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.
2,152
datasets available to search
ShareScore release 0.9.0
Dataset results
2,152 results for “outputs”
Diazotroph Elemental Use Efficiency Model Output
<p>Dataset containing model output from the PISCES QUOTA model for three experiments performed to explore the impact of global change on the niche and physiology of two explicit marine diazotrophs. The model included diazotroph specific thermal performance curves for growth and elemental use efficiencies (both iron and phosphorus). The experiments were run using the IPCC high emissions RCP8.5 scenario. The explicit diazotroph model for PISCES QUOTA can be found here: https://github.com/lewiswrightson/PISCES-QUOTA-P6Z</p>
rankine_output
<p>Error diagnostics output (npy format) from DA experiments (single_wind_obs, position_obs, localization_tuning, full_network, and cycling) in a 2D vortex model. The error files are used in making all the figures of Ying et al. 2022 "Improving vortex position accuracy with a new multiscale alignment ensemble filter", see https://github.com/myying/rankine for more details about the implementation of EnSRF, MSA in python and the experiments.</p>
Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'
<p><strong>Raw output data from ColabFold modelling for the paper 'Interaction of C21ORF2 with a domain of NEK1 mutated in human diseases is vital for NEK1 function in human cells'</strong></p> <p><strong>File descriptions:</strong></p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_1_model_1_fixed.pdb</strong><br> ColabFold output PDB file - Rank 1 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_2_model_2_fixed.pdb</strong><br> ColabFold output PDB file - Rank 2 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_3_model_4_fixed.pdb</strong><br> ColabFold output PDB file - Rank 3 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_4_model_3_fixed.pdb</strong><br> ColabFold output PDB file - Rank 4 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_relaxed_rank_5_model_5_fixed.pdb</strong><br> ColabFold output PDB file - Rank 5 model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_coverage.png</strong><br> ColabFold output chart - MSA sequence coverage</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_PAE.png</strong><br> ColabFold output chart - PAE for each model</p> <p><strong>NEK11160endC21ORF2_amber_2e60f_plddt.png</strong><br> ColabFold output chart - predicted IDDT per position</p> <p><strong>Supplementary Excel file 1</strong><br> List of residues predicted to be involved in intermolecular interactions, and the type of interaction (based on PDB files for each models, generated using BIOVIA Discovery Studio 2021)</p>
WRF-DART/RTTOV input and (processed) output files for GMD-2022-30
<p>The <em><strong>Exp-01.tar.gz ~ Exp-14.tar.gz (including true_processed.tar.gz and ctrl_processed.tar.gz) </strong></em>store the processed output files. Untarrring each of them will generate a list of subdirectories named after UTC time, txt files and netcdf files. (1) The subdirectories named after the UTC time. For example, 202008180200 denotes 02:00 UTC 18 August 2020. The subdirectory 202008180200 includes preassim_mean.nc and postassim_mean.nc files, which denote the prior and posterior estimates of cloud and non-cloud variables (and diagnosed variable). Both preassim_mean.nc and postassim_mean.nc include seven variables: CWP denotes the cloud water path (kg m<sup>-2</sup>). CWC denotes the mixing ratio (kg kg<sup>-1</sup>) of cloud hydrometers, including cloud droplet, rain, snow, ice, and graupel. P denotes the pressure (hPa), Re_CLOUD denotes the effective radius of liquid water clouds, QVAPOR denotes the water vapor mixing ratio, T denotes the perturbation potential temperature, U denotes the u-wind component, and V denotes the v-wind component. (2)Txt files which are the quantitative metrics of cloud and precipitation simulation errors. FSS_pre.txt and FSS_pos.txt store the fraction skill score for the prior and posterior estimates of cloud coverage. MAE_pre.txt and MAE_pos.txt store the Mean Absolute Error for the prior and posterior estimates of CWP. rain_TS_pre.txt and rain_TS_pos.txt store the TS scores for the prior and posterior estimates of precipitation. rain_MAE_pre.txt and rain_MAE_pos.txt store the MAE for the prior and posterior estimates of rain rate. (3) Netcdf files including mpe_mpi.nc, rain_pre.nc, and rain_pos.nc. mpe_mpi.nc includes the Mean Profile Error (MPE) for the prior and posterior estimates of non-cloud variables (Q, T, U, and V). rain_pre.nc and rain_pos.nc store the results for the prior and posterior estimates of rain rate (mm h<sup>-1</sup>).</p> <p>The <em><strong>nature_run.tar.gz</strong></em> file contains the WRF output files for the nature run.</p> <p>The <em><strong>model_codes.tar.gz</strong></em> file includes the source code of WRF-ARW (v4.1.1), WPS (v4.1), RTTOV (v12.3), and DART (Manhattan release v9.8.0).</p> <p>The <em><strong>input_file.tar.gz</strong></em> file includes the input namelist files for the WRF/WPS/DART models (tool) for the nature run, control run, and fourteen cycled data assimilation experiments. In addition, the input observation sequence files for the DART tool at each analysis time are provided.</p> <p>The <em><strong>Figure_script.tar.gz</strong></em> file includes the visualization scripts of Figure 1~Figure 16 in the manuscript.</p>
BioExcel Use Case 1: collection of output data from molecular dynamics simulation
<p>The Use Case aims to address all the challenges related to antibody design through an integrative approach combining the core BioExcel software comprising of GROMACS, HADDOCK and PMX.</p> <p>The folder contains the GROMACS output files (xtc and pdb file). Molecular Dynamics simulations have been performed with GROMACS version 2020 and CHARMM36 force field. The input files and scripts of the final protocol are publicly available on BioExcel GitHub https://github.com/bioexcel/BioExcel-UseCase1.</p> <p>The Use Case 1 protocol was presented at the BioExcel Summer School on Biomolecular Simulation in 2021 (see <a href="https://doi.org/10.5281/zenodo.7009238">https://doi.org/10.5281/zenodo.7009238</a> or <a href="https://youtu.be/_TDKfKX4kwM">https://youtu.be/_TDKfKX4kwM</a>)</p> <p> </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>
Evolution of physical linkage between loci: simulations output
<p>Coupling of multiple barriers to gene-flow, such as divergent local adaptation and reproductive isolation, facilitates speciation. However, alleles at loci that contribute to barrier effects can be dissociated by recombination. Models of linkage between diverging alleles often consider elements that reduce recombination, such as chromosomal inversions and alleles that modify recombination rate between existing loci. In contrast, here we consider the evolution of linkage due to close proximity of loci on the same chromosome. Examples of such physical linkage exist in several species, but in other cases strong associations are maintained without physical linkage. We use an individual-based model to study the conditions under which physical linkage between loci controlling ecological traits and mating preferences might be expected to evolve. We modelled a single locus controlling an ecological trait that acts also as a mating cue<span>.</span> Mating preferences are controlled by multiple loci, formed by mutations that are randomly placed in the "genome", within varying distances from the ecological trait locus, allowing us to examine which genomic architectures spread across the population. </p>
ModelE simulation output used in the study "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size" in Journal of Climate (2024)
<p>The included files are the GISS ModelE output needed to replicate the figures in McGraw et al 2023, "Severe Global Cooling After Volcanic Supereruptions? The Answer Hinges on Unknown Aerosol Size"</p> <p>Most of the data herein is output from GISS ModelE2.2 simulations that did not include interactive aerosol microphysics and chemistry. Instead, aerosol extinction and effective radius were input into the model from scaled Easy Volcanic Aerosol [Toohey et al, GMD 2016] output, as described in this study's Methods section. To calculate volcanic temperature impacts and forcings at combinations of injected sulfur mass and peak effective radius (Reff) that were not simulated, we used 2D linear interpolation with the scipy function 'Rbf'.</p> <p>Separately included is output from GISS ModelE2.1 with MATRIX interactive aerosol microphysics and chemistry [Bauer et al, ACP 2008]. Note that the injections were scaled to match that a 6.5 Tg sulfur (S) injection in ModelE2.1/MATRIX best replicated the aerosol optical depth (AOD) and effective radius observations of the 1991 Pinatubo event despite this injection being most commonly considered an 9 Tg S injection. Hence, to produce the 1000 Tg S eruption, a 722 Tg S injected was simulated. Such a mismatch has been found in other GCMs (eg Mills et al, JGRA 2016) and may be due to aerosol quick-removal processes not represented in these models.</p> <p>Please note that simulated eruption masses are in this dataset listed in units of Tg S, but in the publication are in Tg SO2 (Tg S x 2).</p> <p>Data from other modeling studies included in Fig. 1 and tree ring estimates in Figs. S2 & S4 can be found within the cited studies.</p> <p>For additional information, please contact zachary.mcgraw@columbia.edu</p>
Simulation outputs
<p>This datasets contains netcdf output files of the simulations analyzed in Colin et al. "Groundwater feedbacks on climate change in the CNRM global climate model" submitted to Journal of Climate.</p> <p> </p> <p><strong>1. Monthly and daily outputs file names </strong><br> <br> Monthly outputs : SimulationName_arpsfx_monthly_VariableName_year1-year2.nc<br> Daily outputs : SimulationName_arpsfx_daily_VariableName_year1-year2_JJAS.nc<br> <br> <em>Note : the daily outpus are available only for the "extended boreal summer" months, i.e. June, July, August, September</em><br> <br> <strong>2. Simulation names </strong><br> <br> PIC_FLD_632_NCB : named <strong>PIr</strong> in the paper,<br> without groundwater, under Pre-industrial conditions,<br> run over the 1850-1939 (year1-year2) period.<br> <br> PIC_GWFLD_632_NCB_clean : named <strong>PIa</strong> in the paper,<br> with groundwater, under Pre-industrial conditions,<br> run over the 1880-1969 (year1-year2) period.<br> <br> 4xCO2_FLD_632_NCB : named <strong>C4r</strong> in the paper,<br> without groundwater, under 4xCO2 conditions (after stabilization),<br> run over the 1850-1939 (year1-year2) period.<br> <br> 4xCO2_GWFLD_632_NCB_clean : named <strong>C4a</strong> in the paper,<br> with groundwater, under 4xCO2 conditions (after stabilization),<br> run over the 1880-1969 (year1-year2) period.<br> <br> <em>Note : The years' numbering does not matter as it does not correspond to actual years. Each simulation was run for 90 years (after the spin-up procedures) in a stabilized climate under pre-industrial and 4xCO2 conditions.</em><br> <br> <strong>3. Variable names</strong><br> <br> et : evapotranspiration<br> inflitr : liquid water inflitration in the soil<br> lai : Leaf Area Index<br> pr : precipitation<br> rzwc : root zone water content<br> tas_max : maximum daily 2-meter air temperature<br> tas_min : minimum daily 2-meter air temperature<br> tran : plants transpiration<br> vegstress : Soil Wetness Index computed with a weighting of soil water content with root density along vertical layers<br> wg : liquid soil water content in each vertical layers (sdepth)<br> wtd : water table depth<br> <br> <em>Note : wtd is defined on the river routing model (CTRIP) grid, at 0.5° resolution. All the other variables are defined on the grid common to the atmospheric model (Arpege-Climat) and land surface model (ISBA), at ~1° resolution.</em><br> <br> <em>The variables units and any further needed informations are included in files metadata.</em><br> <br> <strong>4. Other files (fixed fields)</strong><br> <br> areacella.nc : grid cell areas of the ISBA/Arpege-Climat grid (Land Surface model and atmospheric model),<br> areacellr.nc : grid cell areas of the CTRIP grid (River Routing model),<br> land_area_fraction.nc : fraction of land surface in each cell of the the ISBA/Arpege-Climat grid,<br> groundwater_mask.nc : groundwater mask, defined on the CTRIP grid,<br> groundwater_mask_on_Isba-Arpege_grid.nc : groundwater mask interpolated onto the ISBA/Arpege-Climat grid (using the "remapcon" function of CDO),<br> root_depth.nc : root depth, defined on the ISBA/Arpege-Climat grid,<br> root_depth_on_CTrip_grid.nc : root depth interpolated onto the CTRIP grid (using the "remapcon" function of CDO).</p>
Coordinates and output files for stationary points determined for the reaction between 1,4-ditrimethylsilyl-1,4-dihydropyridine and unsaturated nitriles.
<p>The .zip folder contains coordinates and output files (containing energies and frequencies) of the different stationary points found for the analyzed reactions, all of them in .xyz format. IRC output files are also included.</p>
Databases and information systems for research output: digital humanities outlook (DARIAH Bibliographical Data Working Group, September, 30th 2022)
<p>The video recording of the workshop "Databases and information systems for research output: digital humanities outlook" organized by DARIAH Bibliographical Data Working Group.</p>
SolarWalk Dataset: Occupant Identification using Indoor Photovoltaic Harvester Output Voltage
<p>We present the dataset containing time-series open circuit output voltage traces of indoor photovoltaic cell corresponding to occupant door crossing events to perform smart home occupant identification. We collect shadow patterns of five participants from two different doors in two rooms of a building. We collect a total of 900 door entry and exit events during different hours of the day. We sample the voltage at 50 hz and provide the raw timestamped data. We also pre-process the data to filter the event of interest and label the data with occupant id and type of door events. We provide two example scripts to demonstrate how to process raw data and apply machine learning models for occupant identification using solar cell voltage samples. </p>
Output Data: gcam_v5p3_seasia
<p>Outputs of the Southeast Asia GCAM branch, cities configuration. Scenarios included are the reference (BAU) case, and two policy scenarios for Thailand ("low" and "high" sectoral policies).</p>
Model outputs for wild ungulates occurrence and hunting yield abundance at European scale
<p>These are the outputs of a model for the occurrence and hunting yield (HY) density of wild ungulates not only for widely distributed species in Europe, but also for those ones which have a constrained distribution and ii) to compare the output of occurrence with observed HY. Random Forest function was used for modelling occurrence of species. We used occurrence data available from the past 30 years, and HY data (period 2015-2020) from records collected by <em>ENETWILD</em>. Like previous models based on HY, the response variable was the maximum number of wild ruminants annually hunted in 2015-2020 hunting seasons divided by the area (km<sup>2</sup>) of the corresponding administrative unit (HY density). Models based on HY were statistically downscaled to make predictions to 10x10km squares. Occurrence data models indicated a good predictive performance for most species, showing that the model framework proposed have improved results in comparison to previous models. The transferability of models into new regions was limited by the exposure of species to environmental conditions. As for HY models, the calibration plots showed a good and linear predictive performance for widely distributed species, as well as constrained distributed species. Overall, our results were consistent with the expected abundance distribution of widely distributed species.</p>
Pharokka v 1.1.0 Benchmarking Input Output and Script
<p>This record contains all benchmarking input FASTA and output files for the Pharokka manuscript. Enterobacteria Phage Lambda (Genbank accession J02459), Staphylococcus phage SAOMS1 (Genbank accessionMW460250) and 673 crAss-like metagenome assembled phage genomes from the human gut taken from Yutin et al 2021 (https://doi.org/10.1038/s41467-021-21350-w). </p> <p>Each input was benchmarked with 3 runs: 1) Pharokka v1.1.0 using PHANOTATE as a gene predictor 2) Pharokka v1.1.0 specifying Prodigal as gene predictor, and 3) Prokka v1.14.6 using a version of the PHROGs HMM database that has been reformatted for use with Prokka found at the following URLs (http://s3.climb.ac.uk/ADM_share/all_phrogs.hmm.gz https://millardlab.org/2021/11/21/phage-annotation-with-phrogs/ ).</p> <p>Benchmarking was conducted on an Intel® Xeon® CPU E5-4610 v2 @ 2.30GHz specifying 16 threads for Pharokka and 16 cpus for Prokka. Coding densities for each contig in the Prokka output were calculated using the python script calc_gff_coding_density_prokka.py available in the tarball. Prokka coding densities are included in the Prokka_CDS_Coding_Densities directory.</p> <p> </p>
Dispersion of the folded waveguide and output power of the Travelling Wave Tube amplifier in W band.
<p>Datasets of Dispersion of the folded waveguide and beam line (Fig1) and output power (Fig 2) of the paper "Fabrication of W-band TWT for 5G small cells backhaul" for IVEC 2017.</p> <p>Both MAGIC3D and CST- Particle StudioS were used for particle in cell simulations of the whole amplifier. Both the the simulators confirmed more that than 40 W on the full band 923 – 95 GHz as shown in Fig.2 The simulations included the couplers and the RF windows. Specific simulations for the design of the electron optics, the windows and the collector were performed.</p> <p> </p> <p> </p>
Basic graben GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains three archives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li><strong><em>Graben_base.7z</em></strong> is a GeoModeller three dimensional model of the Mansfield syncline area (Victoria, Australia). This model is natively available in every GeoModeller install under the name of "Tutorial case study H".</li> <li><strong><em>MCUE_Graben_nopoletransform.7z</em></strong> is the MCUE output for non pole transfomed foliations.</li> <li><strong><em>MCUE_Graben_poletransform.7z</em></strong> is the MCUE output for non transfomed foliations.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @: https://www.paraview.org/download)</li> <li>.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> </ul>
Mansfield (Victoria, Australia) area original GeoModeller model and relevant MCUE outputs
<p>Companion dataset to the paper:</p> <p><em>Monte Carlo Simulations for Uncertainty Estimation in 3D Geological Modeling, A Guide for Disturbance Distribution Selection and Parameterization</em><em>, </em>Evren Pakyuz-Charrier, Mark Lindsay, Vitaliy Ogarko, Jeremie Giraud, Mark Jessell</p> <p>This dataset contains three archives pertaining to Monte Carlo Uncertainty Estimation (MCUE) in implicit 3D geological models:</p> <ol> <li><strong><em>Mansfield_Base.7z</em></strong> is a GeoModeller three dimensional model of the Mansfield syncline area (Victoria, Australia). This model is natively available in every GeoModeller install under the name of "Tutorial case study H".</li> <li><strong><em>MCUE_Mansfield_nopoletransform.7z</em></strong> is the MCUE output for non pole transfomed foliations.</li> <li><strong><em>MCUE_Mansfield_poletransform.7z</em></strong> is the MCUE output for non transfomed foliations.</li> </ol> <p>In the output archive files:</p> <ul> <li>xml files are the GeoModeller project saves, these can be opened with Intrepid's free viewer for GeoModeller 3.3 3.4 (available @: http://www.intrepid-geophysics.com/ig/index.php?page=geomodeller-update)</li> <li>.vo, .isi and .vop1 files are the lithological voxet exports in binary GOCAD format, these can be opened with the aforementionned viewer or Geocando (available @: https://sourceforge.net/projects/geocando)</li> <li>.vtk files are the MCUE uncertainty indexes outputs, these can be opened with Paraview (available @:https://www.paraview.org/download)</li> <li>.csv file is the MCUE probabilistic model output, it can be opened with any text editor /spreadsheet</li> </ul> <p> </p>
Input and Output files for "Contributions to Streamflow and Sea Level Rise in High Mountain Asia from 2003-2009 Glacier Recession"
<p>All files below were prepared by Collin B. Lawrence. </p> <p>All ARCIDs correspond to the HydroSHEDS Dataset for Asia (Lehner et al., 2008). (http://www.hydrosheds.org/)</p> <p>GLDAS data are from the Global Land Data Assimilation System (Rodell et al., 2004). (https://ldas.gsfc.nasa.gov/gldas/)</p> <p>Q_JJA_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are averaged for the months of June, July, and August from the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>Q_annual_(model) contains three columns: 1) ARCID, 2) subsurface and surface runoff for that particular reach, and 3) accumulated subsurface and surface runoff. All flows are in m<sup>3</sup> s<sup>-1</sup>, and are yearly averages for the years 2003 – 2009. CLM, MOSAIC, NOAH, and VIC were the subset of models used from the Global Land Data Assimilation System (GLDAS).</p> <p>phi_i_JJA is the accumulated subsurface and surface runoff for the CLM, MOSAIC, NOAH, and VIC model average. The June, July, and August output was averaged over the years 2003 – 2009. Column 1 is ARCID and the accumulated runoff is expressed in m<sup>3</sup> s<sup>-1</sup>.</p> <p>phi_i_JJA_err is the standard error of the model mean in phi_i_JJA.</p> <p>phi_g_JJA contains the accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for the months of June, July, August from 2003 - 2009.</p> <p>phi_g_JJA_err is the standard error of the model mean in phi_g_JJA.</p> <p>phi_g_annual contains the annually averaged accumulated glacier recession flow in m<sup>3</sup> s<sup>-1 </sup>for 2003 – 2009.</p> <p>lambda.csv contains the fraction of streamflow from glacier recession.</p>
Exemplar Output to demonstrate FAIR Projection from a CSV file
<p>This is 1000 lines of sample data from UniProt that I will use to demonstrate the ability of FAIR Projection to dynamically project it out as RDF triples</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.