datasets
Training and evaluation data, with the modality, task and licence stated up front. Listed live from the Hugging Face Hub.
Network-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.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.Marvel_network
Dataset Card for Marvel Network
This is a dataset for Marvel universe social network, which contains the relationships between Marvel heroes.
Dataset Description
The Marvel Comics character collaboration graph was originally constructed by Cesc Rosselló, Ricardo Alberich, and Joe Miro from the University of the Balearic Islands. They compare the characteristics of this universe to real-world collaboration networks, such as the Hollywood network, or the one created by… See the full description on the dataset page: https://huggingface.co/datasets/ShimizuYuki/Marvel_network.proto-social-network-canal-barra
Canal Barra Digital Archaeology Dataset
This dataset preserves structured historical evidence related to Canal Barra, a Brazilian digital community founded in 1996 around the #barra IRC channel on the BRASnet network.
Canal Barra combined IRC communication, web-based profiles, persistent nicknames, access-level governance and recurring in-person meetings in Rio de Janeiro. The dataset supports historical and academic investigation into Canal Barra as an early… See the full description on the dataset page: https://huggingface.co/datasets/raphaelnercessian/proto-social-network-canal-barra.benchmark-dataset-different-gpu-workload
GPU catalog × LLM workload VRAM benchmark
Summary
Tabular benchmark in CSV form: each row pairs a catalog GPU (gpu_id, gpu_display_name, catalog_gpu_vram_gb) with a concrete LLM inference-style workload (model, parameter count, context length, precision, batch size, concurrent users). The file records math_engine VRAM component estimates (weights, KV cache, activations, overhead, totals, tier), a document_engine recommended VRAM value, a short comparison summary… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-dataset-different-gpu-workload.zero_trust_network_security_logsSocial_Network_Ads.csvskills_network_vector_databasenetwork-QnA-datasetbenchmark-finetune-lora-v1
Odyn benchmark: LoRA fine-tuning peak VRAM (V1)
Curated benchmark rows for validating GPU memory estimators during LoRA fine-tuning. Each row pairs a published or measured expected peak VRAM with inputs to a math engine (model size, context length, batch, LoRA rank, precision, parallelism) plus optional VRAM breakdown and provenance.
This dataset is not Alpaca-style training JSONL. It is evaluation ground truth for placement / scheduler memory models (Odyn Smart Digester math… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-finetune-lora-v1.benchmark-finetune-dpo-v1
Odyn benchmark: DPO LoRA fine-tuning peak VRAM (V1)
Curated benchmark rows for validating GPU memory estimators during DPO + LoRA fine-tuning. Each row pairs a published or measured expected peak VRAM with inputs to a math engine (model size, context length, batch, LoRA rank, precision, parallelism) plus optional VRAM breakdown and provenance.
This dataset is not preference-pair training JSONL (UltraFeedback-style). It is evaluation ground truth for placement / scheduler memory… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-finetune-dpo-v1.benchmark-dpo-hyperparameters-v1
Odyn benchmark: DPO LoRA fine-tuning hyperparameters (V1)
Curated benchmark of real, cited DPO + LoRA fine-tuning configurations for validating a hyperparameter advisor. Each row is a published or measured config (from a framework example, model card, or write-up) with its hyperparameters — learning rate, LoRA rank/alpha/dropout, epochs, batch, beta, loss type, gradient checkpointing — plus the dataset it trained on and per-field provenance.
Schema
Column… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-dpo-hyperparameters-v1.network-routing-coherence-stability-v0.1What this repo is for
Detect routing instability before it becomes an outage.
It tests whether a model can read routing health signals and decide if the network is stable.
It targets daily operator reality:
BGP route flaps
slow convergence
path asymmetry
packet loss and jitter spikes
SD-WAN path oscillation
EVPN churn
How to use
feed a row as context
ask the question
model must output exactly one token
coherent or incoherent
Residential_Mobility_and_Kinship_Network_SizeFull methodology & context
Residential Mobility and Kinship Network Size (Person-Level, Anonymized)
A person-level, de-identified dataset for studying how residential mobility relates to the
size of a person's recorded kinship network, across age groups and U.S. Census regions.
Each row is one person, described only by coarse, generalized attributes. ~750,000 rows.
How this differs from the state-migration dataset. This dataset is not about
origin→destination flows. It carries… See the full description on the dataset page: https://huggingface.co/datasets/ZaraMann/Residential_Mobility_and_Kinship_Network_Size.network-dns-resolution-coherence-risk-v0.1What this repo is for
Detect DNS instability before services fail.
Covers real operational signals:
rising resolution latency
SERVFAIL spikes
authoritative mismatch
cache poisoning
missing failover resolvers
Used by:
ISPs
cloud providers
enterprises
SRE teams
network-control-plane-data-plane-coherence-risk-v0.1
What this repo is for
Detect when routing looks correct but forwarding fails.
This is a classic outage pattern:
routing tables show reachability
traceroute path deviates
blackholes appear
ping success falls
packet loss rises
A model that detects this early can cut incident time fast.
HNSCC-MultiOmics-10-Cancer-Hallmark-Gene-Network
HNSCC MultiOmics Cancer Gene Hallmark Network Patient Dataset
This dataset consists of network and adjacency matrix files related to head and neck squamous cell carcinoma (HNSCC) patient data. It is intended for research purposes in the field of cancer genomics and network analysis.
License
This dataset is available under the cc-by-nc-sa-4.0 License.
Dataset Files
There are three main files in this dataset:
hnscc.patient.chg.network.pth: This is a PyTorch… See the full description on the dataset page: https://huggingface.co/datasets/VatsalPatel18/HNSCC-MultiOmics-10-Cancer-Hallmark-Gene-Network.network_intrusion_detection_logsnetwork-capacity-demand-coherence-risk-v0.1What this repo is for
Detect when network demand is about to exceed available capacity.
Used for:
ISP backbone planning
cloud scaling
enterprise bandwidth forecasting
datacenter expansion
If demand grows faster than capacity
outages follow.
benchmark-dataset-finetune
Fine-Tuning VRAM Benchmark Dataset
Benchmark dataset for evaluating the accuracy of the Odyn Smart Digester VRAM Math Engine for fine-tuning workloads.
Compares the V1 (initial) and V2 (updated) engine estimates against expected peak VRAM values sourced from published research papers and hardware measurements.
Dataset Details
10 workload rows — all with gradient checkpointing enabled
Methods covered — LoRA (bf16) and QLoRA (NF4)
Models — Llama 2 7B, Llama… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/benchmark-dataset-finetune.network-observability-reality-coherence-gap-v0.1What this repo is for
Detect when dashboards say everything is fine
but the network is failing.
Common real-world pattern:
metrics green
alerts silent
users complaining
synthetic probes failing
hidden outages
This closes the network domain.
network-traffic-pattern-anomaly-coherence-risk-v0.1What this repo is for
Detect when traffic stops behaving normally.
Operators care about:
sudden spikes
source shifts
protocol mix change
unexpected egress
sustained anomalies
This is one of the highest-value early warning datasets.
lora-hyperparameter-benchmark-v1
Odyn benchmark: LoRA fine-tuning hyperparameter configs (V1)
Curated benchmark of real, cited LoRA and QLoRA fine-tuning configurations for validating a hyperparameter advisor. Each row is a published or measured supervised (SFT) LoRA config with its hyperparameters (learning rate, LoRA rank/alpha/dropout, epochs, batch, sequence length, gradient checkpointing), the dataset it trained on, and per-field provenance.
Schema
Column
Type
Description
id… See the full description on the dataset page: https://huggingface.co/datasets/odyn-network/lora-hyperparameter-benchmark-v1.Resource-Allocation-of-a-wireless-network5G-Network-Energy-Consumption
5G Network Energy Consumption Dataset
This dataset provides normalized real-world measurements of energy consumption and operational data from a large-scale 5G network deployment.
It includes eight days of measurements collected from more than 1,000 RRU/AAUs, covering 12 different hardware products.
The dataset is intended to support research in energy-efficient mobile networks, network optimization, and data-driven modeling of 5G systems.
📂 Dataset Structure
The… See the full description on the dataset page: https://huggingface.co/datasets/netop/5G-Network-Energy-Consumption.alphafold-allosteric-network-fracture-detection-v0.1
Goal
Detect when a mutationfractures the allosteric communication networklinking distal regions to the active site.
Proteins function throughlong-range signal transmission.A mutation can break that signalwithout altering the binding site itself.
This dataset trains systems to detectnetwork fracture beforefunctional collapse appears.
Required outputs
fracture_flag
fracture_horizon_steps
critical_path_loss
network_resilience_index
allosteric_gain_change… See the full description on the dataset page: https://huggingface.co/datasets/ClarusC64/alphafold-allosteric-network-fracture-detection-v0.1.network-congestion-control-coherence-risk-v0.1What this repo is for
Detect congestion collapse before users feel it.
Covers daily operator problems:
bufferbloat
queue overflow
packet drop spikes
flow starvation
QoS failure
latency jitter
Models trained here can flag unstable traffic patterns before outages occur.
network-telemetry-signal-coherence-loss-v0.1What this repo is for
Detect when monitoring systems stop reflecting reality.
Covers real operator risk:
missing metrics
delayed telemetry
alert storms
false positives
blind operation
If telemetry coherence fails
operators lose visibility before outages happen.
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