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882 results for “3D models”
Niko Bellic Gta Iv 3D Model gta 4 By YUG BAMANYA
you can use this model for making game and animation this character is from gta Iv Source: Objaverse 1.0 / Sketchfab
Rothenberg 3D Modell Final
Burgstall Rothenberg bei Lipperts im Landkreis Hof E 50,30437 N 11,78705 Wallgräben noch deutlich sichtbar Source: Objaverse 1.0 / Sketchfab
3D model of Leonidas. Modelo 3D de Leónidas.
Processing 37 images, 3D model of a little statue of the spartan king Leonidas, known by his battle in Termopilae. Mediante 37 fotografías, modelo 3D de una pequeña estatua del rey Leónidas, conocido por su participación en la batalla de las Termópilas. Source: Objaverse 1.0 / Sketchfab
Clonfert-doorway-3d-model
THIS IS THE DOORWAY TO CLONFERT CATHEDRAL,AN EXAMPLE OF IRISH ROMANESQUE. [Attribution link] [The Digital Heritage Age (@DH_Age)](http://) Source: Objaverse 1.0 / Sketchfab
Disney Concert Hall 3D Model
Disney Concert Hall Constructive Projects 2 Made in Rhino 7 Other Constructive details from Disney Concert Hall: Disney Concert Hall/Constructive Detail/01 https://skfb.ly/onE7B Disney Concert Hall/Constructive Detail/02 https://skfb.ly/onE7V Disney Concert Hall/Constructive Detail/03 https://skfb.ly/onEqq Disney Concert Hall/Constructive Detail/04 https://skfb.ly/onEr6 Disney Concert Hall/Constructive Detail/05 https://skfb.ly/onErR Disney Concert Hall/Constructive Detail/06 https://skfb.ly/onE8U Disney Concert Hall/Constructive Detail/07 https://skfb.ly/onEsq Disney Concert Hall/Constructive Detail/08 https://skfb.ly/onEsF Disney Concert Hall/Constructive Detail/09 https://skfb.ly/onEtJ Disney Concert Hall/ConstructiveD/10 https://skfb.ly/onEzV Disney Concert Hall/Constructive Detail/10/Corte por Fachada 1 https://skfb.ly/onE98 Disney Concert Hall/Constructive Detail/11/Corte por Fachada 2 https://skfb.ly/onE9P Disney Concert Hall/Constructive Detail/12/Corte por Fachada 3 https://skfb.ly/onEuo Source: Objaverse 1.0 / Sketchfab
Roman Circus of Tarragona 2/3. 3D Model
The construction of Roman Circus ended in Domitian Age and it was in use until Vth century. It's an area of 4 hectares. **Scientific project**: [ARREL- Recercaixa 2015](http://www.icac.cat/recerca/projectes-de-recerca/projecte/arrel-aplicacio-de-jocs-seriosos-en-entorns-col%E2%80%A2laboratius-per-la-transmissio-del-patrimoni-cultural-de-catalunya/) [See Modern City above Circus](https://skfb.ly/66Sys) [See medieval City](https://skfb.ly/6oXAq) Source: Objaverse 1.0 / Sketchfab
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>
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>
Final products from "3D MODELING AS A CONSERVATION TOOL TO CHARACTERIZE ENDANGERED SEASONALLY FLOODED ECOSYSTEMS IN THE VOLTA GRANDE DO XINGU, AMAZON FOREST, PARÁ, BRAZIL"
<p>In these .zip folders, you will find the products generated from flight missions carried out between November 7th to 14th, 2021, in the Volta Grande do Xingu, Pará, Brazil. <br>These results are presented as an integral part of the article titled '3D MODELING AS A CONSERVATION TOOL TO CHARACTERIZE ENDANGERED SEASONALLY FLOODED ECOSYSTEMS IN THE VOLTA GRANDE DO XINGU, AMAZON FOREST, PARÁ, BRAZIL' published on the doctoral thesis "Characterization and monitoring of the flooding dynamics and seasonally flooded environments of the Volta Grande do Xingu through remote sensing", available at <a href="https://doi.org/10.11606/T.106.2023.tde-02022024-211517">https://doi.org/10.11606/T.106.2023.tde-02022024-211517</a>. To understand the data processing methodology that led to these results, please refer to the thesis.<br>Each folder represents a flight mission. They are named by date and flight number (DD_MM_YYYY_FLIGHT#).<br>Within each folder, there are six files resulting from the processed flights: The georeferenced orthophoto and Digital Surface Model, which are raster files (.TIFs), the dense point cloud (.las), and the files composing the 3D Model generated by Agisoft Metashape (extensions .OBJ, .MTL, and .JPEG).<br>The .OBJ file is the primary file for visualizing the model, including the three-dimensional mesh formed by the points of the point cloud and containing geometry, texture, and color information. <br>The .MTL file contains the material description associated with the OBJ file and includes information about the visual properties of the model, such as texture, reflections, and materials. <br>The .JPEG files are the texture images used on the Digital Surface Model to generate the 3D model. This information provides the model with its realistic properties.<br>The .OBJ, .MTL and .JPEG files need to be together in the same folder for a complete 3D Model visualization (i.e., shape, color, and texture).<br>When using this data, please cite Affonso, A. A. (2023). <em>Caracterização e monitoramento da dinâmica de alagamento e dos ambientes sazonalmente alagáveis da Volta Grande do Xingu através de sensoriamento remoto</em>. Tese de Doutorado, Instituto de Energia e Ambiente, Universidade de São Paulo, São Paulo. doi:10.11606/T.106.2023.tde-02022024-211517. Recuperado em 2024-04-14, de www.teses.usp.br</p>
Cryo-EM and X-ray crystallography ligands represented as 3D voxel grids for training deep learning models
<p>Ligand datasets used to train and evaluate the models studied in <em>"Ligand Identification using Deep Learning</em><em>"</em> by Karolczak, J. <em>et al.</em></p> <p>The blobs_full.tar.gz and cryoem_blobs.zip files contain compressed 3D numpy arrays (*.npz) of all the ligand blobs extracted from X-ray and cryo-EM PDB deposits prior to quality filtering. The npz file names correspond to the PDB ID, chain, residue number, and ligand name of the extracted blob. The cmb_data.csv file contains the tabular data used to train the CheckMyBlob model. The X-ray data were later divided into training and testing subsets according to the xray_train.csv and xray_holdout.csv files, respectively. The ligand_mapping.csv file contains the mapping from ligand IDs to ligand group names. Finally, the cryoem_qscores.csv file contains Q-scores that were used to filter cryo-EM ligands.</p>
Application Case 3: Data sets consisting of 3D scans, 3D model and the derived metadata, from two different software programs
<p>In this repository we provide 3D scan projects and the 3D models processed from them with their metadata using the example of a wood sample. The metadata was generated using our metadata generation script, which is described in the referenced publication.</p> <p>The 3D scan projects were created in different software (atos v6.2, atos 2016 and zeiss 2023). For each there is a scan project, a 3D model and the generated metadata with and without uri in this repository.</p> <p>The publication in which this application case is included: Homburg, T., Cramer, A., Raddatz, L. <em>et al.</em> Metadata schema and ontology for capturing and processing of 3D cultural heritage objects. <em>Herit Sci</em> <strong>9</strong>, 91 (2021). <a href="https://doi.org/10.1186/s40494-021-00561-w">https://doi.org/10.1186/s40494-021-00561-w</a></p> <p>Python scripts for exporting metadata can be found here: <a href="https://github.com/i3mainz/3dcap-md-gen/tree/0.1.3">GitHub - i3mainz/3dcap-md-gen</a></p>
Stacked cross correlation functions for the MeSO-net network and derived 3D Vs model
<p>This dataset contains two major types of data.</p> <p>1) The yearly stacked cross-correlation functions between 296 MeSO-net stations covering the Kanto basin, Japan.</p> <p>2) A 3D radially anisotropic Vs model for the Kanto basin. The grid increment for the longitude and latitude is 0.01 degrees and that for the depth is 0.1 km.</p> <p>The complete station list for the 296 MeSO-net stations can be found on the Github page of https://github.com/chengxinjiang/Jiang_Kanto_anisotropy. </p>
Dataset of "Hybrid modeling on 3D hydraulic features of a step-pool unit"
<p>In this repository you can find the data for the submission "Hybrid modeling on 3D hydraulic features of a step-pool unit" by Zhang et al. to Earth Surface Dynamics.</p> <p>The topographic models of the step-pool unit made of natural stones after FAVORization in FLOW3D for the six flow rates are kept in .stl files which were named after the flow rates (L/s). The mesh size for the step-pool feature is 2.5 mm for X, Y and Z directions. The locations, water level and flow velocity for the inlet boundary in all the numerical models are presented in the excel file. </p>
Building a Ngalawa Double Outrigger Logboat in Bagamoyo, Tanzania: A Craftsman at his Work. 3D Model and Documentary Film Files.
<p>The 3D model and documentary film detailing the building of the <em>Bahari Yetu, Urithi Wetu</em> <em>ngalawa</em> accompany an article on building a Ngalawa, a double outrigger logboat. The article documents master logboat-builder Alalae Mohamed’s construction of a <em>ngalawa</em> fishing vessel in Bagamoyo, Tanzania, in 2019. The <em>ngalawa</em> is an extended logboat with double outrigger and lateen sail: used by low-income, artisanal fishers. It is the most common marine vessel type of the East African coast. This article follows the construction process from Alalae’s selection and the felling of the tree(s) to the launching of the vessel. It outlines the tools and materials used, details the sequence he followed, and presents his choices and considerations made along the way. It is accompanied by a documentary film recording the construction process, a 3D digital model of the vessel and detailed construction drawings. </p>
Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling."
<p>Supplementary dataset for "High-resolution Finite Fault Slip Inversion of the 2019 Ridgecrest Earthquake using 3D Finite Element Modeling." </p>
Environmental (oxygen) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include subsurface (100 to 200m) oxygen concentration.</p>
Environmental (biogeochemical) conditions on the NW African coast from 3D reanalysis models
<p>This dataset contain hydrodynamic and biogeochemical variables extracted from the CMEMS service (<a href="https://marine.copernicus.eu/">https://marine.copernicus.eu/</a>) covering the NW region of the African coast and the period 1993 to 2019. The environmental dataset include: nitrate concentration, phosphate concentration and chlorophyll-a concentration.</p>
Visualisation of gradient flow in the 3D SU(2) Georgi-Glashow model
<p>Visualisation of gradient flow applied to a hot configuration containing ’t Hooft-Polyakov<br> monopoles and antimonopoles.</p> <p>Red and blue dots represent positive and negative magnetic monopoles. Isosurfaces of Higgs field expectation value are shown in green and yellow.</p> <p>Simulation was done on a 64^3 lattice at y = 0.03, x = 0.35 and β = 8.<br> Flow begins at ξ = 0 a, and ends at ξ = 4.38 a.</p> <p>Video made with VisIt by Riikka Seppä.<br> Simulation code developed by Lauri Niemi.</p>
Realistic 3D avian vocal tract model demonstrates how shape affects sound filtering (Passer domesticus)
<p><span>Despite the complex geometry of songbird's vocal system, it was typically modelled as a tube or with simple mathematical parameters to investigate sound filtering. Here, we developed an adjustable computational acoustic model of a sparrow's upper vocal tract (<em>Passer domesticus</em>), derived from micro-CT scans. We discovered that a 20% tracheal shortening or a 20° beak gape increase caused the vocal tract harmonic resonance to shift towards higher pitch (11.7% or 8.8%, respectively), predominantly in the mid-range frequencies (3-6 kHz). The oropharyngeal-esophageal cavity (OEC), known for its role in sound filtering, was modelled as an adjustable 3D cylinder. For a constant OEC volume, an elongated cylinder induced a higher frequency shift than a wide cylinder (70% versus 37%). We found that the OEC volume adjustments can modify the OEC first harmonic resonance at low frequencies (1.5–3 kHz) and the OEC third harmonic resonance at higher frequencies (6-8 kHz). This work demonstrates the need to consider the realistic geometry of the vocal system to accurately quantify its effect on sound filtering and show that sparrows can tune the entire range of produced sound frequencies to their vocal system resonances, by controlling the vocal tract shape, especially through complex OEC volume adjustments.</span></p>
Data from: Integrating 3D models with morphometric measurements to improve volumetric estimates in marine mammals
<p>1. Studies of body condition are key to understanding the health, bioenergetics, and ecological roles of marine mammals. Due to challenges in studying marine mammals at sea, body condition is often approximated using metrics representing the size of the dorsal surface visible from aerial imagery, but quantifying variability in body volume would enable a more holistic understanding of bioenergetics. Further, the number and location of measurements needed to accurately quantify body condition has received little attention. Three-dimensional (3D) models provide a promising tool for representing morphology and providing holistic estimates of marine mammal body condition when combined with field-based morphometric measurements.</p> <p>2. We use humpback whales (Megaptera novaeangliae) to demonstrate the utility of 3D models for estimating body condition in marine mammals. We integrate morphometric measurements taken from Unoccupied Aerial Vehicles (UAVs) with scalable 3D models to generate estimates of humpback whale body volume. We assess which and how many morphometric measurements are required to accurately estimate body volume and compare the error between volume estimates derived from 3D models and previously developed models representing volume as a series of ellipses. Using UAV measurements, we assess the contribution of each morphometric measurement to volumetric estimates, and quantify the error produced by all combinations and numbers of morphometric measurements (131,072 combinations).</p> <p>3. Error in volume estimates from 3D models generated with as few as five width measurements was <5% compared to the full models and was lower than the error produced when using five width measurements with the elliptical approach. We suggest that by conserving the external morphology of marine mammals, 3D models allow body volume and body condition to be estimated accurately with few measurements.</p> <p>4. We provide code and guidelines for creating 3D models using the open-source software Blender and for assessing which measurements are needed to accurately capture the morphology of cetaceans. The 3D modeling approach we present will facilitate studies of intra- and interannual changes in body volume in marine mammals, which is vital to providing a more holistic understanding of bioenergetics and to assessing responses to environmental change and anthropogenic stressors.</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.