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9,113 results for “Sampling”
samples inventory - single-stringer coupons for EoL research - ReINTEGRA
<p>Open access to experimental data generated by the ReINTEGRA project (GA 886609, H2020, European Union, through Clean Sky 2 JU) along the research, at single-stringer coupon level, of the End-of-Life of novel welded Al-Li aerostructures. Research pertaining to Task 1.1 (WP1), Deliverable D1.</p> <p>Processed data originated from calliper and precision balance measurements conducted by AZTERLAN, to determine weight and dimensions of coupons supplied by ecoTECH partners. The file contains the physical description of samples from 15 references of coupons, being each reference a unique combination of stringer and skin alloys, welding technique, stringer configuration, coating and FSW sealant. The coupon samples have been used for investigating cutting strategies and decoating methods (WP2), remelting set-up (WP3) and pre-scrap characterisation protocols (WP4). The experimental plan, with estimation of number of samples required of each reference, was outlined as part of Task 1.1 (WP1).</p> <p> </p>
MadGraph proton pair to top pair sample dataset (100 events)
<p>This is a small dataset for the purpose of code testing and examples with the HEP + ML packages produced to aid the research at the University of Southampton.</p>
Supporting publication for 'Prevalence sample-based guidance for reporting 2021 data'
<p>The record is aimed at helping the reporting countries to submit their sample-based level data to the EFSA Data Collection Framework. We include here two excel files and one XML file, and we give below specific information on their use.</p> <p>The two Excel documents help in mapping terms from the matrix catalogue ZOO_CAT_MATRIX used in the aggregated prevalence data model to FoodEx2 codes, and offer examples on how prevalence data can be reported using SSD2 and how data are aggregated afterwards. The XML file is the same example as in the Excel file with similar title but in the XML format that allows for it be uploaded in the Data Collection Framework.</p>
Cortical oscillations support sampling-based computations in spiking neural networks
<p>This archive contains the scripts for generating the data and figures and the data of the publication: "Cortical oscillations support sampling-based computations in spiking neural networks".</p> <p>The different parts of the material are grouped into separate archives to enable modular usage and can be individually downloaded as required:</p> <ul> <li>The archive spike-based-tempering_scripts.tgz contains all the scripts to reproduce the simulation data and figures.</li> <li>The file software.img contains the third-party software needed to execute the simulations of the current-based experiments.</li> <li>The archive current-based_experiments_data includes the scripts and the simulation data for the current-based experiments.</li> </ul>
BioMates - Coding of identifiers for WP1-samples and -blends
<p>Ablative Fast Pyrolysis (AFP) is the first step in the BioMates-concept to convert herbaceous biomass into co-feed with reliable properties for conventional refineries (www.biomates.eu). The document provides the coding behind the identifiers used for samples and sample blends produced by Fraunhofer UMSICHT via AFP within the H2020-project BioMates. Thus, it supplements the data-set-document “BioMates - WP1: Novel pyrolysis oil from non-food/feed biomass - Data sheet: Identifiers for samples and blends”, DOI:10.24406/fordatis/156</p>
Random Sample of Open Source Ventilators
<p>This is a random sample of references to open source ventilators based on the data published by the PubInv project (https://github.com/PubInv/covid19-vent-list CC0-1.0). It was used to verify technology and documentation readiness scales introduced in a CIRP design conference paper. This upload shall make the data set used for the conference paper publicly accessible.</p>
Chesapeake Bay and Baltic Sea phytoplankton sample metadata
<p>We analyze the relationship between species richness, salinity and resource use efficiency from 10712 summer (June to September) surface phytoplankton samples from the Chesapeake Bay (n=3967) and the Baltic Sea (n=6745). As sample species richness (alpha diversity) has a U-shape distribution along an estuarine salinity gradient and species richness is known to scale with resource use efficiency – an important ecosystem function, we hypothesized that the ecosystem function can be predicted from salinity.</p>
Tara Pacific samples provenance and environmental context - version 2
<p>This publication includes the provenance metadata and environmental context of all samples generated by the Tara Pacific Expedition. The metadata fields and parameters are detailed in the readme files. Provenance is given in a single UTF-8 encoded tab-separated-values file. Environmental context is provided in eleven UTF-8 encoded tab-separated-values files, all with the same structure, but each providing a different statistic: </p> <ul> <li>"n" = number of values</li> <li>"mean" = mean value</li> <li>"stdev" = standard deviation</li> <li>"P05" = 5 percentile, i.e. minimum (Q0)</li> <li>"P25" = 25 percentile, i.e. first quartile (Q1)</li> <li>"P50" = 50 percentile, i.e. median (Q2)</li> <li>"P75" = 75 percentile, i.e. third quartile (Q3)</li> <li>"P95" = 95 percentile, i.e. maximum (Q4)</li> <li>"dt" = lag in time, i.e. difference between the collection date/time of the sample and that of the environmental context provided</li> <li>"dxy" = lag in horizontal space, i.e. distance between the collection location of the sample and that of the environmental context provided</li> <li>"dz" = lag in vertical space, i.e. difference between the collection depth/altitude of the sample and that of the environmental context provided</li> </ul> <p>Missing value terms are:</p> <ul> <li>"nav" = not-available, i.e. the expected information is not given because it has not been collected or generated</li> <li>"npr" = not-provided, i.e. the expected information has been collected or generated but it is not given, i.e. a value may be available in a later version or may be obtained by contacting the data providers</li> <li>"nac" = confidential, i.e. the expected information has been collected or generated but is not available openly because of privacy concerns</li> <li>"nap" = not-applicable, i.e. no information is expected for this combination of parameter, environment and/or method, e.g. depth below seabed cannot be informed for a sample collected in the water or the atmosphere</li> </ul>
Random sample of habitat suitability for wolves in Scotland
<p>A rule-based habitat suitability model was created for wolves (<em>Canis lupus</em>) in mainland Scotland. Six variations of the model were run, in order to test sensitivity to changes in input values. In order to test for difference in the outputs of the six models, 500 random points were sampled from all six models, and then a test for statistical difference performed. This dataset constitutes the values of the 500 random points, where 1 indicates complete suitability and 0 indicates complete unsuitability.</p>
FIGURE 3 Morphological trait sampling for all bird families. AVONET contains 718,662 in AVONET: morphological, ecological and geographical data for all birds
FIGURE 3 Morphological trait sampling for all bird families. AVONET contains 718,662 individual trait measurements, all of which are used to calculate species averages. However, sampling per species varies across families depending on taxonomy. Upper phylogram shows sampling under BirdLife International (11,009 species in 243 families). Families where sampling completeness is below 75% indicated by lighter shading. Most families with lower sampling are species poor (numbers in black circles show species richness). Lower panels show that sampling improves under more conservative taxonomic treatments of eBird (10,661 species in 249 families) and BirdTree (9993 species in 194 families). Coloured bars indicate the proportion of species in each family measured to different levels of completeness. 'Complete set' means a full set of all 9 core morphological traits (not necessarily from the same individual). 'Individuals' means any individual bird with one or more traits measured
A GC-IRMS method for measuring sulfur isotope ratios of carbonyl sulfide from small air samples
<p>This dataset contains supporting files, figures and datasets for the method of sulfur isotope measurements of carbonyl sulfide and ambient air measurements in the Netherlands during 2020 and 2021.</p>
Sample Inventory Data
<p>This is an experimental publication of a sample data set. The goal is to test out low-threshold options for university collections to generate persistent identifiers in situations where no formal digital infrastructure is available.</p>
TELL sample output data
<p>This dataset contains sample output data for TELL. The sample dataset includes four years of sample future data (2039, 2059, 2079, and 2099) that comes from IM3's future WRF runs under the RCP 8.5 climate scenario with SSP5 population forcing. Note that the GCAM-USA output used in this simulation is sample data only. As such the quantitative results from this set of sample output should not be considered valid.</p>
Fig. 2 in A new lineage of Galapagos giant tortoises identified from museum samples
Fig. 2 Statistical parsimony haplotype network of the mitochondrial control region (668 bp) for 129 contemporary individuals from San Cristóbal and six historical specimens collected in 1906, as well as 28 representative haplotypes from the other species of Galapagos giant tortoise. The name of the island where each species occurs is labeled with capital letters, with current taxonomy in italics. Haplotypes are represented as black circles on the network, the size of the circle is proportional to the frequency of the haplotype in the analysis. Open circles represent unsampled, hypothesized haplotypes, and hash marks indicate a single mutational change. Reticulations reflect uncertainty in relationships, or homoplasy.
Fig. 4 Barplots depicting K in A new lineage of Galapagos giant tortoises identified from museum samples
Fig. 4 Barplots depicting K = 2 for the STRUCTURE analysis using the SNP (12 192 loci) and microsatellite (21 loci) genotypes for the contemporary San Cristóbal population (n = 64). Each bar represents an individual and the proportion of the bar that is each color represents the membership of that individual to the two clusters. The order of individuals is the same in both plots, black boxes around bars highlight individuals with a greater than 0.4 discrepancy in assignment proportions between the analyses.
Fig. 3 in A new lineage of Galapagos giant tortoises identified from museum samples
Fig. 3 Bayesian Inference maximum clade credibility cladogram showing relationships among the San Cristóbal historical samples from the cave and collected alive in 1906, and a reference dataset of 93 Galapagos giant tortoise haplotypes and three outgroups based on the mitochondrial control region (alignment length 718 bp), estimated using BEAST with a strict clock and Birth Death tree. The numbers on the branches are the posterior probability support values. § indicates the C. chathamensis type specimen. The name of the island where each clade is found is in capitals, with current taxonomy in italics.
Fig. 1 in A new lineage of Galapagos giant tortoises identified from museum samples
Fig. 1 Map of the Galapagos Archipelago, indicating the locations of each Chelonoidis species, with San Cristóbal Island enlarged in the inset map. Island names are in capital letters. The approximate location of the cave where the bones were found in 1906 is marked, as is the approximate location where CAS 8133 was collected alive and the region of Punta Pitt.
Two fish in a pod. Data from a self-sampling pilot program to separate between black hake species in W-Africa
<p>Data from self-sampling pilot trial in Senegal and Mauritanian waters where two species of black hake were separated manually onboard fishing vessels and the results then validated by genetic analysis. </p>
SSP: An R package to estimate sampling effort in studies of ecological communities
<p>SSP (simulation-based sampling protocol) is an R package that uses simulations of ecological data and dissimilarity-based multivariate standard error (MultSE) as an estimator of precision to evaluate the adequacy of different sampling efforts for studies that will test hypothesis using permutational multivariate analysis of variance. The procedure consists in simulating several extensive data matrixes that mimic some of the relevant ecological features of the community of interest using a pilot data set. For each simulated data, several sampling efforts are repeatedly executed and MultSE calculated. The mean value, 0.025 and 0.975 quantiles of MultSE for each sampling effort across all simulated data are then estimated and standardized regarding the lowest sampling effort. The optimal sampling effort is identified as that in which the increase in sampling effort does not improve the highest MultSE beyond a threshold value (e.g. 2.5 %). The performance of SSP was validated using real data. In all three cases, the simulated data mimicked the real data and allowed to evaluate the relationship MultSE – n beyond the sampling size of the pilot studies. SSP can be used to estimate sample size in a wide variety of situations, ranging from simple (e.g. single site) to more complex (e.g. several sites for different habitats) experimental designs. The latter constitutes an important advantage in the context of multi-scale studies in ecology. An online version of SSP is available for users without an R background.</p>
UCIS4EQ: sample outputs
<p>ChEESE PD1 workflow, Urgent Computing Integrated Services for Earthquakes (UCIS4EQ) performs complete end-to-end runs, from classifying an earthquake as an urgent computing candidate, through pre-processing, submitting HPC simulations, all the way to post-processing. The workflow is still in development and accessible only to developers, but here we present a sample output of the UCIS4EQ end-to-end run with Salvus (https://mondaic.com/) with maximum frequency at 5 Hz. With this run we were reproducing an alert for the Samos-Izmir October 30th, 2020 earthquake. Following a manual trigger of this past event, the workflow performed a set of 66 simulations that have then been post-processed together to generate a single synthetic estimate of the ground motion proxies that is uploaded automatically to B2DROP after the execution is finished. The files including the estimates are published here in netCDF format along with a short README about the content.</p>
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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.