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4,107 results for “pointing”
South Entrance, British Museum [point cloud]
Quick scan of the Museum's iconic South entrance. 25 photos, Canon g7x, Agisoft PhotoScan Source: Objaverse 1.0 / Sketchfab
L'Arc by Urs Fischer (Point cloud)
This is a point cloud of the monumental statue l'Arc by Urs Fischer. This 11 meters high arch in sparkling aluminum, with an undulatory shape, now stands in front of Station F the world's biggest startup campus. This point cloud has been created with and [Ouster](https://ouster.com/) OS1 3D LiDAR sensor with [Exwayz](https://www.exwayz.fr/) SLAM software. Source: Objaverse 1.0 / Sketchfab
Active garment recognition and target grasping point detection using deep learning
<p>Data used in the experiments of the research paper</p> <p>E. Corona, G. Alenyà, T. Gabas, and C. Torras, “Active garment recognition and target grasping point detection using deep learning,” Pattern Recognition, vol. 74, pp. 629–641, 2018. </p>
Widening Multi-Beam Scan Angle of Conventional Waveguide Lens Antennas by Increasing Focal Points for Multi-Feed Excitations
<p><span>The paper investigates an alternative approach to improve the achievable beam scan angle of a traditional multi-beam waveguide lens antenna. To overcome this limitation, a solution of treating the waveguide lens as a transmit array consisting of non-identical elements is proposed so that each element of the antenna array can be well-calibrated to improve the maximum scan angular range.</span></p>
Quantum circuits reproduce the experimental two-dimensional many-body localization transition point
<p>abstract: While many studies point towards the existence of many-body localization (MBL) in one dimension, the fate of higher-dimensional strongly disordered systems is a topic of current debate. The latest experiments as well as several recent numerical studies indicate that such systems behave many-body localized—at least on practically relevant timescales. However, thus far, theoretical approaches have been unable to quantitatively reproduce experimentally measured MBL features—an important requirement to demonstrate their validity. In this Letter, we use fermionic quantum circuits as a variational method to approximate the full set of eigenstates of two-dimensional MBL systems realized in fermionic optical lattice experiments. Using entanglement-based features, we obtain a phase transition point in excellent agreement with the experimentally measured value. Moreover, we calculate the filling-fraction-dependent MBL phase diagram. We argue that our approach best captures the underlying charge-density-wave experiments and compute the mean localization lengths, which can be compared to future experiments.</p> <p>Access will be granted upon reasonable request.</p>
Point clouds (LAZ): Towards a spatial data repository for archaeological research in the Romanian Mostiștea Basin and Danube Valley
<p>Spatial data are crucial in archaeological research, where orthophotos, digital elevation models, and 3D models are widely used for mapping, documenting, and monitoring archaeological sites. The introduction of affordable and compact unmanned aerial vehicles (UAVs) has significantly advanced the use of UAV-based photogrammetry in the past 20 years. Recently, compact airborne systems have also enabled the capture of thermal, multispectral, and aerial laser scanning data. This study presents the data acquired with different platforms and sensors at Chalcolithic archaeological sites in Romania's Mostiștea Basin and Danube Valley. Since laser scanning and photogrammetry generate large data volumes, data storage and dissemination must also be carefully considered. Based on a thorough study of system performance, data acquisition and processing methods, and data outputs, a workflow for the systematic mapping and documentation of sites has been proposed. Given the experience obtained in the last 5 summer campaigns (2018-2023), 19 sites have been accurately mapped, of which 5 sites are mapped using airborne laser scanning. 18 sites are documented using multispectral photogrammetry, and for 17 sites, interactive image-based 3D models are acquired using true-color photogrammetry. All data are stored on a publicly accessible website for visualization, as well as on an open-data platform for data exchange. For the multispectral data, a raster tile service has been implemented, allowing the use of the data in a GIS environment.</p>
Dataset and code for "Methods to evaluate subcolumn profiles based on two-point diagnostics"
<p><strong>Description of included files</strong></p> <p>WRF code base (wrf_clubb_silhs.tar.gz)</p> <p>This WRF repo (using the stephens_et_al_2024_lidar_paper branch) can be used to generate both the high-resolution reproductions of LASSO runs with 60-second snapshot output (note that the resulting files will be quite large, it may be convenient to eliminate variables from the history stream), and the coarse-grained, essentially single-column model LASSO reproductions using CLUBB-SILHS. The code base was compiled with gfortran for the CLUBB-SILHS runs, and with ifort for the LES runs. The appropriate test case to compile is "em_crm", and within the wrf_clubb_silhs/WRF/test/em_crm directory there are two bash scripts that will automate the setup of the cases (and in the coarse-grained case, run the model as well). The bash scripts are named runlasso_silhs.bash and runlasso_les.bash, for the coarse-grained and LES versions respectively. For the coarse-grained CLUBB-SILHS cases, the user will need to modify the value of the vertical decorrelation coefficient (vert_decorr_coef) manually. This parameter can be modified in wrf_clubb_silhs/WRF/test/clubb_input/tunable_parameters/silhs_parameters.in.</p> <p> </p> <p>WRF-CLUBB-SILHS files</p> <p>These are output files from coarse-grained versions of the LASSO LES runs, on a 4x4 grid, with CLUBB-SILHS active. Two cases are included:</p> <ol> <li>vdc_10.tar.gz. This test uses the standard SILHS sampling algorithm, with a vertical decorrelation coefficient (VDC, or vert_decorr_coef in the SILHS source code) equal to 10.</li> <li>vdc_10_cdf.tar.gz. Uses new CDF sampling method described in the paper, with VDC=10.</li> <li>vdc_30.tar.gz. Uses standard sampling method, with VDC=30.</li> <li>vdc_30_cdf.tar.gz. Uses new CDF sampling method, with VDC=30.</li> </ol> <p> </p> <p>clubb_release.tar.gz</p> <p>This clubb_release git repo contains a branch, sample_w_using_invrs_cdf_tag_20240719, that includes code for the SILHS inverse CDF sampling method.</p> <p> </p> <p>Python script:</p> <p>The included python script will generate the figures and table shown in the paper, as long as the user has the WRF-CLUBB-SILHS files (included here) and the necessary LASSO and lidar files (not included). Note that the sampling technique in the paper incorporates some randomness so the table values will not be the same as in the paper but should be statistically identical. The script can also be found in the wrf_clubb_silhs.tar.gz code base, at wrf_clubb_silhs/WRF/scripts/stephens_et_al_2024_lidar_paper_figures.py.</p> <ul> <li>As mentioned above, the WRF-CLUBB-SILHS runs can also be reproduced using the included WRF code base and the wrf_clubb_silhs/WRF/test/em_crm/runlasso_silhs.bash script. </li> <li>The raw lidar and value-added product with lidar statistics can be obtained from the ARM Data Discovery site (<a href="https://adc.arm.gov/discovery/#/results">Data Discovery (arm.gov)</a>).</li> <li>The original LASSO files can be obtained through ARM's LASSO Bundle Browser (<a href="https://www.archive.arm.gov/lassobrowser">LASSO Bundle Browser (arm.gov)</a>).</li> <li>The LASSO reproductions with 60-second snapshot output would have to be generated by the user with the WRF code base included here and the wrf_clubb_silhs/WRF/test/em_crm/runlasso_les.bash script.</li> </ul>
SPARCS_WP4_Leipzig_City_Number of bidirectional EV charging points
<p>Number of bidirectional EV charging points in the city of Leipzig with annually captured data for the period between 2017 and 2024</p>
SPARCS_WP4_Leipzig_City_Number of smart EV charging points
<p>Number of smart EV charging points in the city of Leipzig with annually captured data for the period between 2019 and 2024</p>
SPARCS-WP3_Espoo_City_Number of (semi)public EV charging points in Espoo area
<p>Number of (semi)public EV charging points in Espoo area.</p>
Plot-level semantically labelled terrestrial laser scanning point clouds
<p><strong>Abstract</strong></p> <p>Point clouds from Terrestrial Laser Scanning (TLS) are an increasingly popular source of data for studying plant structure and function. However, unlocking their full potential currently requires extensive manual processing to extract ecologically important information. One key task is the accurate semantic segmentation of different plant material within point clouds, particularly wood and leaves, which is required to understand plant productivity, architecture, competition, space optimisation and physiology, and is a key step in common approaches to individual tree extraction. Existing automated semantic segmentation methods are primarily developed for single ecosystem types, and whilst they show good accuracy for biomass assessment from the trunk and large branches, often perform less well within the crown. In this study, we demonstrate a new framework that uses a deep learning architecture developed from PointNet++ and pointNEXT for processing 3D point clouds to provide a reliable semantic segmentation of wood and leaf in TLS point clouds from the tree base to branch tips, applied to diverse natural European forests. Our model combines meticulously labelled data with voxel-based sampling and a novel gated reflectance integration module embedded throughout the feature extraction layers. We evaluate its performance across an extensive dataset, encompassing diverse ecosystem types and sensor characteristics. Our results show consistent outperformance against the most widely used PointNet++-based approach for leaf/wood segmentation on a high-density TLS dataset collected across diverse mixed forest plots across all major biomes in Europe. We tested our model against others’ open data from China, Eastern Cameroon, Germany and Finland, collected using both time-of-flight and phase-shift sensors, finding consistently strong performance, showcasing the transferability of our model to a wide range of ecosystems and sensors. Our newly developed evaluation metric for assessing performance in the outer parts of the canopy, such as in twigs and small branches, found our model to clearly outperform the most widely used approach.</p> <p><strong>Methods</strong></p> <p>Within each country, we scanned a subset of the 30 m x 30 m FUNDIV plots using a Riegl VZ400i TLS instrument (RIEGL Gmbh, Horn, Austria), scanning at 600MHz and with an angular resolution of 0.04 mrad. All plots were scanned following a 10m grid system with a minimum of 16 upright and 16 tilt scans (following Wilkes et al. 2017), with additional scans to minimise occlusion in dense areas, and on the plot perimeter. To ensure high-quality data with minimal noise, scanning was paused when wind conditions rose above 5 m/s (measured with an anemometer on the ground) or when gusts were visually evident. In order to create our labelled dataset, we used a semi-automated approach informed by existing approaches followed by significant manual cleaning. Vicari et al. (2019) found anisotropy, verticality and linearity to be informative features for leaf-wood separation, so we created these geometric features at spatial scales of approx. 5 cm - 0.5 m (using CloudCompare, 2023). Alongside these, we used reflectance and xyz information for each point and labelled leaf-wood by thresholding these features. We followed this with intensive manual checking and cleaning to ensure high label quality, especially in the smaller branches and twigs. Our dense scanning and labelling strategy means that this dataset is an ideal candidate for training and testing the capabilities of processing algorithms. Our dataset comprises nine 10 m x 10 m blocks of x, y, z coordinates with corresponding reflectance values and labels, all at 1 cm resolution achieved through voxel downsampling. This data was used for training and validation. For comprehensive evaluation, we incorporated additional openly available datasets, which we cropped to reduce size and cleaned to rectify erroneous labels. The original, unmodified versions of these datasets can be accessed as follows:</p> <div> <p>*Mspace Lab (2024) ‘ForestSemantic: A Dataset for Semantic Learning of Forest from Close-Range Sensing’, Geo-spatial Information Science. Zenodo. <a href="https://doi.org/10.5281/zenodo.13285640">https://doi.org/10.5281/zenodo.13285640</a>. Distributed under a Creative Commons Attribution Non Commercial No Derivatives 4.0 International licence.</p> <p>Wang, Di; Takoudjou, Stéphane Momo; Casella, Eric (2021). LeWoS: A universal leaf‐wood classification method to facilitate the 3D modelling of large tropical trees using terrestrial LiDAR [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.np5hqbzp6">https://doi.org/10.5061/dryad.np5hqbzp6</a>. Distributed under a Creative Commons 0 1.0 Universal licence.</p> <p>Wan, Peng; Zhang, Wuming; Jin, Shuangna (2021). Plot-level wood-leaf separation for terrestrial laser scanning point clouds [Dataset]. Dryad. <a href="https://doi.org/10.5061/dryad.rfj6q5799">https://doi.org/10.5061/dryad.rfj6q5799</a>. Distributed under a Creative Commons CC0 1.0 Universal licence.</p> <p>Weiser, Hannah; Ulrich, Veit; Winiwarter, Lukas; Esmorís, Alberto M.; Höfle, Bernhard, 2024, "Manually labeled terrestrial laser scanning point clouds of individual trees for leaf-wood separation", <a href="https://doi.org/10.11588/data/UUMEDI">https://doi.org/10.11588/data/UUMEDI</a>, heiDATA, V1, UNF:6:9U7BGTgjjsWd1GduT1qXjA== [fileUNF]. Distributed under a Creative Commons Attribution 4.0 International Deed.</p> </div> <p>*For licensing reasons these data re not included in this repository but can be downladed from the doi provided. </p> <p> </p> <p> </p>
Data from: Point of impact: the effect of size and speed on puncture mechanics
The use of high-speed puncture mechanics for prey capture has been documented across a wide range of organisms, including vertebrates, arthropods, molluscs and cnidarians. These examples span four phyla and seven orders of magnitude difference in size. The commonality of these puncture systems offers an opportunity to explore how organisms at different scales and with differentmaterials,morphologies and kinematics performthe same basic function. However, there is currently no framework for combining kinematic performance with cutting mechanics in biological puncture systems. Our aim here is to establish this framework by examining the effects of size and velocity in a series of controlled ballistic puncture experiments. Arrows of identical shape but varying in mass and speed were shot into cubes of ballistic gelatine. Results from high-speed videography shows that projectile velocity can alter how the target gel responds to cutting. Mixed models comparing kinematic variables and puncture patterns indicate that the kinetic energy of a projectile is a better predictor of penetration than either momentum or velocity. These results form a foundation for studying the effects of impact on biological puncture, opening the door for future work to explore the influence of morphology and material organization on high-speed cutting dynamics.
Abundance of benthic sessile fauna associated with ARMS from the research" Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) for capturing the biological diversity of two coral reefs in the Yucatán Península, México " using the point intersection method (400 points per plate on each side) with the CPCe v4.1 soft-ware (Coral Point Count with Excel extension v4.1)
<p><strong>*Corresponding autor: </strong>edlinguerra@gmail.com</p> <p>This database contains information on the abundance of benthic sessile fauna associated with ARMS from the research “Evaluation of the use of Autonomous Reef Monitoring Structures (ARMS) to estimate cryptic diversity in two coral reefs of the Yucatan Península, México” using the point intersection method (400 points per plate on each side) with the CPCe v4.1 soft-ware (Coral Point Count with Excel extension v4.1) to analyze </p> <p>Krebs, C.J. Estimating Density: Quadrats Counts. In <em>Ecological Methodology</em>; Addison-Wesley Educational Publishers, Inc., 1999; p. 69 ISBN 0321021738</p> <p>Kohler, K.; Gill, S. Coral Point Count with Excel extensions (CPCe): A Visual Basic program for the determination of coral and substrate coverage using random point count methodology. <em>Comput. Geosci.</em> <strong>2006</strong>, <em>32</em>, 1259–1269</p> <div> <p><span lang="EN-US">was produced in collaboration with the Biodiversidad Marina de Yucatán project. </span><a href="https://www.bdmy.org.mx/carteles-publicaciones/" target="_blank" rel="noopener">https://www.bdmy.org.mx/,</a> Universidad Nacional Autonoma de México, Harte Research Institute for Gulf of Mexico Studies and Escuela Nacional de Estudios Superiores</p> </div>
Lu-177 DOTATATE Anonymized Patient Datasets: Multi-Time Point Lu-177 SPECT/CT Scans
<p><span>This publication contains anonymized SPECT/CT scans of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Both the SPECT and the co-registered CT are provided. All images are in DICOM format.</span></p> <p><span>Single-photon emission computed tomography imaging scans (SPECT/CT) were acquired on Siemens Intevo scanners and correspond to Lu-177 patients who have consented to be on dosimetric research trials in our clinics. All patient private information is anonymized using commercial software.</span></p>
Lu-177 DOTATATE Anonymized Patient Datasets: Multi-Time Point Lu-177 Planar Whole Body Scans
<p><span>This publication contains anonymized planar whole body images of two patients. Patient scans were taken at 4 different time points in the week following a therapeutic dose of Lu-177 DOTATATE. Both anterior and posterior views are provided. All images are in DICOM format.</span></p> <p><span>Planar whole body imaging scans were acquired on Siemens Intevo scanners and correspond to Lu-177 patients who have consented to be on dosimetric research trials in our clinics. All patient private information is anonymized using commercial software.</span></p>
Figure 4 from: van Ofwegen LP, Aurelle D, Sartoretto S (2014) A new genus of soft coral (Cnidaria, Octocorallia) from the Republic of Congo (Pointe-Noire Region). ZooKeys 462: 1-10. https://doi.org/10.3897/zookeys.462.8533
Figure 4 - Complexum pusillum sp. n., holotype (RMNH Coel. 41604). A–B spindles of base of colony.
Figure 1 from: van Ofwegen LP, Aurelle D, Sartoretto S (2014) A new genus of soft coral (Cnidaria, Octocorallia) from the Republic of Congo (Pointe-Noire Region). ZooKeys 462: 1-10. https://doi.org/10.3897/zookeys.462.8533
Figure 1 - Location of sampling stations for Complexum pusillum sp. n. (black star).
Aquaculture antimicrobial use point prevalence survey database legend
<p>Aquaculture antimicrobial use point prevalence survey database and legend accompanying https://doi.org/10.1038/s41598-020-78849-3</p>
H2020 Platone German Demonstrator - Active Power Exchange at Grid Connection Point (Medium Voltage/Low Voltage)
<p>The given data are measured values of active power , measured at the low voltage busbar of the medium voltage (MV)/low voltage (LW) grid connection point (secondary substation). The data are provided as 15-minutes mean values in kilowatt.</p> <p><strong>Data Description</strong></p> <ul> <li>p_tei_mean = arithmetic mean of p_tei measured in 1-minute intervals devided by number of samples available for computing within 15 minutes (p_tei_count)</li> <li>p_tei_min = the minimum value (1-minute mean) measured within the period of p_tei_mean (15-minutes)</li> <li>p_tei_max = the maximum value (1-minute mean) measured within the period of p_tei_mean period (15-minutes)</li> <li>p_tei_count = the number a 1-minute mean values available for each15-minute interval.</li> </ul> <p><strong>Field Test Setup</strong></p> <p>The substation includes a transformer feeding a low voltage grid, consisting of 89 households, 450kW of installed PV generation capacity, a large scale battery with 300 kW and 850 kWh. </p> <p>This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 864300</p>
Comparison of Point Cloud and Image-based Models for Calorimeter Fast Simulation
<p>A highly granular calorimeter, similar to CALICE is simulated in Geant4. The calorimeters showers are represented as either images or point clouds. Two state-of-the-art score based diffusion models, on image based and the other point cloud based, are trained on the same set of calorimeter simulations and directly compared to each other.<br> <br> These files include the original Geant4 simulation, an intermediate form of the data required for training, and the samples generated by the image and point cloud models.</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.