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959 results for “GeoMetre”
Wing geometric morphometrics and COI barcoding of Culex pipiens subgroup in the Republic of Korea
<p>Two members of the <em>Culex pipiens</em> subgroup, <em>Culex pallens</em> and <em>Culex pipiens</em> f. <em>molestus</em>, are known to occur in the Republic of Korea (ROK). These species exhibit morphologically similar features and are challenging to distinguish below the species level. Therefore, this study utilized wing geometric morphometrics (GM) on the right wing of the <em>Culex pipiens</em> subgroup, alongside sequencing of the cytochrome <em>c</em> oxidase subunit I (<em>COI</em>) region. Mosquitoes were collected from 11 locations between June and October to minimize regional and seasonal variations. Additionally, <em>Culex pipiens</em> f. <em>pipiens</em>, which is not native to the ROK, was included in the analysis. <em>Culex tritaeniorhynchus</em>, <em>Aedes albopictus</em>, and <em>Anopheles sinensis</em>, the primary vectors in the ROK, were used as outgroups for comparison. All three taxa within the <em>Culex pipiens</em> subgroup could be identified with an 82.4%–97.0% accuracy using GM. However, a comparison of the <em>COI</em> regions of the <em>Culex pipiens</em> subgroup revealed no clear differences between the taxa. These data can be used for accurate identification, contributing to effective mosquito control, in addition to providing a foundation for evolutionary and ecological studies on wing shape differences.</p>
A Large Fault Partially Reactivated During Two Contiguous Seismic Sequences in Central Italy: The Role of Geometrical and Frictional Heterogeneities
<p>Moment tensor catalog for events with M > 3.0, that occurred between January 2009 and April 2021, in Campotosto area, Italy. Moments tensor were calculated by applying the Time Domain Moment Tensor technique, originally proposed by Dreger and Helmberger (1993) and Pasyanos et al. (1996) and successively implemented at INGV by Scognamiglio et al. (2009).</p> <p>The catalog includes:</p> <p>Location of events: time, depth, lat and lon</p> <p>The moment magnitude: Mw</p> <p>The double-couple value: DC</p> <p>The Variance Reduction value: VR</p> <p>The six moment tensor components: Mxx, Mxy, Mxz, Myy, Myz, Mzz</p> <p>The orientation of nodal planes: strike1, dip1, rake1, strike2, dip2, rake2</p> <p> </p>
Data from: Genome-scale annotation of protein binding sites via language model and geometric deep learning
<p>The dataset contains the training and test sets of protein binding sites with DNA, RNA, peptide, protein, ATP, HEM, Zn2+, Ca2+, Mg2+ and Mn2+. Each protein is associated with 3 lines indicating the protein name (PDB accession code and chain), sequence and residue labels (0 for non-binding and 1 for binding), respectively. The ESMFold-predicted structures are also provided.</p>
Quantum mechanical electronic and geometric parameters for DNA k-mers as features for machine learning
<p>With the development of advanced predictive modelling techniques, we are witnessing a steep increase in model development initiatives in genomics that employ high-end machine learning methodologies. Of particular interest are models that predict certain genomic or biological characteristics based solely on DNA sequence information. These models, however, treat the DNA sequence as a mere collection of four, A, T, G and C, letters, thus dismissing the past physico-chemical advancements in science that can enable the use of more intricate information about nucleic acid sequences. Here, we provide a comprehensive database of quantum mechanical and geometric features for all the permutations of 7-meric DNA in their representative B, A and Z conformations. The database is generated by employing the applicable high-cost and time-consuming quantum mechanical methodologies. This can thus make it seamless to associate a wealth of novel molecular features to any DNA sequence, by scanning it with a matching k-meric window and pulling the pre-computed values from our database for further use in modelling. We demonstrate the usefulness of our deposited features through their exclusive use in developing a model for A to C mutation rate constants.</p> <p>The DNA k-mer quantum mechanical parameters can also be found <a href="https://github.com/SahakyanLab/DNAkmerQM" target="_blank" rel="noopener">https://github.com/SahakyanLab/DNAkmerQM</a>, the corresponding research and development code from <a href="https://github.com/SahakyanLab/NucleicAcidsQM" target="_blank" rel="noopener">https://github.com/SahakyanLab/NucleicAcidsQM</a>, and the associated pre-print from <a href="https://doi.org/10.1101/2023.01.25.525597" target="_blank" rel="noopener">https://doi.org/10.1101/2023.01.25.525597</a>.</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>
Data supporting "Transition between mechanical and geometric controls in glacier crevassing processes".
<p>The folders:</p> <ul> <li><strong><em>results_Geometrical_Regime</em></strong></li> <li><strong><em>results_Mechanical_Regime</em></strong></li> <li><strong><em>results_3D</em></strong></li> <li><strong><em>results_varyingLengthSlope</em></strong></li> <li><strong><em>results_varyingVelocity</em></strong></li> </ul> <p>contain the data for spacing and depth of the crevasses in the different simulations. Each simulation folder is named after the ice thickness H and cohesion c. For example, H300c2 means an ice thickness of 300m and a cohesion of 2MPa.</p> <p>The simulation files contain data measured every 10 frames. Empty files imply two possibilities:<br>-the crevasse(s) are away from the window measurement (the window is approximately 1500m wide around the obstacle).<br>-No crevasses are observed or at least no distinguishable enough.</p> <p>**<br>The python code <strong><em>readDataMeasuredInSimulations.py</em></strong> use the <strong><em>readDataPP</em></strong> class to plot the measures from the previous folders.<br>All the figures from the paper can be reproduced using this code.</p> <p>**</p> <p>The full simulation results cannot be transfered as a supplementary material as it is too large (20Go for each simulation). However we provide two full result files: <strong>H300_c0_4.abc</strong> and <strong>H100_c2.abc. </strong>We provide an example of the init.lua file used to launch a simulation with given parameters and configurations. The configurations used in the simulations are all provided in the geometry.zip file.</p> <p>A version of the MPM numerical model can be found in a previous publication at<strong> https://www.nature.com/articles/s43247-021-00179-7. </strong></p> <p> </p>
Data for "A combined experimental and computational exploration of heteroleptic cis-Pd2L2L'2 nanocages through geometric complementarity"
<div>In the following subdirectories are the input and output of GFN2-xTB and DFT calculations for this publication:</div> <div> </div> <div>chemrxiv: <strong><em><a href="https://doi.org/10.26434/chemrxiv-2024-s0mmw">https://doi.org/10.26434/chemrxiv-2024-s0mmw</a></em></strong></div> <div> </div> <div>Published: <strong><em><a href="https://doi.org/10.1002/chem.202403336">https://doi.org/10.1002/chem.202403336</a></em></strong></div> <div> </div> <div>Code repository: <a href="https://github.com/andrewtarzia/simple_het_construction">github.com/andrewtarzia/simple_het_construction</a></div> <div>Zenodo code DOI: <a href="https://doi.org/10.5281/zenodo.13649229">10.5281/zenodo.13649229</a></div> <div> </div> <div>data directory:</div> <div> <ul> <li>a spreadsheet with all final energy values and exchange energy calculations</li> <li>CSD Survey data, NPd_survey_data_261119.csv, for Pd centres</li> </ul> </div> <div>Naming convention for file conversions:</div> <div> <ul> <li>l1: 1DBF</li> <li>l2: 1Ph</li> <li>l3: 1Th</li> <li>la: 2DBF</li> <li>lb: 2Py</li> <li>lc: 2Ph</li> <li>ld: 2Th</li> </ul> </div> <div>Structure naming convention: </div> <div> <ul> <li> <em><strong>mX</strong></em> indicates a homoleptic cage with <em><strong>X</strong></em> Pd atoms, <strong><em>cis</em></strong>/<strong><em>trans</em></strong> are the cis/trans heteroleptic cages, respectively</li> </ul> </div> <div> <p> </p> <p>structures/xtb directory:</p> </div> <div> <ul> <li>contains the structures from GFN2-xTB/ALPB(DMSO) optimisations of stk-generated structures </li> </ul> </div> <div> </div> <div>structures/opt_*METHOD*_SP_*METHOD*_06-02-2024 directories:</div> <div> <ul> <li>All DFT was run by Victor Posligua</li> <li>contains the input files (.com), output files (.log) and structure files (.xyz/.mol) of DFT optimisations and single point energy calculations with each method</li> <li>When the opt method and SP method are the same, the final structure is included in .mol and .xyz formats</li> <li>However, if opt method is different from the SP method, the final structure is not included because only a single-point energy calculation was run. </li> <li>For example, there are no .mol or .xyz files for 'opt_PBE0_SP_B3LYP_06-02-2024’ since the structure is already in 'opt_PBE0_SP_PBE0_06-02-2024’.</li> <li>you’ll find 8 different folders:<br> <ul> <li>opt_PBE0_SP_PBE0_06-02-2024</li> <li>opt_PBE0_SP_B3LYP_06-02-2024</li> <li>opt_PBE0_SP_B97D3_06-02-2024</li> <li>opt_PBE0_SP_HSE_06-02-2024</li> <li>opt_B3LYP_SP_B3LYP_06-02-2024</li> <li>opt_B97D3_SP_B97D3_06-02-2024</li> <li>opt_HSE_SP_HSE_06-02-2024</li> <li>opt_GFN2-xTB_SP_PBE0_06-02-2024</li> </ul> </li> </ul> </div>
Data repository for the paper: Sharp front tracking with geometric interface reconstruction
<h1>Data repository for the paper</h1> <h1><em>Sharp front tracking with geometric interface reconstruction</em></h1> <p> </p> <p>This repository consists of the results data for the paper "Sharp front tracking with geometric interface reconstruction" by Christian Gorges, Fabien Evrard, Robert Chiodi, Berend van Wachem and Fabian Denner. The simulation results stored in this repository have the following data format:</p> <ul> <li> <p>.txt files consisting the raw data used for the plots in the results chapter of the paper</p> </li> <li> <p>.pvtu and .vtu files containing the front mesh data for the rising bubble simulations (Paraview is an exemplary software to view the front mesh data)</p> </li> <li> <p>.py files containing python scripts serving as examples on how to use and plot the raw data of the .txt files</p> </li> </ul> <p>The main folders of this repository are named as the sections in the results chapter of the paper. For instance, the folder translating_droplet contains the data of the "Translating droplet" section. Within the main folders, sub folders contain the raw data for the specific simulations. The naming style of the raw data files and the subfolders for each section is explained in the following.</p> <p><em>stationary_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>translating_droplet</em>: This main folder contains subfolders for all Laplace numbers simulated. "La_120" corresponds to a Laplace number of 120. The file names of the .txt files within the subfolders consist of the Laplace number, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "La_120_ClassicFT_ddx_52.txt" consists of the data for a Laplace number of 120, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%e,%e,%e,%e,%e,%e,%e\n" which corresponds to "Physical time, Physical time / \tau_{mu}, Kinetic energy, RMS velocity, Max velocity, Ca_{max}, U_sigma".</p> <p><em>oscillating_droplet</em>: This main folder contains subfolders for all droplet viscosities simulated. "mu_d_05" corresponds to a droplet viscosity of 0.5. The file names of the .txt files within the subfolders consist of the droplet viscosity, followed by the front tracking method and the d/dx ratio. If roughness smoothing is used it also consists of "WithRoughnessSmoothing". For example "mu_d_05_ClassicFT_ddx_52.txt" consists of the data for a droplet viscosity of 0.5, the classic front tracking method and a d/dx ratio of 52. The content in the .txt files is the following: "%f,%f,%e\n" which corresponds to "Physical time, \tau, r".</p> <p><em>rising_bubbles</em>: This main folder contains subfolders for all rising bubble cases simulated. "Case_1_Classic" corresponds to a case 1 simulated with the classic front tracking method. The .txt files within the subfolders consist of the physical time, followed by the non-dimensional time and the Reynolds number. The .zip files contain the .pvtu and .vtu files for the front meshes.</p> <p>The python scripts have been tested with Python 3.11.5.</p> <p>This project has received funding from the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation), grant number 420239128, and from the European Unions's Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 101026017. This work was supported by the US Department of Energy through the Los Alamos National Laboratory. Los Alamos National Laboratory is operated by Triad National Security, LLC, for the National Nuclear Security Administration of U.S. Department of Energy (Contract No. 89233218CNA000001).</p>
Geometric deep learning improves generalizability of MHC-bound peptide predictions
<p>Full dataset and trained models from the manuscript "<strong>Geometric deep learning improves generalizability of MHC-bound peptide predictions</strong>".</p> <p>"outputs_and-BA_data.zip" contains the networks' outputs for each cross-validation experiment and a "full_dataset.csv" containing the initial BA data.<br>Note: this file has been updated (2024/11/26) due to errors in generating some of the previous csvs. In the earlier version, both MLP and CNN outputs reported were wrong. The correct values are now reported in the updated csvs.</p> <p>"trained_models.zip" contains all the trained models parameters</p> <p>"propedia_ssl.zip" contains all the 3D models from propedia used to train the 3D-SSL</p> <p>"pdb.zip" contains 3D models generated in PANDORA and used to train CNN, GNN and EGNN. It amounts to 145665 .pdb files, one for each human binding affinity entry from the initial dataset from O'Donnell et al. The list of entries used to actually train networks after filtering can be found in outputs_and-BA_data.zip", in the "full_dataset.csv" file. </p> <p> </p> <p>CHANGELOG v4:</p> <p>- In outputs_and-BA-data.zip, updated CNN_AlleleClustered_test_crossval.csv and CNN_shuffled_test_crossval.csv. These file had the wrong IDs paired with the network outputs.The IDs and labels are now consistent with the outputs.</p> <p>- Updated reference from the preprint to the published article. </p> <p> </p>
A 3D dendrite microstructure database of a Ni-base SX based on relational geometric ontology (RGO)
<p>Material: Nickel-base superalloy ERBO/1 (more details: Parsa, A. B., et al. Advanced scale bridging microstructure analysis of single crystal Ni-base superalloys. Adv. Eng. Mater. 2015, 17 (2), 216-230, <a href="https://doi.org/10.1002/adem.201400136">https://doi.org/10.1002/adem.201400136</a>)</p> <p>Casting: Bridgman seed technique; Withdrawal rate: 180 mm/h, Thermal gradient 13.3 K/mm (more details: Hallensleben, P., et al. On the evolution of cast microstructures during processing of single crystal Ni-base superalloys using a Bridgman seed technique, Mat. Des. 2017, 128, 98–111, <a href="https://doi.org/10.1016/j.matdes.2017.05.001">https://doi.org/10.1016/j.matdes.2017.05.001</a>)</p> <p>Sample: Cross sectional slices extracted perpendicular to the growth direction of a single crystal superalloy cylinder (diameter 12mm, length 120 mm).</p> <p>Preperation: Each slice was individually mounted, grinded, polished and etched 6 seconds with an etching solution consisting of 100ml H2O, 100ml HCl, 100ml HNO3 and 3g MoO3. </p> <p>Image acquisition: Optical microscope of type Axio (Carl Zeiss GmbH) equipped with a high-resolution CCD-camera of type Leica DFC320 and stepper-motor driven sample stage of type Tango Desktop (Märzhäuser)</p> <p>--------------------------------------</p> <p>The published data is a compilation of 20 serial sectioned optical micrographs resolving the dendritic microstructure of the sample described above. They show a central region of the specimen at different heights of the cylindrical sample. The names of those micrographs correspond to the z-coordinate in millimeters, i.e. micrograph "012.tif" was extracted at 12mm. For each micrograph, an object detector based on a neural network was used to identify the dendrite core positions. Afterwards, registration algorithms were used to determine the growth directions together with branching and extinction events of all dendrites. Neighboring dendrites were identified by calculating a triangulation for each micrograph.</p> <p>This quantitative data was transformed into a microstructure database stored as a .JSON file using the "Relational Geometric Ontology" approach described in:</p> <p>A.R. Richter, F. Scholz, G. Eggeler, J. Frenzel, P. Thome, Microstructure informatics: Using computer vision for the characterization of dendrite growth phenomena in Ni-base single crystal Superalloys, Materials Characterization, Volume 223, 2025, <a href="https://doi.org/10.1016/j.matchar.2025.114878">https://doi.org/10.1016/j.matchar.2025.114878 </a></p> <p> </p>
Mouse CA1 Calcium Imaging and Behavioural Dataset in 3x3 Geometric Morph Paradigm
<p>The following dataset was collected by Dr. J. Quinn Lee, Dr. Alexandra T. Keinath, and Erica Cianfarano in the laboratory of Dr. Mark P. Brandon. All methods and details are described in the original research article reporting these data published in <em>Neuron</em>: Lee, Keinath, Cianfarano, and Brandon (2025) Identifying representational structure in CA1 to benchmark theoretical models of cognitive mapping. Any use of the following dataset must cite the original publication in Neuron. The code base to reproduce all analyses and figures can be found at: <a href="https://github.com/jquinnlee/georepca1">https://github.com/jquinnlee/georepca1</a></p> <p dir="auto">The dataset (Python joblib files or MATLAB .mat files in the zipped "data" folder) are given names of animal IDs from the original study that can be downloaded from Zenodo and contain the following fields in each file:</p> <p dir="auto">SFPs: spatial footprints (also known as ROI) for every registered cell, centered over each cell. Shape - Dimx, dimy, number of SFPs (ROIs), number of days. If cell is not registered it will be nan along dimx and dimy for a given day.</p> <p dir="auto">blocked: location of blocked (occluded) partitions in 3x3 design of environment. Location of partitions are shown in paper, but are organized in the following way – [[0, 1, 2], [3, 4, 5], [6, 7, 8]]. If no partitions are blocked, value is -1.</p> <p dir="auto">centroids: centroid of spatial footprint. Shape – number of cells, x-y location, number of days.</p> <p dir="auto">envs: environment shape identified with string name</p> <p dir="auto">maps: three types of maps generated from the dataset. “sampling” is the occupancy of animal in each spatial bin, shape – xbins, ybins, number of days. “smoothed” is the event rate map smoothed with 2.5 cm gaussian kernel, shape – xbins, ybins, number of cells, number of days. “unsmoothed” is the same event rate map data without smoothing.</p> <p dir="auto">position: x-y position data for all days. List shape number of days, with shape on each day indicating x-y position in first dimension, and number of temporal bins / frames in second dimension.</p> <p dir="auto">trace: rise-extracted calcium traces, where “1” indicates a significant event. See paper for details on processing pipeline. If cell is not registered on given day, will appear as nan the same shape.</p> <p dir="auto">Precomputed results can also be downloaded in the zipped "results" folder to avoid recomputing main results from scratch using the Github code base linked above.</p>
Supplementary data to "Geometric morphometrics of bilateral asymmetry in Eunotia bilunaris (Eunotiales, Bacillariophyceae) as a tool for the quantitative assessment of teratogenic deviations in frustule shapes"
<p>The supplementary data consist of the files "data.txt" and "R_script.odt" including the landmark coordinates and the R script used for the analyses described in the paper submitted to Symmetry.</p> <p> </p>
Geometrical control of disorder-induced magnetic domains in planar synthetic antiferromagnets
<p>Open data to "Geometrical control of disorder-induced magnetic domains in planar synthetic antiferromagnets", published in Phys. Rev. Materials <strong>6</strong>, L033001 (2022)</p> <p>https://doi.org/10.1103/PhysRevMaterials.6.L033001</p> <p> </p>
Research compendium for 'Practical and technical aspects for the 3D scanning of lithic artefacts using micro-computed tomography techniques and laser light scanners for subsequent geometric morphometric analysis. Introducing the StyroStone protocol'
<p><strong>Abstract:</strong></p> <p>Here, we present a new method to scan a large number of lithic artefacts using three-dimensional (3D) scanning technology. Despite the rising use of high-resolution 3D surface scanners in archaeological sciences, no virtual studies have focused on the 3D digitization and analysis of small lithic implements such as bladelets, microblades, and microflakes. This is mostly due to difficulties in creating reliable 3D meshes of these artefacts resulting from several inherent features (i.e., size, translucency, and acute edge angles), which compromise the efficiency of structured light or laser scanners and photogrammetry. Our new protocol <em>StyroStone</em> addresses this problem by proposing a step-by-step procedure relying on the use of micro-computed tomographic technology, which is able to capture the 3D shape of small lithic implements in high detail. We tested a system that enables us to scan hundreds of artefacts together at once within a single scanning session lasting a few hours. As also bigger lithic artefacts (i.e., blades) are present in our sample, this protocol is complemented by a short guide on how to effectively scan such artefacts using a structured light scanner (Artec Space Spider). Furthermore, we estimate the accuracy of our scanning protocol using principal component analysis of 3D Procrustes shape coordinates on a sample of meshes of bladelets obtained with both micro-computed tomography and another scanning device (i.e., Artec Micro). A comprehensive review on the use of 3D geometric morphometrics in lithic analysis and other computer-based approaches is provided in the introductory chapter to show the advantages of improving 3D scanning protocols and increasing the digitization of our prehistoric human heritage.</p> <p><strong>Content List:</strong></p> <ul> <li><strong>S1. </strong>Step-by-step protocol entitled ‘StyroStone: A protocol for scanning and extracting three-dimensional meshes of stone artefacts using Micro-CT scanners’. Also available on protocols.io (dx.doi.org/10.17504/protocols.io.bzbfp2jn);</li> <li><strong>S2. </strong>Dataset with all raw semilandmark coordinate data (in .xlsx format) used in the validation study;</li> <li><strong>S3. </strong>AGMT3D project. The file “Validation Protocol-MorphoProject.mat” can be used to open the project in the software AGMT3D;</li> <li><strong>S4. </strong>Dataset in .csv format of the principal component score data of the validation study;</li> <li><strong>S5.</strong> R script used to create Figure 2 using the R package ggplot2;</li> <li><strong>S6. </strong>3D models of the experimental bladelets obtained with the Micro-CT scanner used in the validation study. Both .ply and .wrl formats are provided;</li> <li><strong>S7. </strong>3D models of the experimental bladelets obtained with the Artec Micro scanner used in the validation study. Both .ply and .wrl formats are provided.</li> </ul>
Figure 3 in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 3. Canonical variate analysis (CVA) of the anterior margins of coxae.
Figure 2 in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 2. Landmark definitions used in the anterior margins of coxae.
Figure 4 in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 4. Canonical variate analysis (CVA) of dorsoglandularia 1–4.
Figure 5 in Integrative approach of morphology and geometric morphometrics to species delimiation in Torrenticolidae (Acari: Hydrachnidiae)
Figure 5. Canonical variate analysis (CVA) of infracapitulum.
Figure 6 in Classical and geometric morphometric methods reveal differences between specimens of Varroa destructor (Mesostigmata: Varroidae) from seven provinces of Iran
Figure 6. Cluster analysis by UPGMA method based on geometric morphometric measurement.
A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging: geometry and simulation data
<p>This dataset contains the original µCT scan data, the scripts and intermediate results for the generation of the geometrical fiber model, as well as the structural simulation files and their experimental validation data described in the paper <a href="https://journals.sagepub.com/doi/10.1177/00405175221137009">"A novel technique to simulate and characterize a yarn's mechanical behavior based on a geometrical fiber model extracted from micro-CT imaging"</a>, published in Textile Research Journal.</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.