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2,322 results for “precipitations”
Dataset for Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis by T.P. O'Brien et al. submitted to J. Geophysical Res.
<p>Technical reports and data sets for the the paper Electron Precipitation Curtains – Simulating the Microburst Origin Hypothesis. Additional AC6 information can be found at rbspgwy.jhuapl.edu/ac6 and at spdf.gsfc.nasa.gov/pub/data/aaa_smallsats_cubesats/aerocube/aerocube-6/. AC6 data have also been ingested into the main CDAWeb database at cdaweb.gsfc.nasa.gov. Source code related to this data set can be found at https://github.com/tpoiii/dipole_tracer_ac6, or DOI: 10.5281/zenodo.6011631.</p>
Precipitation objects under the current and future climate: WRF 6-km hydroclimate simulation of the western US
<p>This folder includes the precipitation objects that are used in the following manuscript:</p> <p>Chen et al., Sharpening of Cold Season Storms over the Western US.</p> <p>It is generated using WRF V3.8 at PNNL. A historical simulation ("NARR") is done for 1981-2010, and five future simulations ("CanESM2", "CESM1-CAM5", "GFDL-ESM2M", "HadGEM2-ES", "MPI-ESM-MR") are done for 2041-2070 using the Pseudo Global Warming (PGW) approach. For the WRF model configuration and the simulation details, please refer to the abovementioned manuscript and Chen et al. (2018).</p> <p>This is the preliminary version of the dataset that contains the precipitation object features as analyzed in the manuscript. More data (including the WRF raw precipitation output) and the finalized scripts will be included here before the manuscript is published.</p> <p> </p> <p>Reference:</p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, M. Wigmosta, and M. Richmond (2018), Predictability of Extreme Precipitation in Western U.S. Watersheds Based on Atmospheric River Occurrence, Intensity, and Duration, <em>Geophys. Res. Lett.</em> doi: <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2018GL079831">10.1029/2018GL079831</a></p> <p>Chen, X., L. R. Ruby, Y. Gao, Y. Liu, and M. Wigmosta (2023), Sharpening of Cold Season Storms over the Western US, Nat. Clim. Change. doi: <a href="https://www.nature.com/articles/s41558-022-01578-0">10.1038/s41558-022-01578-0</a> </p>
Supplemental Material to "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments"
<p>Supplemental material to manuscript "Consistent quantification of precipitate shapes and sizes in two and three dimensions using central moments" published in IMMJ "Integrating Materials and Manufacturing Innovation" 2022</p>
EPTGODD-WHU: Ensemble Precipitation and Temperature from CMIP6 GCMs optimized by OLS-DT-DNN methods integration (1850-2100)
<p>This monthly global climate dataset EPTGODD-WHU (precipitation and mean temperature variables with grid size of 0.5°×0.5°) was ensembled from 16 selected CMIP6 GCMs. The published dataset was optimized by OLS (Ordinary Linear Square)-DT (Decision Tree)-DNN (Deep Neural Network) methods integration. The CF (Climate and Forecast) v1.6 was employed as the guideline for NetCDF4 format. The periods of temperature files can be divided into historical (1850-1900) and future (2015-2100) periods. For precipitation, this product provides future (2015-2100) period. Three future scenarios (SSP1-2.6, SSP2-4.5 and SSP5-8.5) were selected for both variables. The units of this dataset are degrees Celsius and mm/month for temperature and precipitation, respectively. Each NetCDF4 file in this dataset includes three dimensions (time, latitude (-89.75°N to 89.75°N) and longitude (-179.75°E to 179.75°E)).</p>
FAST loss-cone electron precipitation database
<p>A FAST electron precipitation database derived from FAST EESA observations, covering beginning of mission (October 1996) through 2009.</p> <p>Number flux ('j') and energy flux ('je') moments are produced by integrating EESA measurements over all energies above 70 eV<br> up to the EESA detector limit (30 keV), and over all pitch angles within the earthward portion of the loss cone (see references). </p> <p>================================<br> EXPLANATION OF DATAFRAME COLUMNS<br> ================================</p> <p>'j' : units of #/cm^2-s (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> 'je' : units of mW/m^2 (ALL QUANTITIES ARE POSITIVE, WHERE I HAVE USED THE CONVENTION 'POSITIVE' == 'EARTHWARD')<br> <br> 'jerr' : units of #/cm^2-s (Number flux uncertainty, calculated using the Gershman et al (2015) method)<br> 'jeerr' : units of mW/m^2 (Energy flux uncertainty, calculated using the Gershman et al (2015) method)<br> <br> 'orbit' : FAST orbit number<br> 'alt' : FAST geodetic altitude, in km. (FAST altitude ranges from ~300-4180 km)<br> 'apexmlt' : Magnetic local time in Apex-110 coordinates (see Laundal and Richmond (2016))<br> 'apexmlat' : Magnetic latitude in Apex-110 coordinates (see Laundal and Richmond (2016))</p> <p>'shadowRegion110' : Integer indicator of the region of the Earth's shadow that FAST's field-line footpoint at 110-km altitude lands ind.<br> Takes on values [0,1,2], corresponding to ['Umbra','Penumbra','Sunlit']. <br> Calculated by mapping FAST's location to 110-km altitude in Apex coordinates and then following the methodology of Jia et al. (2015).</p> <p>'mono' = 0,1,2 : 'not monoenergetic','weak monoenergetic','strict monoenergetic'<br> 'broad' = 0,1,2 : 'not broadband','weak broadband','strict broadband'<br> 'diffuse' = 0,1 : 'not diffuse','diffuse'</p> <p>'mono', ' broad', and 'diffuse' follow the Hatch et al. (2016) FAST adaptation of the Newell et al. (2009) classification scheme<br> *NOTE: I do NOT force 'mono' and 'broad' to be exclusive categories! I consider it fine for precipitation to be identified as both 'broad' and 'mono'</p> <p>'drop' : Boolean indicating whether a row should be dropped from the DataFrame</p> <p>==========<br> REFERENCES<br> ==========<br> Gershman, D. J., Dorelli, J. C., F.-Viñas, A., & Pollock, C. J. (2015). The calculation of moment uncertainties from velocity distribution functions with random errors. Journal of Geophysical Research A: Space Physics, 120(8), 6633–6645. https://doi.org/10.1002/2014JA020775</p> <p>Hatch, S. M., Chaston, C. C., & LaBelle, J. (2016). Alfvén wave-driven ionospheric mass outflow and electron precipitation during storms. Journal of Geophysical Research: Space Physics, 121(8), 7828–7846. https://doi.org/10.1002/2016JA022805</p> <p>Hatch, S. M., Labelle, J., Lotko, W., Chaston, C. C., & Zhang, B. (2017). IMF control of Alfvénic energy transport and deposition at high latitudes. Journal of Geophysical Research: Space Physics, 122(12). https://doi.org/10.1002/2017JA024175</p> <p>Jia, X., Xu, M., Pan, X., & Mao, X. (2017). Eclipse Prediction Algorithms for Low-Earth-Orbiting Satellites. IEEE Transactions on Aerospace and Electronic Systems, 53(6), 2963–2975. https://doi.org/10.1109/TAES.2017.2722518</p> <p>Laundal, K. M., & Richmond, A. D. (2016). Magnetic Coordinate Systems. Space Science Reviews, 1–33. https://doi.org/10.1007/s11214-016-0275-y</p> <p>Newell, P. T., Sotirelis, T., & Wing, S. (2009). Diffuse, monoenergetic, and broadband aurora: The global precipitation budget. Journal of Geophysical Research, 114, A09207. https://doi.org/http://dx.doi.org/10.1029/2009JA014326<br> </p>
Model output and analysis scripts for "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity"
<p>Here, we provide annually averaged model output from a 3000-year control simulation of PlaSim–LSG, a climate model of intermediate complexity. Processed variables and a Jupyter notebook to reproduce all figures of the manuscript (Mehling et al.: "High-latitude precipitation as a driver of multicentennial variability of the AMOC in a climate model of intermediate complexity") can also be found in this repository.</p> <p>In addition, a Python implementation of the three-box model proposed in the manuscript can be found in the notebook <em>boxmodel.ipynb</em>.</p>
Data files for figures in "Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming" by Bird et al. 2022
<p>The data files for figures in <em>Deep learning extreme precipitation of the past, present, and under 1.5°C and 2.0°C global warming</em> by Bird, Bodeker and Clem. The data files are provided either as self-describing netCDF files, or .csv files with column descriptors.</p>
Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions"
<p>Data for paper entitled, "Discontinuous Precipitation in Mg-Al Alloy Studied in 3-Dimensions" including:</p> <p>-Raw SEM imaging and EBSD data</p> <p>-Processed SEM images</p> <p>-3D slices</p> <p>-Supplementary summary figure</p> <p>-Supplementary video</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Data and code for: Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent
<p>Code and data to reproduce figures in manuscript entitled "Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent" published in Hydrology and Earth System Sciences (https://hess.copernicus.org/preprints/hess-2022-136/).</p> <p>The contents include three folders, "Codes", "Data", and "Figures". In "Codes" folder, R scripts are listed in the order needed to reproduce the figures. All code is written in R version 4.2.0. Data sets needed to reproduce figures are provided in "Data" folder (Rdata format). The pdf files in "Figures" folder are outputs generated from the corresponding R scripts. Note that final figures in the article were produced by combining multiple figures using a vector graphics software (Inkscape) or PowerPoint. Please contact Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>) with any questions. </p> <p>Preferred citation: Cho, E., Vuyovich, C. M., Kumar, S. V., Wrzesien, M. L., Kim, R. S., and Jacobs, J. M. (2022). Precipitation Biases and Snow Physics Limitations Drive the Uncertainties in Macroscale Modeled Snow Water Equivalent, Hydrol. Earth Syst. Sci., https://doi.org/10.5194/hess-2022-136.</p> <p>Corresponding author: Eunsang Cho (<a href="mailto:eunsang.cho@nasa.gov">eunsang.cho@nasa.gov</a>; <a href="mailto:escho@umd.edu">escho@umd.edu</a>)</p>
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
Monthly Standardized Precipitation Evapotranspiration Index (SPEI) for Australia at 0.05 degree from 1982 to 2014
<p>This monthly SPEI dataset in 1-48 scale is calculated using R's <a href="https://cran.r-project.org/web/packages/SPEI/index.html">SPEI </a>package in 'kernel -- rectangular', 'distribute -- log-Logistic' and 'fit -- ub-pwm' mode, with <a href="http://www.csiro.au/awap/">AWAP'</a>s monthly rainfall and <a href="http://www.bom.gov.au/water/landscape/">ALWB</a>'s potential evapotranspiration.</p>
Regional climate simulations of surface precipitation and temperature for West Africa using COSMO-CLM based on MPI-LR (ECHAM6) and RCP4.5
<p>Regional climate model COSMO-CLM (CCLM) simulations with a horizontal resolution of 0.11° (approx. 12 km) for sub-Saharan West Africa under current and future climate conditions. The CCLM is driven by initial and lateral boundary conditions from the MPI-LR (ECHAM6), based on the emission scenario RCP4.5. The downscaled MPI-LR (ECHAM6) data for surface precipitation (P) and surface temperature (Tmin, Tmax) are provided for the baseline period (1981-2010) and two future time slices, i.e. the 2021–2050 and the 2071–2100 period. </p> <p> </p>
SCOPE Climate: precipitation
<p>SCOPE Climate (Spatially COherent Probabilistic Extended Climate dataset) is a 25-member ensemble of 142-year high-resolution reconstructions of precipitation, temperature and Penmann-Monteith reference evapotranspiration over France, from 1 January 1871 to 29 December 2012. SCOPE Climate results from the statical downscaling of the global extended reanalysis 20CR V2 with the SCOPE method (Caillouet et al., 2016, 2017). SCOPE Climate provides an ensemble of 25 equally-plausible spatially-coherent gridded multivariate time series. Data are available at a daily time step on a 8 km grid over France as 25 files in NetCDF format. Reconstructed values cover grid cells located only within metropolitan France national borders (including Corsica). The SCOPE Climate dataset is fully described by Caillouet et al. (2019).</p> <p>This dataset provides reconstructions of precipitation as a 25-member ensemble of gridded time series. Precipitation values from member 1 should be used with reconstructed temperature/evapotranspiration values from member 1 (and so on). The corresponding temperature dataset can be found at http://doi.org/10.5281/zenodo.1299712, and the evapotranspiration dataset at http://doi.org/10.5281/zenodo.1251843.</p>
Circulation type classifications for surface temperature and precipitation optimized for Italy
<p>The four files are two couple of files for two circulation type classifications (pct9 and san9) optimized for Italy, in order to stratify precipitation and surface temperature respectively.</p> <p>"pct9.cla" and "san.cla" are the circulation type daily series between 1979 and 2015 computed on mean sea level pressure (MSLP) and geopotential height at 500 hPa (500HGT) respectively. Meteorological fields are extracted by the NCEP-NCAR Reanalysis 2 dataset.</p> <p>"pct-nc.txt" and "san9-nc.txt" are the centroid values of MSLP and 500HGT respectively, computed on 9 classes over a spatial domain of 7 X 7 grid points across Italy.</p> <p>These files are created through the COST733 software package (DOI: 10.1002/joc.3920). </p> <p>The pct9 and san9 classifications were selected as the best performing for the stratifacation of precipitation and surface temperature respectively across Italian peninsula, through a sensitivity analysis detailed in a specific study (DOI: 10.1002/joc.5219). In summary several circulation type classifications were computed with different classification methods, number of types and classification variables (i.e. predictands). Then such classifications were compared through the use of proper statistical indexes in order to assess the stratification of the ground-level precipitation and the surface air temperature across Italian peninsula.</p> <p>These two classifications could be evaluated also for other meteorological or environmental variables.</p>
Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem Code and Data
<table> <tbody> <tr> <td>The data here is summary data compiled from all years of the project that lead to the publication Spring Precipitation Amount and Timing Predict Restoration Success in a Semi-Arid Ecosystem with the Journal of Applied Ecology and code to analyze these data. Our study was focused on the Northern Great Basin ecosystem. We conducted surveys at 48 sites over the course of five years (2016-2020). All were located on public lands managed by either the Bureau of Land Management, Idaho Department of Lands, or Oregon State Lands Department. We looked at the influence of management, biotic, abiotic and weather variables predicting seedling establishment success, 45 predictor variables in all. Machine learning techniques were used to select most important predictor variables to be used in future work predicting good seedling establishment windows. </td> </tr> </tbody> </table>
Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon: Implication for identifying trends in dry season rainfall
<h1>Satellite-based precipitation estimates using a dense rain gauge network over the Southwestern Brazilian Amazon.</h1>
Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet
<p>This dataset can be used to reproduce the figures created in Waling et al. 2024, "Using variable-resolution grids to model precipitation from atmospheric rivers around the Greenland ice sheet." Each figure has its own script which can be executed.<br><br></p>
GPC/m: Global Precipitation Climatology by Machine Learning; Quasi-global, Daily, and One Degree Spatial Resolution
<p>A precipitation dataset, Global Precipitation Climatology by Machine Learning (ML), GPC/m, is released.</p> <p>This new precipitation dataset has been produced by machine learning, which is daily from 1979 to 2020 (will be to present), 1° × 1° spatial resolution. Three ML methods are used. Data is produced from outgoing longwave radiation (OLR) and atmospheric circulation from reanalysis. You can download this with DOI.</p> <p>This daily precipitation dataset has been produced by machine learning (ML) methods using satellite observations and atmospheric circulations from reanalysis. The quasi-global daily precipitation dataset has been around for 42 years from 1979 to 2020, which will be updated to the present. The spatial resolution is 1° × 1° zonally global and from 40°S to 50°N. The ML methods are supervised learning, and the reference data are estimated precipitation datasets from 2001 to the present. The input data are somewhat modified based on knowledge of the climatological background. Using the trained statistical models, we predict back to 1979, when daily precipitation data was almost unavailable globally. For now, this GPC/m precipitation dataset version is GPC/m-v1-2024. This data will be updated in the future with added value. The purpose of this dataset is a challenge to produce a climatological dataset by reducing artificial gaps as much as possible for discussion of climatology, climate variability, and climate change. This dataset is very useful for statistical analysis, such as composite analysis and correlation analysis. Disadvantages should also be understood in the description paper (Takahashi, 2024c). Also, I hope that this dataset can contribute to improving the current precipitation datasets, which are based on physical or researcher-explaining algorithms.<br><br>To facilitate analysis of the dataset, it is distributed in Network Common Data Form (netCDF) format and the Grid Analysis and Display System (GrADS) format (with control file). If you would like recently updated data, please contact the creator. If it has already been created, it can be distributed.<br><br><em>Added on September 18, 2024.</em><br>More details are in the preprint paper at this link (<a href="https://doi.org/10.48550/arXiv.2409.09639">Takahashi, 2024, https://doi.org/10.48550/arXiv.2409.09639</a>).</p> <p><em>Added on March 4, 2025.</em><br><strong>Alternative Download Options</strong><br>If you experience slow download speeds from Zenodo, alternative mirrors are available for the dataset files.<br><em><span>However, we kindly request you to download the .ctl file from Zenodo for tracking purposes.</span></em><br>Download NetCDF (.nc) or Binary (.bin) from:<br><a href="https://camo.fpark.tmu.ac.jp/gpcm.html">https://camo.fpark.tmu.ac.jp/gpcm.html</a></p>
Processed data for the manuscript, entitled "Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961–2019"
<p>This is the dataset on annual frequency of heatwaves, heavy precipitation, and heatwave-heavy precipitation events during 1961-2019 at 1776 stations across China. This dataset is the processed results based on daily observations that are provided by the National Meteorological Science Data Center (<a href="http://data.cma.cn/en">http://data.cma.cn/en</a>). In this processed dataset, the "HW_HI" column is the annual frequency of heat-index-based heatwaves; "HW_TW" column is the annual frequency of wet-bulb temperature based heatwaves; "HP" is the annual frequency of heavy precipitation with taking the 95<sup>th</sup> percentile of non-zero precipitation at the threshold. "HWHP_HI"/"HWHP_TW" column indicate annual frequency of heavy precipitation preceded by HW_HI/HW_TW. All the results shown in the manuscript entitled "<strong>Substantial increase in heavy precipitation events preceded by moist heatwaves over China during 1961</strong>–<strong>2019</strong>", are obtained based on this processed dataset.</p>
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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.