Machine Learning
This dataset is composed of a range of biomedical voice measurements from 31 people, 23 with Parkinson's disease (PD). Each column in the table is a particular voice measure, and each row corresponds to one of 195 voice recordings from these individuals ("name" column). The main aim of the data is to discriminate healthy people from those with PD, according to the "status" column which is set to 0 for healthy and 1 for PD.
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This dataset integrates textual, financial, and macroeconomic indicators to support research on bank failure prediction and financial distress forecasting in Vietnam. It includes financial news from the BKAI News Corpus Dataset (2009–2023) and financial crisis data from "A Dataset for the Vietnamese Banking System (2002–2021)" (Tu Le et al., 2022), covering crisis-related events such as restructuring, special control, mergers, and acquisitions.
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48 Views
The AMD3IR dataset is a large-scale collection of Shortwave Infrared (SWIR) and Longwave Infrared (LWIR) images, designed to advance the ongoing research in the field of drone detection and tracking. It efficiently addresses key challenges such as detecting and distinguishing small airborne objects, differentiating drones from background clutter, and overcoming visibility limitations present in conventional imaging. The dataset comprises 20,865 SWIR images with 24,994 annotated drones and 8,696 LWIR images with 10,400 annotated drones, featuring various UAV models.
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561 Views
A significant challenge in racing-related research is the lack of publicly available datasets containing raw images with corresponding annotations for the downstream task. In this paper, we introduce RoRaTrack, a novel dataset that contains annotated multi-camera image data from racing scenarios for track detection. The data is collected on a Dallara AV-21 at a racing circuit in Indiana, in collaboration with the Indy Autonomous Challenge (IAC).
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29 Views
Comprehensive dataset (5000 spectra) of simulated grating biosensor reflections in Excel format. Generated via Lumerical FDTD, it includes 11 parameters (thickness, RI, peak wavelength, FWHM, reflectance, etc.). It is ideal for data visualization, sensor response exploration, and AI/ML benchmarking. The full dataset in Excel format is coming soon! Follow this repository to be notified when it's released. In the meantime, feel free to browse the README for more information about the project.
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324 Views
This dataset provides measurements of cerebral blood flow using Radio Frequency (RF) sensors operating in the Ultra-Wideband (UWB) frequency range, enabling non-invasive monitoring of cerebral hemodynamics. It includes blood flow feature data from two arterial networks, Arterial Network A and Arterial Network B. Statistical features were manually extracted from the RF sensor data, while autonomous feature extraction was performed using a Stacked Autoencoder (SAE) with architectures such as 32-16-32, 64-32-16-32-64, and 128-64-32-16-32-64-128.
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Abstract
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868 Views
Dataset for PerfCam: Digital Twinning for Production Lines Using 3D Gaussian Splatting and Vision Models
KTH Royal Institute of Technology, SCI; AstraZeneca, Sweden Operations
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327 Views
DALHOUSIE NIMS LAB ATTACK IOT DATASET 2025-1 dataset comprises of four prevalent types attacks, namely Portscan, Slowloris, Synflood, and Vulnerability Scan, on nine distinct Internet of Things (IoT) devices. These attacks are very common on the IoT eco-systems because they often serve as precursors to more sophisticated attack vectors. By analyzing attack vector traffic characteristics and IoT device responses, our dataset will aid to shed light on IoT eco-system vulnerabilities.
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364 Views
This dataset comprises Terahertz (THz) images collected to support the research presented in the IEEE Access paper titled Diagnosing Grass Seed Infestation: Convolutional Neural Network Based Terahertz Imaging. The dataset is intended for the detection and classification of grass seeds embedded in biological samples, specifically ham, covered with varying thicknesses of wool. The images were captured at different frequencies within the THz spectrum, providing valuable data for the development of deep-learning models for seed detection.
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49 Views