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510 results for “storms”
GTSM-ERA5-E dataset - Data underlying the paper "Global dataset of storm surges and extreme sea levels for 1950-2024 based on the ERA5 climate reanalysis"
<p>Extreme sea levels, generated by storm surges and high tides, have the potential to cause coastal flooding and erosion. Global datasets are instrumental for mapping of extreme sea levels and associated societal risks. Harnessing the backward extension of the ERA5 reanalysis, we present a dataset containing the statistics of water levels based on a global hydrodynamic model (GTSMv3.0) covering the period 1950-2024. This is an extension of a previously published dataset for 1979-2018 <a href="https://www.frontiersin.org/articles/10.3389/fmars.2020.00263/full" target="_blank" rel="noopener">(Muis et al. 2020)</a>. The timeseries (10-min, hourly mean and daily maxima) are available via the Climate Data Store of ECMWF at DOI: 10.24381/cds.a6d42d60. Using this extended ERA5 dataset, we calculate percentiles and estimate extreme water levels for various return periods globally. The percentiles dataset includes the 1, 5, 10, 25, 50, 75, 90, 95 and 99th percentiles. The extreme water levels include return values for 1, 2, 5, 10, 25, 50, 75 and 100 years, and they are estimated using POT-GPD method applied with a threshold of 99th percentile of the timeseries and using a 72-hour window for declustering peak events, and MLE method for fitting the GPD parameters. The parameters (shape, scale and location) are also supplied with this dataset.</p> <p>Validation of the underlying timeseries and the statistical values shows that there is a good agreement between observed and modelled sea levels, with the level of agreement being very similar to that of the previously published dataset. The extended 75-year dataset allows for a more robust estimation of extremes, often resulting in smaller uncertainties than its 40-year precursor. The present dataset can be used in global assessments of flood risk, climate variability and climate changes.</p> <p>Global modelling of water levels and extreme value analysis are associated with a number of uncertainties and limitations, that are particularly important to consider when conducting local assessments. Please refer to the Usage Notes in the corresponding manuscript (Aleksandrova et al. 2025, paper currently under review) for an overview of limitations.</p>
Pressure oscillations data as storm precursors
<p>The folders contain the derived data product from REMS pressure that study the storm precursors.</p>
Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density
<p>Swarm A/B/C neutral mass density normalized to 400 km to be assimilated by the CTIPe physics based model coupled with the thermosphere ionosphere data assimilation scheme (TIDA). Swarm-A is assimilated and B/C are used for validation purposes. The selected period is March 2015 that contains the St. Patrick's Day storm 2015 between 16-18 of that month.</p>
Sowing storms: how model timestep can control tropical cyclone frequency in a GCM
<p>Supplementary dataset to JAMES article "Sowing storms: how model timestep can control tropical cyclone frequency in a GCM" DOI:10.1029/2021MS002791</p>
BOKURAD X-band radar data of two severe storms in Vienna, Austria
<p>Polarimetric X-band weather radar data of two severe storms in Vienna, Austria occurring on 26 June 2020 and 21 July 2020. The multicell storm on 26 June 2020 produced large hail with reported hail sizes of up to 5 cm. The storm on 21 July passed Vienna as squall line and produced non-severe (< 2 cm) hail.</p> <p>HDF5 data conforms to the OPERA Data Information Model (ODIM) standard.</p>
Datasets supporting the original submission of Harris et al., "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model"
<p>Datafiles used in the analyses described by Harris et al, "A Global Survey of Rotating Convective Updrafts in the GFDL X-SHiELD 2021 Global Storm Resolving Model", to be submitted to the Journal of Geophysical Research.</p> <p>Model output was created by X-SHiELD 2021 <a href="http://doi.org/10.5281/zenodo.6941034">https://doi.org/10.5281/zenodo.6941034</a> described in the paper:</p> <p>Harris, L., Zhou, L., Lin, S.-J., Chen, J.-H., Chen, X., Gao, K., et al. (2020). GFDL SHiELD: A unified system for weather-to-seasonal prediction. <em>Journal of Advances in Modeling Earth Systems</em>, 12, e2020MS002223.<a href="https://doi.org/10.1029/2020MS002223"> https://doi.org/10.1029/2020MS002223</a></p> <p>GPM data used for Figure 6b is derived from</p> <p>Huffman, G.J., E.F. Stocker, D.T. Bolvin, E.J. Nelkin, Jackson Tan (2019), GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), Accessed: 4 August 2021,<a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06"> 10.5067/GPM/IMERG/3B-HH/06</a></p> <p> </p>
Above-anvil Cirrus Plume Producing Storm Tracks
<p>Two separate CSV files containing all warm and cold above-anvil cirrus plume-producing storm tracks used in Murillo and Homeyer (under review). These files contain the hourly storm number, binary above-anvil cirrus plume flag, date and time in UTC, longitude, and latitude positions of the overshooting top associated with the above-anvil cirrus plume-producing storm. The first and second rows contain the variable names and units, respectively, with the remaining rows containing the variable values.</p> <p> </p>
STORMS checklist: Dysbiosis of the enteric microbiota due to Crohn's disease treatment associated with MAIT cell activation
<p>STORMS checklist initiative to standardize reporting of human microbiome research, regarding submission to peer-reviewed journal.</p>
Investigation of the southern hemisphere mid-high latitude thermospheric ∑O/N2 responses to the Space-X storm
<p>This data sets are the data used to plot the figures in the above mentioned paper (Figure 3 to 6)</p> <p>All files are in dimension 288*144*6, 288 stands for longitudes number from -180 to 180 with a resolution of 1.25</p> <p>144 stands for latitude numbers fro -88.75 to 88.75 with a resolution of 1.25. 6 stands for the time, 0:20, 2:20, 4:20, 7:20, 10:20 and 13:20 UT on DOY 34.</p> <p>dON2 stand for the percentage diff of column density ratio of O to N2 between DOY 34 and 32</p> <p>UN stands for zonal wind, VN stands for meridional wind, TN stands for neutral temperature</p> <p>QJO stands for Joule heating rate per unit mass near 160 km</p> <p>POTEN stands for ionosphere potential</p>
Science through ML: Post-storm Cooling Data and Programs
<p>These programs and data are associated with the AGU Space Weather Journal article "Science through Machine Learning: Quantification of Post-storm Thermospheric Cooling".</p>
Convection in future winter storms over Northern Europe - data
<pre><em>We provide the python code to reproduce the figures of the Environmental Research Letter entitled </em><em>"Convection in future winter storms" by S. Berthou et al.</em></pre> <p>Python library requirements:</p> <p>python 3.8.12</p> <p>pandas 1.4.1</p> <p>seaborn 0.11.2</p> <p>matplotlib 3.5.1</p> <p>numpy 1.22.3</p> <p>scipy 1.8.0</p> <p>statsmodel.api 0.13.2</p>
Storm drain water level measurements from Beaufort, North Carolina
<p>This dataset contains water level measurements from within a storm drain in Beaufort, North Carolina, USA. Water level measurements were collected using a Sunny Day Flooding Project (https://tarheels.live/sunnydayflood/) sensor system. The data span from June 22, 2021 to November 30, 2021. Drift-corrected water level measurements relative to the road are in column "road_water_level_adj", and drift-corrected water level measurements relative to NAVD88 are in column "sensor_water_level_adj".</p>
Figure 2 in Record of Markham's Storm Petrel Hydrobates markhami in La Paz, Bolivia
Figure 2. The same individual of Markham's Storm Petrel Hydrobates markhami prepared as a study skin and deposited in the Colección Boliviana de Fauna, Museo Nacional de Historia Natural, La Paz, Bolivia, CBF 5651 (Nicole A. Avalos)
Figure 1 in Record of Markham's Storm Petrel Hydrobates markhami in La Paz, Bolivia
Figure 1. Markham's Storm Petrel Hydrobates markhami found at the Catholic University in La Paz, Bolivia, January 2023 (Ana Serrano R.)
Рис. 2. РаспреΑеΛение трубконосых птиц (А — темноспинный аΛьбатрос, Б — гΛупыш, В — тонкокΛювый буревестник, Г — сизая качурка) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 2. Distribution of tubenoses — (А) Laysan albatross, (Б) Northern fulmar, (В) shorttailed shearwater, (Г) fork-tailed storm-petrel — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects; dotted line indicates a 200 m isobath in Population of seabirds in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan during the winter-spring period of 2020
Рис. 2. РаспреΑеΛение трубконосых птиц (А — темноспинный аΛьбатрос, Б — гΛупыш, В — тонкокΛювый буревестник, Г — сизая качурка) в Охотском море и сопреΑеΛьных воΑах Тихого океана и Японского моря по резуΛьтатам суΑовых учетов в февраΛе — мае 2020 г. (особей/км2 на 10-минутных трансектах). СпΛошными Λиниями показаны учетные трансекты, пунктиром — 200-метровая изобата Fig. 2. Distribution of tubenoses — (А) Laysan albatross, (Б) Northern fulmar, (В) shorttailed shearwater, (Г) fork-tailed storm-petrel — in the Sea of Okhotsk and adjacent waters of the Pacific Ocean and the Sea of Japan in February–May 2020 (birds/km2 on 10-minute transects). Solid lines indicate transects; dotted line indicates a 200 m isobath
Data used in "Storms regulate Southern Ocean summer warming"
<p>The data included in this repository was used to generate the figures in the submitted manuscript "Storms regulate Southern Ocean summer warming" by du Plessis and co-authors.</p> <p><strong>Abstract: </strong>"Sea surface temperature (SST) in the Southern Ocean (SO) is the fingerprint of ocean heat uptake and critical for air-sea interactions. However, SO SST is biased warm in climate models, reflecting our limited understanding of the mechanisms that set its magnitude and variability. An important factor driving SST variability is synoptic-scale weather systems, such as storms, yet their impacts are difficult to directly observe. Using in-situ observations from underwater and surface robotic vehicles in the subpolar SO, we show evidence that storms regulate the summer evolution of SST through altering the mixed layer effective heat capacity and entraining colder water from below. Through these mechanisms, we determine that interannual variations in SO SST reflect changes in storm intensity and prevalence, which, in turn, are driven by the Southern Annular Mode. Our results demonstrate a causal link between storm forcing and lower frequency SST variability, which has implications for addressing SST biases in climate models."</p> <h3><strong>Datasets</strong></h3> <p>The observations in this study were made as a part of the SOSCEx-STORM experiment, which fits into the larger observational programme the Southern Ocean Seasonal Cycle Experiment (Swart et al. 2012). SOSCEx-STORM undertook a twinned deployment of a Wave Glider and a profiling Slocum glider which were piloted in conjunction with each other. The platforms were deployed and retrieved from the R/V Agulhas II at 54°S, 0°E, south of the Polar Front, and sampled together between 20 December 2018 and 8 March 2019. </p> <p><strong>Slocum glider data<br></strong>The glider was equipped with a continuously pumped Seabird Slocum Glider CTD, which was processed with the GEOMAR MATLAB toolbox and vertically gridded to 1 m depth intervals. </p> <p>Relevant data name: <code>slocum_grid_processed.nc</code></p> <p><em>Slocum glider Microstructure data:</em><br>The Webb Teledyne G2 Slocum glider was equipped with a Rockland Scientific Microstructure Profiler (MicroRider). The MicroRider was equipped with two piezo-electric accelerometers and two air-foil shear probes oriented orthogonally. Microstructure data was only collected during the glider climbs to prolong battery life and obtain dissipation estimates as close to the surface as possible. See Nicholson et al. (2022) for details of the MicroRider processing. The mixing layer depth (XLD) was estimated as in Brainnerd and Gregg et al. (1995).</p> <p>Disspitation data name: <code>slocum_eps.nc</code><br>Mixing layer depth data name:<em> </em><code>slocum_xld.nc</code></p> <p><em>Slocum glider SST data:</em> Initial data processing removed temperature data from the upper 2 m during the glider climb phase, and so to obtain an SST value from the Slocum glider temperature profiles, we calculated the median value between 0.5 m and 10 m depth for each dive. </p> <p>Slocum SST data name: <code>slocum_sst_median_10m.nc</code></p> <p><strong>Wave Glider data<br></strong>The Liquid Robotics SV3 Wave Glider was fitted with an Airmar WX-200 Ultrasonic Weather Station mounted on a mast at 0.7 m above sea level, providing wind speed measurements at a rate of 1 Hz, averaged into 1-hour bins. The wind measurements were corrected to a height of 10 m above sea level. Note that the Airmar WX-200 weather station of the Wave Glider was faulty and the wind speed, wind direction and wind stress data was replaced by hourly ERA5 data. </p> <p>Wave Glider data name: <code>WG_era5_1h_processed_28Aug2022.nc</code></p> <p><strong>NOAA OI SST and sea ice<br></strong>Monthly SST data was obtained from the NOAA optimum interpolation (OI) SST V2 product, which uses both in-situ and satellite data from November 1981 to January 202329. Data is provided by the National Centers for Environmental Prediction and made available on a 1◦ grid. All SST data where co-located sea ice concentration was above 0 has been removed from this analysis. NOAA OI SST and sea ice were obtained from<a href="https://psl.noaa.gov/data/gridded/data.noaa"> https://psl.noaa.gov/data/gridded/data.noaa</a><strong>.<br></strong></p> <p>Datasets: <code>sst.mnmean.nc</code>, <code>icec.mnmean.nc</code>, <code>lsmask.nc</code></p> <p><strong>Storm tracking dataset</strong><br>To track storm trajectories, we used storm tracks contained in monthly files for the Southern Ocean identified and used in the JGR-Oceans publication:</p> <p>Lodise, J., Merrifield, S. T., Collins, C., Rogowski, P., Behrens, & J., Terrill,E, (In Review). Global Climatology of Extratropical Cyclones From a New Tracking Approach and Associated Wave Heights from Satellite Radar Altimeter. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018925" rel="nofollow">https://doi.org/10.1029/2022JC018925</a></p> <p>Data can be accessed at <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker</a> </p> <p>All Southern Ocean storm locations can be found at: <code>ec_centers_1981_2020.nc</code></p> <p><strong>Storm radius datasets</strong></p> <p>ERA5 data of air-sea heat flux, 10 m wind speed for all hourly instances where a storm center was within 1000 km of the gliders (Figs. 2 and 3, Extended Data Figs. 2 and 4) </p> <p>Datasets of <code>combined_storms_{variable}_no_ice.nc</code> are data of air-sea heat flux and 10 m wind speed for all instances for 1000 km x 1000 km box around each storm center during summer months from 1981-2020 (> 570,000) used in Figs. 4 and 5. These data are considerably large (combined total >40 GB). Please contact me at marcel.du.plessis@gu.se to find a suitable way to share the data.</p> <p>The data was processed as follows:</p> <p>1. Download storm centers from <a href="https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main">https://github.com/jlodise/JGR2022_ExtratropicalCycloneTracker/tree/main</a></p> <p>2. Run process-lodise-storm-centers.ipynb to save all the cyclone center data as '<code>ec_centers_1981_2020.nc</code>'</p> <p>3. <em>Run filter-cyclone-centers.ipynb</em>:<br> - cut all cyclone centers south of 40S<br> - only choose cyclone centers in DJF<br> - calculates minimum distance to land for each storm<br> - saves a dataset called '<code>ec_centers_1981_2020_with_min_dist_to_land.nc</code>' <br> - contains the variable distance to land for all filtered cyclones<br> - we do this step because it takes about an hour to run<br> - remove cyclones within 500 km from land<br> - remove cyclones less than 24 hours<br> - we are left with 11005 storms<br> - data saved as 'ec_centers_1981_2020_500km_from_land_filtered_24hours'</p> <p>4. Run storm_processing_cutouts.ipynb (this took several days)<br> - loads <code>ec_centers_1981_2020_500km_from_land_filtered_24hours.nc</code><br> - runs through each summer, <em>processes storm_localization.py </em><br> - saves data as <code>storms_{variable}_{year}.nc</code><br> - e.g. <code>storms_winds_1981.nc</code> - winds for all 1000 km radius cyclones in DJF 1981/82</p> <p>5. <em>combine_storm_years.ipyn</em>b <br> - creates datasets for each variable that has storms for all year called <code>combined_storms_{variable}.nc</code></p> <p>6. <em>remove_sea_ice_from_storms.ipnyb </em><br> - makes data nan where sea ice is present in each cyclone<br> - creates datasets called <code>combined_storms_{variable}_no_ice.nc</code></p> <p>7. <em>seasonal-means-storms.ipynb</em><br> - calculates the mean for all storms for each year <br> - saves them one dataset: <code>combined_storms_{variable}_seasonal_means.nc'</code></p> <p><strong>EN4 mixed layer depths</strong><br>We use the EN4 database of quality controlled temperature and salinity profiles from 2004 to 2022 to produce our MLD for the interannual analysis (Good et al. 2013). We use the profiles that contain the Cheng et al. (2014) XBT corrections and Gouretski and Cheng (2020) MBT corrections. We limit the data intake to 2004 as this marks the beginning of the Argo period. All under-ice profiles are removed. We calculate the MLD for each individual profile using the density threshold of de Boyer Montegut et al. (2004) where the density value first exceeds the 10 m reference value by 0.03 kg m-3. We then determine the median MLD value for each month within 3 x 3 degree grid cells, then obtain a mean value for each DJF season per 3 x 3 degree grid cell. </p> <p>Relevant data name: <code>en4_monthly_mixed_layer_depth_median.nc</code></p> <p><strong>Southern Ocean Fronts<br></strong>Position of the Subantarctic Front and Polar Front are from: </p> <div>Sokolov, S. and Rintoul, S.R., 2009. Circumpolar structure and distribution of the Antarctic Circumpolar Current fronts: 1. Mean circumpolar paths. <em>Journal of Geophysical Research: Oceans</em>, <em>114</em>(C11).</div> <div> </div> <div>Relevant data name:<em> </em><code>ACCfronts.csv</code><strong> </strong></div> <div> </div> <div><strong>ERA5<br></strong>The ERA5 data provided was by ECMWF available at <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>.</div> <div> </div> <div>The various datasets used in this study are described below:<strong><br></strong><br>Wind speed, air temperture, dew point temperature for the observational period: <code>ds_era5_vars.nc</code><br>Fluxes for the observational period: <code>ds_era5_flux.nc</code><br>Wind speed, air temperture, dew point temperature, fluxes for the case study day in Figure 3: <code>era5_case_study.nc</code><br>Mean winds and fluxes for each DJF period between 1981 and 2022.: <code>mean_summer_winds_fluxes_1981_2023.nc</code></div> <div>Monthly-mean 10 m wind speed and mean sea level pressure during SOSCEx-Storm: <code>201812_month_avg_wind_mslp.nc</code> and <code>20190102_month_avg_wind_mslp.nc</code></div> <div> </div> <div><strong>Cloud Top Pressure<br></strong>The MODIS Level-2 Cloud product was obtained from <a href="http://dx.doi.org/10.5067/MODIS/MYD06_L2.061">http://dx.doi.org/10.5067/MODIS/MYD06_L2.061</a> (Fig. 3).<strong><br></strong></div> <div> <p>Processed dataset: <code>modis_ctt_ctp.nc</code></p> <p><strong>Southern Annular Mode<br></strong>The SAM is the principal mode of variability in the atmospheric circulation of the Southern Hemisphere mid-and-high latitudes. We use the Marshall SAM Index from station-based observations of the zonal pressure difference between the latitudes of 40◦S and 65◦S.</p> <p>SAM Index was retrieved from <a href="https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based">https://climatedataguide.ucar.edu/climate-data/marshall-southern-annular-mode-sam-index-station-based</a>.</p> <p>SAM dataset: <code>ds_sam.nc</code></p> <p> </p> </div>
Influence of storm sequencing and beach recovery on sediment transport and beach resilience data set at CIEM large scale wave flume.
<p>The Influence of storm sequencing and beach recovery on sediment transport and beach resilience (RESIST) experiments project proposes to study experimentally sequences of storm induced erosion and beach recovery, with a particular focus on the poorly known morphodynamic processes under low energy conditions. Series of large scale experimental tests were done to collect data on the cross-shore hydrodynamics, sediment transport and beach evolution. The main aim of this proposal is to investigate the influence of sequences of beach erosion-recovery in the overall beach profile evolution.</p> <p>The tested wave conditions (2 erosive and 3 Accretive bichromatic conditions) were combined to form three sequences of changing high/mild energy conditions. Each condition started from an initial beach 1/15 handmade profile.</p> <p>The experiments were carried out in the large scale wave flume CIEM at Universitat Politècnica de Catalunya (UPC), Barcelona within the program of Transnational Access of Hydralab+.</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p>
Swash zone response Under grouping Storm Conditions data set produced at the CIEM flume, Hydralab III
<p>The data set here presented reports the large-scale laboratory experiments on the influence of long waves, bichromatic wave groups and random waves on sediment transport in the surf and swash zones. The experiments were done at the CIEM wave flume at UPC, Barcelona, as part of the SUSCO (swash zone response under grouping storm conditions) experiment in the Hydralab III. Fourteen different wave conditions were used, encompassing monochromatic waves, bichromatic wave groups and random waves. The experiments were designed specifically to compare variations in beach profile evolution between monochromatic waves and unsteady waves with the same mean energy flux. Each test commenced with approximately the same initial profile</p> <p>Due to its size, the data set can not be placed on this repository and will be provided on demand. Please contact with the authors or with the data manager of the CIEM installation.</p> <p>More information can be found on the published papers:</p> <p>Baldock, T.E., Alsina, J.A., Caceres, I., Vicinanza, D., Contestabile, P., Power, H. and Sanchez-Arcilla, A., 2011. Large-scale experiments on beach profile evolution and surf and swash zone sediment transport induced by long waves, wave groups and random waves. Coastal Engineering, Vol. 58, pp. 214-227.</p> <p>Vicinanza, D., Baldock, T., Contestabile, P., Alsina, J., Cáceres, I., Brocchini, M., Conley, D., Andersen, T.L., Frigaard, P. and Ciavola, P., 2011. Swash zone response under various wave regimes. Journal of Hydraulic Research, Vol. 49, pp. 55-63.</p>
Supplementary material for research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history"
<p>This public repository contains mainly datasets generated and analyzed during the current study, closely linked to the research article "Quantifying the risk mitigation efficiency of changing silvicultural systems under storm risk throughout history". Furthermore, the repository contains additional figures and deep dives on the methodological background the research article was built on.</p>
Empirical modeling of the geomagnetosphere for SIR and CME-driven magnetic storms
<p>Data associated with Journal of Geophysical Research: Space Physics article titled: "Empirical modeling of the geomagnetosphere for SIR and CME-driven magnetic storms", by V. A. Andreeva and N. A. Tsyganenko.<br> There are:<br> (1) 'Subset_{1,2,3}.dat' incorporating the spacecraft ephemerids and magnetic field measurements, used for the model fitting, as well as input solar wind parameters for the model;</p> <p>(2) 'Data_format.txt' describing the data format in subset files;</p> <p>(3) 'Model_parameters_set_{1,2,3}.par containing the model parameters, obtained during fitting;</p> <p>(4) 'Subroutine_model_TA15_with_Wi.for' - a set of Fortran subroutine for calculating the external magnetospheric magnetic field according to the model described in the paper. <br> Note, that '1', '2' and '3' in the file's name correspond to SIR-induced storms, storms induced by CME without a shock ahead and CME with a shock ahead. For more details please see the paper.</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.