Large Language Models (LLMs) to better handle technical terminology, academic structures, and formal scientific language.
Primary Languages: English (EN) and Indonesian (ID)
Domain:… See the full description on the dataset page: https://huggingface.co/datasets/Ik45/data-science-en-id.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
task047_miscellaneous_answering_science_questions
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task047_miscellaneous_answering_science_questions
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative Instructions on… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task047_miscellaneous_answering_science_questions.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.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.task701_mmmlu_answer_generation_high_school_computer_science
Dataset Card for Natural Instructions (https://github.com/allenai/natural-instructions) Task: task701_mmmlu_answer_generation_high_school_computer_science
Additional Information
Citation Information
The following paper introduces the corpus in detail. If you use the corpus in published work, please cite it:
@misc{wang2022supernaturalinstructionsgeneralizationdeclarativeinstructions,
title={Super-NaturalInstructions: Generalization via Declarative… See the full description on the dataset page: https://huggingface.co/datasets/Lots-of-LoRAs/task701_mmmlu_answer_generation_high_school_computer_science.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.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.openthoughts4-science-26041-prompts-qwen3-4b-n8-flattened-logprobs-k16
OpenThoughts-4 Science SDG: Qwen3-4B (n=8, top-16 logprobs)
Synthetic generations from
Qwen/Qwen3-4B
on the Marin OpenThoughts-4 science SDG prompt
set.
Each prompt is sampled n=8 times, and for every generated token the dataset
stores the chosen-token log probability plus the top-16 log probabilities
over the vocabulary, enabling distillation, KL-style fine-tuning,
reranking, and uncertainty analysis.
Generation setup
Field
Value
Generator model… See the full description on the dataset page: https://huggingface.co/datasets/marin-community/openthoughts4-science-26041-prompts-qwen3-4b-n8-flattened-logprobs-k16.science-cot-dataset
ExpertData Science — Scientific Reasoning
Expert-Annotated · Rights-Cleared · Ground-Truth Verified · PII-Clean
Each record captures a complete experimental or theoretical reasoning chain:
Hypothesis → Methodology → Causal Chain → Validated Conclusion.
Extracted from peer-reviewed papers across physics, biology, materials science, astrophysics, and neuroscience using structured scientific-reasoning extraction.
This dataset is produced by the ExpertData-Factory pipeline
(Mine →… See the full description on the dataset page: https://huggingface.co/datasets/expertdata-factory/science-cot-dataset.tubitak-science-olympiad-tr
TUBITAK Science Olympiad Dataset
This dataset contains multiple-choice and open-ended scientific questions sourced from the TUBITAK (The Scientific and Technological Research Council of Turkey) Science Olympiads spanning various years. It is intended to serve as a benchmark for evaluating the advanced analytical, mathematical, and computational reasoning capabilities of Large Language Models (LLMs) in the Turkish language.
The dataset comprises approximately 2700 problems across… See the full description on the dataset page: https://huggingface.co/datasets/ytu-ce-cosmos/tubitak-science-olympiad-tr.Rehber-CoT-Science
🧬 Rehber-CoT-Science: Turkish Scientific Reasoning Dataset
Turkish Scientific Computational Reasoning (Chain-of-Thought) Dataset
Multi-step scientific problem-solving dataset with verifiable Python code and detailed explanations
Dataset • Author
📌 Changelog
Eski sürümlere erişim: Branch menüsünden v1 seçebilirsiniz.
Version
Date
Changes
v2.0
24.12.2025
✨ Yeni explained_answer alanı eklendi, Statistics domain eklendi, 712 örneğe genişletildi… See the full description on the dataset page: https://huggingface.co/datasets/batuhanozkose/Rehber-CoT-Science.applied-science-qa
Applied Science QA
2,472 question and answer pairs across seven scientific disciplines, written by
practitioners and scored by their peers on the Stack Exchange network.
Used to fine-tune a model for the Adaption Labs AutoScientist Challenge, Part 2,
Science category. The resulting model beat its own base model 60 to 40 on
Adaption's held-out Science test set.
What is in it
Column
Description
question
The question title plus the opening of the asker's… See the full description on the dataset page: https://huggingface.co/datasets/flamiinngo/applied-science-qa.nvidia-Nemotron-Science-Math
NVIDIA Nemotron Science and Math Reasoning
This is an unofficial, curated collection derived from NVIDIA's open-source Nemotron datasets. It is designed specifically to train language models in complex scientific and mathematical reasoning by providing structured chain-of-thought (CoT) examples.
To ensure efficiency, the shortest available CoT sequence was chosen for each question, filtering out redundant variations while preserving the core logical progression toward the final… See the full description on the dataset page: https://huggingface.co/datasets/agentlans/nvidia-Nemotron-Science-Math.bvg_science_qwen_4b_not_easy
BVG Qwen3-4B science not-easy prompts
This is the exact two-split dataset artifact used by the BVG Qwen3-4B science
experiments. It is published as a DatasetDict with train and validation
splits.
Split
Rows
SHA-256 of canonical JSON rows
train
11,121
e985f63334809b43e8cffa971829bff6c2159fe08d79630b2fbbda9d22bc0831
validation
997
c2ea31a3e676b2c28c72daaddb9023c9163cd6671c7cf3afd2e305f7fc206480
The active experiment TOMLs consume the complete train split. Their… See the full description on the dataset page: https://huggingface.co/datasets/graf/bvg_science_qwen_4b_not_easy.sonnet3.5_science_conversationsThis dataset features sharegpt structured dialogues focused on a variety of advanced scientific topics. The content reflects a high level of scientific expertise, providing in-depth information on complex subjects.