datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.judged_science_completionsFrench-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.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_sciencecommunity-science-paper-v2science-datalake
Science Data Lake
A unified, portable science data lake integrating 7 scholarly datasets (~525 GB Parquet) with cross-dataset DOI normalization, 13 scientific ontologies (1.3M terms), and a reproducible ETL pipeline.
Note: One additional source (Semantic Scholar S2AG) is supported by the pipeline but is not redistributed here due to its API terms of service. See Not Included in This Upload below.
What's Unique
This dataset enables queries… See the full description on the dataset page: https://huggingface.co/datasets/GodotCN/science-datalake.EU-Science-Commonsmmu_apogee_dr17
mmu_apogee_dr17 HATS Catalog Collection
This is the collection of HATS catalogs representing mmu_apogee_dr17.
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… See the full description on the dataset page: https://huggingface.co/datasets/hugging-science/mmu_apogee_dr17.szl-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.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.qwq_mix_qwen3_scienceopenthoughts4-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.pdf_science_questions_verified_r1_traces__2_24_25
Dataset card for pdf_science_questions_verified_r1_traces__2_24_25
This dataset was made with Curator.
Dataset details
A sample from the dataset:
{
"url": "https://www.ttcho.com/_files/ugd/988b76_01ceeff230b24cbbb0125b2bfa3f3475.pdf",
"filename": "988b76_01ceeff230b24cbbb0125b2bfa3f3475.pdf",
"success": true,
"page_count": 37,
"page_number": 1,
"question_choices_solutions": "QUESTION: What is the identity of X in the reaction 14N + 1n \u2192… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/pdf_science_questions_verified_r1_traces__2_24_25.mmu_hsc_pdr3_wide_21
mmu_hsc_pdr3_wide_21 HATS Catalog Collection
This is the collection of HATS catalogs representing mmu_hsc_pdr3_wide_21.
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… See the full description on the dataset page: https://huggingface.co/datasets/hugging-science/mmu_hsc_pdr3_wide_21.science_materialsqwq_mix_r1_scienceQwen2.5-7B-Instruct_qwq_mix_r1_science_eval_2870
mlfoundations-dev/Qwen2.5-7B-Instruct_qwq_mix_r1_science_eval_2870
Precomputed model outputs for evaluation.
Evaluation Results
AIME24
Average Accuracy: 60.67% ± 2.20%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions
1
70.00%
21
30
2
53.33%
16
30
3
53.33%
16
30
4
66.67%
20
30
5
63.33%
19
30
6
66.67%
20
30
7
60.00%
18
30
8
46.67%
14
30
9
63.33%
19
30
10
63.33%
19
30
fineweb-1m-samplediffing-stats-gemma-2-2b-crosscoder-l13-mu4.1e-02-lr1e-04 Contains maximum activating examples for all the features of our crosscoder trained on gemma 2 2B layer 13 available here: https://huggingface.co/Butanium/gemma-2-2b-crosscoder-l13-mu4.1e-02-lr1e-04/blob/main/README.md
base_examples.pt contains all the maximum examples of the feature on a subset of validation test of fineweb
chat_examples.pt is the same but for lmsys chat data
chat_base_examples.pt is a merge of the two above files.
All files are of the type dict[int, list[tuple[float… See the full description on the dataset page: https://huggingface.co/datasets/science-of-finetuning/diffing-stats-gemma-2-2b-crosscoder-l13-mu4.1e-02-lr1e-04.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.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.science_biologyopenthoughts4-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-tool-use-conversations
Science Tool-Use Conversations
This dataset contains 11,405 synthetic conversations about science questions. GLM-5.3 generated both the user and assistant messages. The assistant could run commands in shellsim, an in-memory shell and Python simulator. Each row includes a system message, the user-visible conversation, a tool-call transcript, and the tool definition. Some conversations contain no tool calls.
The questions come from the so_openq split of… See the full description on the dataset page: https://huggingface.co/datasets/open-athena/science-tool-use-conversations.IEEE2026_BigData_MAS-4-Science-Matching
SciAgentTrace
An execution-layer trace resource for scientific-agent workload characterization.
A protocol fixes who reasons, what each role can see, when feedback returns, and
when a workflow stops. Those choices determine the sequence of model requests
that produces an answer, so protocol design is also workload design. Two
workflows that consume similar token totals can issue very different request
sequences. SciAgentTrace records that difference.
The matched core runs the… See the full description on the dataset page: https://huggingface.co/datasets/AgentsSci/IEEE2026_BigData_MAS-4-Science-Matching.e1_science_longest_phiQwen2.5-7B-Instruct_qwq_mix_qwen3_science_eval_8179
mlfoundations-dev/Qwen2.5-7B-Instruct_qwq_mix_qwen3_science_eval_8179
Precomputed model outputs for evaluation.
Evaluation Results
Summary
Metric
AIME24
AMC23
MATH500
JEEBench
GPQADiamond
LiveCodeBench
CodeElo
CodeForces
AIME25
HLE
LiveCodeBenchv5
HMMT
Accuracy
61.7
88.8
88.4
67.5
54.7
52.1
25.8
27.1
49.0
11.2
40.7
32.7
AIME24
Average Accuracy: 61.67% ± 1.27%
Number of Runs: 10
Run
Accuracy
Questions Solved
Total Questions… See the full description on the dataset page: https://huggingface.co/datasets/mlfoundations-dev/Qwen2.5-7B-Instruct_qwq_mix_qwen3_science_eval_8179.science-communication-video-v1
Science Communication Video Preview
This preview contains four short educational animation videos from the Thordata Multidisciplinary Science Communication Video Collection:
acetaldehyde oxidation
how volcanoes form
how typhoons form
solar wind and aurora
Each sample presents one focused knowledge topic through a coherent visual sequence. The videos are useful for demonstrating video captioning, visual question answering, and cross-modal reasoning.
Contents… See the full description on the dataset page: https://huggingface.co/datasets/thordata/science-communication-video-v1.