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510 results for “storms”
Leaf area index following the ice storm of January 1998 at the Hubbard Brook Experimental Forest
This dataset includes measurements of changes in Leaf Area Index (LAI) collected as part of a long-term study of the recovery of the forest canopy following the ice storm of January 1998. Measurements were taken in Watershed 1 and Watershed 6 between July 1998 and July 2006. These data were gathered as part of the Hubbard Brook Ecosystem Study (HBES). The HBES is a collaborative effort at the Hubbard Brook Experimental Forest, which is operated and maintained by the USDA Forest Service, Northern Research Station.
Extratropical Storms (1885-1996) Count by Month (USA)
This data set consists of the monthly counts of the number of storms passing through designated 2.5-degree lat and 5.0 degree long. grid cells. The data are from the published tracks of the centers of cyclones across North America published by NOAA in Monthly Weather Review and Mariners Weather Log. Storm centers were defined by closed isobars that persisted for 6 or more hours. The coordinates given for each counting cell define the northeast corner of the grid cell. Thus longitude 120 and LAT45 is the cell that extends from longitude -120 to -125 and latitude 42.5 to 45.
Rapid Outer Radiation Belt Flux Dropouts and Fast Acceleration during the March 2015 and 2013 Storms: The Role of ULF Wave Ttansport From a Dynamic Outer Boundary
<p>Duplicate copy of the electron phase space density provided for the Geospace Environment Modeling (GEM) challenge event in March 2013 selected by the <em>Quantitative Assessment of Radiation Belt Modeling</em> focus group. The original copy of the data is available from <a href="https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg">https://drive.google.com/drive/u/0/folders/0ByNhSbWkAgdfaGt6TnJMcElhUTg</a></p> <p> </p> <p>Data Providers:<br> Michael G. Henderson (LANL; mghenderson@lanl.gov)<br> Steven K. Morley (LANL; smorley@lanl.gov)</p> <p>This data product provides electron phase space density from the Van Allen Probes<br> ECT suite of instruments. The data are calculated similarly to the method described<br> in Morley et al. (2013), with some differences that are noted below.</p> <p>The files are provided in HDF5 format, so the files are self-describing and contain<br> ISTP-style metadata. The files should be directly readable with:<br> - SpacePy (http://sourceforge.net/p/spacepy)<br> - import the spacepy.datamodel module, use the function fromHDF5 to read the data<br> - Autoplot (http://autoplot.org)<br> - MatLab and IDL provide convience routines for reading HDF5</p> <p>Method<br> ------<br> Starting with directional differential flux data from HOPE, MagEIS and REPT, we<br> calculate the PSD as a function of energy, pitch angle, position and time.<br> Following the same basic method given by Morley et al., we transform this to phase <br> space density as a function of the three adiabatic invariants (M, K, L*); note that<br> where Morley et al. used a relativistic Maxwellian fit to the flux spectrum, these<br> data use a smoothing spline fit so that more complex spectral shapes can be<br> represented. Note also that Morley et al. only used REPT, where these files represent<br> the energy ranges of MagEIS and REPT, but also use HOPE to constrain the fit at low<br> energies.</p> <p>While the pitch angles are determined using the EMFISIS data, all three adiabatic <br> invariants are derived from a magnetic field model. These PSD data files use the<br> Tsyganenko and Sitnov (2005) model (aka TS04, T05 or TS05). The models were run using<br> the "definitive" Qin-Denton data files provided by the RBSP ECT-SOC. These files<br> should be made available through the QARBM google drive. </p> <p><br> Caveats<br> -------<br> These data should be considered preliminary. They have undergone a limited amount of<br> verification and prior to publication the data providers should be contacted. New<br> versions of these data may be generated at some point - we do not expect noticeable <br> changes to the data present.<br> Some gaps may be present in the files that are due to calculation of the adiabatic <br> invariants failing. The issues causing these gaps have been resolved in the underlying <br> software, but the data have not yet been regenerated.</p> <p><br> References<br> ----------<br> Morley, S. K., M. G. Henderson, G. D. Reeves, R. H. W. Friedel, and D. N. Baker (2013), <br> Phase Space Density matching of relativistic electrons using the Van Allen Probes: REPT results,<br> Geophys. Res. Lett., 40, 4798-4802, doi:10.1002/grl.50909.</p> <p>Tsyganenko, N. A., and M. I. Sitnov (2005), <br> Modeling the dynamics of the inner magnetosphere during strong geomagnetic storms, <br> J. Geophys. Res., 110, A03208, doi:10.1029/2004JA010798.</p> <p> </p> <p>Also included is the copy of the LANLgeoMag software used in the paper provided on <a href="https://github.com/drsteve/LANLGeoMag">https://github.com/drsteve/LANLGeoMag</a> </p> <p>Copyright (c) 2014, Los Alamos National Security, LLC All rights reserved. Copyright 2014. Los Alamos National Security, LLC. This software was produced under U.S. Government contract DE-AC52-06NA25396 for Los Alamos National Laboratory (LANL), which is operated by Los Alamos National Security, LLC for the U.S. Department of Energy. The U.S. Government has rights to use, reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR LOS ALAMOS NATIONAL SECURITY, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is modified to produce derivative works, such modified software should be clearly marked, so as not to confuse it with the version available from LANL.</p> <p>Additionally, redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of Los Alamos National Security, LLC, Los Alamos National Laboratory, LANL, the U.S. Government, nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY LOS ALAMOS NATIONAL SECURITY, LLC AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL LOS ALAMOS NATIONAL SECURITY, LLC OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.</p> <p><br> </p>
A statistical study on the local time dependence of equatorial spread F (ESF) irregularities and their relation to low latitude Es layers under geomagnetic storms
<p>The data can be downloaded from this site about our work.</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
Responses of White-throated sparrows to simulated winter storm cues
<p class="MsoNoSpacing">These data were used in the publication "Increased frequency of exposure to simulated winter storm cues negatively affects white-throated sparrows (<em>Zonotrichia albicollis</em>)" Front. Ecol. Evol. | doi: 10.3389/fevo.2020.00222</p> <p>Climate change is causing changes in weather patterns and more frequent extreme weather events. Although birds are often able to cope with and respond to inclement weather with physiological and behavioral responses, as weather events become more severe or frequent the adaptive coping responses of many species may be pushed beyond their limits. We investigated the effects of experimental recurrent inclement winter weather cues on body composition, glucocorticoid hormones, and behavior of white-throated sparrows (<em>Zonotrichia albicollis</em>). We used a hypobaric climatic wind tunnel to simulate storms by transiently decreasing barometric pressure and temperature, and measured behavioral responses, body composition, and baseline corticosterone levels in birds exposed, or not exposed (control), to different frequencies of simulated storms. In study 1, experimental birds were exposed to one storm per week over 9 weeks. In study 2, experimental birds were exposed to two storms per week over 12 weeks. Birds exposed to one simulated storm per week had higher fat and lean masses than control birds, with no differences in the amount of time groups spent feeding. This change in body composition suggests that birds were coping by increasing energy stores. In contrast, birds exposed to two simulated storms per week had lower fat masses compared to control birds, even though they spent more time feeding. Experimental birds in study 2 also had lower baseline corticosterone levels than controls. These changes suggest that the coping response observed in study 1 was overcome in study 2. These findings provide novel experimental evidence that birds detect and respond to changes in temperature and barometric pressure independent of other storm-related cues. One simulated storm per week resulted in potentially adaptive responses of increase mass. However, increasing the frequency of storm exposure to twice per week may exceed a physiological threshold for tolerance to which these songbirds are able to cope. These results also experimentally demonstrate that repeated exposure to inclement weather cues can directly affects birds' energy reserves, with strong implications for survival as severe weather events continue to become more prevalent.</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
Data related to: The recurring role of storm disturbance on black sea bass (Centropristis striata) movement behaviors in the Mid-Atlantic Bight
<p>Summer storm events are a significant source of disturbance in the Middle Atlantic Bight (MAB) that cause rapid destratification of the water column. Storm-driven mixing can be considered as a summertime disturbance regime to demersal communities, characterized by the recurrence of large changes in bottom water temperatures. Black sea bass are a model ubiquitous demersal species in the MAB, as their sedentary behavior exposes them to summer storm disturbances and the physiological stresses associated with thermal destratification. To better understand the responsiveness of black sea bass to storm impacts, we coupled biotelemetry with a high resolution Finite Volume Community Ocean Model (FVCOM). During the summers of 2016-2018, 8-15 black sea bass were released with acoustic transponders at each of three reef sites, which were surrounded by data-logging receivers. Data were analyzed for activity levels, reef departures, and fluctuations in temperature, current velocity, and turbulent kinetic energy. Movement rates were depressed with each consecutive passing storm, and late-season storms were associated with permanent evacuations. Consecutive, compounding increases in bottom temperature associated with repeated storm events were identified as the primary depressor of local movement. Storm-driven increases in turbulent kinetic energy and current velocity had comparatively smaller, albeit significant, effect. The need to better understand the effect of storms on fish populations in the MAB is relevant in understanding both coincident anthropogenic impacts as well as future fisheries management.</p>
Data from: Ecology can inform genetics: disassortative mating contributes to MHC polymorphism in Leach's storm-petrels (Oceanodroma leucorhoa)
Studies of MHC-based mate choice in wild populations generally test hypotheses by assuming female choice and male-male competition, whether or not mate choice dynamics have been previously determined for the species under study. Here we examined mate choice patterns in a small burrow-nesting seabird, the Leach's storm-petrel (Oceanodroma leucorhoa), using the Major Histocompatibility Complex (MHC). The life history and ecology of this species is extreme: both partners work together to fledge a single chick during the breeding season, a task that requires regularly traveling hundreds of kilometers to and from foraging grounds over a six to eight-week provisioning period. Using a five-year dataset unprecedented for this species (n=1027 adults and 925 chicks), we found a positive relationship between variation in female reproductive quality and heterozygosity at Ocle-DAB2, a MHC class IIB locus. Contrary to previous reports rejecting disassortative mating as a mechanism for maintaining genetic polymorphism in this species, here we show that males make significant disassortative mate choice decisions. Variability in female reproductive success suggests that the most common homozygous females (Ocle-DAB201 / Ocle-DAB201) may be physiologically disadvantaged and, therefore, less preferred as lifelong partners for choosy males. The results from this study support the role of mate choice in maintaining high levels of MHC variability in a wild seabird species, and highlights the need to incorporate a broader ecological framework and sufficient sample sizes into studies of MHC-based mating patterns in wild populations in general.
Caspar Herman Storms salong - Ladegården
Caspar Herman Storms salong Oslo ladegård er en staselig lystgård fra 1700-tallet. Huset ble bygget på kjellermurene etter borgermester Christen Mules gård, der kong James VI av Skottland og prinsesse Anna ble gift i 1589. Det var Christiania-fruen Karen Toller som bygget huset i 1720-årene. Hennes barnebarn Caspar Herman Storm ga Ladegårdens hovedbygning sin form som gården har beholdt helt opp til vår tid. Han eide hele området fra Alna gård (ved Østre Aker kirke) og ut til strandlinja ved Bjørvika, og fra Tøyen og Vaterlands bro, helt til Alnaelvas bredd. På Caspar Herman Storms tid var hagen på sitt største, med pryd- og nytteplanter, flere uthus og veksthus. Denne salongen er den flotteste i Ladegården, med gulldekorerte veggtapeter fra Caspars tid. Derfor er rommet oppkalt etter han. Created in RealityCapture by Gaute Gunleiksrud from 1102 images in 08h:39m:00s. Source: Objaverse 1.0 / Sketchfab
WACCM-X simulated data for Wu et al. (2023) "Investigation of the physical mechanisms of the formation and evolution of equatorial plasma bubbles during a moderate storm on September 17, 2021"
<p>This dataset contains all the data related to WACCM-X simulated result figures in article "Investigation of the physical mechanisms of the formation and evolution of equatorial plasma bubbles during a moderate storm on September 17, 2021". The data includes simulated hmF2 data over the equatorial region for September 17th, 2021, during 18-23UT, from WACCM-X simulations; RTI data at 250-400km altitude for September 16th and 17th, 2021, obtained from WACCM-X simulated results during 18-23UT; Individual integrated components data of the RTI calculation at 350km altitude for September 16th and 17th, 2021, during 18-23UT, derived from WACCM-X simulations; Total zonal electric fields, PPEF zonal electric fields, Total vertical plasma drifts, and PPEF vertical plasma drifts data for September 16th to 17th, 2021 at 0 longitude between 40°S and 40°N latitude, spanning 0-24UT, from WACCM-X simulations.</p>
Data repository for Lin et al. (2022) "Origin of Dawnside Subauroral Polarization Streams during Major Geomagnetic Storms"
This dataset contains the necessary data and plotting tools supporting the paper titled "Origin of Dawnside Subauroral Polarization Streams during Major Geomagnetic Storms", by Lin et al., 2022. The data includes solar wind/IMF data on 20 November 2003, DMSP F16 measurements of electron precipitation energy flux, electron density, cross track ion drift velocity, magnetic perturbation from 13:51 UT to 14:31 UT on 20 November 2003; MAGE model simulation results of EnFlux, Vhorz, and FAC along the same DMSP trajectory; MAGE simulation results of zonal ion drift, FAC, and magnetospheric equatorial plasma pressure at 06:30 UT and 18:30 UT; MAGE/RCM outputs of ring current pressure at 06 MLT and 18 MLT; RCM outputs of effective potential; CHIMP simulation results of test particle ions at 06:30 UT and 18:30 UT.
Ionosphere-Thermosphere Data Published in "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms"
<p>This dataset supports the Journal of Geophysical Research publication "Responses of the Thermosphere and Ionosphere System to Concurrent Solar Flares and Geomagnetic Storms" by Qian et al., 2019. The data files are selected output and related analyses from the thermosphere-ionosphere-electrodynamics general circulation model (TIEGCM). The format of the data files are either IDL save files or NetCDF files or ASCII.</p>
WACCM-X simulated data for Wu et al.'s paper on "The Formation Mechanism of Merged EIA during a Storm on November 4, 2021"
This dataset contains all the data related to WACCM-X simulated result figure in paper "The Formation Mechanism of Merged EIA during a Storm on November 4, 2021". The data includes simulated NMF2 at 0° longitude (40°S - 40°N latitude), between 9UT and 24UT on November 3rd and 4th; electron density, WI, V, d(O+)/dt, d(O+)(chem), d(O+)(ExB), d(O+)(wind), and d(O+)(ambi) at 0° longitude (40°S - 40°N latitude) between 100-500 km from 9UT to 24UT on November 3rd and 4th.
TIEGCM simulations associated with the February 2016 geomagnetic storm
<p>These are simulation results from the thermosphere-ionosphere-electrodynamics GCM (TIEGCM) described in the manuscript "Importance of the lower atmospheric forcing and magnetosphere-ionosphere coupling in simulating neutral density during the February 2016 geomagnetic storm" by Maute et al. submitted to Frontiers. The study is focused on examining the effect on the neutral density of different lower boundary forcing and different high latitude forcing methods. The simulations are compared to Swarm-C neutral density along the satellite orbit.</p> <p>There are three different TIEGCM simulations which output is provided and vary by their forcing</p> <p>1. lower atmospheric forcing by WACCM-X/SD perturbations and mean, high latitude foricng via field-aligned current (labeled WacXBP_FAC) </p> <p>2. lower atmospheric forcing by climatological Global Scale Wave Model GSWM) (perturbations) and climatological background, high latitude forcing via field-aligned current (labeled Climate_FAC) </p> <p>3. lower atmospheric forcing by WACCM-X/SD perturbations and mean, high latitude forcing via Weimer empirical model (labeled WacXBP_Weimer) </p> <p>There are two additional simulations for which the neutral density is provided</p> <p>4. lower atmospheric forcing by WACCM-X/SD perturbations and climatological background, high latitude foricng via field-aligned current (labeled WacXP_Bclimate_FAC)</p> <p>5. lower atmospheric forcing by WACCM-X/SD symmetric perturbations (with respect to geographic latitude) and mean, high latitude foricng via field-aligned current (labeled WacXBPsym_FAC)</p>
Data for paper titled "Penetrating electric field during the Nov 4 2021 Geomagnetic Storm"
<p>The data is part of publication for a paper titled "Penetrating electric field during the Nov 4 2021 Geomagnetic Storm" to be submitted to the JGR Space Physics. The data contains simulation from the NCAR MAGE model of the Nov 4, 2021 Geomagnetic storm. We used the simulation to study the penetrating electric field, which affects the low latitude region ionosphere.</p>
Data for "Low- and mid-latitude ionospheric response to the 2013 St Patrick's Day geomagnetic storm in the American Sector: GITM simulation"
<p>The dataset stores the GITM simulation results for the study "Low- and mid-latitude ionospheric response to the 2013 St Patrick's Day geomagnetic storm in the American Sector: GITM simulation"</p>
Data for "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
GITM Simulation results for the paper "Assessment of using field-aligned currents to drive the Global Ionosphere Thermosphere Model: A case study for the 2013 St Patrick's Day geomagnetic storm"
Data for "Impact of Precipitation Mass Sinks on Midlatitude Storms in Idealized GCM Simulations over a Wide Range of Climates"
<p>Code, simulation input files, and postprocessed simulation output data supporting "Impact of Precipitation Mass Sinks on Midlatitude Storms in Idealized GCM Simulations over a Wide Range of Climates", submitted to Weather and Climate Dynamics. Enclosed README file provides detailed descriptions of the archive contents.</p>
Data for: The Clam Before the Storm: A Meta Analysis Showing the Effect of Combined Climate Change Stressors on Bivalves
<p>These data were used to conduct a meta-analysis (as descirbed in the pre-print; The Clam Before the Storm: A Meta Analysis Showing the Effect of Combined Climate Change Stressors on Bivalves). These data can be used to reproduce our analysis.</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.