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57 results for “Water masses”
Water quality, temperature, ash-free dry mass, photosynthetic activate radiation (PAR), and zooplankton data from a warming and DOC subsidy experiment, 2020 - 2021.
This dataset includes chlorophyll-a concentrations, periphyton biomass estimates, water quality measurements, and qualitative observations from a large-scale mesocosm experiment conducted in the Green Lakes Watershed, Colorado. The experiment was designed to test how earlier lake ice-off and increased dissolved organic material (DOM), associated with terrestrial plant encroachment in alpine watersheds, interactively influence aquatic food webs. In fall 2019, twenty 2600L “megacosms” were established at Sandy Corner (3300 m ASL; 40.042289, -105.584006), left to fill with snowmelt, and maintained throughout the 2020 open water season. The experiment followed a 2 × 2 randomized block design manipulating ice-off timing (via black vs. beige tank coloration) and DOM inputs (presence/absence of willow leaf packs), with five replicates per treatment. All tanks were seeded with sediments and zooplankton from both alpine and montane lakes (Green Lake 1 and Green Lake 4), and instrumented with thermistors recording surface and hypolimnion temperature every two hours year-round. Periphyton growth was monitored using clay tiles, sampled across five time points. Chlorophyll-a concentrations were extracted from filtered water samples and analyzed spectrophotometrically. Periphyton biomass was estimated via ash-free dry mass (AFDM) determinations, based on the mass lost on combustion of material scraped from tiles. Water quality was measured 1–2 times weekly using a YSI ProPlus multiprobe and Li-Cor quantum sensor, and snow/ice cover was qualitatively assessed monthly during winter.
Mass of wastes discharged directly from vessels to the water column
<p>Data set of mass discharge of selected pollutants from shipping in the European region. Created in the framework of the project Evaluation, control and Mitigation of the EnviRonmental impacts of shippinG Emissions (EMERGE).</p> <p>To determine the mass of discharged pollutants, AIS-based ship emission modelling of discharge volumes is combined with results of water effluent analysis. For the discharge volumes, the Ship Traffic Emission Assessment Model (STEAM) is used. The total discharge volume of wastes is comprised of five waste streams: open/close scrubber, grey, black and ballast water. For the content of pollutants in the five waste streams, a bibliographic database of waste stream pollutant concentrations compiled during the project is used.</p>
Eulerian and Lagrangian diagnostics of the dynamical properties of the water masses sampled during the Tara Pacific Expedition 2016-2018
<p>In order to provide a description of the dynamical properties of the water masses sampled, different Eulerian and Lagrangian diagnostics were calculated. </p> <p>For each of the 246 stations sampled, we proceeded as follows.</p> <p>We identified the water mass sampled at the given station. This was considered as a stadium shape with the two semi-circles centered on the starting and ending points of the transect, respectively. The radius of the stadium semi-circles was considered 0.1°, which is in accordance with previous studies25,29,30. The stadium was filled with virtual particles separated by 0.01°.</p> <p>For each virtual particle inside the stadium shape, we calculated an Eulerian or Lagrangian diagnostic (described above). The Eulerian diagnostics were extracted directly from the velocity field of the day of sampling. Concerning the Lagrangian diagnostics, these were obtained by advecting the virtual particle backward in time for an amount of time 𝞽 from the day of sampling day_S. For the Lagrangian betweenness, the advection was performed between day_S+𝞽/2 and day_S-𝞽/2, so that the advective time window was centered on the sampling day (details in25).</p> <p>For the Lagrangian diagnostics, we used the following advective times 𝞽: 5, 10, 15, 20, 30, and 60 days. The only exception is the retention time, which, by construction, was calculated only with the largest advective time, namely 𝞽=60 days.</p> <p>Once that, a given diagnostic (Eulerian or Lagrangian) was calculated for all the virtual particles filling the stadium shape, we calculated the mean value, and the 25, 50, and 75 percentiles. The percentiles were calculated in order to quantify the spatial variation of the diagnostic inside the stadium shape. Therefore, we associated each station with four values (mean, 25, 50, and 75 percentiles) of a given diagnostic.</p> <p> Furthermore, two different velocity fields were used, which are described as follows. </p> <p>Both the velocity fields were downloaded from E.U. Copernicus Marine Environment Monitoring Service (CMEMS, http://marine.copernicus.eu/). The first velocity field used was MULTIOBS_GLO_PHY_REP_015_004 [GlobEkmanDt]. This was produced by combining the altimetry derived geostrophic velocities and modeled Ekman surface currents. It had a spatial resolution of 0.25° and a temporal resolution of one day. The second velocity field was GLOBAL_REANALYSIS_PHY_001_030 [GloryS12]. It was obtained by a NEMO model assimilating altimetry and other observations. It had a spatial resolution of 1/12° and a temporal resolution of 1 day.</p> <p>The following Eulerian diagnostics were calculated:</p> <ul> <li> <p>Absolute velocity ([Uabs], m s-1): sqrt(u2+v2), where u and v are the zonal and meridional components of the horizontal velocity field used (described below)</p> </li> <li> <p>Kinetic energy ([Ekin], m2 .s-2): 0.5*(u2+v2)</p> </li> <li> <p>Divergence ([EulerDiverg], d-1): du/dx + dv/dy</p> </li> <li> <p>Vorticity ([Vorticity], d-1): dv/dx - du/dy</p> </li> <li> <p>Okubo-Weiss ([OW], d-2): s2-vorticity2, where s2 is (du/dx-dv/dy)2 + (dv/dx+du/dy)2. If negative, it indicates that the station sampled was inside an eddy.</p> </li> </ul> <p>The following Lagrangian diagnostics were calculated:</p> <ul> <li> <p>Finite-Time Lyapunov Exponents ([Ftle], d-1): it indicates the rate of horizontal stirring, and it is a means to quantify the intensity of turbulence in a given region. FTLE are commonly used to identify Lagrangian Coherent Structures, i.e. barriers to transport. In this case, a strong FTLE value indicates a region separating water masses which were far away backward in time.</p> </li> <li> <p>Lagrangian betweenness ([betw], adimensional): this diagnostic draws inspiration from Lagrangian Flow Network Theory26. It can identify regions which act as bottlenecks for the circulation, in that they receive waters coming from different origins, and that are then spread over several different destinations. These can represent possible hotspots driving biodiversity25.</p> </li> <li> <p>Lagrangian Divergence ([LagrDiverg], d-1). This diagnostic was calculated by integrating the Eulerian divergence along the backward trajectories. If positive, it indicates a water mass that, during the previous days, was subjected to a strong divergence, thus to a possible upwelling. If negative, it indicates a strong convergence, thus possible downwelling.</p> </li> <li> <p>Retention Time ([RetentionTime], d). This diagnostic indicates how many days a water mass has spent inside an eddy in the previous period. If the water mass is outside an eddy, then its retention time is set to zero.</p> </li> </ul>
Revisiting Interior Water Mass Responses to Surface Forcing Changes and the Subsequent Effects on Overturning in the Southern Ocean
<p>This dataset contains processed model data used in</p> <p>Tesdal, J.-E., A. MacGilchrist, G., Beadling, R. L., Griffies, S. M., Krasting, J. P., & Durack, P. J. (2023). Revisiting interior water mass responses to surface forcing changes and the subsequent effects on overturning in the Southern Ocean. Journal of Geophysical Research: Oceans, 128, e2022JC019105. <a href="https://doi.org/10.1029/2022JC019105">https://doi.org/10.1029/2022JC019105</a>.</p> <p>The above publication uses two coupled climate models (AOGCMs), GFDL-CM4 and GFDL-ESM4, to assess the impact of perturbations in wind stress and Antarctic ice sheet melting on the Southern Ocean meridional overturning circulation (SO MOC) and associated water mass transformations (WMT).</p> <p>The attached archive includes netCDF files to recreate all figures and tables in <a href="https://doi.org/10.1029/2022JC019105">Tesdal et al. (2023)</a>, including overturning streamfunction (moc), volume storage change (dVdt), surface water mass transformation (swmt), meridional volume transports (mvt) zonal mean potential density referenced to 2000 dbar (sigma2) and mixed layer depth (mld). These variables are derived from preindustrial control (piControl) and idealized perturbation runs of Antarctic melting (Antwater), wind stress (Stress), as well as the combination (Antwater-Stress) using the Flux-Anomaly-Forced Model Intercomparison Project (FAFMIP) protocol.</p> <p>The FAFMIP protocol (<a href="https://doi.org/10.5194/gmd-9-3993-2016">Gregory et al., 2016</a>) involves adding perturbations to the surface fluxes that are computed within the atmosphere-ocean general circulation model (AOGCM) from the state of the system (<a href="https://doi.org/10.1029/2005JC003421">Lowe and Gregory, 2006</a>; <a href="https://doi.org/10.1088/1748-9326/9/3/034004">Bouttes and Gregory, 2014</a>). The perturbations in this dataset were technically added as a flux adjustment similar to that formerly used in AOGCMs (<a href="https://doi.org/10.1007/BF01053472">Sausen et al., 1988</a>).</p> <p>The data files contain processed model output and do not include any raw model output. Model data from the piControl runs of CM4 and ESM4 are available at the Earth System Grid Federation archive (<a href="https://esgf-node.llnl.gov/projects/cmip6">https://esgf-node.llnl.gov/projects/cmip6</a>). The forcing fields (perturbations) used in the perturbation experiments can be found at <a href="https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans">https://github.com/becki-beadling/Beadling_et_al_2022_JGROceans</a>. Python scripts and Jupyter notebooks to reproduce the tables and figures can be accessed at <a href="https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans">https://github.com/jetesdal/Tesdal_et_al_2023_JGROceans</a>.</p> <p><strong>Contents</strong>:</p> <ul> <li>Overturning streamfunction (moc)</li> <li>Volume storage change (dVdt)</li> <li>Surface water mass transformation (swmt)</li> <li>Meridional volume transports (mvt) </li> <li>Zonal-mean potential density referenced to 2000 dbar (sigma2)</li> <li>Mixed layer depth (mld) </li> <li>Antarctic shelf mask</li> <li>Static grid files</li> </ul> <p><strong>Models</strong>:</p> <ul> <li>GFDL-CM4</li> <li>GFDL-ESM4</li> </ul> <p><strong>Simulations</strong>:</p> <ul> <li>Preindustrial control (piControl)</li> <li>Experiment with a 0.1 Sv freshwater perturbation entering at the Antarctic coast (Antwater)</li> <li>Experiment with zonal and meridional wind stress perturbations (Stress)</li> <li>Experiment with combined perturbation of both Antarctic melting and wind stress (Antwater-Stress)</li> </ul> <p><strong>NetCDF file name structure</strong>:<br> <model>_<simulation>_<member_id>_<domain>_<time_period>_<variable>.nc</p> <ul> <li>model: CM4, ESM4</li> <li>simulation: control, antwater, stress, antwaterstress</li> <li>member_id (only for antwater, stress, antwaterstress): 251, 290, 332 (CM4), 101, 151, 201 (ESM4)</li> <li>domain: global, so</li> <li>time_period: yyyy-yyyy (first year to last year)</li> <li>variable: e.g., moc_rho2_online_lores, dVdt_rho2_online_lores, swmt_sigma2_005, sigma2_jmd95_zmean</li> </ul>
The photooxidation of dissolved organic matter in surface waters analyzed by Fourier-transform ion cyclotron resonance mass spectrometry.
Dissolved organic matter (DOM) plays an important role in carbon cycling in natural waters. The processing of DOM in these waters can occur via photooxidation, or interaction with sunlight. This processing can lead to the production of CO2, and also the alteration of organic compounds that make up DOM. It is likely that the extent of photooxidation is at least partially determined by the chemical composition of DOM. Fourier-transform ion cyclotron resonance mass spectrometry (FT-ICR MS) was used to characterize the dissolved organic matter at the molecular level for all water samples, both before and after light exposure to better understand the photooxidation of DOM. Chemical formulas were assigned to mass to generated mass to charge ratios using a custom script in R, resulting in a list of chemical formula assignments for each DOM sample, at multiple light exposure time points.
Most Super-Earths Have Less Than 3% Water: Mass-Radius Relations
<p>Mass-radius relations for rocky super-Earths, related to the models constructed in "Most Super-Earths Have Less Than 3% Water" by James G. Rogers, Caroline Dorn, Vivasvaan Aditya Raj, Hilke E. Schlichting, and Edward D. Young.</p> <p>We provide two .csv files for the scenarios of super-Earths with and without outgassed mantles, respectively. Each file contains planet masses and radii (measured in Earth units) under the scenario of stripped and retained steam atmospheres. These are provided for a range in total water mass fractions (X_H2O) and equilibrium temperature (Teq). Note that all models have an Earth-like 32.5 % iron-core mass fraction. The water mass fractions of stripped models are less than that of retained atmospheres.</p> <p>To extract a single mass-radius relation for a desired scenario, filter a file for planets of a given (retained) water mass fraction and equilibrium temperature. </p>
The impact of Indonesian Throughflow constrictions on eastern Pacific upwelling and water-mass transformation
<p>Netcdf data and Matlab processing scripts for the article: </p> <p>Eabry, Holmes and Sen Gupta (2022): The impact of Indonesian Throughflow constrictions on eastern Pacific upwelling and water-mass transformation. Journal of Geophysical Research: Oceans. <a href="https://doi.org/10.1029/2022JC018509">https://doi.org/10.1029/2022JC018509</a></p> <p>Included are netcdf files with output from the ACCESS-OM2 1-degree ocean model averaged over years 500-600 of the spin-up simulation. CONTROL indicates the control simulation (realistic ITF topography), OPENITF indicates the Open ITF experiment and DIFF indicates difference files between the two. Please refer to the meta-data within the netcdf files for more information. Scripts to help with plotting standard variables are part of the COSIMA cookbook repository at <a href="https://github.com/COSIMA/cosima-recipes">https://github.com/COSIMA/cosima-recipes</a>.</p> <p>An example script Control_WMT_budget.m is provided to plot the control WMT budget and can be easily modified to plot the Open ITF or anomalous WMT budget. This script uses the Pacific masks found in mask.mat. The small tendency term is provided separately as dV_dt_nrho.mat.</p>
LILBID mass spectrum of water
<p>LILBID mass spectrum of water, shown in "Developing a Laser Induced Liquid Beam Ion Desorption Spectral Database as Reference for Spaceborne Mass Spectrometers" by Klenner et al (2022), published in Earth and Space Science.</p>
Data and results for manuscript "Small scale characterization of vine plant root water uptake via 3D electrical resistivity tomography and Mise-à-la-Masse method"
<p>This package contains measured raw ERT and MALM data used to generate the plots in the manuscript.</p> <p> </p>
A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin
<p>Isotopic data from the article in JGR A: A 4.5 year-long record of Svalbard water vapor isotopic composition documents winter air mass origin</p> <p><strong>CLDS_2020_ground_iso_vapor_1h.dat</strong></p> <p><strong>CLDS_2020_zeppelin_iso_vapor_1h.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: humidity in ppmv</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column: not to take into account</p> <p><strong>CLDS_2020_iso_precip.dat </strong></p> <p>first column: date in matlab format</p> <p>second column: temperature at noon in degre C</p> <p>thrid column: d18O in per mil</p> <p>forth column: dD in per mil</p> <p>fifth column:type of precip 1: water & 2 : snow & 3 : other (melt, etc.)</p>
Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment: Model Output Data
<p>This dataset contains model output for the simulations presented in <em>"Neodymium isotopes as a paleo-water mass tracer: A model-data reassessment, Quaternary Science Reviews 279 (2022), 107404".</em></p> <p>The NetCDF4 files contain the following variables:</p> <p>3D fields:</p> <ul> <li>Potential Temperature</li> <li>Salinity</li> <li>North Atlantic dye tracer</li> <li>Epsilon Nd</li> <li>Nd concentration</li> </ul> <p>2D field:</p> <ul> <li>AMOC stream function</li> </ul>
Figure 1 in Short Communication Mass mortality of Giant Water Bug Lethocerus indicus (Lepeletiler & Serville, 1825) [Heteroptera: Belostomatidae] in Thiruvananthapuram, Kerala State, South India
Figure 1. The dead specimens of Lethocerus indicus (Lepeletiler & Serville, 1825).
Supplementary Table S1. Combined analysis of variance containing the degrees of freedom (DF), mean squares (MS), P value (P val.), mean, coefficient of experimental variation (CEV%) and selective accuracy (SA) for the traits of luminosity (L*), chromaticity a* (a*), chromaticity b* (b*), grain length (length, mm), grain width (width, mm), grain thickness (thickness, mm), mass of 100 grains (Mass, g), normal grains (Ng, %), water absorption (absorption, %), cooking time (Ct, min:s), and concentrations of potassium (K, g kg-1 dry matter - DM), phosphorus (P, g kg-1 DM), calcium (Ca, g kg-1 DM), magnesium (Mg, g kg-1 DM), iron (Fe, mg kg-1 DM), zinc (Zn, mg kg-1 DM), and copper (Cu, mg kg-1 DM) obtained in 25 common bean cultivars evaluated in four experiments carried out from 2019 to 2021
<p><strong><span>Table S1.</span></strong><span> Combined analysis of variance.</span></p> <p><strong><span>Indirect selection for multiple technological and nutritional traits in common bean cultivars under different degrees of multicollinearity</span></strong></p> <p><strong><span>Bragantia, 2024.</span></strong></p>
Tracing the imprint of river runoff on Arctic water mass transformation [dataset]
<p>This dataset contains the underlying data for the manuscript Lambert et al., Tracing the imprint of river runoff<br> variability on Arctic water mass transformation, submitted to JGR-Oceans</p> <p>-----------------------------------<br> Both files contain variables with the general notation:<br> S..., which are the cumulative salt fluxes;<br> S..2, which are the salinity-transformation fluxes;<br> T..., which are the cumulative heat fluxes; and<br> T..2, which are the temperature-transformation fluxes.</p> <p>-----------------------------------<br> In the file crfdata.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> emp: evaporation-precipitation, small en neglected in the manuscript<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> fsiso/ftiso: isopycnal diffusion of salt/heat<br> fsdia/ftdia: diapycnal diffusion of salt/heat<br> sec: advection across the collective Arctic gateways</p> <p>Each variable is of size [4,12,nS] or [4,12,nT] where nS is the number of salinity bins, equal to the length of variable S<br> and nT is the number of temperature bins, equal to the length of variable T</p> <p>The first dimension is ordered as follows:<br> 0: delta_s, the equilibrium response to a 30% increase in total Arctic river runoff<br> 1: tau_s, the e-folding time scale of this response in months<br> 2: std, the standard deviation of the control value<br> 3: ctrl, the average control value</p> <p>The second dimension indicates the calendar month</p> <p>-----------------------------------------<br> In the file pp2.nc, the variable names contain:<br> slrx: surface salinity restoring term<br> rnf: river runoff<br> ice: ice melt<br> brnx: brine rejection including the penetration into subsurface layers<br> qns: nonsolar surface heat flux<br> qswx: heat flux due to shortwave radiation including the penetration into subsurface layers<br> adv: advection across the collective Arctic gateways<br> dif: total isopycnal + diapyncal diffusion</p> <p>Each variable is of size [2,nS] or [2,nT]</p> <p>The first dimension is:<br> 0: explained model variance between 0 and 1<br> 1: explained model variance where correlations with p>.05 equal NaN</p>
Shift of seed mass and fruit type spectra along longitudinal gradient: High water availability and growth allometry
<p>CE1 Propagule traits vary among biomes along geographical gradients such as longitude, but the mechanisms that underlie these variations remain unclear. This study aims to explore seed mass variation patterns of different biome types along a longitudinal gradient and their underlying variation mechanisms by involving an in-depth analysis on the variation of seed mass, fruit type spectra, growth forms and dispersal mode spectra in Inner Mongolia and northeastern China. Plant community characterization and seed collection were conducted in 26 sites spreading over five vegetation types and covering 622 species belonging to 66 families and 298 genera. We found there are significantly declining trends for mean seed mass, vertebrate-dispersed species richness and fleshy-fruited species richness along a longitudinal gradient from forests to desert grasslands. However, we also found the lowest average seed mass and the smallest proportion of species dispersed by vertebrates occurring at typical grasslands in the five biomes. The variations of average seed mass display high congruence CE2 with transition of growth form spectra. The selection for these propagule attributes is driven mainly by climatic factors such as precipitation, temperature, soil moisture and evaporation, as well as by internal biotic factors such as growth forms, canopy coverage and leaf area. A hypothesis was provided that environmental factors and botanical traits that favor greater water availability lead to emergence (or speciation) of species with large seeds or fleshy fruits with high water content. Due to greater water availability and increasing leaf area, much more photosynthate (photosynthesis production) and allometric growth then ultimately increase the biome average seed mass from west to east. Phylogenetic signal or diversity are not found to be significantly involved in the effect on the patterns. A novel mechanistic framework and mathematical model are provided to expound seed variation among species or biomes.</p>
CDOM spectral slope (S275-295) as tracers of water masses, CDOM heterogeneity, and 14C-DOC in an oligotrophic marginal sea
<p>The present study is focused on the CDOM vertical profiles in the northern South China Sea. The results suggest humic-like FDOM is controlling the variation of the spectral slope of CDOM (<em>S</em><sub>275-295</sub>). In addition, our results suggest <em>S</em><sub>275-295</sub> could be used as tracers of water mass, CDOM diversity and radiocarbon age of dissolved organic carbon in oligotrophic ocean.</p>
Ocean Gateways and Ocean Circulation Dynamics: Unveiling the Deep Water-Mass properties in the Western Equatorial Pacific and Eastern Indian Ocean since the middle
<p>File contains a dataset related to census counts and stable isotopes that we used to write our manuscript.</p>
Data from: Scaling of thermal tolerance with body mass and genome size in ectotherms: a comparison between water-and air-breathers
Open the record for dataset details and reuse information.
Shift of seed mass and fruit type spectra along longitudinal gradient: High water availability and growth allometry
Open the record for dataset details and reuse information.
Data from: Microbe biogeography tracks water masses in a dynamic oceanic frontal system
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN ANOTHER PUBLICATION. PLEASE SEE http://dx.doi.org/10.1098/rsos.160829. Dispersal limitation, not just environmental selection, plays an important role in microbial biogeography. The distance–decay relationship is thought to be weak in habitats where dispersal is high, such as in the pelagic environment, where ocean currents facilitate microbial dispersal. Most studies of microbial community composition to date have observed little geographical heterogeneity on a regional scale (100 km). We present a study of microbial communities across a dynamic frontal zone in the southwest Indian Ocean and investigate the spatial structure of the microbes with respect to the different water masses separated by these fronts. We collected 153 samples of free-living microorganisms from five seamounts located along a gradient from subtropical to subantarctic waters and across three depth layers: (i) the sub-surface chlorophyll maximum (approx. 40 m), (ii) the bottom of the euphotic zone (approx. 200 m), and (iii) the benthic boundary layer (300–2000 m). Diversity and abundance of microbial operational taxonomic units (OTUs) were assessed by amplification and sequencing of the 16S rRNA gene on an Illumina MiSeq platform. Multivariate analyses showed that microbial communities were structured more strongly by depth than by latitude, with similar phyla occurring within each depth stratum across seamounts. The deep layer was homogeneous across the entire survey area, corresponding to the spread of Antarctic intermediate water. However, within both the sub-surface layer and the intermediate depth stratum there was evidence for OTU turnover across fronts. The microbiome of these layers appears to be divided into three distinct biological regimes corresponding to the subantarctic surface water, the convergence zone and subtropical. We show that microbial biogeography across depth and latitudinal gradients is linked to the water masses the microbes persist in, resulting in regional patterns of microbial biogeography that correspond to the regional scale physical oceanography.
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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.