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1,880 results for “Dimensionality”
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>
Multi-Dimensional Data Viewer (MDV) user manual for data exploration: "Systematic analysis of YFP traps reveals common discordance between mRNA and protein across the nervous system"
<table> <tbody> <tr> <td> <p> Please also see the latest version of the repository:<br> <a href="https://doi.org/10.5281/zenodo.6374011">https://doi.org/10.5281/zenodo.6374011</a> and<br> our website: <a href="https://ilandavis.com/jcb2023-yfp">https://ilandavis.com/jcb2023-yfp</a></p> </td> </tr> </tbody> </table> <p> </p> <p>The explosion in the volume of biological imaging data challenges the available technologies for data interrogation and its intersection with related published bioinformatics data sets. Moreover, intersection of highly rich and complex datasets from different sources provided as flat csv files requires advanced informatics skills, which is time consuming and not accessible to all. Here, we provide a “user manual” to our new paradigm for systematically filtering and analysing a dataset with more than 1300 microscopy data figures using Multi-Dimensional Viewer (MDV) -<a href="https://mdv.molbiol.ox.ac.uk/projects/mdv_project/7012?view=RNA+%2F+Protein+Distribution">link</a>, a solution for interactive multimodal data visualisation and exploration. The primary data we use are derived from our published systematic analysis of 200 YFP traps reveals common discordance between mRNA and protein across the nervous system (<a href="https://doi.org/10.1083/jcb.202205129">eprint link</a>). This manual provides the raw image data together with the expert annotations of the mRNA and protein distribution as well as associated bioinformatics data. We provide an explanation, with specific examples, of how to use MDV to make the multiple data types interoperable and explore them together. We also provide the open-source python code <a href="https://github.com/ilandavislab/Annotate.OMERO.Fig">(github link)</a> used to annotate the figures, which could be adapted to any other kind of data annotation task.</p>
Hydraulic scale model experiments on the two-dimensional run-up of impulse wave trains on steep to vertical slopes
<p>This dataset includes the experimental data and videos, which were generated during the study on the run-up of impulse wave trains at the Laboratory of Hydraulics, Hydrology and Glaciology (VAW), ETH Zurich.</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>
Data and Code for Publication "Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3rd to 1st millennia BC Southwest Germany"
<p>Data and code for publication: H. Rathmann, S. Lismann, M. Francken, A. Spatzier, Estimating inter-individual Mahalanobis distances from mixed incomplete high-dimensional data: Application to human skeletal remains from 3<sup>rd</sup> to 1<sup>st</sup> millennia BC Southwest Germany. <em>Journal of Archaeological Science</em> 156: 105802. <a href="https://doi.org/10.1016/j.jas.2023.105802">https://doi.org/10.1016/j.jas.2023.105802</a></p> <p>The repository contains:</p> <ul> <li>“R code for FLEXDIST.txt”: R code for executing FLEXDIST, a tool to estimate inter-individual Mahalanobis-type distances, taking correlations among variables into account, applicable to multiple variable scales (nominal, ordinal, continuous, or any mixture thereof), accommodating missing values, and handling high-dimensional data. <strong>Please refer to the latest version of this repository for the most up-to-date R code</strong>.</li> <li>“data.csv”: Pre-processed dataset comprising 85 dental morphological features collected from 64 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany used for analysis.</li> <li>“complete dataset.xlsx”: Complete dataset comprising 199 dental morphological features collected from 144 archaeological human remains from Final Neolithic to Early Iron Age Southwest Germany.</li> </ul>
Data for: Bound impurities in a one-dimensional Bose lattice gas: low-energy properties and quench-induced dynamics
<p>Dataset for <em>Bound impurities in a one-dimensional Bose lattice gas: </em><em>low-energy properties and quench-induced dynamics</em> [<a href="https://scipost.org/SciPostPhysCore.7.3.049">SciPost Phys. Core 7, 049 (2024)</a>].</p>
Dataset underlying the manuscript: MAViS: Modular Autonomous Virtualization System for Two-Dimensional Semiconductor Quantum Dot Arrays
<p>Datasets underlying the manuscript. Information on how to run the scripts is detailed in the README file.</p>
Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan - Supporting Movie Files
<p>This is a collection of movies related to the manuscript "Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan" by McLaughlin et al to describe kidney organization using 3D light sheet fluorescence microscopy. The preprint manuscript associated with these movies is </p> <p>Three Dimensional Multiscalar Neurovascular Nephron Connectivity Map of the Human Kidney Across the Lifespan</p> <p>Liam McLaughlin, Bo Zhang, Siddharth Sharma, Amanda L. Knoten, Madhurima Kaushal, Jeffrey M. Purkerson, Heidy Huyck, Gloria S. Pryhuber, Joseph P. Gaut, Sanjay Jain</p> <p>bioRxiv 2024.07.29.605633; doi: <a href="https://doi.org/10.1101/2024.07.29.605633">https://doi.org/10.1101/2024.07.29.605633</a></p> <p>Movie Legends</p> <p>Movie 1: 3D view of the entire slice showing key structures.<br>3D light sheet fluorescence microscopy 5x movie of reference adult sample SK3, demonstrating glomeruli, collecting<br>ducts, nerves, and blood vessels. 0:00s — Raw signal. 0:10s — Segmentations. Annotations are in the movie.</p> <p><br>Movie 2: Relationship of nerves with glomeruli and juxtaglomerular apparatus.<br>The movie depicts innervation of glomeruli in 2D optical sections, containing glomeruli, Tuj1(labels TUBB3)-stained<br>nerves, and CGRP-stained sensory nerves. 0:22s — Innervation of the JGA. 0:33 s— Innervation of the Macula Densa.<br>0:47s — Innervation of the outer boundary of the Bowman’s Capsules.</p> <p><br>Movie 3: Neuro-nephron connectivity.<br>The movie explores innervation between different structures of the same nephron, and between nephrons in both 3D and<br>2D optical sections, containing glomeruli, Tuj1 (TUBB3)-stained nerves, CGRP-stained sensory nerves, proximal<br>(convoluted) tubule, thick ascending limb, distal convoluted tubule, and collecting duct. 0:00-1:53min — 3D<br>relationships. 0:38s — Innervation of glomerulus JGA. 1:02min — Post-JGA innervation of medullary ray structures.<br>1:54min-end — 2D relationships. 2:38min — Interglomerular/internephron innervation.</p> <p><br>Movie 4: Neurovascular – nephron patterns in the medulla.<br>The movie shows innervation pattern within the medulla. 0:00s—5x adult medullary innervation pattern in 3D;<br>0:34s—in 2D also showing Vasa Recta and Collecting Duct; 0:48s—in 3D at 20x resolution. 1:04min—20x adult<br>medullary innervation of proximal tubule, thick ascending limb, and Vasa Recta in 3D; 1:31min—view if the previous in<br>2D. 2:11min—5x adult medullary innervation pattern in 3D; 2:40min—in 2D also showing Vasa Recta and Collecting<br>Duct; 2:54min—in 3D at 20x resolution; 3:15—in 2D at 20x resolution.</p> <p><br>Movie 5: Network motifs.<br>Exploring 3D neuroglomerular networks at 5x and 20x resolution. 0:00s — Raw 5x signal from young adult sample SK2.<br>0:16s— Segmented 5x SK2 with 20x coregistrations. 0:26 — Exploring SK2 20x FOV. 0:38 — 20x network featuring<br>hourglass motif. 1:19 5x “Type I” network in SK2. 1:41 — Segmented 5x adult SK3 sample featuring a “Type 2” network<br>containing a lattice motif. 2:23 — Exploring SK3 20x FOV, featuring a network with a lattice motif.</p> <p><br>Movie 6: LSFM movie of pediatric kidney<br>3D lightsheet 5x image of neonatal sample SK414, containing glomeruli, collecting ducts, nerves, and blood vessels.<br>0:00s — Raw signal. 0:22s — Segmentations. 1:34min — Overlayed segmentations.</p> <p><br>Movie 7: Neuronephron connectivity time course<br>Exploring neuronephro-networks across a life time course in 1mm3 20x images. 0:00 — Raw neonatal. 0:17sec —<br>Segmented neonatal. 0:47 sec— Raw infant. 0:54 — Segmented infant with network. 1:13min — Raw young adult.<br>1:23min — Segmented young adult with network. 1:33min — Raw adult. 1:43min — Segmented adult featuring network<br>with keychain motif. 1:49min — Raw aged. 1:59min — Segmented aged with network featuring pyramid motif.</p> <p> </p> <p>Movie 8: Mother Glomeruli</p> <p>Evaluating distributions and innervation of mother glomeruli in neuroglomerular networks. 0:00 - Large 20x 3D Network sample SK1 FOV8. 0:13 - Sample SK1 5x Network in 2D. 0:36 - Mother glomerulus neural quantifications SK1. 0:41 Large 20x 3D Network sample SK3 FOV12. 0:56. Large 20x 2D Network sample SK3 FOV12. 1:21 - Mother glomerulus neural quantifications SK3.</p> <p> </p> <p>Movie 9: Segmentations</p> <p>Demonstrating accuracy of segmentations that combine supervised ML with manual validation in Sample SK2 FOV5. 0:00 - AQP2 labelled Collecting Duct and NPHS1 labelled Glomerulus. 0:26 - Tuj1 labelled nerve.</p> <p> </p> <p>metadata_analyzed_images:</p> <p>Metadata for images that were analyzed. Includes metadata .txt files for all stitched, downsampled samples, as well as .csv metadata for certain raw .czi files (pre-processing).</p>
Data set of article entitled: "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study"
<p>Data set of the immagies showed in figure 3 of the article entiteled "Impact of the interfacial Dzyaloshinskii-Moriya interaction on the band structure of one-dimensional artificial magnonic crystals: A micromagnetic study". All files contain the matrix of the dispersion relations of the two analysed Magnonic Crystals: the SAMPLE A and the SAMPLE B for different value of the interfacial Dzyaloshinskii-Moriya interaction (constant D). The first row is the set of values of k-vector, while the first column is the set of value of the frequencies. The other elements of the matrix are the values of the pixel related to the first row and first column. These elements are been obtanied by the Fast Fourier Transform in time and space of the micromagnetic simulations .</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>
Data for "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography"
<p>Raw data used to create figures for the paper "Measurement of temperature induced X-ray tube transmission target displacements for dimensional computed tomography" <a href="https://doi.org/10.1016/j.precisioneng.2021.06.002">https://doi.org/10.1016/j.precisioneng.2021.06.002</a></p> <p>Data is available in tab delimited format (.txt) and in Excel (.xls).</p> <p> </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>
Shape Optimization of Thermoacoustic Systems Using a Two-Dimensional Adjoint Helmholtz Solver
<p>rijke stands for Rijke tube and tswc stands for turbulent swirl combustor.</p> <p>mesh_*.xml contains the mesh in xml format.</p> <p>p_dir_*.xml and p_adj_*.xml contain the direct and adjoint fields in xml format.</p> <p>omega_*.txt contains the eigenvalue.</p> <p>my_dict_*.pickle contains the control points and the shape derivatives as byte streams.</p> <p>The dataset also contains the pvd (ParaView Data) files for the amplitude and phase of the direct and adjoint fields.</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>
Data for: Mobile impurities interacting with a few one-dimensional lattice bosons
<p>Dataset for <em>Mobile impurities interacting with a few one-dimensional lattice bosons</em> (<a href="https://iopscience.iop.org/article/10.1088/1361-6455/acb51b">10.1088/1361-6455/acb51b</a>). It corresponds to exact diagonalization results for energies and bipolaron sizes.</p> <p>The files correspond to the following figures in the preprint:</p> <p>Fig. 1a: Ep_UBB2.dat <br> Fig. 1b: Ep_UBB4.dat <br> Fig. 1c: Ep_UBB6.dat <br> Fig. 1d: Ep_UBB8.dat <br> Fig. 2: Ep_UBI50.dat<br> Fig. 5a: Ebp_UBB2.dat <br> Fig. 5b: Ebp_UBB4.dat <br> Fig. 5c: Ebp_UBB6.dat <br> Fig. 5d: Ebp_UBB8.dat <br> Fig. 6: Ebp_UBI50.dat <br> Fig. 7a: rbp_UBB2.dat <br> Fig. 7b: rbp_UBB4.dat <br> Fig. 7c: rbp_UBB6.dat <br> Fig. 7d: rbp_UBB8.dat <br> Fig. 8: rbp_UBI50.dat</p>
Disorder-free localization transition in a two dimensional lattice gauge theory
<p>Data files for Figs 2 and 3 from the paper "Disorder-free localization transition in a two dimensional lattice gauge theory".</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>
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.