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5,635 results for “3D”
3D PALM LexA-PAmCherry in E.coli MG1655
<p>3D PALM in near TIRF conditions of LexA-PAmCherry fusion chromosomally-tagged in MG1655 background<br><br>Data set used in</p> <p><em><span>Anisotropic DBSCAN for 3D SMLM Data Clustering</span></em></p> <p> </p>
3D Stereo Body Pose Estimation - Evaluation Plots
<p>Evaluation plots for the "3D Stereo Body Pose Estimation" project, aimed at estimating 3d keypoints from humans captured using a OAK-D camera.</p> <p>The project is originated in the context of the activities of the postgraduate course IA904 - Model Project in Visual Computing, offered in the first semester of 2024, at Unicamp, under the supervision of Prof. Dr. Leticia Rittner and Prof. Paula D. Paro Costa, both from the Department of Computer and Automation Engineering (DCA) of the Faculty of Electrical and Computer Engineering (FEEC).</p> <p>The <a href="https://github.com/Disciplinas-FEEC/IA904-2024S1/tree/main/projetos/3DStereoBodyPoseEstimation">project page</a> have a full description of the project (portuguese).</p>
Dataset for '3D printing of customizable transient bioelectronics and sensors'
<p>This data set contains the data collected during the FNS project Green Piezo (Grant no. 179064) in association with the recent publication entitled “3D printing of customizable transient bioelectronics and sensors”.</p> <p>This work aims to study and demonstrate the fabrication by 3D printing of devices made of transient materials, i.e. materials that can break down and degrade in an environment of choice. Biodegradable electronic devices have potential in tackling the issue of electronic waste and present an opportunity for new types of implantable and/or wearable devices that can resorb after their lifecycle is completed. A bioresorbable elastomer and a conductive carbon-based ink are printed by direct-ink writing, thanks to an in depth study of their dispense behavior. Several sensors are shown as demonstrators (strain, pressure, electrodes). The data that was collected in the frame of this work is present in this repository. More information about the contents of the dataset is present in the included README file.</p>
Supplementary dataset for "Magnetic recording fidelity of basalts through 3D nanotomography, 2024"
<p>This repository contains raw data and scripts needed to reproduce the results presented in "Magnetic recording fidelity of basalts through 3D nanotomography, 2024". These include slice-and-view image stacks (<a href="../api/records/11369780/draft/files/VesuviusSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">VesuviusSnVTiffStack.tif</a> for the Vesuvius dataset and <a href="../api/records/11369780/draft/files/HeklaSnVTiffStack.tif/content" target="_blank" rel="noopener noreferrer">HeklaSnVTiffStack.tif</a> for the Hekla Volume) for the samples discussed in the manuscript. These were used to generate 3D meshes of magnetite grains in the volume using the methodology described in the manuscript. <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Vesuvius.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Vesuvius.7z</a> and <a href="../api/records/11369780/draft/files/Individual%20Meshes%20Hekla.7z/content" target="_blank" rel="noopener noreferrer">Individual Meshes Hekla.7z</a> contain the individual 3D mesh .pat files, while <a href="../api/records/11369780/draft/files/Hekla%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Hekla Full Volume.stl</a> and <a href="../api/records/11369780/draft/files/Vesuvius%20Full%20Volume.stl/content" target="_blank" rel="noopener noreferrer">Vesuvius Full Volume.stl</a> show full 3D representations of the studied volumes. The individual mesh files can be used as geometry inputs for micromagnetic simulations using the MERRILL suite . Example MERRILL scripts are also included, with <a href="../api/records/11369780/draft/files/LEM_StateMerrilScript.merrill/content" target="_blank" rel="noopener noreferrer">LEM_StateMerrilScript.merrill</a> showing an example of a script used to determine the local energy minimum (LEM) state of a magnetic grain and <a href="../api/records/11369780/draft/files/NEB_Merril_Script.merrill/content" target="_blank" rel="noopener noreferrer">NEB_Merril_Script.merrill</a> showing a script to determine the energy barriers between LEM states used in the calculation of relaxation times. Finally ".csv" files containing grain metrics are also included for both of the studied samples ( <a href="../api/records/11369780/draft/files/HeklaGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">HeklaGrainMetrics.csv</a> and <a href="../api/records/11369780/draft/files/VesuviusGrainMetrics.csv/content" target="_blank" rel="noopener noreferrer">VesuviusGrainMetrics.csv</a> ). These contain information on the size of the individual particles, their morphology, the LEM states they support and the energy barries from the NEB calculation.</p>
Full-Body 3D Human Gait Dataset walking on flat ground
<p>This dataset contains full-body 3D gait data collected from 26 healthy participants (10 males, 16 females) with an average age of 28.19 ± 7.77 years. Data was captured using the Xsens Awinda MTw inertial measurement system, comprising 17 wireless sensors operating at a 60Hz sampling frequency.</p> <p>Key Features:</p> <ul> <li>Full-body motion data using MVN Analyze software's full-body model</li> <li>Anthropometric measurements: height (170.5 ± 8.61 cm), foot length (26.47 ± 1.88 cm), shoulder width (39.32 ± 7.79 cm), and wrist span (131.36 ± 8.85 cm)</li> <li>Four distinct walking paths: Mixed (straight and curved), Circle (3m diameter), Turn (180-degree turns), and Zigzag</li> <li>Total of 1,024,295 frames (17,071.58 seconds) of gait recordings</li> <li>Average of 3,568.97 ± 1,204.26 frames per recording (59.48 ± 20.07 seconds)</li> </ul> <p>The dataset includes various walking patterns designed to capture a wide range of gait characteristics, including straight walks, gentle curves, sharp turns, and zigzag movements. Participants were allowed some freedom in executing turns, particularly in the Zigzag and Mixed paths, to introduce natural variations in gait patterns.</p> <p>This comprehensive dataset is suitable for gait analysis, biomechanics research, and the development of motion synthesis algorithms, particularly those focused on normal walking patterns on a fixed surface with various turning scenarios.</p> <p>Dataset Structure:</p> <ol> <li>'<strong>participants.xlsx</strong>': An Excel file containing participant codes and their anthropometric data.</li> <li>'<strong>data</strong>' folder: Contains subdirectories named with participant codes. <ul> <li>Each participant subdirectory contains CSV files of different gait recordings for that participant.</li> </ul> </li> </ol> <p>This dataset was collected as part of the study:</p> <p><strong>Carneros-Prado, D., Dobrescu, C. C., Cabañero, L., Villa, L., Altamirano-Flores, Y. V., Lopez-Nava, I. H., … & Hervás, R. (2024). Synthetic 3D full-body skeletal motion from 2D paths using RNN with LSTM cells and linear networks. </strong><strong><em>Computers in Biology and Medicine, 180,</em></strong><strong> 108943.</strong></p>
RAKSILA 3D. Laser scanning survey of the street fronts and green areas in Raksila, Oulu (FINLAND)
<p>The video shows the preliminary results of the laser scanner survey of Raksila district in Oulu, Finland. Raksila is an important historical trace in the development of the urban planning of the city of Oulu. The district of Raksila is mainly a well-preserved residential Neighborhood characterized by a strong typicality.The general plan consists of a regular structure and a system of street fronts on the road are ordered and in an homogeneous profile. Despite this, Raksila still has no detailed and updated guidelines capable of managing all different types of interventions allowed (renovation, restoration, repair actions, possible modifications). For this reason, a laser scanner survey and detailed documentation have been created, through which all the elements and characteristics of the place have been defined and collected in sort of atlas and inventory reports. This new documentation is going to constitute the base for the definition of new guidelines, a practical support and analysis for future interventions that can be carried out in total respect of this heritage. This topic is inserted as case study for developing the Research Project n. 746215 entitled "Preserving Wooden Heritage". The project is financed by the European Commission with an Individual Marie S. Curie Fellowship assigned to PostDoctoral Researcher Sara Porzilli, who is working at the University of Oulu - Finland.</p>
Lamminaho Wooden Estate: 3D laser scanner survey and post production results.
<p>The dataset collects detailed information about the architecture and the environment of the historic place of Lamminaho, in Vaala region, Finland. The topic is included in the list of the case studies chosen by post doctoral fellow Sara Porzilli, who is working under Marie S. Curie Fellowship at the University of Oulu, Finland (School of Architecture, Department of "History of Architecture and Restoration Studies"). Supervisor: Prof. Arch. Anna-Maija Ylimaula. The study was promoted also by the "National Board of Antiquities (NBA) - Museovirasto "based in Helsinki (Responsible: Arch. Helena Hirviniemi) operating under the Directorate of the Finnish Ministry of Education and Culture and "Senate Properties" (Responsible: Dr. Juha Keranen), partner and manager working under the Finnish government for the protection and protection of the Finnish heritage present on the territory of Italy and abroad. The research was dedicated to defining the methods for carrying out survey activities on historical wooden architecture, identifying the fundamental aspects of laser scanners methodologies and photogrammetric activities. The work has produced a detailed info-graphic atlas concerning all the buildings located in Lamminaho. The work had a theoretical approach, devoted on research and archival documentation. The results of the research are going to support all the practical activities of restoration and repair necessary in the process of musealization of the area.</p>
Leap Motion Hand Gestures for Interaction with 3D Virtual Music Instruments (LMHGIf3DVMI)
<p>The aim of the dataset is to investigate machine learning real-time gesture recognizer captured with a Leap Motion sensor to control the performance of a virtual 3D musical instrument. The dataset includes from 10-15 samples for each of the 8 gesture classes collected from 10 participants (5 female and 5 male) using the Leap Motion sensor.</p> <p> </p>
3D printed map for blind or visually impaired people
<p>This data set is composed of three parts each having its proper origins, formats and rights. This data set was used to apply the methods of relief editing and image processing to facilitate the production of accessible documentation by having in hand an easy to use interface.</p>
Context-Aware 3D Object Anchoring for Mobile Robots Dataset
<p>This dataset accompanies the following publication:</p> <p>Günther, M.; Ruiz-Sarmiento, J. R.; Galindo, C.; González-Jiménez, J. & Hertzberg, J. <strong>Context-Aware 3D Object Anchoring for Mobile Robots.</strong> <em>Robot. Auton. Syst.</em>, 2018 (accepted)</p> <p>The dataset consists of 15 scenes inspected by a robot equipped with a RGB-D camera driving around a table and turning towards it from different locations. The table contained a number of objects in varying table settings. In total, the dataset contains 1387 seconds of observation and 144 unique objects from 9 categories:</p> <ul> <li>SugarPot</li> <li>MilkPot</li> <li>CoffeeJug</li> <li>MobilePhone</li> <li>Mug</li> <li>Dish</li> <li>Fork</li> <li>Knife</li> <li>Spoon</li> <li>TableSign</li> </ul> <p>Segmentation, tracking and local object recognition was run on the recorded sensor data, and its output (tracked objects and local recognition results) was added to the dataset. Since the objects were observed from multiple perspectives and tracking was lost while the robot was moving from one observation pose to another, the dataset contains more than one track ID for most objects (one for each subsequent observation of the object). Each track ID was manually labeled with the ground truth category of the object it represented. Additionally, all track IDs belonging to the same object were manually grouped together to allow evaluation of the anchoring process. Track IDs that did not correspond to any object on the table (but instead to objects on different tables, pieces of the table itself or other artifacts) were manually removed. In total, out of 432 track IDs, 410 (94.9 %) were associated with true objects, while 22 (5.1 %) were removed as artifacts.</p> <p><br> <strong>File contents</strong></p> <p>All data is provided as rosbags. The naming scheme is as follows:</p> <ul> <li>`*-sensordata.bag.bz2`: The raw sensor data from the robot and all transform data, including localization in a map.</li> <li>`*-perception.bag.bz2`: The object recognition results and ground truth information for the tracked objects.</li> <li>`scene??-pr2-*.bag.bz2`: 5 scenes that were recorded using the PR2 robot.</li> <li>`scene??-calvin-*.bag.bz2`: 10 scenes that were recorded using the Calvin robot.</li> </ul> <p>Both robots used an ASUS Xtion Pro Live as 3D camera.</p> <p>`race_vision_msgs.tar.bz2`: The custom messages used in the `-perception` rosbags, as a ROS Kinetic package.</p> <p><br> <strong>Videos</strong></p> <p>To get a first impression of the dataset, `scene10.mp4` and `scene19.mp4` show the corresponding scenes from the point of view of the robot's RGB camera.</p>
Digital models of test objects captured by RFSAT Ltd using 3D photogrammetry
<p>This data set contains a number of digital models produced via 3D photogrammetric scanning as part of the SCAN4RECO project, funded by the European Horizon'2020 program. Scanning and processing of models was done with Pix4D Mapper and Autodesk ReMake software from images captured with Canon 5DS camera in 50 Megapixel image resolution. Example objects include Byzantine icons painted on wood, oil paintings on canvas and painted Venetian carnival paper masks.</p> <p>Second version of the data set includes historical icons of Saint DImitrios and Saint Archangel Michael, an icon of Saint Mary painted specially for testing SCAN4RECO technologies, as well as models of an original high-relief sculpture from OPD and of its 3D printed copy (made by Fraunhofer-IGD and hand painted by RFSAT)..</p> <p>Selected models can be also seen in the SCAN4RECO Virtual Museum developed by CERTH-ITI:<br> http://scan4reco.eu/scan4reco/content/scan4reco-virtual-museum</p>
Accompanying dataset for: "Flow and detailed 3D morphodynamic data from laboratory experiments of fluvial dike breaching"
<p>This dataset accompanies the manuscrpit "Flow and detailed 3D morphodynamic data from laboratory experiments of fluvial dike breaching" submitted to Scientific Data.</p>
Processed data for the study on "Chromatin 3D interactions mediate genetic effects on gene expression"
<p>This repository contains the processed data that was generated as part of the following study:</p> <p>Delaneau et al. (2019) <strong>Chromatin 3D interactions mediate genetic effects on gene expression.</strong></p> <p><em>Abstract:</em> Studying the genetic basis of gene expression and chromatin organization is key to characterize the effect of genetic variability on the function and structure of the human genome. Here, we unravel how genetic variation perturbs gene regulation using a dataset combining activity of regulatory elements, gene expression and genetic variants across 317 individuals and two cell types. We show that variability in regulatory activity is structured at the intra- and inter-chromosomal levels within 12,583 Cis Regulatory Domains and 30 Trans Regulatory Hubs that highly reflect the local (i.e. Topologically Associating Domains) and global (i.e. open/close chromatin compartments) nuclear chromatin organization. These structures delimit cell type specific regulatory networks that control gene expression/co-expression and mediate the genetic effects of <em>cis</em>- and <em>trans</em>-acting regulatory variants on genes.</p> <p> </p> <p>This repository contains:</p> <ol> <li>Chromatin QTLs for H3K27ac, H3K4me1 and H3K4me3 discovered in 317 Lymphoblastoids Cell Lines (LCLs) and 78 Fibroblasts.</li> <li>Molecular QTLs affecting the activity and structure of Cis Regulatory Domains (CRDs) in LCLs.</li> <li>Basic information about the full set of genetic variants being analyzed in the study.</li> <li>The peak coordinates, their hierarchy based on inter-individual correlation and the CRD calls for both LCLs and Fibroblasts.</li> <li>The functional links discovered in LCLs between CRDs and genes.</li> <li>eQTLs for LCLs.</li> <li>A README file containing the description of the file format for each file.</li> </ol>
3D IQ Test Task (3D-IQTT) - A Dataset for Quantitative Evaluation of 3D Reconstruction from 2D Images
<p>3D reconstruction is mostly evaluated qualitatively. With this dataset, we are introducing a new difficult quantitative task, the 3D IQ test task (3D-IQTT).</p> <p>It is designed to be similar to mental rotation questions found in some IQ tests. Each element in the dataset consists of 4 images: reference object and answers 1-3. One of the answers is the reference object but randomly rotated. For every question, dataset users have to use their model to pick the rotated model out of the 3 possible answers.</p> <p>The dataset encourages semi-supervised or unsupervised 3D reconstruction because it contains a large corpus of unlabeled data and only a small set of labeled data where the correct answer is known.</p> <p>All the images are of blocky 3D shapes floating in space in front of a black background.</p> <p>Demo scripts for loading/processing the dataset can be found at <a href="https://github.com/fgolemo/3D-IQTT">https://github.com/fgolemo/3D-IQTT</a></p> <p>The dataset consists of:</p> <ul> <li> <pre>3diqtt-v2-train.h5 (XZ-compressed)</pre> <strong>(Training Dataset)</strong> <ul> <li> <pre>/labeled</pre> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format: [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> <li> <pre>/unlabeled</pre> <ul> <li> <pre>/questions</pre> format: [100,000 x 4 x 128 x 128 x 3], corresponding to (100k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> </ul> </li> </ul> </li> <li> <pre>3diqtt-v2-test.h5</pre> <strong>(Test Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1].<br> <strong>Important! This is what you have to evaluate yourself on. We have the correct answers but they are not public.</strong></li> </ul> </li> <li> <pre>3diqtt-v2-val.h5</pre> <strong>(Validation Dataset)</strong> <ul> <li> <pre>/questions</pre> format: [10,000 x 4 x 128 x 128 x 3], corresponding to (10k items) x (reference + 3 answers) x (img width) x (img height) x (RGB), np.float32 in range [0,1]</li> <li> <pre>/answers</pre> format [10,000], corresponding to (10k answers), np.uint8, one of the following three items: [0,1,2]</li> </ul> </li> </ul> <p> </p> <p><strong>Important:</strong> Before use, the main training dataset (3diqtt-v2-train.h5.xz) needs to be decompressed. This can take up to 24h depending on your hardware. We apologize for any inconvenience caused by this. The uncompressed file has a size of ~74GB. The reason for this compression was a restriction on the size of individual files. The command for decompression is "<strong>unxz</strong><strong> 3diqtt-v2-train.h5.xz</strong>" on Unix machines.</p> <p><strong>If you use this dataset, please cite it.</strong></p>
3D wind speed and CO2/H20 concentration measurements collected during austral summer 2017/2018 over an ice free surface of a shallow lake located in the Schirmacher oasis, East Antarctica.
<p>The data set includes measurements collected by the integrated CO2 and H2O open-path gas analyzer and 3-D sonic anemometer (Irgason by Campbell Scientific with serial number 1243, https://www.campbellsci.com/irgason). The instrument was operated from 01.01.2018 to 07.02.2018. It was deployed on the north-west shore of the Lake Zub/Priyadarshini (S70° 45′ 41.5″, E011° 44′ 16.6″) on the distance of 10 m from the coast. The instrument was placed on the aluminum tripod on the height of 2 m, and directed to south-eastwards (137 SE). Six metal guidelines were linked to anchors, and the boom was fixed on the tripod. Two rechargeable batteries (12V/33Ah) were used in additional to two solar panels to power supply of the instrument (irgason_deployment.jpg). The format of the output files is given in Irgason_output.pdf. The raw data are packed into the *.dat files (one per day) and then compressed (bz2). The calibration of the Irgason was done 21.08.2017 in the lab of the Finnish Meteorological Institute with standard zero-and-span procedure, and then the instrument is adjusted accordingly.</p>
3D Microphone Array Recording Comparison (3D-MARCo)
<p>3D-MARCo is an open-access database of 3D sound recordings of musical performances and room impulse responses. The recordings were made in the St. Paul’s concert hall in Huddersfield, UK using a total of 71 microphones simultaneously. The main microphone arrays included in the database comprise PCMA-3D, OCT-3D, 2L-Cube, Decca Cubioid, First-order Ambisonics (FOA), Higher-order Ambisonics (HOA) and Hamasaki Square with height. In addition, ORTF, side/height, Voice of God and floor channels as well as a dummy head and spot microphones are included. The sound sources recorded are string quartet, piano trio, piano solo, organ, a cappella group, various single sources and room impulse responses of a virtual ensemble with 13 source positions captured by all of the microphones. 3D-MARCo would be useful for spatial audio research, recording education, critical ear training, etc.</p>
Supplementary dataset: Phoamtonic designs yield sizeable 3D photonic band gaps
<p>Supplementary Dataset of the publication "<a href="http://www.pnas.org/content/116/47/23480">Phoamtonic designs yield sizeable 3D photonic band gaps</a>" by the same authors:</p> <p>M. A. Klatt, P. J. Steinhardt, S. Torquato. <em>Proc. Natl. Acad. Sci. U.S.A.</em> <strong>116</strong>:23480–23486 (2019)<br> <a href="https://doi.org/10.1073/pnas.1912730116">https://doi.org/10.1073/pnas.1912730116</a></p> <p>Database S1 "Gap maps": Raw data of the gap maps listing the gap--mid-gap ratios together with the corresponding rod radii, volume fractions, or dilectric contrast.</p> <p>Database S2 "Configurations": All triply-periodic nets and networks analyzed in this study. The readme file explains the file formats.</p> <p>Database S3 "Parameters and Data": All parameters for the photonic calculations, raw output and post-processed data sets.</p>
Villa Nylander in Haukipudas: 3D laser scanning survey and post production
<p>This report shows some of the drawings elaborated for the 3d laser scanning documentation of an Art art Nouveau Villa situated in Haukipudas, Oulu, Finland.</p>
3D Laser scanning survey of the Rural Farmhouse of Lamminaho in Vaala, FInland
<p>The video shows the results of the laser scanning survey of Lamminaho. The project represents one of the case study chosen for performing the PresWoodenHeritage Marie Curie Project.</p> <p>The survey has been elaborated by using different types of laser scanners and it has been supported by Mitta Company.</p> <p> </p>
3D displacement field and fault-offset measurements for the northern Kaikōura ruptures
<p>Contents:</p> <p>1. East, north and vertical components of the co-seismic displacement field for three faults (the Kekerengu, Jordan and Upper Kowhai faults) that ruptured in the 2016 Kaikoura earthquake, New Zealand (east.tif, north.tif, vertical.tif).</p> <p>2. Shapefiles containing offsets across the faults of interest, measured from the displacement field (shapefiles.zip).</p> <p>3. CSV files containing offsets across the faults of interest, measured from the displacement field (csvs.zip). </p> <p>Our methodology is described in the following manuscript:</p> <p>Howell et al., 2019. 3D surface displacements during the 2016 MW 7.8 Kaikōura earthquake (New Zealand) from photogrammetry-derived point clouds, Journal of Geophysical Research Solid Earth, submitted.</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.