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2,170 results for “clouds”
Expanding CO Shells in the Orion A Molecular Cloud - Additional Figures
<p>Additional figures and captions for the paper "Expanding CO Shells in the Orion A Molecular Cloud", Feddersen et al. (2018). CO channel maps of shells are in fset2.zip, CO position-velocity diagrams are in fset3.zip, and integrated CO and infrared images are in fset4.zip</p>
Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data -- dataset
<p>The data used in the figures in the paper "Fine vertical structures at the cloud heights of Venus revealed by radio holographic analysis of Venus Express and Akatsuki radio occultation data" by Imamura et al. (J. Geophys. Res)</p> <p>The description of the columns in the files are given in the header section.</p>
Optimizing Quality-Aware Big Data Applications in the Cloud — Supporting Data
<p>Supporting experimental data for the paper "Optimizing Quality-Aware Big Data Applications in the Cloud", submitted to IEEE TCC.</p>
Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations - datasets and scripts
<p>Datasets used in the analysis of the article Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations, which was submitted to and subsequently published in Journal of Geophysical Research-Atmosphere. DOI: 10.1029/2018JD029359</p>
Data and scripts for the paper: Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations
<p>This is a collection of the processed data, plotting scripts, and processing scrips used in the making of the publication: "Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations" By Griewank, Schemann, and Neggers. </p> <p>WARNING: These files were used exclusively used by Philipp Griewank on his local work station, and the main purpose of uploading them is for reasons of scientific transparency. They are not highly optimized tools intended for general usage, so comments are hit and miss. Feel free to contact Philipp Griewank (currently philipp.griewank@uni-koeln.de) with any questions. </p>
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Sentinel-2 reference cloud masks generated by an active learning method
<p> <strong>Reference classifications generated with Active Learning for Cloud Detection (ALCD)</strong></p> <p>This data set provides a reference cloud mask data set for 38 Sentinel-2 scenes. These reference masks have been created with the ALCD tool, developed by Louis Baetens, under the direction of Olivier Hagolle at CESBIO/CNES[1]. They were created to validate the cloud masks generated by the MAJA software [2].</p> <p>- The `Reference_dataset` directory contains 31 scenes selected in 2017 or 2018.<br> - The `Hollstein` directory contains 7 scenes that were used to validate the ALCD tool by comparison to manually generated reference images kindlyprovided by Hollstein et al[3]<br> One of these scenes is present in both directories. For the validation of MAJA, the "Hollstein" scenes were not used because of their acquisition at a time period when Sentinel-2 was not yet operational, with a degraded repetitivity of observations.</p> <p><strong># Description of the data structure</strong><br> The name of each scene directory is the name of the corresponding Sentinel-2 L1C product.<br> In the scene directory, three sub-directories can be found.<br> - `Classification`<br> - `Samples`<br> - `Statistics`</p> <p><strong># Description of the files</strong><br> - `Classification/classification_map.tif` --- the main product, which is the classified scene. 7 classes are available. Each one is represented with a different integer.<br> 0: no_data.<br> 1: not used.<br> 2: low clouds.<br> 3: high clouds.<br> 4: clouds shadows.<br> 5: land.<br> 6: water.<br> 7: snow.</p> <p>- `Classification/confidence_enhanced.tif` --- enhanced confidence map of the classification. The values are between 0 and 255 (coded on 1 bit).<br> The original confidence map is, for each pixel, the proportion of votes for the majority class as the classification map has been created via a Random Forest algorithm.<br> A median filter has been applied to this confidence map. Finally, the value was saved on 1 bit, leading to the value being between 0 and 255.</p> <p>- `Classification/contours.png` --- the contours of the classes from the classification map, overlayed on the scene. The color code depends on each class.<br> Green: low and high clouds. Yellow: cloud shadows. Blue: water. Purple: snow.</p> <p>- `Classification/used_parameters.json` --- the parameters that were used to classify the scene. It includes the tile code, the cloudy and clear dates, along with their product reference.</p> <p>- `Samples/` --- this directory contains all the shapefiles, one per class.</p> <p>- `Statistics/k_fold_summary.json` --- results of the 10-fold cross-validation on the scene.<br> 5 metrics are computed, in the order given in the "metrics_names". "all_metrics" is a list of the 10 folds, with the 5 metrics in the correct order for each fold.<br> "means" and "stds" are the means and standard deviations of the 10 folds.</p> <p><br> <strong># References</strong></p> <p>[1] Baetens, L.; Desjardins, C.; Hagolle, O. Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure. <em>Remote Sens.</em> <strong>2019</strong>, <em>11</em>, 433.</p> <p>[2] A multi-temporal method for cloud detection, applied to FORMOSAT-2, VENµS, LANDSAT and SENTINEL-2 images, O Hagolle, M Huc, D. Villa Pascual, G Dedieu, Remote Sensing of Environment 114 (8), 1747-1755, 2010</p> <p>[3] Hollstein, A.; Segl, K.; Guanter, L.; Brell, M.; Enesco, M. Ready-to-Use Methods for the Detection of Clouds, Cirrus, Snow, Shadow, Water and Clear Sky Pixels in Sentinel-2 MSI Images. Remote Sens. 2016, 8, 666</p>
Online Optimization in Cloud Resource Provisioning: Predictions, Regrets, and Algorithms: Virtual Machine ID Dataset
<p>The csv files in this dataset contain the virtual machine IDs used in [1] which correspond to the virtual machine traces in the Azure Public Dataset [2]. The file named "vmtable_lifetime_VMoL_1003.csv" holds the IDs used in Section 5 [1] and the file named "vmtable_lifetime_VMoL_55.csv" holds the IDs used in Section 6 [1]. The first column in both files refers to the ID labels in [1], while the second, third, and fourth columns refer to the Virtual Machine IDs, the Subscription IDs, and the Deployment IDs, respectively.</p> <p> </p> <p>[1] Joshua Comden, Sijie Yao, Niangjun Chen, Haipeng Xing, and Zhenhua Liu. 2019. Online Optimization in<br> Cloud Resource Provisioning: Predictions, Regrets, and Algorithms. Proc. ACM Meas. Anal. Comput. Syst. 3, 1,<br> Article 179 (March 2019).</p> <p>[2] Eli Cortez, Anand Bonde, Alexandre Muzio, Mark Russinovich, Marcus Fontoura, and Ricardo Bianchini. 2017. Resource Central: Understanding and Predicting Workloads for Improved Resource Management in Large Cloud Platforms. In Proceedings of SOSP’17. ACM, New York, NY, USA, 15 pages. https://doi.org/10.1145/3132747.3132772 Dataset access: https://github.com/Azure/AzurePublicDataset (August 2018)</p>
Cloud index fields, cloud motion fields, and supplementary material.
<p>This contains the data used in the paper: "Intra-hour cloud index forecasting with data assimilation." It also includes supplementary material referenced in the above paper.</p> <p>The CI_CMV_data.tar file contains data.nc and data_opt_flow.nc files that are in netCDF4 format. The data.nc files contain cloud index fields derived from GOES-15 geostationary satellite images and wind fields from a Weather Research and Forecasting (WRF) model run. The WRF_run_attributes.txt file contains the parameters of the WRF model that generated the wind fields contained in the data.nc files. The data_opt_flow.nc files contain dense optical flow cloud motion data that were derived using the method described in [1]. The data.nc and data_opt_flow.nc files are in a directory of the form: /yyyy/mm/dd/ corresponding to the year, month and day of the data.</p> <p>The six videos are made from images from the GOES-15 geostationary satellite in the GOES-WEST position. The images are centered around Tucson, AZ. The domain of the images are from a latitude of approximately 27.22° N to 37.22° N and from a longitude of approximately 115.97° W to 105.97° W. All satellite images include a rectangle with dotted edges forming a region over Tucson, AZ that is 40 km from west to east and 56 km from south to north. Some satellite images contain a rectangle with solid edges. This rectangle surrounds a computational domain used in cloud index forecasting. Three of the six videos (sat_images_2014_04, sat_images_2014_05, and sat_images_2014_06) contain the images for April, May and June of 2014. The other three videos (sat_images_2014_04_15, sat_images_2014_04_26, and sat_images_2014_05_29) are for 4/15/2014, 4/26/2014, and 5/29/2014. There are also 500 mb height maps for these three days from <a href="https://www.wpc.ncep.noaa.gov/dailywxmap/">NCEP</a>.</p> <p> </p> <p>[1] Sun, D., Roth, S., Black, M.J., 2010. Secrets of optical flow estimation and their principles, in: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, pp. 2432–2439. doi:10.1109/CVPR.2010.5539939.</p>
Binary asteroseismic modelling: isochrone-cloud methodology and application to Kepler gravity mode pulsators
<p>MESA inlists associated with <a href="https://ui.adsabs.harvard.edu/#abs/2018MNRAS.tmp.2555J/abstract">Johnston et al. (2019)</a>. MESA version 10108.</p> <p>Publication DOI: <a href="https://doi.org/10.1093/mnras/sty2671">10.1093/mnras/sty2671</a></p>
Timelapse of cloud cover during the 21 August 2017 solar eclipse
<p>Timelapse photos of cloud cover during the 21 August 2017 solar eclipse. Location: a small valley in the foothills of the Blue Ridge Mountains, near Charlottesville, VA. Photos were taken using a GoPro camera looking facing approximately SW every 5 seconds between 12:00 - 16:00 EDT (local time).</p>
Equivalence Classes of the LOD Cloud
<p>This data set contains all the 49 million non-singleton equivalence classes resulting from the transitive closure of over 556 million owl:sameAs statements extracted from the LOD Cloud in the 2015 LOD Laundromat crawl. These equivalence classes are the result of the transitive closure of the owl:sameAs links available in the <a href="http://sameas.cc">sameAs.cc</a> data set.</p> <p>We represent these non-singleton equivalence classes using two CSV files:</p> <p>1. id2terms.csv: contains in the first column the equivalence class identifier (randomly generated number) and in the rest of the columns all IRIs belonging to this equivalence class, which theoretically should refer to the same real world entity. In the following, we present an example of one row of this file, where "42467584" in the first column represents the ID of this equivalence class, and the 4 other columns represent the IRIs that are identical after transitive closure: </p> <blockquote> <p>42467584 <http://nl.dbpedia.org/resource/Cnodocentron_trilineatum> <http://sv.dbpedia.org/resource/Cnodocentron_trilineatum> <http://vi.dbpedia.org/resource/Cnodocentron_trilineatum> <http://www.wikidata.org/entity/Q2304468></p> </blockquote> <p>2. terms2id.csv: contains two columns, representing a mapping between each IRI in the sameAs.cc data set involved in a owl:sameAs link with the equivalence class it belongs to. In the following, we present an example of one row in this file:</p> <blockquote> <p><http://nl.dbpedia.org/resource/Cnodocentron_trilineatum> 42467584</p> </blockquote> <p>In addition to the closure of all owl:sameAs links (available in the folder <em><strong>closure_all.zip</strong>), </em>this data set contains an additional two closures, with each closure also containing two CSV files with the same structure as presented above. These two additional closures are the following:</p> <p><strong>- <em>closure_099.zip</em> </strong>represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 1 million probably erroneous owl:sameAs links (with error degree >0.99). This error degree is computed based on the community structure of the network, described in the approach of <a href="https://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf">[Raad et al., 2018]</a>.</p> <p><strong>- </strong><em><strong>closure_04.zip</strong> </em>represents the closure of all owl:sameAs links in the sameAs.cc data set after discarding around 150 million owl:sameAs links (with error degree >0.4). The evaluation conducted in <a href="https://www.cs.vu.nl/~frankh/postscript/ISWC2018.pdf">[Raad et al., 2018]</a> shows that the 400M owl:sameAs links with an error degree <= 0.4 have higher probability of correctness compared to other links.</p> <p>The availability of these 3 different closures allows Linked Data practitioners for the first time to control in practice, the trade-off between (a) using more identity links, possibly not all correct, and benefiting from more contextual information from the LOD Cloud, and (b) using a smaller subset of higher quality identity links for limiting the risk of propagating erroneous identity links and information through the application of owl:sameAs semantics, i.e. transitive, symmetric, reflexive and property sharing.</p>
Impacts of representing heterogeneous distribution of cloud liquid and ice on phase partitioning of Arctic mixed-phase clouds with NCAR CAM5
<p>The model simulation outputs for the paper "Impacts of representing heterogeneous distribution of cloud liquid and ice on phase partitioningof Arctic mixed-phase clouds with NCAR CAM5".</p>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
MOVIE S1. Normal clearance of murine respiratory tract is mediated by discontinuous Muc5b and Muc5ac mucus clouds
<p>Movie S1. Mucus transport in healthy WT mouse trachea. The mucus on the mounted trachea was submerged in a buffer containing Alcian blue, the tissue tilted, and the mucus movement recorded by video microscopy.</p>
ALDIS cloud to ground lightning strike occurrence aggregated to spatiotemporal ERA5 cells (summer months 2010 to 2019)
<p>The dataset contains a binary classification whether at least one cloud-to-ground lightning flash as detected by the ALDIS [1] lightning location system occurred in the previous hour in an ERA5 grid cell. Data with an amplitude between -2 kA and +15 kA are excluded.<br><br></p> <p>The data cover 8.25E to 16.75E longitude and 45.25N to 49.75N latitude.<br><br>Non-commercial use is allowed conditional on proper citation of the data source, ALDIS, and the accompanying manuscript <em>Ehrensperger, G., Simon, T., Mayr, G. J., and Hell, T.: Identifying lightning processes in ERA5 soundings with deep learning, Geosci. Model Dev., 18, 1141–1153, <a href="https://doi.org/10.5194/gmd-18-1141-2025">https://doi.org/10.5194/gmd-18-1141-2025</a>, 2025</em>.</p>
Data for "Source Altitude of Energetic In-cloud Pulses Inside Thunderstorms and Implication for the Intrinsic Brightness of Terrestrial Gamma-Ray Flashes"
<p>The spreadsheet contains detailed information for the EIP events analyzed in this work, including the time, location, peak current, distance to sensors, estimated source height, height error, etc. </p> <p>The compressed file in this dataset contains the low-frequency waveforms of EIPs measured by Duke and FIT sensors. The modeled skywaves are shown in the figures. The cross-correlation coefficient between the measured skywaves and the modeled waveforms of each event are also presented. </p> <p> </p>
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
Open the record for dataset details and reuse information.
MiniMPL data for 'Supercooled liquid water cloud classification using lidar backscatter peak properties'
<p>This depository contains MiniMPL data collected in Christchurch, New Zealand from May 2021 to December 2022 for 'Supercooled liquid water cloud classification using lidar backscatter peak properties' by Whitehead et al. (2024). The dataset contains:</p> <ul> <li> MiniMPL data processed with the Automatic Lidar and Ceilometer Framework (ALCF; Kuma et al., 2021)</li> <li>Reference cloud phase mask</li> <li>G22-Christchurch model-generated cloud phase mask</li> <li>Figures comparing the G22-Davis and G22-Christchurch masks to the reference mask</li> </ul>
FIGURE 3 in A new species of cloud fish of the genus Hypsolebias from Northeast Brazil (Cyprinodontiformes: Rivulidae)
FIGURE 3 | Type-locality of species of Hypsolebias flammeus species-group.
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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)
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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.