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Dataset for Exciton Fine Structure in 2D Perovskites: The Out‐of‐Plane Excitonic State
<p><span>This dataset includes raw photoluminescence and transmission spectra acquired at zero and high magnetic field (identified by the folder name). Samples of different origins have been identified and the folder name includes the institution where the sample was synthetized. In the filename, one can find the information concerning the excitation power used, the wavelength of the excitation laser, the acquisition time, the degree read out on the half-wavelength plate.</span></p> <p><span>PL _ 5K_0.5uW_511.5nm_spot8 _aquisition time _ number of aquisitions _ X.csv</span></p> <p><span> </span></p> <p><span>R _5K_ 1uW_511.5nm _ aquisition time _ number of aquisitions _spot8 _ X.csv</span></p> <p><span> </span></p> <p><span>X: degree on the half-wave plate</span></p> <p><span> </span></p> <p><span>Temperature:5K</span></p> <p><span>Excitation power: x uW </span></p> <p><span>Spot y - spot on a sample</span></p> <p><span>Grating center: zzz nm</span></p>
Data and Software of "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids"
<h1><strong>Id: Data of following publication</strong></h1> <p>title = "Development of a Geometric Modeling Strategy for the Generation of Representative Unit Cells in 2D Braids",<br>journal = "<span>Composite Structures</span>",<br>volume =" 348",<br>pages = "118503",<br>year = "2025",<br>doi = "<a href="https://doi.org/10.1016/j.compstruct.2024.118503" target="_blank" rel="noopener">10.1016/j.compstruct.2024.118503</a>",<br>author = "José Rothkegel, Benjamin Renson, Michael Bruyneel, Ludovic Noels"</p> <p>Data doi on 10.5281/zenodo.10829042</p> <h1>pyRVE</h1> <h2><em>Python Code for Geometrical Generator for Braided Composites RVE</em></h2> <p>pyRVE is a code written in <em>Python</em> using the <em>GMSH API</em> that generates the Representative Unit Cell (RUC) of braided composites. It allows the generation of the RUC of triaxial braided for <em>Diamond</em> and <em>Regular</em> patterns.</p> <h2>Requirements</h2> <p>To run, it requires:</p> <ul> <li>The GMSH Python API, which must be built with OpenCascade support. <ul> <li>Choose a local installation directory; <code>CMAKE_INSTALL_PREFIX=$HOME/local/gmsh</code>, and <code>GMSHPY_INSTALL_DIRECTORY=$HOME/local/gmsh</code> e.g.;</li> <li>Make that directory part of your <code>export PYTHONPATH=$HOME/local/gmsh/lib:$PYTHONPATH</code>.</li> <li>After compiling use <code>make install</code>.</li> </ul> </li> <li>The CM3 app dG3D if the final RVE homogenized solution is needed (<a href="https://gitlab.onelab.info/cm3/cm3Libraries">https://gitlab.onelab.info/cm3/cm3Libraries</a>).</li> <li>Make sure that the latest version of OpenCascade (OCCT) is used. Current used version in occt-V7.8.0.</li> </ul> <h2>Usage</h2> <h3>File Structure</h3> <p>A typical run case must have a file structure, where:</p> <ul> <li><code>brd</code>: the files <code>.brd</code> and <code>.brep</code> are located here. The <code>.brd</code> is a backup of the <code>braidClass</code> instance used in the model saved using <code>pickle</code>, the <code>.brep</code> is the Boundary Representation file that can be opened with <em>GMSH</em>.</li> <li><code>csv</code>: the <code>.csv</code> file saved here is the initial output of the code. It contains the actually used dimensions and the final cover factor of the braid.</li> <li><code>data</code>: It contains <code>.csv</code> files with the material properties and the dimensions of the tows. The original model dimensions are read from here.</li> <li><code>dir</code>: In the case of running the RVE homogenization, the directions of the tow fibers are stored here. They are saved for post processing.</li> <li><code>msh</code>: the mesh file <code>.msh</code> obtained after the geometry geneartion is stores here.</li> <li><code>png</code>: in the case of automatic post processing, png files are stored here.</li> <li><code>res</code>: this folder is used to store the homogenization results. They have to be moved here.</li> <li><code>stp</code>: if acitvated, a <code>.stp</code> file of the geometry is stored here</li> <li><code>svg</code>: the projection of the geometry on the <em>x-y</em> plane is stored here.</li> <li><code>vtk</code>: A copy of the mesh file without the matrix mesh is sotred here as a `.vtk`` file.</li> </ul> <h3>How to Run</h3> <p>We will consider the current file structure to run the example in 000_Base. To run the code, it can be called from the command prompt as</p> <div> <pre><code>python3 ../../source/mainRVE.py --name <i> --pattern <pattern></code></pre> </div> <p>In this case, the <code>--name</code> refers to the index that will be given to the model, where <code><i></code> must be changed to an integer and <code>--pattern</code> refers to the wanted pattern to be used, where <code><pattern></code> must be changed to either <code>dia</code> or <code>reg</code>.</p> <blockquote> <p>Note: <code><code>--name</code>cat</code> can also be used to reproduce the regular pattern benchmark of the paper. In that case, the volume fraction of fiber in the tows is hard coded as the provided value in the reference (i.e. 0.86). For other cases, the volume fraction is evaluated from the tow cross-sections.</p> <p>Note: <code>mainRVE.py</code> must be accesible from the directory where the case is being run. This example shows the usage of the current file structure.</p> </blockquote> <h3>All Command Line Options</h3> <p>The code can be run using further options that serve different purpouses, some serving pre processing needs and other serving run administration. The different command line options are:</p> <ul> <li>Required: <ul> <li><code>--name</code> : it gives a suffix to the run model. It is usually an integer.</li> <li><code>--pattern</code> : indicates the type of pattern to be used to build the geometry. The two current options are <code>dia</code> for diamond and <code>reg</code> for regular.</li> </ul> </li> <li>Optional <ul> <li><code>-dG3D</code>: it indicates that the homogenization of the generated RUC is to be perfomed.</li> <li><code>-GMSH</code> : it indicates that GMSH must be open upon competion of the generation of the mesh.</li> <li><code>-loadModel</code> : it will try to load a premade model. It will ignore <code>--pattern</code>.</li> <li><code>--rndPrm</code> : it will generate randomized geometrical parameters. It can be used to generate batches of results. It takes an argument that can be <code>2</code>, <code>4</code> or <code>6</code>. Currently, <code>2</code> gives a random value for <code>s_axial</code> and <code>theta</code>, <code>4</code> randomizes the same as <code>2</code> and adds <code>h_axial</code> and <code>h_bias</code>, and <code>6</code> randomizes the same as <code>4</code> and adds <code>w_axial</code> and <code>w_bias</code>.</li> </ul> </li> <li>Pre-Processing <ul> <li><code>-refCF</code>: it tells the code to generate a grid of values for <code>s_axial</code> and <code>theta</code> where only the cover factor is obtained. It is meant for posterior graphing purposes.</li> </ul> </li> </ul> <h3>Examples</h3> <p>Following the run options, a few examples are indicated</p> <ul> <li>A basic mesh generation run for the basic data, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg</code></pre> </div> <ul> <li>The generation of the cover factor data and export, considering a <strong>regular pattern</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --pattern reg -refCF</code></pre> </div> <ul> <li>A run for the modified basic data, where the <strong>2</strong> parameters are modified <em>randomly</em>, considering a <strong>regular pattern</strong>, for a model named <strong>2</strong>:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 --pattern reg --rndPrm 2</code></pre> </div> <ul> <li>A run, where model <strong>2</strong> already exists in <code>brd</code> folder but not the <code>.msh</code> and <code>.vtk</code> files:</li> </ul> <div> <pre><code>python3 ../../source/mainRVE.py --name 2 -loadModel </code></pre> </div> <h2>Code Structure</h2> <p>The code is implemented into Python files, where <code>mainRVE.py</code> runs the whole code. The files are:</p> <ul> <li>Braid: <ul> <li><code>braidClass.py</code> :</li> <li><code>bzrPairClass.py</code> :</li> </ul> </li> <li>Geometry <ul> <li><code>bezrClass.py</code> :</li> <li><code>bilnClass.py</code> :</li> <li><code>patchClass.py</code> :</li> <li><code>pntSetClass.py</code> :</li> <li><code>pointClass.py</code> :</li> <li><code>sctnClass.py</code> :</li> <li><code>stripeClass.py</code> :</li> <li><code>surfClass.py</code> :</li> <li><code>surfOffClass.py</code> :</li> </ul> </li> <li>Material: <ul> <li><code>chamis.py</code> :</li> </ul> </li> <li>Tools: <ul> <li><code>dataIO.py</code> :</li> <li><code>postDirection.py</code> :</li> <li><code>tool.py</code> :</li> <li><code>toolData.py</code> :</li> </ul> </li> <li><code>curveClass.py</code> :*</li> </ul> <p> </p> <p> </p>
Dataset for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries
<p>A dataset for publication Datase for publication Reaction Mechanism and Performance of Innovative 2D Germanane-Silicane Alloys SixGe1−xH Electrodes in Lithium-Ion Batteries including all relevant data used in the manuscript. Information on how to use the dataset are included in the readme file.</p>
Agricultural Fields 2D and 3D Models Dataset
<p><strong>Agricultural Fields 2D and 3D Models Dataset</strong></p> <p><strong>Introduction</strong></p> <p>This dataset was created to address the lack of comprehensive datasets in the literature that provide necessary information to evaluate and validate path planning approaches on both 2D and 3D surfaces of agricultural fields. It comprises 30 manually-selected agricultural fields located in France, chosen to cover a diverse range of shapes and sizes (from 1.83 to 13.21 hectares). The dataset includes simple shapes that do not require field decomposition and more complex shapes that necessitate field decomposition, ensuring a broad representation of real-world scenarios. </p> <p>This dataset was initially produced to validate our Complete Coverage Path Planning approach, and we are pleased to make these data available for future research. In sharing this dataset, we kindly ask that users cite this dataset in any publications or presentations that make use of the data. This will help acknowledge our contribution and encourage further collaboration and research in this area.</p> <p><strong>Background</strong></p> <p>Agricultural field shapes result from a complex interplay of historical, geographic, and topographic factors, as well as cultural and economic practices. Fields in countries with a more recent history of land ownership and partitioning may have simpler shapes, while those with more complex histories may have irregular shapes. Geography and topography also influence field shapes, with fields in flat, open areas having simpler shapes than those in mountainous or hilly regions. This dataset focuses on French fields due to the variety of field shapes and the availability of high-precision elevation data from the French government.</p> <p><strong>Dataset Content</strong></p> <p>For each of the 30 agricultural fields, this dataset provides the following information in separate files:</p> <ul> <li>Aerial image (PNG)</li> <li>2D polygon (XML)</li> <li>2D triangulated surface (PLY) with a grid resolution of 0.25 m</li> <li>Elevation grid (PLY) with a grid resolution of 5 m</li> <li>3D triangulated surface (PLY) with a grid resolution of 0.25 m</li> <li>Set of 2D line segments representing access segments (XML)</li> <li>Set of dividing lines for fields 20-30 to decompose them into sub-polygons in different ways</li> <li>The obtained result by our "Advanced 3D Hybrid Path Planning with Multiple Objectives for complete coverage of agricultural field by wheeled robots", which includes: <ul> <li>A way-points in a CSV file</li> <li>An illustration of the result projected on the field surface</li> </ul> </li> </ul> <p><strong>Note:</strong> All coordinates are represented in Cartesian coordinates with centimeter precision.</p> <p>The table below provides links to the field data in the Géoportail platform and coordinates (longitude and latitude) of a point inside each field for all 30 fields. These links and coordinates can be used to access the data and for visualization purposes.</p> <table> <tbody> <tr> <th>Field</th> <th>Link</th> <th>Lon / Lat</th> </tr> </tbody> <tbody> <tr> <td>1</td> <td><a href="https://bit.ly/3FYtuKu">bit.ly/3FYtuKu</a></td> <td>7.435° / 48.7732°</td> </tr> <tr> <td>2</td> <td><a href="https://bit.ly/3WGAyRI">bit.ly/3WGAyRI</a></td> <td>7.474° / 48.7825°</td> </tr> <tr> <td>3</td> <td><a href="https://bit.ly/3zX1vqJ">bit.ly/3zX1vqJ</a></td> <td>2.9205° / 49.8115°</td> </tr> <tr> <td>4</td> <td><a href="https://bit.ly/3DJL0PI">bit.ly/3DJL0PI</a></td> <td>1.6713° / 47.9864°</td> </tr> <tr> <td>5</td> <td><a href="http://bit.ly/3htb8H3">bit.ly/3htb8H3</a></td> <td>3.3216° / 50.6623°</td> </tr> <tr> <td>6</td> <td><a href="https://bit.ly/3WGTfER">bit.ly/3WGTfER</a></td> <td>7.4311° / 48.8245°</td> </tr> <tr> <td>7</td> <td><a href="https://bit.ly/3DP8vqG">bit.ly/3DP8vqG</a></td> <td>2.4845° / 50.3106°</td> </tr> <tr> <td>8</td> <td><a href="https://bit.ly/3NLmQJf">bit.ly/3NLmQJf</a></td> <td>7.5924° / 48.831°</td> </tr> <tr> <td>9</td> <td><a href="https://bit.ly/3EeTvUo">bit.ly/3EeTvUo</a></td> <td>7.4641° / 48.8146°</td> </tr> <tr> <td>10</td> <td><a href="http://bit.ly/3UOyTrv">bit.ly/3UOyTrv</a></td> <td>1.3491° / 48.012°</td> </tr> <tr> <td>11</td> <td><a href="https://bit.ly/3zW7v30">bit.ly/3zW7v30</a></td> <td>3.4701° / 46.652°</td> </tr> <tr> <td>12</td> <td><a href="http://bit.ly/3UMC6I3">bit.ly/3UMC6I3</a></td> <td>7.5742° / 48.8071°</td> </tr> <tr> <td>13</td> <td><a href="https://bit.ly/3TjkOkA">bit.ly/3TjkOkA</a></td> <td>3.578° / 46.7016°</td> </tr> <tr> <td>14</td> <td><a href="https://bit.ly/3UAmdo0">bit.ly/3UAmdo0</a></td> <td>7.4269° / 48.8194°</td> </tr> <tr> <td>15</td> <td><a href="http://bit.ly/3GpjdXZ">bit.ly/3GpjdXZ</a></td> <td>3.5611° / 46.6875°</td> </tr> <tr> <td>16</td> <td><a href="https://bit.ly/3tcGhRN">bit.ly/3tcGhRN</a></td> <td>2.5127° / 48.2645°</td> </tr> <tr> <td>17</td> <td><a href="https://bit.ly/3zW26sE">bit.ly/3zW26sE</a></td> <td>2.6443° / 48.2546°</td> </tr> <tr> <td>18</td> <td><a href="https://bit.ly/3Trsqlq">bit.ly/3Trsqlq</a></td> <td>7.9196° / 48.9513°</td> </tr> <tr> <td>19</td> <td><a href="http://bit.ly/3DWHgKJ">bit.ly/3DWHgKJ</a></td> <td>2.1269° / 46.8124°</td> </tr> <tr> <td>20</td> <td><a href="https://bit.ly/3NN8pnT">bit.ly/3NN8pnT</a></td> <td>1.5874° / 47.1346°</td> </tr> <tr> <td>21</td> <td><a href="https://bit.ly/3DShkA3">bit.ly/3DShkA3</a></td> <td>0.6254° / 49.191°</td> </tr> <tr> <td>22</td> <td><a href="https://bit.ly/3zZK1dg">bit.ly/3zZK1dg</a></td> <td>2.7067° / 50.3336°</td> </tr> <tr> <td>23</td> <td><a href="https://bit.ly/3TmwcMC">bit.ly/3TmwcMC</a></td> <td>7.4416° / 48.7223°</td> </tr> <tr> <td>24</td> <td><a href="http://bit.ly/3E3l8OK">bit.ly/3E3l8OK</a></td> <td>3.1021° / 48.2449°</td> </tr> <tr> <td>25</td> <td><a href="http://bit.ly/3E0Raeq">bit.ly/3E0Raeq</a></td> <td>1.6183° / 49.9655°</td> </tr> <tr> <td>26</td> <td><a href="http://bit.ly/3tvN0Xg">bit.ly/3tvN0Xg</a></td> <td>3.5476° / 50.1441°</td> </tr> <tr> <td>27</td> <td><a href="https://bit.ly/3A0tZ2D">bit.ly/3A0tZ2D</a></td> <td>3.6644° / 48.0046°</td> </tr> <tr> <td>28</td> <td><a href="https://bit.ly/3fTlQGl">bit.ly/3fTlQGl</a></td> <td>1.7086° / 47.2054°</td> </tr> <tr> <td>29</td> <td><a href="http://bit.ly/3hBeLL2">bit.ly/3hBeLL2</a></td> <td>1.6893° / 47.1421°</td> </tr> <tr> <td>30</td> <td><a href="https://bit.ly/3Edm2cN">bit.ly/3Edm2cN</a></td> <td>3.1018° / 48.5853°</td> </tr> </tbody> </table> <p><strong>Hybrid_CCPP_Result Subdirectory</strong></p> <p>Hybrid_CCPP_Result subdirectory contains the results of our path planning algorithm for complete coverage of agricultural fields by wheeled robots. The provided files include way-points in CSV format and an illustration of the result projected on the field surface.</p> <p><strong>Approach Parameters</strong></p> <p>The results were obtained under the following considerations: The driving direction step size ($\ell_s$), the spacing of access segment discretization ($\ell_a$<em>) and the spacing of working trajectory discretization for slope computation </em>($\ell_{slp}$). These parameters were respectively set to $3°$, $0.5m$, and $0.5m$. The values of other parameters are listed in the table below:</p> <table> <tbody> <tr> <th>Parameter</th> <th>Description</th> <th>Value</th> </tr> </tbody> <tbody> <tr> <td>$w$</td> <td>working width</td> <td>3m</td> </tr> <tr> <td>$\gamma_{on}$</td> <td>minimum turning radius - implement on</td> <td>10m</td> </tr> <tr> <td>$\gamma_{off}$</td> <td>minimum turning radius - implement off</td> <td>2.8m</td> </tr> <tr> <td>$V_{on}$</td> <td>average speed - implement on</td> <td>4.5m/s</td> </tr> <tr> <td>$V_{gap}$</td> <td>average speed - implement transition</td> <td>1.5m/s</td> </tr> <tr> <td>$V_{off}$</td> <td>average speed - implement off</td> <td>3.5m/s</td> </tr> <tr> <td>$\ell_t$</td> <td>transition trajectory length</td> <td>1.5m</td> </tr> <tr> <td>$\ell_o$</td> <td>robot-implement offset</td> <td>1.0m</td> </tr> <tr> <td>$\Delta_{mwd}$</td> <td>minimum working distance threshold</td> <td>3m</td> </tr> <tr> <td>$p$</td> <td>number of inner trajectories</td> <td>2</td> </tr> <tr> <td>$g$</td> <td>number of gap-covering trajectories</td> <td>1</td> </tr> <tr> <td>$W_{cov}$</td> <td>weight of $S_{cov}$</td> <td>0.30</td> </tr> <tr> <td>$W_{ovl}$</td> <td>weight of $S_{ovl}$</td> <td>0.15</td> </tr> <tr> <td>$W_{nwd}$</td> <td>weight of $S_{nwd}$</td> <td>0.10</td> </tr> <tr> <td>$W_{otm}$</td> <td>weight of $S_{otm}$</td> <td>0.10</td> </tr> <tr> <td>$W_{slp}$</td> <td>weight of $S_{slp}$</td> <td>0.35</td> </tr> <tr> <td>$W_{s0}$</td> <td>weight of $\ell_{s0}$</td> <td>0.00</td> </tr> <tr> <td>$W_{s1}$</td> <td>weight of $\ell_{s1}$</td> <td>0.10</td> </tr> <tr> <td>$W_{s2}$</td> <td>weight of $\ell_{s2}$</td> <td>0.15</td> </tr> <tr> <td>$W_{s3}$</td> <td>weight of $\ell_{s3}$</td> <td>0.20</td> </tr> <tr> <td>$W_{s4}$</td> <td>weight of $\ell_{s4}$</td> <td>0.25</td> </tr> <tr> <td>$W_{s5}$</td> <td>weight of $\ell_{s5}$</td> <td>0.30</td> </tr> </tbody> </table> <p>For an in-depth understanding of these parameters, we kindly invite you to consult our published article:</p> <p>Pour Arab, D., Spisser, M. & Essert, C. (2024) <em>3D hybrid path planning for optimized coverage of agricultural fields: a novel approach for wheeled robots</em>. Journal of Field Robotics, 1–19. <a href="https://doi.org/10.1002/rob.22422">https://doi.org/10.1002/rob.22422</a></p> <p><strong>Way-point Structure</strong></p> <p>A way-point is represented by the following format:</p> <p>Point X, Point Y, Point Z, Heading, Type, Move</p> <p>where Heading is in radians, and Type and Move are according to the following structures:</p> <pre><code>enum WayPointType { WORKING = 1, TURN_OFF = 2, TURN_ON = 3, TRANSITION_OFF_TO_ON = 4, TRANSITION_ON_TO_OFF = 5 }; enum RobotMove { FORWARD = 1, REVERSE = -1 };</code></pre> <p><strong>WayPointType</strong></p> <ul> <li>WORKING: The robot implement for driving at this point must be on.</li> <li>TURN_OFF: The robot is performing a turn while its implement is off and elevated from the ground.</li> <li>TURN_ON: The robot is performing a turn while its implement is on.</li> <li>TRANSITION_OFF_TO_ON: The robot is traveling a straight transition trajectory for turning on its implement.</li> <li>TRANSITION_ON_TO_OFF: The robot is traveling a straight transition trajectory for turning off its implement.</li> </ul> <p><strong>RobotMove</strong></p> <ul> <li>FORWARD: The robot is moving forward.</li> <li>REVERSE: The robot is moving in reverse.</li> </ul> <p><strong>Files</strong></p> <ul> <li>CSV file contains the way-points generated by our proposed approach.</li> <li>SVG file providing an illustration of the result projected on the field surface.</li> </ul> <p><strong>Usage</strong></p> <p>This dataset is intended for researchers and developers working on path planning algorithms for agricultural applications. Users can leverage the data to evaluate and validate their path planning approaches in various scenarios, from simple to complex field shapes, and on both 2D and 3D surfaces.</p> <p>Please ensure that you cite this dataset appropriately in any publications or presentations that make use of the data.</p>
Dataset for Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions
<p>The dataset contains all relevant data and figures regarding the Figure 4, S4 and S5 of manuscript "Direct Chemical Lithography Writing on 2D Materials by Electron Beam Induced Chemical Reactions".</p> <p>All Figures are in tiff format and all relevant data are in csv formats. </p> <p>The data in csv format are labelled as specified in the corresping images (e.g. Figure4a.csv file corresponds to data used to plot graphs from Figure4a etc.). </p> <p>Axis labeling and units are always specified at the beginning of individual columns. If more than one curve was plotted from the csv file, the conditions can also be found at the beginning of corresponding columns.</p>
Pulseq multi-slice radial 2D phantom data
<p>Multi-slice radial2D Pulseq sequence and acquired phantom data including the trajectory information in the ISMRM raw data format. </p>
Data from: Numerical modelling of bridges in 2D shallow water flow simulations
<p>This repository includes the experimental dataset colleted in the Hydraulics Laboratory of the University of Zaragoza in 2014.</p> <p>The experiments were carried out with different bridge configurations in a straight flume for both steady and transient flow regimes.</p> <p>The data were published originally in:</p> <p>Ratia, H., Murillo, J. and García-Navarro, P. (2014), Numerical modelling of bridges in 2D shallow water flow simulations. Int. J. Numer. Meth. Fluids 75, pp. 250-272. <a href="https://doi.org/10.1002/fld.3892">https://doi.org/10.1002/fld.3892</a></p> <p>This dataset has been made available online to the research community thanks to the support of the project PID2022-137334NB-I00 funded by MCIN/AEI/10.13039/<br>501100011033 and by “ERDF/EU”.</p>
Supplemental materials to "A quasi-2D model of convectively coupled vortices"
<p>math_derivation_note: A hand-written note of key mathematical steps, mostly about section 4 and Appendix C. </p> <p>quasi-2D model.zip: The package of the quasi-2D model code.</p> <p>postprocess_code_quasi2D.zip: The package of the postprocessing codes and intermediate files (.mat) related to the quasi-2D simulations.</p> <p>postprocess_code_CM1.zip: The package of the postprocessing codes and intermediate files (.mat) related to the CM1 simulation.</p> <p>Group_dh.avi: The Group-dh experiments with varying convective intermittency (dh/H). The first, second, and third column shows Group-dh-1, Ref, and Group-dh-2. Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l</em>=30 km) normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_dL.avi: The Group-<em>l</em> experiments with varying convective filter length <em>l.</em> The first, second, and third column shows Ref (<em>l</em>=30 km), Group-<em>l</em>-1 (<em>l</em>=45 km), and Group-<em>l</em>-2 (<em>l</em>=60 km). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_fE.avi: The Group-fE experiments with varying Coriolis parameter f and Ekman number E<em>.</em> They differ in the strength of the rotational flow. The first, second, and third column shows Group-fE-1 (f=1e-5 1/s), Ref (f=1e-4 1/s), and Group-fE-2 (f=2e-4 1/s). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_eta.avi: The Group-eta experiments with varying mesoscale feedback parameter eta<em>.</em> They differ in the strength of the mesoscale feedback. The first, second, and third column shows Group-eta-1 (eta=0), Group-eta-2 (eta=1.2), and Group-eta-3 (eta=1.4). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu) if you have any questions!</p>
Perceptual maps of Heliconiini butterflies: images, 3D spaces, 2D maps, and mimicry ring listings
<h3>Summary</h3> <p>This repository contains<strong> images</strong>, <strong>3D animated spaces</strong>, <strong>2D perceptual maps with GMM</strong>, and <strong>mimicry ring lists</strong> for heliconiine butterflies complementing the analyses presented in this research paper:<em><strong> </strong>"Doré et al., 2025 - Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep."</em>.</p> <h3>Abstract</h3> <p> In 1879, Fritz Müller formulated the first mathematical evolutionary model to explain mutualistic mimicry between coexisting defended prey. Yet, the degree to which local mimicry drives the structure of prey aposematic signals at continental scale remains unclear, because the perception of pattern similarity has never been assessed at large spatial scale. Here, we implement a Citizen Science survey to quantify and analyze the structure of perceived variation in the wing patterns of heliconiine butterflies (Nymphalidae: Heliconiini) throughout the entire Neotropics. Despite a continuum of perceived wing patterns at the continental scale, we show that the convergence of sympatric species into discrete mimicry rings is ubiquitous among communities. These results expand Müller’s historical predictions by supporting the rampant convergence of prey signals across an entire continent. </p> <h3>Contents</h3> <p>This repository contains three folders:</p> <ul> <li><em>"3D_maps"</em> contains the <strong>animated 3D perceptual spaces</strong> of heliconiine wing patterns for the Citizen Science dataset (N = 432) and the Local reference for the five local communities highlighted in the article.</li> <li><em>"Clustering"</em> contains the <strong>2D perceptual maps</strong> and associated <strong>lists of mimicry rings</strong> built for each of the five local communities, for different level of clustering from GMM (K from 5 to 10).</li> <li><em>"Images"</em> contains the 432 <strong>images of dorsal wing patterns</strong> of heliconiine butterflies used in the online survey (<a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>) designed for this study.</li> </ul> <h3>How to cite</h3> <p>Please cite this research article as:</p> <blockquote> <p>Doré, M., Pérochon, E., Aubier, T.G., Le Poul, Y., Joron, M., Elias, M., 2025. Perceptual maps reveal rampant convergence in butterfly wing patterns across the Neotropics. in prep. <a href="https://doi.org/TBA">https://doi.org/TBA</a></p> </blockquote> <h3>Associated ressources</h3> <p>The source codes for the analyses carried out in the study are available on <a href="https://github.com/MaelDore/Perceptual_map_Heliconiini">GitHub</a>.<br> <br>The occurrences data and distribution maps used in this study are publicly available from Zenodo: Occurrences data at <a href="https://doi.org/10.5281/zenodo.10906853">https://doi.org/10.5281/zenodo.10906853</a>; Distribution maps at <a href="https://doi.org/10.5281/zenodo.10903661">https://doi.org/10.5281/zenodo.10903661</a>.</p> <p>The online Citizen Science survey on the perception of mimicry in wing color patterns of heliconiine butterflies is temporary available at <a href="https://memometic.cleverapps.io/">https://memometic.cleverapps.io/</a>.<br>Source code for the online Citizen Science survey are accessible on <a href="https://github.com/MaelDore/Memometic_website">GitHub</a>.</p>
MODYS-video: 2D Human pose estimation data and Dyskinesia Impairment Scale scores from children and young adults with dyskinetic cerebral palsy
<p>The dataset contains the 2D coordinates in pixels of body landmarks (wrists, ankles, shoulders, hips, knees and ankles) extracted from 188 videos of 34 children with dyskinetic cerebral palsy using DeepLabCut [1] and appertaining clinical scores of the Dyskinesia Impairment Scale (DIS) [2].</p> <p>The videos were collected during the item “lying in rest” and “sitting in rest” of the DIS at three time points during a clinical trial on the effect of intrathecal baclofen [3]. Children had a mean age of 14y2m (SD 4.0), 26 were male. Their gross motor function classification system level ranged from IV-V and their manual ability classification system level from III-V. Original videos have length of 4-35 seconds with a resolution of 720x575 pixels and are sampled with 25 Hz. We added stick figures to complement the data for context and ease of understanding. They were created from the 2D coordinates that were extracted with a likelihood >0.8.</p> <p>Clinical scoring was performed by three trained experts (according to the DIS) on the original videos. Within the items “lying in rest” and “sitting in rest” the amplitude and duration of dystonia and choreoathetosis of the trunk, proximal right arm, proximal left arm, proximal right leg and proximal left leg are scored on a 0-4 ordinal scale and calculated towards a percentage score between 0-1.</p> <p>The dataset can be used in a machine learning approach to automatically assess dystonia and choreoathetosis of children with dyskinetic cerebral palsy using 2D coordinates of body points extracted from videos.</p> <p> </p> <p>References:</p> <p>1. Mathis, A., et al., DeepLabCut: markerless pose estimation of user-defined body parts with deep learning. Nat Neurosci, 2018. 21(9): p. 1281-1289.</p> <p>2. Monbaliu, E., et al., The dyskinesia Impairment Scale: a new instrument to measure dystonia and choreoathetosis in dyskinetic cerebral palsy. Dev Med Child Neurol, 2012. 54: p. 278-283.</p> <p>3. Bonouvrie, L.A., et al., The Effect of Intrathecal Baclofen in Dyskinetic Cerebral Palsy: The IDYS Trial. Ann Neurol, 2019. 86: p. 79-90.</p> <p> </p> <p> </p>
Data set for: History-dependent domain and skyrmion formation in 2D van der Waals magnet Fe3GeTe2
<p>This data repository contains the experimental data utilised to produce the figures shown in History-dependent domain and skyrmion formation in 2D van der Waals magnet Fe3GeTe2, by M. T. Birch, L. Powalla, S. Wintz, O. Hovorka, K. Litzius, J. C. Loudon, L. A. Turnbull, V. Nehruji, K. Son, C. Bubeck, T. G. Rauch, M. Weigand, E. Goering, M. Burghard, G. Schütz.</p> <p>Experimental data analysis were performed using Python via Jupyter notebooks, utilising matplotlib, scipy, numpy and h5py libraries. Jupyter notebooks are provided to process some of the experimental data to form the figures shown in the paper.</p> <p><br> Data data is divided into folders:</p> <p>Characterisation_Data - contains EDX, TEM and AFM characterisation of the bulk FGT material and exfoliated flakes.</p> <p>Magnetometry_Data - Includes data files from the MPM3 magnetometry utilised to study the magnetisation of the bulk FGT crystal as a function of temperature and applied magnetic field. The two sub folders divide the files into data acquired with the field applied either parallel to the c axis (OOP - out of plane), or perpendicular to the c axis (IP - in plane).</p> <p>STXM_Data - Contains Scanning Transmission X-ray Microscopy data acquired at the MAXYMUS instrument at the BESSY II synchrotron, Berlin. The data is divided into sections acquired following different sample histories, as reported in the main paper: field sweep, field-cooled and zero-field cooled. Folders containing the extracted skyrmion sizes and stripe domain sizes are included, as well as Python notebooks for plotting Fig. 3, 4 and 5 of the main paper.</p> <p>Spectra_Data - Contains x-ray absorption spectra acquired on a bulk FGT crystal via total electron yield method (Bulk spectra), and spectra acquired on the exfoliated FGT flake by a transmission method (Flake Spectra). The accompanying jupyter notebook demonstrates the plotting and application of XMCD sum rules to the experimental data.</p> <p>LTEM_Data - Contains images acquired via Lorentz Transmission Electron Microscopy of an FGT flake, measured as a function of temperature and applied magnetic field at various tilt angles.</p> <p>Analysis_Scripts - Contains python notebook for plotting the magnetic phase diagrams, and plotting the thickness dependence estimation from the x-ray absorption spectra data.</p> <p>Meanfield_Simulations - Contains the raw output files of the simulations, the .h5 files, and Python notebook plotting scripts. Summary pdf files show the output of all simulation runs for the field sweep performed at each temperature. </p> <p><br> The latest version of this Data Set can be found at the Zenodo DOI:<br> 10.5281/zenodo.6346695</p> <p>Any further requests for materials are welcome, send any inquiries to Max Birch at birch@is.mpg.de</p>
Test datasets for segmentation (2D, 3D, timelapses)
<p>Datasets for testing: 1) Arabidopsis in 3D with membrane labelling for testing Unet, Stardist and denoising models.</p> <p> 2) Ascadian embryo in 3D imaged via light sheet for testing Unet and Stardist models. </p> <p> 3) Carcinoma cells 3D + time data imaged under low SNR conditions for testing Unet, Stardist and Denoising models.</p> <p> 4) Tissue in 2D + time for testing Unet models. Data imaged by Mari Tolonen at the University of Copenhagen.</p> <p> 5) Nuclei in 3D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen. </p> <p> 6) Nuclei cell mask 2D. Raw, GT and model prediction. Data imaged by Mari Tolonen at the University of Copenhagen </p>
Low-frequency anharmonic couplings in bromoform revealed from 2D Raman-THz spectroscopy: From the liquid to the crystalline phase
<p>Raw data for "Low-frequency anharmonic couplings in bromoform revealed from 2D Raman-THz spectroscopy: From the liquid to the crystalline phase"</p>
RNA-seq data of HepG2 cultured in 2D or as organoids
<p>Cancer cells cultured as organoids could better represent cancer cells grown in vivo and were shown to harbor increased stemness compared with cancer cells in 2D culture. Hep G2 cell line was used as parental cell line to establish a liver cancer organoid line. Hep G2 2D culture and organoid culture were subjected to bulk RNA-seq. </p>
Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks
<p>Benchmark data sets of CDPred as described in</p> <p><strong>Prediction of inter-chain distance maps of protein complexes with 2D attention-based deep neural networks</strong></p> <p>Zhiye Guo<sup>1</sup>, Jian Liu<sup>1</sup>, Jeffrey Skolnick<sup>2</sup>, Jianlin Cheng<sup>1*</sup></p> <p><sup>1 </sup>Department of Electrical Engineering and Computer Science, University of Missouri, Columbia, MO 65211</p> <p><sup>2 </sup>School of Biological Sciences, Georgia Institute of Technology, Atlanta, GA 30332-2000</p> <p>*Corresponding author (chengji@missouri.edu)</p> <p>There is four test dataset in this package, each test dataset contains four different folders and one list file. The <strong>afpred_pdb</strong> includes all the corresponding monomer structures predicted by alphafold. The <strong>cdpred_output </strong>includes the prediction results of our tool CDPred for each dataset. The <strong>pre_gen_a3m </strong>includes the multiple sequence alignments file used by CDPred to generate prediction results. And the <strong>true_pdb </strong>includes the fasta file for the test dataset and its heavy atom distance map (h_dist) and carbon alpha distance map (real_dist) that extract from the native structure.</p> <p>HomoTest1: The homodimer test dataset contains 28 targets collect from CASP_CAPRI 10-13</p> <p>HomoTest2: The homodimer test dataset contains 23 targets collect from CASP_CAPRI 13-14</p> <p>HeteroTest1: The heterodimer test dataset contains 9 targets collect from CASP_CAPRI13-14</p> <p>HeteroTest2: The heterodimer test dataset contains 55 targets collect from PDB bank 09-2021 to 11-2021</p>
Data for Vibrational Couplings between Protein and Co-factor in Bacterial Phytochrome Agp1revealed by 2D-IR Spectroscopy
<p>2D-IR data for the bacteriophytochrome Agp1 in the Pr and Pfr states </p>
2D honeycomb transformation into dodecagonal quasicrystals driven by electrostatic forces
<p>This repository contains the SXRD data measured for the 48-18-6 approximant in Sr-Ti-O on Pt(111). The data has been acquired at the SixS beamline of the Synchrotron SOLEIL in France on 19.09.2019.</p> <p>Monochromatic x-rays with photon energy of 11 keV were used to avoid Pt fluorescence (at 11.1 and 13 keV) and consequently reduced the background signal. The diffraction experiment was performed under grazing incidence at an angle of 0.2 °. The orientation matrix was defined relative to the Pt substrate lattice with its lattice constants of a = 392 pm.</p> <p>Additionally, the 3D voxel map generated from the raw data for further use in the binoculars software is provided as well as the input and output of SHELXL, which has been used for structure relaxation.</p>
CARE 2D denoising and upsampling example data
<p>Example data for 2D CARE denoising/upsampling consisting of pairs of low and high signal-to-noise ratio (SNR) 2D images of cells. The high SNR images are acquistions of Human U2OS cells taken from the <a href="https://bbbc.broadinstitute.org/BBBC006/">Broad Bioimage Benchmark Collection</a> and the low SNR images were created by synthetically adding strong read-out and shot-noise and applying pixel binning of 2x2, thus mimicking acquisitions at a very low light level.</p>
2D honeycomb transformation into dodecagonal quasicrystals driven by electrostatic forces
<p>This repository contains the key input and output files used for the density fucntional theory calculations of the paper "Mechanism of 2D Oxide Quasicrystal formation from honeycomb structures" by Sebastian Schenk, Oliver Krahn, Eric Cockayne, Holger L. Meyerheim, Marc deBoissieu, Stefan F"orster, and Wolf Widdra (2022).</p> <p>The calculations were performed using the DFT code VASP, version 5.4.4 [Commercial software is mentioned in this README file to adquately described the procedure. This does not imply an endorsement or recommendation by the National Institute of Standards and Technology, nor that the software used is necessarily the best for the given<br> purpose.]</p> <p>The subfolder large_approximant contains the files for the large Sr<sub>48</sub>Ti<sub>132</sub>O<sub>204</sub> approximant on a Pt monolayer. Subfolders honeycomb/Pt<sub>N</sub> and sigma/Pt<sub>N</sub> contain the files for honeycomb and sigma Ba<sub>8</sub>Ti<sub>24</sub>O<sub>36</sub> structures on Pt trilayers with N Pt per layer per periodic cell. Subfolders honeycomb/Pt<sub>N</sub>/substrate contain the corresponding files<br> for the Pt substrate alone. The honeycomb and sigma structures are at the equilibrium strain as determined by matching interpolated stress results, as described in the Supplementary Information associated with the main Article.</p> <p>The input files are the standard VASP input files: POSCAR (structure information), POTCAR_TITEL (pseudopotential information. Because the VASP pseudopotential files are proprietary, only the titles of the pseudopotentials used are given), KPOINTS (k-point generation) and INCAR (most calculation details). To accelerate the DFT van der Waals calculation, the file vdw_kernel.bindat from the VASP package (not included here) should also be used. The output files are OSZICAR (summarizes energy at each iteration) and OUTCAR (full ouput).</p>
Data set for "Room-temperature electrical control of polarization and emission angle in a cavity-integrated 2D pulsed LED"
<p>Data set for the paper "Room-temperature electrical control of polarization and emission angle in a cavity-integrated 2D pulsed LED"</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.