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64 results for “Radiative forcing”
Dataset for the Roles of Cloud Radiative Forcing in the Onset of MJO in DYNAMO
<p><strong>A journal paper "Roles of Cloud Radiative Forcing in the Onset of MJO in DYNAMO" details the method to create the data.</strong></p> <p>Readme</p> <p>The modeling and observed dataset for generating the figures in this paper is stored here. </p> <p>Contact</p> <p>For any questions related to the dataset, please contact the authors of the paper. </p>
Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record (Data)
<p>README file for ERA5-PRP datasets used in:</p> <p>Raghuraman et al., 2023, Journal of Climate,<br> "Forcing, cloud feedbacks, cloud masking, and internal variability in the cloud radiative effect satellite record"</p> <p>Point of Contact: Shiv Priyam Raghuraman, shivr@alumni.princeton.edu</p> <p>30 ERA5-PRP files:</p> <p>2 files: '2000_2020-allsky_small.nc' and '2000_2020-clearsky_small.nc'</p> <ul> <li>all input quantities varying</li> </ul> <p>2 files: 'clim-allsky-clim-var-all.nc' and 'clim-clearsky-clim-var-all.nc'</p> <ul> <li>all input quantities at climatology</li> </ul> <p>13 files: 'clim-var'</p> <ul> <li>Input quantity X at climatology, rest varying</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>13 files: 'clim-except-var'</p> <ul> <li>Input quantity X varying, rest at climatology</li> <li>X = clouds, q, fal, skt, t, ghg, or o3. Fluxes computed in clear-sky and all-sky, apart from clouds which will only have all-sky.</li> </ul> <p>Other datasets:</p> <p>GFDL AM4 AMIP and Control<br> https://doi.org/10.5281/zenodo.4784726</p> <p>CMIP6 Control, Historical, RFMIP<br> Downloaded from ESGF</p>
Data from "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.
<p>This is the data presented in "Enhanced radiative forcing from aerosol-cloud interactions due to large-scale circulation adjustments" by Guy Dagan, Netta Yeheskel and Andrew I. L. Williams.</p> <p> </p> <p>Please read the README file for explanations about the data.</p> <p> </p>
A monthly shortwave radiative forcing kernel for surface albedo change using CERES satellite data
We present a radiative kernel for surface albedo change founded on a novel, simplified parameterization of shortwave radiative transfer driven with inputs from the Clouds and the Earth’s Radiant Energy System (CERES) Energy Balance and Filled (EBAF) Edition 4.0 products based on a 16-year climatology (2001-2016). Both monthly temporally-explicit and monthly climatological mean CERES albedo change kernels (CACK) are provided with their respective uncertainty layers. Octave script files for generating monthly CACK from CERES EBAF data and demonstrating the application of CACK with user-specified temporal and spatial extents are also included.
Supporting material for "Reappraisal of the effective radiative forcing of ozone-depleting substances"
<p>This is a companion repository, containing data and scripts needed to reproduce the figures and table entries in Morgenstern et al, Reappraisal of the effective radiative forcing of ozone-depleting substances, Geophysical Research Letters, 2020. For more information, please read the README.TXT file which is part of this repository.</p>
Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results
<p>This file contains supporting information for a manuscript submitted for publication.</p> <p>Aviation contrail cirrus and radiative forcing over Europe for six months in 2020 during COVID-19 compared with 2019: Observations and model results </p> <p>U. Schumann, L. Bugliaro, and C. Voigt</p> <p>Corresponding author: Ulrich Schumann (Ulrich.schumann@dlr.de)</p> <p>For details see the README.txt</p> <p> </p> <p> </p>
Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach (Example codes)
<p>## Introduction<br>This folder contains example code used in our paper with the title "Arctic Climate Response to European Radiative Forcing: A Deep Learning Approach". These scripts are intended to demonstrate key functionalities and calculations described in the paper.</p> <p>## Files Description</p> <p>1. **DL_Model_test.py**<br> - Description: Loads the trained deep learning algorithms featured in the paper.<br> - Functionality: Demonstrates the use of the model with example data points.</p> <p>2. **SIC_class_contribution.py**<br> - Description: Calculates the class contribution for Sea Ice Concentration (SIC).<br> - Note: The procedure can be adapted for other fields in a similar manner.</p> <p>## Requirements<br>the requred linrary are listed in the code header</p>
Supporting Data for "Radiation-circulation destabilization of ITCZ position in an idealized GCM: Response to hemispherically asymmetric forcing"
<p>Code and netcdf files of processed Isca simulations to reproduce the figures of the submitted manuscript of Timothy M. Merlis, Chiung-Yin Chang, Pablo Zurita-Gotor, and Isaac M. Held (2024): "Radiation-circulation destabilization of ITCZ position in an idealized GCM: Response to hemispherically asymmetric forcing".</p>
Data for "Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada"
<p>This is the data prepared for submission of "<strong>Snow albedo feedbacks enhance snow impurity-induced radiative forcing in the Sierra Nevada</strong>". Three folders are included: 1) The model output with aerosol deposition in snow (aero); 2) The model output without aerosol deposition in snow (noaero) 3) processed observations used to validate the model results (SPIReS)</p>
Spectral dependence of light absorption and direct radiative forcing of rural carbonaceous aerosol in TSP, PM10, PM2.5, and PM0.1 in northwestern China
<p>Black carbon (BC) and brown carbon (BrC) are major light absorbing components of aerosol, affecting visibility, radiative forcing balance and human health. In this study, we investigated the light absorption and radiative forcing of carbonaceous aerosol in the total suspended particle (TSP), coarse particle (PM<sub>10</sub>: particulate matter with an aerodynamic diameter less than 10 μm, Dp≤10 μm), fine particle (PM<sub>2.5</sub>: Dp≤2.5 μm), and nanoparticle (PM<sub>0.1</sub>: Dp≤0.1 μm) in a rural area of Guanzhong Plain, China. Similar variations of light absorption coefficients and the absorption Ångstrom exponent (AAE) of TSP, PM<sub>10</sub>, and PM<sub>2.5</sub> were observed. Lower light absorption coefficients and higher AAEs were obtained for PM<sub>0.1 </sub>compared with other particle sizes. The direct radiative forcing (DRE) efficiency of BC decreased with size bins of TSP, PM<sub>10</sub>, PM<sub>2.5</sub>, and PM<sub>0.1</sub>, respectively. The DRE of BCs for all particle sizes at top atmosphere (TOA), surface atmosphere (SUF) and the whole atmosphere (ATM) were estimated, and the levels in TSP were ~3.7 times higher than those in PM<sub>0.1</sub>. The optical properties of primary and secondary BrC (PBrC and SBrC) in PM<sub>0.1</sub> were further analyzed. The levels of AAEs indicated that the light absorbing of SBrC was more wavelength dependent than PBrC in PM<sub>0.1</sub>. The DRE of BC, PBrC, and SBrC in PM<sub>0.1</sub> were estimated firstly with the values of 19.9, 2.1, and 1.1 Wm<sup>-2</sup> in the ATM, respectively.</p>
Model simulation data used in "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" (Thor et al., GMD, 2022)
<p>This archive contains files that were used to produce the results published in the article "An inconsistency in aviation emissions between CMIP5 and CMIP6 and the implications for short-lived species and their radiative forcing" by Thor et al.</p> <p>The directory nml contains namelist setups (configuration files) used for each of the simulations that were performed for this study.<br> The used MESSy version is d2.54.0.3-pre2.55-02-2077-g6eca90858-dirty_6eca90858ecb4ee8fb8900681a94a79ac3d612af_2021-01-26T11:10:08+01:00_2021-02-18T09:14:01+0100 for the QCTM simulations and d2.54.0.3-pre2.55-02-1466-g1fb086944_1fb0869442bf4f1bb10a7dd5f36e4bde5c0cf6d7_2020-10-27T18:17:50+01:00_2020-10-27T18:24:16+0100 for the aerosol simulations (http://www.messy-interface.org).</p> <p>The directory figures contains ipython scripts that were used to produce the figures in the paper.</p>
CMIP6 scenarios' radiative forcing of non-CO2 greenhouse gases and aerosols for UVic ESCM simulations (1850-2500)
<h1>Overview</h1> <p>This repository contains the input files for the UVic Earth System Climate Model (ESCM) that are required to simulate the historical period (1850-2014) and the extended CMIP6 SSP-RCP scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, SSP3-7.0, SSP4-3.4, SSP4-6.0, SSP5-3.4, SSP5-8.5 (2015-2500).</p> <p>For simulations of these scenarios, the model is forced with aggregated non-CO2 greenhouse gas radiative forcing, land use cover, aerosol radiative forcing, and either CO2 concentration or CO2 emissions. The radiative forcing of CO2 is calculated internally by the UVic ESCM.</p> <p>The following files are included in this repository:</p> <p><strong>CO2 concentrations (for concentration-driven simulations)</strong></p> <p>A_co2_hist.nc</p> <p>A_co2_119.nc</p> <p>A_co2_126.nc</p> <p>A_co2_245.nc</p> <p>A_co2_370.nc</p> <p>A_co2_434.nc</p> <p>A_co2_460.nc</p> <p>A_co2_534.nc</p> <p>A_co2_585.nc</p> <p> </p> <p><strong>CO2 emissions (for emission-driven simulations)</strong></p> <p>F_co2emit_119.nc</p> <p>F_co2emit_126.nc</p> <p>F_co2emit_245.nc</p> <p>F_co2emit_370.nc</p> <p>F_co2emit_434.nc</p> <p>F_co2emit_460.nc</p> <p>F_co2emit_534.nc</p> <p>F_co2emit_585.nc</p> <p> </p> <p><strong>Land use cover fractions (pasture and crops)</strong></p> <p>L_agricfra_hist_and_ssp119.nc</p> <p>L_agricfra_hist_and_ssp126.nc</p> <p>L_agricfra_hist_and_ssp245.nc</p> <p>L_agricfra_hist_and_ssp370.nc</p> <p>L_agricfra_hist_and_ssp434.nc</p> <p>L_agricfra_hist_and_ssp460.nc</p> <p>L_agricfra_hist_and_ssp534.nc</p> <p>L_agricfra_hist_and_ssp585.nc</p> <p> </p> <p><strong>Aggregated non-CO2 greenhouse gas forcing</strong></p> <p>A_aggfor_hist.nc</p> <p>A_aggfor_119.nc</p> <p>A_aggfor_126.nc</p> <p>A_aggfor_245.nc</p> <p>A_aggfor_370.nc</p> <p>A_aggfor_434.nc</p> <p>A_aggfor_460.nc</p> <p>A_aggfor_534.nc</p> <p>A_aggfor_585.nc</p> <p> </p> <p><strong>Aerosol optical depth</strong></p> <p>A_sulphod_hist.nc</p> <p>A_sulphod_119.nc</p> <p>A_sulphod_126.nc</p> <p>A_sulphod_245.nc</p> <p>A_sulphod_370.nc</p> <p>A_sulphod_434.nc</p> <p>A_sulphod_460.nc</p> <p>A_sulphod_534.nc</p> <p>A_sulphod_585.nc</p> <p> </p> <h1>Detailed description</h1> <h2>1. CO2 concentrations</h2> <p>The CO2 concentrations are provided here as the annual global mean mole fraction of CO2 in ppm and identical with the CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>.</p> <h2>2. CO2 emissions</h2> <p>The CO2 emissions are the same as provided by RCMIP (Meinshausen et al., 2020). Here the Agriculture, Forestry and Other Land Use (AFOLU) emissions are represented as “F_co2eland” emissions. Also, the sector based emissions from Aircraft, the Industrial Sector, International Shipping, Residential Commercial Other, Solvents Production and Application, the Transportation Sector, and Waste are aggregated into the Fossil and Industrial emissions and represented as “F_co2efuel” emissions. Both the F_co2eland and F_co2efuel emissions are finally aggregated into total CO2 emissions represented as “F_co2emit”. These aggregated CO2 emissions are likewise identical to globally averaged CMIP6 input data available at <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a>. All three CO2 emission variables are included in the “F_co2emit*.nc” files. In addition to the SSP-RCP-scenario CO2 emissions also the historical CO2 emissions are included in all files (starting in year 1750).</p> <h2>3. Land use cover</h2> <p>The land-use forcing is provided as the pasture and cropland grid cell fraction (variable names: “L_cropfra” and “L_pastfra”; in file: “L_agricfra.nc”). The UVic ESCM translates pasture and cropland fractions internally into C3 grass or C4 grass fractions, depending on the local conditions. The land-use cover is based on LUH2v2f “states.nc” data (available at <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a>) and has been regridded and reaggregated for the UVic ESCM. The cropland fraction of the UVic ESCM input (“L_cropfra”) is the sum of all crop types given by LUH2v2f (“c3ann”, “c3nfxc”, “c3per”, “c4ann”, “C4per”), whereas the pasture fraction (“L_pastfra”) is the sum of LUH2v2f’s pasture fraction and rangeland fraction (“pastr”, “range”). The land-use forcing covers the period 850-2100.</p> <h2>4. Non-CO2 greenhouse gas radiative forcing</h2> <p>The aggregated radiative forcing of 44 non-CO2 greenhouse gases (GHG) was calculated from the respective atmospheric GHG concentrations (provided by RCMIP for CMIP6, see References), following the approach of Meinshausen et al. 2020 and Etminan et al. 2016. Radiative forcing of tropospheric ozone, stratospheric ozone, and stratospheric water vapor from methane oxidation was calculated as described in Smith et al. 2018.</p> <p>The following non-CO2 GHG are accounted for in the aggregated forcing files (“A_aggfor.nc”):</p> <p>N2O; CH4; CFC11; CFC12; HFC134a; C2F6; C6F14; CF4; HFC23; HFC32; HFC43_10; HFC125; HFC143a; HFC227ea; HFC245fa; SF6; CFC113; CFC114; CFC115; HCFC22; HCFC142B; HCFC141B; HALON1211; HALON1301; HALON2402; CH3BR; CH3CL; CCL4; CH2CL2; CH3CCL3; NF3; HFC365mfc; C3F8; C4F10; HFC236fa; C5F12; CHCL3; cC4F8; HFC152a; SO2F2; C7F16; C8F18; stratospheric and tropospheric O3; water vapor from CH4 oxidation.</p> <h2>5. Aerosol radiative forcing</h2> <p>Aerosol optical depth (AOD) 2D input data for the UVic ESCM was created using a UVic grid with the scripts and data provided by Stevens et al. (2017). The data provided describes nine different plumes globally which are scaled with time to produce monthly aerosol optical depth forcing for the years 1850-2018 (Stevens et al., 2017). For the future projection of the years 2018-2100, the same scripts were run with input data from Fiedler et al. (2019). To extend aerosol optical depth data from 2100 to 2500, the last year of available data (i.e. 2100) was repeated. </p> <p>Since the AOD input caused too great a negative forcing in the historical period, a scaling factor was implemented into the UVic ESCM, which allows to scale aerosol forcing from AOD data. The scaling factor was set to 0.7, which gives a globally averaged forcing of -1.03 Wm<sup>-2</sup> in 2011.</p> <p>Note that the file "A_sulphod_hist.nc" contains not only the data of the historical period (1850-2014) but also the data of the scenario SSP5-8.5 (extended until 2500).</p> <p> </p> <h2>References</h2> <p>Fiedler, S., Stevens, B., Gidden, M., Smith, S. J., Riahi, K., & van Vuuren, D. (2019). First forcing estimates from the future CMIP6 scenarios of anthropogenic aerosol optical properties and an associated Twomey effect. <em>Geoscientific Model Development</em>, <em>12</em>(3), 989-1007.Etminan, M., Myhre, G., Highwood, E., and Shine, K.: Radiative forcing of carbon dioxide, methane, and nitrous oxide: A significant revision of the methane radiative forcing, Geophys. Res. Lett., 43, 12614–12623,<a href="https://doi.org/10.1002/2016GL071930"> </a><a href="https://doi.org/10.1002/2016GL071930">https://doi.org/10.1002/2016GL071930</a>, 2016.</p> <p>Meinshausen, M., Nicholls, Z. R., Lewis, J., Gidden, M. J., Vogel, E., Freund, M., ... & Wang, R. H. (2020). The shared socio-economic pathway (SSP) greenhouse gas concentrations and their extensions to 2500. <em>Geoscientific Model Development</em>, <em>13</em>(8), 3571-3605.</p> <p>Smith, C. J., Forster, P. M., Allen, M., Leach, N., Millar, R. J., Passerello, G. A., & Regayre, L. A. (2018). FAIR v1. 3: a simple emissions-based impulse response and carbon cycle model. <em>Geoscientific Model Development</em>, <em>11</em>(6), 2273-2297.</p> <p>Stevens, B., Fiedler, S., Kinne, S., Peters, K., Rast, S., Müsse, J., Smith, S. J., and Mauritsen, T.: MACv2-SP: a parameterization of anthropogenic aerosol optical properties and an associated Twomey effect for use in CMIP6, Geosci. Model Dev., 10, 433-452, https://doi.org/10.5194/gmd-10-433-2017, 2017</p> <p>RCMIP GHG concentration data:<a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv"> </a><a href="../record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv">https://zenodo.org/record/4589756/files/rcmip-concentrations-annual-means-v5-1-0.csv</a></p> <p>RCMIP Emissions data:</p> <p><a href="https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv">https://rcmip-protocols-au.s3-ap-southeast-2.amazonaws.com/v5.1.0/rcmip-emissions-annual-means-v5-1-0.csv</a></p> <p>Input4mips CO2 concentration data: <a href="https://esgf-node.llnl.gov/search/input4mips/">https://esgf-node.llnl.gov/search/input4mips/</a></p> <p>LUH2 land-use cover data: <a href="https://luh.umd.edu/data.shtml">https://luh.umd.edu/data.shtml</a></p>
Importance of Longwave Radiative Forcing by Icy Clouds in Maintaining Miocene High-latitude Warmth
<p>Supporting Information for<strong> Importance of Longwave Radiative Forcing by Icy Clouds in Maintaining Miocene High-latitude Warmth</strong></p> <p>NetCDF files: B.MIO_400_C5.cam2.h0.last50yr_climo_monthly.nc is from our control experiment, and B.MIO_400_C5_MK_TeoKor_Dem1SLF_2.cam2.h0.last50yr_climo_monthly is from our modified experiment.</p> <p>cldwat2m_micro.F90, microp_aero.F90, microp_driver.F90, and zm_conv.F90 are the source code used in our modified experiment. </p> <p> </p>
Acoustic Radiation Force Impulse Imaging (ARFI) : a New Technique to Assess Liver Elasticity
ClinicalTrials.gov study NCT01082419. IPD Sharing: Not stated. Countries: 1. Publications: 5.
Effective radiative forcing (ERF) experiments with EC-Earth3
<p># Effective radiative forcing (ERF) experiments with EC-Earth3</p> <p>Contact: klaus.wyser@smhi.se<br> Date: 2020-10-19</p> <p>The tarball contains the datasets that have been used to assess the ERF of the modified MACv2-SP forcing that has been prepared for the CovidMIP experiments. All experiments were done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth) that includes the MACv2-SP aerosol forcing including the effects on the cloud droplet activation.</p> <p>Following the CFMIP protocol the ERF is obtained by running the model in atmosphere-only mode with prescribed SST and sea-ice from the piControl simulation. Two experiments are needed to assess the ERF: a control experiment (mctl) in which the all forcings are kept constant at the pre-industrial level, and a sensitivity experiment in which only the MACv2-SP forcing is taken from a different scenario from a specific year. Both control and sensitivty experiment are then run for 55 years.<br> </p> <p>The following MACv2-SP forcings were used in the different experiments:<br> - **mctl**: all forcings as in the piControl experiment<br> - **mba2**: forcing for year 2020 from the new ssp245 baseline dataset<br> - **mba5**: forcing for year 2050 from the new ssp245 baseline dataset<br> - **mbl2**: forcing for year 2020 from the TwoYearBlip dataset<br> - **msg5**: forcing for year 2050 from the StrongGreen recovery dataset<br> - **mmg5**: forcing for year 2050 from the ModerateGreen recovery dataset<br> - **mff5**: forcing for year 2050 from the FossilFuel rebound dataset</p> <p>The following variables from the model output are included in the dataset:<br> - **tsr**: Top of the atmosphere (TOA) SW radiation (all sky)<br> - **tsrc**: TOA SW radiation (clear sky)<br> - **tsra**: TOA SW radiation (cloudy sky)<br> - **ttr**: TOA LW radiation (all sky)<br> - **ttrc**: TOA LW radiation (clear sky)<br> - **ttra**: TOA LW radiation (cloudy sky)<br> - **ssr**: Surface SW radiation (all sky)<br> - **ssrc**: Surface SW radiation (clear sky)<br> - **ssra**: Surface SW radiation (cloudy sky)<br> - **str**: Surface LW radiation (all sky)<br> - **strc**: Surface LW radiation (clear sky)<br> - **stra**: Surface LW radiation (cloudy sky)<br> </p> <p>These variables have been used to compute timeseries and maps of the different components of the ERF:<br> - **erf**: ERF at TOA (all sky)<br> - **erfc**: ERF at TOA (clear sky)<br> - **erfa**: ERF at TOA (cloudy sky)<br> - **erfsfc**: ERF at SFC (all sky)<br> - **erfsfcc**: ERF at SFC (clear sky)<br> - **erfsfca**: ERF at SFC (cloudy sky)</p> <p>The script **stats.sh** has been used to compute the ERF.</p>
SP2 measurement error of BC mixing state and radiative forcing evaluation
<p>The first version of dataset.</p>
Bladder ARFI (Acoustic Radiation Force Impulse) Study
ClinicalTrials.gov study NCT01781832. IPD Sharing: NO. Countries: 1. Publications: 0.
FOcal Radiation for Oligometastatic Castration-rEsistant Prostate Cancer (FORCE)
ClinicalTrials.gov study NCT03556904. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Ultrasound Based Acoustic Radiation Force Impulse Imaging
ClinicalTrials.gov study NCT01781208. IPD Sharing: NO. Countries: 1. Publications: 0.
Lidar Atmospheric Sensing Experiment (LASE) Data Obtained During the Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX)
The Lidar Atmospheric Sensing Experiment (LASE) Tropospheric Aerosol Radiative Forcing Observational Experiment (TARFOX) data set was collected over the Western Atlantic Ocean in July 1996. The overall goal of TARFOX was to reduce uncertainties in the effects of aerosols on climate by determining the direct radiative impacts, as well as the chemical, physical, and optical properties, of the aerosols carried over the western Atlantic Ocean from the United States. LASE is an airborne autonomous DIAL system which produces measurements of aerosols and water vapor vertical profiles from the aircraft altitude down to the surface. Such profiles show the vertical context in which the TARFOX in situ and radiometric measurements are made, thus supporting the vertical extension of the in situ measurements and detecting any unsampled layers or inhomogeneities, which would impact the airborne and satellite radiative flux measurements. Note that the LASE_TARFOX data set is also available under the TARFOX project as the TARFOX_LASE data set. The data files included in these two data sets are identical.
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.