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88 results for “polygon”
Salzburg Database of Polygonal Data
<p>The Salzburg Database is a repository of polygonal areas of various classes and sizes, with and without holes.</p>
Processing of 3-D Polygon Mesh Model and Radio Propagation Simulations in a Cave: Surface Reconstruction from Point Cloud, Simplification of the Mesh, and Ray Tracing
<p><strong>ABOUT</strong></p><p>This repository includes mesh data from cave geometry scanning and processing, and radio propagation data from ray tracing simulations.</p><p>The geometry data is obtained with laser scanning in a cave in Slovenija. </p><p>The geometry processing includes (i) 3-D shape reconstruction - surface reconstruction from point cloud data and (ii) simplification - reduction of the geometric complexity of the 3-D mesh model. </p><p>The radio propagation data is obtained using CloudRT [1] ray-tracing simulator. </p><p>The obtained propagation-related quantities include information about the propagation mechanism, interactions with the geometry, received power, delay, azimuth and elevation angles of arrival and departure, and path loss. </p><p> </p><p><strong>AUTHORS</strong></p><p>Teodora Kocevska, Andrej Hrovat, Tomaž Javornik</p><p>Department of Communication Systems</p><p>Jožef Stefan Institute, SI-1000 Ljubljana, Slovenia</p><p>teodora.kocevska@ijs.si</p><p> </p><p><strong>GEOMETRY PROCESSING</strong></p><p>The cave segment used for the propagation calculations is selected from a point cloud obtained in a cave in Litia, Slovenia. The point cloud is obtained with 3-D laser scanning of the environment. The selected segment is approx. 58 m long. Several parameter configurations were considered for 3-D shape reconstruction, including Poisson surface reconstruction with octree depths of 8, 10, and 12. Geometries that represent the cave shape and have different levels of complexity were created and studied. In the simplification process, one and two-stage simplification was explored using the Quadric Edge Collapse Decimation approach. </p><p> </p><p><strong>RADIO SETUP</strong></p><p>The transmitter (Tx) is fixed at the entrance of the cave and the receiver (Rx) is moved along the cave in 40 positions with a step of 1 m.</p><p>Omnidirectional antennas at the Tx and Rx sites and vertical polarization are considered. The antenna is mounted 1.5 m above the ground.</p><p>The start frequency is 3.5 GHz, the end frequency is 3.6 GHz and the step is 10 MHz. Direct propagation and first-order reflection are considered. </p><p>The cave geometry is represented by a triangular mesh, and the material of the cave is wet earth. The material electromagnetic properties are selected according to the specifications presented in [2].</p><p> </p><p><strong>FOLDER STRUCTURE</strong></p><p>The folder structure is:</p><p> - Polygon_Mesh_Models</p><p> <i># 3-D environment models with varying </i>levels<i> of geometry complexity</i></p><p> - Reconstruction_Segmen1_Poisson_Surface_Reconstruction</p><p> - Simplification_Segment1_Quadric_Edge_Collapse_Decimation</p><p> - Propagation_Data</p><p> <i># Propagation quantities of all rays between a transmitter and receiver</i></p><p> - AllRay_PropData</p><p> - PathLoss</p><p> - readme.txt</p><p> - RayTracing_EnvironmentModel</p><p> <i> # Final environment model used for ray tracing simulations</i></p><p> - Cave_MeshModel.json</p><p> - Cave_MeshModel.skb</p><p> - Cave_MeshModel.skp</p><p> - RayTracing_MaterialProperties</p><p> <i># Properties of the materials in the environment</i></p><p> - materials.json</p><p> - materials.mtl</p><p> - readme.txt</p><p> - Cave_Length.txt</p><p> <i># Length between selected locations in the environment</i></p><p> - Cave_Segment1_visual.png</p><p> <i> # Visualization of the environment segment used for propagation calculation</i></p><p> - readme.txt</p><p> <i># Overall description </i></p><p><strong>REFERENCES</strong></p><p>[1] D. He, B. Ai, K. Guan, L. Wang, Z. Zhong, and T. Kürner, "The Design and Applications of High-Performance Ray-Tracing Simulation Platform for 5G and Beyond Wireless Communications: A Tutorial," in IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 10-27, First quarter 2019, doi: 10.1109/COMST.2018.2865724.</p><p>[2] R. sector of International Telecommunication Union (ITU-R), "Effects of building materials and structures on radio wave propagation above about 100 MHz," International Telecommunication Union, ITU-R Recommendation P.2040-2, 2021.</p><p> </p><p><strong>ACKNOWLEDGEMENT</strong></p><p>This work was supported by the Slovenian Research Agency under grant <strong>J2-3048</strong>.</p><p> </p>
Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery (Polygons)
<p>Automatically generated dataset of glacier outlines from the journal article: "Accelerated change in the glaciated environments of western Canada revealed through trend analysis of optical satellite imagery"</p> <p>Research paper: https://www.sciencedirect.com/science/article/pii/S0034425721005824</p> <p>More information can be found here: https://github.com/bevingtona/glacier_change_western_canada</p>
Dataset of polygons with the contour of 900 juniper shrubs used to track shrub growth from 1977 to 2020 in Sierra Nevada (Spain) using very high resolution aerial and satellite RGB images.
<p><strong>This database provides as polygons the contours of 900 juniper shrubs (<em>Juniperus communis L.</em> and <em>Juniperus sabina L.</em>) along 5 decades (years 1977, 1984, 2001, 2010 and 2020). The contour of each of 900 shrubs manually mapped using the Google Satellite composite for the year 2020) was tracked back in time using orthophotos provided by REDIAM. Contours were obtained by manual annotation as polygon shapefiles in QGIS 3.10.3. Additionally, for the year 2020, the polygons were characterized with five attributes that gather ecological information: Morphotype (Hemispherical, Striped, Senescent, With rock), Presence of surrounding vegetation (Bare Soil, Surrounding Vegetation), Presence of nearby human land-uses (Surrounded by human facilities within 250 meters, Non-anthropized environment) Health status (as percentage of canopy cover with brown foliage: values between 0-5, where 0 corresponds to 100% photosynthetically active cover, decreasing the photosynthetically active cover until category 5 which corresponds to 100% damaged cover), and the subjective annotation certainty of the GIS technician (values between 0-5, where the value 0 corresponds to a very uncertain annotation up to the value 5 which corresponds to a fairly certain annotation). </strong></p>
NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes
<p>This repository holds all of the raw data generated by my C++ code for a paper "NURBS Enhanced Virtual Element Methods for the Spatial Discretisation of the Multigroup Neutron Diffusion Equation on Curvilinear Polygonal Meshes".</p> <p>The C++ code solves the neutron diffusion equation using a novel spatial discretisation called the Virtual Element Method.</p> <p>Alongside the raw data (stored in VTK and HDF5 files) are post-processing python scripts which read the raw data, compute meaningful quantities of interest and generate plots/figures.</p>
Dataset for "Skyrmion states in thin confined polygonal nanostructures"
<p>This dataset provides micromagnetic simulation data collected from a series of computational experiments on the effects of polygonal system shape on the energy of different magnetic states in FeGe. The data here form the results of the study ‘Skyrmion states in thin confined polygonal nanostructures.’</p> <p>The dataset is split into several directories:</p> <p><strong>Data</strong></p> <p><em>square-samples and triangle-samples</em></p> <p>These directories contain final state ‘relaxed’ magnetization fields for square and triangle samples respectively. The files within are organised into directories such that a sample of side length d = 40nm and which was subjected to an applied field of 500mT is labelled d40b500. Within each directory are twelve VTK unstructured grid format files (with file extension “.vtu”). These can be viewed in a variety of programmes; as of the time of writing we recommend either ParaView or MayaVi. The twelve files correspond to twelve simulations for each sample simulated, corresponding to twelve states from which each sample was relaxed - these are described in the paper which this dataset accompanies, but we note the labels are:</p> <p>‘0’, ‘1’, ‘2’, ‘3’, ‘4’, ‘h’, ‘u’, ‘r1’, ‘r2’, ‘r3’, ‘h2’, ‘h3’</p> <p>where:</p> <ul> <li>0 - 4 are incomplete to overcomplete skyrmions,</li> <li>h, h2 and h3 are helical states with different periodicities</li> <li>r1-r3 are different random states</li> <li>u is the uniform magnetisation</li> </ul> <p>The vtu files are labelled according to parameters used in the simulation. For<br> example, a file labelled ‘160_10_3_0_u_wd000000.vtu’ encodes that:</p> <ol> <li> <p>The simulation was of a sample with side length 160nm.</p> </li> <li> <p>The simulation was of a sample of thickness 10nm.</p> </li> <li> <p>The maximum length of an edge in the finite element mesh of the sample was 3nm.</p> </li> <li> <p>The system was relaxed from the ‘u’.</p> </li> <li> <p>‘wd’ encodes that the simulation was performed with a full demagnetizing<br> calculation.</p> </li> </ol> <p><em>square-npys and triangle-npys</em></p> <p>These directories contain computed information about each of the final states stored in square-samples and triangle-samples. This information is stored in NumPy npz files, and can be read in Python straightforwardly using the function numpy.load. Within each npz file, there are 8 arrays, each with 12 elements. These arrays are:</p> <ol> <li>‘E’ - corresponds to the total energy of the relaxed state.</li> <li>‘E_exchange’ - corresponds to the Exchange energy of the relaxed state.</li> <li>‘E_demag’ - corresponds to the Demagnetizing energy of the relaxed state.</li> <li>‘E_dmi’ - corresponds to the Dzyaloshinskii-Moriya energy of the relaxed state.</li> <li>‘E_zeeman’ - corresponds to the Zeeman energy of the relaxed state.</li> <li>‘S’ - Calculated Skyrmion number of the relaxed state.</li> <li>‘S_abs’ - Calculated absolute Skyrmion number - see paper for calculation details.</li> <li>‘m_av’ - Computed normalised average magnetisation in x, y, and z directions for relaxed state</li> </ol> <p>The twelve elements here correspond to the aforementioned twelve states relaxed from, and the ordering of the array is that of the order given above.</p> <p><em>square-classified and triangle-classified</em></p> <p>These directories contain a labelled dataset which gives details about what the final state in each simulation is. The files are stored as plain text, and are labelled with the following structure (the meanings of which are defined in the paper which this dataset accompanies):</p> <ol> <li>iSk - Incomplete Skyrmion</li> <li>Sk, or a number n followed by Sk - n Skyrmions in the state.</li> <li>He - A helical state</li> <li>Target - A target state.</li> </ol> <p>The files contain the names of png files which are generated from the vtu files in the format ‘d_165b_350_2.png’. This example, if found in the ‘Sk.txt’ file, means that the sample which was 165nm in side length and which was relaxed under a field of 350mT from initial state 2 was found at equilibrium in a Skyrmion state.</p> <p><strong>Figures</strong></p> <p><strong><em>square-pngs and triangle-pngs</em></strong></p> <p>These directories contain generated pngs from the vtu files. These are included for convenience as they take several hours to generate. Each directory contains three subdirectories:</p> <p><em>all-states</em></p> <p>This directory contains the simulation results from all samples, in the format ‘d_165b_350_2.png’, which means that the image contained here is that of the 165nm side length sample relaxed under a 350mT field from initial state 2.</p> <p><em>ground-state</em></p> <p>This directory contains the images which correspond to the lowest energy state found from all of the initial states. These are labelled as ‘d_180b_50.png’, such that the image contained in this file is the the lowest energy state found from all twelve simulations of the 180nm sidelength under a 50mT field.</p> <p><em>uniform-state</em></p> <p>This directory contains the images which correspond to the states relaxed only from the uniform state. These are labelled such that an image labelled ‘d_55b_100.png’ is the state found from relaxing a 180nm sample under a 100mT applied field.</p> <p><em><strong>phase-diagrams</strong></em></p> <p>These are the generated phase diagrams which are found in the paper.</p> <p><strong>scripts</strong></p> <p>This folder contains Python scripts which generate the png files mentioned above, and also the phase diagram figures for the paper this dataset accompanies. The scripts are labelled descriptively with what they do - for e.g. ’triangle-generate-png-all-states.py’ contains the script which loads vtu files and generates the png files. The exception here is ’render.py’ which provides functions used across multiple scripts. These scripts can be modified - for example; the function 'export_vector_field' has many options which can be adjusted to, for example, plot different components of the magnetization.</p> <p>In order to run the scripts reproducibly, in the root directory we have provided a Makefile which builds each component. In order to reproduce the figures yourself, on a Linux system, ParaView must be installed. The Makefile has been tested on Ubuntu 16.04 with ParaView 5.0.1. In addition, a number of Python dependencies must also be installed. These are:</p> <ul> <li>scipy >=0.19.1</li> <li>numpy >= 1.11.0</li> <li>matplotlib == 1.5.2</li> <li>pillow>=3.1.2</li> </ul> <p>We have included a requirements.txt file which specifies these dependencies; they can be installed by running 'pip install -r requirements.txt' from the directory.</p> <p>Once all dependencies are installed, simply run the command ‘make’ from the shell to build the Docker image and generate the figures. Note the scripts will take a long time to run - at the time of writing the runtime will be on the order of several hours on a high-specification desktop machine. For convenience, we have therefore included the generated figures within the repository (as noted above). It should be noted that for the versions used in the paper, adjustments have been made after the generation of the figures, (for e.g. to add images of states within the metastability figure, and overlaying boundaries in the phase diagrams).</p> <p>If you want to reproduce only the phase diagrams, and not the pngs, the command ‘make phase-diagrams’ will do so. This is the smallest part of the figure reproduction, and takes around 5 minutes on a high-specification desktop.</p>
Hailstorm Identification and Tracking over Brazil (HIToB): A Storm Polygons Database From GOES ABI Data from 2018 to 2023
<p>This dataset comprises a detailed record of deep convective storm events tracked across South America from 2018 to 2023, utilizing brightness temperature (BT) data from Channel 13 of the GOES-16 Advanced Baseline Imager (ABI) and the TATHU (Tracking and Analysis of Thunderstorms) toolset, that caused hail-fall over Brazil. The database includes storm identification, tracking details, and associated meteorological variables such as brightness temperature statistics inside the storm polygon at each scene and event classifications (e.g., spontaneous generation, continuity, split, merge). The storms were detected and tracked based on brightness temperature threshold of 235 K, with tracking data refined by a 10% overlap criterion between sequential scenes. The tracked convective systems were filtered for intersections in space and time with verified hail reports from Prevots group. The whole family of storm polygons that matched the reports were exported to this database with SpatiaLite enabled dtaa format, in order to make it easier for spatial data queries and analysis. Some example queries using Python library SQLAlchemy are displayed in the code repository as well as the process of creating the tables in the database.<br><br>The data is organized in three tables: "storms", "storm_events" and "intersections". In table "storms" are the records of storm families identifier. Each identifier represents a sequence of storm polygons tracked over subsequent satellite scenes. Table "storm_events" holds the evolution of the storm's geometry through its lifecycle, including BT's mean, minimum and standard deviation inside the storm polygon; as well as storm's pixel count (i.e. storm size). Intersections table stores every instance where a storm event polygon intersects with a hailstorm report's buffer at the corresponding time. In total, there are 9893 intersections belonging to 2172 unique storm families.</p>
Rational polygons with exactly one interior lattice point
<p>This record contains</p> <ul> <li>maximal \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 10\),</li> <li>all \(k\)-rational polygons with exactly one interior lattice point for \(1 \leq k \leq 4\),</li> <li>all Ehrhart quasipolynomials of these polygons for \(1 \leq k \leq 5\).</li> <li>all \(k\)-rational LDP polygons (=almost \(k\)-hollow LDP polygons) for \(1 \leq k \leq 5\).</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by \(k\) (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_k<k>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the \(3\)-rational polygons with normalized area \(30\) having \(5\) vertices are located in "k3_all.h5" under the dataset "/a30/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by \(k\), stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((k\cdot x_1,k\cdot y_1,k \cdot x_2,k\cdot y_2,k \cdot x_3,k \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all \(3\)-rational polygons with normalized area \(30\) are located in "ehrhart_k3.h5" under the dataset "/a30". Each Ehrhart quasipolynomial is stored as a \(3 \times k\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times k\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Rational polygons with no interior lattice points
<p>This record contains</p> <ul> <li>maximal \(k\)-rational polygons with no interior lattice points for \(1 \leq k \leq 21\),</li> <li>all \(k\)-rational polygons with no interior lattice points, except those that can be realized in the strip \( \mathbb{R} \times [0,1] \) for \(1 \leq k \leq 6\),</li> <li>all Ehrhart quasipolynomials of these polygons for \(1 \leq k \leq 6\).</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by \(k\) (so that they are integral). For \(1 \leq k \leq 6\), the files that contain all polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_k<k>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the \(3\)-rational polygons with normalized area \(30\) having \(5\) vertices are located in "all_k3.h5" under the dataset "/a30/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by \(k\), stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((k\cdot x_1,k\cdot y_1,k \cdot x_2,k\cdot y_2,k \cdot x_3,k \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all \(3\)-rational polygons with normalized area \(30\) are located in "ehrhart_k3.h5" under the dataset "/a30". Each Ehrhart quasipolynomial is stored as a \(3 \times k\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times k\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Half-integral polygons with few interior lattice points
<p>This record contains</p> <ul> <li>maximal half-integral polygons with up to 40 interior lattice points,</li> <li>all half-integral polygons with up to 12 interior lattice points,</li> <li>all Ehrhart quasipolynomials of half-integral polygons with up to 16 interior lattice points,</li> <li>all half-integral polygons with up to 15 collinear interior lattice points.</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by two (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_i<i>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the half-integral polygons with \(8\) interior lattice points having normalized area \(60\) with \(5\) vertices are located in "all_i8.h5" under the dataset "/a60/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by two, stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((2\cdot x_1,2\cdot y_1,2 \cdot x_2,2\cdot y_2,2 \cdot x_3,2 \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all half-integral polygons \(8\) interior lattice points having normalized area \(60\) are located in "ehrhart_i8.h5" under the dataset "/a60". Each Ehrhart quasipolynomial is stored as a \(3 \times 2\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times 2\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
3-rational polygons with few interior lattice points
<p>This record contains</p> <ul> <li>maximal 3-rational polygons with up to 5 interior lattice points,</li> <li>all 3-rational polygons with up to 4 interior lattice points,</li> <li>all Ehrhart quasipolynomials of 3-rational polygons with up to 5 interior lattice points.</li> </ul> <p>The maximal polygons are stored as text files with one polygon per line. Each line contains the vertices of the polygon multiplied by two (so that they are integral). The other polygons as well as the Ehrhart quasipolynomials are stored using the <a href="https://www.hdfgroup.org/solutions/hdf5/">HDF5</a> file format. All polygons and Ehrhart quasipolynomials have been obtained using <a href="https://github.com/justus-springer/RationalPolygons.jl">RationalPolygons.jl</a>.</p> <h3>Structure of the HDF5 files for polygons</h3> <p>In the HDF5 files "all_i<i>.h5", the polygons are split into different datasets according to their normalized area and number of vertices. We use "a" to denote the normalized area (twice the euclidian area) and "n" to denote the number of vertices. For example, the 3-rational polygons with \(8\) interior lattice points having normalized area \(60\) with \(5\) vertices are located in "all_i8.h5" under the dataset "/a60/n5". Each dataset of polygons is one-dimensional with one entry per polygon. A polygon is stored using a compound datatype with \(2 \cdot n\) fields of integers. These integers are the vertices of the polygon multiplied by three, stored in column major layout. For example, a triangle with vertices \((x_1,y_1), (x_2,y_2)\) and \((x_3,y_3)\) is stored as the tuple \((3\cdot x_1,3\cdot y_1,3 \cdot x_2,3\cdot y_2,3 \cdot x_3,3 \cdot y_3)\). Moreover, each file contains the special two-dimensional dataset "numbers_of_polygons" that stores the numbers of polygons for a given normalized volume and number of vertices.</p> <h3>Structure of the HDF5 files for Ehrhart quasipolynomials</h3> <p>In the HDF5 files "ehrhart_k<k>.h5", the Ehrhart quasipolynomials are split into different datasets according to the normalized area of the associated polygon. We use "a" for the normalized area (twice the euclidian area). For example, the Ehrhart quasipolynomials of all 3-rational polygons \(8\) interior lattice points having normalized area \(60\) are located in "ehrhart_i8.h5" under the dataset "/a60". Each Ehrhart quasipolynomial is stored as a \(3 \times 3\) integral matrix. Hence each dataset is three-dimensional, with the first dimension to enumerate the quasipolynomials and the latter two dimensions to store each quasipolynomial itself. For the way Ehrhart quasipolynomials are encoded as \(3 \times 3\) integral matrices, we refer to the <a href="https://justus-springer.github.io/RationalPolygons.jl/dev/polygons/#Ehrhart-Theory">relevant documentation of RationalPolygons.jl</a>.</p>
Simulation results: CryoGrid 3 set-up for ice-wedge polygons
<p>This dataset contains numerical simulation results obtained with the CryoGrid 3 permafrost model for a field site in northeast Siberia.</p> <p>The model code and settings which were used to create these results are deposited at <a href="http://doi.org/10.5281/zenodo.2601973">10.5281/zenodo.2601973</a>.</p> <p>A journal article which describes the simulation results scientifically has been published in the scientific journal <em>The Cryosphere</em> as <a href="http://doi.org/10.5194/tc-13-1089-2019">10.5194/tc-13-1089-2019</a>.</p> <p> </p> <p>The dataset contains two types of simulations described in the article</p> <p><em>VALIDATION_200710-201412_*</em>: These simulations of a 7-year period which been used to evaluate the model performance by comparison to field observations.</p> <p><em>LONGTERM_197910-204412_*</em>: These simulations of a 65-year period have been used to investigate the long-term dynamics of the model as described in the article.</p>
Point, polygon, or marker? In search of the best geographic entity for mapping Cultural Ecosystem Services using the online PPGIS tool, "My Green Place."
<p>Excel files include the raw database and the processed data that led to the quadrat analyses. The "Matrix_raw data" file includes the raw data as downloaded from the server. This data was cleaned and organized for its posterior use. "Quadrat analyzes "file includes all the quadrat analyses resulting in each research question in the paper except question four. Question 4 can be seen in the file "Water analysis_Blaarmeersen." All excel files come with a "CODE" tab that describes each of the codes used, their meaning, and ways that were calculated where necessary. Two zip files include all the GIS files. The first one includes the GIS files from which "Matrix_raw data" was built from. The second folder includes the resulting maps from the quadrat analyses. In order to visualize them as in the paper, configure the symbology tab at the GIS software in quantile and the categories number, as shown in the paper.</p> <p>The production of the files in the "GIS_Processed data" folder was done via a repetitive line of commands in ArcGIS pro. The same process was followed for each one of the quadrat analysis described in the paper. Refer to "<a href="https://zenodo.org/api/files/72403e0b-78a9-4bbf-8dca-ed9b8702e3ae/Reproduction%20commands%20and%20parameters.pdf">Reproduction commands and parameters.pdf</a>" for further information.</p>
Polygons with small denominator containing a small number of lattice points
<p>The denominator of a rational polytope \(P\) is an integer \(r\) such that the dilated polytope \(rP\) has lattice point vertices. The size of a polytope is the number of lattice points it contains. This dataset contains polygons with denominator 2 and 3 with small size, classified using a growing algorithm as described in [HHK24].</p> <p>The data consists of files "denom_r_size_k_polygons.txt" which record the denominator \(r\) size \(k\) polygons \(P\). Each entry consists of the vertices and volume of \(rP\), the Ehrhart \(\delta\)-vector/\(h^*\)-vector of \(P\), and an ID number, which is unique among polygons of given size and denominator. Entries are ordered by their ID number. There are 50,564 entries in total.</p> <p><strong>Example entry:</strong></p> <p>ID=1<br>Vertices=[[ 1, 0 ], [ 0, 1 ], [ 3, 5 ]]<br>Volume=7<br>DeltaVec=[ 1, 0, 3, 7, 3, 0 ]</p> <p>If you make use of this data, please cite [HHK24] and the DOI for this data:</p> <p>doi:10.5281/zenodo.14230584</p> <p><strong>References:</strong></p> <p>[HHK24] Girtrude Hamm, Johannes Hofscheier, Alexander Kasprzyk, Classification and Ehrhart Theory of Denominator 2 Polygons. (preprint) arxiv:2411.19183</p>
Northwestern Europe Wildfire Perimeter Polygons [2012-2022]
<p>This dataset contains KML files for wildfires detected from VIIRS across northwestern Europe from 2012 to 2022. Each file contains the fire progression at each satellite overpass. These files were used to detect the rate of spread for each wildfire event to characterize the fire behavior in temperate northwest Europe, a region with limited fire history. </p>
Code and data for "Computational morphology of debris and alluvial fans on irregular terrain using the visibility polygon"
<p>This code is for simulating conical fan morphologies. </p> <p>The code and the dataset can be read/run by using Matlab. The description is as follows:</p> <p>1. Dataset (FieldCase1Input.mat, FieldCase2Input.mat) provides the input data needed to simulate the two field cases. The simulations can be run using the code Case4_FieldCase1.m and Case5_FieldCase2.m. The results can be compared with the provided output data (FieldCase1Output.mat, FieldCase2Output.mat).</p> <p>2. Code for three idealized cases, including the comparison of analytical solutions and model simulations are provided.</p> <p>3. Function VisiPolygon is the algorithm for calculating point visibility polygon. Function FanTopo_slope is the model for simulating fan morphology. </p> <p><br> </p>
Text-fig. 2. Celtis popsii sp. nov., UF 279-34460. a: Growth ring with earlywood of multiple rows of vessels solitary and in radial multiples; latewood vessels in wavy tangential bands, TS. b: Growth ring boundary, latewood vessels in multiples with axial parenchyma confluent, TS. c: Simple perforation plates, alternate intervessel pits, polygonal in outline, TLS. d: Vessel-ray parenchyma pits to right of VRP, oval in outline, with slightly reduced borders, RLS. e: Rays tending to two sizes, some multiseriate rays with distinct sheath cells, multiseriate rays usually with 1 marginal row of square to upright cells, occasionally with 4 or more; uniseriate rays less than 10 cells high, TLS. f: Detail of multiseriate ray with distinct sheath cells, vessel element end walls, TLS. Scale bars: 200 µm in a, e; 100 µm in b; 50 µm in c, f; 20 µm in d. in A Diverse Assemblage Of Late Eocene Woods From Oregon, Western Usa
Text-fig. 2. Celtis popsii sp. nov., UF 279-34460. a: Growth ring with earlywood of multiple rows of vessels solitary and in radial multiples; latewood vessels in wavy tangential bands, TS. b: Growth ring boundary, latewood vessels in multiples with axial parenchyma confluent, TS. c: Simple perforation plates, alternate intervessel pits, polygonal in outline, TLS. d: Vessel-ray parenchyma pits to right of VRP, oval in outline, with slightly reduced borders, RLS. e: Rays tending to two sizes, some multiseriate rays with distinct sheath cells, multiseriate rays usually with 1 marginal row of square to upright cells, occasionally with 4 or more; uniseriate rays less than 10 cells high, TLS. f: Detail of multiseriate ray with distinct sheath cells, vessel element end walls, TLS. Scale bars: 200 µm in a, e; 100 µm in b; 50 µm in c, f; 20 µm in d.
LTER Site Oberes Stubachtal (Austria) - Boundary (polygon, shp)
<p>LTER Site Oberes Stubachtal - Boundary (polygon, shp)</p>
Sensitivity analysis of ice wedge temperature to polygonal microtopography
<p>This repository contains input files, model output, and postprocessing scripts from a sensitivity analysis of ice wedge temperature to polygonal rim height and trough depth. Simulations are constructed in Amanzi-ATS (https://github.com/amanzi/ats), v. 0.86. See the included README file for instructions using this content.</p> <p>This analysis is presented in:</p> <ul> <li>Abolt CJ, Young MH, Atchley AL, Harp DR. 2018. Microtopographic control on the ground thermal regime in ice wedge polygons. <em>The Cryosphere</em>, 12, 1957-1968. DOI:10.5194/tc-12-1957-2018.<br> </li> </ul>
Polygon Aggregration Instances for Monotone Parametric Minimum Cut
<p>This is a collection of 44 benchmark instances for the parametric minimum s-t-cut problem with source-sink-monotone edge capacities. See README.md for further information. </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.