Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
19
datasets available to search
ShareScore release 0.9.0
Dataset results
19 results for “Cloud physics”
Data for the "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" manuscript
<p>This repository contains the post-processed data files for plotting and interpreting the results of "Cirrus cloud thinning using a more physically-based ice microphysics scheme in the ECHAM-HAM GCM" study.</p> <p>The files are all netCDF4 except for the analysis files that show global mean values in a .txt format.</p> <p>The Version 3 & Version 4 tar files are smaller than Version 2 as we performed some code clean-up for some post-processing scripts that excluded redundant data files that were very large and that were not used for plotting or the analysis for the manuscript.</p>
Datasets of synthetic workflows for cyber-physical edge-hub-cloud systems
<p>These datasets of synthetic workflows were generated to evaluate the performance and scalability of a multi-constrained scheduling approach for workflow applications of various structures, sizes, and sensing/actuating requirements in a cyber-physical system (CPS) following the edge-hub-cloud paradigm. The examined CPS comprised four edge devices (i.e., single-board computers, each attached to an unmanned aerial vehicle (UAV) equipped with sensors/actuators) interacting with a hub device (e.g., a laptop), which in turn communicated with a more computationally capable cloud server. All system devices featured heterogeneous multicore processors and varied sensing/actuating or other specialized capabilities. The problem objective was the minimization of the overall latency of the application under deadline, memory, storage, energy, capability, and task precedence constraints.</p> <p>We generated 25 random workflows (task graphs) with 10, 20, 30, 40, and 50 nodes (5 task graphs for each size), utilizing the Task Graphs For Free (TGFF) random task graph generator [1],[2]. Additional task parameters (e.g., execution time, power consumption, memory, storage, output data size, capability) were included post-generation, using appropriate values. More details are provided in README.txt and in [3].<br><br>References:<br>[1] R. P. Dick, D. L. Rhodes, and W. Wolf, "TGFF: Task graphs for free," in Proc. Sixth International Workshop on Hardware/Software Codesign (CODES/CASHE), 1998, pp. 97-101, doi: 10.1109/HSC.1998.666245.</p> <p>[2] R. P. Dick, D. L. Rhodes, and K. Vallerio, "TGFF," https://robertdick.org/projects/tgff/.</p> <p>[3] A. Kouloumpris, G. L. Stavrinides, M. K. Michael, and T. Theocharides, “Optimal multi-constrained workflow scheduling for cyber-physical systems in the edge-cloud continuum,” in Proc. 2024 IEEE 48th Annual Computers, Software, and Applications Conference (COMPSAC), Jul. 2024, pp. 483-492, doi: 10.1109/COMPSAC61105.2024.00072.</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>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
A sample of the training data used in the paper "A Hybrid Physics-AI (HyPhAI) approach for probability fields advection: Application to cloud cover nowcasting"
<p>Copyright (2024) EUMETSAT</p>
SAFARI 2000 Cloud Physics Lidar (CPL) Quicklook Images and Maps
The effect of clouds and aerosols on regional and global climate is of great importance. Two longstanding elements of the NASA climate and radiation science program are field studies incorporating airborne remote sensing and in-situ measurements of clouds and aerosols. These projects involve coordination of ground based and satellite measurements with the airborne observations. The goals of the experiments include testing satellite remote sensing retrievals, development of advanced remote sensing techniques and fundamental advances in knowledge of cloud radiation and microphysical properties. Active lidar profiling is especially valuable because the cloud height structure is measured unambiguously, up to the limit of signal attenuation.The Cloud Physics Lidar (successor to the Cloud Lidar System) is an airborne lidar system designed specifically for studying clouds and aerosols using the NASA ER-2 High Altitude Aircraft. Because the ER-2 typically flies at an altitude of 65,000 feet (20 km), its instruments are above 94% of the Earth's atmosphere, thereby allowing ER-2 instruments to function as spaceborne instrument simulators. The Cloud Physics Lidar provides a unique tool for atmospheric profiling and is sufficiently small and low cost to include in multiple instrument missions.The Cloud Physics Lidar provides a complete battery of cloud physics information. Data products include: (1) Cloud profiling with 30 m vertical and 200 m horizontal resolution at 1064 nm, 532 nm, and 355 nm;(2) Aerosol, boundary layer, and smoke plume profiling;(3) Optical depth estimates (column and by layer); and(4) Extinction profiles. The CPL provides information to permit a comprehensive analysis of radiative and optical properties of optically thin clouds. To determine the effects of particulate layers on the radiative budget of the earth-atmosphere system, certain information about the details of the layer and its constituents is required. The effect of clouds is often referred to as cloud radiative forcing. Cloud radiative forcing, in general, is the alteration that the presence of clouds has on the energy budget. The information required to compute the radiative forcing includes the vertical distribution of short wave cross section, a parameter that the CPL provides up to the limits of optical signal attenuation.Using optical depth measurements determined from attenuation of Rayleigh and aerosol scattering, and using the integrated backscatter, the extinction-to-backscatter parameter can be derived. This permits rapid analysis of cloud optical depth since only lidar data is required; there is no need to use other instrumentation. Using the derived extinction-to-backscatter ratio, the internal cloud extinction profile can then be obtained.The CPL uses photon-counting detectors with a high repetition rate laser to maintain a large signal dynamic range. This dramatically reduces the time required to produce reliable and complete data sets.ORNL DAAC has archived CPL quicklook data samples from the SAFARI 2000 Field Campaign. The samples were provided by the CPL group at the NASA Goddard Space Flight Center (GSFC). The actual Cloud Physics Lidar data are stored at the CPL Web Site at NASA GSFC and can be accessed at [http://virl.gsfc.nasa.gov/cpl/safari2000_pass.htm]. Data users are asked to read and abide by the CPL data usage policy found at [http://virl.gsfc.nasa.gov/cpl/cpl_register.htm]. For systems specifications and other information regarding Cloud Physics Lidar, please visit the Cloud Physics Lidar Home Page at [http://cpl.gsfc.nasa.gov/].
GOES-R PLT Cloud Physics LiDAR (CPL)
The GOES-R PLT Cloud Physics Lidar (CPL) dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR instrument flown aboard the NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign supported post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The CPL instrument is a multi-wavelength backscatter LiDAR that provides multi-wavelength measurements of cirrus clouds and aerosols with high temporal and spatial resolution. Data files are available from April 13, 2017 through May 14, 2017 in HDF-5 format with layer information in ASCII text files. Browse imagery files in GIF format are also available.
GPM Ground Validation Cloud Physics LiDAR (CPL) OLYMPEX V1
The GPM Ground Validation Cloud Physics Lidar (CPL) OLYMPEX dataset consists of extinction profiles, layer optical depth, layer lidar ratio, and aircraft parameter measurements measured by the CPL flown on the NASA ER-2 aircraft during the Global Precipitation Mission (GPM) Olympic Mountains Experiment (OLYMPEX) campaign. The CPL instrument is a multi-wavelength backscatter lidar that provides multi-wavelength measurements of cirrus and aerosols with high temporal and spatial. Data files are available from November 9, 2015 through December 15, 2015 in HDF-5 format with layer information in ASCII text files. Browse imagery files in GIF format contain optical depth and flight path images.
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Quantifying the informativity of emission lines to infer physical conditions in giant molecular clouds
<p>Supplementary figures for Einig et al (2024).</p>
FIREX-AQ ER-2 Cloud Physics Lidar Remotely Sensed Data
FIREXAQ_AerosolCloud_AircraftRemoteSensing_ER2_CPL_Data are remotely sensed data collected by the Cloud Physics Lidar (CPL) onboard the ER-2 aircraft during FIREX-AQ. Data collection for this product is complete.Completed during summer 2019, FIREX-AQ utilized a combination of instrumented airplanes, satellites, and ground-based instrumentation. Detailed fire plume sampling was carried out by the NASA DC-8 aircraft, which had a comprehensive instrument payload capable of measuring over 200 trace gas species, as well as aerosol microphysical, optical, and chemical properties. The DC-8 aircraft completed 23 science flights, including 15 flights from Boise, Idaho and 8 flights from Salina, Kansas. NASA’s ER-2 completed 11 flights, partially in support of the FIREX-AQ effort. The ER-2 payload was made up of 8 satellite analog instruments and provided critical fire information, including fire temperature, fire plume heights, and vegetation/soil albedo information. NOAA provided the NOAA-CHEM Twin Otter and the NOAA-MET Twin Otter aircraft to measure chemical processing in the lofted plumes of Western wildfires. The NOAA-CHEM Twin Otter focused on nighttime plume chemistry, from which data is archived at the NASA Atmospheric Science Data Center (ASDC). The NOAA-MET Twin Otter collected measurements of air movements at fire boundaries with the goal of understanding the local weather impacts of fires and the movement patterns of fires. NOAA-MET Twin Otter data will be archived at the ASDC in the future. Additionally, a ground-based station in McCall, Idaho and several mobile laboratories provided in-situ measurements of aerosol microphysical and optical properties, aerosol chemical compositions, and trace gas species. The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) campaign was a NOAA/NASA interagency intensive study of North American fires to gain an understanding on the integrated impact of the fire emissions on the tropospheric chemistry and composition and to assess the satellite’s capability for detecting fires and estimating fire emissions. The overarching goal of FIREX-AQ was to provide measurements of trace gas and aerosol emissions for wildfires and prescribed fires in great detail, relate them to fuel and fire conditions at the point of emission, characterize the conditions relating to plume rise, and follow plumes downwind to understand chemical transformation and air quality impacts.
CAMEX-4 NOAA WP-3D CLOUD PHYSICS V1
The CAMEX-4 NOAA WP-3D Cloud Physics dataset used the NOAA WP-3D Orion aircraft, which has multiple meteorological and microphysical sensors. These include, for example, cloud particle imagers and temperature and dewpoint probes. CAMEX-4 focused on the study of tropical cyclone (hurricane) development, tracking, intensification, and landfalling impacts using NASA-funded aircraft and surface remote sensing instrumentation. This dataset includes navigation data as well as the meteorological and microphysical data. For further information and to obtain this data, please contact GHRC at support-ghrc@earthdata.nasa.gov
HURRICANE AND SEVERE STORM SENTINEL (HS3) GLOBAL HAWK CLOUD PHYSICS LIDAR (CPL) V1
The Hurricane and Severe Storm Sentinel (HS3) Global Hawk Cloud Physics Lidar (CPL) dataset includes measurements gathered by the CPL instrument during the HS3 campaign which took place during the hurricane seasons of 2011 through 2014 in the Atlantic Ocean basin region. Goals for HS3 included: assessing the relative roles of large-scale environment and storm-scale internal processes; and addressing the controversial role of the Saharan Air Layer (SAL) in tropical storm formation and intensification as well as the role of deep convection in the inner-core region of storms. The CPL instrument returns information on the radiative and optical properties of cirrus clouds and aerosols at a high temporal and spatial resolution. CPL uses the 355, 532, and 1064 nm channels and has a small field of view, which eliminates multiple scattering; it offers 30 m vertical resolution and 200 m horizontal resolution. The CPL instrument measures the total (aerosol plus Rayleigh) attenuated backscatter as a function of altitude at each wavelength. Data is available in netCDF/CF format, from 2012 - 2014.
Cloud Physics LiDAR (CPL) IMPACTS
The Cloud Physics LiDAR (CPL) IMPACTS dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR (CPL) onboard the NASA ER-2 high-altitude research aircraft in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S. Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in HDF-5 format from January 15, 2020, through March 2, 2023.
ACEPOL Cloud Physics Lidar (CPL) Remotely Sensed Data Version 1
ACEPOL Cloud Physics Lidar (CPL) Remotely Sensed Data (ACEPOL_AircraftRemoteSensing_CPL_Data) are remotely sensed measurements collected by the Cloud Physics Lidar (CPL) onboard the ER-2 during ACEPOL. In order to improve our understanding of the effect of aerosols on climate and air quality, measurements of aerosol chemical composition, size distribution, height profile, and optical properties are of crucial importance. In terms of remotely sensed instrumentation, the most extensive set of aerosol properties can be obtained by combining passive multi-angle, multi-spectral measurements of intensity and polarization with active measurements performed by a High Spectral Resolution Lidar. During Fall 2017, the Aerosol Characterization from Polarimeter and Lidar (ACEPOL) campaign, jointly sponsored by NASA and the Netherlands Institute for Space Research (SRON), performed aerosol and cloud measurements over the United States from the NASA high altitude ER-2 aircraft. Six instruments were deployed on the aircraft. Four of these instruments were multi-angle polarimeters: the Airborne Hyper Angular Rainbow Polarimeter (AirHARP), the Airborne Multiangle SpectroPolarimetric Imager (AirMSPI), the Airborne Spectrometer for Planetary Exploration (SPEX Airborne) and the Research Scanning Polarimeter (RSP). The other two instruments were lidars: the High Spectral Resolution Lidar 2 (HSRL-2) and the Cloud Physics Lidar (CPL). The ACEPOL operation was based at NASA’s Armstrong Flight Research Center in Palmdale California, which enabled observations of a wide variety of scene types, including urban, desert, forest, coastal ocean and agricultural areas, with clear, cloudy, polluted and pristine atmospheric conditions. The primary goal of ACEPOL was to assess the capabilities of the different polarimeters for retrieval of aerosol and cloud microphysical and optical parameters, as well as their capabilities to derive aerosol layer height (near-UV polarimetry, O2 A-band). ACEPOL also focused on the development and evaluation of aerosol retrieval algorithms that combine data from both active (lidar) and passive (polarimeter) instruments. ACEPOL data are appropriate for algorithm development and testing, instrument intercomparison, and investigations of active and passive instrument data fusion, which make them valuable resources for remote sensing communities as they prepare for the next generation of spaceborne MAP and lidar missions.
GOES-R PLT Cloud Physics LiDAR (CPL) V1
The GOES-R PLT Cloud Physics Lidar (CPL) dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR instrument flown aboard the NASA ER-2 high-altitude aircraft during the GOES-R Post Launch Test (PLT) field campaign. The GOES-R PLT field campaign supported post-launch L1B and L2+ product validation of the Advanced Baseline Imager (ABI) and the Geostationary Lightning Mapper (GLM). The CPL instrument is a multi-wavelength backscatter LiDAR that provides multi-wavelength measurements of cirrus clouds and aerosols with high temporal and spatial resolution. Data files are available from April 13, 2017 through May 14, 2017 in HDF-5 format with layer information in ASCII text files. Browse imagery files in GIF format are also available.
Cloud Physics LiDAR (CPL) IMPACTS V1
The Cloud Physics LiDAR (CPL) IMPACTS dataset consists of backscatter coefficient, lidar depolarization ratio, layer top/base height, layer type, particulate extinction coefficient, ice water content, and layer/cumulative optical depth data collected from the Cloud Physics LiDAR (CPL) onboard the NASA ER-2 high-altitude research aircraft in support of the Investigation of Microphysics and Precipitation for Atlantic Coast-Threatening Snowstorms (IMPACTS) field campaign. IMPACTS was a three-year sequence of winter season deployments conducted to study snowstorms over the U.S Atlantic Coast (2020-2023). The campaign aimed to (1) Provide observations critical to understanding the mechanisms of snowband formation, organization, and evolution; (2) Examine how the microphysical characteristics and likely growth mechanisms of snow particles vary across snowbands; and (3) Improve snowfall remote sensing interpretation and modeling to significantly advance prediction capabilities. The dataset files are available in HDF-5 format from January 15, 2020 through February 28, 2022.
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.