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29 results for “low clouds”
ICON-LEM Ny-Ålesund low-level clouds polar night and polar day 2021/2022
<h3>Low-level clouds during the polar night and polar day simulated in ICON-LEM for Ny-Ålesund </h3> <p>This data set was created using the ICON-LEM model with ca. 600m resolution and a diagnostic tool "microphysical wrapper". It contains the meteogram output of the Ny-Ålesund column (Svalbard) and the microphysical process rates. The data was created for the polar night (Nov 2021- Feb 2022) and polar day (May - Aug 2022). Clouds are classified as low-level if their cloud top height (CTH) is below 2.5 km and the distance between any cloud with CTH above 2.5 km is at least 500 m higher. The data set was first used and described in the <em>publication: </em></p> <p>T. Kiszler, D. Ori, V. Schemann<em>. </em>(preprint) Microphysical processes involving the vapour phase dominate in simulated low-level Arctic clouds. <em>Atmospheric Physics and Chemistry, </em>https://doi.org/10.5194/egusphere-2023-2986<em><br></em></p> <p>This data is related to the repository <a href="https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics">https://github.com/TracyMcBean/Kiszler_et_al_2023_microphysics</a></p> <p><em>File description:</em></p> <p>*_PN is polar night data</p> <p>*_PD is polar day data</p> <p>LLC_<em>meteo_<yyyymm>_ICONv1</em>_v6.nc : Contains the meteogram variables (thermodynamics, surface variables, hydrometeors)</p> <p>LLC_wrapper_mass_<yyyymm>_ICONv1_v6.nc : Contains hydrometeors masses after diagnostic run of a microphysical wrapper</p> <p>LLC_wrapper_tend_<yyyymm>_ICONv1_v6.nc : Contains the mircophysical process rates showing the mass change per timestep </p> <p>low_cloud_times_v6_*.csv : Contains the date and time when a low-level cloud was detected</p>
Single column 1D radiative transfer simulations during PS106 including low-level-stratus clouds in the central Arctic
<p>The collection of datasets published contain the input parameters and output simulations from a single column 1D radiative transfer simulations using the <strong>R</strong>apid <strong>R</strong>adiative <strong>T</strong>ransfer <strong>M</strong>odel for <strong>G</strong>eneral Circulation Model (GCM) applications (RRTMG).</p><p>The data set contains simulations for the PS106 research cruise conducted in 2017 in the Central Arctic. The simulations are based on remote sensing data which were processed with the Cloudnet algorithm to derive cloud macro - and microphyiscal products. The atmospheric profiles of temperature, pressure, and ozone are from ERA5 (European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis) and values of surface albedo from CERES (Clouds and the Earth's Radiant Energy System) SYN1deg Ed. 4.1.</p>
X-Shooting ULLYSES: Massive Stars at low metallicity - II. DR1: Advanced optical data products for the Magellanic Clouds
<p>Xshooter optical spectroscopic data of Magellanic Clouds targets observed by the ESO Large Program X-Shooting ULLYSES: Massive Stars at low metallicity (PI: Vink; Porgram ID: 106.2011Z). <br><br>This version is identical to the previous version but includes the LMC and SMC atlases and the static calibration files with the new flux models (all arms) and spline anchor points (only UVB).</p>
Dataset for "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis"
<p>This repository contains the data of 512 perturbed-parameter ensemble experiments and CAM5-CLUBB default experiments for the paper "Low-cloud feedback in CAM5-CLUBB: physical mechanisms and parameter sensitivity analysis".</p> <p>In this paper, the quasi-Monte Carlo (QMC) sampling approach is applied to explore the high-dimensional space. 512 samples are generated with the 18 perturbed parameters. For each parameter sample, a pair of experiments is performed: the control one is based on the climatological sea surface temperature (SST), and the 4K experiment applies a uniform +4K SST perturbation to the control experiment. The total of 1024 simulations are then performed. In addition, CAM5-CLUBB default experiments that adopt the default values of the 18 selected parameters as in Bogenschutz et al. (2013) are performed to provide detailed model diagnostics for analyzing physical mechanisms of the cloud feedback, and they include both control and +4K simulations. Each simulation is run for 5 years and 4 months, forced by climatological SSTs. Monthly mean results from the last 5 years are analysed in this study.</p> <p>Note: data uploaded here is annual-mean and the dimension name 'time' in the files (CAM5-CLUBB_PPE_512*.nc) is the number of 512 PPE member.</p>
Low Cloud Amount
<p>The data of cloud amount in summer in the TP(the sources of the Yangtze, Yellow, and Lancang rivers) in the period of 1979 to 2016 are provided by the National Meteorological Information Center , China Meteorological Administration(CMA). </p>
Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations
<p>This dataset contains a comprehensive set of quality-controlled remote sensing observations of low-level mixed-phase clouds collected at the high Arctic site of Ny-Ålesund, between 10 October 2021 and 31 December 2022. Cornerstones of the dataset are observations from a 35-GHz polarimetric scanning Doppler cloud radar and a 94-GHz zenith-pointing Doppler cloud radar. Radar data are complemented with thermodynamic retrievals from a microwave radiometer, liquid base height from a ceilometer and wind fields from large-eddy simulations. All data have undergone extensive quality control, especially the cloud radar data, which are accurately calibrated, matched, and corrected for gas and liquid-hydrometeor attenuation, ground clutter and range folding. This dataset is especially suited for cloud microphysical studies, and the high number of events included allows for the compiling of robust statistics. The dataset is accompanied by a data descriptor article, which is available at <a href="https://doi.org/10.5194/essd-15-5427-2023" target="_blank" rel="noopener">doi.org/10.5194/essd-15-5427-2023</a>.</p> <p> </p> <p><strong>Dataset overview</strong><br>The files include only low-level mixed-phase cloud (LLMPC) events, as well as the 2 hours preceding and following events. Each file contains an individual event, unless multiple events are less than 4 hours apart, in which case they are combined into the same file. LLMPC events are detected by requiring that ice and liquid phase coexist in a cloud layer with top below 2500 m for at least one hour. All radar variables observed in zenith (Doppler moments at 35 and 94 GHz, linear depolarization ratio (LDR) at 35 GHz), as well as microwave radiometer retrievals (temperature (T), liquid water path (LWP), integrated water vapor (IWV)), liquid base height from the ceilometer, and model data (horizontal wind speed and direction) are brought to the same time and range grids (respectively named ‘time_zen’ and ‘range_zen’ in the files). Off-zenith radar variables (reflectivity, differential reflectivity (ZDR), maximum spectral ZDR (sZDRmax), correlation coefficient (RhoHV), differential phase shift (PhiDP), and specific differential phase (KDP)) are stored on separate coordinates (named ‘time_slant’ and ‘range_slant’). All derived corrections are already applied to the data, and stored in the files, in case the user is interested in reconstructing the original data. A number of flags have been included in the files: in particular ‘MPC_detected’ indicates whether a LLMPC event was detected, and ‘liquid_attenuation_correction_flag_zen’ and ‘liquid_attenuation_correction_flag_slant’ indicate whether radar reflectivities were corrected for attenuation due to liquid hydrometeors. Liquid attenuation corrections should be especially taken into account when computing the dual-wavelength ratio (i.e., the difference between reflectivity at 35 GHz and at 94 GHz, both expressed in dBZ), and performing quantitative analyses of reflectivity fields.</p>
Supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations"
<p>This dataset is a supplement to "Low-level mixed-phase clouds at the high Arctic site of Ny-Ålesund: A comprehensive long-term dataset of remote sensing observations", available at <a href="http://doi.org/10.5281/zenodo.7803064">doi.org/10.5281/zenodo.7803064</a>. The additional variables here included are: slow edge velocity, fast edge velocity, and eddy dissipation rate (EDR). All variables are stored on the same time and range grids adopted for the main dataset. Similarly, the event selection and file structure are identical to those of the main dataset.<br><br>Slow and fast edge velocities are derived from Doppler spectra recorded by the zenith-pointing 94-GHz cloud radar. The slow (fast) edge velocity is calculated as the velocity associated with the slowest (fastest) Doppler bin above the peak noise level, belonging to a spectral cluster whose width is at least 5 Doppler bins.<br><br>The EDR is retrieved following the approach by Borque et al. (2016; <a href="http://doi.org/10.1002/2015JD024543">doi.org/10.1002/2015JD024543</a>), using as input the slow edge velocity, and model horizontal wind speed from the main dataset. EDR is retrieved in 5 minute intervals, up to a maximum range of 3 km.<br><br>The detailed documentation of the variables here included can be found in the Supporting Information to the following publication: <a href="https://doi.org/10.1029/2023GL106599" target="_blank" rel="noopener">doi.org/10.1029/2023GL106599</a>.</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>
Data for Coastal Fog and Low Clouds Provide Intertidal Organisms Refugia During Summer Heat
<p>This repository contains data and code relevant to the paper<br>Coastal Fog and Low Clouds Provide Intertidal Organisms Refugia During Summer Heat<br>by Jessica Lundquist, Autumn Nguyen, Steven Pestana, and Eli Schwat<br>contact person: Jessica Lundquist, University of Washington, jdlund@uw.edu<br>submitted to <em>Geophysical Research Letters</em><br>July 29, 2024</p> <p>Files are as follows:</p> <p>Paper files (note, these are submitted files, and may be modified in the final published version):<br>Lundquist_GRL_Manuscript_July29.pdf Manuscript file, main text and figures, submitted July 29, 2024<br>Lundquist_GRL_SuppInfo_July_29.pdf Supplemental Information file, submitted July 29, 2024</p> <p>Movie files of timelapse photographs from each of the sites:<br>timeLapseCattlePoint_July_Oct_2022.mp4 Images from Cattle Point site, facing Lopez Island<br>timeLapseFHL_facingShaw_July_2022.mp4 Images from Friday Harbor Laboratory (FHL) site, facing Shaw Island<br>timeLapseFHL_facingShaw_July_Nov_2022.mp4 Images from Friday Harbor Laboratory (FHL) site, facing Shaw Island<br>timeLapseMtDallas_view_2022.mp4 Images from Mt. Dallas site, looking towards the coast, with FHL toward the left of the image, and Cattle Point towards the right of the image.</p> <p>Temperature data files:<br>SJI_Tdata_site1.nc Temperature and relative humidity data from Hobo datalogger at Friday Harbor Laboratories Weather station location<br>SJI_Tdata_site2.nc Temperature and relative humidity data from Hobo datalogger at Mt. Dallas location <br>SJI_Tdata_site3.nc Temperature and relative humidity data from Hobo datalogger at Cattle Point location<br>SJI_Tdata_site4.nc Temperature and relative humidity data from Friday Harbor Laboratories Weather Station <br>FHairportdata.txt Raw data from Friday Harbor airport weather station.</p> <p>Reanalysis data files:<br>SJI_strongInv2022.txt File of dates used to select days of strong inversion to assess anomalies<br>500mbHtanomalies.205.175.106.80.157.13.39.23.nc 500hPa height anomaly data for Fig 2e map<br>Tanomaly850mbyycompos.205.175.106.80.157.13.47.49.nc 850hPa temperature anomaly data for Fig 2f map</p> <p><br>Matlab files for plotting figures in paper<br>(note that Matlab software is not required to read these files)<br>TRH2.mat Temperature and relative humidity data from in situ locations for 2022<br>Tides2021_23.mat Friday Harbor tide hight data<br>GOESclouds.mat Cloud height data for the locations with surface observations<br>FHairportdata.mat Temperature data from the Friday Harbor airport<br>Lundquist_GRL_FLCC_figurecode.m Matlab code for using the datafiles above to make all of the figures in the paper</p>
Data for: Low temperature ice nucleation of sea spray and secondary marine aerosols under cirrus cloud conditions
<p>Sea spray aerosols (SSA) represent one of the most abundant aerosol types on a global scale and have been observed at all altitudes including the upper troposphere. SSA has been explored in recent years as a source of ice nucleating particles (INPs) in cirrus clouds due to the ubiquity of cirrus clouds and the uncertainties in their radiative forcing. This study expands upon previous works on low temperature ice nucleation of SSA by investigating the effects of atmospheric aging of SSA and the ice nucleating activity of newly formed secondary marine aerosols (SMA) using an oxidation flow reactor. Polydisperse aerosol distributions were generated from a Marine Aerosol Reference Tank (MART) filled with 120 L of real or artificial seawater and were dried to very low relative humidity to crystallize the salt constituents of SSA prior to their subsequent freezing, which was measured using a Continuous Flow Diffusion Chamber (CFDC). Results show that for both primary SSA (pSSA), and the aged SSA and SMA (aSSA+SMA) at temperatures > 220 K, homogeneous conditions (92–97 % relative humidity with respect to water (RHw)) were required to freeze 1 % of the particles. However, below 220 K, heterogeneous nucleation occurs for both pSSA and aSSA+SMA at much lower RHw, where up to 1 % of the aerosol population freezes between 75–80 % RHw. Similarities between freezing behaviors of the pSSA and aSSA+SMA at all temperatures suggest that the contributions of condensed organics onto the pSSA or alteration of functional groups in pSSA via atmospheric aging did not hinder the major heterogeneous ice nucleation process at these cirrus temperatures that has previously been shown to be dominated by the crystalline salts. Occurrence of 1% frozen fraction of SMA, generated in the absence of primary SSA, was observed at/near water saturation below 220 K, suggesting it is not an effective INP at cirrus temperatures, similar to findings in the literature of other organic aerosols. Thus, any SMA coatings on the pSSA would only decrease the ice nucleation behavior of pSSA if the organic components were able to significantly delay water uptake of the inorganic salts, and apparently, this was not the case. Results from this study demonstrate the ability of lofted primary sea spray particles to remain an effective ice nucleator at cirrus temperatures, even after atmospheric aging has occurred over a period of days in the marine boundary layer prior to lofting. We were not able to address aging processes under upper tropospheric conditions.</p>
The diurnal data of the aerosol extinction coefficient of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma
<p><strong>The diurnal data of the the vertical average aerosol extinction coefficient of 0.15-2.5 km of the Mount Qomolangma lidar, as well as precipitation, low cloud cover, relative humidity of three adjacent stations (Tingri, Lazi, Nyalam) of the Mount Qomolangma in July 2018 and July 2019.</strong></p>
Global Classification Dataset of Daytime and Nighttime Marine Low-cloud Mesoscale Morphology
<p>The global classification dataset of daytime and nighttime marine low-cloud mesoscale morphology with six cloud types (Solid stratus, Closed MCC, Open MCC, Disorganized MCC, Clustered Cu and Suppressed Cu). The spatial resolution is 1<sup>o</sup> × 1<sup>o </sup>and the temporal resolution is 5 minutes for the years 2018-2022. They were established based on a deep learning model ResNet-50. Trained on daytime radiance data from MODIS (Moderate Resolution Imaging Spectroradiometer) and daytime retrieved COT (Cloud Optical Thickness), this model achieved a high prediction accuracy and can be applied to nighttime cloud classification. For a detailed introduction to the model, please refer to our article.</p> <p> </p> <h2>Technical info</h2> <p><strong>Product information</strong></p> <ul> <li><strong>File ‘day_xxxx_all.h5’:</strong> Daytime classification of global marine low-cloud morphology for the year xxxx, with a spatial resolution of 1°×1° and a temporal resolution of 5 minutes <ul> <li>date: time of the 1°×1° box, format: 'YYYYDDD.HHHH'</li> <li>lon: central longitude (-180, 180)</li> <li>lat: central latitude (-60, 60)</li> <li>cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu</li> <li>cert: model certainty, the probability that this cloud morphology belongs to the assigned category</li> <li>low_cf: the cloud fraction of low clouds</li> <li>COT_CNN: average cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al. (2022)</li> <li>CER_CNN: average cloud effective radius (CER), retrieved using TIR-CNN model from Wang et al. (2022), in the unit of μm</li> <li>LWP_CNN: average cloud liquid path (LWP), calculated from COT_CNN and CER_CNN, in the unit of g/㎡</li> <li>Sensor_zenith: scene average sensor zenith angle, from MODIS MYD021, in the unit of degree (°)</li> </ul> </li> </ul> <div> <ul> <li><strong>File 'night_xxxx_all.h5':</strong> Nighttime classification of global marine low-cloud morphology for the year xxxx, with a spatial resolution of 1°×1° and a temporal resolution of 5 minutes <ul> <li>same variables as daytime</li> </ul> </li> </ul> <ul> <li><strong>File 'example.xlsx':</strong> A sample of the variable data from our cloud classification dataset, showcasing the classification results of a MODIS granule captured on January 1, 2018, at 00:25 UTC. This sample is provided to help users better understand the content of our dataset.</li> </ul> <p> </p> <p><strong>Training, Validation, Test dataset</strong></p> <ul> <li>Originating from the same classification dataset, same variables, only differ in sample size</li> <li><strong>Files 'training_dataset.h5', 'validation_dataset.h5', and 'test_dataset.h5'</strong><strong>:</strong> <ul> <li>date: time of the 128×128 pixels, format: 'YYYYDDD.HHHH'</li> <li>lat: central latitude of the 128×128 scene</li> <li>lon: central longitude of the 128×128 scene</li> <li>cat: category of the cloud morphology. The numbers 0-5 represent each of the six categories: 0-Solid stratus, 1-Closed MCC, 2-Open MCC, 3-Disorganized MCC, 4-Clustered Cu, 5-Suppressed Cu</li> <li>CTH: cloud top height, in-cloud average value, in the unit of km</li> <li>COT_retrieved: cloud optical thickness (COT), retrieved using TIR-CNN model from Wang et al. (2022), 128×128 pixels</li> <li>LWP: cloud liquid path (LWP) from MODIS MYD06, in-cloud average value, in the unit of g/㎡</li> <li>Sensor_zenith: scene average sensor zenith angle, from MODIS MYD021, in the unit of degree (°)</li> <li>emis_29: radiance data from thermal infrared channel 29 (8.7μm), 128×128 pixels</li> <li>emis_31: radiance data from thermal infrared channel 31 (10.8 μm), 128×128 pixels</li> <li>emis_32: radiance data from thermal infrared channel 32 (12.0 μm), 128×128 pixels</li> <li>i: the row number of the top-left pixel of 128 ×128 scene in the MODIS granule</li> <li>j: the column number of the top-left pixel of 128 ×128 scene in the MODIS granule</li> </ul> </li> </ul> </div>
Data used in "Observation of secondary ice production in clouds at low temperatures"
<p>To support open research, the data used in Korolev et al 2022 "Observation of secondary ice production in clouds at low temperatures" submitted to Atmospheric Chemistry and Physics has been provided here. <br> This in-situ data was collected by Environment and Climate Change Canada (ECCC) in collaboration with the National Research Council (NRC) on the NRC Convair-580 research aircraft on March 25 2017 between 11:00 and 12:00 UTC.<br> Radar data included was collected and processed by N. Cuong and M. Wolde of NRC Canada. <br> </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>
Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations - datasets and scripts
<p>Datasets used in the analysis of the article Mechanisms behind the low-cloud optical depth response to temperature in ARM site observations, which was submitted to and subsequently published in Journal of Geophysical Research-Atmosphere. DOI: 10.1029/2018JD029359</p>
The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback
<p>Supporting data for "The relationship between the present-day seasonal cycles of low clouds in the mid-latitudes and cloud-radiative feedback", by K. Furtado, Y. Tsushima and P. R. Field.</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Data for: Low temperature ice nucleation of sea spray and secondary marine aerosols under cirrus cloud conditions
Open the record for dataset details and reuse information.
Data and codes for figures in "Open water in sea ice causes high bias in polar low-level clouds in GFDL CM4"
Open the record for dataset details and reuse information.
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>
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.