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35 results for “data cube”
Data and Source Codes used in "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"
<p>Data and Source Codes used in the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>Advection Test (ADV): East-West A_TST (100km, Cube), C_TST (25km, Cube), E_TST (5km, Cube),</p> <p> North-South K_TST (100km, Cube), M_TST (25km, Cube), O_TST (5km, Cube) </p> <p>Barotropic Test (BAR): A_TST5 (100km, Cube), Y_TST4 (100km, RLL), C_TST3 (5km, Cube), C_TST1 (5km, RLL)</p> <p>Baroclinic Test (BCL): J_TST30 (100km, Cube), J_TST20 (100km, RLL)</p>
Data set associated with the paper "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"
<p>New data set associated with the revision of the paper "Development of a Global Quasi-3-D Multiscale Modeling Framework: <br> I. Vector Vorticity Model on Cubed Sphere as Cloud-Resolving Component"</p> <p>The title of the paper has been changed to "Implementation of the Vector Vorticity Dynamical Core on Cubed Sphere for Use in the Quasi-3-D Multiscale Modeling Framework"</p> <p>New simulated data set of the advection test is in the folder ADVEC_NEW; New simulated data set of the barotropic instability test is in the folder BARO_NEW; New simulated data set of the baroclinic instability test is in the folder BCL_NEW</p>
Chemical imaging data collected on small wood cubes after impregnation-treatment with phenol formaldehyde resin
<p>This dataset contains UV microspectrophotometry (UMSP) and near infrared (NIR) imaging data from the following publication: Altgen M., Awais M. Altgen D., Klüppel A., Koch G., Mäkelä M., Olbrich A., Rautkari L. (2022) Chemical imaging to reveal the resin distribution in impregnation-treated wood at different spatial scales. Materials & Design, DOI: <a href="https://doi.org/10.1016/j.matdes.2022.111481">https://doi.org/10.1016/j.matdes.2022.111481</a>.</p> <p>The data was measured on small beech wood cubes (15x15x15 mm<sup>3</sup>) that were impregnation-treated with a low molecular weight phenol formaldehyde resin. Experimental details can be found in the publication.</p> <p>The file “NIR sample IDs with weight and dimensional changes.csv” contains the sample IDs as well as the weight percent gains and dimensional changes caused by the resin treatment of each sample in the dataset. To generate the NIR image files, a region of interest of 881 x 384 pixels was selected from the raw image files to produce an image that contains the sample surrounded by background. The spectral data was corrected using the calibration reflectance target values and then converted to absorbance. Each NIR image is stored in a separate MATLAB file (.mat) with the sample ID as the file name.</p> <p>The file "UMSP sample IDs.csv" contains the sample IDs of the UMSP images. The folder "UMSP image profiles.zip" contains the corresponding UMSP image profiles, which are stored as excel files (.xlsx) with the sample IDs as file names. The files contain the absorbance at 278 nm per pixel with a pixel resolution of 0.25 x 0.25 µm<sup>2</sup>.</p>
Wi-Fi (CSI and RSSI) data of six basic knife activities for cooking (chopping, cubing, French cutting, julienning, mincing, and slicing)
<p>To gather the dataset, we asked two participants to perform six basic knife activities. The layout of the system experiment is provided in Fig. 4. As it illustrates, we put the receiver on the right side and the ESP32 transceiver on the left side of the performing area. The performing area is a cutting board (30 x 46 cm) in this experiment. Each participant performs each activity five times in the performing area. The data is recorded using a customized version of ESP32-CSI-tool [38] on the laptop that helps us to record and save each data in a separate file. After recording all 60 data entries, we used Python code to extract the clean data from all generated text by the tool. The clean data is stored in a database and creates the dataset.</p>
Bowles et al. (2020) - G-Cubed - XRD data
<p>X-ray diffraction (XRD) data set associated with the manuscript:</p> <p>Bowles, J.A., A. Morris, M.A. Tivey, and I. Lascu (2020), Magnetic mineral populations in lower oceanic crustal gabbros (Atlantis Bank, SW Indian Ridge): Implications for marine magnetic anomalies, <em>Geochemistry, Geophysics, Geosystems, doi:</em>10.1029/2019GC008847.</p> <p>See README file for additional details.</p>
Supporting data used in the paper: Xi Chen, 2020, The LMARS based shallow-water dynamical core on generic gnomonic cubed-sphere geometry
<p># Simulation results of the unstaggered shallow water model</p> <p>This repository contains the supporting data used in the paper: Xi Chen, 2020, The LMARS based shallow‐water dynamical core on generic gnomonic cubed‐sphere geometry, DOI: 10.1029/2020MS002280</p> <p><br> Organization of the repository:</p> <p>The tar archive with this data submission has a:</p> <p>doc directory contains a README.md with information regarding naming conventions to label the model configurations for a shallow water test simulation. Additional information can also be found in README.md. Table 4 in the paper provides additional details.</p> <p>The data directory contains the supporting data files (NetCDF format). </p> <p> </p>
Deconvolved HI data cube for best T_exc
<p>Deconvolved HI data cube for best T_exc.</p> <p>The data were used for the paper "<em>Assessing the Impact of Hydrogen Absorption on the Characteristics of the Galactic Center Excess" </em>by Pohl, Macias, Coleman, and Gordon (2022)</p>
Data set "Narrow, fast, and "cool" mantle plumes caused by strain-weakening rheology in Earth's lower mantle" (G-cubed)
<p>This archive contains data related to the manuscript “Narrow, fast, and “cool” mantle plumes caused by strain-weakening rheology in Earth’s lower mantle” by Gülcher et al., (G-cubed). It contains output quantities and selected fields for the numerical models shown and discussed in the paper (M###). Moreover, it contains the input parametric files implemented in StagYY. Please read the README.dat file for more information. </p>
Supplementary Data for "Phosphorus variations in volcanic sequences reveal the linkage between regional tectonics and terrestrial biota evolution" in G-Cubed.
<p>Supplementary data tables for Ma et al. (2022) associated with the paper entitled "Phosphorus variations in volcanic sequences reveal the linkage between regional tectonics and terrestrial biota evolution" published in <em>G-Cubed</em>.</p>
A global land-use data cube 1992-2020 based on the Human Appropriation of Net Primary Production: Dataset 3
<p>This dataset is part of the LUIcube, a global dataset on land-use at 30 arcsecond spatial resolution. The LUIcube includes information on area, the change in NPP due to land conversions (HANPP<sub>luc</sub>), the harvested NPP (including losses, HANPP<sub>harv</sub>), and the NPP remaining in ecosystems after harvest (NPP<sub>eco</sub>) for 32 land-use classes in annual time-steps from 1992 to 2020. A detailed description of the LUIcube is available in the accompanying publication.</p> <p>The layers of land-use areas are provided in square kilometers (km²) per grid cell. All NPP flows are provided in tC/yr per grid cell. Adding HANPP<sub>harv</sub> to NPP<sub>eco</sub> results in the actual NPP available before harvest (NPP<sub>act</sub>=NPP<sub>eco</sub>+HANPP<sub>harv</sub>), and adding HANPP<sub>luc</sub> to NPP<sub>act</sub> results in the potential NPP available in the hypothetical absence of land use (NPP<sub>pot</sub>=NPP<sub>act</sub>+HANPP<sub>luc</sub>) for the given land-use class. Area-intensive values (in gC/m²/yr) can be calculated by dividing the NPP flows by the area of the respective land-use class per grid cell. HANPP in % of NPP<sub>pot</sub> can be calculated by summing up HANPP<sub>harv</sub> and HANPP<sub>luc</sub> and dividing it by NPP<sub>pot</sub>. Areas and NPP flows of land-use classes can be aggregated to calculate their overall HANPP.</p> <p>This Zenodo repository provides data on following land-use classes: grazing land with (almost) no trees (GL-notrees); cropland used for production of other fibres (CL-OFIB); fallow cropland areas (CL-FALL)</p>
Supplementary Data Tables for Stolper et al. (2022) Constraints on Early Paleozoic deep-ocean oxygen concentrations from the iron geochemistry of the Bay of Islands ophiolite in G-Cubed
<p>Supplementary data tables for Stolper et al. (2022) associated with the paper entitled "Constraints on Early Paleozoic deep-ocean oxygen concentrations from the iron geochemistry of the Bay of Islands ophiolite" published in <em>G-Cubed</em>.</p>
Data cubes corresponding to the article "Three-dimensional Structure of the Corona during WHPI Campaign Rotations CR-2219 and CR-2223"
<p>CONTENTS of REPOSITORY:</p> <p>The shared data corresponds to 3D tomographic reconstructions and 3D MHD simulations of the solar corona and solar wind of WHPI targets CR-2219 and CR-2223. Each 3D cube of data is specified in a uniform spherical grid, as described in the IDL codes that allow reading the cubes into memory. The specific shared products are: </p> <p>3D Tomographic reconstructions:<br> * DEMT: Electron density (Ne) and temperature (Te) in the height range 1.02-1.25 Rsun (based on AIA images).<br> * WL-SRT: Electron density (Ne) in the height range 2.5-6.0 Rsun (based on LASCO-C2 data). </p> <p>3D MHD steady-state simulations using the AWSoM model:<br> * Electron density (Ne) and temperature (Te).<br> * Magnetic field components (Br, Bth, Bph).<br> * Radial component of the solar wind velocity (Vr).</p> <p>INSTRUCTIONS: The IDL script included contains comments describing how to read into memory the data.<br> </p>
Stimuli Data Experiment on LEV (IEM CUBE)
<p>Loudspeaker stimuli of experiment.</p>
G0.412+0.052 continuum image & data cubes (ALMA project 2016.1.00949.S)
<p>This dataset contains data from the ALMA project 2016.1.00949.S for G0.412+0.052 as presented in Williams et al. (2022). These data include the dust continuum, and data cubes for SiO (5-4), 13CO (2-1), and three transitions of H2CO. Both primary beam- and non primary beam-corrected data are included.</p>
Supplementary Data for "Phosphorus variations in volcanic sequences reveal the linkage between regional tectonics and terrestrial biota evolution" in G-Cubed.
<p>Supplementary data tables for Ma et al. (2022) associated with the paper entitled "Phosphorus variations in volcanic sequences reveal the linkage between regional tectonics and terrestrial biota evolution" published in <em>G-Cubed</em>.</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.