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In this dataset, we have compiled a comprehensive collection of 16,592 agri-food retail images across various classes commonly found in grocery and supermarket environments. To ensure generalizability, the dataset was collected using two distinct sources: a smartphone and an Intel RealSense Depth Camera (D435i), under diverse, real-world conditions, such as shelf inclinations, lighting levels, and different angles. The dataset is structured into two main subsets: unlabeled and labeled. The unlabeled subset is curated for key computer vision tasks relevant to retail applications, including classification, object detection, and product recognition. The labeled subset consists of 2,416 samples with detailed centroid annotations, making it suitable for On-Shelf Availability (OSA) estimation, counting, or multi-task learning approaches. Altogether, both subsets serve as valuable benchmarks for evaluating and testing automated inventory monitoring systems and real-time retail analytics applications.

Dispersivity-Induced Crack Patterns

AMDNet23 is a curated dataset of 2000 high-quality fundus images compiled from six public sources: Ocular Disease Recognition, DR_200, Fundus Dataset, RFMiD, HRF, and ARIA. It includes 4 balanced classes with 400 images each: Normal, Diabetes, Cataract, and Age-related Macular Degeneration (AMD). All images were enhanced using advanced preprocessing techniques (Gamma correction, CLAHE and so on) to improve diagnostic clarity. This dataset supports research in deep learning-based ocular disease detection and classification.

El documento "Blockchain: Evolución y Definición como base de datos" explora el desarrollo histórico de la tecnología blockchain, desde sus antecedentes en los años 90 (como los trabajos de Haber, Dai y Szabo) hasta su consolidación con Bitcoin (Nakamoto, 2008) y Ethereum (Buterin, 2014). Destaca su evolución desde un sistema monetario hasta una "base de datos descentralizada", analizando sus características únicas: inmutabilidad, consenso distribuido y estructura criptográfica.

The chromatograms and mass spectra used in the manuscript figures were provided in numerical format in this dataset.

Protein of unknown function - conserved; Leishmania infantum (strain JPCM5)

The Excel file contains raw simulation data related to the performance of Double Arrowhead auxetic structures. It includes parameters such as wall thickness, number of unit cells, and material type, along with measured outputs like energy absorption (EA) and deformation characteristics under impact loading. This data is used to evaluate and optimize the crashworthiness of auxetic structures in marine applications. The PDF file outlines the methodology, hardware specifications, and software tools utilized for the numerical simulation of Double Arrowhead auxetic structures. It details the simulation setup, including boundary conditions, loading scenarios (such as drop test), and meshing strategies. Additionally, the document specifies the computing hardware and the software used (ANSYS) for conducting finite element analysis.

This publication contains the code and data used in the paper "A Semiparametric Frontier with Group-Specific Random Effects in Inefficiency". Specifically: 1. Data on Chilean hydroelectric power plants (plants.xls) 2. R code for estimating the model (code_gre.R)

Supplemental Table 1. Baseline Comorbidities and Use of Medications of Patients treated with Mohs Micrographic Surgery with vs. without Cardiovascular Disease Before and After Propensity Score Matching (PSM), Including 30-Day and 3-Month Analyses.

This dataset is a structured, high-precision compilation of environmental and solar irradiance data sourced from NASA POWER, covering India's major geographic zones over the span of one year (January to December 2022). It includes seven critical parameters—such as solar radiation, temperature, cloud cover, albedo, and precipitation—essential for evaluating solar power generation potential. The primary goal of the dataset is to aid in the identification of optimal locations for solar energy infrastructure by applying geospatial and machine learning techniques. Carefully preprocessed for consistency and organized for ease of use, this dataset is not only useful for current solar site suitability analysis but also offers long-term value to researchers, urban planners, and policymakers. It supports advanced analytics like clustering, classification, and visualizations, and can serve as a foundation for predictive modeling, transfer learning, and sustainability-oriented decision-making in the field of renewable energy.