datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
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.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.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.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.synthetic-science-v2-sample
Synthetic Scientific Research Threads — v2 (sample)
A synthetic continual-learning benchmark: each episode is a coherent sequence of
short fictional scientific research documents about a single made-up entity, with
per-document QA anchors. Later documents build on, revise, or supersede earlier
ones. Designed to stress test-time / meta-learning approaches where a model must
adapt to a stream of documents and answer questions grounded in what it has just
seen.
This is a sample… See the full description on the dataset page: https://huggingface.co/datasets/HerrHruby/synthetic-science-v2-sample.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.NCERT_Science_10thscience_leaderboard_submissionThis dataset contains the results used for Science Leaderboard
NCERT_Science_8thNCERT_Political_Science_12thsynthlabs-GLM-5.2-Science
GLM-5.2 Science Synth Reasoning
Synthetic reasoning traces for science questions from the GLM-5.2 science dataset. Each record contains a complex scientific question with SYNTH-style reasoning and a generated answer.
Dataset Summary
33,014 records (605 dupes + 726 incomplete/truncated removed from 34,345 source)
33,014 reasoning turns (99.9% format compliance)
Average 3,094 chars per reasoning trace
Models Used
Model
Records… See the full description on the dataset page: https://huggingface.co/datasets/mkurman/synthlabs-GLM-5.2-Science.NCERT_Science_6thNCERT_Science_9thNCERT_Science_7thNCERT_Political_Science_11thNCERT_Science_10th