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55 results for “bedrock”
Bedrock radioactivity influences the rate and spectrum of mutation - Orthologous genes
<p>Alignments of the 2490 orthologous genes used in the article "Natural Bedrock radioactivity influences the rate and spectrum of mutation" to estimate the mutational spectrum and synonymous substitution rate.</p> <p>To compute accurate synonymous substitution rate, we removed genes with short sequences (<half of the alignment) and genes strongly supporting another phylogeny using ProfileNJ <a href="https://paperpile.com/c/Klqlpb/W5sS">(Noutahi et al. 2016)</a> with a bootstrap threshold of 90%, resulting in a subset of 769 genes listed in the file "List_769_1-to-1_orthologs_EvolutionRate.txt".</p> <p>Transcriptome paired-end reads used to define these orthologous genes have been deposited to the European Nucleotide Archive and are available under the study ID PRJEB14193.</p> <p>Sequences were aligned with Prank<a href="https://paperpile.com/c/Klqlpb/pilh"> (Löytynoja & Goldman 2008)</a> using a codon model and sites ambiguously aligned were removed with Gblocks <a href="https://paperpile.com/c/Klqlpb/c5kb">(Castresana 2000)</a>.</p>
Water chemistry, bedrock geology, and land cover data for Pennsylvania headwater streams: 2007-2022.
This data package contains all data necessary to run random forest and regression analyses featured in the article: 'Influence of bedrock geology on headwater stream pH' by G. Moyer, K. Frantz, and M. Shank. The data include water chemistry, land cover, and geologic formations for 271 headwater streams in Pennsylvania. Data from two previously published papers are also included: Ponce et al. (1979) and Lynch and Dise (1985), which were used as validation datasets.
Multiple Element Limitation in Northern Hardwood Ecosystems (MELNHE): Soil Profile Rock Characteristics by Bedrock Type in Bartlett and Hubbard Brook, 2004-2018
Soils in our northeastern forests were formed in parent materials deposited by glaciers. The direction and distance of glacial movement can be used to predict the source of glacial till at “downstream” points on the landscape (Bailey 1992). The goal of this project was to identify the rocks excavated from the soil pits in each of the plots and then to use that data to validate the glacial till model. The minority of rocks in the soil pits matched the bedrock, showing the importance of glacial movement. Additional detail on the MELNHE project, including a data table of site descriptions and a pdf file with the project description and diagram of plot configuration can be found in this data package: https://portal.edirepository.org/nis/mapbrowse?scope=knb-lter-hbr&identifier=344. 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. Literature cited: Bailey, S.W., 1992. Lithologic composition and rock weathering potential of forested, glacial-till soils (Vol. 662). US Department of Agriculture, Forest Service, Northeastern Forest Experiment Station.
Cordilleran ice sheet improved bedrock simulations continuous variables
<p>These data contain a subset of time-dependent glacier model output variables. The <em>ghf70</em> data files are an update on the reference below, fixing significant problems affecting the computation of the bedrock deformation in response to ice load (PISM Github issues <a href="https://github.com/pism/pism/issues/370">#370</a> and <a href="https://github.com/pism/pism/issues/377">#377</a>) and the computation of ice temperature (PISM Github issue <a href="https://github.com/pism/pism/issues/371">#371</a>). The other data files additionally include spatially-variable geothermal heat flux (<em>dav13</em>, <em>gou11comb</em>, <em>gou11simi</em>, <em>sha04</em>), different lithospheric rigidity (<em>eet30km</em>) or mantle viscosity (<em>num1e21</em>), and higher horizontal resolution (<em>3km</em>).</p> <p><strong>Reference:</strong></p> <ul> <li>Seguinot, J., Rogozhina, I., Stroeven, A. P., Margold, M. and Kleman, J.: Numerical simulations of the Cordilleran ice sheet through the last glacial cycle, <em>The Cryosphere</em>, 10(2), 639–664, doi:<a href="https://doi.org/10.5194/tc-10-639-2016">10.5194/tc-10-639-2016</a>, 2016.</li> </ul> <p><strong>File names:</strong></p> <p><code>cisbed.{res}.{forcing}.{ex.100a|ts.10a}.{ghf}.{props}.nc</code></p> <ul> <li>Horizontal resolution: <ul> <li><em> 10km</em>: 10 km horizontal resolution</li> <li><em>5km</em>: 5 km horizontal resolution</li> <li><em>3km</em>: 3 km horizontal resolution</li> </ul> </li> <li>Temperature forcing: <ul> <li><em>epica</em>: EPICA ice core temperature forcing</li> <li><em>grip</em>: GRIP ice core temperature forcing</li> </ul> </li> <li>Variable types: <ul> <li><em>ex.100a</em>: spatial diagnostics every hundred years</li> <li><em>ts.10a</em>: scalar time-series every ten years</li> </ul> </li> <li>Geothermal heat flow: <ul> <li><em>ghf70</em>: constant 70 mW m-2 heat flow</li> <li><em>dav13</em>: Davies (2013) geothermal heat flow map</li> <li><em>gou11comb</em>: Goutorbe et al. (2011) best combination method</li> <li><em>gou11simi</em>: Goutorbe et al. (2011) similarity method</li> <li><em>sha04</em>: Shapiro and Ritzwoller (2004) heat flow map</li> </ul> </li> <li>Bedrock properties <ul> <li><em>eet30km</em>: lithosphere elastic thickness of 30 km</li> <li><em>num1e21</em>: astenosphere viscosity of 1e21 Pa s</li> </ul> </li> </ul> <p><strong>Data format:</strong></p> <p>The data use compressed netCDF format. For quick inspection I recommend ncview. Spatial diagnostics (<em>*.ex.100a.nc</em>) can be converted to GeoTIFF (and other GIS formats) e.g. using GDAL:</p> <p><code>gdal_translate NETCDF:filename.nc:variable -b band filename.variable.band.tif</code></p> <p>The list of variables (subdatasets) can be obtained from ncdump or gdalinfo. Band information can be displayed with:</p> <p><code>gdalinfo NETCDF:filename.nc:variable</code></p> <p>Variable long names, units, PISM configuration parametres and additional information are contained within the netCDF metadata.</p> <p><strong>Funding:</strong></p> <p>Swiss National Supercomputing Centre (CSCS) grants s573 and sm13 to J. Seguinot, Swiss National Science Foundation (SNSF) grants no.~200020-169558 and 200021-153179/1 to M. Funk, and Research Foundation – Flanders (FWO) Odysseus Type II project G0DCA23N 'GlaciersMD' to H. Zekollari.</p> <p><strong>Changelog:</strong></p> <ul> <li>Version 1: <ul> <li>Initial version.</li> </ul> </li> </ul>
Bedrock surface topography of Latvia
<p>A new map and digital bedrock surface elevation model of Latvia is presented with a horizontal resolution of 250 m. The local bedrock comprises largely undisturbed layers of Palaeozoic and Mesozoic sedimentary rocks covered by up to 200 m thick Quaternary strata composed mostly of glacigenic and marine sediments. The bedrock surface elevation model and the corresponding map were created by tessellation interpolation of the bedrock surface based on more than 20,000 boreholes and constrained by the present land surface. The known locations of paleo-incisions buried under Quaternary cover are indicated. <br> <br> The presented map is prepared as a georeferenced dataset comprising four raster bands: elevation of the bedrock surface above sea level, locations of boreholes used for the construction of the elevation of the bedrock surface, locations, where buried valleys in geological boreholes were uncovered, and thickness of Quaternary sediments:</p> <ul> <li>Band_1 - digital elevation model of the bedrock surface topography of Latvia,</li> <li>Band_2 - thickness of Quaternary sequence,</li> <li>Band_3 - well locations marking the top of the bedrock, used to construct the map (count per cell),</li> <li>Band_4 - well locations interpreted to be recovering paleo incisions (count per cell).</li> </ul> <p>This research is funded by the Latvian Council of Science, project “Spatial and temporal prediction of groundwater drought with mixed models for multilayer sedimentary basin under climate change”, project No. lzp-2019/1-0165.</p>
Hubbard Brook Experimental Forest Bedrock Geology: GIS Shapefile
This coverage was obtained in digital form from Chris Barton of the USGS. Bedrock geology in the Hubbard Brook Valley was mapped by C.C. Barton, R.H. Comerlo, and S.W. Bailey, August 1994 to August 1995. The Map is entitled "BEDROCK GEOLOGIC MAP OF HUBBARD BROOK EXPERIMENTAL FOREST AND MAPS OF FRACTURES AND GEOLOGY IN ROADCUTS ALONG INTERSTATE 93, GRAFTON COUNTY, NEW HAMPSHIRE" and was approved for publication on August 28, 1995.
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
Weights for automatic two-dimensional bedrock fracture trace mapping from outcrop images
<p>This file contains weights for automatic two-dimensional bedrock fracture trace mapping. The weights have been trained on manually mapped traces (https://doi.org/10.5281/zenodo.7077574) that were digitized from UAV-acquired drone orthomosaics from along the shoreline of Loviisa, South-East Finland (https://doi.org/10.5281/zenodo.7077518). Code for automatic mapping and weights generation is available on GitHub (https://github.com/nialov/ALSA).</p> <p>Data is published as Hierarchical Data Format, version 5 (HDF5).</p> <p>The work in automatic mapping was done as part of a Geological Survey of<br> Finland project, Kallioperän Rikkonaisuus, during 2021-2022.</p>
The surface deformation induced by thermal expansion of bedrock, based on the the uniform elastic sphere model
<p>The surface deformation induced by thermal expansion of bedrock(TEB), based on the the uniform elastic sphere model in the manuscript submitted to JGR: Solid Earth, including:</p> <p>1. Input data of TEB model:</p> <p><strong>Spherical harmonics coefficients of land surface temperature:</strong> Cosine terms (detrended); Sine terms (detrended) </p> <p>(The coefficients are based on temperature data provided by Physical Sciences Laboratory (PSL) of the National Oceanic and Atmospheric Administration; <u>https://psl.noaa.gov/data/gridded/data.cpc.globaltemp.html</u>)</p> <p>2. Output data of TEB model: </p> <p><strong>The 3-dimensional TEB displacements </strong> <strong>on the 0.5</strong><strong>°×</strong><strong>0.5</strong><strong>°</strong><strong>global grid:</strong> annual variations of East, North, Up components</p>
Text-fig. 2. Profile of the Fox Passage test pit, shape of December 2012. The letters A, B, C mark three fossiliferous layers, the letter D marks a highest part of the palaeontologically sterile bedrock. in The Mammalian Fauna Of Barová Cave (Moravian Karst, The Czech Republic)
Text-fig. 2. Profile of the Fox Passage test pit, shape of December 2012. The letters A, B, C mark three fossiliferous layers, the letter D marks a highest part of the palaeontologically sterile bedrock.
Multi-scale Fractures and Water Invasion Dynamics in Buried-Hill Bedrock Gas Reservoirs of the Jianbei Slope, Qaidam Basin, China
<p>In this study, efforts are made to comprehensively analyze the multi-scale fracture development, the dynamic water intrusion, and the favorable remaining gas areas in the ancient buried-hill bedrock gas reservoir in the Jianbei slope of the Qaidam Basin, based on integrated analyses of thin sections, FMI imaging logging, three-dimensional seismic data, and dynamic production data. Fractures in the bedrock are demonstrated to be controlled by tectonic stress, magma intrusion, hydrothermal activity, and weathering and leaching. The complex fracture genesis leads to differences in the scale and type of bedrock fractures with multiple scales and high angles. The dip angle of the fracture gradually increases from the unconformity on the top of the bedrock downward inside the bedrock. Meanwhile, the dip angle may decrease from the center to both sides due to the difference in the fracture strength of the fault zone. Fractures are mostly near-east-west oriented and they normally intersect with the ground stress at an acute angle. Effective fractures can provide advantageous seepage channels for fluids. Formation water is demonstrated to rapidly channel through large-scale fractures while laterally invading through small-scale fractures in the gas reservoir. The western area of the reservoir is featured by intensive water invasion, rapid pressure drop, and fast production decline, while the eastern area is characterized by weak water invasion as well as relatively stable pressure and productivity. Three relatively independent water-seal gas systems with JB1-3, JBH1-3 and J3 Wells as the center are formed, and the remaining gas is enriched.</p>
Guerrero Copper Sheet in Bedrock No.3 (2019)
Site 2-04-8MO02343: Pieces of crumpled copper sheeting are driven into a hole in the limestone bedrock, as well as fused to the surface of it. The metal sheeting once covered the lower hull of the ship to prevent shipworm infestation and fouling of the bottom. Model created June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Guerrero Copper Sheet on Bedrock No.1 (2019)
Site 2-04-8MO02343: A piece of copper sheet is found fused to the sea bottom at the site of a shipwreck believed to be the Havana-based pirate slave ship *Guerrero*, sunk near Key Largo in 1827. This piece is likely a remnant of copper sheathing used to protect the lower part of the ship from shipworms and fouling. A copper-alloy tack sits on the piece, evidence of the type of fastener used to attach the copper to the ship's hull. This same feature was rendered earlier with images taken in 2012 (see https://skfb.ly/6pwSy), but this newer render is much clearer. Model created June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Limestone bedrock in Whiting Bay, Waterford.
Ballysteen Formation limestone bedrock in Whiting Bay, Waterford, Ireland, at low tide just after the full moon, end of February 2021. xref https://poly.cam/capture/9A295BC5-9145-4D3F-8E54-36C20A0B9623 for texture #testing Source: Objaverse 1.0 / Sketchfab
Guerrero Copper Sheet in Bedrock No.2 (2019)
Site 2-04-8MO02343: Pieces of crumpled copper sheet driven into holes in the limestone bedrock, as well as fused to the surface of it. This metal sheeting once covered the lower hull of the ship to prevent shipworm infestation and fouling of the bottom. Model created June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Guerrero Copper Spike on Bedrock (2019)
Site 2-04-8MO02343 Copper Spike. This spike, approximately 16 centimeters long, sits in a shallow recess in the bedrock. It is one of many copper spikes found across the shipwreck site. This model was made in June, 2019. Scale = 5 centimeters. Source: Objaverse 1.0 / Sketchfab
Data for: Experiment on hydrodynamic characteristics of plunging flows in bedrock canyon bends
<p>The dataset is about the hydrodynamic characteristics of plunging flows in bedrock bends. The dataset includes raw data of bed topography of the Ciping Reservoir canyon section of Jinghe River and the bed of the flume experiments, flow field along the center profiles of plain- and undulating-beds conditions, and the bed shear stress of plain- and undulating-beds conditions.</p>
Shapefiles for Glen Torridon Bedrock Ridges, Ripples, Transverse Aeolian Ridges, Wavelength, and Topographic Profiles
<p>Zipfiles containing polyline shapefiles produced with ESRI's ArcMap 10.6 tracing the location and extent of ridges and transverse aeolian ridges (TARs) in the Glen Torridon region of Aeolis Mons (informally Mount Sharp), Gale crater, Mars. </p>
Mapping and characterization of Martian intercrater bedrock plains supplementary files
<p>This dataset contains a shapefile of mapped intercrater bedrock plains exposures, processed THEMIS multispectral imagery (atmospherically-corrected data, DCS imagery, and emissivity spectra), and CRISM stamplists used for VNIR parameter mapping.</p>
Experimental data for Dynamic cover effects in lateral bedrock channel bank abrasion: Experiment and model comparison
<p>Experimental data for bank erosion.xlsx contains the data used for the figures in the paper, and the distribution of bedrock bank erosion in the longitudinal direction in Run 1 - Run 18.</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)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.