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898 results for “three-dimensional”
Three-dimensional super-Yang--Mills theory on the lattice and dual black branes --- data release
<p>This HDF5 file collects data and analysis results for non-perturbative lattice field theory calculations investigating three-dimensional maximally supersymmetric SU(N) Yang--Mills theory on a skewed euclidean torus, and its holographic connection to dual D2-brane solutions in supergravity. See the README for further information.</p>
Haptic three-dimensional curved surface exploration fMRI dataset
Open the record for dataset details and reuse information.
Data to "Predicting precision grip grasp locations on three-dimensional objects"
<p>This record contains experimental and analysis scripts (written in Matlab) as well as raw and processed data to reproduce the results shown in:</p> <p>Klein, L. K. ^, Maiello, G. ^, Paulun, V. C., & Fleming, R. W. (in press). <br> Predicting precision grip grasp locations on three-dimensional objects. PLOS Computational Biology<br> ^co-first authors </p> <p>A preprint version of the manuscript is currently available at: https://doi.org/10.1101/476176</p>
The Plunging of Hyperpycnal Plumes on Tilted Bed by Three-Dimensional Large-Eddy Simulations
<p><strong>Abstract:</strong> Theoretical and experimental interest in the transport and deposition of sediments from rivers to oceans has increased rapidly over the last two decades. The marine ecosystem is strongly affected by mixing at river mouths, with for instance anthropogenic actions like pollutant spreading. Particle-laden flows entering a lighter ambient fluid (hyperpycnal flows) can plunge at a sufficient depth, and their deposits might preserve a remarkable record across a variety of climatic and tectonic settings. Numerical simulations play an essential role in this context since they provide information on all flow variables for any point of time and space. This work offers valuable Spatio-temporal information generated by turbulence-resolving 3D simulations of poly-disperse hyperpycnal plumes over a tilted bed. The simulations are performed with the high-order flow solver Xcompact3d, which solves the incompressible Navier-Stokes equations on a Cartesian mesh using high-order finite-difference schemes. Five cases are presented, with different values for flow discharge and sediment concentration at the inlet. A detailed comparison with experimental data and analytical models is already available in the literature. The main objective of this work is to present a new data-set that shows the entire three-dimensional Spatio-temporal evolution of the plunge phenomenon and all the relevant quantities of interest.</p> <p><strong>Description:</strong> Data from the five simulations are included (cases 2, 4, 5, 6, and 7). The output files from Xcompact3d were converted to NetCDF, including coordinates and metadata, aiming to be more friendly than raw binaries.</p> <p>More details, including examples about how to read and plot the dataset using Python and xarray, are available at <a href="https://github.com/fschuch/the-plunging-flow-by-3D-LES">GitHub</a>.</p>
AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE
<p>This repository contains all geometrical data and metadata belonging to the paper AN OPEN-SOURCE, THREE-DIMENSIONAL GROWTH MODEL OF THE MANDIBLE by the MAGIC Amsterdam research consortium. The following contents are uploaded:</p><p><strong>shapeVectors_original.csv</strong> | shape vectors of the original data<br><strong>shapeVectors_rescaled.csv</strong> | shape vectors of the rescaled data<br>678 x 62589 matrices where the rows are samples and the columns are shape vectors. The shape vectors are formatted<i> [x1, x2, x3, ..., y1, y2, y3, ..., z1, z2, z3, ...].</i></p><p><strong>PCA_coeff_original.csv</strong> | principal component coefficients of the original data<br><strong>PCA_coeff_rescaled.csv</strong> | principal component coefficients of the rescaled data<br>62589 x 677 matrices where each row of these matrices is a variable (x-, y-, or z-coordinate of a vertex) and each column is a principal component.</p><p><strong>PCA_score_original.csv</strong> | principal component scores of the original data<br><strong>PCA_score_rescaled.csv</strong> | principal component scores of the rescaled data<br>678 x 677 matrices where rows correspond to samples and columns correspond to principal components.</p><p><strong>PCA_latent_original.csv</strong> | principal component variances of the original data<br><strong>PCA_latent_rescaled.csv</strong> | principal component variances of the rescaled data<br>677 x 1 vectors where each element is an eigenvalue of a principal component.</p><p><strong>PCA_mu_original.csv</strong> | mean of the original data<br><strong>PCA_mu_rescaled.csv</strong> | mean of the rescaled data<br>1 x 62589 vectors that represent the average shape vector. All (centered) data can be reconstructed as follows: <i>shapeVectors = PCA_score * PCA_coeff' + PCA_mu.</i></p><p><strong>PCA_standardDeviations_original.csv</strong> | standard deviations of each sample for each principal component of the original data.<br><strong>PCA_standardDeviations_rescaled.csv</strong> | standard deviations of each sample for each principal component of the rescaled data.<br>677 x 678 matrices where the rows are principal components and the columns are samples. The standard deviations were calculated as follows: <i>PCA_standardDeviations = PCA_score' ./ sqrt(PCA_latent).</i></p><p><strong>metadata.csv</strong> | This matrix contains the age in years (first column) and biological sex (second column, 1 = male and 2 = female) for all samples (rows).</p><p><strong>connectivityList.csv</strong> | This matrix defines the mesh of the 3D model of the mandible. The vector in each row represents which vertices define a triangle. Indexing starts at 0, so for use in e.g. Matlab, add 1 to all elements.</p>
Three-dimensional thermal structure of East Asian continental lithosphere
<p>This data set includes 3 supplement data files and 1 readme file for 3D thermal structure of East Asian continental lithosphere. </p>
Data and Workflow to: Three-dimensional buoyant hydraulic fracture growth: constant release from a point source (Möri and Lecampion, (2022))
<p>This upload contains the relevant scripts, notebooks, and datasets to reproduce the numerically obtained results of the Journal article "Three-dimensional buoyant hydraulic fracture growth: constant release from a point source" by Möri and Lecampion, (2022).</p>
Supporting data for publication: The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences.
<p><span>This repository contains the supplementary data used in the publication Roche et al., 2024 (The role of the three-dimensional geometry of fault steps on event migration during fluid-induced seismic sequences), including (1) the seismicity catalogues from Cahuilla, Yellowstone and West Bohemia, modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016), and (2) the pictures series used to build isochrone contour maps.</span></p> <p><span><span>1.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The seismicity catalogues from Cahuilla, Yellowstone and West Bohemia are modified from Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016). The catalogues include the hypocentre location, relative time, and magnitude for non-filtered and filtered data. General information on each catalogue and filtering and modifications can be found in the associated publication.</span></p> <p><span> Dataset list:</span></p> <ul> <li><span>Cahuilla Catalogues (modified from Ross et al., 2019): </span></li> <ul> <li><span>Original data: File name: VR_sup_0021_Cah_All</span></li> <li><span>Filtered data: File name: VR_sup_0022_Cah_Filter</span></li> </ul> <li><span>Bohemia 2008 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0023_Boh_08_All</span></li> <li><span>Filtered data: File name: VR_sup_0024_Boh_08_Filter</span></li> </ul> <li><span>Bohemia 2014 Catalogues (modified from Haintzl et al., 2016): </span></li> <ul> <li><span>Original data: File name: VR_sup_0025_Boh_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0026_Boh_14_Filter</span></li> </ul> <li><span>Yellowstone Catalogs (modified from Shelly et al., 2013): </span></li> <ul> <li><span>Original data: File name: VR_sup_0027_Yell_14_All</span></li> <li><span>Filtered data: File name: VR_sup_0028_Yell_14_Filter</span></li> </ul> </ul> <p><span>The files are text files tab-delimited, with the following headers:</span></p> <ul> <li><span>Index: 1 by default</span></li> <li><span>Easting(m): hypocenter Easting in meters </span></li> <li><span>Northing(m): hypocenter Northing in meters </span></li> <li><span>Depth(m): hypocenter depth in meters </span></li> <li><span>Mw: magnitude</span></li> <li><span>Relative Time(s): date of the origin time in the format </span></li> </ul> <p><span><span>2.<span> </span></span></span><span>Seismicity catalogues</span></p> <p><span>The pictures series are images of seismicity at a regular time interval for each studied step.</span></p> <p><span>Dataset list:</span></p> <ul> <li><span>Step C1: File name: VR-sup-0012-Pictures_C1.</span></li> <li><span>Step C2: File name: VR-sup-0013-Pictures_C2.</span></li> <li><span>Step C3: File name: VR-sup-0014-Pictures_C3.</span></li> <li><span>Step C4: File name: VR-sup-0015-Pictures_C4.</span></li> <li><span>Step Y1: File name: VR-sup-0016-Pictures _Y1.</span></li> <li><span>Step B1I: File name: VR-sup-0017-Pictures _B1I.</span></li> <li><span>Step B1II: File name: VR-sup-0018-Pictures _B1II.</span></li> <li><span>Step B2: File name: VR-sup-0019-Pictures _B2.</span></li> <li><span>Step B3: File name: VR-sup-0020-Pictures _B3.</span></li> </ul> <p><span>Each file contains a series of pictures in JPEG format. For each picture, events in the overlying and underlying segments are indicated in blue and red. The full circles represent the events occurring during the last interval. The empty circles represent the events occurring in the previous intervals.</span></p> <p><span>If you find these data useful in your research, please cite Roche et al. (2024), as well as the relevant papers Ross et al. (2020), Shelly et al. (2013) and Hainzl et al. (2016).</span></p>
Statistical analysis and dataset for: Three-dimensional body reconstruction enables quantification of liquid consumption in small invertebrates
<p>Linked to the journal article published in bioRxiv (https://doi.org/10.1101/2024.06.14.599002).</p> <p><em><strong>Abstract</strong></em></p> <p>Quantifying feeding patterns provides valuable insights into animal behaviour. However, small invertebrates often consume incredibly small amounts of food. This renders traditional methods, such as weighing individuals before and after food acquisition, either inaccurate or prohibitively expensive. Here, we present a non-invasive method to quantify food consumption of small invertebrates whose body expands during feeding. Using the markerless pose estimation software DeepLabCut, we three-dimensionally track the body of Argentine ants, <em>Linepithema humile</em>. Using these extracted markers, we developed an algorithm which computationally reconstructs the ant’s body, directly measuring volumetric change over time. Moreover, we provide measures of accuracy and quantify the ant’s feeding response to a range of sucrose concentrations, as well as a gradient of caffeine-laced sucrose solutions. Small invertebrates are often prolific invasive species and disease vectors, causing significant ecological and economical damage. Understanding their feeding behaviour could be an important step towards effective control strategies.</p> <p> </p> <ul> <li><strong>VolEst_C1_volume_calculation_multiprocessing.py</strong>: Takes as input H5 3D DeepLabCut files, calculates the gaster volume at every frame using seven different methods and outputs these as CSV files.</li> <li><strong>VolEst_C2_interactive_GUI.py</strong>: Given a folder with Volume CSV files, interactively plots the volume over time, 3D coordinates tracked by DeepLabCut and the frame of interest for both cameras.</li> <li><strong>VolEst_C3_linear_regression.py</strong>: Applies a linear regression to each feeding event tracked and provides measures of interest such as crop load and consumption rate.</li> <li><strong>VolEst_C4_statistical_analysis</strong>: Complete statistical analysis and code for the manuscript.</li> <li><strong>VolEst_D1_sucrose_density.csv</strong>: Data obtained to quantify the density of sucrose solutions of varying molarity.</li> <li><strong>VolEst_D2_accuracy_weight_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D3_accuracy_weight.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the weight-volume accuracy measurements.</li> <li><strong>VolEst_D4_accuracy_nanoliter_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D5_accuracy_nanoliter.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the volume-volume accuracy measurements.</li> <li><strong>VolEst_D6_sucrose_caffeine_consumption_metadata.csv</strong>: Manually collected metadata pertaining to experimental conditions, subjects, and treatments for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_D7_sucrose_caffeine_consumption.zip</strong>: Folder containing the raw points tracked using DeepLabCut, all relevant data obtained from the algorithms created, and a sample video of the experiment for the sucrose and caffeine dilutions application measurements.</li> <li><strong>VolEst_Camera_A-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera A.</li> <li><strong>VolEst_Camera_B-Henrique-2023-09-20.zip</strong>: DeepLabCut labels and trained network for camera B.</li> <li><strong>VolEst_base.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> <li><strong>VolEst_platform.stl</strong>: 3D file for the resin platform used in the experimental validation of the setup.</li> </ul>
Insights into non-axisymmetric instabilities in three-dimensional rotating supernova models with neutrino and gravitational-wave signatures
<p>The data of the gravitational wavefroms of core-collapse supernovae, which are used in Takiwaki, Kotake, and Foglizzo, (2021), Monthly Notices of the Royal Astronomical Society, Volume 508, Issue 1, pp.966-985</p>
A three-dimensional map of the Milky Way using 66,000 Mira variable stars
<p>We provide here full Table 1 from Iwanek, P., et al., 2023, "A three-dimensional map of the Milky Way using 66,000 Mira variable stars", ApJS (accepted for publication, DOI: 10.3847/1538-4365/acad7a), which contains mean magnitudes, distances, extinction values, and photometric chemical types for 65,981 Galactic Miras (Iwanek2023_Table1_GalMirasDist.txt file). The corner plot, i.e., the two-dimensional projections of the multi-dimensional posterior parameter spaces fitted to the Galactic Miras distribution is presented in Figure Iwanek2023_corner_plot.png.</p> <p> </p> <p>Patryk Iwanek is partially supported by Kartezjusz program No. POWR.03.02.00-00-I001/16-00, founded by the National Centre for Research and Development, Poland. Szymon Kozłowski acknowledges the support from the National Science Centre, Poland, via grant OPUS 2018/31/B/ST9/00334. </p> <p>This publication makes use of data products from WISE, which is a joint project of the University of California, Los Angeles, and the Jet Propulsion Laboratory/California Institute of Technology, funded by the National Aeronautics and Space Administration (NASA). This work is based in part on archival data obtained with the Spitzer Space Telescope, which was operated by the Jet Propulsion Laboratory, California Institute of Technology under a contract with NASA.</p>
Three-dimensional magnetic reconnection in particle-in-cell simulations of anisotropic plasma turbulence (Simulation Data)
<p>This folder contains the output of the following simulation: </p> <p>We use the explicit Plasma Simulation Code (PSC, Germaschewski et al.2016) to simulate eight anisotropic counter-propagating Alfvén waves in an ion-electron plasma. The anisotropy of the initial fluctuation is set up according to the theory of critical balance by Sridhar & Goldreich (1994) and Goldreich & Sridhar (1995) at the small scale end of the inertial range: <span class="math-tex">\(k_{\parallel} d_{i} = C (|k_{\perp}|d_{i})^{2/3}\)</span>, where <span class="math-tex">\(C= 10^{-4/3}\)</span>. The normalization parameters are the speed of light <span class="math-tex">\(c = 1\)</span>, the vacuum permittivity <span class="math-tex">\(\epsilon_{0} = 1\)</span>, the magnetic permeability <span class="math-tex">\(\mu_{0} = 1\)</span>, the Boltzmann constant <span class="math-tex">\(k_{b}=1\)</span>, the elementary charge <span class="math-tex">\(q=1\)</span>, the ion mass <span class="math-tex">\(m_{i}=1\)</span>, the density of ions and electrons <span class="math-tex">\(n_{i}=n_{e}=1\)</span> and the ion inertial length <span class="math-tex">\(d_{i}=c/\omega_{pi}\)</span> where <span class="math-tex">\(\omega_{pi}=\sqrt{n_{i}q^{2}/m_{i}\epsilon_{0}}\)</span> is the ion plasma frequency. We set <span class="math-tex">\(\beta_{s,\parallel}=1\)</span> and <span class="math-tex">\(T_{s,\parallel}/T_{s,\perp}=1\)</span>, where <span class="math-tex">\(\beta_{s,\parallel}=2 n_s \mu_{0} k_{B}T_{s,\parallel}/B_{0}^{2}\)</span> is the ratio between the plasma pressure parallel to the background magnetic field <span class="math-tex">\(\mathbf{B}_{0}\)</span> and the magnetic pressure and $T_{s,\parallel}$ is the parallel temperature. The magnetic field is normalised to <span class="math-tex">\(B_{0}=V_{A}/c\)</span>, where <span class="math-tex">\(V_{A}=B_{0} / \sqrt{\mu_{0}n_{i}m_{i}}\)</span> is the ion Alfvén speed. We use 100 particles per cell (100 ions and 100 electrons), a mass ratio of <span class="math-tex">\(m_{i}/m_{e} = 100\)</span> so that <span class="math-tex">\(d_e = 0.1 d_{i}\)</span> where <span class="math-tex">\(m_{e}\)</span> is the electron mass and <span class="math-tex">\(d_{e}\)</span> is the electron inertial length. The simulation box size is <span class="math-tex">\(L_{x} \times L_{y} \times L_{z} = 24d_{i}\times24d_{i}\times125d_{i}\)</span> and the spatial resolution is <span class="math-tex">\(\Delta x =\Delta y = \Delta z = 0.06d_{i}\)</span>. We use a time step <span class="math-tex">\(\Delta t =0.06/ \omega_{pi}\)</span>. In our normalisation, the Debye length <span class="math-tex">\(\lambda_{D}=d_{i}\sqrt{\beta_{i}/2}V_{A}/c\)</span> defines the minimum spatial distance that needs to be resolve in the simulation and <span class="math-tex">\(\lambda_D=0.07d_i\)</span>.</p> <p>This output corresponds to <span class="math-tex">\(t=120 \omega_{pi}\)</span>. </p> <p>These data were produced using the Data Intensive at Leicester (DIaL) facility provided by the DiRAC project<br> dp126 "Identifying and Quantifying the Role of Magnetic Reconnection in Space Plasma Turbulence".</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - volume filtering verification
<p>For each of the four ROIs (R1 to R4 in file names) we pick few sub-regions (annotated in R?_legend.png) and show the changes between sections (temporally encoded) from initial (registered) input, marked "1" in video to the final result of volume filtering, marked "5" in video.</p> <p>Subregion number j in ROI number i is bears the video file name "R<i>_verification_volume_-_region_<j>_FullHD.mov".</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - registered sections (input data)
<p>These are the 3500x3500 ROI data, selected from complete scans.</p> <p>Following procedure was applied:</p> <p>- Coarse registration (Ulrich et al, 2014)<br> - ROI selection, 4k x 4k regions<br> - Normalisation<br> - Fine-grain registration (Lobachev et al, 2016)<br> - Crop to the center to obtain 3500x3500 size.</p> <p>We estimated the slice thickness to be 7 µm.</p>
Three-dimensional arrangement of human bone marrow microvessels revealed by immunohistology in undecalcified sections - overview videos
<p>We reconstruct the 3D shape of the micro-vasculature in human bone marrow from serial sections.</p> <p>This upload shows three kinds of overview data.</p> <p>- R[n]_overview_... shows an overview of the mesh from ROI n as video<br> - R[n]_input_animated_... shows a thumb cinema overlay of initial input data<br> - R[n]_both.png shows a still with a frame from overview video (blue) and diameter measurement video (red).</p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS
<p>This dataset is relative to the paper entitled: "First Three-dimensional Quantification of Planktic Food Chain lower levels (Copepods) for the Ross Sea region Marine Protected Area (RSRMPA), Antarctica: Using FAIR-inspired legacy data with Machine Learning, and Open Source GIS" publishing in journal Diversity (MPDI).</p> <p>Abstract:</p> <p>Zooplankton is a fundamental group in all aquatic ecosystems located the base of the food chain. It forms a link between the lower trophic levels with secondary consumers and shows marked fluctuations of populations with environmental change, especially reacting to heating and water acidification. At sea copepod crustaceans account for app. 70% in abundance of zooplankton and are a target of monitoring activities in key areas such as the Southern Ocean. In this study we have used FAIR-inspired legacy data (dating back to the ‘80s) collected in the Ross Sea by the Italian National Antarctic Program in GBIF.org. Together with other open-access GIS data sources and tools it allows generating, for the first time, three-dimensional predictive distribution maps for twenty-six copepod species. These predictive maps were obtained by applying machine learning techniques to grey literature data, which were visualized in open-source GIS platforms. In a Species Distribution Modeling (SDM) framework we used machine learning with three types of algorithms (TreeNet, RandomForest and Ensemble) to analyze the presence and absence of copepods at different areas and depth classes in function of environmental descriptors obtained from the Polar Macroscope Layers present in Quantartica. The models allow for the first time to map-predict the food chain in quantitative terms showing the relative index of occurrence (RIO) and identified the presence for each copepod species analyzed in the Ross Sea. Our results show marked geographical preferences that vary with species and trophic strategy. This study demonstrates that machine learning is a successful method in accurately predicting Antarctic copepod presence, also providing useful data to orient future sampling and management of wildlife and conservation.</p>
Data file for: Three-Dimensional Electrical Imaging Across the Cona Woka Rift and Yalaxiangbo Dome in Southern Tibetan Plateau
<p>The magnetotellurics data were used to study the lithospheric electrical structure across the Cona Woka rift and Yalaxiangbo dome in the southern Tibetan plateau, conducted by Institute of Geophysical and Geochemical Exploration, Chinese Academy of Geological Sciences. The data file of CN6.dat was generated by the Matlab code EM3DVP.</p> <p>You are recommended to refer to the Kelbert et al., 2014 paper: https://doi.org/10.1016/j.cageo.2014.01.010 for a brief understanding of the data file formats.</p> <p> </p>
Three-dimensional building and mobility infrastructure of the CONUS
<p>Humanity's role in changing the face of the earth is a long-standing concern, as is the human domination of ecosystems. Geologists are debating the introduction of a new geological epoch, the 'anthropocene', as humans are 'overwhelming the great forces of nature'. In this context, the accumulation of artefacts, i.e., human-made physical objects, is a pervasive phenomenon. Variously dubbed 'manufactured capital', 'technomass', 'human-made mass', 'in-use stocks' or 'socioeconomic material stocks', they have become a major focus of sustainability sciences in the last decade. Globally, the mass of socioeconomic material stocks now exceeds 10e14 kg, which is roughly equal to the dry-matter equivalent of all biomass on earth. It is doubling roughly every 20 years, almost perfectly in line with 'real' (i.e. inflation-adjusted) GDP. In terms of mass, buildings and infrastructures (here collectively called 'built structures') represent the overwhelming majority of all socioeconomic material stocks.</p><p>This dataset features intermediate mapping results for estimating material stocks in the CONUS (see related identifiers) on a 10m grid based on high resolution Earth Observation data (Sentinel-1 + Sentinel-2), Microsoft building footprints, NLCD Impervious data, and crowd-sourced geodata (OSM). These data may also be useful on their own.</p><p><strong>Provided layers @10m resolution</strong><br>- Building height<br>- Building type<br>- Building area<br>- Impervious fraction<br>- street, and rail area<br>- Building and street climate zones<br>- County zones<br>- State masks<br>- EQUI7 correction factors</p><p><strong>Spatial extent</strong><br>This dataset covers the whole CONUS. </p><p><strong>Temporal extent</strong><br>The maps are representative for ca. 2018.</p><p><strong>Data format</strong><br>The data are organized in 100km x 100km tiles (EQUI7 grid), and mosaics are provided.</p><p><strong>Further information</strong><br>For further information, please see the main publication.<br>A web-visualization of the resulting dataset is available <a href="https://ows.geo.hu-berlin.de/webviewer/us-stocks/">here</a>.<br>Visit our <a href="https://boku.ac.at/understanding-the-role-of-material-stock-patterns-for-the-transformation-to-a-sustainable-society-mat-stocks">website</a> to learn more about our project MAT_STOCKS - Understanding the Role of Material Stock Patterns for the Transformation to a Sustainable Society.</p><p><strong>Publication</strong><br>D. Frantz, F. Schug, D. Wiedenhofer, A. Baumgart, D. Virág, S. Cooper, C. Gómez-Medina, F. Lehmann, T. Udelhoven, S. van der Linden, P. Hostert, and H. Haberl (2023): Unveiling patterns in human dominated landscapes through mapping the mass of US built structures. <i>Nature Communications</i> <strong>14</strong>, 8014. <a href="https://doi.org/10.1038/s41467-023-43755-5">https://doi.org/10.1038/s41467-023-43755-5</a></p><p><strong>Funding</strong><br>This research was primarly funded by the European Research Council (ERC) under the European Union's Horizon 2020 research and innovation programme (MAT_STOCKS, grant agreement No 741950). </p><p><strong>Acknowledgments</strong><br>We thank the European Space Agency and the European Commission for freely and openly sharing Sentinel imagery; USGS for the National Land Cover Database; Microsoft for Building Footprints; Geofabrik and all contributors for OpenStreetMap.This dataset was partly produced on <a href="https://eodc.eu/">EODC</a> - we thank Clement Atzberger for supporting the generation of this dataset by sharing disc space on EODC, and Wolfgang Wagner for granting access to preprocessed Sentinel-1 data.</p>
Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.
<p>Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere</p>
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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.