datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dblp-discovery-dataset
Dataset Card for DBLP Discovery Dataset (D3)
Dataset Summary
DBLP is the largest open-access repository of scientific articles on computer science and provides metadata associated with publications, authors, and venues. We retrieved more than 6 million publications from DBLP and extracted pertinent metadata (e.g., abstracts, author affiliations, citations) from the publication texts to create the DBLP Discovery Dataset (D3). D3 can be used to identify trends in research… See the full description on the dataset page: https://huggingface.co/datasets/jpwahle/dblp-discovery-dataset.2026-08-20-odcv-feature-discovery-difficult-advice-716-5-pct-vs-numina-control
LLM-driven feature discovery over ODCV-Bench rollouts from TWO matched Qwen3.6-27B LoRA arms — 9,284 filtered instruction rows plus 716 rows that differ only in kind (constitution-grounded difficult advice vs NuminaMath chain-of-thought) — asking which reasoning and action properties separate the two models, and which go with the judged misalignment.
field
value
experiment
LLM-driven feature discovery over ODCV-Bench rollouts from TWO matched Qwen3.6-27B LoRA arms — 9… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-08-20-odcv-feature-discovery-difficult-advice-716-5-pct-vs-numina-control.hf-coding-tools-traces-discovery
HuggingFace AI Coding Tools — Agent Traces
This dataset rehydrates the benchmark results from
davidkling/hf-coding-tools-dashboard
into the JSONL session format consumed by the
Hugging Face Agent Trace Viewer.
What's inside
31 sessions, one per (tool, model, effort, thinking) configuration
9,022 query → response turns total (≈18,044 events)
Tools covered: claude_code, codex, copilot, cursor
Models: claude-opus-4-6, claude-sonnet-4-6, claude-sonnet-4.6, composer-2… See the full description on the dataset page: https://huggingface.co/datasets/davidkling/hf-coding-tools-traces-discovery.2026-07-29-msm-philosophy-spec-focused-discovery
Petri audit: Petri adaptive audit of the MSM philosophy-spec AFT checkpoint: 10 seed archetypes x 3 epochs (30 audits) probing for concerning agentic behaviour, with two-round adversarial validation of every flagged transcript.
Petri audit — qwen-3-32b-philosophy-spec-msm-aft-cot @ 9a00c85c
Brief finding
No seed replicated. Ten seed archetypes were each run for three epochs. Under
the pre-committed bar — a candidate must hold in a majority of its… See the full description on the dataset page: https://huggingface.co/datasets/dougalldeepmind/2026-07-29-msm-philosophy-spec-focused-discovery.scaling_law_discovery_results
Scaling Law Discovery Results Dataset
Results dataset for the paper: "Can Language Models Discover Scaling Laws?"
This dataset contains the complete collection of results from the Scaling Law Discovery (SLDBench) benchmark, where various AI agents attempt to discover mathematical scaling laws from experimental LLM training data.
🔗 Quick Links
Resource
Link
📄 Paper
arXiv:2507.21184
📊 Original Benchmark
SLDBench Dataset
🧪 Benchmark Code… See the full description on the dataset page: https://huggingface.co/datasets/pkuHaowei/scaling_law_discovery_results.red-pill-drug-discovery-formulation
🔴 RED-PILL
Research Enhanced Dataset for Pharmaceutical Innovation in Learning & Language
The first open instruction-tuning dataset for drug discovery & formulation development.
Built for fine-tuning Heretic-ablated models that won't refuse your pharmaceutical R&D questions.
⚡ Quick Start
from datasets import load_dataset
# Load the full dataset
ds = load_dataset("saidutta69/red-pill-drug-discovery-formulation"… See the full description on the dataset page: https://huggingface.co/datasets/saidutta69/red-pill-drug-discovery-formulation.adaption-african-history-discoveries
This dataset is a remastered version prepared using Adaption's Adaptive Data platform.
adaption-african_history_discoveries
This dataset consists of instruction-response pairs covering contemporary discoveries and reassessments in African history from 2020 to 2026. Samples feature news snippets and research summaries alongside factual contextual analyses of archaeological finds, oral tradition documentations, genetic studies, and colonial-era historical re-evaluations. Each… See the full description on the dataset page: https://huggingface.co/datasets/Svngoku/adaption-african-history-discoveries.ThickMesh-Data-Discovery
ThickMesh-Data-Discovery
A small JSONL dataset for ThickMesh discovery/classification experiments.
"This is not an algorithm. This is a trap for the patent system. Learn it, fork it, but do not lock it."
Contents
4 splits files: ThickMesh-zero-split_'0-3'.jsonl — primary dataset (one JSON object per line)
Apache 2.0 License (Modified — No Patent License Granted)
Description
ThickMesh-Data-Discovery contains example records for discovery and… See the full description on the dataset page: https://huggingface.co/datasets/usermma/ThickMesh-Data-Discovery.discover-and-prove
MiniF2F-Hard & FIMO-Hard
Expert-reannotated Hard Mode variants of the MiniF2F and FIMO theorem-proving
benchmarks, released with our paper Discover and Prove: An Open-source Agentic
Framework for Hard Mode Automated Theorem Proving in Lean 4 (ACL 2026).
In Hard Mode, the final answer is not embedded in the formal statement:
the system must first discover the answer before constructing a formal proof —
mirroring what a human competitor actually faces. Each solution-style… See the full description on the dataset page: https://huggingface.co/datasets/liuchengwu/discover-and-prove.Self-Discover-MM-Instruct-Alpacacleo-value-discovery
Cleo Value-Discovery Benchmark
A small (66-question), held-out benchmark for a failure mode that ordinary text-to-SQL evaluations miss:
questions whose correct SQL depends on a literal that lives in the data, not the schema.
The schema tells you a column is named status; only the data reveals its values are {'O','C','X'}.
The schema shows to_date; only the data reveals that "current" is encoded as the sentinel
'9999-01-01'. A one-shot text-to-SQL model has to guess these… See the full description on the dataset page: https://huggingface.co/datasets/dreeseaw/cleo-value-discovery.Self-Discover-MM-InstructThis dataset was synthetically generated using the Mistral Medium model for a project I am currently developing. It draws inspiration from the Self-Discover framework outlined in a paper by Google Deepmind 1. While this implementation is a basic interpretation and does not fully capture the essence of the original framework, it resulted in a robust Instruct dataset that meets the project's objectives. Further details will be shared upon the project's release. Below is the Python code utilized… See the full description on the dataset page: https://huggingface.co/datasets/Crystalcareai/Self-Discover-MM-Instruct.ai-drug-discovery-papers
AI for Drug Discovery Papers — FineSet
A research-paper dataset on AI for Drug Discovery Papers, assembled, deduplicated, and quality-scored by
FineSet from arXiv and Semantic Scholar.
📸 This is a dated snapshot — generated 2026-06-19.
It is not auto-updated. Research on AI for Drug Discovery Papers moves fast — new papers land on arXiv every
week. Want this same dataset refreshed daily, on a topic you choose? See the bottom. ↓
Why this dataset
Quality-scored:… See the full description on the dataset page: https://huggingface.co/datasets/fineset-io/ai-drug-discovery-papers.cleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research元データ: https://huggingface.co/datasets/moremilk/CoT_Reasoning_Scientific_Discovery_and_Research
使用したコード: https://github.com/LLMTeamAkiyama/0-data_prepare/tree/master/src/CoT_Reasoning_Scientific_Discovery_and_Research
データ件数: 3,733
平均トークン数: 1,193
最大トークン数: 2,489
合計トークン数: 4,453,517
ファイル形式: JSONL
ファイル分割数: 1
合計ファイルサイズ: 23.2 MB
加工内容：
メタデータ列の解析と新列生成: metadata列（辞書型）を解析し、その中のreasoningをthought列に、difficultyをdifficulty列に展開しました。解析に失敗した行は除外されました。また、元のmetadata列は削除されました。
難易度によるフィルタリング:… See the full description on the dataset page: https://huggingface.co/datasets/LLMTeamAkiyama/cleand_moremilk_CoT_Reasoning_Scientific_Discovery_and_Research.color-animal-discoverydiscovery-bench-simplifiednous-symbolic-discovery-100k
nous-symbolic-discovery-100k
This dataset was created using the Claude Dataset Skill.
green-bear-discoveryahodo-discovery
AHODO Discovery Dataset v0.3
AHODO is a cross-institutional discovery and rights/provenance metadata dataset for African humanities and humanities-adjacent resources. This v0.3 distribution contains 11,650 records. It is a discovery registry, not a corpus of the works it describes or a representative sample of African humanities. It is not presented as an AI-training dataset.
Interactive search
Canonical Zenodo archive and DOI
Zenodo record
Public GitHub repository… See the full description on the dataset page: https://huggingface.co/datasets/Lincoln-Rwodzi/ahodo-discovery.discovery-in-practice
Discovery in Practice
Three complete articles from https://discoveryinpractice.com/. Two are by Andrew Stewart; one Bench Tip is credited to Discovery in Practice. This is an article corpus, not a collection of experimental measurements. Preserve scientific limitations, citations, attribution, canonical links, and revision dates when reusing it. The train split is the dataset loader label; there is no evaluation split or benchmark claim.
Original article content is CC BY 4.0.… See the full description on the dataset page: https://huggingface.co/datasets/andrewinpractice/discovery-in-practice.