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2,170 results for “clouds”
Dataset in support of the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Dataset supporting the article: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e
<p>This dataset contains the measurement data, model outputs and Python (v3.10) scripts that are used to produce figures and tables in the paper: Implementation and evaluation of updated photolysis rates in the EMEP MSC-W chemical transport model using Cloud-J v7.3e.</p> <p>The newly created module providing the interface with Cloud-J in the EMEP MSC-W and BoxChem models is called "CloudJ_mod.f90." The modules from the Cloud-J code itself are gathered in the "ModsCloudJ_mod.f90" file in the CloudJ sub-folders.</p> <p>The measurement data and supporting MATLAB scripts used to create the ATom-1 data files read in by the Python scripts can be downloaded from https://doi.org/10.3334/ORNLDAAC/1651</p>
Comparison of Acidity and Chemical Composition of Summertime Cloud Water and Aerosol at an Alpine Site in Northwest China: Implication for Elevated pH of Cloud
<p>Low molecular weight organic acids and water-soluble ion data of Cloud water and PM2.5 samples in Mt. Hua</p>
Blast Furnace Raw Material Granularity Recognition Model Based on Deep Learning and Multimodal Fusion of 3D Point Cloud
<p>Provide data code</p>
Developing a Method to Automatically Extract Road Boundary and Linear Road Markings from MMS Point Cloud using OBB Collision Detection Techniques
<p>This video demonstrates the application of our method in a software tool for constructing road boundaries and lane marking data.</p>
Datasets for "Irradiance and cloud optical properties from solar photovoltaic systems" (final version)
<p>This dataset contains all the relevant data for the algorithms described in the paper "<a href="https://amt.copernicus.org/articles/16/4975/2023/">Irradiance and cloud optical properties from solar photovoltaic systems</a>", which were developed within the framework of the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> project.</p> <p><strong>Input data:</strong></p> <ol> <li><a href="http://www.cosmo-model.org/">COSMO</a> weather model data (DWD) as NetCDF files (cosmo_d2_2018(9).tar.gz) <ol> <li>COSMO atmospheres for <a href="http://www.libradtran.org/">libRadtran</a> (cosmo_atmosphere_libradtran_input.tar.gz)</li> <li>COSMO surface data for calibration (cosmo_pvcal_output.tar.gz)</li> </ol> </li> <li><a href="https://aeronet.gsfc.nasa.gov/">Aeronet</a> data as text files (MetPVNet_Aeronet_Input_Data.zip)</li> <li>Measured data from the <a href="https://www.h-brs.de/de/satelliten-und-meteorologie-unterstuetzte-vorhersage-der-energieerzeugung-von-pv-anlagen-auf">MetPVNet</a> measurement campaigns as text files (MetPVNet_Messkampagne_2018(9).tar.gz) <ol> <li>PV power data</li> <li>Horizontal and tilted irradiance from pyranometers</li> <li>Longwave irradiance from pyrgeometer</li> </ol> </li> <li>MYSTIC-based lookup table for translated tilted to horizontal irradiance (gti2ghi_lut_v1.nc)</li> </ol> <p><strong>Output data:</strong></p> <ol> <li>Global tilted irradiance (GTI) inferred from PV power plants (with calibration parameters in comments) <ol> <li>Linear temperature model: MetPVNet_gti_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_gti_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Global horizontal irradiance (GHI) inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined GHI averaged to 60 minutes and compared with COSMO data <ol> <li>Linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_ghi_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> <li>Cloud optical depth inferred from PV power plants <ol> <li>Linear temperature model: MetPVNet_cod_cf_inversion_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_cf_inversion_results_faiman.tar.gz</li> </ol> </li> <li>Combined COD averaged to 60 minutes and compared with COSMO and APOLLO_NG data <ol> <li>Linear temperature model: MetPVNet_cod_inversion_combo_60min_results_linear.tar.gz</li> <li>Faiman non-linear temperature model: MetPVNet_cod_inversion_combo_60min_results_faiman.tar.gz</li> </ol> </li> </ol> <p><strong>Validation data:</strong></p> <ol> <li>COSMO cloud optical depth (cosmo_cod_output.tar.gz)</li> <li>APOLLO_NG cloud optical depth (MetPVNet_apng_extract_all_stations_2018(9).tar.gz)</li> <li>COSMO irradiance data for validation (cosmo_irradiance_output.tar.gz)</li> <li><a href="https://www.soda-pro.com/web-services/radiation/cams-radiation-service">CAMS</a> irradiance data for validation (CAMS_irradiation_detailed_MetPVNet_MK_2018(9).zip)</li> </ol> <p><strong>How to import results:</strong></p> <p>The results files are stored as text files ".dat", using Python multi-index columns. In order to import the data into a Pandas dataframe, use the following lines of code (replace [filename] with the relevant file name):</p> <p>import pandas as pd<br>data = pd.read_csv("[filename].dat",comment='#',header=[0,1],delimiter=';',index_col=0,parse_dates=True)</p> <p>This gives a multi-index Dataframe with the index column the timestamp, the first column label corresponds to the measured variable and the second column to the relevant sensor</p> <p><strong>Note:</strong></p> <p>The output data has been updated to match the latest version of the paper, whereas the input and validation data remains the same as in Version 1.0.0</p>
Cloud Factory - x1470y1400z1400
<p>Binary AREPO Cloud Factory grid used in a tutorial notebook that skeletonizes and analyzes clouds within the grid. </p>
Stability of Biogenic Amino Acids in Concentrated Sulfuric Acid: Implications for the Habitability of Venus' Clouds
<p><strong>DESCRIPTION OF THE DATASET: </strong><br> <br> <strong>Supplementary Datasets for the paper published in Astrobiology, Venus 2 Special Collection:</strong><br> </p> <p><em>Supplementary Dataset S1:</em> The original 1D <sup>1</sup>H and <sup>13</sup>C NMR data collected in 81% w/w and 98% w/w concentrated sulfuric acid at all three time intervals for all 20 amino acids. Supplementary Dataset S1 also contains original 1D <sup>1</sup>H and <sup>13</sup>C NMR data for modified amino acids such as <em>S</em>-sulfocysteine, <em>O</em>-sulfoserine, oxidized cysteine, oxidized methionine as well as original 1D <sup>1</sup>H and <sup>13</sup>C NMR data for tests with formic acid and FeO.<br> </p> <p><em>Supplementary Dataset S2:</em> The original 2D <sup>1</sup>H-<sup>13</sup>C HMQC NMR data for all 20 amino acids collected in 98% w/w concentrated sulfuric acid.</p> <p> </p>
Data for ice nucleation measurement of cloud seeding aerosols
<p>This data is for the ice nucleation ability measurement and size characterization of cloud-seeding particles</p>
TOOCAN cloud mask
<p>This dataset comprises a high cloud mask derived from the TOOCAN dataset (Fiolleau et al., 2020) within the region spanning from 0N to 9.6N latitude and 140E to 149.6E longitude. The TOOCAN dataset offers calibrated infra-red brightness temperatures across the entire tropical band in the period 2012-2016.<br> The images have 0.04° spatial resolution and 30 minute temporal frequency.</p> <p>Fiolleau, T., R. Roca, S. Cloché, D. Bouniol, P. Raberanto, 2020: Homogenization of geostationary infrared imager channels for cold cloud studies using Megha-Tropiques/ScaRaB. IEEE Trans. Geosci. Remote Sens., vol 58, no. 9, pp. 6609-6622. doi: 10.1109/TGRS.2020.2978171</p>
Arraylake: A Cloud-Native Data Lake Platform for Earth System Science
<p>The vast amount of earth system data available today is an incredible resource for understanding our planet and confronting the challenge of climate change. Traditionally, a few large organizations have provided most of the data, and users have downloaded data to local computers. This way of working is becoming increasingly infeasible as data volumes grow and as AI-based methods demand direct access to full-scale data archives. With essentially infinite compute and storage capacity, cloud computing has the potential to revolutionize our interaction with weather and climate data, allowing everyone to bring their own compute workloads to bear against a single shared copy of the data. Over the past years, via our work in the Pangeo project, we have prototyped a cloud-native approach to weather and climate data in the cloud, combining scalable computing technologies such as Xarray and Dask with analysis-ready, cloud-optimized data in formats like Zarr. While these tools show great potential, they remain difficult to deploy and use in an operational context for many scientists and institutions.</p> <p>Motivated by this challenge, we founded Earthmover, a company aimed at democratizing access to state-of-the-art cloud-native data analytics, and built Arraylake, a data platform which enables teams of any size to manage and analyze weather and climate data in the cloud. Arraylake users can access high-quality public datasets alongside their own private data, all via the high-performance Zarr data standard. This talk describes Arraylake’s architecture, novel version control system for data, and approach to supporting all common climate data formats (NetCDF, HDF5, Grib, Tiff, Zarr) via a single, user-friendly interface. Via a short demo, we illustrate how Arraylake helps overcome common data management challenges that have henceforth limited widespread adoption of cloud computing in earth system science.</p>
FIGURE 3 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 3. Head and thorax of Dixella machiotla sp. nov., male. A. Head (frontal view), B. Antenna (lateral view), C. Thorax (lateral view), D. Thorax (dorsal view), E. Claws (last tarsal segments of the foreleg (lateral view). Scale: A, B, E= 0.1 mm; C–D= 0.5 mm. Abbreviations. cl= claw; flgm I to IV= first to four flagellomeres; pe= pedicel; tsm IV–V= tarsomere four and fifth.
FIGURE 1 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 1. Distribution in Mexico of Dixella aliciae (red circle) and Dixella machiotla sp. nov. (black square).
FIGURE 5 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 5. Male genitalia of Dixella machiotla sp. nov. A. Tergum 9 (dorsal view), B. Tergum 10 (ventral view), C. Bottom view; D. Top view. Scale = 0.1 mm. Abbreviations. ae= aedeagus; a lob= anterior lobe of gonocoxite; b tg 10= base tergum 10; goncx= gonocoxite; gonst= gonostylus; pa= paramere; st 9= sternite 9.
FIGURE 2 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 2. Male genitalia of Dixella aliciae. A. Ventral view, B. Lateral view. Scale= 0.1 mm. Abbreviations. a lob= anterior lobe of gonocoxite; goncx= gonocoxite; gonst= gonostylus.
FIGURE 7 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 7. Last abdominal segments of Dixella machiotla sp. nov., female. A. Ventral view, B. Lateral view. Scale= 0.1 mm. Abbreviations. cer= cercus; p lob= posterolateral lobe ot tergum 9; seg 10= segment 10; tg 9= tergum 9.
FIGURE 6 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 6. Male genitalia of Dixella machiotla sp. nov. A B. Ventral view, B. Lateral view. Scale A, C= 0.1 mm; B=0.064 mm. Abbreviations. ae= aedeagus; a lob= anterior lobe of gonocoxite; b tg 10= base tergum 10; goncx= gonocoxite; gonst= gonostylus; lob= anterior lobe tergum 10; pa= paramere; st 9= sternite 9; tg 9= tergum 9; tg 10= tergum 10.
FIGURE 4 in Description of a new species of Dixella from a cloud forest in Tlanchinol, Hidalgo and first record of Dixella aliciae (Johannsen) in Mexico (Diptera: Dixidae)
FIGURE 4. Wings of Dixella machiotla sp. nov. (dorsal view). A. Male, B. Female. Scale= 0.5 mm. Abbreviations. C= costal vein; CuA= anterior branch of cubital vein; CuP= posterior branch of cubital vein; M1 = first branch of media; M2 = second branch of media; M3+4 = third and fourth branch of media; m-cu= medial-cubital crossvein; m-m= medial crossvein; Rs= radial sector; R1 = anterior branch of radius; R2 = upper branch of second branch of radius; R3 = lower branch of second branch of radius; R2+3= second branch of radius; R 4+5 = third branch of radius; r-m= radialmedial crossvein; Sc = subcostal vein.
Cloudsim Cloud Computing Simulation Platform and data set Creators
<p>In this uploaded file, Cloudsim-AHP is the cloud computing simulation platform, in which the VM placement method has been modified to the VM placement strategy based on AHP multidimensional decision making method proposed in this paper. The "data set" file is the inbuilt data set in the above mentioned cloud simulation platform.</p>
Ananias Chapel, Syria Point Cloud
Lidar data, clean,edit, and uniform The Chapel of Saint Ananias, also known as the House of Saint Ananias, is a small underground chapel located in the ancient city of Damascus, Syria. The site is connected to the biblical story of Jesus's disciple, Saul of Tarsus, later known as St. Paul the Apostle. Over many centuries, non-Christian rulers have continually destroyed the chapel, rebuilt as a pagan temple and mosque before Franciscans rebuilt it in 1814. More info: https://doi.org/10.26301/j4g0-ek82 Cyark 2019 Ananias Chapel - LiDAR - Terrestrial . Collected by Directorate General of Antiquities and Museums . Distrubuted by Open Heritage 3D. Source: Objaverse 1.0 / Sketchfab
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.