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419 results for “capture data”
A Facial Motion Capture System Based on Neural Network Classifier Using RGB-D Data-Figure 1. Peak of facial expression in database
<p>In this study, data are obtained from the Kinect camera that benefits from colorful images and depth data. Kinect can record colorful and depth data simultaneously at 30 frames per second. The data are collected from the person who initially pose in front of the camera with normal face mode and then the various modes are represented. It should be noted that data are obtained at different distances from the Kinect camera and in different lighting conditions. Figure 1 shows various facial modes in our database.</p>
Spatial capture-recapture data of Darwin's frogs captured between 2014-2017
<p>Search-encounter spatial capture-recapture data from <em>R. darwinii</em> individuals captured between 2014-2017 at two plots located near Neltume, Southern Chile.</p> <p>x and y coordinates in meters are provided for each capture as text files. These are matrices with 64 columns (secondary capture occasions) and 311 rows (frogs). The 16 primary capture occasions are separated by a 3-month period, and each of these occasion is composed of four secondary capture occasions. With these data you can reconstruct the capture-history matrix used in non-spatial capture-recapture models.</p> <p>Snout-to-vent length (SVL) during each capture occasion are provided in mm for each frog. With these data you can reconstruct the age of the individuals (juveniles or adults).</p> <p>Id data is provided for each individual. The first column represents the frog’s code, and the second one represents the plot where the frog was captured (1= HUI1, 2= HUI2).</p> <p>Any question can be addressed to andresvalenzuela.zoo@gmail.com</p>
Zenodo data and software citation links captured by the Asclepias Broker
<p>The dataset was retrieved from the Asclepias Broker early January 2019 after having performed a full harvesting and deduplication cycle from a clean database with zero citation links.</p> <p>The dataset contains citation links from three discovery systems: the NASA Astrophysics Datasystem (ADS), Crossref Event Data and Europe PMC. Only citation links with a target DOI in the DOI prefix 10.5281 (Zenodo’s DOI prefix) were kept.</p>
Raw data for High-speed shear mixing: a versatile energy-efficient ultra-fast strategy for solvent-free amine-functionalised solid CO2 adsorbents for direct air capture
<p><strong>Specification of affiliations:</strong></p> <ul> <li>Pavol Suly - Centre of Polymer Systems</li> <li>Barbora Hanulikova - Centre of Polymer Systems</li> <li>Abdulkadir Bozarslan - Centre of Polymer Systems</li> <li>Milan Masar - Centre of Polymer Systems</li> <li>Michal Urbanek - Centre of Polymer Systems</li> <li>Eva Domincova Bergerova - Centre of Polymer Systems</li> <li>Michal Machovsky - Centre of Polymer Systems</li> <li>Ivo Kuritka - Centre of Polymer Systems</li> </ul> <p> </p> <p>Raw data for the research paper. Information on the data collection are described in the manuscript. </p>
Linked collectors and determiners for: Capture of Primary Biodiversity Data for West African Plants.
Natural history specimen data linked to collectors and determiners held within, "Capture of Primary Biodiversity Data for West African Plants". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/f455bc89-8b18-4ad2-a982-cb961022a3ab">https://bionomia.net/dataset/f455bc89-8b18-4ad2-a982-cb961022a3ab</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/f455bc89-8b18-4ad2-a982-cb961022a3ab">https://gbif.org/dataset/f455bc89-8b18-4ad2-a982-cb961022a3ab</a>. Formatted as a Frictionless Data package.
Supplementary data for "Deep learning for industrial processes: Forecasting amine emissions from a carbon capture plant"
<p>A preliminary analysis of the data already has been discussed in <a href="https://dx.doi.org/10.2139/ssrn.3812299">10.2139/ssrn.3812299</a>.</p> <p><strong>Raw data</strong></p> <p>Raw measurement data is in the Excel files `day*_raw.xlsx`.</p> <p><strong>Model</strong></p> <p>Covariate and label scaler objects are serialized in joblib format in the following files:</p> <ul> <li>20210812_y_transformer_co2_ammonia_reduced_feature_set</li> <li>20210812_y_transformer__reduced_feature_set</li> <li>20210812_x_scaler_reduced_feature_set</li> </ul> <p>Checkpoints of the models are in the `*.pth.tar` files. An example for loading the models is:</p> <pre><code class="language-python">from pyprocessta.model.tcn import TCNModelDropout model_cov = TCNModelDropout( input_chunk_length=8, output_chunk_length=1, num_layers=5, num_filters=16, kernel_size=6, dropout=0.3, weight_norm=True, batch_size=32, n_epochs=100, log_tensorboard=True, optimizer_kwargs={"lr": 2e-4}, ) model_cov.load_from_checkpoint('20210814_2amp_pip_model_reduced_feature_set_darts')</code></pre> <p>which assumes that the checkpoints are placed as `model_best.pth.tar` in a folder called `20210812_2amp_pip_model_reduced_feature_set_darts`.</p> <p> </p>
Suggested Taxonomy: Tracking Technologies to Effectively Capture and Input Key Data on the Blockchain
<p>Within the paper titled "Transparency with Blockchain and Physical Tracking Technologies: Enabling Traceability in Raw Material Supply Chains" (Mater. Proc. 2021, 5(1), 1; <a href="https://doi.org/10.3390/materproc2021005001">https://doi.org/10.3390/materproc2021005001</a>), we consider the majority of tracking technologies to be part of the IoT ecosystem and suggest a taxonomy with their key features, benefits and use cases in the mining industry. Although technologies such as markers and QR/Barcodes are not necessarily electronic devices, they can integrate with other IoT objects and provide or qualify a digital identity. The common element connecting all these technologies is that they include functionalities that can capture and communicate granular, timely, relevant and accurate data, which can be automatically or manually entered into the blockchain. </p> <p>We have analysed the following most common physical tracking methods which will be described and exemplified in more detail below:</p> <ul> <li> <p>Video monitoring (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t001">Table 1</a>)</p> </li> <li> <p>Bar and QR codes (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t002">Table 2</a>)</p> </li> <li> <p>Markers and taggants (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t003">Table 3</a>)</p> </li> <li> <p>Cellular, near range and low power network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t004">Table 4</a>)</p> </li> <li> <p>Satellite network tracking tools (<a href="https://www.mdpi.com/2673-4605/5/1/1/htm#table_body_display_materproc-05-00001-t005">Table 5</a>)</p> </li> </ul>
Datasets and OpenLCA foreground data processes for the article: Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment
<p>This data set contains the supplementary data sets (1-3) and exported foreground data processes from OpenLCA for the manuscript “Understanding environmental trade-offs and resource demand of direct air capture technologies through comparative life-cycle assessment”, submitted to Nature Energy.</p> <p>This repository contains:</p> <ul> <li>Supplementary data set 1: Ancillary calculations and numerical values for HT-Aq DAC</li> <li>Supplementary data set 2: Ancillary calculations and numerical values for TSA DAC</li> <li>Supplementary data set 3: Ancillary calculations and numerical values shown in plots and table 3</li> <li>Foreground data from OpenLCA. OpenLCA process model for different cases of HT-Aq DAC and TSA DAC. To re-run the LCA calculations, OpenLCA (freeware) and the Ecoinvent 3.5 database (license required) need to be installed on a standard desktop computer or laptop with at least 8 GB RAM.</li> </ul>
Pond bat capture and diet data from the Netherlands
<p>The goal of this study was to describe the spatial segregation and diet of Pond bats (<em>Myotis dasycneme</em>). A wide range of water-bound habitats throughout the Netherlands was sampled, including marshes, lakes, rivers, wetlands and waterways. For all the locations water depth and soil type were determined. Life animals were captured during 471 nights using a mist net. No Pond bats were captured during 134 of those nights. Water depth and soil type was based on top 10 vector maps (www.pdok.nl/geo-services) with information about both variables. Each captured individual was placed in a separate cotton holding bag until it was weighted, sexed, and the reproductive status and age were assessed by observation of external characteristics. Before dissecting, the dry weight of each faecal pellet was measured with an electronic scale. All samples were dissected under a Carl Zeiss Discovery V20 stereomicroscope. All identifiable fragments were photographed and stored for later use. </p> <p>Genetic analysis: The pellets were ground to a fine powder in liquid nitrogen with a mortar and pestle using the protocol of the commercial Qiagen QIAamp DNA Stool Mini Kit in a special Ancient DNA facility dedicated to work with samples with degraded DNA and following established protocols to avoid contamination such as the inclusion of extraction blanks. Subsequently, aliquots of each extraction were further purified using Promega PCR purification columns. Amplifications of the ~313 bp long mitochondrial COI mini-barcoding marker were performed using forward primer ZBJ-ArtF1c 5’-AGATATTGGAACWTTATATTTTATTTTTGG-3’ and reverse primer ZBJ-ArtR2c 5’- WACTAATCAATTWCCAAATCCTCC-3’. The ~157 bp long mitochondrial 16S barcoding marker was amplified using the forward primer P7_FO-16S 5’- RGACGAGAAGACCCTATARA-3’ and P7_R0-16S 5’-ACGCTGTTATCCCTAARGTA-3’. Primers were labelled for DNA metabarcoding with IonExpress labels. The PCR was carried out in 30 microliter reactions containing 0.20 µl Qiagen taq 5u/µl, 3 µl 10x Qiagen buffer, 2 µl 2,5mM dNTP’s, 0,5 µl 10 µM forward primer, 0,5 µl 10 µM reverse primer, 1,5 µl 25mM MgCl<sub>2, </sub>0,5 µl 10 mg/ml BSA, 19.80 µl MiliQ and 2 µl template. Amplifications were performed using the following PCR programme: 5 min denaturation at 95°C followed by 40 cycles of 20 seconds denaturation at 95°C, 20 seconds annealing at 50°C and 1-minute elongation at 72°C. Final elongation was conducted at 72°C for 7 minutes on a C1000 Biorad PCR machine. Primer dimer and other contaminants were removed by using 0.9x Ampure XP beads (Agencourt) to which the PCR products were bound. The beads were washed with 150 microliter 70% EtOH twice and resuspended in 20 microliter TE buffer. Cleaned PCR products were quantified using an Agilent 2100 Bioanalyzer DNA High sensitivity chip. An equimolar pool was prepared of the amplicon libraries at the highest possible concentration. This equimolar pool was diluted according to the calculated template dilution factor to target 10-30% of all positive Ion Sphere Particles. Template preparation and enrichment was carried out with the Ion One Touch 200 Template kit with use of the Ion One Touch System, according to the manufacturer's protocol. The quality control of the Ion One Touch 200 Ion Sphere Particles was done with the Ion Sphere Quality Control kit using a Life Qubit 2.0. The enriched Ion Spheres were prepared for sequencing on a Personal Genome Machine (PGM) with the Ion PGM 200 Sequencing kit as described in the protocol and deposited on an Ion-314 chip (520 cycles per run) in three consecutive sequencing runs. Reads obtained from Ion Torrent sequencing were automatically sorted into separate sequence files based on the MID labels by the Ion Torrent software. The reads were further processed with PRINSEQ (version 0.20.3) with the following settings: a minimum read length of 100 bp, trimming to 140 bp, minimum mean quality of Q24 per read, additional trimming of '3 end bases with a Q lower than 24 and removal of full duplicate sequences. Filtered reads were clustered into Operational Taxonomic Units (OTUs) defined by a sequence similarity of at least 97% using CD-HIT-EST. Singletons were omitted. For each cluster the representative sequences were BLASTed with the NCBI-blast+ software package (version 2.2.28+) against either the NCBI GenBank nucleotide database or a custom database containing all Arthropod sequences located on the Barcode of Life Database. BLAST hits were filtered according to the following criteria: minimum hit length of a 100 bp, minimum hit similarity of 97% and a maximum e-value of 0.05. Reference databases of Dutch species such as http://www.nederlandsesoorten.nl/ were used to check if a species had been recorded for the Netherlands. All species not (yet) known for The Netherlands were reduced to genus level or to the family level if the genus is also unknown to occur.</p> <p> </p> <p>Description of the data files:</p> <p> </p> <p><strong>pelletsMicroscopy.csv</strong></p> <p>Details of individual faecal pellets with prey remains analysed using microscopic analysis.</p> <p>ID: pellet ID</p> <p>sex: sex of the caught Pond bat individual</p> <p>age: age class of the bat</p> <p>date: capture night</p> <p>province: province in which the bat was captured</p> <p>x and y: spatial coordinates within the Netherlands</p> <p>waterDepth: water depth in meters at the capture location</p> <p>pelletWeight: weight of the pellet in grams</p> <p>year: capture year</p> <p>dayOfYear: day of the year, since January 1</p> <p>period: I: end of hibernation till May 20, II May 21- June 29, III: June 30- July 30 and IV: July 31 till hibernation</p> <p>meanPreyWeight: average weight of prey in mg</p> <p>peat: whether the capture site was located in peatland or not</p> <p>propPupae: the proportion of pupae of chironomids: the number of pupae divided by the total number of organisms of all species in a pellet</p> <p>evenness: Pielou’s evenness of the abundance of prey items in a pellet</p> <p>shannon: Shannon index of the diversity of prey items in a pellet</p> <p>saFemale and saMale: sexual activity status of females and males at the time of capture</p> <p>temp: mean temperature (in 0.1 degrees Celsius) during the first two hours after sunset during the capture night</p> <p>wind: mean wind speed (in 0.1 m/s) during the first two hours after sunset during the capture night</p> <p>nPupae: number of Chironomidae pupae found in a pellet</p> <p>nPrey: total number of prey items found in a pellet</p> <p> </p> <p><strong>pelletsMetabarcoding.csv</strong></p> <p>Details of individual faecal pellets analysed using metabarcoding. </p> <p>ID: pellet ID</p> <p>sex: sex of the caught Pond bat individual</p> <p>reproductiveState: reproductive status of the bat</p> <p>age: age class of the bat</p> <p>mature: sexually mature (1) or not (0)</p> <p>date: capture night</p> <p>peat: whether the capture site was located in peatland or not</p> <p>province: province in which the bat was captured</p> <p>x and y: spatial coordinates within the Netherlands</p> <p>waterDepth: water depth in meters at the capture location</p> <p>pelletWeight: weight of the pellet in grams</p> <p> </p> <p><strong>preyWeight.csv</strong></p> <p>Mean weight (milligram) of taxonomic and developmental prey groups, including information on length (mm) and width (mm) of each group.</p> <p> </p> <p><strong>orderPellet.csv</strong></p> <p>Number of pellets in which at least 1 prey of a certain taxon is found, separately per sex of the bat and per analysis method (microscopy or metabarcoding). Total number of analyzed pellets was 365 for females – microscopy, 170 for males – microscopy, 95 for females – metabarcoding, and 65 for males – metabarcoding. </p> <p> </p> <p><strong>taxaCountsMicroscopy.csv</strong></p> <p>Number of prey individuals per taxonomic group found by using morphological analyses (microscopy) of faecal pellets of Pond bats. For each group the number of observations are given separately for male and female bats.</p> <p> </p> <p><strong>taxaCountsMetabarcoding.csv</strong></p> <p>Number of prey individuals per taxonomic group found by using metabarcoding of faecal pellets of Pond bats. For each group the number of observations are given for both male and female bats.</p>
Data from: Tall, heterogenous forests improve prey capture, delivery to nestlings, and reproductive success for Spotted Owls in southern California
<p>Predator-prey interactions can be profoundly influenced by vegetation conditions, particularly when predator and prey prefer different habitats. Although such interactions have proven challenging to study for small and cryptic predators, recent methodological advances substantially improve opportunities for understanding how vegetation influences prey acquisition and strengthen conservation planning for this group. The California Spotted Owl (<em>Strix</em> <em>occidentalis</em> <em>occidentalis</em>) is well-known as an old-forest species of conservation concern, but whose primary prey in many regions – woodrats (<em>Neotoma</em> spp.) – occurs in a broad range of vegetation conditions. Here, we used high-resolution GPS tracking coupled with nest video monitoring to test the hypothesis that prey capture rates vary as a function of vegetation structure and heterogeneity, with emergent, reproductive consequences for Spotted Owls in Southern California. Foraging owls were more successful capturing prey, including woodrats, in taller multilayered forests, in areas with higher heterogeneity in vegetation types, and near forest-chaparral edges. Consistent with these findings, Spotted Owls delivered prey items more frequently to nests in territories with greater heterogeneity in vegetation types and delivered prey biomass at a higher rate in territories with more forest-chaparral edge. Spotted Owls had higher reproductive success in territories with higher mean canopy cover, taller trees, and more shrubby vegetation. Collectively, our results provide additional and compelling evidence that a mosaic of large tree forests with complex canopy and shrubby vegetation increases access to prey with potential reproductive benefits to Spotted Owls in landscapes where woodrats are a primary prey item. We suggest that forest management activities that enhance forest structure and vegetation heterogeneity could help curb declining Spotted Owl populations while promoting resilient ecosystems in some regions.</p>
Model for Calibrating a Model to Thirty Years of Data to Capture the Accumulation of Chloride from Winter Deicers in a Shallow Aquifer
<p>We created a ten layer model to simulate the flow and transport of chloride in Will County’s shallow aquifer with Groundwater Vistas software. This model is transient with yearly stress periods starting in 1950 and ending in 2020.</p> <p>The groundwater flow model was developed using the U.S. Geological Survey (USGS) finite difference code MODFLOW-NWT (McDonald and Harbaugh 1988; Niswonger et al., 2011) within the graphical user interface Groundwater Vistas 7.24 (Rumbaugh and Rumbaugh, 2020). We simulated chloride transport with the MODFLOW post-processing package MT3D-USGS (Bedekar et al., 2016).</p> <p>See Methods Section of 'Calibrating a Model to Thirty Years of Data to Capture the Accumulation of Chloride from Winter Deicers in a Shallow Aquifer'</p> <p>With questions email Cecilia Cullen (ccullen3@illinois,edu) or Daniel Abrams (dbabrams@illinois.edu) </p>
IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization
<p>IMU, magnetometer, and motion capture data from a UAV used for indoor magnetic field mapping and localization. </p> <p>This data is split up into two folders. </p> <p>"stationary_magnetometer_data" 0.1Hz samples of a RM3100 magnetometer that was kept in one position from July 2022 through February 2023. The data is partitioned into separate files for analytical convenience of our research. Each file here is a CSV with a timestamp (synchronized with chrony to a central computer) and the three components of the measured magnetic field. We did not calibrate this stationary magnetometer.</p> <p>"UAV_and_mocap_data" has many subfolders. Each subfolder is labeled by a date and a small description of the goals for that test segment. There is a single "EXPLANATION" file in each subfolder that gives more detail on the provided data. The data here includes the trajectory flown by the UAV, outdoor calibration data to adjust the raw magnetometer measurements, and IMU/motion capture data for each listed flight test. The EXPLANATION file should explain what trajectory was flown for each individual flight test. </p>
Data from: How to use discrete choice experiments to capture stakeholder preferences in social work research
<p>The primary article (cited below under "Related works") introduces social work researchers to discrete choice experiments (DCEs) for studying stakeholder preferences. The article includes an online supplement with a worked example demonstrating DCE design and analysis with realistic simulated data. The worked example focuses on caregivers' priorities in choosing treatment for children with attention deficit hyperactivity disorder. This dataset includes the scripts (and, in some cases, Excel files) that we used to identify appropriate experimental designs, simulate population and sample data, estimate sample size requirements for the multinomial logit (MNL, also known as conditional logit) and random parameter logit (RPL) models, estimate parameters using the MNL and RPL models, and analyze attribute importance, willingness to pay, and predicted uptake. It also includes the associated data files (experimental designs, data generation parameters, simulated population data and parameters, simulated choice data, MNL and RPL results, RPL sample size simulation results, and willingness-to-pay results) and images. The data could easily be analyzed using other software, and the code could easily be adapted to analyze other data. Because this dataset contains only simulated data, we are not aware of any legal or ethical considerations.</p>
Data from: Using transcriptome sequencing and pooled exome capture to study local adaptation in the giga-genome of Pinus cembra
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Data from: Spider venom potency exhibits phylogenetic prey-specificity but does not trade-off with body size or silk use in prey capture
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Data from: Exon capture museomics deciphers the nine-banded armadillo species complex and identifies a new species endemic to the Guiana Shield
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Data from: Phylogenomics of a genus of ‘Great Speciators’ reveals rampant incomplete lineage sorting, gene flow, and mitochondrial capture in island systems
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Capturing local nuisance flooding events with HOBO pendant G data loggers in Key West, Florida US
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Morphological and DNA sequence data generated by Sanger sequencing and target capture methods for moss plants in the genus Fissidens from herbarium specimens
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Data from: Tall, heterogenous forests improve prey capture, delivery to nestlings, and reproductive success for Spotted Owls in southern California
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