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464 results for “high density”
Dataset of comprehensive Full-notch creep tests (FNCT) of selected high-density polyethylene (PE-HD) materials
<p>The dataset provided in this repository comprises data obtained from a series of full-notch creep tests (FNCT) performed on selected high-density polyethylene (PE-HD) materials (for further details, see section 1 Materials in this document) in accordance with the corresponding standard ISO 16770 [1]. </p><p>The FNCT is one of the mechanical testing procedures used to characterize polymer materials with respect to their environmental stress cracking (ESC) behavior. It is widely applied for PE-HD materials, that are predominantly used for pipe and container applications. It is based on the determination of the time to failure for a test specimen under constant mechanical load in a well-defined and temperature controlled liquid environment. The test device used here also allows for continuous monitoring of applied force, specimen elongation and temperature.</p>
Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?
<p>Socioecological theory attributes social variation in female-bonded species to differences in within- and between-group competition, shaped by food distribution. Strong between-group contests are expected over large, monopolisable resources, but not when low-quality food is distributed across large, undefended home ranges. Within-group contests are expected to be more frequent with increasing heterogeneity in feeding sites. We tested these predictions in female Asian elephants, which show traits associated with infrequent contests – predominant graminivory, overlapping home ranges, and high fission-fusion. We examined how agonistic interactions within and between female elephant clans (social groupings) vary with food distribution and competitor density. We found stronger between-clan contests than that known from neighbouring forests and more frequent agonism between females between clans than within clans. Such strong between-clan contest is attributable to food patchiness as the Kabini grassland in the study area had three times the grass biomass as adjacent forests. Within-clan agonism was also frequent but was not influenced by food distribution, contradicting socioecological predictions. Contrary to recent claims, increasing within-clan agonism with group (party) size showed that ecological constraints operate despite high fission-fusion in Asian elephants. Thus, despite graminivory and fission-fusion, within-clan and between-clan agonism can be frequent, especially at high population density.</p>
High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers
<p><span>In species with large and complex genomes such as conifers, dense linkage maps are a useful for supporting genome assembly and laying the genomic groundwork at the structural, populational and functional levels. However, most of the 600+ extant conifer species still lack extensive genotyping resources, which hampers the development of high-density linkage maps. In this study, </span><span><span>we developed a linkage map relying on 21,570 SNP makers in </span></span><span>Sitka spruce (<em>Picea sitchensis</em> [Bong.] Carr.)</span><span><em><span>, </span></em></span><span><span>a long-lived conifer from western North America that is widely planted for productive forestry in the British Isles. </span></span><span>We used a single-step mapping approach to efficiently combine RAD-Seq and genotyping array SNP data for 528 individuals from two full-sib families. As expected for spruce taxa, the saturated map contained 12 linkages groups with a total length of 2,142 cM. The positioning of 5,414 unique gene coding sequences allowed us to compare our map with that of other Pinaceae species, which provided evidence for high levels of synteny and gene order conservation in this family. We then developed an integrated map for <em>P. sitchensis</em> and <em>P. glauca</em> based on 27,052 makers and 11,609 gene sequences. Altogether, these two linkage maps, the accompanying catalog of 286,159 SNPs and the genotyping chip developed herein opens new perspectives for a variety of fundamental and more applied research objectives, such as for the improvement of spruce genome assemblies, or for marker-assisted sustainable management of genetic resources in Sitka spruce and related species.</span></p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
F I G U R E 1 in Targeted census of lionfishes (Scorpaenidae) reveals high densities in their native range
F I G U R E 1 Lionfishes observed during targeted surveys at three sites at Moorea, French Polynesia. A total of 90 transects (total area surveyed = 3600 m2) were conducted across three depths ["Reef Crest" (c. 1–3 m depth); "Mid" (3 m below the reef crest, c. 4–6 m depth); and "Deep" (6 m below the reef crest, c. 7–9 m depth)] at the three locations (a). Three species of lionfish were observed: clearfin lionfish Pterois radiata (b, e); spotfin lionfish Pterois antennata (c, f); and twinspot lionfish Dendrochirus biocellatus (d, g). Density is averaged (mean ± se) across the three locations. Site 1, Site 2, Site 3
Development of a high-density 665 K SNP array for rainbow trout genome-wide genotyping. Supplemental VCF file
<p>Single nucleotide polymorphism (SNP) arrays, also named « SNP chips », enable very large numbers of individuals to be genotyped at a targeted set of thousands of genome-wide identified markers. We used preexisting variant datasets from USDA, a French commercial line and 30X-coverage whole genome sequencing of INRAE isogenic lines to develop an Affymetrix 665 K SNP array (HD chip) for rainbow trout. In total, we identified 32,372,492 SNPs that were polymorphic in the USDA or INRAE databases. A subset of identified SNPs were selected for inclusion on the chip, prioritizing SNPs whose flanking sequence uniquely aligned to the Swanson reference genome, with homogenous repartition over the genome and the highest Minimum Allele Frequency in both USDA and French databases. Of the 664,531 SNPs which passed the Affymetrix quality filters and were manufactured on the HD chip, 65.3% and 60.9% passed filtering metrics and were polymorphic in two other distinct French commercial populations in which, respectively, 288 and 175 sampled fish were genotyped. Only 576,118 SNPs mapped uniquely on both Swanson and Arlee reference genomes, and 12,071 SNPs did not map at all on the Arlee reference genome. Among those 576,118 SNPs, 38,948 SNPs were kept from the commercially available medium-density 57K SNP chip. We demonstrate the utility of the HD chip by describing the high rates of linkage disequilibrium at 2 kb to 10 kb in the rainbow trout genome in comparison to the linkage disequilibrium observed at 50 kb to 100 kb which are usual distances between markers of the medium-density chip.</p> <p> </p> <p>File submitted correspond to the supplementary data 1 of the publication (under submission) : INRAE_USDA_MAF1.vcf.gz</p>
Directional excitation of a high-density magnon gas using coherently driven spin waves
<p>Data corresponding to the figures of the main text of: "Directional excitation of a high-density magnon gas using coherently driven spin waves "</p>
Enabling spectrally resolved single-molecule localization microscopy at high emitter densities: Dataset
<p>The data in this dataset accompanies the various figures present in the publication 'Enabling spectrally resolved single-molecule localization microscopy at high emitter densities'. Contained are tiff files used to create the figures 2-4 and Supplementary figures 1 and 2, as well as csvs after processed with the steps described in the paper (and contained in protocol text files).</p>
3DHD CityScenes: High-Definition Maps in High-Density Point Clouds
<p><strong>Overview</strong></p> <p>3DHD CityScenes is the most comprehensive, large-scale high-definition (HD) map dataset to date, annotated in the three spatial dimensions of globally referenced, high-density LiDAR point clouds collected in urban domains. Our HD map covers 127 km of road sections of the inner city of Hamburg, Germany including 467 km of individual lanes. In total, our map comprises 266,762 individual items.</p> <p>Our corresponding paper (published at ITSC 2022) is available <a href="https://www.researchgate.net/publication/364309881_3DHD_CityScenes_High-Definition_Maps_in_High-Density_Point_Clouds">here</a>.<br> Further, we have applied 3DHD CityScenes to map deviation detection <a href="https://www.researchgate.net/publication/368983255_DNN-Based_Map_Deviation_Detection_in_LiDAR_Point_Clouds">here</a>. </p> <p>Moreover, we release code to facilitate the application of our dataset and the reproducibility of our research. Specifically, our 3DHD_DevKit comprises:</p> <ul> <li>Python tools to read, generate, and visualize the dataset,</li> <li>3DHDNet deep learning pipeline (training, inference, evaluation) for<br> map deviation detection and 3D object detection.</li> </ul> <p>The DevKit is available here:</p> <p><a href="https://github.com/volkswagen/3DHD_devkit">https://github.com/volkswagen/3DHD_devkit</a>.</p> <p>The dataset and DevKit have been created by <a href="https://de.linkedin.com/in/christopher-plachetka-42b325115">Christopher Plachetka</a> as project lead during his PhD period at Volkswagen Group, Germany.</p> <p>When using our dataset, you are welcome to cite:</p> <pre><code>@INPROCEEDINGS{9921866, author={Plachetka, Christopher and Sertolli, Benjamin and Fricke, Jenny and Klingner, Marvin and Fingscheidt, Tim}, booktitle={2022 IEEE 25th International Conference on Intelligent Transportation Systems (ITSC)}, title={3DHD CityScenes: High-Definition Maps in High-Density Point Clouds}, year={2022}, pages={627-634}}</code></pre> <p><strong>Acknowledgements </strong></p> <p>We thank the following interns for their exceptional contributions to our work.</p> <ul> <li><a href="https://www.linkedin.com/in/sertolli/">Benjamin Sertolli</a>: Major contributions to our DevKit during his master thesis</li> <li>Niels Maier: Measurement campaign for data collection and data preparation</li> </ul> <p>The European large-scale project Hi-Drive (<a href="http://www.Hi-Drive.eu">www.Hi-Drive.eu</a>) supports the publication of 3DHD CityScenes and encourages the general publication of information and databases facilitating the development of automated driving technologies.</p> <p><strong>The Dataset</strong></p> <p>After downloading, the 3DHD_CityScenes folder provides five subdirectories, which are explained briefly in the following.</p> <p>1. Dataset</p> <p>This directory contains the training, validation, and test set definition (train.json, val.json, test.json) used in our publications. Respective files contain samples that define a geolocation and the orientation of the ego vehicle in global coordinates on the map.</p> <p>During dataset generation (done by our DevKit), samples are used to take crops from the larger point cloud. Also, map elements in reach of a sample are collected. Both modalities can then be used, e.g., as input to a neural network such as our 3DHDNet.</p> <p>To read any JSON-encoded data provided by 3DHD CityScenes in Python, you can use the following code snipped as an example.</p> <pre><code class="language-python">import json json_path = r"E:\3DHD_CityScenes\Dataset\train.json" with open(json_path) as jf: data = json.load(jf) print(data)</code></pre> <p>2. HD_Map</p> <p>Map items are stored as lists of items in JSON format. In particular, we provide:</p> <ul> <li>traffic signs,</li> <li>traffic lights,</li> <li>pole-like objects,</li> <li>construction site locations,</li> <li>construction site obstacles (point-like such as cones, and line-like such as fences),</li> <li>line-shaped markings (solid, dashed, etc.),</li> <li>polygon-shaped markings (arrows, stop lines, symbols, etc.),</li> <li>lanes (ordinary and temporary),</li> <li>relations between elements (only for construction sites, e.g., sign to lane association).</li> </ul> <p>3. HD_Map_MetaData</p> <p>Our high-density point cloud used as basis for annotating the HD map is split in 648 tiles. This directory contains the geolocation for each tile as polygon on the map. You can view the respective tile definition using QGIS. Alternatively, we also provide respective polygons as lists of UTM coordinates in JSON.</p> <p>Files with the ending .dbf, .prj, .qpj, .shp, and .shx belong to the tile definition as “shape file” (commonly used in geodesy) that can be viewed using QGIS. The JSON file contains the same information provided in a different format used in our Python API.</p> <p>4. HD_PointCloud_Tiles</p> <p>The high-density point cloud tiles are provided in global UTM32N coordinates and are encoded in a proprietary binary format. The first 4 bytes (integer) encode the number of points contained in that file. Subsequently, all point cloud values are provided as arrays. First all x-values, then all y-values, and so on. Specifically, the arrays are encoded as follows.</p> <ul> <li>x-coordinates: 4 byte integer</li> <li>y-coordinates: 4 byte integer</li> <li>z-coordinates: 4 byte integer</li> <li>intensity of reflected beams: 2 byte unsigned integer</li> <li>ground classification flag: 1 byte unsigned integer</li> </ul> <p>After reading, respective values have to be unnormalized. As an example, you can use the following code snipped to read the point cloud data. For visualization, you can use the pptk package, for instance.</p> <pre><code class="language-python">import numpy as np import pptk file_path = r"E:\3DHD_CityScenes\HD_PointCloud_Tiles\HH_001.bin" pc_dict = {} key_list = ['x', 'y', 'z', 'intensity', 'is_ground'] type_list = ['<i4', '<i4', '<i4', '<u2', 'u1'] with open(file_path, "r") as fid: num_points = np.fromfile(fid, count=1, dtype='<u4')[0] # print(num_points) # Init for k, dtype in zip(key_list, type_list): pc_dict[k] = np.zeros([num_points], dtype=dtype) # Read all arrays for k, t in zip(key_list, type_list): pc_dict[k] = np.fromfile(fid, count=num_points, dtype=t) # Unnorm pc_dict['x'] = (pc_dict['x'] / 1000) + 500000 pc_dict['y'] = (pc_dict['y'] / 1000) + 5000000 pc_dict['z'] = (pc_dict['z'] / 1000) pc_dict['intensity'] = pc_dict['intensity'] / 2**16 pc_dict['is_ground'] = pc_dict['is_ground'].astype(np.bool_) fid.close() print(pc_dict) # Visualization # Normalize (due to large UTM values) x_utm = pc_dict['x'] - np.mean(pc_dict['x']) y_utm = pc_dict['y'] - np.mean(pc_dict['y']) z_utm = pc_dict['z'] xyz = np.column_stack((x_utm, y_utm, z_utm)) viewer = pptk.viewer(xyz) viewer.attributes(pc_dict['intensity']) viewer.set(point_size=0.03)</code></pre> <p>5. Trajectories</p> <p>We provide 15 real-world trajectories recorded during a measurement campaign covering the whole HD map. Trajectory samples are provided approx. with 30 Hz and are encoded in JSON.</p> <p>These trajectories were used to provide the samples in train.json, val.json. and test.json with realistic geolocations and orientations of the ego vehicle.</p> <ul> <li>OP1 – OP5 cover the majority of the map with 5 trajectories.</li> <li>RH1 – RH10 cover the majority of the map with 10 trajectories.</li> </ul> <p>Note that OP5 is split into three separate parts, a-c. RH9 is split into two parts, a-b. Moreover, OP4 mostly equals OP1 (thus, we speak of 14 trajectories in our paper). For completeness, however, we provide all recorded trajectories here. </p>
Sample dataset to accompany Hamilton, Chang, Lee, & Chang. Semi-automated anatomical labeling and inter-subject warping of high-density intracranial recording electrodes in electrocorticography
<p>This dataset accompanies the following paper: <br> Hamilton, Chang, Lee, and Chang. Semi-automated anatomical labeling and inter-subject warping of <br> high-density intracranial recording electrodes in electrocorticography</p> <p>This data includes an anonymized and de-identified CT and T1 MRI scan, plus all intermediate and final files<br> produced by the img_pipe software for testing and instructional purposes. This subject had a right hemisphere implantation including high density grids, strip electrodes, and depth electrodes.</p> <p>img_pipe software and installation instructions can be found at http://github.com/changlabucsf/img_pipe</p> <p>If you wish to follow along yourself, we recommend creating a new subject in your Freesurfer $SUBJECTS_DIR, <br> then copy the acpc and CT directories from this dataset into that new subject directory. </p> <p>The electrode montage is provided in test_subj_montage.txt and describes the type of electrodes implanted<br> (grid, strip, or depth) and their general location. </p>
Fig. 3 in Short Communication High-Density Cultivation of the Marine Ciliate Uronema marinum (Ciliophora, Oligohymenophorea) in Axenic Medium
Fig. 3. Growth chart of U. marinum in PGY medium and bacterized filtered seawater. The density of ciliate cells was measured every 12 hours after inoculation using a hemocytometer. The final data points are 419 cells/μl in PGY medium and 11 cells/μl in bacterized filtered seawater.
Fig. 2 in Short Communication High-Density Cultivation of the Marine Ciliate Uronema marinum (Ciliophora, Oligohymenophorea) in Axenic Medium
Fig. 2. PCR amplification of the bacterial SSU-rDNA on 1% agarose gel. A1, A2, and A3 are parallel samples extracted from the axenic culture in PGY medium. B1, B2, and B3 are parallel samples extracted from the culture in bacterized filtered seawater. M – DNA ladder.
Fig. 1 in Short Communication High-Density Cultivation of the Marine Ciliate Uronema marinum (Ciliophora, Oligohymenophorea) in Axenic Medium
Fig. 1. Uronema marinum from life (A, B, E, F), after protargol (C, D) and DAPI-staining (G, H). A, B – lateral-ventral view of typical cell (B, from Pan et al. 2010); C, D – ventral and dorsal view of the same specimen (from Pan et al. 2010); E – 72 hours after inoculating into PGY medium; F – 168 hours after inoculating into PGY medium; G – Uronema marinum in axenic culture, demonstrating the absence of bacteria; H – Uronema marinum in bacterized filtered seawater cultivating system, arrowheads indicate bacteria that are active in the realtime viewing conditions; M1–3 – membranelles 1–3, PM – paroral membrane, Sc – scutica. Scale bars: 20 μm.
Constructing a high-density linkage map to infer the genomic landscape of recombination rate variation in European Aspen (Populus tremula)
<p>Data sets and files for linkage map construction and for inferring recombination rate variation in <em>Populus tremula</em>. Associated scripts for analyses can be found at <a href="https://github.com/parkingvarsson/Recombination_rate_variation">https://github.com/parkingvarsson/Recombination_rate_variation</a> </p>
Refined Mass and Geometric Measurements of the High-Mass PSR J0740+6620: Probability Density Functions and their Credible Intervals
<p>This Zenodo entry contains files for data used by Fonseca et al. (2021), The Astrophysical Journal Letters, 915, L12, which presents an analysis of radio-timing data for PSR J0740+6620 observed with the Green Bank Telescope and the Canadian Hydrogen Intensity Mapping Experiment telescope. See the attached README for a description of the attached data products and their use.</p>
Improving wheat yield prediction using secondary traits and high-density phenotyping under heat stressed environments
<p>A primary selection target for wheat (Triticum aestivum) improvement is grain yield. However, the selection for yield is limited by the extent of field trials, fluctuating environments, and the time needed to obtain multiyear assessments. Secondary traits such as spectral reflectance and canopy temperature (CT), which can be rapidly measured many times throughout the growing season, are frequently correlated with grain yield and could be used for indirect selection in large populations particularly in earlier generations in the breeding cycle prior to replicated yield testing. While proximal sensing data collection is increasingly implemented with high-throughput platforms that provide powerful and affordable information, efficient and effective use of these data is challenging. The objective of this study was to monitor wheat growth and predict grain yield in wheat breeding trials using high-density proximal sensing measurements under extreme terminal heat stress that is common in Bangladesh. Over five growing seasons, we analyzed normalized difference vegetation index (NDVI) and CT measurements collected in elite breeding lines from the International Maize and Wheat Improvement Center at the Regional Agricultural Research Station, Jamalpur, Bangladesh. We explored several variable reduction and regularization techniques followed by using the combined secondary traits to predict grain yield. Across years, grain yield heritability ranged from 0.30 to 0.72, with variable secondary trait heritability (0.0–0.6), while the correlation between grain yield and secondary traits ranged from−0.5 to 0.5. The prediction accuracy was calculated by a cross-fold validation approach as the correlation between observed and predicted grain yield using univariate and multivariate models. We found that the multivariate models resulted in higher prediction accuracies for grain yield than the univariate models. Stepwise regression performed equal to, or better than, other models in predicting grain yield. When incorporating all secondary traits into the models, we obtained high prediction accuracies (0.58–0.68) across the five growing seasons. Our results show that the optimized phenotypic prediction models can leverage secondary traits to deliver accurate predictions of wheat grain yield, allowing breeding programs to make more robust and rapid selections.</p>
Impact of larval high density on the performance of Anastrepha ludens: competition or feeding facilitation?
<p>A critical point in diet management is maximizing density as a strategy for reducing costs. Artificial diets elaborated with large particle sizes have increased volume, possess high porosity and aeration capacity, are more penetrable and facilitate the movement of larvae, and increase the bioavailability of nutrients. The present experiment consisted in demonstrating that the bulking agent facilitates feeding and could have more capacity to support a high density of larvae with a minimal effect of competition on the life-history traits of <em>Anastrepha ludens</em>. The results indicate that density affected larval and pupal weight, but not pupation at 24 h, adult emergence, and flier percentage, which remained unchanged. However, there was an increase in yield and bioconversion. Larvae in high density conditions aggregated to increase the effect of regurgitation of amylases and proteases as a strategy to metabolize the food prior to ingestion through enzymes secreted in the saliva, contributing thus to feeding facilitation and the uptake of ingested food, which decreased the negative impact of competition in high density conditions. High density leads to an increase in food consumption and a consequent increase in digestive enzyme activity, contributing to the bioavailability of macronutrients ─ proteins, carbohydrates, and lipids ─ in the diet.</p> <p>Identifying the properties of bulking agents and other ingredients and their interaction under high-density conditions is essential to develop novel artificial diets and improve the mass-rearing strategies for the SIT.</p> <p> </p>
Do food distribution and competitor density affect agonistic behaviour within and between clans in a high fission-fusion species?
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High-density genetic linkage mapping in Sitka spruce advances the integration of genomic resources in conifers
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Evolution under pH stress and high population densities leads to increased density-dependent fitness in the protist Tetrahymena thermophila
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.