datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
rhizomorphic-networks-data
Rhizomorphic Networks — Data and Analysis Outputs
This dataset contains the experimental image data, segmentation outputs, and
downstream analysis results used for the quantitative analysis of
Armillaria gallica rhizomorphic networks.
The directory structure is organised according to the main stages of the analysis
pipeline:
Rhizomorphic Networks/
│
├── 01_raw inputs/
│ ├── control/
│ ├── furnace/
│ └── nutrient density/
│
├── 02_segmentation outputs/
│ ├── raw… See the full description on the dataset page: https://huggingface.co/datasets/lamm-mit/rhizomorphic-networks-data.Network_Defense_Symmetric_Competitive102,400,000 timesteps, Multi-Agent Reinforcement Learning
Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Timesteps
-The Red Agent’s goal is to discover vulnerabilities, elevate privileges, compromise assets, and maintain persistence. Its action space can be modeled after phases of the
MITRE ATT&CK framework.
-The Blue Agent’s goal is to maintain system availability, reduce the attack surface, detect malicious behavior… See the full description on the dataset page: https://huggingface.co/datasets/privateboss/Network_Defense_Symmetric_Competitive.tcga-ov-multiomics-network-derived-results
TCGA-OV Multiomics Network Derived Results
This dataset contains derived, publication-ready outputs from a reproducible TCGA-OV multi-omics network analysis pipeline.
Current status
Primary manuscript target: Journal of Biomedical Informatics
Preferred bundle: manuscript/journal_of_biomedical_informatics/
Current JBI main manuscript status:
required statement-of-significance table included
main-paper combined tables/figures reduced to a compliant <=8
sequential in-text… See the full description on the dataset page: https://huggingface.co/datasets/hssling/tcga-ov-multiomics-network-derived-results.Network_Defense_Symmetric_Competitive102,400,000 timesteps, Multi-Agent Reinforcement Learning
Total Environment Steps= 10 parallel environments × 7,000 episodes ×2,048 steps= 102400000 Training Timesteps
-The Red Agent’s goal is to discover vulnerabilities, elevate privileges, compromise assets, and maintain persistence. Its action space can be modeled after phases of the
MITRE ATT&CK framework.
-The Blue Agent’s goal is to maintain system availability, reduce the attack surface, detect malicious… See the full description on the dataset page: https://huggingface.co/datasets/TorontoMetropolitanUniversity/Network_Defense_Symmetric_Competitive.ROVR-Open-Dataset
ROVR Open Dataset
Introduction
Welcome to the ROVR Open Dataset repository! This dataset is designed to empower autonomous driving and robotics research by providing rich, real-world data captured from ADAS cameras and LiDAR sensors. The dataset spans 50+ countries with over 20 million kilometers of driving data, making it ideal for training and developing advanced AI algorithms for depth estimation, object detection, and semantic segmentation.… See the full description on the dataset page: https://huggingface.co/datasets/ROVR-Network/ROVR-Open-Dataset.dream-network-prop-cards
Dream Network — Artifact & Prop Reference Cards (20)
20 fully-annotated weapon, artifact, and prop reference cards from the Dream Network universe. Each card is a self-contained 1:1 collectible item sheet featuring a 3D hero showcase render, inset material/angle details, and structured RPG stat annotations.
Companion to:
dream-network-player-cards (The Characters)
dream-network-environment-cards (The Locations)
unhinged-cast-20 (The Original Character Turnarounds)
Together… See the full description on the dataset page: https://huggingface.co/datasets/TheMindExpansionNetwork/dream-network-prop-cards.dream-network-environment-cards
Dream Network — Environment Location Cards (20)
20 fully-annotated environment reference cards — the locations of the Dream Network, each a self-contained 1:1 card: a cinematic establishing view plus alternate views and a full worldbuilding stat panel rendered into the image.
Companion to dream-network-player-cards (the characters) and unhinged-cast-20 (their turnaround sheets). Together they form a complete production bible: who the characters are, what they look like, and… See the full description on the dataset page: https://huggingface.co/datasets/TheMindExpansionNetwork/dream-network-environment-cards.dream-network-player-cards
Dream Network — Character Player Cards (20)
20 fully-annotated character reference cards for the Dream Network cast. Each 1:1 card is a complete, self-contained character reference: full-body turnaround, expression lineup, and a personality stat panel — all rendered into the image itself.
This is the companion to unhinged-cast-20 (which holds the multi-view turnaround sheets). Those sheets show what they look like; these cards explain who they are.
What every card… See the full description on the dataset page: https://huggingface.co/datasets/TheMindExpansionNetwork/dream-network-player-cards.dream-network-prop-3d-assets
Dream Network — Isolated 3D Asset Prop Renders (20)
20 clean, isolated 3D prop and weapon reference renders from the Dream Network universe on pure neutral studio backgrounds. Designed specifically for Image-to-3D pipelines (Tripo3D, Trellis, InstantMesh, Hunyuan3D, Rodin, CSM, NeRF/Gaussian Splatting) and 3D modeling texture reference.
All cards, frames, text overlays, and background clutter have been stripped away, leaving only the centered object with crisp silhouettes, clean… See the full description on the dataset page: https://huggingface.co/datasets/TheMindExpansionNetwork/dream-network-prop-3d-assets.network_diagram_for_llama_trainingTensor-Network-Hackathon-2024ai-tube-onigiri-network
Description
🍙 Onigiri Network is the first AI-generated anime channel.
Model
HotshotXL
LoRA
KappaNeuro/studio-ghibli-style
Style
Studio Ghibli Style
Voice
Cloée
Prompt
A video channel which produces anime episodes of fictional franchises.
Stories should be short, about 1 minute or 2, but full of action and fun.
It will NEVER create content from existing artists or studio.
Instead it will create its own artistic stories, content… See the full description on the dataset page: https://huggingface.co/datasets/jbilcke-hf/ai-tube-onigiri-network.network_segmentationbangbros-networkMalicious-Network-Resource-Detectionddf-networknetwork-monitoring-vmdogfart-network