Loading...
Loading...
Image classification, object detection, segmentation, face recognition, OCR, image generation, video understanding
15,925 datasets
The UCDP Georeferenced Event Dataset provides disaggregated data on individual events of organized violence in Bangladesh. Events are geocoded to the level of individual villages and have temporal durations disaggregated to single days. The dataset is based on research from Uppsala University and was last updated on 2026-03 03.
UCDP's most disaggregated dataset covers individual events of organized violence in Bahrain. Events are geo-coded down to the village level and have temporal durations disaggregated to single days. The dataset is published under a CC-BY-3.0-IGO license and was last updated on 2026-03-03.
UCDP's most disaggregated dataset covers individual events of organized violence with precise geo-coding to village level and temporal disaggregation to single days. The data is sourced from the Uppsala Conflict Data Program and documented in the Journal of Peace Research and UCDP codebooks. It was last updated on March 3, 2026.
UCDP's most disaggregated dataset covers individual events of organized violence occurring at a specific time and place. Events are geo-coded down to the level of individual villages and have temporal durations disaggregated to single days. The dataset is published by the Department of Peace and Conflict Research at Uppsala University and was last updated on March 3,ζ们εη°2026.
The UCDP Georeferenced Event Dataset provides disaggregated data on individual events of organized violence. Events are geo-coded to the level of individual villages and have temporal durations disaggregated to single days. The dataset is sourced from the Uppsala Conflict Data Program and was last updated on 2026-03-03.
Argentina data from the Uppsala Conflict Data Program's most disaggregated dataset, covering individual events of organized violence. The dataset is sufficiently fine-grained to be geo-coded down to the level of individual villages, with temporal durations disaggregated to single days. It is sourced from UCDP research publications and was last updated on the HDX platform in March 2026.
Individual events of organized violence in Angola, geo-coded to the level of villages and disaggregated to single days. The dataset is the Uppsala Conflict Data Program's most disaggregated event dataset. It is based on research published in the Journal of Peace Research and maintained by the Department of Peace and Conflict Research at Uppsala University.
UCDP's most disaggregated dataset covers individual events of organized violence in Algeria. Events are geo-coded to the village level and have temporal durations disaggregated to single days. The dataset is based on research from Uppsala University and is licensed under CC-BY-3.0-IGO.
Albania is the geographic focus of this dataset of individual organized violence events. The data is the Uppsala Conflict Data Program's most disaggregated dataset, with events geocoded to villages and dated to single days. It was last updated on March 3, 2026, and is shared under a CC-BY-3.0-IGO license.
UCDP's most disaggregated dataset records individual events of organized violence with precise location and timing. Events are geo-coded to the level of individual villages and have temporal durations disaggregated to single days. The dataset is based on research from Uppsala University and is licensed under CC-BY-3.0-IGO.
Fields Data provides this operational presence (3W) dataset for Cavite Province, Philippines, detailing humanitarian organizations, their sectors of activity, and specific locations. Updated in March 2026, the data follows the Humanitarian Exchange Language (HXL) standard for interoperability.
Humanitarian 3W data (Who, What, Where) for Biliran Province, Philippines, identifies organizations and sectors across specific locations. Published by Fields Data and updated in March 2026, the records utilize HXL tags to facilitate coordination between operational partners.
4840 paleomagnetic measurements from 271 rock samples collected at 40 sites near McMurdo Sound, Antarctica. The data were published by Tauxe et al. in 2004 and are archived in the MagIC database under grant OPP 022940. Measurements and interpretations can be reviewed and downloaded via a persistent URL.
Alaska is covered by 47 selected Advanced Very High Resolution Radiometer (AVHRR) scenes from NOAA polar-orbiting satellites 6, 7, 8, and 9. The imagery has a spatial resolution of 1.1 km at nadir and is organized by 7.5' or 15' quadrangles. Data originates from the National Oceanic and Atmospheric Administration (NOAA) and is distributed via NASA's Earthdata platform.
Peruvian STEM academics dataset contains 770 subjects and 44 columns. It includes 7 sociodemographic variables and responses to the ATE scale (23 items) and OJS scale (15 items). The data was authored by Luis Fidel AbregΓΊ Tueros and published on figshare in 2024-2025.
Raw Landsat digital data covering most of Alaska, acquired from 1972 onward and ongoing. The dataset contains 585 records, growing at a rate of 5-10 records per year, and is provided by the USGS Alaska Field Office. Data is organized into 7.5' or 15' quads and stored on sequential magnetic tapes.
A dataset titled 'efficientnet_1e-2-res' is hosted on Kaggle. The title suggests a connection to the EfficientNet family of convolutional neural networks, likely containing model weights, training results, or related outputs. No further metadata such as author, size, or description is provided.
EfficientNet_1e-3-res likely contains pre-trained model weights for the EfficientNet architecture. The dataset is published on the Kaggle platform. Its specific contents, such as the training data or exact model variant, require verification after download.
A dataset published on Kaggle, likely containing model weights or parameters for an EfficientNet architecture. The specific content, such as the number of layers or training details, is not described in the available metadata. The author, organization, and last update date are unknown.
Kaggle hosts a dataset titled 'efficientnet_1e-1-res'. The title suggests a connection to the EfficientNet family of convolutional neural networks, which are commonly used for image classification tasks. The dataset likely contains model weights, parameters, or related image data, but specific details on content, size, and authorship are unavailable from the provided metadata.