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Dataset results
180 results for “Cloud dataset”
NPM3D dataset with instance labels used in paper "Toward Accurate Instance Segmentation in Large-scale LiDAR Point Clouds"
<p>NPM3D (https://npm3d.fr/paris-carla-3d) consists of mobile laser scanning (MLS) point clouds collected in four different regions in the French cities of Paris and Lille, where each point has been annotated with two labels: one that assigns it to one out of 10 semantic categories and another one that assigns it to an object instance. When inspecting the data, we found 9 cases where multiple tree instances had not been separated correctly (i.e., they had the same ground truth instance label). These cases were manually corrected using the CloudCompare software (https://www.cloudcompare.org), and 35 individual tree instances were obtained. Our variant of the dataset with 10 semantic categories and enhanced instance labels is publicly available.</p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</p>
Dataset for "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach"
<p>This dataset contains the raw model outputs for the paper titled "Atmospheric oxygen isotopic fractionation in clouds: a bin–resolved microphysics model approach" by Thibault Hiron and Andrea Flossmann.</p> <p>The structure of the data and the explaination for the filenames are to be found in the ReadMe.txt file.</p>
Automated vegetation cover estimation from close-range photogrammetric point clouds in mountain terrain for comparison of vegetation location properties - Dataset
<p>Vegetation cover data of the used plots, showing values for manually digitized, in-situ, and photogrammetric methods.</p>
A Survey on the Adoption of Patterns for Engineering Software for the Cloud - Response Dataset
<p>This work takes as a starting point a collection of patterns for engineering software for the cloud and tries to find how they are regarded and adopted by professionals. We investigate (1) their relevance for professional software developers, (2) the extent to which product and company characteristics influence their adoption, and (3) how adopting some patterns might correlate with the likelihood of adopting others. For this purpose, we surveyed 102 practitioners using an online questionnaire. </p> <p>Amongst other findings, we conclude that most companies are using these patterns, with the overwhelming majority (97%) using at least one. We observe that the mean pattern adoption tends to increase as companies mature, namely when varying the product operation complexity, active monthly users, and company size. Finally, we establish clear correlations in the adoption of specific pairs of patterns, with conditional probabilities as high as 94%, which hints on how some practices are dependent or influence the adoption of others.</p>
Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership. SPCM Atlas Dataset.
<p>The SPCM (SFiNCs Possible Cluster Member) Atlas dataset accompanies the article entitled ``Star Formation In Nearby Clouds (SFiNCs): X-ray And Infrared Source Catalogs And Membership,'' by Getman, Broos, Kuhn, Feigelson, Richert, Ota, Bate, and Garmire, to appear in The Astrophysical Journal Supplement Series. The paper is also available on-line on astro-ph at: https://arxiv.org/abs/1612.05282 . SPCM Atlas is a collection of 25 PDF files. Four pdf files are associated with the SFiNCs star forming region (SFR) Cep OB3b, and 21 pdf files are associated with the remaining 21 SFiNCs SFRs. Full description of SPCM Atlas is given in the Appendix B section of the article. This upload is superseded by a new version, http://doi.org/10.5281/zenodo.345398 .</p>
The MIDI Linked Data Cloud dataset
<p>The study of music is highly interdisciplinary, and thus requires the combination of datasets from multiple musical domains, such as catalog metadata (authors, song titles, dates), industrial records (labels, producers, sales), and music notation (scores). While today an abundance of music metadata exists on the Linked Open Data cloud, datasets containing interoperable symbolic descriptions of music itself, i.e. music notation with note and instrument level information, are scarce. This is the MIDI Linked Data Cloud, a dataset that represents multiple collections of digital music in the MIDI standard format as Linked Data. At the time of writing, the dataset comprises 10,215,557,355 triples of 308,443 interconnected MIDI scores, and provides Web-compatible descriptions of their MIDI events.</p>
Supplemental dataset to "Cloud sync in response to wave-like large-scale forcings"
<p>This dataset deposits the supplemental materials for the manuscript "Cloud sync in response to wave-like large-scale forcings".</p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/math_note.pdf/content" target="_blank" rel="noopener noreferrer">math_note.pdf</a> A hand-written math derivation note for equations in the appendices. </p> <p><a href="https://zenodo.org/uploads/15304862" target="_blank" rel="noopener noreferrer">movie_wL_006_T24hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=24 hours experiment (the reference CM1 simulation).</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T18hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T18hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=18 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_006_T12hours.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_006_T12hours.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio for the wL=0.006m/s and T=12 hours experiment.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/movie_wL_000.avi/content" target="_blank" rel="noopener noreferrer">movie_wL_000.avi</a> A movie of near-surface (z=25m) water vapor mixing ratio, without large-scale wave-like forcing.</p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/input_sounding/content" target="_blank" rel="noopener noreferrer">input_sounding</a> The initial sounding for all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/namelist.input/content" target="_blank" rel="noopener noreferrer">namelist.input</a> The namelist file for launching all CM1 simulations. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/postprocessing_CM1.zip/content" target="_blank" rel="noopener noreferrer">postprocessing_CM1.zip</a> The postprocessing code of the CM1 simulations, including intermediate output files (.mat) in data postprocessing. </p> <p><a href="https://zenodo.org/api/records/14164798/draft/files/microscopic_model.zip/content" target="_blank" rel="noopener noreferrer">microscopic_model.zip</a> The MATLAB code for the microscopic model. </p> <p><a href="https://zenodo.org/api/records/15304862/draft/files/cm1.F/content" target="_blank" rel="noopener noreferrer">cm1.F</a> The CM1 script where the large-scale vertical velocity is programmed. You can copy it directly to your CM1/src/ path. </p> <p> </p> <p>Feel free to send an email to Dr. Hao Fu (haofu@nju.edu.cn) if you have any questions!</p> <p> </p>
CloudTracks: A Dataset for Localizing Ship Tracks in Satellite Images of Clouds
<p>The CloudTracks dataset consists of 1,780 MODIS satellite images hand-labeled for the presence of more than 12,000 ship tracks. More information about how the dataset was constructed may be found at <a href="http://github.com/stanfordmlgroup/CloudTracks">github.com/stanfordmlgroup/CloudTracks</a>. The file structure of the dataset is as follows:</p><p>CloudTracks/<br> full/<br> images/<br> (sample image name) mod2002121.1920D.png<br> jsons/<br> (sample json name) mod2002121.1920D.json</p><p>The naming convention is as follows:<br>mod2002121.1920D: the first 3 letters specify which of the sensors on the two MODIS satellites captured the image, mod for Terra and myd for Aqua. This is followed by a 4 digit year (2002) and a 3 digit day of the year (121). The following 4 digits specify the time of day (1920; 24 hour format in the UTC timezone), followed by D or N for Day or Night.</p><p>The 1,780 MODIS Terra and Aqua images were collected between 2002 and 2021 inclusive over various stratocumulus cloud regions (such as the East Pacific and East Atlantic) where ship tracks have commonly been observed. Each image has dimension 1354 x 2030 and a spatial resolution of 1km. Of the 36 bands collected by the instruments, we selected channels 1, 20, and 32 to capture useful physical properties of cloud formations.</p><p>The labels are found in the corresponding JSON files for each image. The following keys in the json are particularly important:</p><p>imagePath: the filename of the image.<br>shapes: the list of annotations corresponding to the image, where each element of the list is a dictionary corresponding to a single instance annotation. The dictionary has a key with value "shiptrack" or "uncertain" which is the label of the annotation and the corresponding value is a linestrip detailing the ship track path.</p><p>Further pre-processing details may be found at the GitHub link above. If you have any questions about the dataset, contact us at:<br><a href="mailto:mahmedch@stanford.edu">mahmedch@stanford.edu</a>, <a href="mailto:lynakim@stanford.edu">lynakim@stanford.edu</a>, <a href="mailto:jirvin16@cs.stanford.edu">jirvin16@cs.stanford.edu</a></p>
Dataset to "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " by Zmijewski, Dziekan & Pawlowska
<p>The archive contains datasets, run scripts, time series and plotting scripts used when preparing the paper: P. Zmijewski, P. Dziekan and H. Pawlowska "Modeling Collision-Coalescence in Particle Microphysics: Numerical Convergence of Mean and Variance of Precipitation in Cloud Simulations Using University of Warsaw Lagrangian Cloud Model (UWLCM) 2.1 " submitted to Geoscientific Model Development in March 2023.</p>
Simulation dataset and plotting scripts used for journal article "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024)
<p>Simulation data underlying all figures presented in "Impact of acidity and surface modulated acid dissociation on cloud response to organic aerosol" by Sengupta et al. (2024). DOI: 10.5194/acp-24-1467-2024</p> <p>The data for each figure and the plotting scripts are included in a zip file labelled by the figure number as presented in the paper and accompanying supplement.</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Dataset: Planetary-scale waves seen in thermal infrared images of Venusian cloud top
<p>The data archive contains data used in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al.</p> <p>The contents of the directories are as follows.</p> <p>"data_used_in_figures" : The table data used in the figures are given in Excel and CSV. The table format is described in the data files. An image in NetCDF format is also included.</p> <p>"time_series_of_brightness_temperature_gradient" : The files contain the time series of the longitudinal gradient of Venusian cloud's brightness temperature measured by LIR onboard JAXA's Venus orbiter Akatsuki. The data were derived and analyzed in the paper "Planetary-Scale Waves Seen in Thermal Infrared Images of Venusian Cloud Top" by Kajiwara et al. The filename represents the latitude for each time series (For example, "10N" means 10 degrees north, and "EQ" means the equator). In all files, the first column gives the approximate elapsed time in days from 18 May 2017: the exact dates are given in the paper (Table S1 in the Supporting Information). The second column gives the longitudinal gradient of the brightness temperature in unit of K/degree.</p>
STAR NDSI collection: A cloud-free MODIS NDSI dataset (2001–2020) for China
<p><strong>If you used our dataset, please cite our reference:</strong></p> <p><strong>Jing, Y., Li, X., and Shen, H.: STAR NDSI collection: a cloud-free MODIS NDSI dataset (2001–2020) </strong></p> <p><strong>for China, Earth Syst. Sci. Data, 14, 3137–3156, https://doi.org/10.5194/essd-14-3137-2022, 2022.</strong></p> <p>1. A recent 20-year cloud-free MODIS normalized difference snow index (NDSI) collection for </p> <p>China (excluding sea area) is generated using a Spatio-Temporal Adaptive fusion method with </p> <p>erroR correction (STAR).</p> <p>2. The STAR NDSI collection is derived from daily 1-km MODIS NDSI datasets (MOD10A1 and </p> <p>MYD10A1).</p> <p>3. It is provided using a WGS_1984_UTM_48N projection, with the data format of TIFF images.</p> <p>4. Each ZIP contains NDSI data for one hydrological year (August to next July). After </p> <p>uncompressing into the TIFF format, the files are named as "NDSI_ yyyymmdd_Daily_500M_</p> <p>V01.tif" for NDSI data and "QA_yyyymmdd_Daily_500M_V01.tif" for quality assessment (QA) data. </p> <p>5. The accuracy of this collection has been well validated by the in-situ snow depth observations </p> <p>and Landsat OLI NDSI maps. The detailed information can be found in the published paper.</p> <p>6. The NDSI value ranges from 0, 1-100. The value of water is set to 255. The fill value is set to 127.</p> <p>7. Disclaimer: The spatial scope of the dataset is for research convenience. Please refer to official</p> <p>release for a standard map of China, http://211.159.149.56/index.html.</p> <p>8. The new version includes data from 2001 to 2020, while the old version includes data from</p> <p>2012 to 2020 (https://doi.org/10.5281/zenodo.5644386).</p> <p>9. In case any questions arise, please do not hesitate to contact us (yhjing@whu.edu.cn).</p>
Dataset for "Difference of photometric properties between regular and non-regular Miras in the Magellanic Clouds"
<p><strong>Summary: </strong>This deposit supplements the manuscript, "<em>Difference of photometric properties between regular and non-regular Miras in the Magellanic Clouds</em>", accepted to the Astronomical Journal (17 February 2022).</p> <p><strong>Description of contents: </strong>This repository contains the numerical values for all of the features for the data (including the training set) and classification results. The specific files included in this deposit are as follows:<br> <br> ReadMe.txt This Readme file.<br> Feature_table_with_training_data.txt Feature Table with training data<br> Feature_table_with_classify_data.txt Feature table with classify data<br> Classify_result_table.txt Mira Regular/Non-regular <br> machine-learning classification</p> <p><strong>System requirements:</strong> The files are formatted according to the machine readable format used by the AAS Journals and CDS/VizieR. Specific information on the structure of MRT files can be found at:</p> <p> AAS: <a href="https://journals.aas.org/mrt-overview/">https://journals.aas.org/mrt-overview/</a><br> CDS: <a href="http://cds.u-strasbg.fr/doc/catstd.htx">http://cds.u-strasbg.fr/doc/catstd.htx</a></p> <p> These files can be read in python using the astropy package:</p> <pre><code class="language-python">from astropy.table import Table data = Table.read("Classify_result_table.txt", format="ascii.cds")</code></pre> <p> or with the most recent version of TOPCAT (> Version 4.8)</p> <p><strong>Additional comments: </strong>The features of training data and classify data were calculated by Python package Feature Analysis for Time Series (FATS), and the result was presented in the file names "Feature_table_with_training_data.txt" and "Feature_table_with_classify_data.txt". Then these two data set were used in our machine learning process. The classification result of Mira was presented in the file "Classify_result_table.txt".<br> </p>
Replicated radiation of a plant clade along a cloud forest archipelago dataset
<p>Raw data complementary of https://github.com/eaton-lab/Orienotinus-phylogeny repository</p> <p>Assembly output of <em>Oreinotinus </em>species (<em>Viburnum</em>, Adoxaceae) using ipyrad pipeline and morphological matrix:</p> <p>List of files:</p> <ul> <li><code>full_dataset.loci</code> Custom format that shows each individual locus with variable sites indicated.</li> <li><code>full_dataset.phy </code>Concatenated RAD loci in Phylip format</li> <li><code>full_dataset.seqs.hdf5</code> Sequences formated as HDF5 file</li> <li><code>full_dataset.snps</code> Phylip files that includes only variable sites (SNPs)</li> <li><code>full_dataset.snps.hdf5</code> SNPs formated as HDF5 file</li> <li><code>full_dataset.snpsmap</code> Information about location (locus) of SNPs</li> <li><code>full_dataset_stats.txt</code> Statistics about the assembly</li> <li><code>leaf_traits_measurements.tsv</code> TSV file that contains leaf measurments used in the discriminant analyses.</li> <li><code>canopy_data.txt</code> Canopy cover data</li> <li><code>central_plateau_data.txt</code> Macroenvironmental data for the Chiapas central plateau</li> <li><code>full_distribution.txt</code> Specimen metadata and coordinates for all the points for Oreinotinus distribution</li> <li><code>leaf_wetness_temp.txt</code> Climate station data for leaf wetness, temperature</li> <li><code>leaf_traits_measurements.tsv</code> TSV file that contains leaf measurments used in the discriminant analyses.</li> <li><code>tree_accessions.txt</code> Species-level tree accessions with specimen metadata</li> </ul>
Commodity Dataset | Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform
<p>(Commodity data in raster format) Supplementary materials for “Retrieving the National Main Commodity Maps in Indonesia Based on High-Resolution Remotely Sensed Data Using Cloud Computing Platform” that had been published on Land MDPI (2020). doi:<a href="https://doi.org/10.3390/land9100377">10.3390/land9100377</a> </p> <p>The data included:</p> <p>1) Raster data of commodity maps (TIFF Compressed in ZIP)</p> <p>2) READ ME for the dataset (DOCX)</p> <p>3) Legend for raster data in ArcGIS Format (LYR)</p> <p> </p>
Dataset for Bate (2022): Dust coagulation during the early stages of star formation: molecular cloud collapse and first hydrostatic core evolution
<p>This data set contains 12 smoothed particle hydrodynamics (SPH) dump files that were used to produce some of the figures in the journal paper:</p> <p>Bate, Matthew. R., 2022, Monthly Notices of the Royal Astronomical Society, accepted 13 May 2022</p> <p>Each of the SPH dump files is from a different calculation of the early stages of star formation: the gravitational collapse of a molecular cloud core, including dust coagulation. Each SPH dump file gives the state of the SPH calculation when the maximum temperature reached 1500 K, except for the beta=0.05 cases which give the state when the maximum hydrogen number density reaches 10^{14} cm^{-3}. The calculations were each performed using 3 million SPH particles and differed by their initial rotation rate, which was parameterised by beta=0, 0.0025, 0.005, 0.01, 0.02, and 0.05 (the magnitude of the ratio of the rotational and gravitational potential energies). Dump files from calculations that include and exclude envelope turbulence are provided (both are used for Figure B1). The dump files associated with each calculation are:</p> <p>beta=0: B1M0123 (does not include envelope turbulence)<br> beta=0.0025: B1M2123 (does not include envelope turbulence)<br> beta=0.005: B1M5123 (does not include envelope turbulence)<br> beta=0.01: B1M1128 (does not include envelope turbulence)<br> beta=0.02: B1M2126 (does not include envelope turbulence)<br> beta=0.05: B1M5109_b05_NoEnvTurb (does not include envelope turbulence)</p> <p>beta=0.0: B1M0123_b0_EnvTurb<br> beta=0.0025: B1M2177_b0025_EnvTurb<br> beta=0.005: B1M5209_b005_EnvTurb<br> beta=0.01: B1M1219_b01_EnvTurb<br> beta=0.02: B1M2221_b02_EnvTurb<br> beta=0.05: B1M5321_b05_EnvTurb</p> <p>The SPH dump files are Fortran binary files written in big endian format and generated by the sphNG code (Benz 1990; Bate 1995; Bate & Keto 2015). They can be read, visualised, and manipulated using the free, publicly available SPLASH visualisation code (which reads sphNG dump files), written by Daniel J. Price, that can be downloaded from: </p> <p>http://users.monash.edu.au/~dprice/splash/ </p> <p>The SPLASH configuration files used to produce Figs. 10,11,12,and B1 in Bate (2022) are included with this dataset in a gzipped tar file.</p> <p> </p>
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.