datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
llm-network-study-data
LLM-Network-Study-Data
Per-request network captures (.pcapng) collected by the
LLM-Network-Study benchmark harness (benchmark.py and the
per-workload test scripts). Each directory holds one capture file per request,
named request_<id>_run<n>_<timestamp>.pcapng.
A directory name encodes four dimensions:
<capture-env>_<provider/model>_<workload>[_<dataset/variant>]_results
Dimension legend
Dimension
Values
Meaning
Capture env
ethernet
Wired connection to… See the full description on the dataset page: https://huggingface.co/datasets/wayslab/llm-network-study-data.bitcoin-network-propagation
Bitcoin network propagation
Timestamped block and transaction announcements received from connected Bitcoin peers, with peer metadata and advertised relay-fee floors. These observations support analysis of announcement timing and differences between connected peers.
Contents
Table
Record
bitcoin_block_announcements
A peer's announcement of a block, timestamped on receipt
bitcoin_transaction_announcements
A retained transaction announcement from a peer… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/bitcoin-network-propagation.dogecoin-network-propagation
Dogecoin network propagation
Timestamped block and transaction announcements received from connected Dogecoin peers, with peer metadata and advertised relay-fee floors. These observations support analysis of announcement timing and differences between connected peers.
Contents
Table
Record
dogecoin_block_announcements
A peer's announcement of a block, timestamped on receipt
dogecoin_transaction_announcements
A retained transaction announcement from a… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/dogecoin-network-propagation.bitcoin-cash-network-propagation
Bitcoin Cash network propagation
Timestamped block and transaction announcements received from connected Bitcoin Cash peers, with peer metadata and advertised relay-fee floors. These observations support analysis of announcement timing and differences between connected peers.
Contents
Table
Record
bitcoin_cash_block_announcements
A peer's announcement of a block, timestamped on receipt
bitcoin_cash_transaction_announcements
A retained transaction… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/bitcoin-cash-network-propagation.network_security_questionsThis dataset contains a single file full of network security questions in Chinese.
Could be used as good initial sources for scrapers, though not good as your browsing history.
litecoin-network-propagation
Litecoin network propagation
Timestamped block and transaction announcements received from connected Litecoin peers, with peer metadata and advertised relay-fee floors. These observations support analysis of announcement timing and differences between connected peers.
Contents
Table
Record
litecoin_block_announcements
A peer's announcement of a block, timestamped on receipt
litecoin_transaction_announcements
A retained transaction announcement from a… See the full description on the dataset page: https://huggingface.co/datasets/dataforge-labs/litecoin-network-propagation.Networking_Commands_DatasetNetworking Commands Dataset
Overview
This dataset is (networking_dataset) contains 750 unique Cisco-specific and general networking commands (NET001–NET750), designed for red teaming AI models in cybersecurity. It focuses on testing model understanding, detecting malicious intent, and ensuring safe responses in enterprise networking environments. The dataset includes both common and obscure commands, emphasizing advanced configurations for adversarial testing.
Dataset Structure
The dataset is… See the full description on the dataset page: https://huggingface.co/datasets/darkknight25/Networking_Commands_Dataset.bridge_network_open_clipub-networking-dataset-2024-2network_papernetwork_security_questionsThis dataset contains a single file full of network security questions in Chinese.
Could be used as good initial sources for scrapers, though not good as your browsing history.
global-meteor-network
Global Meteor Network Trajectory Data
Credit: NASA/ESA
Part of a dataset collection on Hugging Face.
Dataset description
Individual meteor trajectory solutions from the Global Meteor Network (GMN), a worldwide network of 500+ all-sky cameras operated by volunteer astronomers. Each row is one detected meteor with orbital elements derived from multi-station triangulation.
The GMN was founded in 2018 and has grown to cover all longitudes from Europe, the… See the full description on the dataset page: https://huggingface.co/datasets/juliensimon/global-meteor-network.Strandset-Rust-v1
Strandset-Rust-v1
Overview
Strandset-Rust-v1 is a large, high-quality synthetic dataset built to advance code modeling for the Rust programming language.Generated and validated through Fortytwo’s Swarm Inference, it contains 191,008 verified examples across 15 task categories, spanning code generation, bug detection, refactoring, optimization, documentation, and testing.
Rust’s unique ownership and borrowing system makes it one of the most challenging languages for… See the full description on the dataset page: https://huggingface.co/datasets/Fortytwo-Network/Strandset-Rust-v1.network_instruct_mcq_2481ncbi-dataset-for-genome-network-pretrainingNetwork-Intrusion-Detection-DataTON_IoT_network
TON IoT Network
The TON IoT train test network dataset provided by https://research.unsw.edu.au/projects/toniot-datasets
Dataset Details
The datasets have been called 'ToN_IoT' as they include heterogeneous data sources collected from Telemetry datasets of IoT and IIoT sensors, Operating systems datasets of Windows 7 and 10 as well as Ubuntu 14 and 18 TLS and Network traffic datasets. The datasets were collected from a realistic and large-scale network designed at the… See the full description on the dataset page: https://huggingface.co/datasets/codymlewis/TON_IoT_network.network-intrusion-detection
network-intrusion-detection
Dataset Description
This dataset contains cybersecurity events collected from honeypot infrastructure.
The data has been processed and feature-engineered for machine learning applications in threat detection and security analytics.
Feature Categories
Network Features
Connection flow statistics (bytes, packets, duration)
Protocol-specific metrics
Geographic information
IP reputation data
Behavioral Features… See the full description on the dataset page: https://huggingface.co/datasets/pyToshka/network-intrusion-detection.synthetic-social-networks
Synthetic Social Networks (Dataset)
Raw experimental outputs from the Synthetic Social Networks study:
59,776 in-character LLM-agent posts from 528 production trials, and
64,562 posts total when the original pipeline-verification runs are
included. The artifact combines an exploratory stage with a separately
frozen, preregistered 448-trial matched-exposure confirmation. Each production
trial includes peer-vote traces from in-character voting by other agents.… See the full description on the dataset page: https://huggingface.co/datasets/ranausmans/synthetic-social-networks.multi-modal-derived-brain-network
PPMI Connectivity Graphs — HF Staging (Derivatives)
This dataset ships ready-to-use functional brain connectivity graphs derived from the PPMI cohort in a BIDS-ish derivatives layout. For each subject and parcellation, we include:
ROI time-series (*_desc-timeseries_parc-<name>.mat)
Pearson correlation connectivity matrix (*_desc-correlation_matrix_parc-<name>.mat)
JSON sidecars with summary fields (nodes, measure, symmetric/weighted flags)
Contents
data/… See the full description on the dataset page: https://huggingface.co/datasets/pakkinlau/multi-modal-derived-brain-network.bridge_network_mlm_hidden_statesNetwork_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.lens-network-traffic
Lens Network Traffic Classification Benchmark
Downstream network-traffic classification data used to evaluate Lens, a knowledge-guided
foundation model for network traffic (TMLR). It bundles the 12 classification tasks
from the Lens paper as HuggingFace dataset configurations, each with train / validation /
test splits and a unified schema.
ℹ️ All tasks are derived from publicly available academic traffic datasets
obtained via the NetBench benchmark (Qian et al., 2024); the… See the full description on the dataset page: https://huggingface.co/datasets/Charles59/lens-network-traffic.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.network-packet-flow-header-payloadEach row contains the information of a network packet and its label. The format is given below:
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.saf_communication_networks_english
Dataset Card for "saf_communication_networks_english"
Dataset Summary
Short Answer Feedback (SAF) dataset is a short answer dataset introduced in Your Answer is Incorrect... Would you like to know why? Introducing a Bilingual Short Answer Feedback Dataset (Filighera et al., ACL 2022) as a way to remedy the lack of content-focused feedback datasets. This version of the dataset contains 31 English questions covering a range of college-level communication networks topics -… See the full description on the dataset page: https://huggingface.co/datasets/Short-Answer-Feedback/saf_communication_networks_english.optical-network-data
Optical Network Dataset
This dataset contains ITU-T and IEEE 802.3 standards, along with their embeddings. The dataset is organized as follows:
/documents/Standards/IEEE Standards/: Contains IEEE standards.
/documents/Standards/ITU-T Recommendations/: Contains ITU-T Recommendations.
/embeddings/: Contains embeddings as .npy files.
Documents.db: Database for indexing chunks and metadata.
metadata.json: Describes dataset categories, structure, and metadata.
How to use… See the full description on the dataset page: https://huggingface.co/datasets/Anishanks/optical-network-data.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.NADW-network-attacks-dataset
Network Traffic Dataset for Anomaly Detection
Overview
This project presents a comprehensive network traffic dataset used for training AI models for anomaly detection in cybersecurity. The dataset was collected using Wireshark and includes both normal network traffic and various types of simulated network attacks. These attacks cover a wide range of common cybersecurity threats, providing an ideal resource for training systems to detect and respond to real-time network… See the full description on the dataset page: https://huggingface.co/datasets/onurkya7/NADW-network-attacks-dataset.