This dataset contains 535 recordings of heart and lung sounds captured using a digital stethoscope from a clinical manikin, including both individual and mixed recordings of heart and lung sounds; 50 heart sounds, 50 lung sounds, and 145 mixed sounds. For each mixed sound, the corresponding source heart sound (145 recordings) and source lung sound (145 recordings) were also recorded. It includes recordings from different anatomical chest locations, with normal and abnormal sounds.
The dataset used in this study consists of Airborne LiDAR Bathymetry (ALB) waveform data collected by the Norwegian Mapping Authority via Field Geospatial AS. It covers the Fjøløy Island area in Stavanger, Norway, a region characterized by fjords and diverse submerged environments. The dataset is proprietary and was provided to the authors under a research collaboration agreement.
Two subsets were extracted from the full dataset:
Each waveform file contains:
This dataset comprises 325 histopathological images of clear cell renal cell carcinoma (ccRCC) tissue sections, designed to characterize vascular morphology based on CD31 immunohistochemical staining. Each image was scanned at 10× magnification and annotated with global proportions for three distinct vascular patterns: high-branching (HB), low-branching (LB), and sinusoidal (SN). The provided annotations include the relative distribution of each vascular class per image.
The Lemon Leaf Disease Dataset (LLDD) is a high-quality image dataset designed for training and evaluating machine learning models for lemon leaf disease classification. The dataset contains 9 classes of images of healthy and diseased lemon leaves, such as; Anthracnose. Bacterial Blight, Citrus Canker, Curl Virus, Deficiency Leaf, Dry Leaf, Healthy Leaf, Sooty Mould, Spider Mites, making it suitable for tasks such as plant disease instance segmentation, detection, image classification, and deep learning applications in agriculture.
The IP102 dataset comprises 75,222 images of 102 pest species, out of which 12 classes are chosen for detection tasks. The custom pest dataset contains 10 categories of pests commonly found in crops like rice, maize, soybean, and canola. It includes images captured under varied real-world conditions, such as different lighting, occlusion, and complex backgrounds, making it highly representative of practical agricultural scenarios.
QuaN is a collection of specially designed datasets for exploring the impact of noise quantum machine learning and other applications. The presented work focuses on the transformation of clean datasets into noisy counterparts across diverse domains, including MNIST-handwritten digits datasets, Medical MNIST, IRIS datasets and Mobile Health datasets. The dataset is created using noise from classical and quantum domains.
For the semantic segmentation to be effectively done, a labelled flood scene image dataset was created. This initiative was undertaken with official permission obtained from the BBC News Website and YouTube channel, providing a valuable dataset for our research. We were granted permission to use flood-related videos for research purposes, ensuring ethical and legal considerations. Specifically, videos were sourced from the BBC News YouTube channel. The obtained videos were then processed to extract image frames, resulting in a dataset comprising 10,854 images.
The existing datasets lack the diversity required to train the model so that it performs equally well in real fields under varying environmental conditions. To address this limitation, we propose to collect a small number of in-field data and use the GAN to generate synthetic data for training the deep learning network. To demonstrate the proposed method, a maize dataset 'IIITDMJ_Maize' was collected using a drone camera under different weather conditions, including both sunny and cloudy days.
