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ROV cm-scale seafloor surveys at 1840 m depth in Monterey Canyon
<p>This dataset contains 5-cm lateral resolution multibeam sonar bathymetry and 1-cm lateral resolution lidar bathymetry from ~1840 m water depth in Monterey Canyon, offshore Moss Landing, California. The data were acquired by a remotely operated vehicle (ROV) aboard the R/V Western Flyer operated by the Monterey Bay Aquarium Research Institute (MBARI). The multibeam sonar and lidar data were collected using a Reson SeaBat 7125 400-kHz multibeam sonar and a 3D at Depth SL1 subsea lidar, respectively.</p> <p>These surveys were all conducted between November 2015 and March 2017 as one component of the Coordinated Canyon Experiment (CCE) led by MBARI geologist Charlie Paull, in collaboration with researchers from the United States Geological Survey, the Ocean University of China, the National Oceanography Centre in Southampton, England, and the University of Hull, England.</p> <p>For a detailed description of the methodology used to collect, process, and grid these data, please refer to the associated journal article: <em><strong><This info will be updated once the paper is published></strong></em></p> <p>The primary results from the CCE were described in:</p> <p>Paull, C.K., Talling, P.J., Maier, K.L., Parsons, D., Xu, J., Caress, D.W., Gwiazda, R., Lundsten, E.M., Anderson, K., Barry, J.P., Chaffey, M., O'Reilly, T., Rosenberger, K.J., Gales, J.A., Kieft, B., McGann, M., Simmons, S.M., McCann, M., Sumner, E.J., Clare, M.A., Cartigny, J., (2018). Powerful turbidity currents driven by dense basal layers. Nature Communications, 9: 1-9. <a href="https://doi.org/10.1038/s41467-018-06254-6">https://doi.org/10.1038/s41467-018-06254-6</a></p> <p>A general description of the CCE can be found in an article from the 2017 MBARI annual report: <a href="https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment">https://annualreport.mbari.org/2017/story/coordinated-canyon-experiment</a></p> <p><br> This dataset contains the following gridded bathymetry data in both GeoTIFF and ESRI ASCII grid format (.asc) in WG84 Geographic (EPSG: 4326):<br> <br> 5-cm lateral resolution multibeam sonar bathymetry grids:</p> <ol> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_November2015 <ul> <li>Multibeam bathymetry data collected between November 18 - 20, 2015</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_May2016 <ul> <li>Multibeam bathymetry data collected between May 10 - 12, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_October2016 <ul> <li>Multibeam bathymetry data collected between October 26 - 28, 2016</li> </ul> </li> <li>CCE_LASS_5cm_multibeam_MontereyCanyon_March2017 <ul> <li>Multibeam bathymetry data collected between March 7 - 12th, 2016</li> </ul> </li> </ol> <p>1-cm lateral resolution lidar bathymetry grids:</p> <ol> <li>CCE_LASS_1cm_lidar_MontereyCanyon_May2016 <ul> <li>a portion of the survey area mapped by lidar between May 10 - 12, 2016</li> </ul> </li> </ol>
Global input datasets for use in constraints on global seafloor biogenic methane production from deterministic and machine learning modeling
<p>This dataset includes 9 grids used as model input for manuscript "Constraints on global seafloor biogenic methane production from deterministic and machine learning modeling". Additionally, there are four grids (heat flow, total organic carbon, porosity, and crust age) for which variable uncertainty was given.</p> <p>Grids here are available in xyz (longitude in decimal degrees, latitude in decimal degrees, and variable) ascii file format. Each reference is below is the grids native reference. For more information on the creation of these grids please visit the main manuscript.</p> <p>Below are respective file names and variable name/units:</p> <p>Dataset 1: Elevation in Meters (+ indicates above sea level, - below sea level)</p> <p>Tozer, B., Sandwell, D. T., Smith, W. H. F., Olson, C., Beale, J. R., & Wessel, P. (2019). Global bathymetry and topography at 15 arc sec: SRTM15+. <em>Earth and Space Science</em>, 6. https://doi.org/10.1029/ 2019EA000658</p> <p>Dataset 2: Seawater Density in Kilograms per Cubic Meter</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 3: Seawater Temperature in Degrees Celcius </p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 4: Seawater Salinity in Percent Salinity Units</p> <p>Boyer, T. P., Antonov, J. I., Baranova, O. K., Garcia, H. E., Johnson, D. R., Mishonov, A. V., … Grodsky, A. (2013). World Ocean Database 2013. In S. Levitus, A. Mishonov (Ed.), Technical Ed.; <em>NOAA Atlas NESDIS</em> 72 (pp. 209).</p> <p>Dataset 5: Heat Flow in Milliwatts per Square Meter</p> <p>Global Heat Flow Compilation Group (2013). Component parts of the World Heat Flow Data Collection. <em>PANGAEA</em>, https://doi.org/10.1594/PANGAEA.810104</p> <p>Hornbach, M. J., Harris, R. N. & Phrampus, B. J. (2020). Heat flow on the U.S. Beaufort Margin, Arctic Ocean: Implications for ocean warming, methane hydrate stability, and regional tectonics. <em>Geochemistry, Geophysics, Geosystems</em>, 21(5). e2020GC008933. https://doi.org/10.1029/2020GC008933</p> <p>Dataset 6: Sediment Thickness in Meters</p> <p>Straume, E. O., Gaina, C., Medvedev, S., Hochmuth, K., Gohl, K., Whittaker, J. M., … Hopper, J. R. (2019). GlobSed: updated total sediment thickness in the world’s oceans. <em>Geochemistry, Geophysics, Geosystems</em>, 20(4), 1756–1772.</p> <p>Dataset 7: Seafloor Porosity in Fraction</p> <p>Martin, K. M., Wood, W. T., & Becker, J. J. (2015). A global prediction of seafloor sediment porosity using machine learning. <em>Geophysical Research Letters</em>, 42(24), 2015GL065279. https://doi.org/10.1002/2015GL065279</p> <p>Dataset 8: Seafloor Total Organic Carbon in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 9: Crust Age in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p> <p>Dataset 10: Seafloor Porosity Uncertainty in Fraction</p> <p>Dataset 11: Seafloor Total Organic Carbon Uncertainty in Percent Dry Weight</p> <p>Lee, T.R., Wood, W.T., & Phrampus, B.J. (2019). A machine learning (kNN) approach to predicting global seafloor total organic carbon. <em>Global Biogeochemical Cycles</em>. 33, 37–46, doi:10.1029/2018GB005992.</p> <p>Dataset 12: Heat Flow Uncertainty in Milliwatts per Square Meter</p> <p>Dataset 13: Crust Age Uncertainty in Million Years</p> <p>Müller, R. D., Sdrolias, M., Gaina, C., & Roest, W. R. (2008). Age, spreading rates, and spreading asymmetry of the world’s ocean crust. <em>Geochemistry, Geophysics, Geosystems</em>, 9, Q04006. https://doi.org/10.1029/2007GC001743</p>
Subducting seafloor anomalies promote porphyry copper formation
<p>Source data used to interrogate relationships between subducting seafloor anomalies and porphyry copper deposits presented in:</p> <p>Mather, B., Müller, R.D., Alfonso, C.P., Wright, N.M., Seton, M. (In prep.) Subducting seafloor anomalies promote porphyry copper formation.</p> <p>Python notebooks and scripts can be found in the <a href="https://github.com/brmather/SeafloorAnomalies">GitHub repository</a>.</p>
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C). in Species of warm-water origin Laternula elliptica (King, 1832) (Mollusca: Bivalvia: Laternulidae), a widespread mollusk in recent Antarctica
Рис. 3. Фотографии Laternula elliptica, сделанные около cтанции «Прогресс», ВосточнаЯ Антарктида. L. elliptica на морском дне с медкими камнЯми или гравием, глубина 27 м (А); несколько сифональных отверстий L. elliptica над поверхностью мЯгких осадков вокруг голотурии Staurocucumis turqueti, глубина 27 м (В); раковина L. elliptica (длина около 110 мм) на снегу около майны сраЗу после иЗвлечениЯ иЗ воды (С); пустые раковины L. elliptica на морском дне, глубина 56 м (D); раковина L. elliptica (вид с дорсального краЯ) на мЯгких осадках с камнЯми, покрытыми иЗвестковыми водорослЯми, глубина 30 м (Е); пара сифональных отверстий L. elliptica на поверхности мЯгких осадков, глубина 27 м (F). Фотографии О. Савинкина (A, B, D–F) и В. Потина (С). Fig. 3. Photographs of Laternula elliptica taken near «Progress» Research Station (East Antarctica). Softshelled clam L. elliptica on sea bottom with small stowns or gravel, depth 27 m (A); several open siphons of L. elliptica above soft bottom sediments around holothurian Staurocucumis turqueti, depth 27 m (B); a shell of L. elliptica (length about 110 mm) on snow near a dive hole just after dragging out of water (C); empty shells of L. elliptica on seafloor, depth 56 m (D); a shell of Laternula elliptica (dorsal view) on soft deposits among stones, covering by Lithothamnion, depth 30 m (E); pair of siphonal opening of L. elliptica on surface of soft sediments, depth 27 m (F). Photographs are taken by O. Savinkin (A, B, D–F) and V. Potin (C).
Dataset for the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings"
<p>The results of the analysis in the paper "Accelerating Seafloor Uplift of Submarine Caldera near Sofugan Volcano, Japan, Resolved by Distant Tsunami Recordings" published in Geophysical Research Letters are available here. For the details of the file, please see Readme.pdf.</p>
SDUST2023BCO: a global seafloor model determined from multi-layer perceptron neural network using multi-source differential marine geodetic data
<div> <p>SDUST2023BCO.nc is the global marine bathymetric model covering 80°S~80°N and 0°~360°E on 1′×1′ grids. The dataset contains geospatial information (latitude, longitude), SDUST2023BCO bathymetric model and an attachment data.</p> </div>
Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism
<h2>Abstract</h2> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Estimated reflectance hyperspectral libraries for Vigo sediment sample, seafloor sand samples and marine organism</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Estimated reflectance hyperspectral libraries created for sand samples from seafloor at Vigo sea zone 1, 2 and 3, sediment samples from Vigo fieldwork Sept. 2023 and some Vigo marine organisms such as sea cucumber, sea pens, sea stars, coral, seaweed.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance hyperspectral signature, library, sediment, sand, sea cucumber, sea star, coral, seaweed</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>25.05.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>CSV</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>0.25m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS, info@ecotone.com</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
Seismic Model of the Seafloor Sediment and Shallow Oceanic Crust of the Alaska-Aleutian Subduction Zone at the Alaska Peninsula
<p>This dataset is supplementary to</p> <blockquote> <p>Zheng, Mengjie, Sheehan, Anne, Liu, Chuanming, Wu, Mengyu, & Ritzwoller, Michael. (2024). Characterizing Sub-Seafloor Seismic Structure of the Alaska Peninsula Along the Alaska-Aleutian Subduction Zone. <em>Journal of Geophysical Research: Solid Earth</em>, <em>129</em>(11), e2024JB029862. <a href="https://doi.org/10.1029/2024JB029862">https://doi.org/10.1029/2024JB029862</a></p> </blockquote> <p>This dataset contains files of sub-seafloor S-wave velocities and sediment properties.</p>
Data from: How long does a brachiopod shell last on a seafloor? Modern mid-bathyal environments as taphonomic analogues of continental shelves prior to the Mesozoic Marine Revolution
<p class="MsoNormal">Carbonate skeletal remains are altered and disintegrate at yearly to decadal scales in present-day shallow-marine environments with intense bioerosion and dissolution. Present-day brachiopod death assemblages are invariably characterized by poor preservation on continental shelves, and abundant articulated shells of brachiopods with well-preserved brachidia are thus not expected to be preserved if not rapidly buried. However, such preservation is paradoxically observed in shallow-water Paleozoic and Mesozoic brachiopod assemblages. Here, we show that a bathyal death assemblage time-averaged to several millennia (Adriatic Sea) consists of sediment-filled articulated shells of <em>Gryphus</em> <em>vitreus</em> with complete brachidia. Postmortem age distributions indicate that disintegration half-lives exceed several centuries (~500-1,700 years). The high frequency of articulated but centuries-old shells (>50%) and the fitting of taphonomic models to postmortem ages indicate that disarticulation half-life is unusually long (~200 years). Rapid sediment filling of shells (1) inhibited disarticulation, loop fragmentation and colonization by coelobites and (2) induced precipitation of ferromanganese oxides at redox fronts within shells. Sediment-filled articulated shells, however, still resided at the sediment-water interface as indicated by encrusters and sponges that infested them after death. Sediment-filled shells disintegrated through bioerosion and wear when residence time in the taphonomically active zone exceeded ~2,000 years. We suggest that the articulation paradox is driven by the Mesozoic Marine Revolution (MMR) that escalated predation, bioturbation and organic matter recycling, all intensifying shell disintegration. A scenario with slow disarticulation in bathyal environments can be an analogue of conditions leading to preservation of articulated shells in shallow-water assemblages prior to the MMR.</p>
Evaluating a poroelastic model via pore pressure signals in seafloor sediments [data set]
<p>The csv files consist of pressure data collected off the coast of Camp Pendleton between 10 February 2021 and 25 February 2021, and include both pore pressure data from two instrumented surrogates and pressure data from a Nortek Signature. Timestamps are in posix time; pressure is in kPa. The time series for Surrogate A are prefixed "surrA"; those for Surrogate B are prefixed "surrB". The Nortek Signature time series is prefixed "Sig1000".</p>
Seafloor and volcanic seismic horizons from NZ3D
<p>This file (horizons.csv) contains picked seismic horizons for the seafloor and top of volcanic basement in the NZ3D volume within 15 km of the deformation front. The columns are formatted as crossline, inline, seafloor depth (m), top of volcanic basement (m).</p> <p>Raw and processed NZ3D seismic data can be downloaded here: https://www.marine-geo.org/tools/entry/MGL1801.</p>
Rock magnetic data sets for Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism
<p>The rock magnetic data sets in "Coupled detachment faulting and hydrothermal circulation at 49.7°E Southwest Indian Ridge revealed by seafloor magnetism" was studied, including the density, magnetic susceptibility, NRM, Q ratio and other parameters of rock samples , as well as the thermomagnetic curves, hysteresis loops, FORCs, AF and TD demagnetization.</p>
Data from: How long does a brachiopod shell last on a seafloor? Modern mid-bathyal environments as taphonomic analogues of continental shelves prior to the Mesozoic Marine Revolution
Open the record for dataset details and reuse information.
Data from: Marine heatwaves amplify benthic community metabolism and solute flux in a seafloor heating experiment
Open the record for dataset details and reuse information.
Seafloor Density Measurements, Prediction, and Associated Uncertainty for "Predicting global marine sediment density using the random forest regressor machine learning algorithm"
<p>Global seafloor density prediction results using the random forest regressor machine learning algorithm. </p> <p>Dataset S1. Seafloor density measurements. Columns are labeled with a header and include associated drilling project and measurement type for each sample. File format: CSV text file</p> <p>Dataset S2. Seafloor density prediction results from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p> <p>Dataset S3. Seafloor density prediction standard deviation from the random forest regressor machine learning algorithm at 5×5-arc minute resolution. Units are g/cm^3. File format: netCDF (.nc)</p>
Predator-prey overlap in three dimensions: cod benefit from capelin coming near the seafloor
<p>Spatial overlap between predator and prey is a prerequisite for predation, but the degree of overlap is not necessarily proportional to prey consumption. This is because many of the behavioural processes that precede ingestion are non-linear and depend on local prey densities. In aquatic environments, predators and prey distribute not only across a surface, but also vertically in the water column, adding another dimension to the interaction. Integrating and simplifying behavioural processes across space and time can lead to systematic biases in our inference about interaction strength. To recognise situations when this may occur, we must first understand processes underlying variation in prey consumption by individuals. Here we analysed the diet of a major predator in the Barents Sea, the Atlantic cod (Gadus morhua), aiming to understand drivers of variation in cod's feeding on its main prey capelin (Mallotus villosus). Cod and capelin only partly share habitats, as cod mainly reside near the seafloor and capelin inhabit the free water masses. We used data on stomach contents from ~2000 cod individuals and their surrounding environment collected over 12 years, testing hypotheses on biological and physical drivers of variation in cod's consumption of capelin, using Generalized Additive Models. Specifically, effects of capelin abundance, capelin depth distribution, bottom depth, and cod abundance on capelin consumption were evaluated at a resolution scale of 2 km. We found no indication of food competition as cod abundance had no effect on capelin consumption. Capelin abundance had small effects on consumption, while capelin depth distribution was important. Cod fed more intensively on capelin when capelin came close to the seafloor, especially at shallow banks and bank edges. Spatial overlap as an indicator for interaction strength needs to be evaluated in three dimensions instead of the conventional two when species are partly separated in the water column.</p>
Seafloor pressure data from 2019 deployment at the Hikurangi subduction zone, New Zealand
<p>We include here hourly seafloor pressure time series (and locations) from a 2019 deployment at the Hikurangi subduction zone, used in "Using seafloor geodesy to detect vertical deformation at the Hikurangi subduction zone: insights from self-calibrating pressure sensors and ocean general circulation models", a paper submitted to JGR: Solid Earth in January 2022.</p> <p><strong>APG_hikurangi_2019.json/.mat: </strong>Seafloor pressure time series data (JSON and MATLAB format) from a deployment at the Hikurangi subduction zone in 2019. The files contain the hourly time series in datetime (UTC) and pressure in hectopascals, with the convention that a decrease in pressure is equivalent to a reduction in the height of the water column (seafloor uplift).</p> <p>The only processing that has been applied to the data is filtering using a 2-day corner lowpass filter for all sites, and the A-0-A correction for the POBS sensors (which are therefore drift corrected). All APGs not equipped with A-0-A still contain sensor drift. Each time series has been adjusted using the mean of the absolute data, which is why the time series for the sites plot about zero - amplitude has been preserved.</p> <p><strong>locations_APG_hikurangi_2019.csv: </strong>Locations of the seafloor pressure sites from a deployment at the Hikurangi subduction zone in 2019. Indicated for each site are the sensor’s institute (UTIG - University of Texas Institute for Geophysics, Austin, USA; GNS Science - GNS Science, New Zealand; LDEO - Lamont-Doherty Earth Observatory, Columbia University, USA; KU - Kyoto University and Tohoku University, Japan), A-0-A drift correction capability, deployment longitude, latitude, and depth, and whether there are usable data. The sensors without usable data either contained data logger issues or were not recovered, and are not included in APG_hikurangi_2019.json/.mat.</p>
Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan by a new analysis method for distributed acoustic sensing data using a seafloor cable and seismic interferometry"
<p>Cross-spectra used in "Detailed S-wave velocity structure of sediment and crust off Sanriku, Japan, derived from distributed acoustic sensing data collected using a seafloor cable with seismic interferometry", by Shun Fukushima, Masanao Shinohara, Kiwamu Nishida, Akiko Takeo, Tomoaki Yamada, and Kiyoshi Yomogida </p> <p>For more information, please contact Shun Fukushima (s-fuku@eri.u-tokyo.ac.jp)</p>
Extensive Early Marine Seafloor Cementation in a Modern Epeiric Sea Induced by Seawater Properties and a Shallow Redox Boundary below the Seafloor
<p>supplement information for Extensive Early Marine Seafloor Cementation in a Modern Epeiric Sea Induced by Seawater Properties and a Shallow Redox Boundary below the Seafloor</p>
Interferometric array data for UK-Canada seafloor cable
<p>Dataset for interferometric data showing in Marra et al, "Optical interferometry–based array of seafloor environmental sensors using a transoceanic submarine cable".<br> Data in .MAT file format. Sampling rate and start time showing in the filename. Data is frequency deviation (Hz).</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)
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.