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352 results for “Radiation effects”
Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation
<p>Satellite measurements of backscattered UV radiation are sensitive to the presence of UV-absorbing aerosols in the atmosphere. These measurements are commonly used for determining the concentration of atmospheric trace gases such as O<sub>3</sub>, SO<sub>2</sub>, and H<sub>2</sub>CO.</p> <p>The theoretical results in this dataset describe the effects of UV-absorbing mineral dust and carbonaceous smoke aerosols on these backscatter satellite measurements between 300-400 nm. The information provided is independent of any specific trace gas retrieval algorithm and does not require detailed a priori knowledge of aerosol and surface properties.</p> <p>The results are derived from the analysis of the radiative transfer model simulations performed with the optical property data used in the Ozone Monitoring Instrument (OMI) UV aerosol retrieval algorithm, OMAERUV. Results from this algorithm have been validated with Aerosol Robotic Network (AERONET) observations (Jethva & Torres, 2011; Torres et al., 2018).</p> <p>This dataset is associated with the following publication:</p> <p>Jethva, H., Haffner, D., Bhartia, P. K., & Torres, O. (2022). Estimating Spectral Effects of Absorbing Aerosols on Backscattered UV Radiation, Earth Space Sci., Accepted.</p>
Exploring the Relationship Between Upper Ocean States and the Falling Ice Radiative Effects using ECCO Product and Global Climate Models
<p><strong><span>Sensitivity test using CESM1-CAM5 following CMIP5 protocool from 1980-2005</span></strong></p> <p><strong><span>NOS: no falling ice radiative effects (FIREs), four data sets</span></strong></p> <p><strong><span>SON: with FIREs, for data sets</span></strong></p> <p><strong><span> Xsize = 362 Ysize = 182 Zsize = 18</span></strong></p> <p><strong><span>Format: netcdf</span></strong></p> <p><strong><span>Upper 200 meter ocean variables</span></strong></p> <p><strong><span>Annual mean (ANN)</span></strong></p> <p><strong><span>CESM2-var-NOS (or SON)-ANN.nc, var = (UO, VO, WO, TO) = (zonal velocity, meridional velocity, ascending velocity, potential temperature) : (cm/s, cm/s, cm/s, K)</span></strong></p>
Dataset for "Droplet collection efficiencies inferred from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions"
<p>This dataset in includes MODIS-CloudSat CFODD reference data, the updated Warm Rain Diagnostics implemented in COSPv2.0, RANSAC regression analysis, and figure production scripts associated with the manuscript “Droplet collection efficiencies estimated from satellite retrievals constrain effective radiative forcing of aerosol-cloud interactions”<br> Authors: Beall, Charlotte, M.; Ma, Po-Lun; Christensen, Matthew W.; Mülmenstädt, Johannes; Varble, Adam; Suzuki, Kentaroh; Michibata, Takuro<br> Journal: Atmospheric Chemistry & Physics (submitted, 2023)</p>
A Novel Model Hierarchy Isolates the Limited Effect of Supercooled Liquid Cloud Optics on Infrared Radiation
<p>This dataset contains data used in and resulting from an upcoming paper. For further detail on methodology and experiments, see that paper.</p> <h2>Supercooled liquid water optics</h2> <h3>Complex refractive indices (CRIs)</h3> <ul> <li>Water_DW_300.txt</li> <li>water_RFN_240K.txt</li> <li>water_RFN_253K.txt</li> <li>water_RFN_263K.txt</li> <li>water_RFN_273K.txt</li> </ul> <p>Water_DW_300.txt is sourced from Downing & Williams 1975 (https://doi.org/10.1029/JC080i012p01656). water_RFN_240K.txt, water_RFN_253K.txt, water_RFN_263K.txt, and water_RFN_273K.txt are sourced from Rowe et al. 2020 (https://doi.org/10.1029/2020JD032624).</p> <h3>CESM lookup tables of liquid water optics</h3> <ul> <li>CESM_CRI_RFN_240K.nc</li> <li>CESM_CRI_RFN_253K.nc</li> <li>CESM_CRI_RFN_263K.nc</li> <li>CESM_CRI_RFN_273K.nc</li> </ul> <p>These optics sets were created from the corresponding Rowe et al. 2020 CRI.</p> <p> </p> <h2>SCAM output</h2> <p>History files for the four MPACE SCAM runs.</p> <ul> <li>Control: tutorial.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>240K optics: cri240K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>263K optics: cri263K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> <li>273K optics: cri273K_test.FSCAM.mpace.cam.h0.2004-10-05-07171.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850_UVnudge1980-2018 data</h2> <p>Data used to create graphs shown in PAPER from the F1850_UVnudge1980-2018 experiment. For the variable FLDS (downwelling longwave flux at the surface), each optics set has a mean, count (n), and standard deviation file. These statistics are calculated over the 39 years of the model run and across all 3 ensemble members. </p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test_nudge_long.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>B1850_UVnudge1980 data</h2> <p>Data used to create graphs shown in PAPER from the B1850_UVnudge1980 experiment. For each optics set there is a mean, count (n), and standard deviation file. These statistics are calculated over the 1 year of the model run and across all 10 ensemble members for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.control_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.avg.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.n.Mean.All_data.non_filtered.nc</li> <li>b.e22.B1850.f09_g17.cri263K_test_nudge.FLDS.std.Mean.All_data.non_filtered.nc</li> </ul> <p> </p> <h2>F1850 data</h2> <p>Data used to create graphs shown in PAPER from the F1850 experiment. For each optics run there is a mean, count (n), and standard deviation file. These statistics are calculated over the 40 years of the model run for the variable FLDS (downwelling longwave flux at the surface).</p> <p>Control optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.control_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>240K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.avg.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri240K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>263K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri263K_test.FLDS.std.All_data.non_filtered.nc</li> </ul> <p>273K optics:</p> <ul> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.avg.All_data.non_filtered.nc </li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.n.All_data.non_filtered.nc</li> <li>f.e22.F1850.f09_f09_mg17.cri273K_test.FLDS.std.All_data.non_filtered.nc</li> </ul>
Supplementary data to "Radiative forcing and equivalent effective chlorine due to hydrochlorofluorocarbons peaked in 2021"
<p><span>README for Supplementary data to “</span><span>Radiative forcing and equivalent effective chlorine due to hydrochlorofluorocarbons peaked in 2021”</span></p> <p> </p> <p><span>This repository contains 4 folders:</span></p> <p><span>1) agage: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the AGAGE network.</span></p> <p><span>2) noaa: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the NOAA network.</span></p> <p><span>3) vollmer: contains the inputs to the 12-box model and the derived monthly and annual mole fractions (global and semi-hemispheric) using measurements from the measurements published in Vollmer et al. (2021).</span></p> <p><span>4) Projections: contains a csv file with the merged mole fractions (i.e., mean) from the various networks and the projected quantities.</span></p> <p> </p> <p><span>The 12-box model and the method used to quantify global mean mole fractions are available via GitHub (https://github.com/mrghg/py12box (last accessed 5 March 2024) and https://github.com/mrghg/py12box_invert (last accessed 5 March 2024)) and Zenodo (https://doi.org/10.5281/zenodo.6857447 and https://doi.org/10.5281/zenodo.6857794).</span></p> <p> </p> <p><span>AGAGE data are also available at http://agage.mit.edu/data/agage-data (last accessed 5 March 2024) and https://data.ess-dive.lbl.gov/ (current dataset <a href="https://doi.org/10.15485/1998580"><span>https://doi.org/10.15485/1998580</span></a>) and newer data can be made available upon request. The most recent NOAA atmospheric observations are available at https://gml.noaa.gov/aftp/data/hats/hcfcs/ (last accessed 5 March 2023). </span></p> <p> </p> <p><span>References:</span></p> <p><span>Vollmer, M. K. et al. Unexpected nascent atmospheric emissions of three ozone-depleting hydrochlorofluorocarbons. Proc Natl Acad Sci USA 118, e2010914118 (2021).</span></p>
Data for the publication "Radiative effects of precipitation on the global energy budget and Arctic amplification"
<p>This dataset includes a set of 15yr simulations using the MIROC6 global aerosol-climate model with 1) diagnostic precipitation, 2) prognostic precipitation without radiative effect of precipitation, and 3) prognostic precipitation with radiative effect of precipitation.</p> <p>The data are used in the manuscript entitled "Radiative effects of precipitation on the global energy budget and Arctic amplification".</p>
Ecological specialization, rather than the island effect, explains morphological diversification in an ancient radiation of geckos
Island colonists are often assumed to experience higher levels of phenotypic diversification than continental taxa. However, empirical evidence has uncovered exceptions to this 'island effect'. Here, we tested this pattern using the geckos of the genus Pristurus from continental Arabia and Africa and the Socotra Archipelago. Using a recently published phylogeny and an extensive morphological dataset, we explore the differences in phenotypic evolution between Socotran and continental taxa. Moreover, we reconstructed ancestral habitat occupancy to examine if ecological specialization is correlated with morphological change, comparing phenotypic disparity and trait evolution between habitats. We found a heterogeneous outcome of island colonization. Namely, only one of the three colonization events resulted in a body size increase. However, in general, Socotran species do not present higher levels or rates of morphological diversification than continental groups. Instead, habitat specialization explains better the body size and shape evolution in Pristurus . Particularly, the colonization of ground habitats appears as the main driver of morphological change, producing the highest disparity and evolutionary rates. Additionally, arboreal species show very similar body size and head proportions. These results reveal a determinant role of ecological mechanisms in morphological evolution and corroborate the complexity of ecomorphological dynamics in continent–island systems.
Dataset for "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation"
<p>These documents are supplements to the "Air-sea interactions on Titan: effect of radiative transfer on the lake evaporation and atmospheric circulation" paper published by the same authors in The Planetary Science Journal in 2022.</p> <p>Are made available:</p> <p>-the Supporting Information document on the performed sensitivity study,<br> "paper_mtWRF_lake_RT_220825_SI.pdf"</p> <p>-the Fortran source code of the radiative transfer module developed for this work,<br> "module_ra_gray.F"</p> <p>-all the netCDF simulation outputs and a list describing their parameters,<br> "run-##.nc.gz"<br> "list_simulations_2D_paper2022_RT_zenodo.pdf"</p> <p>-the Python codes to plot figures from the netCDF output files,<br> "mtwrf_analysis_#D_#.py"</p>
Development and evaluation of E3SM-MOSAIC: Spatial distributions and radiative effects of nitrate aerosol
<p>FC20TR-MOZ_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MZT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SLOW_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_WGT_PD</p> <p>FC20TR-MOZ_MOSAIC_AIKDST_MTC-SPLC_NUG_PD_V2beta4_ANN_200501_201412_climo.nc 10-yr mean for MTC_SPLC_PD</p> <p> </p> <p>NO3_TM_2005-2014.nc 10-yr mean nitrate burden</p> <p>NO3_AQCH_GAEX_2005-2014.nc 10-yr mean for nitrate chemistry production</p> <p>NO3_DRF_2005-2014.nc 10-yr mean nitrate direct forcing between PD and PI</p> <p>NO3_INDRF_2005-2014.nc 10-yr mean nitrate indirect forcing between PD and PI</p> <p> </p> <p>NH4_TM_2005-2014.nc 10-yr mean for ammonium burden</p> <p>NH4_DRF_2005-2014.nc 10-yr mean ammonium direct forcing between PD and PI</p> <p> </p> <p>SO4_TM_2005-2014.nc 10-yr mean for sulfate burden</p> <p>SO4_DRF_2005-2014.nc 10-yr mean sulfate direct forcing between PD and PI</p> <p> </p> <p>CCN3_1850.nc Cloud condensation nuclei number concentrations at 0.1% super saturation at PI</p> <p>CCN3_2005-2014.nc CCN3 at PD</p> <p>CCN3_NONO3_1850.nc CCN3 at PI without nitrate formation</p> <p>CCN3_NONO3_2005-2014.nc CCN3 at PD without nitrate formation</p> <p> </p> <p>CDNC_*.nc cloud droplet number concentrations</p> <p>CLDFRC_*.nc cloud fraction</p> <p>CWP_*.nc cloud liquid water path</p> <p> </p> <p> </p>
Calculation data for "Scattering of Radiation Belt Electrons by Fast Magnetosonic Waves: Considering the Kinetic Effects"
<p>Here are the calculation data for "Scattering of Radiation Belt Electrons by Fast Magnetosonic Waves: Considering the Kinetic Effects", using the format of ".mat". </p> <p>Term of "cold_plasma_dispersion_relation.mat" is the numerical results of cold plasma dispersion relation for MS waves;</p> <p>Term of "kinetic_dispersion_relation.mat" is the numerical results of kinetic dispersion relation for MS waves, using the WHAMP code by K. Rönnmark;</p> <p>Term of "Diffusion_coefficients_cold.mat" is the diffusion rates calculated based on the cold plasma dispersion relation;</p> <p>Term of "Diffusion_coefficients_kinetic.mat" is the diffusion rates calculated based on the kinetic dispersion relation;</p> <p>To be noted, the "D_{\alpha\alpha}_Bw2" in term "Diffusion_coefficients_kinetic.mat" is derived from the inferred wave magnetic field power spectral density.</p>
Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021
<p>data for Rapid decline of aerosol absorption coefficient and aerosol optical properties effects on radiative forcing in urban areas of Beijing from 2018 to 2021</p>
Input and Output simulation data of the THOR GCM for the paper Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme
<p>The input and ouput simulation data of the THOR GCM for Dynamical and radiative effects resulting from the deep non-hydrostatic vs deep quasi-hydrostatic equations in the global circulation model THOR with an added non-grey radiative transfer scheme</p> <p>Global circulation models (GCMs) play an important role in contemporary investigations of exoplanet atmospheres. Different GCMs evolve various sets of dynamical equations which can result in obtaining different atmospheric properties between models. In this study, we investigate the effect of different dynamical equation sets on the atmospheres of hot Jupiter exoplanets. We compare GCM simulations using the quasi-primitive dynamical equations (QHD) and the deep Navier-Stokes equations (NHD) in the GCM THOR. We utilise a two-stream non-grey "picket-fence" scheme to increase the realism of the radiative transfer scheme. We perform GCM simulations covering a wide parameter range grid of system parameters in the population of exoplanets. Our results show significant differences between simulations with the NHD and QHD equation sets at lower gravity, higher rotation rates or at higher irradiation temperatures. The parameter exploration shows the relevance of choosing dynamical equation sets dependent on system and planetary properties.Climate states of hot Jupiters seemed to be more diverse than previously thought. There are exceptions to prograde superrotation. Overall, our study shows the evolution of different climate states which arise just due to different selection of Navier-Stokes equations and approximations. We show the shortcomings of approximations in GCMs made for Earth, but used for non Earth-like planets.</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Data from: Convolutional neural networks trained on internal variability predict forced response of TOA radiation by learning the pattern effect
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Ecological specialization, rather than the island effect, explains morphological diversification in an ancient radiation of geckos
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Dataset - Three-dimensional radiative transfer effects on airborne and ground-based trace gas remote sensing
<p>This dataset was created by Marc Schwaerzel (marc.schwaerzel@empa.ch) and is intended to get along with the Schwaerzel et al. (2020) AMT publication (amt-2020-146) . The data and the data structure is described in the<em> <strong>readme.txt</strong></em> file.</p> <p>The dataset contains:</p> <p>- libRadtran input files</p> <p>- libRadtran output</p> <p>- name lists</p> <p>- GRAL simulation outputs</p>
Data for the paper: An energetic view on the geographical dependence of the fast aerosol radiative effects on precipitation
<p>This is the data presented in "An energetic view on the geographical dependence of the fast aerosol radiative effects on precipitation", Dagan et al 2021 JGR.</p> <p>names of files:</p> <p>*aqua* is for aqua-planet simulations, while *AMIP* is for AMIP simulations. *id* is for the simulations with the idealised aerosol perturbations in AMIP type of simulations. sur* are for surface variables, while d_*zonal* and d_mar* present the devotion zonal and meridional cross-sections as in the paper </p> <p>The names of the variables are as in the paper. </p>
Data for: Assessing the Impacts of Falling Ice Radiative Effects on the Seasonal Variation of Land Surface Properties
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Data and codes for manuscript "A study on radiative efficiencies and warming effects of halocarbons"
<p>Data and codes for “A study on radiative efficiencies and warming effects of halocarbons”, including the 998-band radiative transfer model for calculating the radiative efficiency, scripts for plotting and corresponding data.</p>
Impact of host climate model on contrail cirrus effective radiative forcing estimates
<p>Data to reproduce the figures in the article 'Impact of host climate model on contrail cirrus effective radiative forcing estimates'.</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.