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2,170 results for “clouds”
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>
Balloon-borne Aerosol-Cloud Interaction Studies (BACIS): Field campaigns to understand and quantify aerosol effects on clouds
<p>The dataset uploaded on Zenodo consists of in-situ measurements from specialised balloon borne sondes used in the field campaign named BACIS (Balloon borne Aerosol Cloud Interaction Studies) conducted from Gadanki, a location in Southern Peninsular India. Note that raw data from the observations is only uploaded here. There is lot more post processing of data carried out during our analysis for brining out meaningful data. Hence the users are cautioned in using the raw data for analysis and kindly advised to approach us for the post processed, quality controlled data. </p>
Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds
<p>This is data from several atmosphere-only GCM experiments used to investigate the impacts of changing mixed-phase microphysical parameters in the CAM6 atmospheric model. Details and results from these simulations is presented in the submitted manuscript "Using satellite observations to evaluate model microphysical representation of Arctic mixed-phase clouds". A preprint of this manuscript can be found at https://www.essoar.org/doi/10.1002/essoar.10506728.2.</p> <p>An included README file describes organization of files. For any questions, please contact jonah.shaw@colorado.edu.</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>
Data for the publication "Assessing the potential for simplification in global climate model cloud microphysics"
<p>This repository contains the data for the paper:</p> <p>Authors: Ulrike Proske, Sylvaine Ferrachat, David Neubauer, Martin Staab, and Ulrike Lohmann<br> Titel: Assessing the potential for simplification in global climate model cloud microphysics<br> Date: 2022</p> <p>Note that the scripts can be found in the accompanying package (https://doi.org/10.5281/zenodo.5506588)</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>
Assets for 'Phase correlation on the edge for estimating cloud motion' submitted to Atmospheric Measurement Techniques
<p>1. CMV-26-07-2016_ARM-SGP.gif Sample cloud motion vectors from TSI camera images over the United States Atmospheric Radiation Measurement user facility’s Southern Great Plains site.</p> <p>2. raindrop_02-01-2017_ARM-SGP.gif Rotation of cloud motion vectors from raindrop contaminated TSI camera.</p>
Multi-temporal Structure from Motion ponit clouds of riparian vegetation
<p>the dataset consists of three pointclouds and two NIR orthomosaics generated through a Structure from Motion standard workflow of the same forested area. The study area is typical riparian habitat vegetation. The data were acquired in different phenological stages:</p> <p>the first acquisition was realised in leaves-off conditions (march 2020)</p> <p>The second acquisition was realised in June 2020</p> <p>the third acquisition was realised in July 2020.</p> <p>Reference system: WGS84/32N [EPGS: 32632]</p> <p>For further information regarding the data processing please refer to https://doi.org/10.3390/rs13091756<br> </p>
Dataset for Cloud Identification in Mars Daily Global Maps with Deep Learning
<p><strong>Overview:</strong></p> <p>This repository stores cloud masks and MDGMS for Martian Years (MYs) 28-33. MDGMs were obtained from Harvard Dataverse (<a href="https://doi.org/10.7910/DVN/U3766S">https://doi.org/10.7910/DVN/U3766S</a>), and cloud masks were created using the cloudmask model (<a href="https://github.com/03kalven/cloudmask">https://github.com/03kalven/cloudmask</a>). The cloud masks contained in the binary folders have already been binarized using the threshold of 0.912. This dataset is considerably smaller in size than the floating-point cloud mask dataset and is suited for researchers that prefer to use the default threshold of 0.912.</p> <p> </p> <p><strong>Quick breakdown of the files and folders:</strong></p> <ul> <li>phase folders contain the complete set of MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>phase_binary folders contain the complete set of binary MDGMs and cloud masks for Mars Reconnaisance Orbiter mission phases P, B, G, D, F, and J (MYs 28-33)</li> <li>view_masks.ipynb has a few handy methods to plot cloud masks and MDGMs</li> </ul> <p> </p> <p><strong>Phase folders:</strong></p> <p>Each folder is organized based on phase (P, B, G, D, F, J) and subphase (_01 to _23). In each subphase, there are cloudmasks and mdgms folders, as well as a .txt file with MY and solar longitude (Ls) data for each day.</p> <p> </p> <p><strong>MDGM and cloud mask formats:</strong></p> <ul> <li>mdgm: JPEG, 3600x1801</li> <li>cloudmask: (NETCDF4_CLASSIC data model, file format HDF5): <ul> <li>dimensions(sizes): x(3600), y(1801)</li> <li>variables(dimensions): float32 longitude(x), float32 latitude(y), float32/int16 cloudmask(y, x)</li> </ul> </li> </ul> <p>The cloud masks' values for any pixel are -999 for NaN and a float from 0 to 1 reporting the model's confidence in that pixel being a cloud. The cloud masks can be binarized using get_cloudmask() included in view_masks.ipynb. A binarized mask would report -999 for NaN, 0 for no cloud, and 1 for cloud. The default threshold is 0.912, but this value can be adjusted if desired. The cloud masks' (0,0) coordinate is the lower left corner of the map, so it may be needed to flip the cloudmask vertically before plotting on a Martian map. The cloud mask NetCDF files are constructed the same way as Wang and González Abad's (<a href="https://doi.org/10.7910/DVN/WU6VZ8">https://doi.org/10.7910/DVN/WU6VZ8</a>).</p>
Individual TLS tree clouds collected from both Alto Tajo and Cuellar in Spain.
<p>36 30 x 30 m plots across two sites in Spain were scanned using a Leica HDS6200 scanner (3.2mm resolution at 10 m) following a grid pattern (see Owen et al., 2021 for more details). All 16 scans were co-registered and individual trees segmented using a combination of treeseg (Burt et al. 2019) and manual extraction/refinement. Trees in this database have been zero-centred and downsampled to 5 cm for the purpose of classifying species using deep learning. Individual tree species ID (Quercus faginea, Quercus ilex, Pinus nigra, Pinus sylvestris and Pinus pinaster) were appended to each tree using a combination of field derived hand drawn stem maps and TLS derived stem maps. Whether a tree is a multi-stem or single-stem has also been included in the metadata file.</p> <p>Owen et al 2021 - https://doi.org/10.1111/1365-2745.13670</p>
VENuS cloud mask training dataset
<p>Training dataset for cloud masking for VENμS satellite images. Samples are pixel-wise labelled scenes over the land of Israel, with a special focus on the southern, arid area.</p> <p> </p> <p>Contains three classes:</p> <p>* 0: Cloud-less pixels<br> * 1: Thick clouds<br> * 2: Thin clouds</p>
Tracking the photomineralization mechanism in irradiated lab-generated and field-collected brown carbon samples and its effect on cloud condensation nuclei abilities
<p>Data set of the data presented in figures and tables in our manuscript on the photomineralization of brown carbon samples: ammonium sulfate-methylglyoxal solutions, Suwannee River fulvic acid isolates, firewood smoke and ambient aerosols from Padua, Italy.</p>
3DHD CityScenes: High-Definition Maps in High-Density Point Clouds
<p><strong>Overview</strong></p> <p>3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.</p> <p>Our corresponding paper (published at ITSC 2022) is available <a href="https://www.researchgate.net/publication/364309881_3DHD_CityScenes_High-Definition_Maps_in_High-Density_Point_Clouds">here</a>.<br> Further, we have applied 3DHD CityScenes to map deviation detection <a href="https://www.researchgate.net/publication/368983255_DNN-Based_Map_Deviation_Detection_in_LiDAR_Point_Clouds">here</a>. </p> <p>Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:</p> <ul> <li>Python tools to read, generate, and visualize the dataset,</li> <li>3DHDNet deep learning pipeline (training, inference, evaluation) for<br> map deviation detection and 3D object detection.</li> </ul> <p>The DevKit is available here:</p> <p><a href="https://github.com/volkswagen/3DHD_devkit">https://github.com/volkswagen/3DHD_devkit</a>.</p> <p>The dataset and DevKit have been created by <a href="https://de.linkedin.com/in/christopher-plachetka-42b325115">Christopher Plachetka</a> as project lead during his PhD period at Volkswagen Group, Germany.</p> <p>When using our dataset, you are welcome to cite:</p> <pre><code>@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}</code></pre> <p><strong>Acknowledgements </strong></p> <p>We thank the following interns for their exceptional contributions to our work.</p> <ul> <li><a href="https://www.linkedin.com/in/sertolli/">Benjamin Sertolli</a>: Major contributions to our DevKit during his master thesis</li> <li>Niels Maier: Measurement campaign for data collection and data preparation</li> </ul> <p>The European large-scale project Hi-Drive (<a href="http://www.Hi-Drive.eu">www.Hi-Drive.eu</a>) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.</p> <p><strong>The Dataset</strong></p> <p>After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.</p> <p>1. Dataset</p> <p>This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.</p> <p>During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.</p> <p>To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.</p> <pre><code class="language-python">import json json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)</code></pre> <p>2. HD_Map</p> <p>Map items are stored as lists of items in JSON format. In particular, we provide:</p> <ul> <li>traffic signs,</li> <li>traffic lights,</li> <li>pole-like objects,</li> <li>construction site locations,</li> <li>construction site obstacles (point-like such as cones, and line-like such as fences),</li> <li>line-shaped markings (solid, dashed, etc.),</li> <li>polygon-shaped markings (arrows, stop lines, symbols, etc.),</li> <li>lanes (ordinary and temporary),</li> <li>relations between elements (only for construction sites, e.g., sign to lane association).</li> </ul> <p>3. HD_Map_MetaData</p> <p>Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.</p> <p>Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.</p> <p>4. HD_PointCloud_Tiles</p> <p>The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.</p> <ul> <li>x-coordinates: 4 byte integer</li> <li>y-coordinates: 4 byte integer</li> <li>z-coordinates: 4 byte integer</li> <li>intensity of reflected beams: 2 byte unsigned integer</li> <li>ground classification flag: 1 byte unsigned integer</li> </ul> <p>After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.</p> <pre><code class="language-python">import numpy as np import pptk file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['<i4', '<i4', '<i4', '<u2', 'u1'] with open(file_path, "r") as fid: num_points = np.fromfile(fid, count=1, dtype='<u4')[0] # print(num_points) # Init for k, dtype in zip(key_list, type_list): pc_dict[k] = np.zeros([num_points], dtype=dtype) # Read all arrays for k, t in zip(key_list, type_list): pc_dict[k] = np.fromfile(fid, count=num_points, dtype=t) # Unnorm pc_dict['x'] = (pc_dict['x'] / 1000) + 500000 pc_dict['y'] = (pc_dict['y'] / 1000) + 5000000 pc_dict['z'] = (pc_dict['z'] / 1000) pc_dict['intensity'] = pc_dict['intensity'] / 2**16 pc_dict['is_ground'] = pc_dict['is_ground'].astype(np.bool_) fid.close() print(pc_dict) # Visualization # Normalize (due to large UTM values) x_utm = pc_dict['x'] - np.mean(pc_dict['x']) y_utm = pc_dict['y'] - np.mean(pc_dict['y']) z_utm = pc_dict['z'] xyz = np.column_stack((x_utm, y_utm, z_utm)) viewer = pptk.viewer(xyz) viewer.attributes(pc_dict['intensity']) viewer.set(point_size=0.03)</code></pre> <p>5. Trajectories</p> <p>We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.</p> <p>These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.</p> <ul> <li>OP1 – OP5 cover the majority of the map with 5 trajectories.</li> <li>RH1 – RH10 cover the majority of the map with 10 trajectories.</li> </ul> <p>Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here. </p>
KappaSet: Sentinel-2 KappaZeta Cloud and Cloud Shadow Masks
<p><strong>General information</strong></p> <p>The dataset consists of 9251 labelled sub-tiles from 1038 Sentinel-2 (S2) Level-1C (L1C) products distributed over the globe. In terms of seasonal distribution, S2 products can be divided into the following groups:</p> <ul> <li> <p>Winter products: 29 austral and 142 boreal S2 products</p> </li> <li> <p>Sprint products: 45 austral and 257 boreal S2 products</p> </li> <li> <p>Summer products: 30 austral and 293 boreal S2 products</p> </li> <li> <p>Autumn products: 29 austral and 213 boreal S2 products</p> </li> </ul> <p>Each S2 product was oversampled at 10 m resolution for 512 x 512 pixels sub-tiles. From each S2 product, the most challenging ~5 sub-tiles per product were selected for labelling. Each selected L1C S2 product represents different clouds, such as cumulus, stratus, or cirrus, which are spread over various geographical locations around the world. The classification pixel-wise map consists of the following categories:</p> <ul> <li> <p>0 – UNDEFINED: pixels that the labeler is not sure which class they belong to;</p> </li> <li> <p>1 – CLEAR: pixels without clouds or cloud shadows;</p> </li> <li> <p>2 – CLOUD SHADOW: pixels with cloud shadows;</p> </li> <li> <p>3 – SEMI TRANSPARENT CLOUD: pixels with thin clouds through which the land is visible; include cirrus clouds that are on the high cloud level (5-15km).</p> </li> <li> <p>4 – CLOUD: pixels with cloud; include stratus and cumulus clouds that are on the low cloud level (from 0-0.2km to 2km).</p> </li> <li> <p>5 – MISSING: missing or invalid pixels.</p> </li> </ul> <p>The dataset was labelled using Computer Vision Annotation Tool (CVAT) and Segments.ai. With the possibility of integrating an active learning process in Segments.ai, the labelling was performed semi-automatically. The distribution of the dataset is presented in the Figure below. Color represents the season from which the product was chosen.</p> <p>The dataset limitations must be considered: the data mostly covers terrestrial regions (around 91%) and includes some water areas (around 9%); only around 7% of the dataset contains snow. Current sub-tiles do not have georeferencing. </p> <p><strong>Contributions and Acknowledgements</strong></p> <p>The data were annotated by Olga Wold, Mariana Rohtsalu, Nikita Murin, Joosep Truupõld and Fariha Harun. The data verification and Software Development were performed by Indrek Sünter, Heido Trofimov, Anton Kostiukhin, Marharyta Domnich, Mihkel Järveoja, Olga Wold and Tetiana Shtym. The methodology was developed by Kaupo Voormansik, Indrek Sünter, Marharyta Domnich and Tetiana Shtym.</p> <p>The data were collected, processed, and checked as a part of “KappaMask: AI-based Cloudmask Processor for Sentinel-2” project. We thank Segments.ai team for providing a wonderful annotation tool that was actively used to prepare the dataset. In the end, we thank European Space Agency (ESA) for supporting, advising, and funding the project.</p> <p>The project was funded by <em><strong>European Space Agency,</strong></em> Contract No. 4000132124/20/I-DT.</p>
Dataset of "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM"
<p>This dataset contains the GrADS data of Venus GCM results used for figures in the paper "Vertical-Wind-Induced Cloud Opacity Variation in Low Latitudes Simulated by a Venus GCM" by H. Karyu et al. (2022). </p> <p>The file 'dataset_day1' contains the three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of temperature (unit: K), zonal wind velocity (unit: m/s), meridional wind velocity (unit: m/s), vertical wind velocity (unit: m/s), geopotential height (unit: m), cloud mass mixing ratio of mode 1, 2, 2', 3 particles, mass mixing ratio of sulfuric acid, air density (unit: kg/m<sup>3</sup>), cloud mass mixing ratio changing rate of mode 1, 2, 2', 3 particles (unit: 1/s), in snapshots of every 3 hours for the periods of the first Venusian days (117 Earth days). The file 'dataset_day2' contains the same for the second Venusian days.</p> <p>The file 'cloudtau-wc' contains three-dimensional (X: longitude, Y: latitude, Z:altitude (km)) data of column-integrated optical depth (COD) of mode 1, 2, 2' 3 particles and column mass abundance of mode 1, 2, 2' 3 particles (unit: kg/m<sup>2</sup>), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). The COD at each altitude corresponds to the integrated value from the top of the atmosphere, and the column mass abundance of each altitude corresponds to the integrated value from the bottom of the atmosphere. The COD is calculated with the cloud mass mixing ratio stored in the file ‘dataset’ and extinction efficiency shown in the paper.</p> <p>The file 'stf-wc' contains two-dimensional (Y: latitude, Z:altitude (km)) data of mass stream function (unit: kg/s) and residual mass stream function (unit: kg/s), in snapshots of every 3 hours for the periods of 2 Venusian days (234 Earth days). One should refer to Holton (2004) for the definition of the (residual) mass stream function.</p> <p>The files 'dataset_comp' and 'taudataset_comp' are composite mean data of 'dataset' and 'cloudtau-wc', respectively, in snapshots of every 3 hours for the period of 30 days starting from day 86 of the simulation (Earth day). The composite mean is calculated by averaging atmospheric parameters with respect to the frame moving at the rotation period of 7.1-day.</p> <p>The file ‘scripts’ contains FORTRAN scripts and some additional files to derive the atmospheric parameters stored in 'cloudtau-wc’, 'stf-wc’, 'dataset_comp' and ‘taudataset_comp' from the GCM output file ‘dataset’. Please refer to the ‘README.txt’ contained in ‘scripts’ for how to use FORTRAN scripts, required input files and their output.</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p> </p>
CRAAS: Cloud Regime dAtAset based on the CLAAS-2.1 climate data record
<p>The Cloud Regime dAtAset based on the CLAAS-2.1 climate data record (CRAAS) is a dataset of cloud regimes derived from cloud properties from the <a href="https://wui.cmsaf.eu/safira/action/viewDoiDetails?acronym=CLAAS_V002_01">CLAAS-2.1</a> climate data record. Such a cloud regime dataset can provide detailed insight in the cloud climatology over the region of interest and also in climate monitoring through the concept of the cloud regimes.</p> <p>CRAAS covers a region over Europe (30°N to 60°N and from 11°W to 37°E) and it extends from 2004 to 2017. The generated Joint Cloud Histograms (JCHs) of Cloud Top Pressure (CTP) and Cloud Optical Thickness (COT), <br> as well as the derived labeled data points of the cloud regime classification are available on a 1°x1° degree resolution and every 15 minutes.</p> <p>Two sets of yearly files can be found in the dataset. Those containing the generated JCHs from the CLAAS-2.1 climate data record (example filename: 'craas_jch_2004.v1.nc') <br> and those including the labeled data points of the cloud regime classification (example filename: 'craas_label_2004.v1.nc').<br> The files are provided in netCDF4 format, following the NetCDF Climate and Forecast Metadata Conventions-Version 1.8 (CF-1.8).</p>
Detection of standing retention trees in boreal forests with airborne laser scanning point clouds and multispectral imagery
<p>1. In a landscape consisting primarily of intensive forestry interspersed with some protected areas, multifunctional forestry with retention trees can play a crucial role in nature conservation. Accurate mapping of retention trees is important for guiding landscape-level conservation and forest management and improving landscape connectivity. Sizeable dead and living retention trees play a particularly important ecological role but even their large-scale inventory is often intensive through field work and/or inaccurate. We aimed to detect and classify retention trees using the novel nationwide Finnish airborne laser scanning (ALS) data (~ 5 pulses/m<sup>2</sup>) in conjunction with unrectified color-infrared (CIR) aerial imagery. 2. Applying photogrammetric principles, we added spectral information from the CIR imagery to the ALS-derived point cloud. For a training dataset of 160 retention trees from 19 stands and a geographically separate validation dataset of 79 trees from 8 stands, we segmented trees via individual tree detection (ITD), removed most trees belonging to the regenerating vegetation layer, and classified trees into living conifers, living broadleaves, and dead trees by linear discriminant analysis. 3. The detection rate via ITD differed considerably for dead and living trees, with 41.7% of all dead and 83.8% of all living trees being detected with relatively low commission error rates. Dead trees with smaller diameters and heights were more likely missed, while grouping caused living tree omission. For classification into living conifers, living broadleaves, and dead trees, an overall accuracy of 67.3% was achieved in training and 71.2% in validation data only ALS-derived metrics. When adding spectral metrics, the overall accuracies were 79.6% and 61.0% for training and validation, respectively. 4. Our findings imply that wall-to-wall large-scale high density ALS data can be used to detect retention trees rather accurately – even larger dead trees – and that metrics derived solely from ALS data can accurately classify detected retention trees into living conifers, living broadleaves, and dead trees. Considering the ecological value of retention trees, our results are promising and indicate that ALS data of the studied pulse density are a cost-effective option for large area mapping of retention trees in countries with such data available.</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.