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2,170 results for “clouds”
ASP Great House, USA, Point cloud
Point cloud data, clean and uniform to easy visualization. Site Description The Annaberg Sugar Plantation in the Virgin Islands is part of one of the largest historic migrations of people and resources throughout the world. For centuries, the Virgin Islands was a crossroads for indigenous peoples, colonial powers, and the transatlantic slave trade. The plantation structures that the National Park Service preserves on the site today demonstrate the agricultural success of the Danish Colonial era, but also its human cost. A 40-foot high windmill constructed with sails to direct the wind was state of the art at the time of its completion in 1805. Not far from the windmill, built into the foundation of the sick house, the Annaberg "Dungeon" served as a means of punishment for enslaved laborers....more info: https://doi.org/10.26301/vv0h-dq68 CyArk 2019: Annaberg Plantation - LiDAR - Terrestrial , Photogrammetry . Collected by CyArk , Trimble . Distrubuted by Open Heritage 3D. https://doi.org/10.26301/vv0h-dq68 Source: Objaverse 1.0 / Sketchfab
Point-Cloud (3D Scan)
Point-Cloud 3D Scan of a historical site in northern Switzerland Source: Objaverse 1.0 / Sketchfab
Databases generated for Manuscript titled "Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures"
<p>Databases generated for manuscript "<strong>Quantifying downward radiative fluxes from nighttime Martian water ice clouds: Applications to thermal modeling of surface temperatures</strong>"</p> <p>There are two zip files containing generated databases:</p> <p>The zip file titled "database.zip" contains generated database for calculated fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtb.csv (e.g. 000N000Etb.csv for 0°N, 0°E). Each .csv file contains four separate columns and variable rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to Thermal Emission Spectrometer (TES) measured temperature. "Tau" corresponds to calculated Dust visible opacities using the methodology presented in this work. The rows in each file vary based on the temporal observations from TES at each location. </p> <p>The zip file titled "fitdatabase.zip" contains generated database for fitted fluxes using the methodology mentioned in the manuscript. The database spans calculated fluxes in one degree bins for latitudes spanning 30° to -10° N and longitudes spanning 0° to 360°. There are 14760 separate .csv files that are for each one by one degree bin. The title of each file contains its coordinates in the format XXXNXXXEtbf.csv (e.g. 000N000Etbf.csv for 0°N, 0°E). Each .csv file contains six separate columns and three hundred and sixty rows. The columns have headers corresponding to specific values. "ls" corresponds to solar longitude or date based on Mars' orbit around the Sun. "Flux" corresponds to calculated flux based on the methodology presented on the manuscript. "Delta-T" is the difference in temperature comparing modeled temperature compared to measured temperature. The fitting algorithm interpolates points between values in the calculated flux database and applies a rolling mean fit with a window spanning ten degrees in solar longitude centered at each calculated flux point. "FLAG" indicates the amount of points of calculated flux points that exist within the ten degree window centered at each flux point to demonstrate to the user how much data had to be fitted. "From Ls" shows the leftmost edge of the rolling mean fit window. "To Ls" shows the rightmost edge of the rolling mean fit window. The rows in each file correspond to one degree of solar longitude the fitting algorithm was designed to cover each solar longitude bin. </p> <p> </p>
Dataset for "Enhancing Cloud Detection in Sentinel-2 Imagery: A Spatial-Temporal Approach and Dataset"
<p>This dataset is built for time-series Sentinel-2 cloud detection and stored in Tensorflow TFRecord (refer to https://www.tensorflow.org/tutorials/load_data/tfrecord).</p> <p>Each file is compressed in 7z format and can be decompressed using Bandzip or 7-zip software.</p> <p><strong>Dataset </strong><strong> </strong><strong>Structure</strong>:</p> <p>Each filename can be split into three parts using underscores. The first part indicates whether it is designated for training or validation ('train' or 'val'); the second part indicates the Sentinel-2 tile name, and the last part indicates the number of samples in this file.</p> <p>For each sample, it includes:</p> <ol> <li>Sample ID;</li> <li>Array of time series 4 band image patches in 10m resolution, shaped as (n_timestamps, 4, 42, 42);</li> <li>Label list indicating cloud cover status for the center \(6\times6\) pixels of each timestamp;</li> <li>Ordinal list for each timestamp;</li> <li>Sample weight list (reserved);</li> </ol> <p>Here is a demonstration function for parsing the TFRecord file:</p> <pre><code>import tensorflow as tf # init Tensorflow Dataset from file name def parseRecordDirect(fname): sep = '/' parts = tf.strings.split(fname,sep) tn = tf.strings.split(parts[-1],sep='_')[-2] nn = tf.strings.to_number(tf.strings.split(parts[-1],sep='_')[-1],tf.dtypes.int64) t = tf.data.Dataset.from_tensors(tn).repeat().take(nn) t1 = tf.data.TFRecordDataset(fname) ds = tf.data.Dataset.zip((t, t1)) return ds keys_to_features_direct = { 'localid': tf.io.FixedLenFeature([], tf.int64, -1), 'image_raw_ldseries': tf.io.FixedLenFeature((), tf.string, ''), 'labels': tf.io.FixedLenFeature((), tf.string, ''), 'dates': tf.io.FixedLenFeature((), tf.string, ''), 'weights': tf.io.FixedLenFeature((), tf.string, '') } # The Decoder (Optional) class SeriesClassificationDirectDecorder(decoder.Decoder): """A tf.Example decoder for tfds classification datasets.""" def __init__(self) -> None: super().__init__() def decode(self, tid, ds): parsed = tf.io.parse_single_example(ds, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) sample_dict = { 'tid': tid, # tile ID 'dates': dates, # Date list 'localid': parsed['localid'], # sample ID 'imgs': decoded, # image array 'labels': label, # label list 'weights': weight } return sample_dict # simple function def preprocessDirect(tid, record): parsed = tf.io.parse_single_example(record, keys_to_features_direct) encoded = parsed['image_raw_ldseries'] labels_encoded = parsed['labels'] decoded = tf.io.decode_raw(encoded, tf.uint16) label = tf.io.decode_raw(labels_encoded, tf.int8) dates = tf.io.decode_raw(parsed['dates'], tf.int64) weight = tf.io.decode_raw(parsed['weights'], tf.float32) decoded = tf.reshape(decoded,[-1,4,42,42]) return tid, dates, parsed['localid'], decoded, label, weight t1 = parseRecordDirect('filename here') dataset = t1.map(preprocessDirect, num_parallel_calls=tf.data.experimental.AUTOTUNE) # </code></pre> <p><strong>Class Definition:</strong></p> <ul> <li>0: clear</li> <li>1: opaque cloud</li> <li>2: thin cloud</li> <li>3: haze</li> <li>4: cloud shadow</li> <li>5: snow</li> </ul> <p><strong>Dataset Construction:</strong></p> <p>First, we randomly generate 500 points for each tile, and all these points are aligned to the pixel grid center of the subdatasets in 60m resolution (eg. B10) for consistence when comparing with other products. <br>It is because that other cloud detection method may use the cirrus band as features, which is in 60m resolution. </p> <p>Then, the time series image patches of two shapes are cropped with each point as the center.<br>The patches of shape \(42 \times 42\) are cropped from the bands in 10m resolution (B2, B3, B4, B8) and are used to construct this dataset.<br>And the patches of shape \(348 \times 348\) are cropped from the True Colour Image (TCI, details see sentinel-2 user guide) file and are used to interpreting class labels.</p> <p>The samples with a large number of timestamps could be time-consuming in the IO stage, thus the time series patches are divided into different groups with timestamps not exceeding 100 for every group.</p>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
Data for "Africa's Climate Response to Marine Cloud Brightening Strategies is Highly Sensitive to Deployment Region"
<p>The data presented here were used the following study:</p> <p>Africa's Climate Response to Regional Marine Cloud Brightening Strategies.<span> </span></p> <p><span>Romaric C. Odoulami<sup>1</sup>, Haruki Hirasawa<sup>2,3</sup>, Kouakou Kouadio<sup>4</sup>, Trisha D. Patel<sup>1</sup>, Kwesi A. Quagraine<sup>5,6</sup>, Izidine Pinto<sup>6,7</sup>, Temitope S. Egbebiyi<sup>6</sup>, Babatunde J. Abiodun<sup>6</sup>, Christopher Lennard<sup>6</sup>, Mark G. New<sup>1</sup> </span></p> <p><sup><span>1</span></sup><span>African Climate and Development Initiative, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>2</span></sup><span>School of Earth and Ocean Sciences, University of Victoria, Victoria, BC, Canada.</span></p> <p><sup><span>3</span></sup><span>Department of Atmospheric Sciences, University of Washington, Seattle, Washington, USA</span></p> <p><sup><span>4</span></sup><span>Laboratory of Atmospheric Physics and Fluid Mechanics, University Felix Houphouet-Boigny, Abidjan, Côte d’Ivoire.</span></p> <p><sup><span>5</span></sup><span>National Center for Atmospheric Research (NCAR), Boulder, Colorado, USA.</span></p> <p><sup><span>6</span></sup><span>Climate System Analysis Group (CSAG), Environmental and Geographical Science Department, University of Cape Town, Cape Town, South Africa.</span></p> <p><sup><span>7</span></sup><span>Royal Netherlands Meteorological Institute (KNMI), De Bilt, The Netherlands.</span></p> <p> </p> <p>The datasets contains six folders. Each of them contains two folders: (a) "precipitation_indices" containing all precipitation indices and (b) "temperature_indices" containing all temperature indices for the African domain analysed in this study.</p> <p> (i) "historical"<br> This folder contains all historical precipitation and temperature indices analysed.</p> <p> (ii) "ssp245"<br> This folder contains all ssp245 precipitation and temperature indices analysed.</p> <p>(iii) "ssp245_MCB_R1"<br> This folder contains all ssp245+MCB_R1 (MCB over Northeast Pacific: MCB_NEP) precipitation and temperature indices analysed.</p> <p> (iv) "ssp245_MCB_R2"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Pacific: MCB_SEP) precipitation and temperature indices analysed.</p> <p> (v) "ssp245_MCB_R3"<br> This folder contains all ssp245+MCB_R2 (MCB over Southeast Atlantic: MCB_SEA) precipitation and temperature indices analysed.</p> <p> (vi) "ssp245_MCB_R1R2R3"<br> This folder contains all ssp245+R1R2R3 (MCB simultaneously over Northeast Pacific, Southeast Pacific, and Southeast Atlantic: MCB_ALL) precipitation and temperature indices analysed.</p> <p> </p>
Data associated with "Deforestation-driven increases in shallow clouds are greatest in drier, low-aerosol regions of Southeast Asia"
<p>This .zip file contains the semi-processed data required to reproduce the figures in "Deforestation-driven increases in shallow clouds are greatest in drier, low-aerosol regions in Southeast Asia" (Leung, Grant, and van den Heever 2024; pre-print doi: 10.5194/egusphere-2023-1722). </p> <p>Inside the compressed folder are:</p> <ul> <li>base_forest_data.pq: parquet file (readable in Python, see associated code) containing the processed UMD Global Forest Cover and EC-JRC Global Surface Water data scaled to same ~1km resolution as MODIS grid</li> <li>matched_points/: directory containing parquet files of each deforestation event with cloud properties before and after deforestation, as well as mean cloud properties before/after deforestation for corresponding control pixels (see Supporting Information of above manuscript for full descriptions)</li> <li>deforest_effect/bootstrapped/: directory containing .csv files for points in Figures 2-4 of above manuscript</li> </ul>
Citizen Science Assoziationen Word Cloud
<p>Assoziationen, die Teilnehmer*innen des Forums Citizen Science (November 2023 in Freiburg, <a href="https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023">https://www.buergerschaffenwissen.de/veranstaltungen/forum-citizen-science-2023</a>) mit dem Begriff Citizen Science in Verbindung brachten. Dargestellt als Word Cloud mit Inkscape.</p> <p>Beantwortet wurde die Frage “Welche drei Begriffe fallen dir zu Citizen Science ein?” schriftlich von 31 Personen. Es wurden 97 Antworten genannt, davon 72 unterschiedliche. Sehr lange Antworten wurden für die grafische Darstellung vereinfacht (insgesamt zwei Begriffe, siehe Dokument Citizen Science Assoziationen-Datensammlung_12-03-24.xlsx).</p> <p>Die Word Cloud-Visualisierung darf beliebig unter folgender Angabe verwendet werden: "Tim Kiessling (CC BY NC 4.0), https://doi.org/10.5281/zenodo.10810212". Die Vektor-Grafikdatei ist verfügbar um Änderungen durchzuführen (Citizen Science Assoziationen_12-03-24.svg). </p>
AssemblyNetV2: A Dataset for Predicting Exploded Views Through Point Clouds
<p>AssemblyNetV2 contains two datasets: the original AssemblyNet dataset, resampled with face normals, and a novel dataset for predicting whether parts can be disassembled, also containing both points and normals. The original AssemblyNet dataset contains 5420 samples while the new dataset contains 16337 samples of 512-point and 1024-point point clouds of mixed industrial assemblies and LEGO models.</p> <p>The new dataset for part removal contains annotations for binary classification of whether a part can be removed from an assembly. The intended use case of the two datasets is to predict exploded views of assemblies by iteratively removing parts in the predicted directions.</p>
3D Point Clouds of Trees and Apple Fruit Annotated with Thermal Data
<p>The data set captures four measurements during fruit growth: 06/28/2022 (15:00), 07/12/2022 (15:00), 09/01/2022 (15:00), 09/06/2022 (13:00)</p> <p>Additionally, diurnal courses are provided for three days: </p> <table> <tbody> <tr> <td> <p>Date</p> </td> <td> <p>Time</p> </td> </tr> <tr> <td> <p>09/21</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>09/22</p> </td> <td> <p>07:00, 08:00, 10:00, 12:00, 13:00, 18:00</p> </td> </tr> <tr> <td> <p>10/05</p> </td> <td> <p>06:30, 07:00, 09:00, 10:00, 11:00, 16:00</p> </td> </tr> </tbody> </table> <p> </p> <p>The data set was measured in Blocks (A-D) of trees (T) on apple (A) fruit and is stored as compressed zip files, capturing raw, preprocessed, and manually recorded reference (ground truth) data.</p> <p>1. zip files entitled Raw_YYYY_MM_DD_Block[A-C]-[R, L] for seasonal data and Raw_YYYY_MM_DD_BlockD-[R, L]_Hour__:__ for diel data</p> <p>- raw data of LiDAR 3D point clouds - txt files</p> <p>- raw image data by thermal camera - txt files</p> <p>2. zip files entitled YYYY_MM_DD or DailyAcquisitions:</p> <p>- preprocessed (merged) sensor data of temperature-annotated 3D point clouds of canopies - csv files</p> <p>- preprocessed data, capturing manually segmented point clouds of temperature-annotated fruit - txt files</p> <p>3. Microsoft Excel files entitled References and Weather data:</p> <p>- raw data, representing reference data of fruit - xlsx file</p> <p>- raw data of weather conditions - xlsx file</p>
Word Cloud of Sky News Australia's Video Comments
<p>Word Cloud graph created on Python with a Google Colab Notebook. Dataset of YouTube comments of Sky News Australia's video about France Abortion Law as a constitutional right, collected from Communalytic. </p>
Word Cloud of CBS US' Video Comments
<p>Word Cloud graph created on Python with a Google Colab Notebook. Dataset of YouTube comments of CBS US' video about France's Abortion Law made as a constitutional right, collected from Communalytic. </p>
Investigation of cirrus clouds properties in the Tropical Tropopause Layer using high-altitude limb scanning near-IR spectroscopy during the NASA-ATTREX Experiment
<p>Data results of the findings of the AMT-2023-85 research article, titled: "Investigation of cirrus clouds properties in the Tropical Tropopause Layer using high-altitude limb scanning near-IR spectroscopy during the NASA-ATTREX Experiment", submitted to the Atmospheric Measurement Techniques journal on 20 Apr 2023.</p>
Xenium Cloud Optimized Geotiff Test
<p>Test dataset for Cloud Optimized Geotiff</p>
Final point cloud results of DAR-MVSNet on DTU dataset
<p>DAR-MVSNet在DTU数据集上的最终点云结果。</p>
Final point cloud results of DAR-MVSNet on BlendedMVS dataset
Open the record for dataset details and reuse information.
Final point cloud results of DAR-MVSNet on Tanks and Temples benchmark
<p>Final point cloud results of DAR-MVSNet on Tanks and Temples benchmark.</p>
EVALUACIÓN DE LOS SERVICIOS DE CLOUD COMPUTING EN INSTITUTOS DE EDUCACIÓN SUPERIOR EN MEDIOS RURALES
<p>Datos recolectados para la tesis de maestria titulada: EVALUACIÓN DE LOS SERVICIOS DE CLOUD COMPUTING EN INSTITUTOS<br>DE EDUCACIÓN SUPERIOR EN MEDIOS RURALES</p>
Continuous Initial Breakdown Development of In-cloud Lightning Flashes
<p>The data contained in the MATLAB .fig files support the manuscript submitted to JGR-Atmospheres titled "Continuous Initial Breakdown Development of In-cloud Lightning Flashes".</p>
Point clouds and QSM for 598 individually scanned tree branches to support "Terrestrial laser scanning to reconstruct branch architecture from harvested branches"
<p>A collection of 598 harvested branches scanned in high resolution using terrestrial LiDAR. Branches were collected from GEM forest plots in Malaysia, Australia and Brazil.</p> <p>Branch nomenclature is <em>PLOT</em>-<em>TREE</em>-B<em>N</em><S or SH> where <em>PLOT </em>codes can be found in the manuscript, <em>N</em> refers to the sample number of branch harvested from a tree, and S and SH refer to sun or shade branch respectivetly.</p> <p>For each branch there are 3 files: </p> <ul> <li>Unfiltered point clouds (raw_pc) are clipped from the original data only and have had no post processing applied</li> <li>Filtered point clouds (filtered_pc) have been filtered according to the steps in the manuscript</li> <li>QSMs were produced using <em>treegraph </em>(<a href="https://doi.org/10.5281/zenodo.5226212">https://doi.org/10.5281/zenodo.5226212</a>)</li> </ul> <p>Polygon File Format (.ply) files can be viewed in software such as CloudCompare.</p> <p>Full details of methods can be found in: Wilkes, P., Shenkin, A., Disney, M., Malhi, Y., Bentley, L. P., & Vicari, M. B. (2021). Terrestrial laser scanning to reconstruct branch architecture from harvested branches. <em>Methods in Ecology and Evolution</em>, 12, 2487–2500. <a href="https://doi.org/10.1111/2041-210X.13709">https://doi.org/10.1111/2041-210X.13709</a></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.