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154 results for “parameterization”
Underlying data for "Evaluating parameterization protocols for hydration free energy calculations with the AMOEBA polarizable force field"
<p>This dataset includes underlying data for the publication "Evaluating<br /> parameterization protocols for hydration free energy calculations with the<br /> AMOEBA polarizable force field"</p> <p>Contents:<br /> Modified valence parameters for the Poltype software (valence.py). This can be<br /> substituted for the existing valence.py module packaged with Poltype to make the<br /> parameter assignment changes detailed in the article supplementary information. </p> <p>Results files for each parameter set (*.txt). Each consists of a 4 x 47 array of<br /> numbers. Rows correspond to entries for each sequential ligand. The first column<br /> in each row is the experimental hydration free energy. The following three rows<br /> are computational hydration free energy predictions from three independent<br /> repeat simulations.</p> <p>Script for analysis of results files (analyse_hfe.py). Short script to produce<br /> descriptive statistics for packaged datasets. Expects input files in the syntax<br /> of *.txt (i.e. 4 x 47 arrays)</p>
Data related to "Free infragravity waves on the inner shelf: Observations and Parameterizations at two Southern California beaches"
<p><strong>Abstract: </strong></p> <p>Co-located pressure and velocity observations in 10-15m depth are used to estimate the relative contribution of bound and free infragravity (IG) wave energy to the IG wave field. Shoreward and seaward going IG waves are analyzed separately. At the Southern California sites, shoreward propagating IG waves are dominated by free waves, with the bound wave energy fraction <30% for moderate energy incident sea-swell and <10% for low energy incident sea-swell. Only the 5% of records with energetic long swell show primarily bound waves. Consistent with bound IG wave theory, the energy scales as the square (frequency integrated) sea-swell energy, with a higher correlation with swell than sea energy. Seaward and shoreward free IG energy is strongly tidally modulated. The ratio of free seaward to shoreward propagating IG energy suggests between 50-100% of the energy radiated offshore is trapped on the shelf seaward of 10-15m and redirected shoreward. Remote sources of IG energy are small.<br><br>The observed linear dependency of free seaward and shoreward IG energy on local sea-swell wave energy and tide are parameterized with good skill (R2 ~ 0.90). Free (random phase) and bound (phase-coupled) IG waves are included in numerically simulated timeseries for shoreward IG waves that are used to initialize (~ 10m depth) the numerical nonlinear wave transformation SWASH. On the low slope study beach, wave runup is only weakly influenced by free shoreward propagating waves observed at the offshore boundary (foreshore slope = 0.02). <br> </p> <p><strong>Plain Language Summary:</strong></p> <p>Infragravity (IG) waves are long-period (every 25 sec to 2.5 min) waves that contribute to coastal flooding and beach erosion. IG waves, generated near the shoreline by short-period sea-swell (SS) wave groups (known by surfers as "sets"), have long wavelengths (100s of m) and do not curl and break like ordinary sea and swell waves. Instead, they can bounce off the beach face and propagate seaward. Our study concerns IG waves on the inner shelf (10-15m depth, ~ 500-700m offshore), seaward of the region of IG generation. Similar to previous observations in Hawai'i and North Carolina, we find most of the bounced, seaward going IG energy cannot reach deep water and is trapped on the continental shelf. We develop an observation-based estimate IG wave energy on the inner shelf as a function of SS wave energy and tide level. Finally. we show with a numerical model that IG wave runup at the shoreline is influenced only weakly by IG waves on the inner shelf.<br> </p> <p><strong>About the data: </strong></p> <p>The PUV MATLAB data structure is saved in the .mat file provided. Data is aggregated from numerous deployments of Nortek Vectors (PUV) in San Diego County between 2019 and 2022, at Torrey Pines State Beach and Cardiff State Beach. This data was collected by the <a href="https://siocpg.ucsd.edu/">Coastal Processes Group</a> at the Scripps Institution of Oceanography. </p> <p>2488 3 hour at 2Hz timeseries of pressure, cross-shore and alongshore velocity are provided. </p> <ul> <li><em>time</em> is given in UTC time as a MATLAB datetime.</li> <li><em>MOP</em> is the location of the instrument for a specific timeseries given as a MOP number (corresponding to the MOP lines given in O'Reilly et al. (2016) and used by the Coastal Data Information Program (CDIP). The MOP number can be used to obtain shorenormal angle and location of the sensor from <a href="https://cdip.ucsd.edu/mops/">https://cdip.ucsd.edu/mops/</a>.</li> <li><em>depth</em> is given as the mean depth over the 3h pressure records in meters and includes the mean tidal elevation.</li> <li><em>P</em> is the detrended pressure data in meters, detided and the mean depth removed.</li> <li><em>U</em> is the detrended cross-shore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+x is onshore).</li> <li><em>V </em>is the detrended alongshore velocity data in meters/second, with the main tidal constituents M2, S2 and K1 and the mean depth removed, and rotated to be shorenormal according the the MOP angle (+y is north).</li> <li><em>sensor_offsets</em> includes both the sensor offset from the sea floor in meters of the pressure sensor and the current meter. These can be used to surface correct the timeseries.</li> </ul> <p>The timeseries are grouped by instrument.</p>
Dataset: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model
<p>Codes and figures for the paper: Neutron star properties with careful parameterization in the (axial)vector meson extended linear sigma model</p>
Responses of Pine Island and Thwaites glaciers to melt and sliding parameterizations
<p>Pine Island and Thwaites glaciers are the two largest contributors to sea level rise from Antarctica. Here we examine the influence of basal friction and melt in determining projected losses. We examine both Weertman and Coulomb friction laws with explicit weakening as the ice thins to flotation, which many friction laws include implicitly via the effective pressure. We find relatively small differences with the choice of friction law (Weertman or Coulomb) but find losses are highly sensitive to the rate at which the basal traction is reduced as the area above the grounding line thins. Consistent with earlier work on Pine Island Glacier, we find sea level contributions from both glaciers vary linearly with the melt volume averaged over time and space, with little influence from the spatial or temporal distribution of melt. Based on recent estimates of melt from other studies, our work simulations suggest that melt-driven combined sea-level rise contribution from both glaciers is unlikely to exceed 10 cm by 2200. We do not include other factors, such as ice shelf breakup that might increase loss, nor factors such as increased accumulation and isostatic uplift that may mitigate loss.</p>
Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components - All Figures and Datasets
<p>Figures and Datasets for "Parameterized Task Graph Scheduling Algorithm for Comparing Algorithmic Components"</p> <ul> <li>dataset.zip: All datasets evaluated</li> <li>figures.zip: All plots (1080) for all results for our paper</li> </ul>
Lightning Prediction in the Tehran Region Using the WRF Model with Multiple Physical Parameterizations and an Ensemble Approach
<p><span>The Grid Analysis and Display System (</span>GrADS)<span> </span><span>and</span><span> </span><span>Python</span><span> </span><span>scripts and the output data from simulations that we used in this study.</span></p>
Code and partial data used in "Vertically recurrent neural networks for sub-grid parameterization"
<p>This repository contains the RNN training and evaluation code used in the paper<em> Vertically recurrent neural networks for sub-grid parameterization</em></p> <p> </p> <ul> <li> The radiative transfer emulation data can be accessed with through a Climetlab plugin (<a href="https://pypi.org/project/climetlab-maelstrom-radiation/">Climetlab-maelstrom-radiation</a>). <p>Datasets are downloaded and explained in the demo notebook here <a href="https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb" rel="nofollow">https://git.ecmwf.int/projects/MLFET/repos/maelstrom-radiation/browse/notebooks/demo_radiation.ipynb</a></p> In addition the full training and testing code used in the paper is uploaded here (<em>pu-maelstrom-radiation.tar.gz</em>).</li> <li> </li> </ul> <p>Three parameterization problems from earlier studies are also included (we have modified the code from these papers to incorporate RNNs): </p> <ul> <li>non-orographic gravity wave drag (<a href="https://doi.org/10.1029/2021MS002477">Chantry et al. 2021</a>) <ul> <li>Based on TensorFlow</li> <li>This repository uses the <em>CliMetLab </em>plugin and<strong> downloads the data from the European Weather Cloud</strong></li> </ul> </li> <li>non-local parameterization (<a href="https://doi.org/10.1029/2022MS002984">Wang et al. 2022</a>) <ul> <li>The new code is based on TensorFlow, so you'll need both PyTorch and TensorFlow to run everything</li> <li><strong>See original paper for data access</strong></li> </ul> </li> <li>moist physics (Han et al. <a href="https://doi.org/10.1029/2022MS003508">2023</a>, <a href="https://doi.org/10.1029/2020MS002076">2020</a>) <ul> <li>Based on TensorFlow and PyTorch. This one has the most additions, e.g. code to generate a TensorFlow TFRecord dataset from the raw netCDF data archived in the original paper, autoregressive training and experimental model architectures in PyTorch</li> <li><strong>See original paper for data access</strong></li> </ul> </li> </ul> <p>Each of the code repos (unpack the tars) have an updated README.</p> <p>References:</p> <table> <tbody> <tr> <td> <div>Chantry, M., Hatfield, S., Dueben, P., Polichtchouk, I., & Palmer, T. (2021). Machine learning emulation of gravity wave drag in numerical weather forecasting. <em>Journal of Advances in Modeling Earth Systems</em>, <em>13</em>(7), e2021MS002477</div> <div> </div> <div> <div>Han, Y., Zhang, G. J., Huang, X., & Wang, Y. (2020). A moist physics parameterization based on deep learning. <em>Journal of Advances in Modeling Earth Systems</em>, <em>12</em>(9), e2020MS002076.</div> </div> <div> </div> <div>Han, Y., Zhang, G. J., & Wang, Y. (2023). An ensemble of neural networks for moist physics processes, its generalizability and stable integration. <em>Journal of Advances in Modeling Earth Systems</em>, <em>15</em>(10), e2022MS003508</div> <div> </div> <div>Wang, P., Yuval, J., & O’Gorman, P. A. (2022). Non‐local parameterization of atmospheric subgrid processes with neural networks. <em>Journal of Advances in Modeling Earth Systems</em>, <em>14</em>(10), e2022MS002984.</div> </td> </tr> <tr></tr> </tbody> </table> <div> </div>
" Description and evaluation of a new contrail cirrus 2 parameterization in the ARPEGE-Climat atmospheric 3 model " datasets
<p>This repository contains the data files used for the analyses presented in the paper. The dataset includes variables of interest for the two main simulations (CONTFREE and CONTNUDGED) for the year 2019. </p> <ul> <li>"totcon" variable represents the integrated contrail cirrus coverage.</li> <li>"rst", respectively "rstcotra", represent the net downward shortwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net downward shortwave radiation contribution. </li> <li>"rlut", respectively "rlutcotra", represent the net upward longwave radiation at the top of the atmosphere for the radiative call with contrails perturbation, respectively without perturbation. The difference between these two variables provides the contrail cirrus net upward longwave radiation contribution. </li> <li>"pissr" represents the probability of the gridbox being ice supersaturated. This variable is provided for pressure levels 200,225, and 250hPa.</li> <li>"clhcalipso" represents the integrated coverage of "high clouds" (>400hPa). </li> </ul>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Model simulations using a parameterization of convective organization effects
<p>We propose a parameterization scheme of convective organization effects based on a moisture-distribution approach. We implement it into a regional climate model and evaluate its performance against a convection-permitting model simulation. The related model simulations are included in this dataset.</p>
Dataset and code for "A parameterization of non-hydrostatic pressure transport terms in second- and third-order turbulence equations"
<p>Description of included files:</p> <p> </p> <p><strong>Cloud Layers Unified By Binormals (CLUBB) content</strong></p> <p>The zipped up "clubb_release.tar.gz" folder contains the CLUBB code base used to generate the simulations used in this paper. The ifort compiler was used to produce the simulations used in the paper.</p> <p>The zipped up "clubb_files.tar.gz" folder contains BOMEX, DYCOMS2_RF01, and Wangara 1D simulations for "default" runs (without new pressure transport terms) and "new" runs (with new pressure transport terms). Setup files are also included which give the parameter and flag values for each run. The files contained in clubb_files.tar.gz are:</p> <ol> <li>bomex_setup_dflt.txt</li> <li>bomex_zt_dflt.nc</li> <li>bomex_zm_dflt.nc</li> <li>bomex_sfc_dflt.nc</li> <li>dycoms2_rf01_setup_dflt.txt</li> <li>dycoms2_rf01_zt_dflt.nc</li> <li>dycoms2_rf01_zm_dflt.nc</li> <li>dycoms2_rf01_sfc_dflt.nc</li> <li>wangara_setup_dflt.txt</li> <li>wangara_zt_dflt.nc</li> <li>wangara_zm_dflt.nc</li> <li>wangara_sfc_dflt.nc</li> <li>bomex_setup_new.txt</li> <li>bomex_zt_new.nc</li> <li>bomex_zm_new.nc</li> <li>bomex_sfc_new.nc</li> <li>dycoms2_rf01_setup_new.txt</li> <li>dycoms2_rf01_zt_new.nc</li> <li>dycoms2_rf01_zm_new.nc</li> <li>dycoms2_rf01_sfc_new.nc</li> <li>wangara_setup_new.txt</li> <li>wangara_zt_new.nc</li> <li>wangara_zm_new.nc</li> <li>wangara_sfc_new.nc</li> </ol> <p>The CLUBB code base used for this paper can also be obtained by cloning the repo at https://github.com/larson-group/clubb_release.git. The relevant git branch is "stephens_2022_press_trans_paper" and the relevant git commit hash is 937db014df79318655b4fb6a469a7b77529b2e74. Compiled with ifort, the stephens_2022_press_trans_paper branch will generate the "new" simulations without further modifications. To generate the "dflt" simulations, set C_wp2_pr_dfsn = C_wp3_pr_dfsn = 0 in input/tunable_parameters/tunable_parameters.in, and set l_use_tke_in_wp2_wp3_K_dfsn = .false. in input/tunable_parameters/configurable_model_flags.in.</p> <p> </p> <p><strong>System for Atmospheric Modeling (SAM) content</strong></p> <p>SAM LES files with horizontal average variables:</p> <ol> <li>BOMEX_64x64x75_100m_40m_1s.nc</li> <li>DYCOMS_RF01_96x96x320.nc</li> <li>WANGARA_64x64x80_100m_40m_LES.nc</li> </ol> <p>The SAM code base used for this paper can be obtained by cloning the repo at https://github.com/larson-group/sam_clubb.git. The relevant git commit hash is db3e00a09cb5c0503e1bf3a728603fe690cb9a3e. The gfortran compiler was used to generate the runs used in the paper.</p> <p> </p> <p><strong>Python script</strong></p> <p>The included python script "stephens_2022_press_trans_paper_figures.py" can be used along with the SAM and CLUBB files to generate the plots shown in the paper, with some simple user modifications. This script can also be found in the stephens_2022_press_trans_paper branch of the clubb_release repo, in the "postprocessing" folder.</p>
Data and analysis scripts for "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt"
<p>The data relates to the analysis of "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt" and is in support and reference to the work titled as the same for a research article authored by Choudhary and Voigt. The file 'data.zip' contains different directories named as per the computations related to particular analysis as mentioned below:</p> <table> <tbody> <tr> <td> <p>Directory names and content inside 'data.zip'</p> </td> </tr> <tr> <td> <p>Name of directory</p> </td> <td> <p>Content</p> </td> </tr> <tr> <td> <p>trajectories </p> </td> <td> <p>Output from run of LAGRANTO tool on regridded ICON simulations<strong><sup>*</sup></strong> (the input data is not provided here). The output contains computations of 'warm conveyor belt (WCB)' trajectories and different variables traced along them which are stored in different files named as outtrace1.nc and so on. The subdirectories are named as per the resolution, convection and cloud microphysics parametrization used in model setup, for e.g. '0.025convon/1m' corresponds to 2.5 km resolution with parametrized convection and 1-moment cloud microphysics. The file with name ending with 'box' corresponds to different subclasses of trajectory as described in the work. </p> </td> </tr> <tr> <td> <p>dhr</p> </td> <td> <p>diabatic heating rate, traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>dynamical variables</p> </td> <td> <p>av- absolute vorticity, pv- potential vorticity and w- vertical wind traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>statistics</p> </td> <td> <p>statistics (different statistical computation of WCB parcels ascent for 1- and 2- moment cloud microphysics, details described in work)</p> </td> </tr> <tr> <td> <p>theta_e</p> </td> <td> <p>equivalent potential temperature for 2.5 km explicit convection simulation (.pkl file) for 2016.09.22 12UTC, 2016.09.23 12UTC and 2016.09.24 12UTC in files numbered 1, 2 and 3 respectively.</p> </td> </tr> <tr> <td> <p>ipv</p> </td> <td> <p>same as above but for isentropic potential vorticity</p> </td> </tr> <tr> <td> <p>mslp</p> </td> <td> <p>mslp.nc- same as above but for mean sea level pressure field </p> </td> </tr> <tr> <td> <p>cyclone_track</p> </td> <td> <p>File name with convention 'nawdexnwp-5km-mis-0001_vladiana_cyclonepressure.nc' corresponds to file containing central mean sea level pressure of cyclone Vladiana during the simulation period for 5 km resolution with parametrized convection and 1-moment cloud microphysics. </p> <p>coord_1m.npy- the coordinate/ location of cyclone Vladiana based on central mean sea level pressure </p> </td> </tr> <tr> <td> <p>pte</p> </td> <td> <p>Pressure tendency equation (PTE) analysis. Subdirectories are named after different resolutions of simulation with 1-moment cloud microphysics and parametrized convection (except for 10, 5 and 2.5 km where convection is explicit).</p> <p>Further, the .npy files are named as per the different vertically integrated tendency terms of PTE equation they represent, as mentioned below:</p> <p>dp_dt: surface pressure</p> <p>dfi_dt: geopotential (upper boundary of integral)</p> <p>i_diab_res: diabatic heating (including residual)</p> <p>i_itt: virtual temperature</p> <p>i_tadv: temperature advection</p> <p>i_vmt: vertical motions</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>*</strong>Note: The original ICON simulations which are not part of this dataset were carried out by Prof. Aiko Voigt (University of Vienna) at the Mistral High Performance Computing system of the German Climate Computing Center (DKRZ) in Hamburg, Germany. The primary data of the ICON simulations (run scripts, namelists, scripts for lateral boundary data) are published at KITopen of Karlsruhe Institute of Technology, https://doi.org/10.5445/IR/1000123695. Note that the KITopen dataset includes all simulations of \cite{svchd20}, from which a subset is analyzed here.</p> <p> </p> <p>The file 'scripts.zip' contains two directories which is related with analysis presented in the work:</p> <p>'plotting_scripts' includes script for creating different plots/ figures showcased in the work </p> <p>'processing_scripts' includes computations for producing different quantities/ data that are named as described above in the table for 'data.zip'</p> <p> </p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Trained Models
<p>Trained machine learning models and scaling values used in the paper "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model."</p>
Climate Impacts of Parameterizing Subgrid Partitioning of Land Surface Heat Fluxes to the Atmosphere with the NCAR CESM1.2
<p>The modified code as well as the CAM5 output for all the simulations in this study (V0 for the CTL run, CON1 for the EXP run, and PCON1R for EXP_COR run).</p> <p>The CESM1.2.1-CAM5.3 source code can be downloaded through the CESM official website https://www.cesm.ucar.edu/models/cesm1.2/cesm/doc/usersguide/x290.html#download_ccsm_code. Its output files are named in V0*.nc.</p> <p>The modified code for the EXP run in the study is in CON1.tar, with its CAM5 output files named in CON1*.nc.</p> <p>The modified code for the EXP_COR run in the study is in PCON1R.tar, with its CAM5 output files named in PCON1R*.nc</p>
Code for APJAS publication - Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate
<p>In this repository, we include the source codes for WRF microphysics parameterization used in the APJAS publication "Numerical errors in ice microphysics parameterizations and their effects on simulated regional climate"</p> <p>There are three WDM6 codes for simulations. The original WDM6 code (ORG) using parameter defined by Hong et al (2004), the revised WDM6 code (NEW) those revised by removing the numerical errors, and the additional WDM6 code (SEN) for sensitivity experiment adopting the column-shaped parameters.</p> <p>In supplement, several cloud-ice characteristics presented in the paper were induced in detail and compared with Hong et al (2004) and this study.</p>
Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES)
<p>Data and code for "Non-local parameterization of atmospheric subgrid processes with neural networks" (Wang et al. 2022 submit to JAMES). Detailed description of the files in README.txt.</p>
Model output of: Excitation of an MJO event in response to a transient sea surface warming in Super-Parameterized CAM
<p>Recent studies have suggested that the Madden-Julian Oscillation (MJO) could be generated in the atmospheric adjustment to equatorial heating anomalies in models properly resolving moist convection. Here we use Super-Parameterized CAM to simulate atmospheric response to transient sea surface warming in the equatorial Indian Ocean, which leads to the excitation of an MJO event, robust across different ensemble members, in addition to the much-expected equatorial Rossby and Kelvin waves. A moist static energy (MSE) budget analysis suggests that longwave and surface turbulent latent heat flux anomalies are predominantly responsible for the MJO excitation; however, these terms oppose its eastward propagation. In contrast, advection of MSE generally weakens the MJO amplitude, but contributes to its eastward propagation. These results highlight the role of the MJO in the moist atmospheric adjustment to equatorial heating, with implications for the MJO mechanisms and prediction.</p>
Replication Data for: ``Impact of Parameterized Isopycnal Diffusivity on Shelf-Ocean Exchanges under Upwelling-Favorable Winds: Offline Tracer Simulations Augmented by Artificial Neural Network''
<p>This dataset contains the modified MAMEBUS source code, configuration files for the MITgcm and MAMEBUS simulations, model diagnostics used in the paper, and scripts to train the Artificial Neural Networks.</p>
Data and code for training neural network parameterizations from an near-global aqua-planet simulation
<p>This commit contains the code, coarse-grained data, processed training data, neural network models, and coupled NN-GCM simulations. It can be extracted by running</p> <pre><code>tar xzf <archive></code></pre> <p>While this archive contains code (it is slightly out of date). This is the up-to-date code: <a href="https://zenodo.org/record/3248586">https://zenodo.org/record/3248586</a></p> <p>Move the "nn", "debiased", and "data" folders from this archive into that code directory.</p> <p> </p> <p> </p>
Replication Package: Reducing the Maintenance Effort for Parameterization of Representative Load Tests Using Annotations
<p>This is the replication package for the publication <em>Reducing the Maintenance Effort for Parameterization of Representative Load Tests Using Annotations</em>, Journal of Software Testing, Verification and Reliability (STVR), Special Issue on Testing Extra-Functional Properties. It contains the JSON/YAML schema of the Input Data and Properties Annotation (IDPA) as well as the experiment artifacts, the experiment results, and the R-scripts we used for the analysis.</p> <p>The README.md (or README.pdf) contains further descriptions and instructions.</p>
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.