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2,155 results for “Ridging”
Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Figure 12. - APlacotrochidescylindrica, holotype, Museum of Tropical Queensland G55627, off Queensland BPlacotrochidesfrustum, holotype, USNM 36451, Lesser Antilles; paratype, NMC, Hudson 4B, Lesser Antilles C Placotrochuslaevis, USNM 81994, Great Barrier Reef, Australia D Falcatoflabellumraoulensis, upper image, holotype, Museum of New Zealand, CO 258, Kermadec Ridge; lower images, paratype, USNM 94313, Kermadec Ridge. Scale bars: 1mm (A); 2 mm (B); 10 mm (C), except for basal scar, which is 5 mm; 1 mm (D), except latera view, which is 5 mm.
Figure 11. - ATruncatoflabellumarcuatum, lateral and calicular views, holotype, NZOI H633, Norfolk Ridge; edge view, paratype, USNM 94280, Norfolk Ridge B Blastotrochusnutrix, USNM 97553, Siboga, Indonesia C Placotrochidesscaphula, USNM 94273, NZOI G941, New Zealand D Placotrochidesminuta, holotype, Australian Museum G16747, Flores Sea. Scale bars: 10 mm (A–C), except for calice of A and basal scar of B, which are 5 mm; 1 mm (D).
Figure 11. - ATruncatoflabellumarcuatum, lateral and calicular views, holotype, NZOI H633, Norfolk Ridge; edge view, paratype, USNM 94280, Norfolk Ridge B Blastotrochusnutrix, USNM 97553, Siboga, Indonesia C Placotrochidesscaphula, USNM 94273, NZOI G941, New Zealand D Placotrochidesminuta, holotype, Australian Museum G16747, Flores Sea. Scale bars: 10 mm (A–C), except for calice of A and basal scar of B, which are 5 mm; 1 mm (D).
Volatile (C, N, Ar) variability in MORB and the respective roles of mantle source heterogeneity and degassing: the case of the Southwest Indian Ridge
<p>Location, isotopic compositions of δ13C and δ18O of CO2, δ15N of N2 and C, N and Ar abundances in vesicles of SWIR basaltic glasses</p>
Dataset to "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds"
<p>The dataset is connected to the paper "Fine-sediment erosion and ridge morphodynamics in coarse-grained immobile beds".</p> <p>The dataset is subdivided in "topographic" and "PIV" data. Each dataset refers to an experiment presented in the paper.</p> <p>The filenames of the topographic data specify the location where the data were collected ("upstream" or "downstream"). The topographic data contain the bed elevations measured around each sphere over a measurement area as wide as the channel and covering nine patterns of spheres (see paper for a definition of the pattern of spheres). Each topographic dataset contains four structures:</p> <p>- A time vector "T" identifying the sampling times at which the bed topography was measured (unit: h)</p> <p>- A space vector "x" identifying the streamwise position of the spheres with the origin of the coordinate system positioned at the beginning of the measurement areas (upstream or downstream) - unit cm</p> <p>- A space vector "y" identifying the transverse position of the spheres with the coordinate system centred in the middle of the channel and positively oriented towards the left side of the flume looking in streamwise direction - unit cm</p> <p>- A matrix "Z" identifying the protrusion levels of the spheres (as defined in the paper) as a function of space and time (x times y times T) - unit cm</p> <p> </p> <p>The PIV data were acquired during experiments 1b and 5b at the upstream location. The data are a collection of the velocity fields measured above the top of the spheres and over two pattern of spheres in the middle "m" and in the quarter plane "q" of the channel for three different time instants (t1, t2, t3) during the experiments. Each dataset contains 6 variables:</p> <p>- the "sampling time" of the bursts - unit s</p> <p>- the "acquisition time" of the PIV run during the experiments - unit h</p> <p>- a vector "x" identifying the streamwise position of the velocity vectors for a coordinae system centred in the centre of the sphere and pointing in the longitudinal direction.</p> <p>- a vector "z" identifying the vertical direction of the coordinate system with the origin at the top of the sphere and pointing upwards - unit cm</p> <p>- a matrix "vx" containing 1000 fields of the streamwise velocity component - unit cm/s</p> <p>- a matrix "vz" containing 1000 fields of the vertical velocity component - unit cm/s</p>
Bulk geochemistry and in situ sulfur isotopes of hydrothermal deposits from the Lucky Strike vent field, Mid-Atlantic Ridge
<p>This is the dataset presented in the article <em>"Effects of substrate composition and subsurface fluid pathways on the geochemistry of seafloor hydrothermal deposits at the Lucky Strike Vent Field, Mid-Atlantic Ridge". </em>This includes Tables 1 and 2 found in the article as well as Tables S1 and S2 from the Supporting Information. Table 1 is the chemical composition of hydrothermal samples from the Lucky Strike vent field and Table S1 is an extended version of Table 1 that includes elements that were largely below the detection limit. Table 2 is the dataset for in situ sulfur isotope analyses of marcasite, pyrite, and chalcopyrite for samples from Lucky Strike. Table S2 is a compilation of modern seafloor hydrothermal sites that includes Lucky Strike, Menez Gwen, TAG, Snake Pit, Broken Spur, Rainbow, Logatchev, Beebe, Kairei, Yuhuang-1, and Daxi.</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>
FOCI model output used in the study by Ivanciu et al. - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change
<p>This dataset contains the model output used in the analysis presented in the study by Ivanciu et al., 2022 - On the ridging of the South Atlantic Anticyclone over South Africa: the impact of Rossby wave breaking and of climate change. Four ensembles of three simulations each were performed with the global coupled climate model FOCI (Flexible Ocean and Climate Infrastructure, Matthes et al., 2020). Details about the ensembles can be found in the above-mentioned publication. The files containing "past" in their name belong to the ensemble "PAST", the files containing "future" in their name belong to the ensemble "FUTURE", the files containing "future_GHG" in their name belong to the ensemble "GHG" and the files containing "future_Ozone" in their name belong to the ensemble "OZONE" from the publication.</p>
A highly depleted and subduction-modified mantle beneath the slow-spreading Mohns Ridge
<p><strong>Supplementary Information. </strong>A word file containing the supplementary information.</p> <p><strong>Table 1. </strong>An Excel spreadsheet containing whole-rock geochemical results.</p> <p><strong>Table 2. </strong>An Excel spreadsheet containing electron microprobe analysis of orthopyroxene, clinopyroxene, and Cr-spinel.</p> <p><strong>Table 3. </strong>An Excel spreadsheet containing trace element concentrations of orthopyroxene</p> <p><strong>Table 4. </strong>An Excel spreadsheet containing Nd isotopic compositions of orthopyroxene.</p> <p><strong>Supplementary Table 2. </strong>An Excel spreadsheet containing LA-ICP-MS secondary standard values.</p>
Pillbox Winchelsea Morlais Ridge
A World War II concrete machine gun emplacement on Morlais Ridge (at the junction with Willow Lane), Winchelsea Beach, East Sussex, England. When the pillbox was built the sea came much further inland, the trees nearby did not exist and the occupants had a clear view out over the Channel. EDOBID: e02175 https://archaeologydataservice.ac.uk/archives/view/dob/ai_full_r.cfm?refno=10732 784 photos taken in April 2022 with a Sony a7R III and processed in Reality Capture. The top could not be photographed due to the pillbox's height and surroundings. Source: Objaverse 1.0 / Sketchfab
ICESat-2 Sea Ice Surface Topography from the University of Maryland-Ridge Detection Algorithm: Coastal Alaska
<p>This dataset is derived from the ICESat-2 Global Geolocated Photon Height Product (ATL03, <a href="https://nsidc.org/data/atl03/versions/6" target="_blank" rel="noopener">Neumann et al., 2023</a>), release 006, using the University of Maryland-Ridge Detection Algorithm (UMD-RDA, <a href="https://doi.org/10.1029/2022GL100272" target="_blank" rel="noopener">Duncan & Farrell, 2022</a>). The UMD-RDA is applied to ATL03 granules on a per-shot basis, nominally resulting in elevation measurements at ICESat-2's along-track sampling of ~0.7 m.</p> <p><strong>Data Bounds (lon,lat): </strong></p> <p><em>LL corner: (-169.71, 68.32), UR corner: (-137.1, 72.18)</em></p> <p> </p> <p><strong>File Format</strong></p> <p>Coastal Alaska data are provided in .xz compressed format and decompress to ascii text. The columns are as follows:</p> <ol> <li>Longitude (degrees)</li> <li>Latitude (degrees)</li> <li>Time (seconds since 2018-01-01)</li> <li>Elevation (meters, relative to DTU18 Mean Sea Surface)</li> </ol> <p>Data are provided monthly for the period December 2021 (202112) to May 2022 (202205).</p> <p> </p> <p><strong>Users of this dataset are asked to cite the following publication:</strong></p> <p><em>Duncan, K. and Farrell, S. L. (2022). Determining Variability in Arctic Sea Ice Pressure Ridge Topography with ICESat-2. Geophys. Res. Lett., 49, e2022GL100272. https://doi.org/10.1029/2022GL100272</em></p> <p> </p> <p><em>This dataset is supported by NASA Cryosphere Grant 80NSSC20K0966</em></p> <p> </p> <p>For any questions regarding this dataset you can contact Kyle Duncan by email: kduncan at umd . edu</p>
Dataset for the NC article: Deep mantle earthquakes linked to CO2 degassing at the Mid-Atlantic Ridge
<p>The obtained earthquake catalogue, picked P- and S-arrivals, and 1-D velocity models in the Mid-Atlantic Ridge in the equatorial Atlantic ocean, using a recent temporary array of seafloor seismometers.</p> <p>Related article:<br>Yu, Z., Singh, S.C., Hamelin, C. <em>et al.</em> Deep mantle earthquakes linked to CO<sub>2</sub> degassing at the mid-Atlantic ridge. <em>Nat Commun</em> <strong>16</strong>, 563 (2025). https://doi.org/10.1038/s41467-024-55792-9</p>
FIgS. 26–29. Urodacus butleri, n. sp., paratype ♂ (WAM T85141). 26. Dextral hemispermatophore, dorsal aspect. 27–29. Detail of capsule lamellae, dorsal, ental, and ventral aspects. Abbreviations: Al, anterior lobe; C, capsule; Cp, conical process; DTR, distal transverse ridge; L, lamella; Lh, lamellar hook; Lhp, lamellar hook process; IBL, internobasal lobe; T, trunk. Scale bars = 0.5 mm.
FIgS. 26–29. Urodacus butleri, n. sp., paratype ♂ (WAM T85141). 26. Dextral hemispermatophore, dorsal aspect. 27–29. Detail of capsule lamellae, dorsal, ental, and ventral aspects. Abbreviations: Al, anterior lobe; C, capsule; Cp, conical process; DTR, distal transverse ridge; L, lamella; Lh, lamellar hook; Lhp, lamellar hook process; IBL, internobasal lobe; T, trunk. Scale bars = 0.5 mm.
Expression data of the flowering time genes in chickpea, extracted from Ridge et al. (2017). Plant Physiology 175, 802-815.
<p>This data is supplementary to the following paper: Gursky, V.V., Kozlov, K.N., Nuzhdin, S.V., and Samsonova, M.G. (2018) Dynamical Modeling of the Core Gene Network Controlling Flowering Suggests Cumulative Activation from the <em>FLOWERING LOCUS T </em>Gene Homologs in Chickpea. <em>Frontiers in Genetics</em>. 9:547. doi: 10.3389/fgene.2018.00547</p> <p>The data was obtained by digitizing Figure 5 of the following paper: Ridge, S., Deokar, A., Lee, R., Daba, K., Macknight, R. C., Weller, J. L., and Tar'an, B. (2017). The chickpea Early flowering 1 (Efl1) locus is an ortholog of arabidopsis ELF3. <em>Plant Physiology </em>175, 802-815. doi:10.1104/pp.17.00082</p> <p>The archive contains files (in csv format) with the expression data of each of the following ten genes: <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, <em>FTc</em>, <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, for the cultivars CDC Frontier and ICCV 96029 and for two growth conditions (long day, LD, and short day, SD). Each file is named according to the following scheme: <Gene name>_<Cultivar name>_<Growth conditions>.csv. Each file contains values in the following three columns (separated by commas): time (in days after sowing), relative transcription level (%ACTIN), and standard error. In the case of the genes <em>AP1</em>, <em>FD</em>, <em>TFL1a</em>, <em>TFL1c</em>, and <em>LFY</em>, the standard error was assumed equal to the size of the points in the figure when the actual error range was smaller than that size (and, thus, not visible in the figure). In the case of the genes <em>FTa1</em>, <em>FTa2</em>, <em>FTa3</em>, <em>FTb</em>, and <em>FTc</em>, the standard error was recorded as 0 for such points (the error for these genes was not used in the study).</p> <p>The data was extracted with the help of the web-based tool <em>WebPlotDigitizer</em> (https://automeris.io/WebPlotDigitizer).</p>
Biophysical models of persistent connectivity and barriers on the northern Mid-Atlantic Ridge
<p>This contains four data files that are all matlab binary files (.mat)</p> <p><strong>all_vent_sites.mat</strong></p> <p>This is a Matlab data file containing the <strong>longitude (column 1)</strong>, <strong>latitude (column 2)</strong>, of all vent sites used in the simulations. Column 3 specifies whether a vent-site is a <strong>known vent site (=1)</strong> or a <strong>ghost vent-site (=0)</strong></p> <p> </p> <p><strong>probeData_20W60W_04S45N.mat</strong></p> <p>This is a Matlab data file containing data on Argo probe cycles used to estimate average ocean currents that drive the particle tracking simulations. The variables in the file are:</p> <ul> <li><strong>fl</strong> Argo float ID </li> <li><strong>depth </strong>parking depth of the Argo float (m)</li> <li><strong>longlatStart </strong>longitude and latitude for the start of one dive cycle</li> <li><strong>longlatEnd </strong>longitude and latitude for the end of one dive cycle</li> <li><strong>month</strong> month of the dive cycle</li> <li><strong>year</strong> year of the dive cycle</li> <li><strong>timeStep</strong> number of days between the start and end of a dive cycle</li> <li><strong>distStep</strong> distance between the start and end positions of a cycle (km)</li> <li><strong>velocity</strong> average velocity of the Argo float over one dive cycle (km/day)</li> <li><strong>pos</strong> the mid-point position of the Argos float for each cycle</li> </ul> <p> </p> <p> </p> <p><strong>vent_connectivity_data.mat</strong></p> <p>This is a Matlab data file containing the connectivity data from the particle tracking simulations. The variables in this file are:</p> <ul> <li><strong>bbox_all</strong> The coordinates for the 64 target boxes</li> <li><strong>particleCount</strong> The number of larval particles starting in each of the 64 target boxes. This should be 100000 for all target boxes</li> <li><strong>connectTime</strong> A 64x64x500 array giving number of particles making a connection between two target boxes. connectTime(i,j,t) = number of particles from box i that have passed though box j in a time <= t. The 500 times correspond to the vector tVec.</li> <li><strong>leaveTime </strong>A 64x500 array giving the time taken for particles to leave their initial target box. leaveTime(i,t) = number of particles starting in box i that leave the box in a time <=t. The 500 times correspond to the vector tVec.</li> <li><strong>C_critical </strong> Critical connection probability</li> <li><strong>tVec</strong> A vector of simulation times. This should be 500 time points starting at day 1 up to day 500</li> <li><strong>tMax</strong> The maximum simulation time (days)</li> </ul> <p> </p> <p> </p> <p><strong>sim_larval_dispersal.mat</strong></p> <p>This is a Matlab data file that contains the dispersal distances of all the simulated larval particles for six planktonic larval durations. The variables in this file are:</p> <ul> <li><strong>bbox_all </strong>The coordinates for the 64 target boxes</li> <li><strong>tMax</strong> The maximum simulation time (days). This is the planktonic larval duration.</li> <li><strong>distAll </strong>The dispersal distance (km) within a given planktonic larval duration (tMax)</li> <li><strong>startAll</strong> The target box where a simulated larval particle started. The position of this box is given by bbox_all</li> </ul> <p> </p>
Figure 7. Stenothoe menezgweni, DIVA 2 in Stenothoidae (Crustacea: Amphipoda) of hydrothermal vents and surroundings on the Mid-Atlantic Ridge, Azores Triple Junction zone
Figure 7. Stenothoe menezgweni, DIVA 2, PL26, holotype female. Habitus of the holotype.
FIG. 3. — Heptnerina confusa n. gen., n in A new genus and species of deep-sea cyclopoid (Crustacea, Copepoda, Cyclopinidae) from the Mid-Atlantic Ridge (Azores Triple Junction, Lucky Strike)
FIG. 3. — Heptnerina confusa n. gen., n. sp.,
Blue Ridge Escarpment Drainage Divide Retreat Code
<p>The data and code in this repository was developed for publication in the manuscript: </p> <p>Stokes, MF, Larsen, IJ, Goldberg, SL, McCoy, SW, Prince, PP, & Perron, JT, The erosional signature of drainage divide motion along the Blue Ridge escarpment, JGR: Earth Surface (in review in October, 2022)</p> <p>There are four main directories in the repository. </p> <p>Directory 1: Cosmogenics</p> <p>Includes Table S1 and S2 in the manuscript which report sample locations, 10-Be concentrations, and modelled erosion rates. Also includes data and parameters used in LSDTopotools (Mudd et al. 2016, ESurf) (https://lsdtopotools.github.io/) to prepare the input data for the online erosion rate calculator formerly known as CRONUS (Balco et al., 2008, Geochronology) (https://hess.ess.washington.edu/). </p> <p>Directory 2: MapData</p> <p>All digital elevation models and shapefiles used for topographic analyses. This directory is not needed to run the river long-profile models or to examine the erosion rate results. However, if interested in exactly reproducing our results and following our topographic analyses, this directory will be necessary.</p> <p>Directory 3: TopographicAnalysis</p> <p>Scripts used to analyze topography and create most of the figures in the manuscript. The scripts in this folder use TopoToolbox. TopoToolbox Schwanghart, W., Scherler, D. (2014): TopoToolbox 2 – MATLAB-based software for topographic analysis and modeling in Earth surface sciences. Earth Surface Dynamics, 2, 1-7. [DOI: 10.5194/esurf-2-1-2014]</p> <p>Directory 4: Simulations </p> <p>Code used for numerical models of the topographic evolution of river long-profiles following river capture. It is not necessary to download the data in MapData to get started with the simulation code. Scripts to replicate the experiments in our paper can be found in the sub-directories "DanSimulations" and "RoaSimulations". Toy results are deposited in the directories "RoanokeSimulationResults" and "DanSimulationResults". </p> <p>The functions used in the simulations are in the subdirectory "LongProfileCode". If LongProfileCode is used in work that results in publication please cite one of the following papers in which the MIT Geomorphology landscape evolution model (Tadpole) was developed:</p> <p>Perron, J.T., W.E. Dietrich and J.W. Kirchner (2008), Controls on the spacing of first-order valleys. Journal of Geophysical Research, 113, F04016, doi: 10.1029/2007JF000977.</p> <p>Perron, J.T., J.W. Kirchner and W.E. Dietrich (2009), Formation of evenly spaced ridges and valleys. Nature, 460, 502–505, doi: 10.1038/nature08174.</p> <p> </p>
Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: Static 2D models and effects of paleo-seawater chemistry
<p>DePaolo et al. Gcubed 2022 data files</p> <p><strong>Thermo-hydro-chemical simulation of mid-ocean ridge hydrothermal systems: </strong></p> <p><strong>Static 2D models and effects of paleo-seawater chemistry </strong></p> <p> </p> <p>In this folder are input and output files for v3.68 of TOUGHREACT that contain all of the files illustrated in the manuscript plus many more. Also included is v3 TOUGHREACT reference manual, which gives more information on all of the input and output files.</p> <p>In each folder there are a sequence of run folders, each containing input files (flow.inp, solute.inp, chemical.inp, MESH, GENER, plus a thermodynamic database with filename like “tkslth06acp3isi9.dat.” Also included are raw tecplot files (flowvector.tec, flowdata.tec, rct_sfarea.tec, rctn_rate.tec, min_SI.tec, minerals.tec, aqconc.tec) and other output files (all “.out” files). In some cases the .tec files, which are combined files with output for both fractures and matrix, have been separated into separate fracture and matrix files with names like “flowvector_frc.tec,” “flowvector_mtx.tec,” aqconc_frc.tec,” “aqconc_mtx.tec” to allow plotting of fracture and matrix properties separately.</p> <p>Some folders also contain .tiff or .png files that are 2D color contour plots as shown in the manuscript. All of these plots were made with Paraview (<a href="https://www.paraview.org/">https://www.paraview.org</a>) which is open-source.</p> <p>Each folder labeled like “Modern SW fastcpx Sr8…” contains several subfolders each labeled with the model year at which the run ends, like 2000, 2600, 2700, 2800, … which correspond to the warmup steps described in the manuscript:</p> <p>The typical procedure used to achieve the results reported here is (with some minor variations):</p> <ol> <li>Run the simulation for 2000 model years with 50% of the final heating from below and minimal chemical reactions. RSA for primary minerals in both matrix and fractures are set to 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup>cm<sup>2</sup>/g for secondary minerals, which yields chemical reaction rates about 500 times slower than for a more realistic system.</li> <li>Run for an additional 600 model years with the full heating from below and RSA’s at 10<sup>-6</sup> cm<sup>2</sup>/g and 2 x 10<sup>-6</sup> cm<sup>2</sup>/g. This step yields a steady state temperature and flow field with the full heating from below. Less time is needed than for the first phase because the fluid flow velocities are higher with higher heating rates.</li> <li>Run an additional 100 years; RSA’s increased to 10<sup>-5</sup> cm<sup>2</sup>/g and 2 x 10<sup>-5</sup> cm<sup>2</sup>/g</li> <li>Run 100 years; RSA’s at 10<sup>-4</sup> cm<sup>2</sup>/g and 2 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 years; RSA’s at 2 x 10<sup>-4</sup> cm<sup>2</sup>/g and 4 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 3 x 10<sup>-4</sup> cm<sup>2</sup>/g and 5 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 50 years; RSA’s at 4 x 10<sup>-4</sup> cm<sup>2</sup>/g and 8 x 10<sup>-4</sup> cm<sup>2</sup>/g*</li> <li>Run 100 additional years*</li> </ol> <p>After step 8 the system has been running for 3100 model years, but only 150 years with full reactions, which is long enough to get close to quasi-steady state fluid chemistry (there is no true steady state for chemistry because the rock mineralogy is changing with time). For each of the steps marked with an asterisk, an alternative procedure is to use high RSA’s for fracture minerals, up to 50 times higher. </p> <p>In some folders there are additional subfolders extending in model time up to 3400 years.</p>
Ice-floe based records of seismic events at 85°E Gakkel Ridge, Arctic Ocean
<p>This dataset contains seismic events recorded by seismometers on drifting ice<br> floes at 85°E volcano Gakkel Ridge, Arctic Ocean. The experiment is described in<br> Korger & Schlindwein (2014). After a reanalysis and selection of suitable<br> earthquakes, the data were reused by Koulakov et al.(2022) for a seismic<br> tomography. The data set contains waveform files and phase picks of the selected<br> events in Nordic Format along with the station coordinates.</p>
"Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" - calcareous nannofossil census data
<p>This is a data supplement (<strong>Dataset A</strong>) to the paper "Microfossil evidence for trophic changes during the Eocene–Oligocene transition in the South Atlantic (ODP Site 1263, Walvis Ridge)" by Bordiga et al., 2015a (https://doi:10.5194/cp-11-1249-2015). Note that data are tabulated against depth in core (meters composite depth, mcd). Please refer to <strong>Table 1</strong> in Bordiga et al. (2015) for age-depth model.</p> <p><strong>Dataset A</strong>. Calcareous nannofossil census data (ODP Site 1263)</p> <p>One file (ODP 1263 dataset A_Bordiga et al. 2015.xls) containing:<br> Sample information; Raw counts, relative (%) and absolute abundances (N/ g) of all species and size-based groups detected (as illustrated in Figure S2 of the original publication).</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.