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.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.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.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.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.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.science-theory-textbooksS1-Omni-Corpus-10K
S1-Omni-Corpus-10K
An open-source scientific multimodal reasoning dataset subset for S1-Omni
🧬 Model Introduction
S1-Omni is a unified scientific multimodal reasoning model for scientific understanding, prediction, and generation. It is developed by the ScienceOne AI team of the Chinese Academy of Sciences.
S1-Omni addresses fragmented scientific AI capabilities with a shared backbone for cross-disciplinary, cross-modal, and cross-task understanding and reasoning… See the full description on the dataset page: https://huggingface.co/datasets/ScienceOne-AI/S1-Omni-Corpus-10K.SciGenEdit-10K
SciGenEdit-10K
An Open Dataset for Scientific Image Generation and Editing
English | 简体中文
📖 Introduction
SciGenEdit-10K is a public subset released with the S1-Omni-Image project. It is designed for research on scientific image generation, scientific image editing, and multi-turn scientific image generation and editing.
S1-Omni-Image is a unified multimodal model developed by the ScienceOne team at the Chinese Academy of Sciences for scientific… See the full description on the dataset page: https://huggingface.co/datasets/ScienceOne-AI/SciGenEdit-10K.science_chemistryszl-science-forum-corpus
Science Forum Pilot
Explore original summaries and metadata from two operator-authored topics used to formulate review hypotheses.
Artifact: Two-topic metadata pilot · Stage: Training unauthorized
Explore in Command Lab · Build · Evidence
Before you use it
This is not a forum scrape, representative sample, model-training dataset or scientific benchmark.
Original annotations do not grant rights to linked forum posts; expansion requires separate access, reuse and… See the full description on the dataset page: https://huggingface.co/datasets/SZLHOLDINGS/szl-science-forum-corpus.piibench
piibench — a carrier-aware benchmark for PII detection in outbound traffic
The full rationale, metric by metric and with every known limitation stated, is in
DESIGN.md. This card is the short version.
Leaderboard: ScienceSoft/piibench-leaderboard
· ScienceSoft's entrant: ScienceSoft/scnsoft-pii-encoder
piibench measures how well a model detects personal data in the text formats that outbound
traffic to AI assistants actually carries. Public PII benchmarks score prose, whereas… See the full description on the dataset page: https://huggingface.co/datasets/ScienceSoft/piibench.Darwin-ScienceThis repository contains the dataset of the research paper Data Darwinism -- Part1: Unlocking the Value of Scientific Data for Pre-training.
The dataset is currently being uploaded and is expected to be released within one week.
Nemotron-SFT-Science-v2-Sharded
Nemotron-SFT-Science-v2-Sharded
Byte-preserving sequential 100 MB JSONL shards of selected files from nvidia/Nemotron-SFT-Science-v2. All credit for data generation and upstream curation belongs to the source authors. See the upstream dataset card for attribution, source descriptions and license terms.
Included files: vendor.jsonl, so.jsonl, rqa.jsonl, syn_mcq.jsonl.
No filtering, shuffling, normalization, tokenization or truncation was performed. Complete records and all… See the full description on the dataset page: https://huggingface.co/datasets/sunnysetia/Nemotron-SFT-Science-v2-Sharded.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.wild-science-theory-textbooksComputer-Science-Conversational-Dataset-IndicScience-Logits-1.2M
Logits-Based Finetuning
• 🤗 Data
• 🤗 ScienceLLaMA-3B
• 🤗 ScienceLLaMA-1B
• 🐱 Code
• 📃 Paper
This is a repo of a large-scale 1.2M logits dataset for Logits-Based Finetuning, which integrates the strengths of supervised learning and knowledge distillation by combining teacher logits with ground truth labels. This preserves both correctness and linguistic diversity.
Performance
Train
Data: huggingface
Readme: Installation Guide… See the full description on the dataset page: https://huggingface.co/datasets/JingyaoLi/Science-Logits-1.2M.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.malaysian_journal_of_analytical_sciencescience-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.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
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.realistic-bpe5-science-math-10bSO-Python_QA-Data_Science_and_Machine_Learning_classdrtulu_v2_nemotron_science_v1_mcq_0205DataPRM-ScienceAgentBenchverisci-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.