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76 results for “2D Model”
2d U-net models trained to segment human placental maternal/fetal blood volumes and blood vessels from syncrotron micro-CT data along with a sample data volume.
<p>This dataset contains a 512 x 512 x 512 pixel volume taken from an imaging dataset of human placental tissue collected at Diamond Light Source Manchester Imaging Branchline, I13-2 on visits MG23941 and MG22562 using in-line high-resolution synchrotron-sourced phase contrast micro-computed X-ray tomography. This data is saved in HDF5 format with a uint8 datatype. Alongside this are two 2d binary U-net models that have been trained to segment this data. One model segments the data into regions of maternal/fetal blood volume, the other segments the blood vessels. Both models were trained using the fastai python package, which utilises the pytorch library. These models were used to segment the data in our paper "A massively multi-scale approach to characterising tissue architecture by synchrotron micro-CT applied to the human placenta" which can be found at <a href="https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1">https://www.biorxiv.org/content/10.1101/2020.12.07.411462v1</a>. The code used for training the U-net models and for predicting the segmentation of the data volume can be found at <a href="https://github.com/DiamondLightSource/placental-segmentation-2dunet">https://github.com/DiamondLightSource/placental-segmentation-2dunet</a> and is published at <a href="https://doi.org/10.5281/zenodo.4252562">https://doi.org/10.5281/zenodo.4252562</a> </p>
Auxiliary files and data to generate eddy flux and validate 2D model for MALTA
<p>This repository contains the following directories to accompany the manuscript 'A Zonally-Averaged Global Atmospheric Transport Model for Long-lived Trace Gases', submitted to JAMES:</p><p>1) <strong>GEOSChem </strong>This directory contains the run directory template and (slurm) runscript to generate the tracer fields used to generate the eddy fluxes. The GEOSChem model will have to be installed locally to run this, and the run directory built to your local area. It may be easiest to just copy the relevant bits in /Tracer_2D_template/ (i.e., the .rc files, /RestartFiles/, input.geos, reset_restart.py and species_database.yml) into a GEOSChem Transport run directory and change the directories in the copied files. If using slurm on an HPC, just change the directories in the runtracers_inputs.sh script to match that of your own HPC. Else, a different script will have to be written copying the slurm functionality.</p><p>2) <strong>GEOSChem_SF6 </strong>This directory contains the monthly mean SF6 mole fractions generated using GEOSChem used to validate the 2D model MALTA. Emissions come from the EDGAR v4.2 emissions inventory. Emissions after 2008 continue to use 2008 as the emissions value.</p><p>3) <strong>CFC11_inversion</strong> This directory contains the relevant script and files to quantify emissions of CFC-11 using an output mole fraction from the TOMCAT 3D model using MALTA, and compare these to the TOMCAT emissions used to generate the mole fractions. The directory paths at the beginning of the main script in CFC11_inversion.py must be changed to point to the remaining files in the /CFC11_inversion/ directory, and a save directory must be specified, before running locally. MALTA must be installed to run this.</p><p>4) <strong>singapore.dat </strong>This file contains the QBO winds above Singapore, taken from https://www.geo.fu-berlin.de/en/met/ag/strat/produkte/qbo/index.html</p><p> </p>
2D synthetic hydrothermal models
<p>2D hydrothermal models of 1 km × 1 km in the horizontal and depth directions, with a grid size of 50 × 50, created by TOUGH2 software. The 2D models were used to evaluate physics-informed neural networks in the following paper:</p><p>Ishitsuka, K. and Lin, W. (2023) Physics-informed neural network for inverse modeling of natural-state geothermal systems, Applied Energy, 337, 120855. <a href="https://doi.org/10.1016/j.apenergy.2023.120855">https://doi.org/10.1016/j.apenergy.2023.120855</a></p><p>https://www.sciencedirect.com/science/article/pii/S0306261923002192</p><p>In the files, "x" and "z" indicate horizontal and vertical coordinates in meter. The columns of "pres", "temp" and "log10PER" indicate pressure, temperature and logarithm of permeability, respectively. </p>
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.1. Shell digitised with different methods. The photogrammetry model was captured with a 100 mm Macro lens and processed with Agisoft Photoscan. The visual comparison of the mollusc shows a similar level of detail between photogrammetry and MechScan for the external surfaces, with still a bit more detail for the MechScan. The HDI Advance has a much lower resolution.
Fig. 6.14. Ishango rod. The left 3D model was acquired with a in Handbook of best practice and standards for 2D+ and 3D imaging of natural history collections
Fig. 6.14. Ishango rod. The left 3D model was acquired with a µCT many years ago. The middle one is scanned with the MechScan structured light scanner. The right one is the combination of both the µCT scan, the structured light scan and the texture of the photogrammetry model.
2D EBSD Dataset of the Alpha and Beta Phase Orientations for a Hot-Rolled Model Zircaloy-4 with 7 wt.% Nb Alloy
<p>A set of electron backscatter diffraction (EBSD) data files for a model Zircaloy-4 with 7 wt.% Nb addition alloy following hot-rolling. </p> <p>The 2D data set contains EBSD maps recording the microstructure and texture evolution, from a beta-processed (and annealed) starting condition, following rolling at a temperature of 725C to 50% and 75% reduction. Data for annealing of the rolled materials (750C for 2 hours) is also included. <em>Note, phases in the ctf files marked as 'Titanium Cubic' refer to measurement of the 'Zirconium Cubic' phase.</em></p> <p>Please see our accompanying paper for analysis of the 2D measurements - as well as analysis of a 3D reconstruction from <a href="https://doi.org/10.5281/zenodo.3785084">10.5281/zenodo.3785084</a> - and for interpretation of the coupled crystallographic texture evolution;</p> <p>C.S. Daniel, A. Garner, P.D. Honniball, L. Bradley, M. Preuss, P.B. Prangnell, J. Quinta da Fonseca, Co-deformation and dynamic annealing effects on the texture development during alpha–beta processing of a model Zr-Nb alloy, Acta Materialia 205 (2021) 116538. <a href="https://doi.org/10.1016/j.actamat.2020.116538">10.1016/j.actamat.2020.116538</a></p>
2D P-wave velocity model of the northern Hikurangi margin.
<p>You should find three files attached that include (1) a table that maps x-coordinates to longitude/latitude, (2) the Vp model with columns for x, depth, and Vp, and (3) a table of interfaces that include the topography (value 1) and the Moho (value 2). The zero x coordinate is arbitrarily the location of the westernmost shot in the Bay of Plenty from the SHIRE seismic survey of 2017, so x values range from ~190 to 400 km.</p> <p>This P-wave velocity model is presented in: Gase, Andrew C., et al. "Crustal structure of the northern Hikurangi margin, New Zealand: Variable accretion and overthrusting plate strength influenced by rough subduction." <em>Journal of Geophysical Research: Solid Earth</em> 126.5 (2021): e2020JB021176.</p>
Code and Velocity Data for Sustained indentation in 2D models of continental collision involving whole mantle subduction
<p>Contains the Python code for all models and resolution tests using the <a href="https://www.underworldcode.org/intro-to-underworld">underworld geodynamics code</a> in "Sustained indentation in 2D models of continental collision involving whole mantle subduction" submitted to GJI</p>
Prototyping 3D Virtual Learning Environments with X3D-based Content and Visualization Tools-Figure 9. Room model generated with Autodesk 123D Catch - the 2D model
<p>Structure from motion was used for rapid modeling of a small room with all its objects. Two files were generated, a Wavefront obj and mtl (corresponding to the texture). The 3D model was post-processed with MeshLab, during which several filters were applied to clean up the model. The mesh model was also connected with the scanned model, by choosing at least 4 connection points. The 2D and 3D results are shown in Figures 9, 10. A post-processing could also be performed using the Autodesk 123D Catch web application.</p>
2D P-wave velocity model for Bay of Plenty, New Zealand
<p>The files contain the geographic projection, P-wave velocities, and earth layer model for the Bay of Plenty, New Zealand. The methods and data are presented in the article: Seismic Evidence of Magmatic Rifting in the Offshore Taupo Volcanic Zone, New Zealand, Gase et al. 2019 (<a href="https://doi.org/10.1029/2019GL085269">https://doi.org/10.1029/2019GL085269</a>).</p>
Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour
<p>An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model.</p> <p>References</p> <p>[1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p>
Numerical Modeling of Asteroid Impact in 2D Cylindrical Coordinates
<p>Additional material and code for the Bachelor Thesis "Numerical Modeling of Asteroid Impact in 2D Cylindrical Coordinates" </p>
Input and output data from simulations of 2D valves and 3D inflow-outflow model using particle methods
<p>Input and output data of open-source softwares for computational fluid dynamics simulation involving fluid-structure interaction.</p> <p> </p> <p><strong>Data from two studies</strong></p> <ol> <li>Verifications of the weakly-compressible smoothed particle hydrodynamics (WCSPH) method, open-source code <a href="https://www.sphinxsys.org">SPHinXsys</a>, when applied to the flow of idealized 2D valve models.</li> <li>Validations of inflow-outflow model in moving particle semi-implicit (MPS) method, open-source code <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow">PolyMPS</a>.</li> </ol> <p> </p> <p><strong>Folders and Files</strong></p> <p><strong>valve-2D.zip </strong>is the folder with data from the idealized models of vertical and curved 2D valves:</p> <ul> <li>Vertical valves with parameters provided in <a href="https://doi.org/10.1016/j.jcp.2010.08.005">Gil et al., 2010</a></li> <li>Curved valves with parameters provided in <a href="http://doi.org/10.1007/s00466-013-0890-3">Wick, 2014</a></li> <li>source files (.cpp): input data (physical and numerical parameters) for SPHinXsys</li> <li>text files: SPHinXsys (.dat) and Reference (.tsv) results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>inflow-outflow-3D.zip </strong>is the folder with data from the inflow-outflow model in MPS:</p> <ul> <li>Fluid physical properties of water <ul> <li><span>\(\rho=1000kg/m^3 , \,\, \nu=10^{-6}m/s^{-2}\)</span></li> </ul> </li> <li>Pipes of length <span>\(L=0.15m\)</span>: <ul> <li>circular section of diameter <span>\(D=0.1m\)</span>.</li> <li>square section of sides <span>\(S=0.1m\)</span>.</li> </ul> </li> <li>Constante pressure variation (<span>\(\Delta P = 30 \,\, or \,\, 50 \,\, Pa\)</span>) between inflow and outflow: <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L}, \\ \Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> </ul> <ul> <li>Sinusoidal pressure variation (<span>\(\Delta P =700Pa \,\, , \,\, T = 2.0s\)</span>) between inflow and outflow <ul> <li><span>\(\frac{\partial p}{\partial x} = - \frac{\Delta P}{L} \sin \omega t \, \\ \omega = \frac{2\pi}{T} \\ Delta P = P_{outflow} - P_{inflow}\)</span></li> </ul> </li> <li>input data (.json, .grid, .stl): physical properties, numerical parameters and geometries for PolyMPS can be found at <a href="https://github.com/rubensamarojr/polymps/tree/inOutflow/input">https://github.com/rubensamarojr/polymps/tree/inOutflow/input</a></li> <li>text files (.txt): PolyMPS and OpenFOAM results</li> <li>python files (.py): Generates the graphics</li> </ul> <p> </p> <p><strong>References</strong></p> <p><a href="https://doi.org/10.1016/j.jcp.2010.08.005">A. J. Gil. The Immersed Structural Potential Method for haemodynamic applications. J. Comput. Phys., 229 (2010), pp. 8613-8641</a></p> <p><a href="https://doi.org/10.1007/s00466-013-0890-3">T. Wick. Flapping and contact FSI computations with the fluid–solid interface-tracking/interface-capturing technique and mesh adaptivity. Comput Mech 53, 29–43 (2014)</a></p> <p><a href="https://doi.org/10.1016/j.cma.2014.10.040">D. Kamensky, et al. An immersogeometric variational framework for fluid–structure interaction: Application to bioprosthetic heart valves Comput. Methods Appl. Mech. Engrg., 284 (2015), pp. 1005-1053</a></p> <p><a href="https://doi.org/10.1016/j.cma.2015.12.023">C. Kadapa et al. A fictitious domain/distributed Lagrange multiplier based fluid–structure interaction scheme with hierarchical B-Spline grids. Comput. Methods Appl. Mech. Engrg., 301 (2016), pp. 1-27</a></p> <p><a href="https://doi.org/10.1016/j.jcp.2015.10.015">Jie Liu. A second-order changing-connectivity ALE scheme and its application to FSI with large convection of fluids and near contact of structures. J. Comput. Phys., 304 (2016), pp. 308-423</a></p>
2D and 3D coral models imaged in Curaçao: George, Mullinix, et al PeerJ 2021
Open the record for dataset details and reuse information.
2D Herbig Ae/Be disk models for HD far-infrared lines (Kama et al. 2020)
<p>In this work 2D physical-chemical models of Herbig Ae/Be protoplanetary disks have been run to relate HD far-infrared line fluxes to gas masses. The tables give DALI model parameters and matching HD far-infrared line fluxes used in Kama et al. 2020 (A&A 634, A88 (2020); doi:10.1051/0004-6361/201937124)</p>
2D numerical modelling: the case study of the Arno River at Greve junction
<p>files description:</p> <p>- "nuova mesh_barra.2dm" is the input mesh file for BASEMENT;</p> <p>- "veg. surveys.docx" shows the data collected during the vegetation surveys activities.</p>
Orthophotos and 2D hydraulic modelling results used for habitat suitability modelling of the River Inn section (river km 35.3-48) in SE Germany
<p>The aerial RGB picture acquisition was performed on October 7 (bypass channel) and 11 (side channel) 2022 using a DJI-Matrice 210 V2 RTK drone. For the image acquisition, the drone mounted the DJI Zenmuse X5S RGB camera. The flight was realized at an altitude of about 120 m, ensuring a lateral and longitudinal overlap of the images of about 80%. Gound Control Points (GCPs) have been disposed along the study site, and their position georeferenced using a Emlid Reach RS2 RTK GPS system. After data collection, an RGB orthomosaic with a spatial resolution of 25 cm was generated, using PIX4Dmapper v4.7.5 (www.pix4d.com).</p><p>The hydrodynamic simulations were performed with the freeware software BASEMENT v3.2 (https://basement.ethz.ch), which solves the 2D shallow-water equations using a finite volume approach over two-dimensional unstructured meshes. Computational meshes were created using the QGIS plugin BASEmesh 2, with spatially varying element sizes, which were set to be finer in areas expected to be suitable for spawning and as nursery grounds, or when needed to more accurately represent the local morphological complexity.</p>
Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries data
<p>Deforming subduction zone finite element model temperature, velocity, surface and flux field data as reported in the work:</p> <p>N. Sime, C. R. Wilson and P. E. van Keken<br> Thermal modeling of subduction zones with prescribed and evolving 2D and 3D slab geometries.</p>
Temperature, pressure, permebility datasets of a 2D hydrothermal model
<p>The numerical datasets are temperatures, pressures, permeabilities of a 2D natural-state hydrothermal system simulated using TOUGH2.</p> <p>The numerical datasets were used to evaluate a neural network proposed in the following article:</p> <p>Ishitsuka et al. Modeling unobserved geothermal structures using a physics-informed deep neural network with transfer learning of prior knowledge, under review.</p> <p>#----------------------------------------------------------------------------------------------</p> <p>(Dataset 1: Dataset_Reference.csv, Pretrain_Datasets.zip)</p> <p>The hydrothermal system had a 2D space with a horizontal width of 2 km and a depth of 1.6 km. The system consists of five geological units with different permeability, and had a fault-controlled permeable region extending vertically. The hydrothermal fluid flowed though the vertical permeable zone.</p> <p>(Dataset2: Dataset_Reference_Ungaran-Base.csv, Pretrain_Datasets_Ungaran-Base.zip)</p> <p>The permeability structure of the numerical model is constructed based on the hydrothermal model in Jatmiko et al. (2022). A 10 km long and 5 km deep region of the numerical model is divided into 5,000 elements at 100 m intervals. In the hydrothermal model, the hydrothermal upflow zone is around the summit of the mountain.</p> <p>Jatmiko BW et al. Resource assessment of Ungaran geothermal field using numerical model and monte carlo simulation. IOP Conference Series: Earth and Environmental Science 2022;1031:2021.</p> <p>#----------------------------------------------------------------------------------------------</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>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.