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478 results for “3D data”
Pose-estimated 3D data of infant spontaneous activity from Helsinki and Pisa
<p>This dataset contains pose-estimated 3D (and 2D proxy) data as well as trained models for generating infant Kinetic Age, collected from research trials in Helsinki and Pisa. The dataset is organized into separate archives for metadata, data streams, trained models, and predictions. Below is a detailed breakdown of the dataset contents:</p> <p><strong>Metadata</strong></p> <ul> <li><em>metadata/combined.csv</em><br> - test_id: Unique identifier for each infant.<br> - corrected_age: Corrected age of the infant in days.<br> - outcome: Neurodevelopmental outcome labels (0 typical, 1 weak impairment, 2 MNI).</li> </ul> <p><strong>Data</strong></p> <ul> <li><em>data/features.csv</em><br> - Handcrafted movement features computed for each, by experts annotated, useful video segment.</li> <li><em>data/streams/combined/*.feather</em><br> - 3D recording segment, with 18 J, B, V, A streams over 600 time steps.</li> <li><em>data/streams_2d/combined/*.feather </em><br> - 2D recording segment, with 18 J, B, V, A streams over 600 time steps.</li> </ul> <p><strong>Results</strong></p> <ul> <li><em>results/model/fold_n/...</em><br> - train_predictions.npy: Predictions made on the training set.<br> - val_predictions.npy: Predictions made on the validation set.<br> - best_model.ckpt: Checkpoint file containing the saved model weights.<br> - metadata.json: Metadata describing the fold, including training parameters and validation segments.<br> - scatter_*.png: Regression results.</li> </ul> <p><strong>Predictions</strong></p> <ul> <li><em>predictions/jb-aagcn-coord-xy_predictions.csv</em><br> - Model predictions for the 2D model on MNI samples.</li> <li><em>predictions/jb-aagcn-coord_predictions.csv</em><br> - Model predictions for the 3D model on MNI samples.</li> </ul>
Data archive for paper "Machine Learning Emulation of 3D Cloud Radiative Effects"
<p><strong>Overview</strong></p> <p>This archive contains models, data, and the Singularity image to optionally rerun experiments described in "<a href="https://doi.org/10.1029/2021MS002550">Machine Learning Emulation of 3D Cloud Radiative Effects</a>".</p> <p>For the Python tool to generate synthetic data, please refer to the <a href="https://github.com/dmey/synthia">Synthia repository</a>.</p> <p><strong>Prerequisites</strong></p> <ul> <li>Linux or macOS with Bash shell.</li> <li><a href="https://sylabs.io/singularity/">Singularity</a> (tested with version 3.6.3-1.el8)*.</li> <li><a href="https://en.wikipedia.org/wiki/Portable_Batch_System">Portable Batch System</a> (PBS) job scheduler**.</li> </ul> <p>*Please note that all steps require <a href="https://sylabs.io/">Singularity</a> to be installed on your system. If you are looking for information on how to install or use Singularity, please refer to the <a href="https://sylabs.io/docs">Singularity documentation</a>.</p> <p>**Although PBS in not a strict requirement, it is required to run all helper scripts as included in this repository. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the <code>hpc</code> directory (e.g. <code>#PBS -lwalltime=24:00:00</code>).</p> <p><strong>Initialization</strong></p> <p>Deflate the data archive with:</p> <pre><code>./init.sh </code></pre> <p>Compile ecRad with Singularity:</p> <pre><code>./tools/singularity/compile_ecrad.sh </code></pre> <p><strong>Usage</strong></p> <p>To reproduce the results as described in the paper, run the following commands from the <code>hpc</code> folder:</p> <pre><code>qsub -v JOB_NAME=mlp_default ./submit_grid_search_default.sh qsub -v JOB_NAME=mlp_synthia ./submit_grid_search_synthia.sh qsub submit_benchmark.sh </code></pre> <p>then, to plot stats and identify notebooks run:</p> <pre><code>qsub submit_stats.sh </code></pre> <p><strong>License</strong></p> <p>Paper code released under the <a href="./LICENSE.txt">MIT license</a>. Data released under <a href="./data/LICENSE.txt">CC BY 4.0</a>. <a href="https://confluence.ecmwf.int/display/ECRAD">ecRad</a> released under the <a href="./ecrad/LICENSE">Apache 2.0 license</a>.</p>
Data for "Characteristics of earthquake cycles: a cross-dimensional comparison 0D to 3D"
<p>This is the data used in "Characteristics of earthquake cycles: a cross-dimensional comparison 0D to 3D". The paper is currently under review. See README.txt for more information.</p>
Data from: T cell morphodynamics reveal periodic shape oscillations in 3D migration
<p>Surface segmentation data of cytotoxic T cells migrating in 3D collagen matrices, imaged by lattice light-sheet microscopy and used for quantitative morphodynamic analysis in the manuscript: T Cell Morphodynamics Reveal Periodic Shape Oscillations in 3D Migration.</p>
3D Model data from the virtual asset marketplace Sketchfab.
<p>3D Model data from the virtual asset marketplace Sketchfab.</p> <p>Publication: <a href="https://www.mdpi.com/0718-1876/17/3/48">https://www.mdpi.com/0718-1876/17/3/48</a></p>
3D Optical Stochastic Cooling Data
<p>Example data and processing/analysis for 3D Optical Stochastic Cooling at Fermilab's IOTA ring</p>
3D scanning data of the tooth surface of Japanese macaque (NMNS PV 6166-7) and the silicone impression molds of it
<p>3D scanning data of the tooth surface of Japanese macaque (NMNS PV 6166-7) and the silicone impression molds of it are saved in ".vk3", ".mnt", and ".sur" format, which are analyzed in:</p> <p>Sawaura, R., Kimura, Y., and Kubo, M. O. "Accuracy of Dental Microwear Impressions by Physical Properties of Silicone Materials" submitted to Frontiers in Ecology and Evolution.</p> <p>For details, please see the information in the paper.</p>
In Situ Volumetric Imaging and Analysis of FRESH 3D Bioprinted Constructs Using Optical Coherence Tomography (Data and 3D models)
<p>These files contain 3D models and reconstructions of the 3D printed models after OCT imaging of the brain stem, circle of willis, kidney, vestibular apparatus, mixing network, and resolution text. These are from the journal article "In Situ Volumetric Imaging and Analysis of FRESH 3D Bioprinted Constructs Using Optical Coherence Tomography" published in <em>Biofabrication </em>(2022).</p>
Data for: Ascent rates of 3D fractures driven by a finite batch of buoyant fluid
<p>Ascent rates of finite fluid batches:</p> <p>Contains the numerical experiments using https://pyfrac.epfl.ch/</p> <p>Also contains movies and text data from selected analog gelatin experiments of <em>Smittarello, D. 2019 Propagation des intrusions basaltiques. PhD thesis, Université Grenoble Alpes.</em></p> <p>See ReadMe file for more information. </p> <p>Files zipped using 7zip.</p>
Analysis of internal pressure in 3D bent insulated glass units - experimental data (CC-BY)
<p>The experimental data have been collected within the research project “Analysis of internal pressure in 3D bent insulated glass units” (grant number 2021/05/X/ST8/00168) financed by The National Science Centre (NCN) within the MINIATURA 5 programme.</p>
Design 3D CAD data of an oversized-sample 35 GHz EPR resonator with an elevated Q value
<pre>Supplemental data for the manuscript “Design and performance of an oversized 35 GHz EPR resonator with an elevated Q value” General information The CAD dimensions match the dimension of the manufactured resonator/probehead. Not depicted in the CAD file are the modulation coils (the modulation coil holders on the sides of the cavity block are shown) and the mechanical connection between the coupling screw on top flange of the resonator and the movable coupling piston at the bottom. Autodesk Inventor files The assembly of the whole resonator is stored as “Qband_CW_Probenkopf”. All dependent subassemblies and parts can be found in the folder “Q_band_CW_resonator_Inventor”. 3D files for Open source software The complete resonator is stored in the file “Qband_CW_Probenkopf.stl”. Since in this file all resonator components are merged into a single unit and cannot be depicted on their own, all resonator components are also stored seperately as .obj-files in the folder “Qband_CW_Probenkopf_obj_format”.</pre>
3D PanIN volume distribution data
<p>This data was originally published in Braxton,* Kiemen* et al "Three-dimensional genomic mapping of human pancreatic tissue reveals striking multifocality and genetic heterogeneity in precancerous lesions" and was used in Kiemen et al "Power-law growth models explain incidences and sizes of pancreatic cancer precursor lesions and confirm spatial genomic findings."</p>
Human Bone Ultrastructure in 3D (nano-CT+qPRS): Data Archive
<p>This is a Data Archive with original data for the manuscript "Human Bone Ultrastructure in 3D: Mltimodal Correlative Study Combining Nanoscale X-Ray Computed Tomography and Quantitative Polarized Raman Spectroscopy".</p> <p>A README file containing all descriptions can be found in the main folder. Data is stuctured into 4 categories: Light Microscopy, nano-CT tilt series, nano-CT reconstruction and Raman (qPRS) Data. Each category is uploaded as a .zip archive and contains files related to respective technique (in their turn groupped into .zip archives by the name of a showcased sample).<br><br></p>
A 40-year moisture source data for Tibetan Plateau precipitation using a 3D Lagrangian approach
<p>This repository contains the dataset that reproduces the work by Cheng et al. (2024). The zip files contain data in netCDF format. They consist of</p> <ol> <li>Data of Figures 1-5 in the article (Cheng et al. 2024)</li> <li>Moisture sources of precipitation in the Tibetan Plateau (TP) <ul> <li><code>TP_moisture_source_1971-2010.nc</code>: Global map (lon,lat,time) of 40 years of moisture source (mm/day) contributing to the TP precipitation based on the FLEXPART-WaterSip approach</li> <li><code>SR_1971-2010_TP_grids_1x1_XXX.nc</code>: Fractional contributions of different circulation regimes to each of 302 1˚x1˚ grids on the TP</li> </ul> </li> <li>Multi-product ensemble mean precipitation and evapotranspiration</li> <li>Boundary data of TP river basins used in the study</li> </ol> <p>For any enquiries, feel free to contact Dr. Tat Fan (Franklin) Cheng at <a href="mailto:franklin.cheng@ust.hk">franklin.cheng@ust.hk</a>. Please cite our two recent articles if you found the dataset useful. Thank you!</p> <p><strong>References</strong></p> <blockquote> <p>Cheng, T. F., Chen, D., Wang, B., Ou, T. & Lu, M. (2024). Human-induced warming accelerates local evapotranspiration and precipitation recycling over the Tibetan Plateau. <em>Commun Earth Environ </em>5, 388. <a href="https://doi.org/10.1038/s43247-024-01563-9">https://doi.org/10.1038/s43247-024-01563-9</a> </p> <p>Cheng, T. F., & Lu, M. (2023). Global Lagrangian Tracking of Continental Precipitation Recycling, Footprints, and Cascades. <em>Journal of Climate</em>, 36, 1923–1941. <a href="https://doi.org/10.1175/JCLI-D-22-0185.1">https://doi.org/10.1175/JCLI-D-22-0185.1 </a></p> </blockquote>
Data for "3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation"
<p>Segmentation masks and evaluation metrics generated for "3DCellComposer - A Versatile Pipeline Utilizing 2D Cell Segmentation Methods for 3D Cell Segmentation"</p>
Example 3D Underwater Acoustic Pressure Data Sampled Over 24 Hours
Open the record for dataset details and reuse information.
Rapid T1 quantification from high resolution 3D data with model-based reconstruction
<p>In-vivo datasets used in the work "Rapid T1 quantification from high resolution 3D data with model-based reconstruction" with DOI: 10.1002/mrm.27502<br> </p>
Underlying data for "Metabolism of remimazolam in primary human hepatocytes during continuous long-term infusion in a 3D bioreactor system"
<p>The dataset supports the manuscript "Metabolism of remimazolam in primary human hepatocytes during continuous long-term infusion in a 3D bioreactor system" published in the journal "Drug design, development and therapy" in 2019</p>
raw 3D wind data at 30cm above the nebkha surface
<p>The raw wind data over a nebkha collected 2018. The other dataset the results of the quadrant analysis.</p>
Nanoresolution real-time 3D orbital tracking for studying mitochondrial trafficking in vertebrate axons in vivo - Data Set 2
<p>The data set was analyzed in main figures 2, 3 and supplementary figure 5.</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.