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2,170 results for “clouds”
Data for "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction"
<p>This dataset contains the simulation results in "Impact of Gaussian transformation on cloud cover data assimilation for historical weather reconstruction".</p>
Data for the analysis of aquifer-system deformation in the Doñana Natural Space (Spain) using unsupervised cloud-computed InSAR data and wavelet analysis
<p>This are the data necessary to correlate InSAR and hydrogeological information through wavelet analysis, by WaSAR Python script (Jiménez-González & Guardiola-Albert, 2022, http://doi.org/10.5281/zenodo.6334996). The structure and information about the data is the following:</p> <p>PSBAS: Processed Interferometric Synthetic Aperture Radar (InSAR) data from the European Space Agency (ESA) Sentinel-1 satellites to estimate line-of-sight (LOS) ground motion in the period 2014-2020 in the Doñana area (SW Spain). These images have been processed using the P-SBAS approach (Parallel Small BAseline Subset), which is the parallel computing solution for the SBAS processing chain at the ESA Geohazards Exploitation Platform (GEP) by CNR-IREA.</p> <p>Aggregates deformation: Former InSAR information aggregated in polygons</p> <p>Climate: rainfall and ET information in the Doñana area for the 2014-2020 period. Daily records of evapotranspiration and precipitation have been obtained from the agroclimatic stations belonging to the Junta de Andalucía (https://www.juntadeandalucia.es/agriculturaypesca/ifapa/riaweb/web/).</p> <p>Piezometry: piezometry information in Doñana area for the 2014-2020 period. Groundwater level information was provided by the piezometric networks of the Guadalquivir Hydrographic Confederation and the Geological and Mining Institute of Spain.</p> <p>Pump rates: estimated pumping rate time series in the Matalascañas touristic resort</p>
SCM-CNN: A Robust Deep Learning Matting Model for Cloud Removal in Optical Imagery
<p>This is a dataset that can be used for cloud detection and cloud opacity estimation. The data is saved in python-numpy form, and stored in dictionary :dict_keys(['OriginImage', 'Gimage', 'Alpha', 'Trimap', 'CloudMaxDN']) represents the cloud-free remote sensing image, cloud remote sensing image, cloud opacity, trilateration information and cloud brightness respectively. The command {np.load("Path",allow_pickle=True).item()} is used to read, where "Path" is the corresponding path to the file.</p>
Data for manuscript "Long-term variation in the quasi-five-day wave in the top layer of Venus clouds"
<p>The dataset contain the derived cloud motions from Akatsuki LIR level3c and the corrected LIR level3c brightness temperature. The data of each figure used for drawing are also contained.</p>
Dataset for "Secure and Dependable Multi-Cloud Network Virtualization"
<p>Processed data from experiments using Sirius, the SUPERCLOUD network hypervisor</p> <p>Type of data: processed data</p> <p>Hardware/software used:</p> <p>We use one public (Amazon EC2 in Germany – Frankfurt) and one private (the private cloud is our datacenter in Portugal – Lisbon) clouds in the evaluation. In Amazon EC2 we use t2.medium as gateway VMs and t2.micro as normal VMs. The private cloud is based on a rack of Dell R420, with 2 Intel Xeon E5520 quad-core, 2.2 GHz, and 32 GB RAM. VirtualBox managed the VMs, which were configured</p> <p>with 1 CPU and 2GB RAM. The VMs run Ubuntu with Docker (1.13.1) and OvS (2.5.0). The containers were also based in Ubuntu. The hypervisor was hosted in the server of the private Datacenter.</p> <p>Data Format: Text files</p> <p>Source: Simulations and experiments</p> <p>Number of samples: 10 samples in each experiment</p> <p>Total size of samples:</p> <p>Time to create and destroy containers: 1KB</p> <p>Time to setup a virtual network: 1KB</p> <p> </p>
The Cloud that Runs the Mobile Internet: A Measurement Study of Mobile Cloud Services
<p>In order to assess the performance of domains hosted in Cloud Service Providers (CSP), we run a dedicated measurements campaign on the MONROE measurements platform.</p> <p>For this study we use nodes in 4 countries and the Mobile Network Operators (MNO) over which we measure are:</p> <ul> <li>Norway: Telenor, Telia, <strong>Telia (SE)</strong></li> <li>Italy: Vodafone, Wind, TIM</li> <li>Spain: Yoigo, Orange, <strong>Vodafone (IT)</strong></li> <li>Sweden: Telia, Telenor, 3</li> </ul> <p>* MNOs written in bold letters are SIM cards that are roaming internationally (home country code in brackets).</p> <p>We benefit from MONROE’s openness and capabilities to run long-lived active measurements in realistic but controlled scenarios in commercial MNOs.</p> <p>Namely:</p> <ul> <li><strong>DNS test:</strong> We run DNS lookups for each target mobile domain towards: 1) the default MNO DNS resolver, 2) Google's public resolver and 3) OpenDNS's public resolver. This will allow us to compare the quality of the responses and identify possible DNS-level inefficiencies during the replica selection. All our DNS queries include the EDNS edns-client-subnet flag as it may be used by CSPs like EdgeCast and Amazon services to locate the end user and improve the quality of replica selection. If we do not receive a reply, as it occurs for Vodafone (IT) possibly due to the presence of an in-path DNS proxy filtering those requests, we repeat the request without the flag.</li> <li><strong>Traffic performance test:</strong> We measure the TCP connection and TLS session establishment time, if applicable, towards the resolved IP addresses. We repeat the same experiment over TCP ports 80 and 443, in case the MNO performs traffic shaping or deploys TCP-splitting proxies. The TCP connection, as well as the TLS session establishment are triggered by an HTTP GET request for the favicon.ico object. The presence of the object in the server is not relevant as the handshakes are triggered regardless of its existence.</li> <li><strong>Network topology test: </strong>For each resolved IP address, we run UDP traceroutes to study CSP-MNO peering and topological relationships. We also run pings towards the same IP address.</li> </ul> <p>We also log all the related metadata that are provided by the platform.</p> <p>The schema of the database can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/blob/master/supportingFiles/parsers/cloudmap.sql</p> <p>The container code used on the MONROE nodes to generate this dataset can be found at:</p> <p>https://github.com/FoivosMichelinakis/cloudmap/</p> <p> </p> <p>We run the aforementioned experiments in isolation from other experiments continuously over 4 weeks, from April 5, 2017 until May 6, 2017.</p> <p>The combined results of the three tests for a given FQDN produce a "sample". The measurements are continuously run and the periodicity of the same sample varies between 4 and 24 hours. Thus, each sample has several instances across time of day and various radio conditions.</p>
A Point Cloud Dataset of Vehicles Passing Through a Toll Station for use in Training Classification Algorithms
<p>This work presents a point cloud dataset of vehicles passing through a toll station in Colombia to be used to train artificial vision and computational intelligence algorithms. This article details the process of creating the dataset, covering initial data acquisition, range information preprocessing, point cloud validation, and vehicle labeling. Additionally, a detailed description of the structure and content of the dataset is provided, along with some potential applications of its use. The dataset consists of 36,026 total object classes: 31,432 cars, campers, vans and 2-axle trucks with a single tire on the rear axle, 452 minibuses with a single tire on the rear axle, 1158 buses, 1179 2-axle small trucks, 797 2-axle large trucks, and 1008 trucks with 3 or more axles. The point clouds were captured using a LiDAR sensor and Doppler effect speed sensors. The dataset can be used to train and evaluate algorithms for range data processing, vehicle classification, vehicle counting, and traffic flow analysis. The dataset can also be used to develop new applications for intelligent transportation systems.</p> <table> <tbody> <tr> <td>Type</td> <td>Description</td> <td>Quantity</td> </tr> <tr> <td>1</td> <td>Cars, campers, vans and 2-axle trucks with<br>a single tire on the rear axle</td> <td>31,432</td> </tr> <tr> <td>2</td> <td>Minibuses with a single tire on the rear axle</td> <td>452</td> </tr> <tr> <td>3</td> <td>Buses</td> <td>1,158</td> </tr> <tr> <td>4</td> <td>Trucks with 3 or more axles</td> <td>1,008</td> </tr> <tr> <td>5</td> <td>2-axle small trucks</td> <td>1,179</td> </tr> <tr> <td>6</td> <td>2-axle large truck</td> <td>797</td> </tr> <tr> <td>Total</td> <td> </td> <td>36,026</td> </tr> </tbody> </table>
Cloud to Thing Continuum based Sports Monitoring System using Machine Learning and Deep Learning Model
<p><span>Sports monitoring and analysis have seen significant advancements with the integration of cloud computing and continuum paradigms, facilitated by machine learning and deep learning techniques. In this study, we present a novel approach for sports monitoring that seamlessly transitions from traditional cloud-based architectures to a continuum paradigm, enabling real-time analysis and insights into player performance and team dynamics. Leveraging machine learning and deep learning algorithms, our framework offers enhanced capabilities for player tracking, action recognition, and performance evaluation in various sports scenarios. This research proposes a Cloud-to-Thing Continuum based Sports Monitoring System utilizing Machine Learning (ML) and Deep Learning (DL) models. The system integrates data acquisition, preprocessing, feature extraction, cloud-based processing, continuum paradigm integration, and decision-making stages. It leverages innovative techniques such as Improved Mask R-CNN for pose estimation, hybrid metaheuristic algorithms with Generative Adversarial Network (GAN) for classification, and fuzzy decision-making Based on the integrated analysis, decisions are made regarding player performance, team strategies, and tactical adjustments. The continuum approach ensures a balance between centralized cloud processing and distributed edge processing, optimizing resource utilization and reducing latency. Through this system, real-time analysis of sports events is achieved, enabling immediate feedback for time-sensitive applications.</span></p>
Cloud_ICA: A deterministic cloud-overlap algorithm for generating a complete set of independent column atmospheres
<p>In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community's concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here, we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate‐relevant scales. We build benchmarking capability around a solar heating code (Solar‐J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice‐cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m−2 using a full climate re‐creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ∼2 W m−2 with greater latitudinal biases and are likely to affect a model's ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.</p>
Dataset of 4D conserved tracers for convection simulated by large eddy model and cloud resolving model
<p>There are conserved tracers and active flag for convection used for diagnosis of bulk entrainment rate for four convection cases in this dataset. Total water and moist static energy are selected as tracer for shallow convection (BOMEX and RICO) and deep convection (GATE and KWAJEX), respectively. The two variables simulated by large eddy model for shallow convection and cloud resolving model for deep convection are four-dimension variables with horizontal scales, vertical altitude, and time. </p> <p>The size of domain simulated for BOMEX and RICO is 6.4 km with horizontal grid spacing of 100 m, and that GATE and KWAJEX is 256 km with horizontal grid spacing of 1 km. Besides, vertical layers in the simulation are 75 levels with spacing of 40m for BOMEX and 100 levels with spacing of 40m for RICO. For KWAJEX and GATE, the model was set up with 64 levels vertically, which gradually increases from 75 m at the surface to a spacing of 400 m through the troposphere and a larger spacing of 1 km in the Newtonian damping region. The model is integrated for 6 hours for BOMEX, 24 hours for RICO, 52.25 days for KWAJEX, and 20 days for GATE. Here, the range of time in these variables The four-dimension variables are saved every 3 seconds for shallow convection, and every 6 minutes for deep convection for two consecutive days.</p>
Solutions for Reproducibility in Empirical Research: Virtual Machines, Containers, Environment Management Packages, and Cloud Platforms
<p>This image provides a comprehensive overview of various technologies and platforms used to enhance the reproducibility of empirical research. It is divided into several sections:</p> <ol> <li><strong>Virtual Machines (VMs): </strong>the left section of the image illustrates the architecture of VMs with Type 1 and Type 2 hypervisors. <br> - <em>Type 1 Hypervisor </em>runs directly on the hardware, providing high efficiency and performance. Examples include VMware ESXi, <strong>Microsoft Hyper-v</strong>, and Xen Project.<br> - <em>Type 2 Hypervisor</em> runs on an existing operating system, offering flexibility at the cost of some performance. Examples include <strong>Oracle VirtualBox</strong>, VMware Workstation, and Parallels.</li> <li><strong>Containers: </strong>the middle section of the image explains the containerization concept, which shares the host operating system's kernel, making containers more lightweight than VMs. Technologies like <strong>Docker</strong> and <strong>Kubernetes</strong> are shown as popular solutions for container orchestration.</li> <li><strong>Environment Management Packages: </strong>the top right section focuses on tools for managing software dependencies and environments. <strong>renv</strong> (for R) and <strong>Conda</strong> (for Python and other languages) are highlighted as key tools for creating reproducible research environments.</li> <li>Cloud Platforms: the bottom right section features various cloud-based platforms that facilitate reproducible research by providing scalable and shareable computational environments. Platforms include <strong>Google Colab</strong>, <strong>Posit Cloud</strong>, JupyterHub, <strong>Binder</strong>, Nextjournal, OpenShift, and <strong>Code Ocean</strong>.</li> </ol> <p>Together, these solutions provide a robust framework for ensuring that empirical research can be reliably reproduced and validated by others, addressing the challenges of dependency management, environment consistency, and computational resource availability.</p>
Dataset: DT Cloud Acquisition Corporation (DYCQR) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Big Tree Cloud Holdings Limited (DSY) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: DT Cloud Acquisition Corporation (DYCQU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: DT Cloud Acquisition Corporation (DYCQ) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Themes Cloud Computing ETF (CLOD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Global X Cloud Computing ETF Global X Cloud Computing ETF (CLOU) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Consensus Cloud Solutions, Inc. (CCSI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: WiMi Hologram Cloud Inc. (WIMI) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: WisdomTree Cloud Computing Fund (WCLD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.