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2,170 results for “clouds”
NERC MSTRF Sky-Camera Time-Lapse Video for 2010-09-07 05:30 UTC (showing widespread development of Cumulonimbus clouds, some of which have extensive Anvils).
<p>A time-lapse video showing widespread development of Cumulonimbus clouds, some of which have extensive Anvils. A variety of other cumuloform clouds can be seen. The origin of the widespread cirrus cloud seen during the second half of the sequence is probably outflow from Cumulonimbus anvils. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2008-10-05 11:00 UTC (showing Cumulus humilis clouds, which are also known as fair weather cumulus).
<p>A time-lapse video showing Cumulus humilis clouds, which are also known as fair weather cumulus. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</p>
NERC MSTRF Sky-Camera Time-Lapse Video for 2008-01-02 15:00 UTC (showing Altocumulus lenticularis clouds revealing Mountain Wave activity).
<p>A time-lapse video showing Altocumulus lenticularis clouds revealing Mountain Wave activity. Notice how the positions of the clouds remain fixed relative to the landscape. This contrasts with the lower-level (Cumulus) clouds, seen at the beginning of the sequence, which move downstream with the wind. This video has been created from images taken by the NERC MST Radar Facility's Sky-Camera, which is located near Aberystwyth in West Wales. The images are freely available, under an Open (UK) Government License, from http://tinyurl.com/nerc-mstrf-sky-camera/ . For an explanation of the atmospheric phenomena that can be seen, download the resource available at http://cedadocs.ceda.ac.uk/1259/ .</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2007-05-21 03:00 UTC (showing Altocumulus undulatus clouds).
<p>A time-lapse video showing Altocumulus undulatus clouds. The cloud layer initially appears to be of Stratocumulus type, with relatively poorly-defined undulatus elements. However, the layer soon breaks up to give well-defined Altocumulus undulatus. Cirrus clouds and contrails can be seen at a higher level towards the end of the sequence. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2007-09-04 17:50 UTC (showing Stratocumulus clouds that develop wave-like asperitas features).
<p>A time-lapse video showing Stratocumulus clouds that develop wave-like asperitas features. During the early part of the sequence, there appear to be layers of cloud at two distinct levels. These are lit differently by the setting sun. The asperitas features begin to appear approximately half way through the sequence. Altocumulus undulatus clouds can be seen towards the end. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2009-03-25 17:25 UTC (showing Altocumulus fluctus and Altocumulus undulatus clouds followed by Altocumulus lenticularis)
<p>A time-lapse video showing Altocumulus fluctus and Altocumulus undulatus clouds followed by Altocumulus lenticularis. The short-lived fluctus cloud feature is caused by Kelvin-Helmholtz wave activity, which is generated where the wind speed changes sharply with altitude. The initial telephoto image shows the characteristic breaking-wave pattern. This was taken (at 17:37 UTC) at the same location as the camera used to to generate the video sequence, but shows a narrower field of view. The wave activity is also revealed by the Altocumulus undulatus cloud elements, which form in parallel bands. There is evidence of mountain wave activity throughout the sequence and Altocumulus lenticularis becomes the dominant cloud type towards the end. These clouds remain stationary relative to the landscape rather moving with the wind. Occasional contrails and Cirrus clouds can be seen at a higher level (one of the contrails develops undulatus features). These remain illuminated for longer than the Altocumulus clouds as the sun sets. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</p>
Data from the NASCENT campaign used in the publications: "Conditions favorable for secondary ice production in Arctic mixed-phase clouds" and "Understanding the history of two complex ice crystal habits deduced from a holographic imager"
<p>This repository contains the data from the Ny‐Ålesund AeroSol Cloud ExperimeNT campaign (NASCENT). This data were used to produce the figures in the two papers:</p> <p>(1) Pasquier, J. T., Henneberger, J., Ramelli, F., Korolev, A.,Wieder, J., Lauber, A., Li, G., David, R. O., Carlsen, T., Gierens, R., Maturilli, M., and Lohmann, U.: Understanding the history of two complex ice crystal habits deduced from a holographic imager, Geophys. Res. Lett., accepted, 2022</p> <p> </p> <p>(2) Pasquier J. T., Henneberger J., Ramelli F., Lauber A., David O. D., Wieder J., Carlsen T., Gierens R., Maturilli M., and Lohmann U.:Conditions favorable for secondary ice production in Arctic mixed-phase clouds, ACP, accepted.</p> <p>More information can be found in the README files.</p> <p> </p> <p>The scripts to reproduced the Figures are available on Zenodo</p> <p>(1) https://doi.org/10.5281/zenodo.7402296</p> <p>(2) https://doi.org/10.5281/zenodo.7407107</p>
NOAA PSL CL31 Ceilometer Backscatter and Cloud Base Height Data for SPLASH
<p>This dataset contains daily files from a CL31 ceilometer manufactured by Vaisala that was deployed at Roaring Judy in the East River Watershed in Colorado (38.7169321 N, 106.853031 W, 2494 m above mean sea level) from 21 October 2021 to 28 January 2022 as part of the National Oceanic and Atmospheric Administration (NOAA) Study of Precipitation, the Lower Atmosphere, and Surface for Hydrometeorology (SPLASH) campaign. </p> <p>The files contain backscatter profiles, cloud base heights, and visibility. The ceilometer measures vertical profiles of backscatter using laser technology. From the backscatter profiles, cloud base height and vertical visibility are determined using with the Vaisala software CL-view. For details on the instrument specifics and the methods, see the manufacturer manual (cl31usersguide.pdf). </p> <p>The file format is the original Vaisala format (.DAT). For a description of the format see the manufacturer manual. To convert the .DAT file format to netcdf format, the open source command line Python program ‘cl2nc’ (https://github.com/peterkuma/cl2nc) can, for example, be used. </p> <p>The file naming conventions for the .DAT files are as follows:</p> <p>NOAA_PSL_CL31_Roaring Judy_yyyymmdd_HH.DAT</p> <p>with</p> <p>yyyy: Year</p> <p>mm: Month</p> <p>dd: Day</p> <p>HH: Hour when the first sample was written to the file</p> <p>The time stamp of all data is in UTC.</p>
Dataset for "Surface-Atmosphere Decoupling Prolongs Cloud Lifetime Under Warm Advection Due To Reduced Entrainment Drying"
<p>Dataset for "Surface-Atmosphere Decoupling Prolongs Cloud Lifetime Under Warm Advection Due To Reduced Entrainment Drying". The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
Replication Package for: Streaming vs. Functions: A Cost Perspective on Cloud Event Processing
<p>In cloud event processing, data generated at the edge is processed in real-time by cloud resources. Both distributed stream processing (DSP) and Function-as-a-Service (FaaS) have been proposed to implement such event processing applications. FaaS emphasizes fast development and easy operation, while DSP emphasizes efficient handling of large data volumes. Despite their architectural differences, both can be used to model and implement loosely-coupled job graphs. In this paper, we consider the selection of FaaS and DSP from a cost perspective. We implement stateless and stateful workflows from the Theodolite benchmarking suite using cloud FaaS and DSP. In an extensive evaluation, we show how application type, cloud service provider, and runtime environment can influence the cost of application deployments and derive decision guidelines for cloud engineers.</p>
Anisotropic Radiance/Reflectance Simulations for Clouds
<p>This data file contains anisotropic (sensor viewing azimuth angles range from 0 to 360° of every 5°) radiance and reflectance calculations at the top of the atmosphere for clouds.</p> <p> </p> <p><strong>Radiative transfer parameters:</strong></p> <p>1) sensor viewing zenith angle of 45°;</p> <p>2) solar zenith angle of 60°;</p> <p>3) solar azimuth angle of 0 (north);</p> <p>4) surface albedo (Lambertian): 0.03;</p> <p>5) cloud optical thickness: 10;</p> <p>6) cloud effective radius: 12 micron;</p> <p>7) cloud vertical location: 0.5 km to 1 km.</p> <p> </p> <p><strong>Data description:</strong></p> <p>rad_555: 1D array (size of 73), radiance at 555 nm, units in W/m²/nm/sr;</p> <p>ref_555: 1D array (size of 73), reflectance at 555 nm;</p> <p>rad_vis: 1D array (size of 73), broadband (350 - 700 nm) radiance, units in W/m²/nm/sr;</p> <p>ref_vis: 1D array (size of 73), broadband (350 - 700 nm) reflectance;</p> <p>vaa: 1D array (size of 73), sensor viewing azimuth angles, units in degree;</p> <p>vza0: single value, sensor viewing zenith angle, units in degree;</p> <p>saa0: single value, solar azimuth angle, units in degree;</p> <p>sza0: single value, solar zenith angle, units in degree.</p> <p> </p> <p><strong>Notes:</strong></p> <p>This data file is generated by the code included in EaR<strong>³</strong>T (<a href="https://github.com/hong-chen/er3t">https://github.com/hong-chen/er3t</a>, function <example_rad_02_libera_adm> in <examples/00_er3t_lrt.py>). The listed parameters such as solar and sensor viewing geometries, cloud optical properties, surface albedo etc. can be customized.</p>
Supplementary Figures for "Patchy Forsterite Clouds in the Atmospheres of Two Highly Variable Exoplanet Analogs" (Vos+ 2023)
<p>Supplementary figures for "Patchy Forsterite Clouds in the Atmospheres of Two Highly Variable Exoplanet Analogs" (Vos et al. 2023; doi: 10.3847/1538-4357/acab58). The figures show posterior distributions from the retrievals for models that were not chosen as the preferred model. </p>
CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"
<p>Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).</p>
Simulations of stratocumulus cloud fields using RAMS model
<p>The simulations of the stratocumulus are performed with a domain size of km ( bin points) for 3 hours. The horizontal resolution is fixed at 100 m, and the vertical bin spacing is 50 m. The initial state of the simulations (DYCOMS-II case) is based on vertical profiles of potential temperature, moisture, and horizontal winds, that were adapted from Stevens et al. (2003). From these initial fields, 4 additional simulations are carried out by slightly modifying the temperature profiles to check their effects on the stratocumulus field and notably on entrainment rates. In addition, one extra simulation is realized by modifying the humidity profile. In summary, these 6 simulations are as follows: case 1) ‘Control’ is the basic simulation with the unmodified fields; case 2) ‘Control + layer 150 m’ is as ‘Control’ but the temperature inversion is 150 m above that of ’Control’ (less brutal than ‘Control’), expecting more entrainment; case 3) ‘Control + layer 300 m’ is as ‘Control’ but the temperature inversion is 300 m above that of ’Control’; case 4) ‘Control - 4K’ is as ‘Control’ but with a smaller temperature inversion, expecting more mixing; case 5) ‘Control + 4K’ is as ‘Control’ but with a stronger temperature inversion; and case 6) ‘Extra’ is as ‘Control’ but initialized using a slightly modified water vapor profile. Detailed application of the datasets is described in our under reviewing paper (Shang et al 2022).</p> <p>Reference:</p> <p>Shang, H., Hioki, S., Penide, G., Cornet, C., Letu, H., and Riedi, J.: Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-674, in review, 2022.</p> <p>Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S., Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S. M., Malinowski, S. P., Morley, B., Petters, M. D., Rogers, D. C., Russell, L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J., Takagi, H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, M. C.: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II, Bulletin of the American Meteorological Society, 84, 579-594, 10.1175/BAMS-84-5-579 %J Bulletin of the American Meteorological Society, 2003.</p>
Data of "Aerosol-cloud interaction enhanced the Tibetan Plateau warming " - version1
<p>The data (version 1) of "Aerosol-cloud interaction enhanced the Tibetan Plateau warming ".</p> <p>This is source observation data of figures in the manuscript "Aerosol-cloud interaction enhanced the Tibetan Plateau warming". The manuscript is currently in the status of initial submission. </p> <p> </p>
DEM, rock point clouds, and 3D morphological features of Martian rocks in the landing region of Zhurong rover
<p>Rocks on the Martian surface and their three-dimensional (3D) morphology record the geological evolution of Mars and its interaction history with outer space. China's Zhurong Mars rover has traveled on Mars for nearly 2 kilometers and obtained a wealth of data, enabling the fine-scale measurement and morphological analysis of 3D Martian rocks. With 178 high-resolution Zhurong NaTeCams stereo images acquired in rings covering large circular areas, we extracted 6,185 3D rocks from centimeter-scale to meter-scale distributed along the traverse of the Zhurong rover using an automatic approach. This dataset includes the digital elevation model (DEM), extracted rocks in point clouds, and the 3D morphological features of rocks derived from the Zhurong NaTeCams images, obtained in six areas along the traverse of the Zhurong rover.</p> <p>To use this dataset, please cite "Li, Y., Xiao, Z., Ma, C., Zeng, L., Zhang, W., Peng, M., & Li, A. (2023). Extraction and Analysis of Three‐dimensional Morphological Features of Centimeter‐scale Rocks in Zhurong Landing Region. <em>Journal of Geophysical Research: Planets</em>, e2022JE007656."</p>
Urban cloud patterns and environmental factors dataset supporting for paper: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023)
<p> </p> <p>The dataset supporting for the publication: "How do Cities Modify Local Cloud Patterns?" Vo T.T., Hu L., Xue L., Li Q., Chen S. (2023), PNAS. </p> <ul> <li>The data format is in tabular form (.csv, comma delimited). Each row is the <strong>monthly </strong>aggregated <span class="math-tex">\(\Delta_{CloudCover}\)</span> for <strong>each </strong>city for daytime and nighttime, associated with the environmental factors used to study the relationship in the research. More specifically, the description of each factor associated with its unit formatted in the table are listed as follows: <ul> <li>the spatial differences in cloud coverage (in percent) between urban and adjacent background domain (<span class="math-tex">\(\Delta_{CloudCover}\)</span>): <strong>cldmeand </strong>(in percent) </li> <li>the city size (<span class="math-tex">\(log_{10}A\)</span>, A in km<sup>2</sup>) as an indicator of surface roughness and anthropogenic emission: <strong>CitySize </strong>(in <span class="math-tex">\(log_{10}(km^{2})\)</span>)</li> <li>the differential urban-background surface heating measured by land surface temperature (<span class="math-tex">\(\Delta LST\)</span>): <strong>Tempdiff </strong>(in K)</li> <li>the moisture availability measured by annual precipitation (P): <strong>precipannualbackground </strong>(in mm)</li> <li>the energy availability measured by mean annual temperature (<span class="math-tex">\(\overline{T}\)</span>): <strong>tempannualbackground </strong>(in <span class="math-tex">\(^{o}C\)</span>)</li> <li>the longitudes and latitudes for the defined city: <strong>xcoords </strong>(longitudes, in meters) and <strong>ycoords </strong>(latitudes, in meters)</li> </ul> </li> <li>Other columns: <ul> <li><strong>NAME10</strong>: the name of urban domain or city defined in the study (CENSUS 2010)</li> <li><strong>time</strong>: daytime (around 13 LT) or nighttime (around 1 LT). </li> <li><strong>monthtext</strong>: specifies the month (January to December)</li> <li><strong>urban_type</strong>: the geographical location of the defined urban area (either inland, coastal or mountainous city) </li> </ul> </li> </ul>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Timelapse footage of deep convective clouds in New Mexico produced during the DCMEX field campaign
<p>Timelapse footage of clouds taken during the<a href="https://cloudsense.ac.uk/dcmex/"> Deep Convective Microphysics Experiment</a> (DCMEX) research project, funded by the UK Natural Environment Research Council.</p> <p>Cameras were pointed towards the Magdalena mountains. The main camera location was Socorro airport, a secondary location was Econolodge, Socorro, and a third location was Magdalena airport.</p> <p>These are a subset of the footage collected. 20s interval photographs from a number of days are available from another archive.</p> <p>During these timelapses the FAAM aircraft was flying through the clouds collecting thermodynamic, dynamics, microphysics and aerosol <a href="https://catalogue.ceda.ac.uk/uuid/b1211ad185e24b488d41dd98f957506c">measurements</a>. Radars were also sometimes operational.</p> <p>The full campaign and data is decribed in detail in <a href="https://essd.copernicus.org/articles/16/2141/2024/">Finney et al. (2024) ESSD</a>.</p> <p><strong>Video descriptions</strong></p> <p>19th July - A number of convective cloud bursts, but no anvil formed over the Magdalena mountains during this footage.</p> <p>23rd July - Cumulus are present and growing from the start of the footage. Cloud bases stay rooted to the mountain. Shear appears low until a detrainment layer is reached.</p> <p>27th July - Footage begins with clear skies. Strong low levels winds carry clouds northward. Deep convective clouds form and are detrained south westward. Later in the footage scene becomes overcast with high cloud, and this suppresses the earlier deep convection. Late in the camera 1 footage a gust front cloud passes across the scene.</p> <p>29th July - Footage taken from the Magdalena airport and begins with fast moving cumulus cloud. The cloud grows and moves over the camera.</p> <p>31st July - Cumulus clouds form almost immediately over the Magdalena mountains and steadily grow from multiple thermals. Cloud bases stay rooted to the mountain, but shear aloft carries cloud northward from a detrainment layer. The cloud takes a structure similar to anvil but winds are strong and cloud in the detrainment layer appears to be fairly long-lived, warping the anvil shape somewhat.</p> <p>2nd August - Footage begins overcast but clears. Cumulus clouds and congestus begin to form, with strong low level winds carrying them southward.</p> <p><strong>Related datasets</strong></p> <p>Facility for Airborne Atmospheric Measurements; Finney, D.; Blyth, A.; Gallagher, M.; Wu, H.; Nott, G.J.; Biggerstaff, M.; Sonnenfeld, R.G.; Daily, M.; Walker, D.; Dufton, D.; Bower, K.N.; Boeing, S.; Choularton, T.W.; Crosier, J.; Groves, J.; Field, P.; Coe, H.; Murray, B.J.; Lloyd, G.; Marsden, N.A.; Flynn, M.; Hu, K.; Thamban, N.M.; Williams, P.I.; Connolly, P.J.; McQuaid, J.B.; Robinson, J.; Cui, Z.; Burton, R.R.; Carrie, G.; Moore, R.; Abel, S.J.; Tiddeman, D.; Aulich, G.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Morris, F.; Hampton, J. (2022): DCMEX: Collection of in-situ airborne observations, ground-based meteorological and aerosol measurements and cloud imagery for the Deep Convective Microphysics Experiment. NERC EDS Centre for Environmental Data Analysis, <em>30 April 2024</em>. <a href="https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C">https://dx.doi.org/10.5285/B1211AD185E24B488D41DD98F957506C</a></p> <p>Individual image archive for camera 1 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 11 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1">https://dx.doi.org/10.5285/b839ae53abf94e23b0f61560349ccda1</a></p> <p>Individual image archive for camera 2 - Finney, D.; Groves, J.; Walker, D.; Dufton, D.; Moore, R.; Bennecke, D.; Kelsey, V.; Reger, R.S.; Nowakowska, K.; Bassford, J.; Blyth, A. (2023): DCMEX: cloud images from the NCAS Camera 12 from the New Mexico field campaign 2022. NERC EDS Centre for Environmental Data Analysis, 15 December 2023. <a href="https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce">https://dx.doi.org/10.5285/d1c61edc4f554ee09ad370f6b52f82ce </a></p> <p>Oklahoma University radar data - <a href="https://doi.org/10.5281/zenodo.8051426">https://doi.org/10.5281/zenodo.8051426</a></p> <p> </p>
Point clouds data "Waterfall height sets the mechanism and rate of upstream retreat"
<p>Point clouds data on the erosion shape of waterfalls in our experiment in the paper "Waterfall height sets the mechanism and rate of upstream retreat".</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.