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1,477 results for “earth”
Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi."
<p>Data for "Integrated ab initio modelling of atomic order and magnetic anisotropy for rare-earth-free magnet design: effects of alloying additions in L1$_0$ FeNi", published in npj Comput. Mater. <strong>10</strong> 272 (2024).</p> <p>Version 2 contains additional results relating to Ni-rich systems.</p> <p>Version 3 contains data relating to vibrational considerations for the A1-L1$_0$ transition in equiatomic FeNi.</p> <p>Version 4 contains date pertaining to the Curie temperatures of the disordered, partially ordered, and fully ordered alloys considered in this work.</p>
Dataset for manuscript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model"
<p>Datasets and Jupyterlab python script for plotting all figures relevant to the mansucript "Gaps in our understanding of ice-nucleating particle sources exposed by global simulation of the UK Earth System Model" by Herbert et al.</p> <p>https://egusphere.copernicus.org/preprints/2024/egusphere-2024-1538/</p> <p>Data needs to be unzipped and paths (input and output) updated in the jupyterlab python script.</p> <p> </p>
Small orbital forcing changes alter the ocean circulation of Earth's next future supercontinent: Simulations
<p>This dataset contains the simulations from the manuscript "Small orbital forcing changes alter the ocean circulation of Earth's next future supercontinent".</p> <p>From the methods section of the manuscript:<br>The simulations are carried out using the fully-coupled ocean-atmosphere general circulation model ROCKE-3D version Planet 1.0 (R3D1) (Way et al., 2017). The model is run at a horizontal resolution of 4 x 5 degrees and simulations were integrated for 5000 and 7000 years for PD and future forcing, respectively, to ensure they were in equilibrium. The topography and bathymetry is the one used by Way et al. (2021) in their simulation 2 (Aurica). This includes a fully dynamic ocean 4252 m deep and land elevations close to present day mean topography and elevations between 1 and 858 m.<br>Due to high uncertainties in its future evolution, atmospheric composition is kept constant at year 1850 levels, following the setup in Way et al. (2021): the atmosphere is dominated by nitrogen with 21% oxygen, 285 ppmv CO2, 0.3 ppmv N2O and 0.79 ppmv CH4, and no aerosols or ozone are included.<br>Two simulations are presented: PD_Aurica which which has present-day day-length (24 hrs) and insolation (1361 Wm-2) and F_Aurica where we apply orbital forcing consistent with 250 Ma into the future - day length is increased by 0.5 hrs and insolation is increased by 2.6 Wm-2 (see Way et al., 2021). </p> <p>PD_Aurica corresponds to the output files with the following names: ANN4500-4999.*F_AURICA_RAND_PD_03.nc<br>F_Aurica corresponds to the output files with the following names: ANN6500-6999.*F_AURICA_RAND_PD_01.nc</p> <p>The diagnostic files including AGC, AIJ, AIJK and AIJL in the filename contain atmospheric diagnostics and outputs, whereas those that include OIJ, OIJL, OJL and OTJ contain ocean diagnostics and outputs.</p> <p><span><span><span><a href="https://science.gsfc.nasa.gov/author/533569006/35432">Way M. J.</a></span></span><span>, </span></span><span><span><span><a href="https://science.gsfc.nasa.gov/author/830545035/35432">I. Aleinov</a></span></span><span>, </span></span><span><span>D. S. Amundsen</span><span>, </span></span><span><span><a title="See all authors for this publication">et al.</a> </span></span> <span>2017.</span> <span> "Resolving Orbital and Climate Keys of Earth and Extraterrestrial Environments with Dynamics (ROCKE-3D) 1.0: A General Circulation Model for Simulating the Climates of Rocky Planets." </span> <em>The Astrophysical Journal Supplement Series</em> <strong>231</strong> <span><strong>(1)</strong>: </span> <span>12</span> <span> [<a href="http://dx.doi.org/10.3847/1538-4365/aa7a06">10.3847/1538-4365/aa7a06</a>]</span></p> <p><span><span><span><a href="https://science.gsfc.nasa.gov/author/533569006/42520">Way M. J.</a></span></span><span>, </span></span><span><span>H. S. Davies</span><span>, </span></span><span><span>J. C. Duarte</span><span>, </span></span><span><span><a title="See all authors for this publication">et al.</a> </span></span> <span>2021.</span> <span> "The Climates of Earth’s Next Supercontinent: Effects of Tectonics, Rotation Rate, and Insolation." </span> <em>Geochemistry, Geophysics, Geosystems</em> <strong>22</strong> <span><strong>(8)</strong>: </span> <span> [<a href="http://dx.doi.org/10.1029/2021gc009983">10.1029/2021gc009983</a>]</span></p> <p> </p> <p> </p>
Fault Traces Dataset for Zou and Fialko Earth and Space Sciences Manuscript
<p>The 'xx_fault.dat' contains the linked fault traces of different regions (nz: Northern New Zealand; nv: Basin and Range Province; ca: Ventura County, California; np: Pennsylvania and Northern New Jersey); The 1st and 2nd columns are UTM coordinates; The 3rd column is the random number assigned for distinguishing each fault traces.</p> <p>The 'xx_len.dat' contains the length of each fault trace in the corresponding regions, in km.</p> <p>The other '.dat' files and the 'SunData.xls' contain the length of fractures from outcrop and lab data. All of them are frequency distribution, except the 'LaHouve_Villemin.dat' which is already in cumulative distribution. The unit of 'SunData.xls' is mm; for the two 'Bahat' datasets is cm; for the rest of the outcrop data is m.</p> <p>The .m files are the codes for calculating cumulative length distribution, frequency density distribution, and fault connection.</p> <p> </p> <p> </p> <p>For any questions please contact Xiaoyu Zou via x3zou@ucsd.edu</p>
How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations: Data and Analysis
<p>Dataset includes simulation output produced by a TERRA simulation for `How to assess similarities and differences between mantle circulation models and Earth using disparate independent observations'. </p> <p> </p> <h3><strong>Description of data file contents</strong></h3> <ul> <li><strong>NC*comp.tar.gz</strong> - compressed archives containing NetCDF files (file-per-process) with TERRA grid data including temperature, velocity, interpolated bulk composition, denisty, and voscosity fields. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>. *dump number</li> <li><strong>NC_seis_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with predicted seismic properties at the resolution of the TERRA grid generated from the present day state of the simulated mantle, including elastic and anelastic Vs and Vp, bulk sound velocity, and predicted density from mineral phyiscs tables. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>NC_hpes_037.tar.gz</strong> - compressed archive containing NetCDF files (file-per-process) with interpolated abundances at the resolution of the TERRA grid for isotopes including the heat-producing elements ^40^K, ^232^Th, ^235^U and ^238^U. Can be read using <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>P_files_037.tar.gz</strong> - compressed archive of TERRA P-files (particle files).</li> <li><strong>C_files_037.tar.gz</strong> - compressed archive of TERRA C-files (grid state files) - together with the P-files describe the full present day state of the simulation. </li> <li><strong>seis_filtered_037.tar.gz</strong> - compressed archive (file-per-layer) with reparameterised and seismically filtered (against S40RTS) present day Vs field.</li> <li><strong>seis_tables.tar.gz</strong> - compressed archive contianing lookup tables of seismic properties for the 3 principal lithologies assumed in the TERRA simulation (harzburgite, lherzolite and basaltic crust).</li> <li><strong>density_037.sph</strong> - Spherical harmonic coefficients for the density field in format to be read by the <a title="propagator" href="https://zenodo.org/records/12696774" target="_blank" rel="noopener">propagator matrix code.</a></li> <li><strong>plumes.pkl, ridges.pkl</strong> - Files containing tracer particle information for particles associated with plumes and ridges. </li> <li><strong>plumes_ridges.py</strong> - Python script containing example code for reading and plotting plumes.pkl and ridges.pkl files. </li> <li><strong>ptcls_rdgs_plms.py, interrogate_particles.py</strong> - Python script and module containing required functions for carrying out post processing routine generating the plumes.pkl and ridges.pkl files. Requires <a title="terratools" href="https://github.com/mantle-convection-constrained/terratools" target="_blank" rel="noopener">terratools</a>.</li> <li><strong>hst.dat </strong>- Time series of key simulation properties including mantle temperature profile used for calcualting CMB heat flux. </li> <li><strong>terra, interra</strong> - TERRA executable and input parameter file.</li> <li><strong>pyflowng.zip</strong> - Compressed directory containing version of the `pyflowng` code used in this work.</li> <li><strong>mode_splitting_methods.zip</strong> - Compressed directory contianing synthetic splitting function predictions and maps.</li> </ul> <p> </p> <h3><strong>Dump Numbers</strong></h3> <p>Below is a table of dump numbers (final three digits of file names) and the corresponding model times.</p> <table> <tbody> <tr> <td><strong>Dump number </strong></td> <td><strong>Model time (Ma)</strong></td> </tr> <tr> <td>037</td> <td>0 (present day)</td> </tr> <tr> <td>027</td> <td>10</td> </tr> <tr> <td>026</td> <td>20</td> </tr> <tr> <td>025</td> <td>30</td> </tr> <tr> <td>024</td> <td>40</td> </tr> <tr> <td>023</td> <td>50</td> </tr> <tr> <td>022</td> <td>60</td> </tr> <tr> <td>021</td> <td>70 </td> </tr> <tr> <td>020</td> <td>80</td> </tr> <tr> <td>019</td> <td>90</td> </tr> <tr> <td>018</td> <td>100</td> </tr> </tbody> </table>
Earth System Model-based Life Cycle Assessment of Ocean Alkalinity Enhancement
<ol> <li>“Fig2data.xlsx”, “Fig5data.xlsx” and “Fig4data.xlsx” are the original data to create Fig.2, Fig.3 and Fig.5. The data are calculated from UVic results.</li> <li>“Fig4.txt” is the code to create Fig.4 by Pyferret from UVic results.</li> <li>“X.f” are the update codes in our UVic model.</li> </ol>
DEVELOPMENT OF AN INNOVATIVE PROCESS INVOLVING THE USE IONIC LIQUIDS FOR THE RECOVERY AND PURIFICATION OF RARE EARTH FROM PERMANENT MAGNETS AND NiMH BATTERIES
<p>Experimental data</p>
A "Dirty" Footprint: Soil macrofauna biodiversity and fertility in Amazonian Dark Earths and adjacent soils
<p>Amazonian rainforests once thought to hold an innate pristine wilderness, are increasingly known to have been densely inhabited by populations showing a diverse and complex cultural background prior to European arrival. To what extent these societies impacted their landscape is unclear. Amazonian Dark Earths (ADEs) are fertile soils found throughout the Amazon Basin, created by pre-Columbian societies as a result of more sedentary habits. Much is known of the chemistry of these soils, yet their zoology has been neglected. Hence, we characterised soil macroinvertebrate communities and activity in these soils at nine archaeological sites and adjacent reference soils in three Amazonian regions, totaling eighteen sampling sites. Furthermore, we characterized various soil chemical and physical properties associated with soil fertility.</p> <p>The current dataset contains data on soil macroinvertebrate biodiversity (26 taxa), with a special focus on termites, earthworms and ants. It also contains data on soil macromorphology, bulk density and porosity, soil carbon, nitrogen, macro and micronutrients, magnetic susceptibility and apparent electrical conductivity. The results show similar overall macrofauna morphospecies richness in ADE and adjacent soils, but distinct communities in each soil type. They also show higher soil fauna activity in ADEs when compared to adjacent reference soils, associated with greater earthworm populations. Finally, they also confirm the well-known high soil fertility in ADEs compared with adjacent soils. Land use was an important determinant of both macrofauna biodiversity and soil fertility. These findings support the idea that humans have built and sustained a contrasting high fertility ecosystem that persisted until our days, altering biodiversity distribution patterns in Amazonia.</p>
A comprehensive approach to detect hybridization sheds light on the evolution of Earth's largest lizards
<p>Hybridization between species occurs more frequently in vertebrates than traditionally thought, but distinguishing ancient hybridization from other phenomena that generate similar evolutionary patterns remains challenging. Here, we used a comprehensive workflow to discover evidence of ancient hybridization between the Komodo dragon (<em>Varanus komodoensis</em>) from Indonesia and a common ancestor of an Australian group of monitor lizards known colloquially as sand monitors. Our data comprise >300 nuclear loci, mitochondrial genomes, phenotypic data, fossil and contemporary records, and past/present climatic data. We show that the four sand monitor species share more nuclear alleles with <em>V</em>. <em>komodoensis</em> than expected given a bifurcating phylogeny, likely as a result of hybridization between the latter species and a common ancestor of sand monitors. Sand monitors display phenotypes that are intermediate between their closest relatives and <em>V</em>. <em>komodoensis</em>. Biogeographic analyses suggest that <em>V</em>. <em>komodoensis</em> and ancestral sand monitors co-occurred in northern Australia. In agreement with the fossil record, this provides further evidence that the Komodo dragon once inhabited the Australian continent. Our study shows how different sources of evidence can be used to thoroughly characterize evolutionary histories that deviate from a treelike pattern, that hybridization can have long-lasting effects on phenotypes, and that detecting hybridization can improve our understanding of evolutionary and biogeographic patterns.</p>
Stress Drop Catalog for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence" - Kemna et al. 2021 JGR - Solid Earth
<p>Catalog with stress drop estimates for "Spatio-temporal evolution of earthquake static stress drop values in the 2016-2017 Central Italy seismic sequence"</p> <p>Kemna et al., 2021, JGR: Solid-Earth, https://doi.org/10.1029/2021JB022566.</p> <p>Description of columns:</p> <p><strong>Earthquake information</strong></p> <ul> <li>ID - INGV Earthquake ID</li> <li>Latitude - Latitude in Degrees</li> <li>Longitude - Longitude in Degrees</li> <li>Depth - Depth in km</li> <li>Magnitude_INGV - Magnitude reported by INGV</li> <li>Origin_UTC - UTC Origin Time in ISO Format</li> <li>Catalog - Catalog source of specific event. See section 2.1 for details</li> <li>Profile_distance_norcia - Distance of earthquake from Norcia Mainshock location projected onto a NW-SE trending line</li> <li>Dayafter_20160101 - Day after start of catalog in float</li> </ul> <p><strong>Single spectra fitting estimates</strong></p> <ul> <li>mw_s_mean - Moment Magnitude averaged over station estimates</li> <li>mw_s_err - 95% error (from delete-one jackknife-mean)</li> <li>m0_s_mean - Seismic Moment in Nm averaged over station estimates</li> <li>m0_s_err - 95 % error(from delete-one jackknife-mean)</li> <li>fc_s_sssa_mean - Corner frequency estimate averaged over station estimates</li> <li>fc_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_sssa_mean - Stress drop estimate averaged over station estimates</li> <li>strdrop_s_sssa_err - 95 % error(from delete-one jackknife-mean)</li> <li>sample_size_s_sssa - Number of stations with an estimate</li> <li>azimuthal_gap_s_sssa - Maximum azimuthal gap</li> <li>alpha_vel - P-wave velocity in m/s at Hypocenter</li> <li>beta_vel - S-wave velocity in m/s at Hypocenter</li> </ul> <p><strong>Cluster-event method estimates</strong></p> <ul> <li>fc_s_cema_mean - Corner frequency estimated averaged over clusters</li> <li>fc_s_cema_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_cema_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_cema_err - 95 % error</li> </ul> <p><strong>Spectral Ratio fitting estimates</strong></p> <ul> <li>fc1_s_rsta_mean - Target event corner frequency estimate using automatic source spectra fitting averaged over eGfs</li> <li>fc1_s_rsta_err - 95 % error(from delete-one jackknife-mean)</li> <li>strdrop_s_rsta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>strdrop_s_rsta_err - 95 % error</li> <li>egf_number_rsta_s - Number of eGfs for each target event</li> <li>fc1_s_rrta_mean - Target event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc1_strdrop_s_rrta_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> <li>fc2_s_rrea_mean - eGf event corner frequency estimate using semi-automatic spectral ratiofitting averaged over eGfs</li> <li>fc2_strdrop_s_rrea_mean - Stress drop estimate using Magnitude estimate from single spectra fitting</li> </ul> <p><strong>Magnitude-normalized stress drop</strong></p> <ul> <li>prio_strdrop_s - Which type of estimate is used</li> <li>magbin_s - Magnitude bin to which event is associated</li> <li>prio_strdrop_s_magbinmean - Stress drop mean for specific magnitude bin</li> <li>prio_strdrop_s_magbinstderr - 95 % error(from delete-one jackknife-mean)</li> <li>prio_strdrop_s_magnitude-normalized - Magnitude-normalized stress drop estimate</li> </ul>
An Assessment of Nonhydrostatic and Hydrostatic Dynamical Cores at Seasonal Time Scales in the Energy Exascale Earth System Model (E3SMv1)
<p>This is the companion data for the manuscript of the same title, submitted to Journal of Advances in Modeling Earth Systems on 09/03/2021. </p> <p><strong>summer: </strong>this folder contains part of the model outputs I ran on NERSC Cori in 2020-2021 corresponding to the summer simulations in the manuscript.</p> <p><strong>winter: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2020-2021 corresponding to the winter simulations in the manuscript.</p> <p><strong>ne256: </strong>this folder contains part of the outputs I ran on NERSC Cori in 2021 corresponding to the ne256 simulations in the manuscript.</p> <p><strong>bubble</strong>: this folder includes the namelists used in the rising bubble experiments.</p> <p><strong>script: </strong>this folder includes an example of a script to generate the realistic SCREAM simulation </p> <p> </p> <p>Unfortunately, model outputs are too large (~ 30 TB). Therefore, I only provide mean data, used directly to generate figures, in this repository. All model output are archived on tape at NERSC. </p> <p>For more details, refer to the manuscript, or contact me (wrliu@ucdavis.edu). </p>
Mapping the irrecoverable carbon in Earth's ecosystems
<p>These datasets provide global maps of carbon density (aboveground, belowground biomass carbon and soil organic carbon stocks) for the year 2010 and 2018 at ~300-m spatial resolution in Mg ha-1 (Coordinate System: WGS 1984, float format). Input maps were collected from published literature, and where necessary, updated to cover the focal time period. These updates were applied to the manageable carbon, vulnerable carbon and irrecoverable carbon maps. Manageable carbon is carbon in terrestrial and coastal ecosystems that could experience an anthropogenic land-use conversion event . Vulnerable carbon is the carbon that would be that would be released in a typical land-use conversion. Irrecoverable carbon is the carbon that, if lost, would not recover by mid-century. Datasets are disaggregated for carbon density in biomass or soils. To view these datasets, go to: <a href="https://irrecoverable.resilienceatlas.org/map">https://irrecoverable.resilienceatlas.org/map</a>.</p>
Clay minerals control rare earth elements (REE) fractionation in Brazilian mangrove soils
<p>XRD data from different size fractions of Brazilian mangrove soils, supporting the manuscript entitled <strong><em>Clay minerals control rare earth elements (REE) fractionation in Brazilian mangrove soils, </em></strong>submitted to the journal <strong><em>Catena</em>.</strong></p>
Dataset for the Speciation and Coordination of a Deep Earth Carbonate-Silicate-Metal Melt
<p>This upload includes speciation files for the paper The Speciation and Coordination of a Deep Earth Carbonate-Silicate-Metal Melt. </p>
Demonstration that the special relativity theory is wrong and that the Earth (as well as any other mass) is surrounded b y a media.
<p>This is a demonstration that the inertial reference frames of the special theory of relativity are wrong when applied to the associated wave of a particle. It follows that the Earth must be surrounded by a media, which is the reference for all velocities on the surface of the Earth. The same media is of course surrounding all masses.</p>
Core-mantle partitioning and the bulk Earth abundances of hydrogen and carbon: implications for their origins
Open the record for dataset details and reuse information.
Dataset: Powering Earth's ancient dynamo with silicon 2 precipitation
<p>Collated experimental dataset used in this study. Study, temperature, pressure and normalised molar concentrations of Fe, Si, O, C, S, Mg, FeO, SiO2 and MgO in metal and silicate from partitioning experiments.</p>
The giant clam photosymbiosis is a physically optimized photoconversion system for the most intense sunlight on Earth
<p>Giant clams are photosymbiotic with unicellular algae ("zooxanthellae") organized in the clam's mantle tissue. This tissue has an especially low albedo for a photosynthetic system, generally less than 10\% at all visible wavelengths. This efficient absorbance of light occurs in the ecological context of the high solar irradiances in intertidal habitats near the equator. At these light levels, photosynthetic systems typically adapt to absorb less light in order to prevent radiative damage to chloroplasts. Giant clams are therefore unusual. If the giant clam photosymbiosis proves to be simultaneously efficient at absorption and at phototransduction at these irradiances, they are potentially remarkably productive and an important source of bioinspiration. We showed previously that the clams organize algae into vertical pillars in the mantle tissue. The clams' iridocytes, or optically structured skin cells on the surface of the tissue, then function to evenly distribute incoming solar irradiance along the vertical faces of the pillars. The result is that zooxanthellae in the system absorb solar power at lower rates than that of incoming solar flux. The overall energetic performance of this phtooconversion scheme has, however, been difficult to characterize given the complex three-dimensional structure and the fact that it is coupled to a much more voluminous, respiring animal. Here we use a combination of photochemical characterization and new quantitative modeling of data from the literature to estimate the photochemical efficiency as a function of incoming irradiance of the initial electron-transfer events. Our approach is to consider the clam mantle tissue in isolation as a meta-material for photoconversion. To do this, we developed a method to directly measure the system's photochemical efficiency with spatial resolution of 10's of microns using optical microprobes threaded through the tissue. These experimental efficiency data then serve as ground-truthing for a subsequent reanalysis of photosynthesis-irradiance curves of clams taken from the literature. For this quantitative re-analysis, we incorporated the clam system's quantum efficiency as a function of irradiance per cell into a Monte Carlo model of radiative transfer among cells to find the tissue's area-specific oxygen evolution apart from any sinks. We found that cells located within the dense clam system had fluorescence transients (i.e., Kautsky curves), a direct measure of the efficiency of PS II) that were very slow and of low intensity, particularly for a dense system, consistent with photochemical efficiencies generally greater than 50\% and often greater than 90\%. When incorporated into a larger computational model, we found that mature Tridacnid clams can efficiently perform photoconversion of light energy into chemical energy at light intensities many times more intense than the maximum time-averaged environmental radiance, or even the solar constant. The intensities to which the clam is adapted, however, can be found in strong wave-lensed pulses of irradiance that are characteristic of the clams' habitats. This surprising result makes sense if the system has evolved to both avoid damage from and utilize the power in the intense pulses of light that result from wave-lensing. Our model predicts that by evolving to compensate for the intense pulses of solar energy produced by wave-lensing, the clam system can perform photochemical conversion of radiation at intensities many times greater than the solar constant at around 90\% quantum efficiency. This result in turn suggests a strategy for organic, engineered materials performing photoconversion under solar concentration.</p>
Shortwave radiation budget under Arctic Sea ice in Earth System models
<p>Contains data and scripts of the study " Improving the representation of shortwave radiation budget under Arctic Sea ice in Earth System models using observations", submitted to Journal of Geophysical Research - Oceans. </p>
Waveform data for centroid moment tensor solutions presented in publication "Bayesian seismic source inversion with a 3-D Earth model of the Japanese islands"
<p>This dataset contains waveform data for centroid moment tensor solutions inferred using Hamiltonian Monte Carlo sampling algorithm and a 3-D Earth model of the Japanese islands. Specifically, it includes processed observed waveforms from the Full Range Seismograph Network of Japan (F-Net, http://www.fnet.bosai.go.jp) and synthetic waveforms for the maximum-likelihood solutions as well as Global Centroid Moment Tensor (GCMT) solutions for all study events inverted at different periods. Detailed description of the dataset is included in the README file. </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.