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4,107 results for “pointing”
PMMA 3 point bending test until failure loaded in displacement
<p>This document present the 3 point bending test results for PMMA. Digital Image Correlation (DIC) was used to measure displacements during the test. Results obtained provide the force-strain plot over time. Such results could be compared with simulation results or used as part of an experimental dataset.<br> Raw results and a complete dataset are provided with this data package.</p>
Muscle activity, ground reaction forces and pointing performance during postural control tasks in healthy adults
<p>To investigate the muscle coordination during postural control, we recorded muscle activity and postural dynamics in healthy human adults. Fourteen participants performed postural tasks in which postural stability and pointing behaviour was varied. The data set contains electromyography of 36 muscles distributed across the body and ground reaction forces recorded during postural control tasks. A full factorial design was used. Stability was either not challenged or challenged in the anterior-posterior or medial-lateral direction. In addition, participants were asked to either relax their arms or to perform an unimanual or a bimanual pointing task. In the pointing task, participants held a laser pointer and pointed it on a target in front of them. Pointing performance was recorded using a video recording of the laser beam on the target area.</p> <p>InformationData.pdf – Description of data acquisition and file structure<br> EMG.zip – EMG data<br> FP.zip – Force plate data<br> Video.zip – Video feed</p>
Detection of Structural Components in Point Clouds of Existing RC Bridges
<p><em>The cost and effort </em><em>of</em><em> modelling existing bridges from point clouds currently outweighs the perceived benefits of the resulting model. There is a pressing need to automate this process. Previous research has achieved the automatic generation of surface primitives combined with rule-based classification to create labelled cuboids and cylinders from point clouds. While these methods work well in synthetic datasets or idealized cases, they encounter huge challenges when dealing with real-world bridge point clouds, which are often unevenly distributed and suffer from occlusions. In addition, real bridge geometries are complicated. In this paper, we propose a novel top-down method to tackle these challenges for detecting slab, pier, pier cap, and girder components in reinforced concrete bridges. This method uses a slicing algorithm to separate the deck assembly from pier assemblies. It then detects and segments pier caps using their surface normal, and girders using oriented bounding boxes and density histograms. Finally, our method merges over-segments into individually labelled point clusters. The results of 10 real-world bridge point cloud experiments indicate that our method achieves an average detection precision of 98.8%. This is the first method of its kind to achieve robust detection performance for the four component types in reinforced concrete bridges and to directly produce labelled point clusters. Our work provides a solid foundation for future work in generating rich Industry Foundation Classes models from the labelled point clusters.</em></p>
Fig. 5. Bogidiella veneris n. sp. from Venus Bay, South Australia. A, B, F, paratype 3 (3.1 mm female); C-E, holotype (3.6 mm female ). A, pereiopod 3; B, enlarged dactylus of pereiopod 4; C, pereiopod 5; D, pereiopod 6; E, pereiopod 7; F, pleopod 1. arrow pointing at enlarged retinaculae. Scale bar A, C-F 5 0.1 mm.
Fig. 5. Bogidiella veneris n. sp. from Venus Bay, South Australia. A, B, F, paratype 3 (3.1 mm female); C-E, holotype (3.6 mm female ). A, pereiopod 3; B, enlarged dactylus of pereiopod 4; C, pereiopod 5; D, pereiopod 6; E, pereiopod 7; F, pleopod 1. arrow pointing at enlarged retinaculae. Scale bar A, C-F 5 0.1 mm.
Fig. 2. Bogidiella veneris n. sp. from Venus Bay, South Australia. A-C, holotype (3.6 mm female); E, H, paratype 1 (3.0 mm female); D, F, G, paratype 3 (3.1 mm female). A, head; B, epimera 1-3; C, coxal plates 1-7 (from left to right), with brood plates exemplarily shown on coxa 4, and gills on coxae 4 and 5; D, antenna 1, with arrows pointing at enlarged accessory flagellum (above) and aesthetasc (below); E, antenna 2; F, maxilla 1; G, enlarged serrate setae from outer plate of maxilla 1; H, maxilla 2. Scale bars: A-E 5 0.1 mm; F, H 5 0.1 mm.
Fig. 2. Bogidiella veneris n. sp. from Venus Bay, South Australia. A-C, holotype (3.6 mm female); E, H, paratype 1 (3.0 mm female); D, F, G, paratype 3 (3.1 mm female). A, head; B, epimera 1-3; C, coxal plates 1-7 (from left to right), with brood plates exemplarily shown on coxa 4, and gills on coxae 4 and 5; D, antenna 1, with arrows pointing at enlarged accessory flagellum (above) and aesthetasc (below); E, antenna 2; F, maxilla 1; G, enlarged serrate setae from outer plate of maxilla 1; H, maxilla 2. Scale bars: A-E 5 0.1 mm; F, H 5 0.1 mm.
e-ROSA data points' dataset
<p>This dataset holds the metadata for the data points uploaded/mapped on http://map.aginfra.eu/</p>
Fractal sampling point nesting
<b>Description: </b><p>This dataset contains a table showing the nesting of sampling points within the fractal sampling design at the SAFE project and an R code file used to create the nesting from a GIS file of the core sampling locations.<br><br>There are five fractal levels across 17 sites: six experimental blocks (A-F), two edge transects (LFE, VJR) and three sets of triplets of sites to sample specific habitats (Old growth: OG1 - 3, Oil palm: OP1 - 3, Twice logged forest: LF1 - 3). Within each site, there are sampling points at five fractal scales with fractal order 1 as the smallest scale and fractal order 5 being the largest scale. This file identifies the child - parent relationships of points across the fractal sampling levels. For more details, see <a>https://www.safeproject.net/dokuwiki/safe_gis/safe_fractal</a>.</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/1"><b>SAFE CORE DATA</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=179">here</a></p><p><b>Files: </b>This dataset consists of 2 files: Fractal_point_nesting.xlsx, Nested_fractal_table_maker.R</p><p><b>Fractal_point_nesting.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Fractal sampling point nesting</b> (described in worksheet Fractal_point_nesting)</p><p>Description: This table contains one row for each fractal order 1 sampling point (finest scale) and then identifies the parent sampling points at the higher fractal scales (2-5). Note that not all sites have a central fractal order 5 point.</p><p>Number of fields: 9</p><p>Number of data rows: 579</p><p>Fields: </p><ul><li><b>Site</b>: Site ID prefix (Field type: ID)</li><li><b>Habitat</b>: Focal habitat of sampling site (Field type: Categorical)</li><li><b>Logging</b>: Logging history of sampling site (Field type: Categorical)</li><li><b>Frag_Area</b>: Estimated area of fragment (Field type: Numeric)</li><li><b>FirstOrder</b>: First order fractal point (Field type: Location)</li><li><b>SecondOrder</b>: Second order fractal parent (Field type: Location)</li><li><b>ThirdOrder</b>: Third order fractal parent (Field type: Location)</li><li><b>FourthOrder</b>: Fourth order fractal parent (Field type: Location)</li><li><b>FifthOrder</b>: Fifth order fractal parent (not applicable for all sites) (Field type: Location)</li></ul></li></ol><p><b>Nested_fractal_table_maker.R</b></p><p>Description: This R code creates the table stored in Fractal_point_nesting from the SAFE Core sampling stations GIS layer. </p><p><b>Date range: </b>2010-01-01 to 2020-01-01</p><p><b>Latitudinal extent: </b>4.6350 to 4.7716</p><p><b>Longitudinal extent: </b>116.9474 to 117.7031</p>
Freezing point of water, water content and proportion of freezing water in Heracleum sosnowskyi plants
<p><strong>Method plant tissue of analysis using differential scanning calorimetry (DSC)</strong></p> <p>Differential scanning calorimetry was used to assess the risk of tissue plant damage by low negative temperatures. We measured the freezing point of water, the water content and the proportion of freezing water in plant samples using the DSC – 60 Shimadzu calorimeter (Japan). The samples were buds and seeds of <em>Heracleum sosnowskyi</em> (hogweed) plants. The samples were placed in an aluminum pan with a volume of 0.1 cm<sup>3</sup>. The sample was cooled at a rate of 1 °C/min from +5 °C to -30 °C. The water crystallization temperature was determined at the beginning of the phase transition peak. After measurements, the material was dried at 105 °C to a constant dry mass.</p> <p>The following calculations were applied:</p> <ol> <li>A – DSC data (water crystallization temperature, ºC);</li> <li>B – wet weight sample (mg);</li> <li>C – dry weight sample (mg);</li> <li>D = B-C – (water content in sample, mg);</li> <li>E – DSC data (heat from water freezing, mJ);</li> <li>F= E× 335 – (frozen water in sample, mg);</li> <li>335 –The heat of frozen for wateris approximately (mJ/mg);</li> <li>G = D-F – (non-freezing water, mg);</li> <li>H = (100%/D) × F – (frozen water in sample, %);</li> <li>I = (D/B) ×100 – (water content in sample, %);</li> <li>J= D/C (mg H<sub>2</sub>O/mg dry weight sample).</li> </ol> <p> The results of the analysis are presented in the file «The_results_DSC_analysis_of_buds_seeds.xls».</p> <p>Measurements were carried out in September 2018. The samples were buds and seeds of <em>Heracleum sosnowskyi</em> (hogweed) plants. Geographical coordinates vegetation plants 61.645333, 50.733196.</p>
Dirac gaugino benchmark points from arXiv:1812.09293
<p><strong>Benchmark points, in SUSY Les Houches Accord (SLHA) format</strong>, from the paper "LHC limits on gluinos and squarks in the minimal Dirac gaugino model", <strong><a href="http://arXiv.org/abs/arXiv:1812.09293">arXiv:1812.09293</a></strong>; for 2 TeV gluino and 2.6 TeV squark masses.</p> <p>We also provide the <strong>UFO</strong> and <strong>SPheno models</strong> (exported from <a href="https://sarah.hepforge.org">SARAH</a>), and the <a href="http://spheno.hepforge.org/">SPheno</a> <strong>input files</strong> for creating the DG benchmark points. For usage in SPheno, expand DiracGauginos_SPheno.zip to a folder "DiracGauginos" in the SPheno root directory and compile with</p> <p> <code>make Model=DiracGauginos</code></p> <p>This produces the library <code>./lib/libSPhenoDiracGauginos.a</code> and the executable <code>./bin/SPhenoDiracGauginos</code>. (Tested with SPheno-4.0.3)</p> <p>Note that compared to the version exported from <a href="https://sarah.hepforge.org">SARAH</a>, we have changed <code>InputOutput.f90</code> to have an SLHA1-like ordering of the squarks, so that, for instance, PDG 1000006 is always the lighter stop.</p> <p>The two additional scripts provided serve to add the QNUMBERS blocks and change "DECAY1L" to "DECAY" in the SLHA files. This is necessary for event generation with <a href="https://cp3.irmp.ucl.ac.be/projects/madgraph/">MadGraph5</a> / <a href="http://home.thep.lu.se/Pythia/">Pythia</a>.</p>
Freezing point of water, water content and proportion of freezing water in Heracleum sosnowskyi seedlings with radicles
<p><strong>Method plant tissue of analysis using differential scanning calorimetry (DSC)</strong></p> <p>Differential scanning calorimetry was used to assess the risk of tissue plant damage by low negative temperatures. We measured the freezing point of water, the water content and the proportion of freezing water in plant samples using the DSC – 60 Shimadzu calorimeter (Japan). The samples were seedlings with radicles of <em>Heracleum sosnowskyi</em> (hogweed) plants from under snow. The samples were placed in an aluminum pan with a volume of 0.1 cm<sup>3</sup>. The sample was cooled at a rate of 1 °C/min from +5 °C to -30 °C. The water crystallization temperature was determined at the beginning of the phase transition peak. After measurements, the material was dried at 105 °C to a constant dry mass.</p> <p>The following calculations were applied:</p> <ol> <li>A – DSC data (water crystallization temperature, ºC);</li> <li>B – wet weight sample (mg);</li> <li>C – dry weight sample (mg);</li> <li>D = B-C – (water content in sample, mg);</li> <li>E – DSC data (heat from water freezing, mJ);</li> <li>F= E× 335 – (frozen water in sample, mg);</li> <li>335 –The heat of frozen for water is approximately (mJ/mg);</li> <li>G = D-F – (non-freezing water, mg);</li> <li>H = (100%/D) × F – (frozen water in sample, %);</li> <li>I = (D/B) ×100 – (water content in sample, %);</li> <li>J= D/C (mg H<sub>2</sub>O/mg dry weight sample).</li> </ol> <p> </p> <p>The results of the analysis are presented in the file «The_results_DSC_analysis_of_seedlings_Heracl_sosn_2017.xls».</p> <p>Measurements were carried out in March 2017. The samples were seedlings with radicles of <em>Heracleum sosnowskyi</em> (hogweed) plants from under snow. Geographical coordinates vegetation plants 61.645333, 50.733196.</p>
NIH3T3_point_locations for RNA seqFISH+ experiments
<p>These source data consist of the point locations of individual decoded mRNA dots in seqFISH+ experiments on NIH/3T3 cells. DAPI_experiment folder contains the DAPI stainings with other beads images. ROIs_experiment folders are the manual segmentation performed in ImageJ. We only performed segmentation on the whole single cells. In seqFISH+_NIH3T3_point_locations zip folder, there are 3 matlab files . One is gene name file, the other two files are point locations for replicate 1 and 2 seqFISH+ experiments in NIH/3T3 cells. The columns of the data represent field of view(FOV), Cell , and Gene. For example : FOV1 , Cell 5, Gene : Bgn to retrieve the point locations. </p>
Dataset: An Open Dataset and Collection Tool for BMS Point Labels
<p>This dataset consists of a set of CSV files each representing a "dump" of point labels from a set of buildings. Most of the files only contain a single column (point labels); several of them also contain other dimensions such as engineering units, Haystack tags and other BACnet object properties.</p>
Data for the publication "Singular charge fluctuations at a magnetic quantum critical point"
<p>Data sets of the figures in the publication "Singular charge fluctuations at a magnetic quantum critical point"</p> <p>Preprint: arXiv:1808.02296</p>
Figure. Camera traps points (l) in study area. in Camera trapping of medium and large-sized mammals in western Black Sea deciduous forests in Turkey
Figure. Camera traps points (l) in study area.
The dataset used in the article "A point cloud graph neural network for protein-ligand binding site prediction"
Open the record for dataset details and reuse information.
Hydrolysis of SeCN and SCN in the active site of TcDH: QM/MM stationary points
<p>Equilibrium geometry configurations of the enzyme-substrate complexes (ES), transition states (TS) and reaction products (EP) for the SeCN and SCN hydrolysis in the active site of the TcDH enzyme calculated at the QM(uPBE0-D3/6-31G**)/MM(AMBER99 level) using NWChem software. The "qm" in the file name denotes that only QM part is included.</p>
Linear morphometric analysis data of lithic points from Lovedale, Free State, South Africa
<p>Linear morphometric analysis data of lithic points from the Middle Stone Age site of Lovedale and other Middle Stone Age localities in the Modder River basin, Free State, South Africa.</p>
Supplementary material for "Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion"
<p>This Zenodo archive contains supplementary data and code for the paper <strong>Moment tensor and point force estimation, with applications to earthquakes, landslides, and the 2017 DPRK nuclear explosion</strong> (submitted to <em>Geophysical Journal International</em>). The archive provides scripts and data to reproduce some of the figures and results presented in the paper / supplementary material.</p>
Validation Points at Municipality of Xiromero
<p>In the context of “Protecting the Inner Ionian Archipelago and Formicula” project, data collection of groundtruthing points has been conducted for habitat classification with the objective of identifying the distribution of Posidonia oceanica near aquaculture facilities in the Municipality of Xiromero. This project was implemented by iSea and funded by Blue Marine Foundation.</p>
GNSS amplitude scintillation index (S4) and ionospheric pierce point (IPP) from Zhuhai on 11 May 2024
<p>The ionospheric pierce point (IPP) location is named as "IPP_lla_CXX", sampled by 1-sec (86400 a day), where the columns are latitude, longitude, and altitude, respectively.</p> <p>The amplitude scintillation index (S4) is named as "S4_CXX_SYSU_132", sampled by 1-minute cadence (1440 a day).</p> <p>The data are provided in MATLAB format.</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.