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2,170 results for “clouds”
The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales
<p>This is the companion data for the manuscript titled 'The June 2012 North American Derecho: A testbed for evaluating regional and global climate modeling systems at cloud-resolving scales', submitted to the Journal of Advances in Modeling Earth Systems in September 2022.</p> <p>derecho_simulation_result: this folder contains part of the SCREAM RRM outputs I ran on NERSC Cori in 2021-2022 corresponding to the simulation in Table 1 of the manuscript.</p> <p>wrf_simulation_result: this folder contains part of the WRF outputs run by Jianfeng Li from PNNL (jianfeng.li@pnnl.gov) in 2022 corresponding to the simulations in Table 4 of the manuscript.</p> <p>plot_script: this folder includes python scripts to plot figures shown in the manuscript.</p> <p>ASOS_station: this txt file contains the processed ASOS station wind speed used in the manuscript.</p> <p><br> Unfortunately, all model outputs are large (~ 3.8 TB for SCREAM RRM and 3.1 TB for WRF). Therefore, I only provide the variables (i.e., OLR, precipitation, composite radar reflectivity, and 10-m wind speed) used directly to generate figures in this repository. All model outputs are archived on tape at NERSC.</p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu).</p>
The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc
<p>Data files for Dharmawardena et al., accepted MNRAS, 2022, <em>The three-dimensional structure of Galactic molecular cloud complexes out to 2.5 kpc.</em></p> <p>In this works we map the Interstellar Dust Extinction and Density in 3D using <em>Dustribution</em> code: Dharmawardena et al., 2022, Astronomy & Astrophysics, Volume 658, id.A166, 30 pp. <a href="https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html">https://www.aanda.org/articles/aa/full_html/2022/02/aa41298-21/aa41298-21.html</a></p> <p>The <em>Dustribution</em> code can be accessed from <a href="https://github.com/Thavisha/Dustribution">https://github.com/Thavisha/Dustribution</a></p> <p>The latest results from Dustribution can be interactively viewed and downloaded from <a href="https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html">https://www2.mpia-hd.mpg.de/homes/tmueller/projects/ThavishaDustDensity/index.html</a></p> <p>Data available here:</p> <p>- Trained gpy functions for all sixteen mapped regions which can be used to predict on any selected spherical or cartesian grid. </p> <p>-predicted cubes of 3D dust extinction and corresponding lbd boundary grids</p> <p>- predicted cubes of dust densities on cartesian grids and corresponding xyz boundary grids. </p> <p>- <em>astrodendro</em> derived dendrograms for all sixteen molecular cloud regions.</p> <p>- Tables of <em>astrodendro</em> produced parameters and calculated error bars for each region's substructure as described in appendix C of paper. </p> <p>- Tables 1,2,3 in main body of paper in .csv format. </p> <p>- 3D rendering videos of the sixteen mapped regions.</p>
Cloud-based User Entity Behavior Analytics Log Data Set
<p>This respository contains the CLUE-LDS (CLoud-based User Entity behavior analytics Log Data Set). The data set contains log events from real users utilizing a cloud storage suitable for User Entity Behavior Analytics (UEBA). Events include logins, file accesses, link shares, config changes, etc. The data set contains around 50 million events generated by more than 5000 distinct users in more than five years (2017-07-07 to 2022-09-29 or 1910 days). The data set is complete except for 109 events missing on 2021-04-22, 2021-08-20, and 2021-09-05 due to database failure. The unpacked file size is around 14.5 GB. A detailed analysis of the data set is provided in [1].</p><p>The logs are provided in JSON format with the following attributes in the first level:</p><ul><li><strong>id</strong>: Unique log line identifier that starts at 1 and increases incrementally, e.g., 1.</li><li><strong>time</strong>: Time stamp of the event in ISO format, e.g., 2021-01-01T00:00:02Z.</li><li><strong>uid</strong>: Unique anonymized identifier for the user generating the event, e.g., old-pink-crane-sharedealer.</li><li><strong>uidType</strong>: Specifier for uid, which is either the user name or IP address for logged out users.</li><li><strong>type</strong>: The action carried out by the user, e.g., file_accessed.</li><li><strong>params</strong>: Additional event parameters (e.g., paths, groups) stored in a nested dictionary.</li><li><strong>isLocalIP</strong>: Optional flag for event origin, which is either internal (true) or external (false).</li><li><strong>role</strong>: Optional user role: consulting, administration, management, sales, technical, or external.</li><li><strong>location</strong>: Optional IP-based geolocation of event origin, including city, country, longitude, latitude, etc.</li></ul><p>In the following data sample, the first object depicts a successful user login (see <i>type: login_successful</i>) and the second object depicts a file access (see <i>type: file_accessed</i>) from a remote location:</p><blockquote><p>{"params": {"user": "intact-gray-marlin-trademarkagent"}, "type": "login_successful", "time": "2019-11-14T11:26:43Z", "uid": "intact-gray-marlin-trademarkagent", "id": 21567530, "uidType": "name"}</p><p> {"isLocalIP": false, "params": {"path": "/proud-copper-orangutan-artexer/doubtful-plum-ptarmigan-merchant/insufficient-amaranth-earthworm-qualitycontroller/curious-silver-galliform-tradingstandards/incredible-indigo-octopus-printfinisher/wicked-bronze-sloth-claimsmanager/frantic-aquamarine-horse-cleric"}, "type": "file_accessed", "time": "2019-11-14T11:26:51Z", "uid": "graceful-olive-spoonbill-careersofficer", "id": 21567531, "location": {"countryCode": "AT", "countryName": "Austria", "region": "4", "city": "Gmunden", "latitude": 47.915, "longitude": 13.7959, "timezone": "Europe/Vienna", "postalCode": "4810", "metroCode": null, "regionName": "Upper Austria", "isInEuropeanUnion": true, "continent": "Europe", "accuracyRadius": 50}, "uidType": "ipaddress"}</p></blockquote><p>The data set was generated at the premises of <a href="https://www.huemer-group.com/">Huemer Group</a>, a midsize IT service provider located in Vienna, Austria. Huemer Group offers a range of Infrastructure-as-a-Service solutions for enterprises, including cloud computing and storage. In particular, their cloud storage solution called <a href="https://www.huemer-group.com/hbox/">hBOX</a> enables customers to upload their data, synchronize them with multiple devices, share files with others, create versions and backups of their documents, collaborate with team members in shared data spaces, and query the stored documents using search terms. The hBOX extends the open-source project <a href="https://nextcloud.com">Nextcloud</a> with interfaces and functionalities tailored to the requirements of customers.</p><p>The data set comprises only normal user behavior, but can be used to evaluate anomaly detection approaches by simulating account hijacking. We provide an implementation for identifying similar users, switching pairs of users to simulate changes of behavior patterns, and a sample detection approach in our <a href="https://github.com/ait-aecid/clue-lds">github repo</a>.</p><p>Acknowledgements: Partially funded by the FFG project DECEPT (873980). The authors thank Walter Huemer, Oskar Kruschitz, Kevin Truckenthanner, and Christian Aigner from Huemer Group for supporting the collection of the data set.</p><p><strong>If you use the dataset, please cite the following publication:</strong></p><p>[1] M. Landauer, F. Skopik, G. Höld, and M. Wurzenberger. <a href="https://doi.org/10.1109/BigData55660.2022.10020672">"A User and Entity Behavior Analytics Log Data Set for Anomaly Detection in Cloud Computing"</a>. <a href="http://bigdataieee.org/BigData2022/"><i>2022 IEEE International Conference on Big Data - 6th International Workshop on Big Data Analytics for Cyber Intelligence and Defense (BDA4CID 2022)</i></a>, December 17-20, 2022, Osaka, Japan. IEEE. [<a href="https://www.skopik.at/ait/2022_bigdata.pdf">PDF</a>]</p>
Nanopore sequencing data analysis using Microsoft Azure cloud computing service
<p>Genetic information provides insights into the exome, genome, epigenetics and structural organisation of the organism. Given the enormous amount of genetic information, scientists are able to perform mammoth tasks to improve the standard of health care such as determining genetic influences on outcome of allogeneic transplantation. Cloud-based computing has increasingly become a key choice for many scientists, engineers and institutions as it offers on-demand network access and users can conveniently rent rather than buy all required computing resources. With the positive advancements of cloud computing and nanopore sequencing data output, we were motivated to develop an automated and scalable analysis pipeline utilizing cloud infrastructure in Microsoft Azure to accelerate HLA genotyping service and improve the efficiency of the workflow at lower cost. In this study, we describe (i) the selection process for suitable virtual machine sizes for computing resources to balance between the best performance versus cost-effectiveness; (ii) the building of Docker containers to include all tools in the cloud computational environment; (iii) the comparison of HLA genotype concordance between the in-house manual method and the automated cloud-based pipeline to assess data accuracy. In conclusion, the Microsoft Azure cloud-based data analysis pipeline was shown to meet all the key imperatives for performance, cost, usability, simplicity and accuracy. Importantly, the pipeline allows for the ongoing maintenance and testing of version changes before implementation. This pipeline is suitable for data analysis from MinION sequencing platforms and could be adopted for other data analysis application processes.</p>
Data for "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation"
<p>Data for the manuscript "Kinetic limitations affect cloud condensation nuclei activity measurements under low supersaturation" by Tao et al.</p>
Cloud-free multispectral Landsat geomedian of Mexico for the year 2011
<p>Cloud-free national image, generated with the Open Data Cube (https://www.opendatacube.org/) with 1177 Landsat 5 Scenes and 2362 Landsat 7 Scenes with a total of 90% of the scenes with Tier 1 quality from the year 2011 Summarized with the Geomedian Algorithm. In Cloud Optimized GeoTIFF (COG) format (https://www.cogeo.org/).</p>
Metabarcoding for biodiversity inventory blind spots: A test case using the beetle fauna of an insular cloud forest
<p>Soils harbour a rich arthropod fauna, but many species are still not formally described (Linnaean shortfall), and the distribution of those already described is poorly understood (Wallacean shortfall). Metabarcoding holds much promise to fill this gap, however, nuclear copies of mitochondrial genes, and other artefacts lead to taxonomic inflation, which compromises the reliability of biodiversity inventories. Here we explore the potential of a bioinformatic approach to jointly "denoise" and filter non-authentic mitochondrial sequences from metabarcode reads to obtain reliable soil beetle inventories and address open questions in soil biodiversity research, such as the scale of dispersal constraints in different soil layers. We sampled cloud forest arthropod communities from 49 sites in the Anaga peninsula of Tenerife (Canary Islands). We performed whole organism community DNA (wocDNA) metabarcoding, and built a local reference database with COI barcode sequences of 310 species of Coleoptera for filtering reads and the identification of metabarcoded species. This resulted in reliable haplotype data after considerably reducing nuclear mitochondrial copies and other artefacts. Comparing our results with previous beetle inventories, we found: (i) new species records, potentially representing undescribed species; (ii) new distribution records, and; (iii) validated phylogeographic structure when compared with traditional sequencing approaches. Analyses also revealed evidence for higher dispersal constraint within deeper soil beetle communities, compared to those closer to the surface. The combined power of barcoding and metabarcoding contribute to mitigate the important shortfalls associated with soil arthropod diversity data, and thus address unresolved questions for this vast biodiversity fraction.</p>
Point Cloud-Mediveal Hermitage of Alarcos, Spain
The medieval hermitage of Nuestra Señora de Alarcos is located 10 kilometers from Ciudad Real (Spain) in the Archaeological Park of Alarcos. Possibly it was built between the 13th and 14th centuries on top of the Alarcos hill, next to the Guadiana river. This place had previously been an important Iberian fortified (oppida) town. During the Middle Ages a castle was built and an attempt was made to build a walled city that was never completed. The hermitage has three naves and two chapels. The naves are separated from each other by ten limestone pillars that support eight pointed arches characteristic of Gothic art. The capitals of the pillars are decorated with leaves and with representations of human heads. Outside there is a large rose window decorated with six-petal flowers that illuminates the interior of the building. Source: Objaverse 1.0 / Sketchfab
Metropolis Maschinenmensch - Point Cloud
Metropolis is a 1927 German expressionist science-fiction drama film directed by Fritz Lang. Written by Thea von Harbou in collaboration with Lang, it stars Gustav Fröhlich, Alfred Abel, Rudolf Klein-Rogge and Brigitte Helm. Erich Pommer produced it in the Babelsberg Studios for Universum Film A.G. The silent film is regarded as a pioneering science-fiction movie, being among the first feature-length movies of that genre. Filming took place over 17 months in 1925–26 at a cost of over five million Reichsmarks. Made in Germany during the Weimar Period, Metropolis is set in a futuristic urban dystopia and follows the attempts of Freder, the wealthy son of the city master, and Maria, a saintly figure to the workers, to overcome the vast gulf separating the classes in their city and bring the workers together with Joh Fredersen, the city master. The film's message is encompassed in the final inter-title: "The Mediator Between the Head and the Hands Must Be the Heart". Source: Wikipedia Source: Objaverse 1.0 / Sketchfab
Abraham Lincoln Sparse Cloud
Bronze bust by Gutzon Borglum, located in the Special Collections Research Center, University of Kentucky Source: Objaverse 1.0 / Sketchfab
Court Cell Raw Point Cloud
The raw pointcloud from an AR worldmap scan of one of the felons' cells at York Castle Museum. Scanned using ARKit. Source: Objaverse 1.0 / Sketchfab
Saint Helena Chapel - Photogrammetry Point cloud
Photogrammetric point cloud of the Chapel of Saint Helena Jerusalem. The Chapel of Saint Helena is a 12th-century Armenian church in the lower level of the Church of the Holy Sepulchre in Jerusalem, constructed during the Kingdom of Jerusalem. The Armenians call it the Chapel of St. Gregory the Illuminator, after the saint who brought Christianity to the Armenians. Wikipedia Source: Objaverse 1.0 / Sketchfab
MASE cabin and cloud probe data
<p>All data necessary to reproduce observed cloud top flight mean effective radius from the PDI, CAS, CIP and PVM cloud probes aboard the CIRPAS Twin Otter during the MASE campaign are contained herein. The script compile_all_reff.m (for Matlab/Octave) can be used to reproduce the data and understand the format of each probe data file; make sure to change file paths accordingly. PDI and PVM effective radius are available from the "<date>_Ps.mat" files as the variables "s_reff" and "s_reff_xg," respectively. CAS+CIP effective radius is reconstructed from time series of binned drop concentration from each instrument (corresponding to file names "Cas_TO_<date>02.dat" and "cip_conc_<date>.csv") and is output as "reff_caps" from compile_all_reff.m. </p> <p>Geometric mean diameter grids for each probe are contained in the files "dp_<inst>.mat" and cloud top leg times are in the file "reff_datestimes.mat" (time in UTC; columns 2 and 3 are leg start and end time, respectively; note: leg times are included for MASE, POST and VOCALS, not just MASE) and the file "reff_proj.mat" identifies the field campaign associated with each entry in "reff_datestimes." </p> <p>POST and VOCALS datasets may be obtained freely from data.eol.ucar.edu to reconstruct all observed effective radii used in Witte et al. "MODIS retrievals of cloud effective radius in marine stratocumulus exhibit no significant bias," submitted to GRL October 2017.</p>
The role of ions in new-particle formation in the CLOUD chamber: supporting data
<p>Data that is presented in the publication "The role of ions in new-particle formation in the CLOUD chamber", ACP 2017.</p>
Coastal bluff point clouds derived from SfM near Elwha River mouth, Washington from 2016-04-18 to 2020-05-08
<p>Point Clouds of an approximately 2.0 km alongshore reach of seaward-facing coastal bluff faces on the Strait of Juan de Fuca, Washington State, were derived using structure-from-motion (SfM) photogrammetry from digital photos collected at least quarterly between 2016 and 2022. The point clouds were derived to assess spatial and temporal patterns of erosion on the bluff face and deposition at the base of the bluff. Photos from Miller, et al. (2022) were aligned using a modified USGS published workflow (Over, et al., 2022) with Agisoft Metashape Professional 1.8.5. Photos were aligned within a single chunk in a 4D approach described by Wernette, et al. (2022), and the sparse point cloud was filtered by reconstruction uncertainty (Ru) and projection accuracy (Pa). Dense point clouds were generated independently for each survey date by disabling all cameras except for a single photo date and then generating the dense cloud. This was repeated for each of the 30 photo survey dates, resulting in 30 dense point clouds (one point cloud per photo survey date).</p>
Spectral study of rare upward developing, circling, and branching cloud-to-ground lightning
<p>The development of cloud-to-ground (CG) lightning channel is one of the main bases reflecting the discharge characteristics. Time-resolved spectra of CG lightning with eight return strokes were recorded by a slit-less high-speed spectroscope. We found an unusual phenomenon in which a branch developed upward from a node into the cloud exhibited a circling structure after the first return stroke. To investigate the propagation properties of this lightning, characteristic parameters, such as the two-dimensional speed of the leader, node temperature, and electron density, are analyzed. The physical mechanism of the branch is discussed. Spectral analysis reveals that the main channel temperature and electron density were approximately 2.72–3.07 × 10<sup>4</sup> K and 0.23–2.03 × 10<sup>18</sup> cm<sup>-3</sup>, respectively. The temperature and electron density increase as the branch approaches the cloud base, similar to the feature of the main channel approaching the ground. The temperature and electron density of the node are higher than at other locations in the circular channel, indicating that massive charge accumulated around the node. Lightning occurred in the Qilian Mountains in a complex landscape, which easily yielded an uneven distribution in the local electric field of the air, resulting in channel bending, kinking, and even circle development. The phenomenon of the branch developed from the node to the cloud may be due to the fact that the branch is formed during the return stroke process.</p>
The PHAse Cloud Type (PHACT) product
<p><strong>Description</strong></p> <p>The PHAse Cloud Type (PHACT) product development has been guided by recent ground-based observations to document the topmost liquid-bearing cloud layers for different cloud types (ice over liquid, liquid only, liquid seeded by ice above, mixed-phase, multilayer and single layer etc.) and for thin (optical depth < 3) and opaque (optical depth > 3) clouds. PHACT uses instantaneous CALIPSO-GOCCP cloud profiles (Cesana et al., 2013; Chepfer et al., 2010) at a 333 m along-track resolution and for 480 m altitude bins. Liquid- and ice-dominated cloudy bins are discriminated using the polarization state of the CALIPSO lidar return, different for non-spherical crystals and spherical droplets. </p> <p>PHACT documents ice-only (no liquid in the column), mixed-phase (liquid with ice below, contiguous or not) and liquid (with possibly ice above) cloud covers. Additionally, the phase cloud cover is computed as the number of cloud phase type profiles divided by the total number of profiles, consistent with the common definition of cloud cover:</p> <p>CCphase = Nphase / Nprofiles<br><br>Where phase means either ice, liquid or mixed-phase.</p> <p>Therefore, the sum of all cloud covers cannot be greater than 1. PHACT contains a mixed-phase category, which is not available in CALIPSO-GOCCP. Mixed-phase clouds are defined as liquid clouds with ice or undefined-phase clouds underneath, directly or not. Undefined-phase clouds are defined as cloudy bins that are located below highly reflective cloud bins. At subfreezing temperatures, undefined-phase clouds are typically mixed-phase clouds(Cesana et al. 2016).<br>BAsed on a validation study against in situ aircraft measurements, the maximum disagreement fraction between CALIPSO-GOCCP instantaneous profiles, which are used to create PHACT statistics, and five in situ aircraft flights is 11.8% when accounting for in situ aircraft measurement uncertainty (Cesana et al. 2016; their Table 3). </p> <p> </p> <p><span><em>PHACT2D_200701-202012_avg_2.5x2.5_3.1.2_v1.52.nc</em>: This file contains the monthly mean 2D cloud covers for the different cloud phase types (all documented in the file), over a 2.5˚x2.5˚ grid for the 2007-2020 period.</span></p> <p><span><em>PHACT3D_200701-202012_avg_2.5x2.5_3.1.2_v1.52.nc</em>: This file contains the monthly mean 3D cloud fractions for the different cloud phase types (all documented in the file), over a 2.5˚x2.5˚ horizontal grid and for 480m altitude bins from 0 to 19.2 km, for the 2007-2020 period.</span></p> <p> </p> <p><strong>References</strong></p> <p>Cesana, G.V., O. Pierpaoli, M. Ottaviani, L. Vu, Z. Jin and I. Silber, 2024: <a href="https://pubs.giss.nasa.gov/abs/ce08200c.html">The correlation between Arctic sea ice, cloud phase and radiation using A-train satellites</a>. Atmos. Chem. Phys., accepted.</p> <p><span>Cesana, G.</span>, H. Chepfer, D. Winker, B. Getzewich, X. Cai, O. Jourdan, G. Mioche, H. Okamoto, Y. Hagihara, V. Noel, and M. Reverdy, 2016: <a href="https://pubs.giss.nasa.gov/abs/ce00200u.html">Using in situ airborne measurements to evaluate three cloud phase products derived from CALIPSO</a>. J. Geophys. Res. Atmos., <strong>121</strong>, no. 10, 5788-5808, doi:10.1002/2015JD024334.</p> <p>Cesana, G., and H. Chepfer, 2013: <a href="https://pubs.giss.nasa.gov/abs/ce04200y.html">Evaluation of the cloud thermodynamic phase in a climate model using CALIPSO-GOCCP</a>. J. Geophys. Res. Atmos., <strong>118</strong>, no. 14, 7922-7937, doi:10.1002/jgrd.50376.</p> <p>Chepfer, H., S. Bony, D. Winker, <span>G. Cesana</span>, J.L. Dufresne, P. Minnis, C.J. Stubenrauch, and S. Zeng, 2010: <a href="https://pubs.giss.nasa.gov/abs/ch03410n.html">The GCM-Oriented CALIPSO Cloud Product (CALIPSO-GOCCP)</a>. J. Geophys. Res., <strong>115</strong>, no. D4, D00H16, doi:10.1029/2009JD012251.</p>
DARDAR 1˚x1˚ gridded statistics of cloud fraction by cloud phase type over the Arctic for the period 2007 - 2010
<p><strong>Description</strong></p> <p>These files, further described below, contain the necessary DARDAR variables to reproduce the figures and tables of Cesana et al. (2024), which are not publically available. The cloud phase statistics have been processed over the Arctic only (60˚N to 82˚N) for the years 2007 to 2010, and accumulated onto a 1˚x1˚ spatial grid, using the DARDAR instantaneous files (Delanoe and Logan, 2010).</p> <p><em>dardar_grid_2D_2007-2010.nc</em>: This file contains the monthly mean total, ice, liquid and mixed 2D cloud covers (clt, clt_ice, clt_water and clt_mixed, respectively), as well as the total number of profiles per grid box (total_profiles), the latitude and longitude (lat, lon) and the time.</p> <p><em>dardar_grid_3D_2007-2010.nc</em>: This file contains the same variables as the 2D files except with the altitude dimension, over 40 480m altitude bins, from 0 to 19.2 km. Alt corresponds to the mid-level altitude of each altitude bins.</p> <p> </p> <p><strong>References</strong></p> <p>Cesana, G.V., O. Pierpaoli, M. Ottaviani, L. Vu, Z. Jin and I. Silber, 2024: <a href="https://pubs.giss.nasa.gov/abs/ce08200c.html">The correlation between Arctic sea ice, cloud phase and radiation using A-train satellites</a>. Atmos. Chem. Phys., accepted.</p> <p>Delanoe, J. and Hogan, R. J., 2010: Combined CloudSat-CALIPSO-MODIS retrievals of the properties of ice clouds, Journal of Geophysical<span> </span>Research: Atmospheres, 115, https://doi.org/https://doi.org/10.1029/2009JD012346.</p>
Data and scripts for "Unraveling secondary ice production in winter orographic clouds through a synergy of in-situ observations, remote sensing and modeling"
<div> <div> <div>This repository contains field observations and processed data from the Weather Research and Forecasting (WRF) model simulations and the Cloud Resolving Model Radar Simulator (CR-SIM), alongside scripts designed to reproduce the figures presented in the paper titled "Unraveling Secondary Ice Production in Winter Orographic Clouds through a Synergy of In-Situ Observations, Remote Sensing, and Modeling." The in-situ and remote sensing measurements were conducted at Mount Helmos in Peloponnese as part of the CALISHTO campaign (https://calishto.panacea-ri.gr/).</div> </div> </div> <div>Preprint accessible at: https://doi.org/10.21203/rs.3.rs-3502790/v1</div>
Global Cloud Biases in Optical Satellite Remote of Rivers - Accompanying Dataset
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
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.