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870 results for “boxing”
Olympic Collection Box
This wooden cash box was used at coffee parties by Finnish-American societies in New York to raise funds for the Finnish Olympic team ahead of the 1932 Winter Games held in Lake Placid, N.Y. The collection drive held in the winter of 1931-32 – in the middle of the Great Depression – among Finnish immigrants in the United States and Canada yielded an impressive sum that eventually covered half of the travel expenses of the Finnish Olympic teams for Lake Placid and for the Summer Games held in Los Angeles. The cash-strapped Finnish Olympic Committee could afford to send only seven athletes to the Winter Games in Lake Placid – and one of them had to pay for his own fare. This box was apparently handed to the team in New York on 20 February 1932, the date seen on the bottom with signatures of the Finnish athletes. Source: Objaverse 1.0 / Sketchfab
Oxygen tank in a box
# This is an [oxygen tank](https://en.wikipedia.org/wiki/Self-contained_self-rescue_device) in a box used by miners # This is a blue tank in a box. It contained oxygen for the miners in order to help them to breath while they were working in the mines. This was essential to their life, since their natural work environment was oftentimes filled with a high amount of [methan or carbon monoxide](https://www.britannica.com/technology/mine-gas). The gas tanks could supply the miners with oxygen for a couple hours, but were really heavy to carry around. This was one of the reasons why they were usually accompanied with a sling or like in this case with a *box*. The words on the back of the tank **Zuurstof-Koffer** are dutch and translate to oxygen tank. The box has a length of ca. 40 cm and a width of ca. 70 cm. created by: Fee Lucie, Wiegandt Location: Nederlands Mijnmuseum, Heerlen Source: Objaverse 1.0 / Sketchfab
Cigar Box Guitar
A Cigar Box Guitar/Tenor Guitar made in maple wood. Reconstructed with CNR - Open Mesh Reconstructor (free photogrammetry software, after registration to the d4science.org platform) https://services.d4science.org/group/rprototypinglab/data-miner?OperatorId=org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.OPEN_MESH_RECONSTRUCTOR_ADVANCED Citation and more information on the software: Coro, G., Palma, M., Ellenbroek, A., Panichi, G., Nair, T., & Pagano, P. (2019). Reconstructing 3D virtual environments within a collaborative e‐infrastructure. Concurrency and Computation: Practice and Experience, 31(11), e5028. https://doi.org/10.1002/cpe.5028 Source: Objaverse 1.0 / Sketchfab
9 Items Box Project
9 Items Box Project I did for work experiance for college. Please follow and leave a like for future contant. Source: Objaverse 1.0 / Sketchfab
storage box 01
3d scan of objects in storage boxes from camberwell collection, London. Source: Objaverse 1.0 / Sketchfab
Wmid-321fb6 - Incomplete medieval box mount
An incomplete copper alloy bar mount, probably from a casket, chest or box, of Medieval dating (c. AD 1200 to AD 1400). Less than 25% of the bar mount is present, consisting of one terminal and part of the bar. The terminal is a lis or lys shape, with a circular attachment hole placed centrally (2.9 mm) around diagonal side grooves.The surviving section of the bar mount has a raised area with bevelled sides, creating a pyramid type effect (11.4 mm by 8.6 mm). The underside is slightly concave along its length, with raised edges. The terminal is bent upwards at an angle of approx 45 degrees. Gilding is still present on the upper surfaces. For more detail, please visit the online database record available at: https://finds.org.uk/database/artefacts/record/id/1059761 Source: Objaverse 1.0 / Sketchfab
Historical Elektronic Levels Box (retopology)
This is a rare artifact that has seen an old measuring instrument box. Source: Objaverse 1.0 / Sketchfab
Box
Low poly box. Made in blender. Original file name and extension: Box.fbx Source: Objaverse 1.0 / Sketchfab
Pouncet-box with a genre scene
Porcelain pouncet-box with a hand-painted court scene ID no.: FZRS/34 Museum: Collection of the Sosenko Family Foundation https://muzea.malopolska.pl/en/objects-list/869 Digitalisation: RDW MIC, Virtual Małopolska project Source: Objaverse 1.0 / Sketchfab
HIGH Kirjelaatikko - Post box
Ensimmäiset kirjelaatikot otettiin Suomessa käyttöön vuonna 1845. Laatikoiden käyttöönotto helpotti postitusta, sillä kirjeiden jättäminen ei ollut enää sidottu postikonttorin aukioloaikoihin. Tämä laatikko on ollut käytössä Porvoon postissa 1890-luvulta alkaen. Laatikot valmistettiin metallista niin, että vain määrämittaiset kuoret mahtuivat niihin. Ne kiinnitettiin esimerkiksi postikonttorin seinään. Laatikoiden keltainen väri yleistyi 1900-luvun alussa. The post boxes were introduced in Finland in 1845. This made mailing easier as sending letters was not tied to post offices' opening hours. This post box was in use from 1890's onwards. Post boxes were made of metal. The measurements of the opening permitted only letters of defined measures to be inserted. Post boxes were often attached to the wall of a post office. Yellow became the predominant color of post boxes in early 20th century. The object (PM-ES-96) and 3D model: Finnish Postal Museum (member of Traffic Museums Association). Source: Objaverse 1.0 / Sketchfab
Dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)
<p>dataset of sediment cores and box cores collected in Central Basin, Ross Sea (Antarctica)</p>
Additional raw video and pose estimation data of top view mouse behavior recordings (marble burying test, light-dark box, fear conditioning box) of acute and chronic stress models
<p>This repository contains raw data for 296 different behavioral recordings of mice (marble burying test, light-dark box, fear conditioning box). These include top view raw video .mp4 files (Videos.zip) and the corresponding .csv pose estimation data (data.zip) obtained with DeepLabCut. The data is from multiple different experiments. The METADATA.csv or METADATA.xlsx files contain all grouping variables and help linking the pose estimation files (located in multiple subfolders of /data) to the video files. Visit https://github.com/ETHZ-INS/BehaviorFlow to find out more about how this data has be used by us.</p>
HARTU PALLETIZING AND BOXING DATASET
<p>This dataset is created using different objects CAD models. There are two distinct datasets: palletizing and multiobject boxing. For each image, the location of each object is provided at the box level with labels in YOLO format. Each dataset is divided on train, val and test subsets.</p> <p>The HARTU project supports this work. This project has received funding from the European Union’s research and innovation programme Horizon Europe under the grant agreement No. 101092100.</p>
Figure 3 in Efficiency of colored modified box traps for sampling of tabanids
Figure 3. Number of collected specimens of T. bromius in different colored traps.
Figure 5 in Efficiency of colored modified box traps for sampling of tabanids
Figure 5. Differences of reflectances of light-colored traps and green color.
Figure 1 in Efficiency of colored modified box traps for sampling of tabanids
Figure 1. Reflection spectra of the used colors.
A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification
<p><strong>Introduction</strong></p> <p>The Robot Inverse Dynamics Dataset is a collection of trajectories and joint torque measurements of two robotic manipulators, a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL. Additionally, the dataset contains the inverse dynamical models and other useful quantities learned to reproduce the results reported on our reference paper "A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification". The proposed model relies on a novel multidimensional kernel, called Lagrangian Inspired Polynomial (LIP) kernel.</p> <p><strong>At a Glance</strong></p> <ul> <li>The size of the unzipped dataset is ~700MB.</li> <li>The dataset contains</li> <ul> <li>collections of joint trajectories and joint torque measurements of two robot manipulators: a 7 DoF Franka Emika Panda, and a 6 DOF MELFA RV4FL.</li> <li>models of the inverse dynamics of the two manipulators learned on the datasets</li> </ul> <li>The main directories are</li> <ul> <li>Simulated_PANDA/ contains the trajectories, models and results obtained on different configurations of a Franka Emika PANDA robot, simulated in sympybotics.</li> <li>Robots/ contains the data, models and results obtained on two real robots, a Franka Emika PANDA and a Mitsubishi Electric MELFA RV4FRL</li> </ul> <li>See the README.md file for a detailed description of the directories.</li> </ul> <p><strong>Other Resources</strong></p> <p>Python code to train the models and reproduce the results in the paper are available <a href="https://github.com/merlresearch/LIP4RobotInverseDynamics">here</a>.</p> <p><strong>Citation</strong></p> <p>If you use the Robot Inverse Dynamics dataset in your research, please cite our contribution:</p> <pre><code>@InProceedings{ title={A Black-Box Physics-Informed Estimator based on Gaussian Process Regression for Robot Inverse Dynamics Identification}, author={Giacomuzzo, G., Dalla Libera, A., Romeres, D.,}, booktitle={IEEE Transaction on Robotics}, year={2024} } </code></pre> <p><strong>License</strong></p> <p>The Robot Inverse Dynamics dataset is released under <a href="https://creativecommons.org/licenses/by-sa/4.0/">CC-BY-SA-4.0 license</a>.</p> <p>All data:</p> <pre><code>Created by Mitsubishi Electric Research Laboratories (MERL), 2024 SPDX-License-Identifier: CC-BY-SA-4.0</code></pre>
Fig. 2. Experimental box with P in Reciprocal Predation Between Preserved And Invasive Species: Adult Bombina Bombina Predate Young Whitebaits Of Alien Fish Perccottus Glenii
Fig. 2. Experimental box with P. glenii and pair of B. bombina.
Forming tests of a uni- and a bidirectional non-crimp fabric for different orientations and geometries (hemisphere, tetrahedron, square box)
<p><strong>Reference:</strong> The data is published as supplementary data for publications and for other researchers to incorporate the data in their work. Details behind the experimental procedure and the methods applied to analyze the tests are published in (please use to reference this data):</p> <p>“B. Schäfer, R. Zheng, J. Colmars, A. Platzer, N. Naouar, P. Boisse, L. Kärger. <em>Experimental analysis of the forming behavior of uni- and bidirectional non-crimp fabrics for different geometries</em>, Composites Part B: Engineering, 287:111765, 2024. <a href="https://doi.org/10.1016/j.compositesb.2024.111765">https://doi.org/10.1016/j.compositesb.2024.111765</a>”</p> <p><strong>Description:</strong> This dataset contains high-resolution images and analyzed numerical data (punch forces, outer contour after forming, local strains measured with DIC) of forming tests on a unidirectional non-crimp fabric (UD-NCF) and a bidirectional non-crimp fabric (Biax-NCF) for different layup configurations and punch shapes (hemisphere, tetrahedron, square box). The tests were conducted for various single- and double-layer configurations with different relative fiber orientations. The experiments were conducted at the “LaMCoS – Laboratoire de Mecanique des Contacts and des Structures” from the “Institut National des Sciences Appliquées de Lyon (INSA Lyon)” in France. The analysis of the data was performed at the “Institute of Vehicle System Technology, Division Lightweight Engineering” from the “Karlsruhe Institute of Technology (KIT)” in Germany. The work was part of the international collaborative research project AMECOMP funded by the ANR in France and the DFG in Germany (DFG: 431354059, ANR: ANR-19-CE06-0031) - <a href="https://doi.org/10.5445/IR/1000172090">https://doi.org/10.5445/IR/1000172090</a>.</p> <p><strong>Test setup and method:</strong> The experimental setup consists of three punch shapes (hemisphere, tetrahedron, square box) with matching planar blank holders and dies. The blank holders apply their own weight to the fabric, and centering pins prevent any in-plane movement. All setups are mounted to a universal testing machine by ZwickRoell. The blank holders and dies are made of transparent methyl methacrylate (PMMA) to allow for the acquisition of digital images by a high-resolution camera. The forming depths are 75 mm, 95 mm, and 85 mm with blank holder weights of 1.7 kg, 6.0 kg, and 1.7 kg for the hemisphere, tetrahedron, and square box punches, respectively. The dimensions and schematics are summarized in the image <em>“Experimental-setup_schematics-dimensions.jpg”</em>. The deformation is measured via Digital Image Correlation (DIC) with a regular grid of white dots of acrylic paint using a stencil with a distance of l<sub>e </sub>= 10 mm.</p> <p><strong>Material:</strong> A unidirectional (UD300) and bidirectional (MD600) non-crimp fabric (NCF) both without binder are used, cf. <em>”Material_schematics.jpg”</em>. The fabrics are manufactured by Zoltek™ and produced from their PX35-50K continuous carbon (CF) fiber heavy tows. Both fabrics are stitched together with a 76 dtex Polyester stitching yarn in a Tricot pattern. A textile layer of UD300 consists of a single fiber layer of aligned CF tows in warp direction with thin glass fibers (GF) in weft direction on the back for improved handleability. A textile layer of MD600 comprises two fiber layers in a 0°/90° orientation. Both fabrics have a similar number of CFs in each fiber layer with about 300 g/m2 in their respective reinforcing directions. </p> <p><strong>Tested configurations for all three punch shapes: </strong></p> <ul> <li> UD-NCF: three types of single-layer ([0°<sub>U</sub>], [45°<sub>U</sub>], [90°<sub>U</sub>]) and two types of double-layer ([0°<sub>U</sub>∕45°<sub>U</sub>], [0°<sub>U</sub>∕90°<sub>U</sub>]) configurations</li> <li> Biax-NCF: two types of single layer ([(0°∕90°)<sub>B</sub>], [(±45°)<sub>B</sub>]) and one type of double-layer [(0°∕90°)<sub>B</sub>∕(±45°)<sub>B</sub>] configurations</li> </ul> <p><strong>Provided data:</strong></p> <ul> <li>High-resolution images of all tested configurations for both materials for the different geometries in the respective .7z archives <a href="https://www.7-zip.org/">(7-zip.org)</a>. [Only one representative image set per configuration due to total size limitations by zenodo.org]</li> <li>Analyzed numerical data (punch forces, outer contours after forming, local strains measured with DIC) for all tests. The data is stored as nested dictionaries in the folder <em>“_analyzed_data”</em> in the respective .json files. A demonstration to access the data via Python and recreate the plots from the original publication is provided in the supplementary Jupyter Notebook (<em>“example_data_analysis.ipynb”)</em></li> </ul>
Results for Several Simple Algorithms on the W-Model for Black-Box Discrete Optimization Benchmarking
<p>The W-Model follows a layered approach, where each layer can either be omitted or introduce a different characteristic feature such as neutrality via redundancy, ruggedness and deceptiveness, epistasis, and multi-objectivity, in a tunable way. The model problem is defined over bit string representations, which allows for extracting some of its layers and stacking them on top of existing problems that use this representation, such as OneMax, the Maximum Satisfisiability or the Set Covering tasks, and the NK landscape. The ruggedness and deceptiveness layer can be stacked on top of any problem with integer-valued objectives.</p> <p>Here we provide some experimental results with several simple algorithms on the W-Model along with the algorithm and experiment executor implementation. The most recent version of the code can be found at <a href="http://www.github.com/thomasWeise/BBDOB_W_Model">http://www.github.com/thomasWeise/BBDOB_W_Model</a>, while the program used in the experiment are attached to this dataset. The implemented algorithms are</p> <ul> <li>1-flip hill climber with restarts,</li> <li>2-flip hill climber with restarts,</li> <li>μ+λ Evolutionary Algorithms (for different values of μ, λ, and different crossover rates),</li> <li>μ+λ Evolutionary Algorithms with Frequency Fitness Assignment (FFA) (for different values of μ, λ, and different crossover rates),</li> <li>exhaustive enumerate (EE), and</li> <li>random sampling (RS)</li> </ul> <p>These algorithms are applied to a range of different parametric setups of the W-Model for single-objective optimization and fixed-length bit string representations.</p> <p>The experiments are executed on a HP Z640 Work Station with 32 GB DDR4-2400 RAM and Intel Xeon E5-2609v4 CPU under Ubuntu Server Linux 16.04, Kernel 4.4.0-116-generic, and with Java 1.8.0_151. Each run was granted at most 1048576 function evaluations (FEs). The total amount of data collected is about 45 GB, with tar.xz compression down to about 1.2 GB. The text files containing the algorithm traces are fairly self-describing.</p> <p>Publications that describe the W-Model:</p> <ul> <li>Thomas Weise and Zijun Wu. Difficult Features of Combinatorial Optimization Problems and the Tunable W-Model Benchmark Problem for Simulating them. In Black Box Discrete Optimization Benchmarking (<a href="http://iao.hfuu.edu.cn/bbdob-gecco18">BB-DOB</a>) Workshop of <em>Companion Material Proceedings of the Genetic and Evolutionary Computation Conference (<a href="http://gecco-2018.sigevo.org/">GECCO 2018</a>)</em>, July 15th-19th 2018, Kyoto, Japan, ISBN: 978-1-4503-5764-7. ACM. doi:<a href="http://dx.doi.org/10.1145/3205651.3208240">10.1145/3205651.3208240</a> / <a href="http://github.com/thomasWeise/BBDOB_W_Model">source codes</a> [This paper contains some errata for the one below]</li> <li>Thomas Weise, Stefan Niemczyk, Hendrik Skubch, Roland Reichle, and Kurt Geihs. A Tunable Model for Multi-Objective, Epistatic, Rugged, and Neutral Fitness Landscapes. In Maarten Keijzer, Giuliano Antoniol, Clare Bates Congdon, Kalyanmoy Deb, Benjamin Doerr, Nikolaus Hansen, John H. Holmes, Gregory S. Hornby, Daniel Howard, James Kennedy, Sanjeev P. Kumar, Fernando G. Lobo, Julian Francis Miller, Jason H. Moore, Frank Neumann, Martin Pelikan, Jordan B. Pollack, Kumara Sastry, Kenneth Owen Stanley, Adrian Stoica, El-Ghazali, and Ingo Wegener, editors, <em>Proceedings of the 10th Annual Conference on Genetic and Evolutionary Computation Conference (GECCO'08)</em>, pages 795-802, July 12-16, 2008, Renaissance Atlanta Hotel Downtown: Atlanta, GA, USA. ISBN: 978-1-60558-130-9, New York, NY, USA: ACM Press. doi:<a href="http://dx.doi.org/10.1145/1389095.1389252">10.1145/1389095.1389252</a> / <a href="http://iao.hfuu.edu.cn/images/publications/WNSRG2008ATMFMOERANFL.pdf">pdf</a> [This paper has some deficits fixed in the paper above]</li> </ul>
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.