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Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
15,883 datasets
The Democratic Republic of the Congo is the geographic focus of this dataset. It contains estimates of the acutely food insecure population, produced by FEWS NET. The data covers the year 2019 and was last updated on the platform in March 2026.
FEWS NET's 2019 estimates for the population in Uganda facing acute food insecurity. The data is provided in multiple formats including CSV, XLSX, and JSON. It was last updated on the HDX platform in March 2026.
FEWS NET data provides acute food insecurity classifications for the Democratic Republic of the Congo. The dataset covers the year 2018 and is available in multiple formats including GEOJSON, CSV, and XLSX. It is licensed under CC-BY-4.0 and was last updated on March 26, 2026.
FEWS NET data on the estimated population facing acute food insecurity in Uganda for the year 2019. The dataset is available in CSV, XLSX, and JSON formats under a CC-BY-4.0 license. It was last updated on the platform in March 2026.
2019 estimates of the acutely food insecure population in the Democratic Republic of the Congo, produced by FEWS NET. The data is provided in multiple formats including CSV, XLSX, and JSON. It was last updated on the platform in March 2026.
UCDP's most disaggregated dataset covers individual events of organized violence in Nigeria. Events are sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single days. The dataset is published by HDX under a CC-BY-IGO license and was last updated on the platform in April 2026.
IMAV 2025 Gate Detection Dataset supports object detection for an indoor drone competition. The dataset was created by blackbeedrones for the 16th International Micro Air Vehicle Conference and Competition held in San Andres Cholula, Mexico. It focuses on detecting gates of three sizes (blue 1.5m, green 1.0m, red 0.5m) within a 10m x 10m arena.
A measure developed by the Greater London Authority's City Intelligence unit, with development work from 2021 to 2023. It aggregates data on multiple aspects of life to track collective wellbeing and sustainability for London's residents over time. The framework was created through a review of other cities, stakeholder consultation, and in-depth qualitative research with Londoners.
A 5.5 KB Excel file compares the performance of different depth estimation algorithms. The dataset, authored by Gobinda Chandra Sarker and last updated in April 2026, is hosted on figshare under a CC-BY-4.0 license. Its small size suggests it likely contains aggregated metrics or summary results from algorithm evaluations.
A 5.5 KB tabular dataset by Muhammad Naeem, last updated in April 2026. It records hyperparameter settings used during the training of a proposed convolutional neural network algorithm for impulse noise detection. The dataset is shared on figshare under a CC-BY-4.0 license.
Montaser Abdelsattar published comparative performance metrics for EfficientNetV2 models applied to photovoltaic cell fault detection on figshare in April 2026. The dataset is 5.5 KB in size and is available in XLS format. It is licensed under CC-BY-4.0.
A tabular dataset comparing model size, memory requirements, and computational complexity across EfficientNetV2 variants. The 5.5 KB Excel file was authored by Montaser Abdelsattar and last updated in April 2026. The dataset likely contains metrics for models applied to computer vision tasks, such as solar panel maintenance.
Montaser Abdelsattar's dataset provides a comparative analysis of defects in monocrystalline and polycrystalline photovoltaic cells. The dataset is 5.5 KB in size and was last updated on April 3, 2026. It is available in XLS format under a CC-BY-4.0 license.
Code files for the EfficientNetV2B0, EfficientNetV2B2, and EfficientNetV2M models used in a computer vision experiment. The 22.1 KB RAR archive was authored by Montaser Abdelsattar and last updated on 2026-04 03. The platform tags suggest the experiment involved preventive maintenance and performance analysis of solar panels.
Amir Ali's dataset provides molecular mechanics Poisson-Boltzmann surface area (MM-PBSA) calculations for protein-ligand binding free energies. The data, last updated in April 2026, is stored in a 9.5 KB XLS file. It appears to focus on evaluating compounds from Caralluma tuberculata for managing type 2 diabetes.
9.5 KB of computational chemistry results for 57 traditionally used bioactive compounds. The data, authored by Amir Ali and last updated in April 2026, was generated using the MM/GBSA approach to calculate binding free energies for proteins like amylase and glucosidase. This method is commonly employed to study binding mechanisms and therapeutic efficacy.
Signal strength metrics for adverse events associated with the antihypertensive drug aliskiren, categorized by System Organ Class (SOC). The dataset was created by Meirong Shan and last updated in April 2026. It is a small dataset, approximately 9.5 KB in size, derived from the FDA Adverse Event Reporting System (FAERS) database.
Jiawen Chen published a processed dataset on figshare in April 2026 to replicate the main findings of a study on urothelial bladder cancer. The 226.8 KB XLSX file contains data related to plasma cell infiltration and biomarker expression. It was created to support the replication of analyses concerning survival outcomes and the immunogenic tumor microenvironment.
Two spreadsheets systematize and analyze the content of Brazilian state vaccination plans against COVID-19. The 'Eixos de Pesquisa (Consolidado)' sheet compares state plans to the national framework across themes like epidemiology, logistics, communication, and budget. The 'Tabela Grupos Prioritários' sheet details the criteria each state used to define professional and population priority groups for early vaccination. This dataset was created by researcher Elize Massard da Fonseca for the Base Documental dos Planos de Vacinação contra a Covid-19 no Brasil project.
LibriBrain contains Magnetoencephalography (MEG) recordings of brain activity synchronized with audiobook stimuli. The dataset was first open-sourced for the 2025 PNPL Competition and includes event annotations and stimulus audio files. It was used to evaluate word decoding from brain data in the paper 'MEG-XL: Data-Efficient Brain-to-Text via Long-Context Pre-Training'.