datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
scifact
SciFact
An MTEB dataset
Massive Text Embedding Benchmark
SciFact verifies scientific claims using evidence from the research literature containing scientific paper abstracts.
Task category
t2t
Domains
Academic, Medical, Written
Reference
https://github.com/allenai/scifact
How to evaluate on this task
You can evaluate an embedding model on this dataset using the following code:
import mteb
task = mteb.get_tasks(["SciFact"])
evaluator = mteb.MTEB(task)… See the full description on the dataset page: https://huggingface.co/datasets/mteb/scifact.scidocs
SCIDOCS
An MTEB dataset
Massive Text Embedding Benchmark
SciDocs, a new evaluation benchmark consisting of seven document-level tasks ranging from citation prediction, to document classification and recommendation.
Task category
t2t
Domains
Academic, Written, Non-fiction
Reference
https://allenai.org/data/scidocs
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/mteb/scidocs.SciCodeThis dataset was presented in SciCode: A Research Coding Benchmark Curated by Scientists.
sci-datasets
Mainly science focused but other datasets exist too!
Einstein models are based on this repo.
scibench
SciBench
SciBench is a novel benchmark for college-level scientific problems sourced from instructional textbooks. The benchmark is designed to evaluate the complex reasoning capabilities,
strong domain knowledge, and advanced calculation skills of LLMs.
Please refer to our paper or website for full description: SciBench: Evaluating College-Level Scientific Problem-Solving Abilities of Large Language Models
.
Citation
If you find our paper useful, please cite our… See the full description on the dataset page: https://huggingface.co/datasets/xw27/scibench.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.science-theory-textbooksSciKnowEval
SciKnowEval
Evaluating Multi-level Scientific Knowledge of Large Language Models
Please refer to our repository and paper for more details.
博学之 ，审问之 ，慎思之 ，明辨之 ，笃行之。
—— 《礼记 · 中庸》 Doctrine of the Mean
The Scientific Knowledge Evaluation (SciKnowEval) benchmark for Large Language Models (LLMs) is inspired by the profound principles outlined in the “Doctrine of the Mean” from ancient Chinese philosophy. This benchmark is designed to assess LLMs based on their proficiency in… See the full description on the dataset page: https://huggingface.co/datasets/hicai-zju/SciKnowEval.SciMDR-EvalNemotron-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.scidocs-rerankingSciCode-Verified
SciCode-Verified
SciCode-Verified is the corrected, human-verified release of the
SciCode scientific-code-generation benchmark.
A problem-by-problem audit identified 263 defects in the 65-problem SciCode test split and
corrected every confirmable defect. The released evaluation set contains 64 main problems and
287 scored subproblems; one original problem is excluded because its specification does not
determine a unique, verifiable answer.
Paper: SciCode-Verified: How Benchmark… See the full description on the dataset page: https://huggingface.co/datasets/shhu2001/SciCode-Verified.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.wild-science-theory-textbooksmath-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.S1-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.Computer-Science-Conversational-Dataset-IndicC4-Eval
C4-Eval
C4-Eval is the evaluation set for C4 Bench, a Chengyu-based benchmark for measuring whether multimodal language models can understand cross-concept creativity. The release contains the original images, the corresponding idiom answers, and the complete task-specific questions used for evaluation.
221 base items: 37 human-designed seed figures and 184 bridge-controlled synthetic figures.
1,105 evaluation instances: five task formulations for every base item.
Language:… See the full description on the dataset page: https://huggingface.co/datasets/sci-m-wang/C4-Eval.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.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.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.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/placeholderlabs/Nemotron-SFT-Science-v2-Sharded.SciInstructscience_chemistrysciclaimeval-shared-task
SciClaimEval Shared Task: All information is available at sciclaimeval.github.io
Evaluation scripts & examples: github.com/SciClaimEval/sciclaimeval-shared-task
More Information: paper
Version Info
Please use the latest version, v1.1.
Changes from v1.0 to v1.1
Compared with v1.0, v1.1 includes the following changes.
Removed Samples
The following 20 samples have been removed:
val_tab_1594
val_tab_0067… See the full description on the dataset page: https://huggingface.co/datasets/alabnii/sciclaimeval-shared-task.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.scicode
Dataset Card for Dataset Name
This dataset card aims to be a base template for new datasets. It has been generated using this raw template.
Dataset Details
Dataset Description
Curated by: [More Information Needed]
Funded by [optional]: [More Information Needed]
Shared by [optional]: [More Information Needed]
Language(s) (NLP): [More Information Needed]
License: [More Information Needed]
Dataset Sources [optional]
Repository: [More… See the full description on the dataset page: https://huggingface.co/datasets/Zilinghan/scicode.scivideobench
SciVideoBench
📄 Paper | 🌐 Project Page | 💻 Code
SciVideoBench is the first comprehensive benchmark for scientific video reasoning, covering disciplines in Physics, Chemistry, Biology, and Medicine.
It provides challenging multiple-choice QA pairs grounded in real scientific videos.
🔬 Overview
Scientific experiments present unique challenges for video-language models (VLMs): precise perception of visual details, integration of multimodal signals (video, audio… See the full description on the dataset page: https://huggingface.co/datasets/groundmore/scivideobench.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.