datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
SpatialLM-Dataset
SpatialLM Dataset
The SpatialLM dataset is a large-scale, high-quality synthetic dataset designed by professional 3D designers and used for real-world production. It contains point clouds from 12,328 diverse indoor scenes comprising 54,778 rooms, each paired with rich ground-truth 3D annotations. SpatialLM dataset provides an additional valuable resource for advancing research in indoor scene understanding, 3D perception, and… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Dataset.argument_quality_ranking_30k
Dataset Card for Argument-Quality-Ranking-30k Dataset
Dataset Summary
Argument Quality Ranking
The dataset contains 30,497 crowd-sourced arguments for 71 debatable topics labeled for quality and stance, split into train, validation and test sets.
The dataset was originally published as part of our paper: A Large-scale Dataset for Argument Quality Ranking: Construction and Analysis.
Argument Topic
This subset contains 9,487 of the arguments only with… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/argument_quality_ranking_30k.SpatialLM-Testset
SpatialLM Testset
Project page | Paper | Code
We provide a test set of 107 preprocessed point clouds and their corresponding GT layouts, point clouds are reconstructed from RGB videos using MASt3R-SLAM. SpatialLM-Testset is quite challenging compared to prior clean RGBD scan datasets due to the noises and occlusions in the point clouds reconstructed from monocular RGB videos.
Folder Structure
Outlines of the dataset files:… See the full description on the dataset page: https://huggingface.co/datasets/manycore-research/SpatialLM-Testset.enabling-independent-research
Overview
This directory contains the Anthropic Insights data we provided to our three external research groups as part of the collaboration detailed in "Enabling independent research on how people use Claude".
Before using this data, we recommend first reading our blog post on this collaboration and the Anthropic Insights paper and blog post. Before drawing conclusions from this data — especially from open-ended clusters — please read "Guidance for Interpreting Open-Ended… See the full description on the dataset page: https://huggingface.co/datasets/Anthropic/enabling-independent-research.Wikipedia_contradict_benchmark
Wikipedia contradict benchmark
Wikipedia contradict benchmark is a dataset consisting of 253 high-quality, human-annotated instances designed to assess LLM performance when augmented with retrieved passages containing real-world knowledge conflicts. The dataset was created intentionally with that task in mind, focusing on a benchmark consisting of high-quality, human-annotated instances.
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/Wikipedia_contradict_benchmark.taiwan-ly-law-research
Taiwan Legislator Yuan Law Research Data
Overview
The law research documents are issued irregularly from Taiwan Legislator Yuan.
The purpose of those research are providing better understanding on social issues in aspect of laws.
One may find documents rich with technical terms which could provided as training data.
For comprehensive document list check out this link provided by Taiwan Legislator Yuan.
There are currently missing document download links in 10th and 9th… See the full description on the dataset page: https://huggingface.co/datasets/aigrant/taiwan-ly-law-research.claim_stance
Dataset Card for Claim Stance Dataset
Dataset Summary
Claim Stance
This dataset contains 2,394 labeled Wikipedia claims for 55 topics. The dataset includes the stance (Pro/Con) of each claim towards the topic,
as well as fine-grained annotations, based on the semantic model of Stance Classification of Context-Dependent Claims (topic target,
topic sentiment towards its target, claim target, claim sentiment towards its target, and the relation between the… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/claim_stance.mac-app-store-apps-metadata
Dataset Card for Macappstore Applications Metadata
📌 Dataset status: static snapshot (no scheduled updates). The data was collected from the public iTunes Search API between December 2023 and January 2024 and reflects the Mac App Store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications Metadata sourced by the public API.
Curated by: MacPaw Way Ltd.
Language(s) (NLP): Mostly EN, DE… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-metadata.AttaQ
AttaQ Dataset Card
The AttaQ red teaming dataset, consisting of 1402 carefully crafted adversarial questions, is designed to evaluate Large Language Models (LLMs) by assessing their tendency to generate harmful or undesirable responses.
It may serve as a benchmark to assess the potential harm of responses produced by LLMs.
The dataset is categorized into seven distinct classes of questions: deception, discrimination, harmful information, substance abuse, sexual content, personally… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/AttaQ.dementor-sft-dataUiPad
UiPad - UI Parsing and Accessibility Dataset
📌 Dataset status: stable release. UiPad was built for the IASA Champ 2024 Challenge and is a complete, fixed research artifact. No further updates are planned.
Curated by: MacPaw Way Ltd.
Language(s): Mostly EN, UA
License: MIT
Overview
UiPad is a dataset created for the IASA Champ 2024 Challenge, focusing on the accessibility and interface understanding of MacOS applications. With growing interest in AI-driven user interface… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/UiPad.warp_Research
Warp Research Dataset (TsFile)
Apache TsFile version of
GotThatData/warp_Research.
Overview
Experimental results from warp-field research, focused on the relationship between warp
factors, energy efficiency, and field characteristics.
Records: ~19,700.
Time period: January 2025.
Features: 15 variables including derived metrics (warp_factor, expansion_rate,
stability_score, max_field_strength, avg_field_strength, energy_efficiency,
efficiency_ratio… See the full description on the dataset page: https://huggingface.co/datasets/THULab/warp_Research.scientific-verification
Scientific Verification Benchmark: NMC Cathodes
Dataset summary
The benchmark contains 50 scientific claims about NMC (lithium nickel manganese cobalt oxide) battery cathodes. Each claim is answered by Claude Opus 5, GPT 5.6 Luna and Gemini 3.1 Pro using a set of 20 open-access papers, producing 150 scored answers. The accompanying reference set contains 1,991 experiment-grounded measurements curated from 227 open-access papers, with experimental conditions and… See the full description on the dataset page: https://huggingface.co/datasets/GenData-Research/scientific-verification.mac-app-store-apps-descriptions
Dataset Card for Macappstore Applications Descriptions
📌 Dataset status: static snapshot (no scheduled updates). This dataset is derived from the December 2023 – January 2024 Mac App Store metadata snapshot and reflects the store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications descriptions extracted from the metadata from the public API.
Curated by: MacPaw Way Ltd.
Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-descriptions.veAgentBench
VeAgentBench Dataset
The VeAgentBench dataset is designed based on specific application scenarios of agents, aiming to test and evaluate the quality of agents generated by full-process agent development frameworks (such as veADK). It focuses on assessing agents' capabilities in tool calling, knowledge base retrieval, memory management, and overall performance.
Updates
2025.11.25 First public release of the dataset, containing a total of 484 questions (145 publicly… See the full description on the dataset page: https://huggingface.co/datasets/bytedance-research/veAgentBench.LLMFineTuningBench
Dataset Card for LLMFineTuningBench
A dataset of over 30,000 LLM fine-tuning experiments, capturing detailed performance metrics from jobs run on high-performance computing (HPC) clusters. It spans a wide range of models, fine-tuning methods, and hardware configurations, and is intended to support research on predictive resource allocation, performance optimization, and cost estimation for LLM fine-tuning workloads.
Dataset Details
Dataset Description… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/LLMFineTuningBench.dxap-research-workflow-excerpts
DXAP historical workflow excerpts and selection aggregates
Three detailed historical case studies: 29 parent/reconciliation event rows, 18 sanitized tool-request/response projections, five starting-state records, two research-child event records and nine proposed scenario questions. It also includes the complete 15-row selection-rank table already published with arXiv:2609.05663v1. These are different views of the same selected material, not independent sample counts to add… See the full description on the dataset page: https://huggingface.co/datasets/DXRG/dxap-research-workflow-excerpts.call-playbook
☎️ The Call Playbook Dataset
Real-world B2B sales conversations for text classification
A dataset by Gong.io Research
Annotated samples drawn from anonymized enterprise sales conversations across 5 binary classification tasks.
📄 Read the paper (ACL Anthology)
·
arXiv
🗂️ Dataset Summary
The Call Playbook Dataset contains annotated samples from real enterprise sales conversations across 5 binary classification tasks… See the full description on the dataset page: https://huggingface.co/datasets/gong-io-research/call-playbook.ChatGPT-Research-Abstracts
ChatGPT-Research-Abstracts
This is a dataset created in relation to a bachelor thesis written by Nicolai Thorer Sivesind and Andreas Bentzen Winje. It contains human-produced and machine-generated text samples of scientific research abstracts.
A reformatted version for text-classification is available in the dataset collection Human-vs-Machine. In this collection, all samples are split into separate data points for real and generated, and labeled either 0 (human-produced) or 1… See the full description on the dataset page: https://huggingface.co/datasets/NicolaiSivesind/ChatGPT-Research-Abstracts.urban-heat-research-corpus
Urban Heat Research Corpus (UHRC) v1.0
What does the world study, invent and report about urban heat? This dataset puts three records of the same problem side by side:
20,422 research papers on urban heat islands and extreme heat in cities (1990–2025) with the claims their abstracts make,
106,458 news articles about heat (2021–2025) coded for 51 subjects, framings and terms, and
4,123 patent families for heat-mitigation technologies (2006–2024) — plus supplementary tables on the… See the full description on the dataset page: https://huggingface.co/datasets/fliarbi/urban-heat-research-corpus.mac-app-store-apps-release-notes
Dataset Card for Macappstore Applications Release Notes
📌 Dataset status: static snapshot (no scheduled updates). This dataset is derived from the December 2023 – January 2024 Mac App Store metadata snapshot and reflects the store as of that period. The dataset is stable and remains available for research use; it is not refreshed on a schedule.
Mac App Store Applications release notes extracted from the metadata from the public API.
Curated by: MacPaw Way Ltd.
Language(s)… See the full description on the dataset page: https://huggingface.co/datasets/macpaw-research/mac-app-store-apps-release-notes.MermaidSeqBench
Dataset Card for MermaidSeqBench
Dataset Summary
This dataset provides a human-verified benchmark for assessing large language models (LLMs) on their ability to generate Mermaid sequence diagrams from natural language prompts.
The dataset was synthetically generated using large language models (LLMs), starting from a small set of seed examples provided by a subject-matter expert. All outputs were subsequently manually verified and corrected by human annotators to ensure… See the full description on the dataset page: https://huggingface.co/datasets/ibm-research/MermaidSeqBench.GeoGPT-CoT-QA
GeoGPT-CoT-QA Dataset: A Large-scale Geoscience Chain-of-Thought QA Dataset for Supervised Fine-Tuning of LLMs
1. Dataset Description
We introduce GeoGPT-CoT-QA Dataset, a large-scale synthetic question–answer (QA) corpus enriched with chain-of-thought (CoT) reasoning traces, developed to support supervised fine-tuning (SFT) of geoscience reasoning models. The GeoGPT-R1-Preview is specifically fine-tuned using this dataset to enhance its geoscience reasoning capabilities.… See the full description on the dataset page: https://huggingface.co/datasets/GeoGPT-Research-Project/GeoGPT-CoT-QA.dementor-matrix-responses
Dementor — matrix model responses
Generated model outputs for the Dementor LLM-imitation / behavioral-inertia study.
Companion to:
Code + prompt splits: https://github.com/lisadunlap/dementor (branch ethan)
Trained adapters (2,122 LoRAs): https://huggingface.co/dementor-research — SFT / DPO /
self-SFT, grouped into per-dataset collections (gsm8k, chatbot_arena, writingprompts, openassistant).
Dataset viewer. This repo is a nested tree of CSV tables plus per-cell cell.json… See the full description on the dataset page: https://huggingface.co/datasets/dementor-research/dementor-matrix-responses.ASRD-Dataset
Adversarial Surface-Form Robustness Dataset (ASRD)
Anonymous Repository for Double-Blind ReviewNeurIPS 2026 Workshop
1. Dataset Overview
Standard safety evaluations of large language models routinely measure model refusal and compliance using canonical plain-text instructions. However, deployed systems frequently encounter non-canonical inputs containing expressive symbols (emojis), character-level substitutions (homoglyphs, leetspeak), structured encodings… See the full description on the dataset page: https://huggingface.co/datasets/asrd-research/ASRD-Dataset.equity-research-dataset
AI-Powered Equity Research — Synthetic Analyst Notes (v2)
Parts 1 & 2 of an end-to-end AI Equity-Research Platform — synthetic generation (Part 1) and a
decision-driven EDA + feature-engineering study (Part 2). Every claim below is a measured number
printed by the notebooks, not an assumption.
0. The question this dataset answers
What factors determine whether an analyst note is bullish, neutral or bearish — and does the
quantitative space still behave like a… See the full description on the dataset page: https://huggingface.co/datasets/Uris001/equity-research-dataset.Arabic-books-and-research-dataset
Arabic reserach and books dataset (ARABD)
This dataset is an extracted cleaned text from more than 60K word files with unique arabic texts never published before.
Dataset diversity
the dataset is diverse from all kind of islamic research: [feqh, hadeeth, tafseer, tahqeeq, ... etc], from new written research to a manuscirpts.
dataset size
the dataset was more than 11GB but after cleaning (pre-processing) it becase a straight 10GB with less noisy chars.… See the full description on the dataset page: https://huggingface.co/datasets/riotu-lab/Arabic-books-and-research-dataset.dementor-matrix-baselinesdx-terminal-pro-research-aggregates
DX Terminal Pro research aggregates
Nine published research records from DX Research Group's Terminal Pro work. These are small aggregate evidence tables. They contain no participant-level decision logs or training trajectories.
The two configurations have different units of analysis:
Configuration
Records
What a row represents
market_behavior
4
A reported event or token-window aggregate from the bounded 21-day real-capital Terminal Pro deployment… See the full description on the dataset page: https://huggingface.co/datasets/DXRG/dx-terminal-pro-research-aggregates.GeoGPT-QA
GeoGPT-QA Dataset: A Large-scale Geoscience QA Dataset for Supervised Fine-tuning of LLMs
1. Dataset Description
We introduce GeoGPT-QA Dataset, a large-scale synthetic question–answer (QA) corpus developed to support supervised fine-tuning (SFT) of geoscience foundation models.
The dataset is derived from open-access geoscience publications distributed under the CC BY license. Using an automated data synthesis pipeline, we generated professional QA pairs from article… See the full description on the dataset page: https://huggingface.co/datasets/GeoGPT-Research-Project/GeoGPT-QA.