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9,113 results for “Sampling”
IODP Expedition 398 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Metrics for two-sample tests: results on JetNet dataset
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/JetNetMetrics" target="_blank" rel="noopener">JetNetMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Metrics for two-sample tests: results on Mixture of Gaussians and Correlated Gaussians models
<p>The repository includes version 1.0 (v1.0) of the code and results corresponding to the GitHub repository <a href="https://github.com/TwoSampleTests/GenerativeModelsMetrics">GenerativeModelsMetrics</a>.</p> <p>Publishing information and arXiv identifier will be added after publication of the main manuscript related to the data.</p>
Acoustic- and Moth sampling at Etonbury Wood (Bedford) - United Kingdom
<p>Moths sampling by hand of led buckets (https://www.vlinderstichting.nl/wat-wij-doen/meetnetten/meetnet-nachtvlinders/ledemmers/) and acoustic sampling by hand of AudioMoths (https://www.openacousticdevices.info/audiomoth) in a silvoarable system</p>
Experimental data for fracture toughness analysis of sandstone and granite samples under fluid saturation conditions
<p>This database includes experimental results from mode I fracture toughness (KIC) tests conducted on saturated rock specimens. Three lithologies were studied: a porous siliceous sandstone (Corvio, C) and two high-strength, low-porosity granites (Blanco Mera, BM and Blanco Alba, BA). Tests were conducted at room pressure and temperature using the pseudo-compact tension (pCT) methodology. Seven different fluids were used: deionized water, methanol, NaCl-saturated water, mineral oil, diesel fuel, an acidic HCl solution, and a caustic NaOH solution.</p>
IODP Expedition 355 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
Experiment to measure the thickness loss due to corrosion of a 5mm uncoated steel sample exposed close to the shore during 6 months
<p>This is the data obtained during a experiment conducted in Grand Canary Island close to the shore to collect data using CEIT's ultrasound sensor node attached to a 5mm uncoated steel sample. We collected during 6 months the measured thickness taking two measures per day. The sample was considerably degraded during that time. The estimated Corrosion Rate (134um/year) was in coherence with the typical corrosion rates in real environments (100-200um/year). </p>
Data for: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models"
<p>This repository contains the raw data to reproduce the paper: "Comprehensive sampling of coverage effects in catalysis by leveraging generalization in neural network models". Within the .tar.gz file, you will find the directory structure described above.</p> <h2>Directory Structure</h2> <h3>`data`</h3> <p>Contains the data to reproduce all figures in the manuscript. Used primarily by the Jupyter Notebooks that plot the data from the paper.</p> <h3>`eval`</h3> <p>Contains the predicted energies according to a MACE model for the following systems and facets:<br>- covsplit (100, 111, 211, 331, 410, 711): The NN model is trained on low-coverage structures and tested on high-coverage structures for a single facet<br>- evencov (100, 111, 211, 331, 410, 711): The NN is trained on even coverages and tested on odd coverages for a single facet<br>- facet (100, 111, 211, 331, 410, 711): the NN is trained on the facet indicated by the folder name (e.g., facet-100 means that the model was trained on Cu(100)) and tested on all of the other facets.<br>- full: the model was trained on all facets and all coverages<br>- slopes (various versions and configurations): the models were trained with different body-order correlation (v) for the Cu(711) facet and tested only on the Cu(711) facet<br>- Rh111: Energies for the Rh(111) + CHOH + CO systems.</p> <h3>`mcmc`</h3> <p>Contains the data for MCMC (Markov Chain Monte Carlo) evaluations for two systems: Cu and Rh<br>- copper-mcmc-public.tar.gz<br>- rhodium-mcmc-public.tar.gz</p> <h3>`models`</h3> <p>Contains the weights and parameters of the best-performing MACE models trained in this work, as selected by the validation loss:</p> <p>File formats: `.model` and `_swa.model` relate to the first-stage of training and the second-stage of training.</p> <h3>`pyscripts`</h3> <p>Python scripts to perform the MCMC sampling given the custom configuration file `sample_cfg.json`.</p> <h3>`scripts`</h3> <p>Shell scripts for evaluation and training the MACE models, along with the hyperparameters used in doing so.</p> <p>- Evaluation scripts (eval-*.sh)<br>- Training scripts (train-*.sh)</p> <h3>`train`</h3> <p>Training, validation, and testing data for all Cu and Rh facets in this work, according to the naming scheme described above.</p> <p>- Rh111<br>- covsplit<br>- evencov<br>- facet<br>- full<br>- slopes</p>
Physico-chemical properties of Saharan dust samples deposited across Europe in March 2022 and collected through a participatory approach
<p>A particularly long and dense episode of airborne dust transport crossed Spain, France and part of Europe from 15 to 18 March 2022. This episode led to significant dust deposits on cars, roofs, etc., which may have caused surprise or concern among the general public. Based on association reports, several media published articles stating that this dust contained an artificial radionuclide, cesium-137 (<sup>137</sup>Cs), the source of which was attributed to the French nuclear tests conducted in the Sahara early in the 1960s.</p> <p>In order to provide a solid and representative scientific basis for a better characterisation of this dust and its radionuclide content, a participatory call for the collection of about ten grams of dust in different sites in France and Europe was launched on 17 March 2022 on the social network Twitter by Olivier Evrard. It was then relayed by Germán Orizaola, generating numerous reactions and the collection of several dozen samples in France, Spain and other neighbouring countries in Europe.</p> <p>The current dataset provides general information about the location/time of sample collection (<em>n</em>=110 samples) and data about the content of dust samples in cesium-137 (<sup>137</sup>Cs), an artificial radionuclide emitted by nuclear atmospheric tests and accidents (Evrard et al., 2020). Samples (2-80g) were analysed using the ultra-low background Germanium HyperPure gamma spectrometry detectors of LSCE installed in the underground facilities at both University Paris-Saclay (Gif-sur-Yvette) and Modane (Underground Lab of Modane, France). Filters were analysed for 80,000 to 200,000 s to obtain sufficient counting statistics (for the detection of the <sup>137</sup>Cs peak at 662 keV). All results were expressed in Bq kg<sup>−1</sup> with activities decay-corrected to the sampling date. Counting efficiencies and reliability were conducted using certified International Atomic Energy Agency (IAEA) standards (IAEA-444, 135, 375, RGU-1 and RGTh-1) prepared in the same containers as the samples.</p> <p>Additional data regarding other physico-chemical properties analysed in these dust samples has been added in June 2024.</p>
IODP Expedition 353 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
IODP Expedition 359 Sample report
Report includes detailed information about samples taken for testing: location/depth, type/form factor, test, request number, and database identifier.
AddressGB Manual Evaluation Sample
<p>Random sample of 7,200 manually geo-coded <a href="https://doi.org/10.5255/UKDA-SN-7481-2">I-CeM</a> addresses. The sample comprises 1,000 addresses from each England and Wales census (1851, 1861, 1881, 1891, 1901, and 1911) and 200 addresses from each Scottish census (1851, 1861, 1871, 1881, 1891, and 1901).</p> <p>Addresses have been linked to two sources of geo-coded address data: GB1900 and OS Open Roads.</p> <p>GB1900 contains transcriptions of text labels from the Second Edition County Series six-inch-to-one-mile maps covering the whole of Great Britain, published by the Ordnance Survey between 1888 and 1914. To obtain GB1900 dataset, visit the <a href="http://www.visionofbritain.org.uk/data/#tabgb1900">GB1900 website</a>.</p> <p>OS Open Roads is the Ordnance Survey's Open access modern road vector data. To obtain the OS Open Roads dataset, visit the <a href="https://www.ordnancesurvey.co.uk/business-government/products/open-map-roads">OS Website</a>.</p>
Sampling study and collection phase (q-interline data)
<p>This dataset contains the spectral data acquired on waste wood samples using a FT-NIR spectrophotometer (Q-Interline A/S, Tølløse, Denmark) provided with the patented spiral sampler (Spiral Sampler, Q-Interline A/S, Tølløse, Denmark). The waste wood samples have been collected in a panel board company located in the Northern part of Italy during two days of sampling (February 18-19, 2020). In detail, 64 samples (16 lots x 4 samples from each lot) were obtained; later each sample has been split into two replicates for a total of 128 samples. Because of an unfortunate computer error (i.e. the computer froze without saving the collected data) only realized after arriving back at the University, the spectral data of two sample replicates have not been stored. Hence, the final dataset consists of 26192 observations and 1091 wavenumbers (around 210 NIR scans per sample replicate). The sample has been analysed with a particle size of 5 mm.</p> <p>The CSV file contains the following information in the columns:</p> <ul> <li>Sample code: it is reporting the sample code where S1 is the number of lot, the successive number is the number of subsample and the last number the NIR replicate. E.g. S01-01_122: lot number 1, subsample number 01, NIR replicate number 122. Please note that we have also letter ‘B’ when we are looking at the second subsample replicate.</li> <li>Lot: number of lot to which the sample belongs (from 1 to 16).</li> <li>Subsample: number 1, 7, 13 or 19 indicating the four subsamples;</li> <li>Replicate: number 1 or 2 indicating first or second replicate.</li> <li>Measurement: the number of the NIR measurement.</li> <li>Spectral data: absorbance values for each sample from 3595.05 cm<sup>-1</sup> to 12004.07 cm<sup>-1</sup>.</li> </ul> <p>The aim behind this dataset is to provide information about the frequency of sampling and number of replicates and scans to perform for describing the waste wood variability (WP1 of WoodSpec project). A correct sampling procedure is fundamental to guarantee an accurate and successful use of a NIR sensor into real industrial applications and improve the waste wood management, especially when dealing with heterogeneous material.</p> <p><em>Funding: The project leading to this application has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No. 838560. </em></p> <p><em>Terms of use: These data are provided "as is", without any warranties of any kind. The data are provided under the Creative Commons Attribution 4.0 International license.</em></p>
GVI: Sample data for computing VGVI. Vancouver, BC and Manchester.
<p>This is a supplement for the <a href="http://doi.org/10.5281/zenodo.5068835">GVI: Greenness Visibility Index R package</a>.</p> <p> </p> <p>Description:</p> <p>This dataset contains raster (TIFF) data for computing the VGVI for the City of Vancouver and Manchester.</p> <p><strong>Greater Manchester:</strong></p> <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/5f6f7d5b-3f4c-4476-bfb8-cda490c9cf0e/lidar-composite-dtm-2017-50cm">LIDAR Composite DTM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DTM_5m.tif<br> </li> </ul> </li> <li>Digital Surface Model (DSM): <ul> <li>Spatial Resolution: 5m</li> <li>Source: <a href="https://data.gov.uk/dataset/0ab507af-cd91-40cb-8524-3efafc267211/lidar-composite-dsm-2017-50cm">LIDAR Composite DSM 2017 - 50cm</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_DSM_5m.tif<br> </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial resolution: 5m</li> <li>Source: <a href="https://doi.org/10.3390/land7010017">Dennis et al. 2017</a></li> <li>Licence: <a href="http://www.nationalarchives.gov.uk/doc/open-government-licence/version/3/">Open Government Licence (OGL)</a></li> <li>File name: GreaterManchester_GreenSpace_5m.tif</li> </ul> </li> </ul> <p> </p> <p><strong>Vancouver:</strong></p> <ul> <li>Digital Terrain Model (DTM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DTM_1m.tif<br> </li> </ul> </li> <li>Digital Surface Model (DSM): <ul> <li>Spatial Resolution: 1m</li> <li>Source: Canada’s Open Government Portal</li> <li>Licence: <a href="https://open.canada.ca/en/open-government-licence-canada">Open Government Licence - Canada</a></li> <li>File name: Vancouver_DSM_1m.tif<br> </li> </ul> </li> <li>Greenspace Mask: <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_GreenSpace_2m.tif<br> </li> </ul> </li> <li>Landuse <ul> <li>Spatial Resolution: 2m</li> <li>Source: Land Cover Classification 2014 - 2m LiDAR</li> <li>Licence: <a href="http://www.metrovancouver.org/data">Metro Vancouver</a></li> <li>File name: Vancouver_LULC_2m.tif</li> </ul> </li> </ul>
EXPLO. Sovjan 2021. Archaeological wood samples metadata
<p>Information on unit provenance, size, presence of sapwood, number of rings and species of the wood samples from the archaeological site of Sovjan, Albania, sampled in 2018 and presented in "<em>The Early Bronze Age dendrochronology of Sovjan (Albania): A first tree-ring sequence of the 24th – 22nd c. BC for the southwestern Balkans</em>", Maczkowski et al., 2021, DOI: <a href="http://dx.doi.org/10.1016/j.dendro.2021.125811">10.1016/j.dendro.2021.125811</a></p>
Multi-decade land use and land cover samples for Brazil based in a stratified sampling design and visual interpretation of Landsat data (1985 — 2018)
<p>This dataset is composed by 85,152 random points throughout the Brazilian territory selected according to a stratified sampling design, based in 127 regular regions and six slope classes (<a href="https://www.usgs.gov/centers/eros/science/usgs-eros-archive-digital-elevation-shuttle-radar-topography-mission-srtm-1-arc?qt-science_center_objects=0#qt-science_center_objects">SRTM</a>). Each sample was visually inspected by three independent interpreters, which associated all the land use and land cover (LULC) changes between 1985 and 2018, on a <strong>yearly basis</strong>, using as reference two <strong>Landsat</strong> images per year, a <strong>MODIS</strong> NDVI time series and high resolution images from <strong>Google Earth</strong>. </p> <p>This process was guided by a <a href="https://www.lapig.iesa.ufg.br/chave/">reference labeling protocol</a> which established the follow LULC classes:</p> <ul> <li><strong>Annual crop:</strong> Areas occupied with short to medium-term crops, usually with a vegetative cycle of less than one year, which after harvest needs to be re-planted. </li> <li><strong>Aquaculture:</strong> Artificial lakes, where aquaculture and/or salt production activities predominate</li> <li><strong>Beach and dune (Other):</strong> Sandy areas, with bright white color, where there is no vegetation predominance of any kind.</li> <li><strong>Forest formation:</strong> Vegetation types with predominance of tree species, with continuous canopy formation</li> <li><strong>Grassland formation:</strong> Grassland formations with predominance of herbaceous stratum</li> <li><strong>Mangrove (Other):</strong> Dense and Evergreen Forest formations, often flooded by tide and associated with the mangrove coastal ecosystem.</li> <li><strong>Mining (Other):</strong> Areas where clear signs of extensive mineral extractions are present, shows clear exposure of the soil by the action of heavy machinery. Only regions surrounding the AhkBrasilien (AHK) and the CPRM digital reference data were considered.</li> <li><strong>Not observed:</strong> Areas blocked by clouds or atmospheric noise, or with absence of ground observation masked out from analysis.</li> <li><strong>Other non-forest natural formations:</strong> Marshes (with fluvio-marine influence).</li> <li><strong>Other non-vegetated area (Other):</strong> Non-permeable surface areas (infrastructure, urban expansion or mining) not mapped into their classes</li> <li><strong>Pasture:</strong> Pasture areas, natural or planted, related with farming activity. In particular in the Pampa and Pantanal biomes part of the area classified as Grassland Formation also includes pasture areas.</li> <li><strong>Perennial crop:</strong> Areas occupied with crops with a long cycle (more than one year), which allow successive harvests without the need for new crop. </li> <li><strong>Rocky outcrop (Other)</strong>: Naturally exposed rocks without soil cover, often with the partial presence of rupicolous vegetation and high slope. </li> <li><strong>Salt flat (Other):</strong> "Apicuns" or Salt flats are formations often without tree vegetation, associated to a higher, hypersaline and less flooded area in the mangrove, generally in the transition between this area and the continent.</li> <li><strong>Savanna formation:</strong> Savanna formations with defined tree and shrub-herbaceous stratum</li> <li><strong>Semi-perennial crop:</strong> Cultivated areas with sugar cane</li> <li><strong>Tree plantation:</strong> Planted tree species for commercial use (e.g. Eucalyptus, Pinus and Araucaria)</li> <li><strong>Urban infrastructure:</strong> Urban areas with predominance of non-vegetated surfaces, including roads, highways and constructions.</li> <li><strong>Water:</strong> Rivers, lakes, dams, reservoir and other water bodies</li> <li><strong>Wetland:</strong> Wetlands with fluvial influence or swampy areas</li> </ul> <p>To enable a proper area estimation and accuracy assessment (<a href="https://www.tandfonline.com/doi/abs/10.1080/01431161.2014.930207">Stehman, 2014</a>) the dataset is provided with the <strong>sampling probability</strong> for each sample (<em>brazil_lulc_samples_1985_2018</em> and <em>brazil_lulc_samples_1985_2018_row_wise</em>) and the <strong>sampling weight</strong> (<em>brazil_lulc_samples_1985_2018_row_wise</em>), which was adjusted to disregard the "<strong>Not observed" </strong>class. The number of votes for the associated LULC class (visual interpretation agreement) and an indication if the sample is between two different LULC<strong> </strong>classes (<strong>border flag</strong>) are also provided.</p> <p>The samples were used to produce several <strong><a href="https://github.com/lapig-ufg/tvi-analysis">area estimation analyses</a></strong>, including land use and land cover dynamics, historical deforestation and agricultural expansion of Brazil. A publication describing in detail the methodology and the analysis is under preparation.</p>
Sampling-event dataset of short-term monitoring in Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines
<p>This is a sampling-event dataset of the short-term monitoring of Poblacion and Kadurong Reefs, two of the marine protected areas Municipality of Liloan, Cebu, Philippines. Water quality and ecological assessments were carried out to monitor the status and trends of biological and physical parameters associated with coral reefs using the standard protocols for surveying tropical marine resources. Specifically, the following measurements were conducted: (1) physico-chemical parameters, (2) phytoplankton and zooplankton occurrence and abundance, (3) fish occurrence and density, and (4) percent cover of benthic components of coral reef. The data can serve as the basis for the formulation and implementation of relevant measures for conservation and protection management of the Poblacion and Kadurong Reefs in Liloan, Cebu, Philippines. </p> <p>In this version, occurrence.csv was revised as described below:</p> <ul> <li>TaxonID for <em>Abudefduf vaigiensis</em> (Quoy & Gaimard, 1825) and <em>Hemiaulus</em> P.A.C. Heiberg, 1863 were corrected.</li> <li>Author names with corrupted characters/symbols were corrected. </li> </ul> <p> </p>
Taste responsiveness and liking of model food samples
<p>This data sheet consisted of children's taste intensity perception and liking to model food samples of grapefruit juice and vegetable broth. In addition, children's responses regarding PROP responsiveness, food familiarity, stated liking, food choice, and neophobia were also reported in this data set. </p>
Impact of the PSR J0740+6620 radius constraint on the properties of high-density matter : Weighted Monte Carlo samples for neutron star observables
<p>This data release contains weighted Monte Carlo samples associated with</p> <p>Legred, Chatziioannou, Essick, Han, and Landry, 2021</p> <p>"Impact of PSR J0740+6620 radius constraint on the properties of high-density matter"</p> <p>Phys. Rev. D 104, 063003;</p> <p>doi:10.1103/PhysRevD.104.063003</p> <p> </p> <p> </p> <p> </p> <p> </p>
Sample generalised Gross-Pitaevskii data for circulation statistics
<p>Sample dataset containing an instantaneous complex wave function field obtained from a three-dimensional generalised Gross-Pitaevskii simulation.</p> <p>The dataset is split into two files: one for the real part, and the other for the imaginary part of the wave function field <span class="math-tex">\(\psi(x, y, z)\)</span>.</p> <p>The dataset resolution is <span class="math-tex">\(256^3\)</span> grid points. The data is encoded as raw binary data, written in little-endian order, in double precision (64-bit floating point precision).</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.