datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
dojo_market_dynamicsHydroGym-environmentsmeta13sphere_IRS_DCE_Topological_Dynamics__Boundary_Dissolution_Physics
Resonance Resonance / IRS-DCE
MASTER README (FULL EXTENDED VERSION)
If you need the other data or pdf check on [https://huggingface.co/datasets/meta13sphere/phaseShift_shell_result_pdf]
[2026-09-22 Update]
MASK_BDP BBRCM: 조건부 운용의 통합 구조
배경·경계·분기·관측·계량을 분해하고 다시 조립하면서, 무엇이 보존되고 어떤 조건에서 결과가 달라지는지 정리한 연구입니다. 상보성 쌍과 제타/RH 관련 변환뿐 아니라 BBRCM 자체에도 같은 변형·스트레스 테스트를 적용했습니다.
주요 성과는 다음과 같습니다.
R32: 원래 함수공간과 정확한 직교사영 조건 아래에서 첫 셀의 잔차를 전체 극한 잔차와… See the full description on the dataset page: https://huggingface.co/datasets/meta13sphere/meta13sphere_IRS_DCE_Topological_Dynamics__Boundary_Dissolution_Physics.KoopmanRL
Dataset Card for KoopmanRL
Dataset Summary
This dataset contains the collected experimental data used for the results of Koopman-Assisted Reinforcement Learning allowing for the full reproduction, and further use of the paper's results. To reproduce the results by running the experiments yourself, please see the source code of KoopmanRL.
Dataset Structure
The dataset of the reinforcement learning experiments for KoopmanRL contains roughly 461MB of Tensorboard… See the full description on the dataset page: https://huggingface.co/datasets/dynamicslab/KoopmanRL.STUZero-Atari-Dynamics
STUZero Atari Dynamics Dataset
Offline dynamics training datasets collected from trained EfficientZero V2 (EZv2) benchmark models on Atari games. Each game's data is stored in a subfolder named {game}_{steps} indicating the game and the number of training steps of the source checkpoint. While all models were trained for 120K steps, best results in some games were attained at earlier checkpoints. The model with best eval scores was used to curate data for each game.… See the full description on the dataset page: https://huggingface.co/datasets/Shivamkak/STUZero-Atari-Dynamics.dynamic-feed
Dynamic Feed — Live AI Data
Fresh, structured data that language models don't have on their own (it changes
faster than training cutoffs). Updated daily from authoritative sources.
Live API & MCP: https://dynamicfeed.ai · remote MCP: https://dynamicfeed.ai/mcp
What's inside
File
Contents
models.json
Current pricing & capabilities for ~250 AI models (USD per 1M tokens, context windows, vision/tool support).
mcp_servers.json
A registry of MCP servers… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/dynamic-feed.humanoid-robots-training-dataset
Dynamic Intelligence — Humanoid Robot Training Dataset
A first-person (egocentric) video dataset of human hand manipulation, designed for training humanoid robot policies via imitation learning. Each episode captures a person performing an everyday household task — folding clothes, moving dishes, opening doors — filmed from a head-mounted iPhone using its built-in LiDAR and depth sensors.
The dataset pairs each video with frame-level 3D hand tracking and camera pose data, giving… See the full description on the dataset page: https://huggingface.co/datasets/DynamicIntelligence/humanoid-robots-training-dataset.dynamic_replicarobotwin_dynamic_27500initial-dynamic-proteins
Initial 2,000-protein dataset
This is the canonical local root for the first complete router dataset: 1,000
single-dominant structured-state proteins (label 0) and 1,000 dynamic or
heterogeneous-state proteins (label 1). The fixed split is 1,400 train, 300
validation, and 300 test proteins.
Place Colab's completed ESMFold result files (<sequence_sha256>.npz) in
esmfold_results/. Then import them with:
uv run python scripts/esmfold_dataset.py import
The importer validates every… See the full description on the dataset page: https://huggingface.co/datasets/archiitecture/initial-dynamic-proteins.EchoNet-Dynamic-unzipped
EchoNet-Dynamic 资产：EchoNet-Dynamic-unzipped
导航 / Navigation：EchoNet-Dynamic｜源数据与分发格式
中文
角色：canonical。 当前 canonical 解压入口；10,032 个 payload 对象。
当前 repo revision：e5eb1aa94d60be651b451d3b62b3e89d75a144a2
最大/主要 payload：VolumeTracings.csv (31581368 bytes)
payload identity：a51a49a8294b031cac7dfb2dc01e02e6efef93a8a1b9d58d607be12ab5a716e7
payload object count：10033
EchoNet 各仓库当前旧 license metadata 并不完全一致，而这些 repo 内没有像 CAMUS 那样的明确统一 LICENSE_TERMS 文件。本轮不凭推测改写 EchoNet… See the full description on the dataset page: https://huggingface.co/datasets/miyuki17/EchoNet-Dynamic-unzipped.complet4r_preprocessed_dynamicreplicaEverMemBench-Dynamic
EverMemBench-Dynamic
A benchmark dataset for evaluating long-term memory capabilities in conversational AI systems. It is part of EverMemBench, the first benchmark designed for long-horizon collaborative memory, introduced in the paper Evaluating Long-Horizon Memory for Multi-Party Collaborative Dialogues — accepted at KDD 2026 (Oral).
Configurations
This dataset has three configurations (subsets):
dialogues
Multi-turn group dialogues spanning ~250… See the full description on the dataset page: https://huggingface.co/datasets/EverMind-AI/EverMemBench-Dynamic.hard-intersection-multimodal-sample
Hard Intersection Multimodal Samples
Release Notes
Release
Description
v1.0.0
Initial public release.
v1.1.0
Added Unreal Engine assets.Fixed issues in the OpenDRIVE map data.Updated the README to improve documentation and usability.
Dataset Summary
Hard Intersection Multimodal Samples is a curated multimodal dataset of accident-prone urban intersection in Japan for autonomous driving research and development.It provides… See the full description on the dataset page: https://huggingface.co/datasets/dynamic-maps/hard-intersection-multimodal-sample.live-facts-snapshot
Live Facts Snapshot
A daily snapshot of verifiable, post-training-cutoff world-state facts — the kind of
ground truth language models cannot know from training data — exported through
Dynamic Feed, a live, verifiable data API whose every response
is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one fact per line, and
every row carries its own source, source_url and measured_at.
Facts covered per day:
tool
facts
upstream source
licence
software_version… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/live-facts-snapshot.p2-dynamic-granger-lasso-resultsDynamicReplica_rawDynamicMCPBench
DynamicMCPBench
A trace-grounded, effect-scored benchmark for LLM agents on live MCP servers.
Tasks are generated forward: an explorer agent drives real MCP tools until a goal
is reached, the recorded trace is distilled into a TaskSpec, and candidates are graded
on whether they reproduce the effects the trace produced — checkpoints, equivalence
sets, minefields, a partial order — never on matching an answer string or a fixed tool
list. Candidates are evaluated under… See the full description on the dataset page: https://huggingface.co/datasets/TokenWasteGroup/DynamicMCPBench.vibepi-060526-dynamicsThis dataset was created using LeRobot.
Dataset Structure
meta/info.json:
{
"codebase_version": "v3.0",
"fps": 20,
"features": {
"action": {
"dtype": "float32",
"names": [
"shoulder_pan.pos",
"shoulder_lift.pos",
"elbow_flex.pos",
"wrist_flex.pos",
"wrist_roll.pos",
"gripper.pos",
"tilt.pos"
]… See the full description on the dataset page: https://huggingface.co/datasets/VibeCuisine/vibepi-060526-dynamics.dynamic_model_informationVanGogh_vs_TreeOilPainting_Torque_Brushstroke_Dynamics_EnergyField_Phase2_2026🚪 Quick Entry: Start Here
What is this dataset (in 2 sentences)
This dataset is not about what a painting looks like.
It is about what physically created it.
Instead of pattern recognition, this system forces AI to perform causal reasoning based on force, motion, and energy encoded in brushstrokes.
What you can do here
With this dataset, you can:
Reconstruct brushstroke motion from a static image
Infer pressure, torque, and stroke velocity
Test whether an AI… See the full description on the dataset page: https://huggingface.co/datasets/HaruthaiAi/VanGogh_vs_TreeOilPainting_Torque_Brushstroke_Dynamics_EnergyField_Phase2_2026.dynamic_earthnetDynamic EarthNet dataset redistributed from https://mediatum.ub.tum.de/1650201 and https://cvg.cit.tum.de/webshare/u/toker/dynnet_training_splits/ under a common tarball for simpler download speeds.
Individual zip files were replaced with tarballs instead.
In the mediatum server version the following directories have the wrong name compared to the given split txt files:
/labels/5111_4560_13_38S
/labels/6204_3495_13_46N
/labels/7026_3201_13_52N
/labels/7367_5050_13_54S
/labels/2459_4406_13_19S… See the full description on the dataset page: https://huggingface.co/datasets/torchgeo/dynamic_earthnet.DynamicVerse
DynamicVerse: A Physically-Aware Multimodal Framework for 4D World Modeling
Overview
DynamicVerse is an integrated framework for dynamic scene understanding and 4D reconstruction. It combines advanced visual models such as Sa2VA, Qwen-VL, DAM, CameraBench, CoTracker, and UniDepth to achieve end-to-end processing from video to 4D scenes.This repository hosts the processed datasets used in the DynamicVerse project. These data cover multiple mainstream dynamic scene… See the full description on the dataset page: https://huggingface.co/datasets/kairunwen/DynamicVerse.Semantic-Flow-Dynamics-SFD
Semantic Flow Dynamics (SFD) — A Formally Specified Social-Science Theory Corpus
TL;DR: 614 Chinese-language formalized social-science concepts across 25
papers, UUID-linked with typed derivation relations (derives_from,
leads_to, falsified_by, …) — usable for knowledge-graph construction,
RAG over structured theory, or as a Chinese formal-reasoning corpus.
Author: 黃正宇 Cheng Yu HuangContact: mthree.tw@gmail.com
What This Dataset Is
This corpus is an ongoing… See the full description on the dataset page: https://huggingface.co/datasets/mthreetw/Semantic-Flow-Dynamics-SFD.groundtruth-dynamic-benchmarking-submissions
Groundtruth Dynamic Benchmarking — Geology — Submissions
Community-submitted evaluation runs against the groundtruth-dynamic-benchmarking geology rubrics, feeding the leaderboard. We are currently running two tracks: model benchmarking (comparing different models with no special harness) and harness benchmarking (comparing different harnesses using a single standard model - GLM 4.7).
Each submission is a pointwise rubric score: one model, scored 0–10 per question against a… See the full description on the dataset page: https://huggingface.co/datasets/EigenformAI/groundtruth-dynamic-benchmarking-submissions.dynamics-of-instruction-tuning
💻 [Github Repo] • 📃 [Paper] • 👀 [Preview]
Update
12/01/23: Corrected ambiguous choices in the validation and test sets of the role-play chat data.
Overview
We introduce DoIT, a collection of over 40k human-curated instruction-output pairs in Chinese. This dataset is organized into ten representative ability categories: (1) STEM subject - Biology, (2) Humanity subject - History, (3) Code Generation, (4) Creative Writing, (5) Language proficiency - Chinese, (6)… See the full description on the dataset page: https://huggingface.co/datasets/ChiyuSONG/dynamics-of-instruction-tuning.earthquakes-daily
Earthquakes Daily — M4.5+ global snapshot
A daily snapshot of every magnitude 4.5+ earthquake in the live USGS feed, exported
through Dynamic Feed — a live, verifiable data API whose every
response is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one JSON
object per earthquake per line.
Live source: https://dynamicfeed.ai (tool: earthquakes, endpoint POST /v1/batch) — keyless, no signup
Upstream source: USGS Earthquake Hazards Program
Licence: US public domain (USGS… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/earthquakes-daily.ai-model-pricing-daily
AI Model Pricing Daily
A daily snapshot of AI model pricing and metadata — flagship and open models across
providers (OpenAI, Anthropic, Google, Mistral, Groq, ...) — exported through
Dynamic Feed, a live, verifiable data API whose every response
is Ed25519-signed. One file per day (data/YYYY-MM-DD.jsonl), one JSON object per
model per line. Because model prices change without notice and post-date every model's
training cutoff, a dated, signed daily series is the form this data… See the full description on the dataset page: https://huggingface.co/datasets/dynamicfeed/ai-model-pricing-daily.dml-fl-iot-ids-dynamic3L-K5safe-alignment-dynamic
safe-alignment-dynamic
Training prompts for score-conditioned SFT / RL and separate reward-model pair sets; nothing here is scored.
sft-prompts/train and rl-prompts/train: the same prompt pool, deduplicated across sources with responses
merged and HH/PKU test prompts removed. rl-prompts additionally marks selection=pku_label_conflict where PKU's
better and safer labels disagree with opposite safety flags; preference_pairs indexes those responses.
This is an annotation, not a… See the full description on the dataset page: https://huggingface.co/datasets/RLLab/safe-alignment-dynamic.