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2,170 results for “clouds”
Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight"
<p>Code/data to accompany publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight". The cloud radar data files contains all the data used in the publication "Using cloud radar to investigate the effect of rainfall on migratory insect flight" by Charlotte E. Wainwright, Sabrina N. Volponi, Phillip M. Stepanian, Don R. Reynolds, and David H. Richter, published in Methods in Ecology and Evolution in 2022. The MATLAB code implements the method described in the paper on the data files included.</p>
ACCESS-AM2 Southern Ocean cloud and radiation data for k-means clustering and analysis
<p>The ACCESS-AM2 (Australian Community Climate and Earth-System Simulator - Atmospheric Model Version 2) data and k-means analysis used for the study described in Fiddes et al. 2022 '<em>Southern Ocean cloud and shortwave radiation biases in a nudged climate model simulation: does the model ever get it right?' .</em> </p> <p>Included files: </p> <ul> <li>modis_cluster_centres_2015-2019.nc - kmeans derived cluster centres for MODIS</li> <li>modis_cluster_labels_2015-2019.nc - kmeans derived cluster labels for MODIS </li> <li>bx400_cluster_labels_2015-2019.nc - kmeans fitted cluster label for model </li> <li>COSP_vars_bx400_2015-2019.nc - model data for analysis </li> </ul> <p>The code that performs the analysis/generates this data and has instructions for where to download MODIS data can be found here: https://github.com/sfiddes/code_for_publications_2022/tree/main/ACCESS_cloud_radiation_eval</p>
FGI ARVO VLS-128 LiDAR Point Cloud, Käpylä, 7th of September 2020
<p>This LiDAR point cloud dataset is collected with a research platform of Finnish Geospatial Research Institute (FGI), called Autonomous Research Vehicle Observatory (ARVO). The dataset was collected with Velodyne VLS-128 Alpha Puck LiDAR, 7th of September 2020 in a suburban environment in the area of Käpylä in Helsinki, the capital of Finland. The environment in the dataset consists of a straight two-way asphalt street, called Pohjolankatu, which starts from a larger controlled intersection at the crossing of Tuusulanväylä (60.213326° N, 24.942908° E in WGS84) and passes by three smaller uncontrolled intersections until the crossing of Metsolantie (60.215537° N, 24.950065° E). It is a typical suburban street with tram lines, sidewalks, small buildings, traffic signs, light poles, and cars parked on both sides of the streets. To collect a reference trajectory and to synchronize the LiDAR measurements, we have used a Novatel PwrPak7-E1 GNSS Inertial Navigation System (INS).</p> <p>The motion distortion of each individual scan has been corrected with a postprocessed GNSS INS trajectory and the scans have been registered with Normal Distributions Transform (NDT). Each point is provided with a semantic label probability vector and the final point cloud is averaged with a 1 cm voxel filter.</p> <p>The steps to create this preprocessed dataset have been described in more detail in the article "<a href="https://arxiv.org/abs/2301.03956">Towards High-Definition Maps: a Framework Leveraging Semantic Segmentation to Improve NDT Map Compression and Descriptivity</a>" published in IROS 2022. However, the number of points in each semantic segment in Table I in Section IV-A are different. The correct values are shown in the table below. This does not affect the results.</p> <table align="left"> <caption>TABLE I: RandLA-Net classified dataset label proportions.</caption> <tbody> <tr> <td><strong>Semantic label</strong></td> <td><strong>No. of points</strong></td> <td><strong>% of all</strong></td> <td><strong>% of used</strong></td> </tr> <tr> <td>Ground</td> <td>14,206,060</td> <td>32.3</td> <td>50.3</td> </tr> <tr> <td>Building</td> <td>7,782,757</td> <td>17.7</td> <td>27.6</td> </tr> <tr> <td>Tree Trunk</td> <td>3,736,775</td> <td>8.5</td> <td>13.2</td> </tr> <tr> <td>Fence</td> <td>2,201,851</td> <td>5.0</td> <td>7.8</td> </tr> <tr> <td>Pole</td> <td>206,983</td> <td>0.5</td> <td>0.7</td> </tr> <tr> <td>Traffic Sign</td> <td>85,316</td> <td>0.2</td> <td>0.3</td> </tr> <tr> <td>Labels used here</td> <td>28,219,742</td> <td>64.1</td> <td>100.0</td> </tr> <tr> <td>Others</td> <td>15,821,962</td> <td>35.9</td> <td> </td> </tr> <tr> <td>Total</td> <td>44,041,704</td> <td>100.0</td> <td> </td> </tr> </tbody> </table>
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</p>
Multi-Temporal Cloud Gap Imputation With HLS Data Across CONUS
<p>This release contains the version 1.0 of the dataset which was used in <a href="https://arxiv.org/abs/2404.19609">Seeing Through the Clouds: Cloud Gap Imputation with Prithvi Foundation Model</a> and is included as one of the tasks in the <a href="https://madewithclay.org/challenge">AI for Earth Challenge 2024</a>.</p>
Dataset for Paper Titled "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations"
<div>The attached two files were used for analysis in the paper "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations." The NetCDF file included planetary boundary layer heights derived from lidar and radiosondes. The CSV file detailed the model configurations for selected case days in simulation sets ID1-5.</div> <div> <div> <p> </p> </div> </div>
Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm
<p>Used data and obtained results for the paper Optimisation of business processes tenant distribution in the Cloud with a genetic algorithm.</p> <p>The reader can find the following files :</p> <ul> <li>configuration_types.csv contains the cloud resource types (the name is the EC2 instance for database, and for the BPM engine separated by an underscore), their price and their capacity</li> <li>tenants_uni.csv contains the customers and their minimum and maximum BPM task throughput</li> <li>results_[number of tenants]_seg.csv files contain the results for the previous heuristic (segmentation only)</li> <li>results_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the iterative heuristic tests</li> <li>solver_<em>[number of tenants]</em>_ga_<em>[duration]</em>.csv files contain the results for the genetic algorithm coupled to the restricted model solved tests</li> </ul>
Science ready spectra of star clusters and their best-fitting models described in the research paper "Using Star Clusters as Tracers of Star Formation and Chemical Evolution: the Chemical Enrichment History of the Large Magellanic Cloud" by Chilingarian & Asa'd
<p>Science ready spectra of star clusters in the Large Magellanic Cloud and their best-fitting templates (alpha-enhanced MILES based simple stellar population models) obtained using the NBursts full spectrum fitting code. Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For each cluster, 5 spectra are provided, which correspond to [alpha/Fe] values from 0.0 to 0.4 dex with a step of 0.1 dex. The only exception is NGC2249, for which only 3 models are provided. The alpha-enhancement value of a model grid used in the fitting procedure is given in the FITS keyword MGFEGRID.</p>
Dataset for "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis"
<p>This repository contains the data of 512 perturbed-parameter ensemble experiments and CAM5-CLUBB default experiments for the paper "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis".</p> <p>In this paper, the quasi-Monte Carlo (QMC) sampling approach is applied to explore the high-dimensional space. 512 samples are generated with the 18 perturbed parameters. For each parameter sample, a pair of experiments is performed: the control one is based on the climatological sea surface temperature (SST), and the 4K experiment applies a uniform +4K SST perturbation to the control experiment. The total of 1024 simulations are then performed. In addition, CAM5-CLUBB default experiments that adopt the default values of the 18 selected parameters as in Bogenschutz et al. (2013) are performed to provide detailed model diagnostics for analyzing physical mechanisms of the cloud feedback, and they include both control and +4K simulations. Each simulation is run for 5 years and 4 months, forced by climatological SSTs. Monthly mean results from the last 5 years are analysed in this study.</p> <p>Note: data uploaded here is annual-mean and the dimension name 'time' in the files (CAM5-CLUBB_PPE_512*.nc) is the number of 512 PPE member.</p>
Sonora 2018: Cloud-free, solar composition, solar C/O substellar atmosphere models and spectra
<p>These models for non-irradiated, substellar mass objects belong to the Sonora 2018 model series, to be described in Marley et al., currently in preparation for submission to Astrophysical Journal.</p> <p>This particular set of model atmosphere structures and associated spectra are for cloudless, solar metallicity, solar C/O ratio objects (relative to Lodders (2010) abundances) with <span class="math-tex">\(3.25 \le \log g \le 5.5\)</span> and <span class="math-tex">\(200 \le T_{\rm eff} \le 2400\,\rm K.\)</span> "Rainout" chemical equilibrium is assumed. Spectra describe the emergent flux from the top of the atmosphere of the object, units in file header. Profiles give temperature and pressure through the model radiative-convective equilibrium structure. Volume mixing ratios for a few gasses of interest are also tabulated. Additional gas species are included in the calculation.</p> <p>Filename specifies <span class="math-tex">\(T_{\rm eff}\)</span> and gravity (in mks units). </p> <p>"co1.0" in version and spectra header nomenclature refers to 1.0 times the solar C/O ratio. Y is the He mass fraction. f_hole is a cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>The two "flux table" files give fluxes, in mJy, for various standard filter bandpasses of interest for each model case.</p> <p> </p> <p> </p> <p> </p>
Mobile Cloud Computing Bibliographic Results from Google Scholar
<p>This dataset contains all the results for the term "Mobile Cloud Computing" on Google Scholar until June 2018. The data was acquired using Publish or Perish. The data has been cleaned such that the wrong and invalid results have been removed, duplicates have been removed. Titles are accurate and fine but authors and publishers info. etc. is still unclean. For textual analysis based on paper titles, this dataset is fine. For any other factor, such as institutional or journal or authorship analysis, this isn't a good choice. </p>
Size-resolved cloud condensation nuclei data collected during the CalWater 2015 field campaign
<p>This repository contains raw and processed data for the size-resolved cloud condensation nuclei instrument deployed during the Calwater-2015 field campaign. It also contains the averaged cluster data presented in the paper "Classification of aerosol population type and cloud condensation nuclei properties in a coastal California littoral environment using an unsupervised cluster model" by Atwood et al. (2019). Details about the datafiles are provided in README.md file in markdown format.</p>
Snow-Cloud Validation Masks for Multispectral Satellite Data.
<p>Geotiffs of manually validated snow, cloud, & clear-sky snow free pixels for 13 Landsat 8 images. These acquisitions are of mid-latitude mountainous regions that contain both snow and cloud cover. Four spectral libraries of snow and cloud are also provided. These are the snow and cloud spectra extracted from both these 13 scenes and the 13 L8 SPARCS Cloud Validation Masks that contained both snow and cloud. 1&2.) Snow and cloud top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands, aggregated from the 26 scenes. 3&4.) The top-of-atmosphere reflectance for the eight Landsat 8 OLI 30 meter optical bands of all snow misidentified as cloud and cloud misidentified as snow by CFMASK, the cloud mask that ships in the BQA file of Landsat 8 Collection 1.</p>
Dataset for "Method to retrieve cloud condensation nuclei number concentrations using lidar measurements"
<p>This repository contains the source data for the manuscript "<strong>Method to retrieve cloud condensation nuclei number concentrations using lidar measurements</strong>" published in <em>Atmospheric Measurement Techniques</em>. In situ measured data from five filed campaigns and corresponding theoretical simulated CCN number concentrations, lidar extinction and backscatter are included.</p>
Data for the publication "The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity"
<p>This repository contains the data for the paper:</p> <p>"Neubauer, D., Ferrachat, S., Siegenthaler-Le Drian, C., Stier, P. Partridge, D. G., Tegen, I., Bey, I., Stanelle, T., Kokkola, H., and Lohmann, U.: The global aerosol-climate model ECHAM6.3-HAM2.3 – Part 2: Cloud evaluation, aerosol radiative forcing and climate sensitivity, Geosci. Mod. Dev., https://doi.org/10.5194/gmd-2018-307, 2019."</p> <p>Each tar-file contains the data (or instructions how to obtain the data) to reproduce a figure or table in our paper.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.2553891)</p> <p> </p>
THOR - point clouds
<p><strong>THÖR</strong> is a dataset with human motion trajectory and eye gaze data collected in an indoor environment with accurate ground truth for the position, head orientation, gaze direction, social grouping and goals. THÖR contains sensor data collected by a 3D lidar sensor and involves a mobile robot navigating the space. In comparison to other, our dataset has a larger variety in human motion behaviour, is less noisy, and contains annotations at higher frequencies.</p> <p>The dataset includes 9 separate recordings in 3 variations:</p> <ul> <li>``One obstacle" - features one obstacle in the environment and no robot</li> <li>``Moving robot" - features one obstacle in the environment and the moving robot</li> <li>``Three obstacles" - features three obstacles in the environment and no robot</li> </ul> <p><strong>THOR - point clouds </strong>is the part of THÖR data set containing bag files with 3D scans collcted during the experiments.</p> <p><strong>Reference:</strong></p> <p>For more details check project website <a href="http://thor.oru.se">thor.oru.se</a> or check our publications:</p> <pre><code>@article{thorDataset2019, title={TH\"OR: Human-Robot Indoor Navigation Experiment and Accurate Motion Trajectories Dataset}, author={Andrey Rudenko and Tomasz P. Kucner and Chittaranjan S. Swaminathan and Ravi T. Chadalavada and Kai O. Arras and Achim J. Lilienthal}, journal={arXiv preprint arXiv:1909.04403}, year={2019} }</code></pre>
Empirical measurements of function placements and executions in a mixed cloud-edge cluster
<p>Empirical measurements used for the Skippy Scheduler, an optimized container scheduler for serverless edge computing in Kubernetes.</p>
UAV-based Lidar point clouds of Experimental Station Britz, Brandenburg, 2024
<p><strong>UAV-based Lidar data of a Experimental Forest Station Britz</strong></p> <ul> <li>Date of acquisition: 09.07.2024</li> <li>Location: Experimental Station Britz, Britz, Brandenburg, Germany</li> <li> <div>DEIMS.iD: <a href="https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762">https://deims.org/8ee82a9b-5086-4547-b5aa-4064e3314762</a></div> </li> <li>UAV: DJI M300 RTK with SAPOS connection</li> <li>Flight altitude above ground level: 40 m</li> <li>Line spacing: 7 m</li> <li>Sensor: Yellowscan Mapper Plus</li> <li>EPSG: 25833; GCG2016</li> <li>Data products: <br>• High-resolution point cloud in .laz format<br>• Yellowscan Cloudstation strip adjustment report<br>• Trimble Applanix POSPAC - diagnostic report</li> </ul> <p><br><strong>Acknowledgment</strong></p> <p>Dr. Tanja Sanders</p> <p>Dr. Marco Natkhin</p> <p>Institute of Forest Ecosystems<br>Johann Heinrich von Thünen Institute<br>Federal Research Institute for Rural Areas, Forestry and Fisheries</p>
Ubuntu One multi-cloud object storage trace sublement to "SkyPIE: A Fast & Accurate Oracle for Object Placement"
<p>These are the workload traces of Ubuntu one used in the evaluation of "SkyPIE: A Fast & Accurate Oracle for Object Placement".</p> <p>These traces derive diverse multi-cloud access pattern on object stores from the trace published in "Dissecting UbuntuOne: Autopsy of a Global-Scale Personal Cloud Back-End." The derivation is described in the SkyPIE paper.</p> <p>The traces are stored in Parquet file format, hence can be read with a Parquet reader such as the one included in Pandas. The file names specify the number of regions issuing accesses and the percentage of accesses to object that originate from regions other than the home region, see the publication.</p>
Data associated with the manuscript: Andean bear tree selectivity for scent-marking in Ecuadorian cloud forests
<p>This is the original version of data used for the manuscript, <em>Andean bear tree selectivity for scent-marking in Ecuadorian cloud forests</em>. The data are in four files following the numerical order and titles of the Results section in the manuscript, i.e. <em>1_PCA.csv</em>, <em>2_Modeling tree selection at the individual-tee level.csv</em>, <em>3_Modeling tree selection on a local spatial scale.csv</em>, <em>4_Modeling formation of marked-tree cluster sites.csv. We used these datasets for our analysis in the program R, the details are provided in the Methods section. </em><span>Our field work was performed in compliance with the Framework Agreement for access to genetic resources called "Biodiversity Study of Ecuador '' made between the Ecuadorian Ministry of Environment and the UTPL. The code for the Agreement is MAE-DNB-CM-2015-0016-M-0002. The research was funded by Bears in Mind, International Association for Bear Research and Management, the Faculty of Environmental Sciences of Czech University of Life Sciences in Prague, National Geographic Society, Nature and Culture International, GIZ Ecuador, and Trailcampro. </span></p>
ScienceDex guides
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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.