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4,107 results for “pointing”
Arrow Point / End Blade XCB-105-2987
Arrow Point / End Blade. XCB-105-2987. 400 BCE-100 CE XCB-105 Adamagan (Aleut for place of walrus hunters) is at the head of Morzhovoi Bay, western Alaska Peninsula. It is a massive village with multiple occupations. When it was occupied 400 BCE-100 CE, it was the largest village in the Arctic with an estimated 1000 people. It also has limited occupations dated 2200-1700 BCE, 1000-600 BCE, and 900-1100 CE. The Western Alaska Peninsula artifacts are presented as a result of the research conducted under grants NSF 9630072, NSF 9814086, NSF 9996372, NSF 9996415, NSF 1139266, NSF 1321411. H. Maschner, Principal Investigator. These artifacts were scanned with either a Faro Edge Arm or a Minolta Vivid 9i. Processed in Geomagic or Polyworks. 2-8 photos were used for texture in Geomagic Wrap. Original digitizing work done at the IVL at Id. St. Univ. Subsequent processing and publication completed at Global Digital Heritage. Source: Objaverse 1.0 / Sketchfab
Madron Baptistry - point cloud
The medieval bapistry close to Madron Well, Cornwall. This model appears transparent as it represents a sample of the measurements taken to create the full textured model. A point cloud allows you to 'see through' the structure, allowing for an alternative understanding of its construction. 3D scan for Penwith Landscape Partnership by the [Curatorial Research Centre](https://curatorialresearch.com). Source: Objaverse 1.0 / Sketchfab
Small03 point cloud
พระพุทธรูปหินทรายขนาดเล็ก องค์ที่ 3 Source: Objaverse 1.0 / Sketchfab
FIGURE 2. Discretized continuous characters. Each point denotes a in Revision and phylogenetic analysis of the orb-weaving spider genus Glenognatha Simon, 1887 (Araneae, Tetragnathidae)
FIGURE 2. Discretized continuous characters. Each point denotes a measured individual (up to three for each species) and the horizontal lines are the limits between states (Mann-Whitney U=0.0, p<0.001). PDP: paracymbium distal portion. PBP: paracymbium basal portion. Ret: Retromarginal tooth.
Data for Evaluation of altered cell-cell communication between glia and neurons in the hippocampus of 3xTg-AD mice at two time points
<p>processed_data.tar.gz contains all files from the data directory associated with the 230418_TS_AgingCCC GitHub project and includes the following:</p> <ul> <li> <p>CellRangerCounts/</p> </li> <ul> <li> <p>post_soupX/ : contains 12 directories for 12 samples, which each contain 3 files obtained from ambient RNA removal with soupX. Below is a representative example, but the post_soupX directory contains one directory for each of the 12 samples:</p> </li> <ul> <li> <p>S01_6m_AD/</p> </li> <ul> <li> <p>barcodes.tsv</p> </li> <li> <p>genes.tsv</p> </li> <li> <p>matrix.mtx</p> </li> </ul> </ul> <li> <p>pre_soupX/ : contains 12 directories for 12 samples, which each contain 2 files obtained from Cell Ranger after aligning fastq files to the reference genome. Below is a representative example, but this directory contains 1 directory for each individual sample:</p> </li> <ul> <li> <p>S01_6m_AD/outs/</p> </li> <ul> <li> <p>filtered_feature_ bc_matrix.h5</p> </li> <li> <p>raw_feature_bc_matrix.h5 </p> </li> </ul> </ul> </ul> <li> <p>PANDA_inputs/</p> </li> <ul> <li> <p>PANDA_exp_files_array.txt: Text files with files paths to expression inputs for PANDA gene regulatory networks.</p> </li> <li> <p>mm10_TFmotifs.txt: Mouse TF motif input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> <li> <p>mm10_ppi.txt: Mouse protein-protein interaction information from SringDB input for PANDA gene regulatory networks. Previously published in Whitlock et al. 2023 </p> </li> </ul> <li> <p>ccc/</p> </li> <ul> <li> <p>nichenet_v2_prior/</p> </li> <ul> <li> <p>gr_network_mouse_21122021.rds : accessed in December 2023, gene regulation network – gene regulatory information from MultiNicheNet</p> </li> <li> <p>ligand_target_matrix_nsga2r_final_mouse.rds: accessed in December 2023, ligand target matrix for mouse from MultiNicheNet.</p> </li> <li> <p>ligand_tf_matrix_nsga2r_final_mouse.rds: accessed in December 2023, mouse ligand tf matrix for signaling path determination from MultiNicheNet</p> </li> <li> <p>lr_network_mouse_21122021.rds : accessed in December 2023, ligand-receptor matrix from MultiNicheNet</p> </li> <li> <p>signaling_network_mouse_21122021.rds : accessed in December 2023, signaling network – protein-protein interaction information from MultiNicheNet for mouse</p> </li> <li> <p>weighted_networks_nsga2r_final_mouse.rds : accessed in October 2023, networks weighted by literature evidence from MultiNicheNet for mouse</p> </li> </ul> <li> <p>multinichenet_output.rds : MultiNicheNet output for 3xTg-AD snRNA-seq data</p> </li> <li> <p>12m_signaling_igraph_objects.rds : list of igraph objects for 93 LRTs and their signaling mediators at 12 months</p> </li> <li> <p>6m_signaling_igraph_objects.rds :list of igraph objects for 2 LRTs and their signaling mediators at 6 months</p> </li> </ul> <li> <p>elisa/: CSV files of measured OD values for every ELISA.</p> </li> <ul> <li> <p>240319_ELISA_Ab40.csv: OD measurements for Ab40</p> </li> <li> <p>240319_ELISA_Ab42.csv: OD measurements for Ab42</p> </li> <li> <p>240319_ELISA_total_tau.csv: OD measurements for Total Tau</p> </li> </ul> <li> <p>panda/: PANDA gene regulatory networks for each time point and condition in excitatory and inhibitory neurons. Used for differential gene targeting.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>pseudobulk/: includes pseudo bulk matrices for every cell type which were used for downstream analyses. Each matrix also includes metadata information on condition and time point.</p> </li> <ul> <li> <p>all_counts_ls.rds: List of all the pseudo bulk matrices (below).</p> </li> <li> <p>astrocytes.rds: pseudobulk matrix for astrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>endothelial_cells.rds: pseudobulk matrix for endothelial cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>ependymal_cells.rds: pseudobulk matrix for ependymal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>excitatory_neurons.rds: pseudobulk matrix for excitatory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>fibroblasts.rds: pseudobulk matrix for fibroblasts. Include time point and condition information for downstream analyses.</p> </li> <li> <p>inhibitory_neurons.rds: pseudobulk matrix for inhibitory neurons. Include time point and condition information for downstream analyses.</p> </li> <li> <p>meningeal_cells.rds: pseudobulk matrix for meningeal cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>microglia.rds: pseudobulk matrix for microglia. Include time point and condition information for downstream analyses.</p> </li> <li> <p>oligodendrocytes.rds: pseudobulk matrix for oligodendrocytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>opcs.rds: pseudobulk matrix for oligodendrocyte progenitor cells. Include time point and condition information for downstream analyses.</p> </li> <li> <p>percicytes.rds: pseudobulk matrix for pericytes. Include time point and condition information for downstream analyses.</p> </li> <li> <p>rgcs.rds: pseudobulk matrix for retinal ganglion cells. Include time point and condition information for downstream analyses.</p> </li> </ul> <li> <p>pseudobulk_split/: Includes pseudo bulk count matrices split by time point and condition. Used for input to PANDA for gene regulatory network construction.</p> </li> <ul> <li> <p>excitatory_neurons_AD12.Rdata</p> </li> <li> <p>excitatory_neurons_AD6.Rdata</p> </li> <li> <p>excitatory_neurons_WT12.Rdata</p> </li> <li> <p>excitatory_neurons_WT6.Rdata</p> </li> <li> <p>inhibitory_neurons_AD12.Rdata</p> </li> <li> <p>inhibitory_neurons_AD6.Rdata</p> </li> <li> <p>inhibitory_neurons_WT12.Rdata</p> </li> <li> <p>inhibitory_neurons_WT6.Rdata</p> </li> </ul> <li> <p>seurat_preprocessing/</p> </li> <ul> <li> <p>filtered_seurat.rds : merged and filtered seurat object</p> </li> <li> <p>integrated_seurat.rds : seurat object integrated using harmony</p> </li> <li> <p>clustered_seurat.rds : clustered seurat object</p> </li> <li> <p>processed_seurat.rds : processed seurat object with final cell type assignments at specified resolution</p> </li> </ul> </ul> <p> </p> <p>Raw data publicly available on GEO under series accession: GSE261596</p>
Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).
<p>Figure S1: Two-tailed intergroup Pearson analysis between patient age, COVID-19 detection, de-livery time, gravida, and termination week (Y-axis) with maternal and neonatal anti-COVID-19 immunoglobulins (IgG and IgM) (X-axis), depicted as a three-point color map (lowest = blue, 0 = white, highest = red) showed no significant correlation (at p>0.05).</p>
Interior points for various geographic areas as defined TIGER/LINE 2006
<p>Interior points (however defined) created for various geographic areas based on TIGER/LINE 2006. Created by Matthew Graham (no warranties). Posted by Lars Vilhuber.</p>
Final point cloud results of SPC-MVSNet on DTU dataset
<p>SPC-MVSNet在DTU数据集上的最终点云结果</p>
Final point cloud results of SPC-MVSNet on Tanks and Temples benchmark
<p>SPC-MVSNet在坦克和寺庙基准上的最终点云结果。</p>
Kinematic Process and Characteristic of Root-Induced Water Flow Using a Visualizing Point Source Infiltration Experiment
<p><span>The video is the temporal results of the 2-D point source infiltration experiment in this paper. ‘G-’ represents the granular experimental group, and ’RG-’ represents the root-granular experimental group. The injection rate of G-1 and RG-1 is 0.25 mm/min, corresponding to the boundary flow rate of 0.03 ml/min. </span></p> <p><span>In this experiment, the glass beads have a particle diameter ranging from 0.6-0.8 mm. The selected root has diameters ranging from 1.3-1.5 mm.</span></p>
Investigating the Eye Movement Characteristics of Basketball Players Executing 3-Point Shot at Varied Intensities and their Correlation with Shot Accuracy
<h2>不同强度三分球运动员眼球运动特征及其与投篮命中率的相关性研究</h2>
Supplementary file 2; Photographs of various camp survey locations that illustrate various characteristics of the study site and points of rationale for the approach taken to data collection, analysis and interpretation
Open the record for dataset details and reuse information.
Datasets for "Detecting rhythmic spiking through the power spectra of point process model residuals"
<p>This repository contains synthetic and empirical datasets that were analyzed in the preprint "Detecting rhythmic spiking through the power spectra of point process model residuals."</p> <p>Relative to the previous version (1.0.0), this new version (1.0.1) only entails changes in the naming of some folders, and the text of an explanatory text file. The changes reflect the reordering of the figures in the newest version of the manuscript.</p>
Multiway Registration Capability Study in Increasing the Accuracy of Registration Results for Infrastructure and Mining Pits Terrestrial Laser Scanner (TLS) Data Point Cloud
<p>This material has presented on 2nd International Conference on Advanced Research in Engineering and Technology in October 25, 2023.</p>
Pointing movements and eye-tracking data_Facilitated Communication Users
<p>The repository contains all the pre-sorted data used for the analysis described in the paper. Data are divided into three:</p> <ul> <li>in A, we report the movement data of each pointing gesture considered in the analysis.</li> <li>in B we report keystrokes' related data.</li> <li>in C we report the sorted eye-tracking data.</li> </ul> <h3>A. User Correct Movements:</h3> <p>Each participant's data is organized into a 1xN cell array in Matlab, where N represents the number of pointing gestures analyzed. Each cell contains an Nx8 column vector with the following information:</p> <ol> <li> <p><strong>Time Information (column 1)</strong>:</p> <ul> <li>Time associated with the pointing gesture (milliseconds).</li> </ul> </li> <li> <p><strong>Arm Coordinates (columns 2,3 and 4)</strong>:</p> <ul> <li>X-axis coordinates (millimetres).</li> <li>Y-axis coordinates (millimetres).</li> <li>Z-axis coordinates (millimetres).</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (Facilitator) (columns 5, and 6) </strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> <li> <p><strong>EMG Deltoid Activation (User) (columns 7 and 8)</strong>:</p> <ul> <li>Rectified EMG deltoid activation.</li> <li>Envelope EMG deltoid activation.</li> </ul> </li> </ol> <h3>B. Users' Keys Pressed with Probability:</h3> <p>Each participant's data is organized into an Nx6 string array, where N represents the number of pointing gestures analyzed. Each array contains the following information:</p> <ol> <li> <p><strong>Key Press Time</strong>:</p> <ul> <li>Absolute time the key is pressed (milliseconds, as recorded by the key-logger).</li> </ul> </li> <li> <p><strong>Time Between Keystrokes</strong>:</p> <ul> <li>Difference in milliseconds between two consecutive keystrokes.</li> </ul> </li> <li> <p><strong>Key Pressed</strong>:</p> <ul> <li>The key that has been pressed.</li> </ul> </li> <li> <p><strong>Pointing Time</strong>:</p> <ul> <li>Time taken by the arm to complete the forward phase of the pointing gesture (seconds).</li> </ul> </li> <li> <p><strong>Character Position</strong>:</p> <ul> <li>Position of the pressed character within the word (spacebar hits are assigned the number 300).</li> </ul> </li> <li> <p><strong>Key Selection Probability</strong>:</p> <ul> <li>Probability (percentage) of the key being selected.</li> </ul> </li> </ol> <h3><strong>C. EyeTracking data sorted</strong></h3> <p>Each participant's data is organized into an N×6 cell array, where N represents the number of pointing gestures analyzed through eye-tracking. The contents of each row are as follows:</p> <ol> <li> <p><strong>Fixation Data (Nx5 vector)</strong>:</p> <ul> <li><strong>N</strong> is the number of fixations related to one pointing gesture.</li> <li>Each vector contains: <ul> <li> <p><strong>The standardized time </strong>is determined by synchronizing the eye fixation with the arm movement. Given the movement duration is scaled from 0 to 10, we identify the moment when the eye-fixation occurs.</p> <p>This standardized time refers to the duration of the fixation relative to the duration of the pointing gesture. In <strong>column 1</strong>, we report the gross time, averaging the beginning and end of the fixation. In<strong> column 4</strong>, we provide the exact standardization at the start, and in <strong>column 5</strong>, the exact standardization at the end of the movement. During analysis, these times are synchronized with the movement duration, and we use the data from column 4.</p> </li> <li> <p><strong>Euclidean distance</strong> between the fixated key and the target key (2nd column).</p> </li> <li><strong>Duration</strong> of each fixation (3rd column).</li> </ul> </li> </ul> </li> <li> <p><strong>Sequence of Fixated Keys</strong>:</p> <ul> <li>Contains the sequence of keys fixated by the user during each pointing gesture.</li> </ul> </li> <li> <p><strong>Arm Movement Information</strong>:</p> <ul> <li>Includes details on the arm movement (same as reported in<strong> file A</strong>) corresponding to the eye-tracking fixation sequence.</li> </ul> </li> <li> <p><strong>Target Key Pressed</strong>:</p> <ul> <li>Indicates the target key pressed by the participant.</li> </ul> </li> <li> <p><strong>Probability of Key Pressed</strong>:</p> <ul> <li>Reports the likelihood of each key being pressed,<strong> as detailed in file B.</strong></li> </ul> </li> <li> <p><strong>Euclidean Distance Between Consecutive Keys</strong>:</p> <ul> <li>Measures the Euclidean distance between two consecutively pressed keys using the keyboard as a reference (refer to the paper text for more details).</li> </ul> </li> </ol> <p> </p> <p> </p>
3D spinal column point clouds of mice with RyR1 mutations and their wild-type littermates
<p>Spinal column point clouds of mice with RyR1 mutations and their wild-type littermates.</p> <p>Part of the paper Ruiz et al., "Reduction of RyR1 expression in muscle spindles causes alterations of the proprioceptive properties and scoliosis in mice carrying recessive Ryr1 mutations"</p>
Antarctic Tipping points triggered by the mid-Pliocene warm climate
<p>Simulated Antarctic mPWP at 16 km resolution files for every AOGCM. </p> <p><strong>enh:</strong> enhancement factor</p> <p><strong>btq: </strong>friction exponent</p> <p><strong>cfrf: </strong>friction coefficient</p>
Naturally occurring horse model of miscarriage reveals temporal relationship between chromosomal aberration type and point of lethality
<p>Released .idata files, sample metadata and array manifest file for the paper "<span>Naturally occurring horse model of miscarriage reveals temporal relationship between chromosomal aberration type and point of lethality"</span></p>
FIGURE 5 in Recommendations For Monitoring Avian Populations With Point Counts: A Case Study In Southeastern Brazil
FIGURE 5: Monthly counts of rare (sightings ≤ 5 for the entire year) species comparing endemic and non-endemic species show that these rare species may be seen in any month of the year. Months are separated by dry and wet seasons.
FIGURE 4 in Recommendations For Monitoring Avian Populations With Point Counts: A Case Study In Southeastern Brazil
FIGURE 4: Comparisons of species richness (A) and abundance (C) by time of day (morning – AM, and afternoon – PM), and separated by endemic and nonendemic species richness (B) and abundance (D) as the difference between AM and PM during 2006 show that more species and individuals were recorded on morning counts. Months begin in April to clearly separate the dry from the wet season. Error bars represent 95% confidence interval.
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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
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International Brain Laboratory public data
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