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9,113 results for “Sampling”
Sample FITS file with non-linear wavelength solution using a cubic spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a cubic spline model.</p> <p>This data is in its original shape.</p>
Sample FITS file with log linear wavelength solution
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a log linear wavelength solution.</p>
Sample FITS file with non-linear wavelength solution using a Legendre model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a Legendre model.</p> <p>This data is in its original shape.</p>
Sample FITS file with non-linear wavelength solution using a linear spline model
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a non-linear wavelength solution using a linear spline model.</p> <p>This data is in its original shape.</p>
Sample FITS file with linear wavelength solution
<p>This file was wavelength calibrated using IRAF and written to a FITS file using a linear wavelength solution which means it has been resample.</p>
Representative Sample Dataset for Resolution-Agnostic Tissue Segmentation in Whole-Slide Histopathology Images
<p>This is a representative sample from the dataset that was used to develop resolution-agnostic convolutional neural networks for tissue segmentation1 in whole-slide histopathology images.</p> <p>The dataset is composed of two parts: <strong>development set</strong> and <strong>dissimilar set</strong>.</p> <p>Sample images from the development set:</p> <ul> <li>breast_hne_00.tif</li> <li>breast_lymph_node_hne_00.tif</li> <li>tongue_ae1ae3_00.tif</li> <li>tongue_hne_00.tif</li> <li>tongue_ki67_00.tif</li> </ul> <p>Sample images from the dissimilar set:</p> <ul> <li>brain_alcianblue_00.tif</li> <li>cornea_grocott_00.tif</li> <li>kidney_cab_00.tif</li> <li>skin_perls_00.tif</li> <li>uterus_vonkossa_00.tif</li> </ul>
Simulated tumor and healthy sample
<p>This dataset provides tumor and corresponding healthy sample from a simulation of somatic indels and SNVs using Venters genome. The reference genome (UCSC hg18) used for CRAM compression is delivered as well. Simulation was done as described here: <a href="https://doi.org/10.1101/741256">https://doi.org/10.1101/741256</a>.</p>
Sample data for analysis of sequence variation in HIV
<p>These are downsampled interleaved paired fastq datasets from Jair et. 2019 (<a href="https://doi.org/10.1371/journal.pone.0214820">https://doi.org/10.1371/journal.pone.0214820</a>). The datasets were prepared by:</p> <ol> <li>Downloading original data from NCBI SRA (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA517147)</li> <li>Trimming contaminating Nextera adapters using trim-galore</li> <li>Mapping reads against nxb2 reference of HIV genome (K03455.1) with BWA MEM</li> <li>Restricting mapped reads to <em>pol</em> gene vicinity (K03455.1:2000-5100)</li> <li>Downsampling mapped data to ~10% of the original with Picard's DownsampleSam</li> <li>Converting BAM to Interleaved Fastq with Picard's SamToFastq</li> <li>Gzipping resultant interleaved paired fastq files</li> </ol>
A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples
<p>[This repository contains the source data for the manuscript "A reference library for Canadian invertebrates with 1.5 million barcodes, voucher specimens, and DNA samples" by deWaard et al., 2019. BioRxiv]</p>
Raw metagenomic data from sweep net samples collected in 2016 as part of the Slikok Creek Watershed Biotic Inventory
<p>We set out to inventory vascular plants, bryophytes, lichens, birds, arthropods, and earthworms on a grid of sites in the portion of Slikok Creek watershed that is on the Kenai National Wildlife Refuge, Kenai Peninsula, Alaska. Occurrence data, images, and field data sheets from this project are available via an <a href="https://arctosdb.org/">Arctos</a> project page at <a href="http://arctos.database.museum/project/10002227">http://arctos.database.museum/project/10002227</a>.</p> <p>This dataset includes the raw FASTQ files from metagenomic processing and associated collection data. Of the 160 sweep net samples collected, 125 were selected for High Throughput Sequencing and shipped to RTL Genomics (<a href="http://rtlgenomics.com">http://rtlgenomics.com</a>) for extraction and sequencing steps. Sequencing was performed on an Illumina MiSeq platform and reads were processed using RTL Genomics’ standard methods with the mlCOIlintF/HCO2198 primer set of Leray et al. (2013), yielding a 313 bp region of the COI gene.</p> <p>Collection data are included in the file <code>ArctosData_43C6167EB1.csv</code> downloaded from Arctos. Extraction methods and sequencing methods provided by RTL Genomics are included in the files <code>Bowser 4869.pdf</code> and <code>Illumina MiSeq Two-Step Method 454 profile only.docx</code>. Primers used are provided in the file <code>Bowser_4869M.txt</code>. The archive <code>FASTQ.zip</code> contains all of the resulting FASTQ files.</p> <p>These sequence data have also been been published to GenBank's Sequence Read Archive in accessions <a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454582">SRR10454582</a>–<a href="http://trace.ncbi.nlm.nih.gov/Traces/sra/?run=SRR10454706">SRR10454706</a> under BioProject <a href="http://www.ncbi.nlm.nih.gov/bioproject/PRJNA427721">PRJNA427721</a>.</p>
A Dataset of Global Land Cover Validation Samples
<p>A dataset of global land cover validation samples in 2015. In order to guarantee the confidence and objective of the validation samples, several existing reference datasets such as GLCNMO2008 training dataset, VIIRS reference dataset, STEP reference dataset, Global cropland reference data and so on, high resolution imagery in the Google earth and time-series NDVI,NDSI values of each related point are integrated to derive the global validation datasets. The dataset is provided in .shp format.</p>
CLDF Dataset derived from List's "Sample Size and Cognate Detection" from 2014
<p>Cite the source of the dataset as:</p> <blockquote> <p>List, Johann-Mattis (2014): Investigating the impact of sample size on cognate detection. Journal of Language Relationship. 11. 91-102. DOI: https://doi.org/10.31826/jlr-2014-110111</p> </blockquote>
Trait records sample: Carnivora traits from traitdb (ZIP of CSV files)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is going to similar to the previous set of CSV files, but with fewer tables and fewer columns, since it is derived from the neo4j traitdb instead of the whole back-end SQL database
Trait records sample: Carnivora traits (JSON tarball)
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This is a gzipped tar file containing a single .json file
Trait records sample: Carnivora traits tarball
This file is part of an API design process and is placed here for internal review. It contains trait records for Carnivora, and neighboring files.<p></p>This tarball contains CSV files for pages, traits, and metadata.
WageIndicator CBA Database - BARWAGE project sample
<p>The WageIndicator Collective Bargaining Agreements Database (CBA Database) includes the coded content of an increasing number of Collective Labour Agreements (CBAs or CLAs). These Collective Agreements have been gathered, annotated and coded by the WageIndicator team and its partners. The coding scheme has more than 700 variables. This dataset contains a subsample of the database, which was collected and analyzed in the context of the BARWAGE project. It includes 1224 coded collectie agreements from 10 European Union member states (Austria, Bulgaria, Czechia, Estonia, France, Italy, Netherlands, Portugal, Slovakia and Spain). </p> <p>Information about the CBA Database can be found <a href="https://wageindicator.org/labour-laws/collective-bargaining-agreements" target="_blank" rel="noopener">here </a>https://wageindicator.org/labour-laws/collective-bargaining-agreements</p> <p>Information about the BARWAGE project can be found <a href="https://wageindicator.org/Wageindicatorfoundation/projects/barwage" target="_blank" rel="noopener">here</a>: https://wageindicator.org/Wageindicatorfoundation/projects/barwage</p>
Data supporting publication: Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples
<p>This repository includes the data corresponding to the figures shown in the journal article entitled Nanoscale Mechanical Manipulation of Ultrathin SiN Membranes Enabling Infrared Near-Field Microscopy of Liquid-Immersed samples, published in small. </p>
Rayleigh Sample Outputs
<p>This zip file contains a sample Rayleigh output dataset generated by using the 'benchmark_diagnostics_input' file from the 'input_examples' directory. This is the same dataset that is needed to run the plotting example notebook Diagnostics_Plotting.ipynb, which is found in Rayleigh's post_processing directory. All data and python files necessary to run that notebook, as well as the notebook itself are contained in this zip file.</p> <p>Note that the latest release of the Rayleigh code can be found at <a href="../records/11391213">https://zenodo.org/records/11391213.</a></p> <p>Documentation can be found at <a href="https://rayleigh-documentation.readthedocs.io">https://rayleigh-documentation.readthedocs.io</a>.</p>
Hyperspectral data of Vigo sediment samples
<h2>Abstract</h2> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> <p>This depository contains data generated within the European S34 project.</p> <h2>Metadata Information</h2> <table> <tbody> <tr> <td> <p><strong>Identification</strong></p> </td> </tr> <tr> <td> <p>Full Title</p> </td> <td> <p>Hyperspectral data of Vigo sediment samples</p> </td> </tr> <tr> <td> <p>Abstract</p> </td> <td> <p>Reflectance and Radiance converted hyperspectral data of 9 sediment samples. The samples were collected by UPORTO and IGME from Vigo Campaign fieldwork in Sept 2023 but they were scanned at Ecotone lab in Trondheim by UHI in February 2024. The data was scanned for both dry and wet sediments.</p> </td> </tr> <tr> <td> <p>Keywords</p> </td> <td> <p>Reflectance estimated hyperspectral data, Radiance converted hyperspectral data, sediments, sand, mineral resource</p> </td> </tr> <tr> <td> <p>Pilot area</p> </td> <td> <p>Ria de Vigo</p> </td> </tr> <tr> <td> <p>Associated resources</p> </td> <td> <p>Report, photo</p> <p>Raw data: <a href="https://doi.org/10.5281/zenodo.13462199">https://doi.org/10.5281/zenodo.13462199</a></p> </td> </tr> <tr> <td> <p>Language</p> </td> <td> <p>English</p> </td> </tr> <tr> <td> <p>URL</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Categories</p> </td> <td> <p>Mineral resources</p> </td> </tr> <tr> <td> <p><strong>Temporal reference</strong></p> </td> </tr> <tr> <td> <p>Creation date (dd.mm.yyyy)</p> </td> <td> <p>3.04.2024</p> </td> </tr> <tr> <td> <p>Revision date (dd.mm.yyyy)</p> </td> <td> <p>3.04.2024</p> </td> </tr> <tr> <td> <p><strong>Quality and validity</strong></p> </td> </tr> <tr> <td> <p>Representation type</p> </td> <td> <p>Other</p> </td> </tr> <tr> <td> <p>Format</p> </td> <td> <p>HDF5</p> </td> </tr> <tr> <td> <p>Lineage</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Spatial resolution</p> </td> <td> <p>1.5m</p> </td> </tr> <tr> <td> <p>Positional accuracy</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Maintenance information</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Coordinate system</p> </td> <td> <p>EPSG 3035</p> </td> </tr> <tr> <td> <p><strong>Constranits related to access and use</strong></p> </td> </tr> <tr> <td> <p>Use limitation</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Access constraint</p> </td> <td> <p>/</p> </td> </tr> <tr> <td> <p>Public/Private</p> </td> <td> <p>Public</p> </td> </tr> <tr> <td> <p><strong>Responsible organisation</strong></p> </td> </tr> <tr> <td> <p>Responsible Contact</p> </td> <td> <p>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Responsible Party</p> </td> <td> <p>Ecotone AS</p> </td> </tr> <tr> <td> <p><strong>Metadata on metadata</strong></p> </td> </tr> <tr> <td> <p>Contact</p> </td> <td> <p>Ecotone AS (info@ecotone.com)</p> </td> </tr> <tr> <td> <p>Metadata language</p> </td> <td> <p>English</p> </td> </tr> </tbody> </table>
CT-Scan Image Dataset of Residual Fluid-Driven Fracture in a Molasse de Villarlod Sandstone Core - Post-Radial Hydraulic Fracture Experiment - M03 Sample
<h3><strong>Dataset Description</strong></h3> <p>This dataset contains high-resolution CT-scan images that capture the residual fracture surface within a core sample of Molasse de Villarlod Sandstone. The core sample was extracted after conducting a radial hydraulic fracture experiment on a 25 × 25 × 25 cm cubic block of sandstone (M03 sample). The experiment was designed to simulate fluid-driven fracture propagation and closure, and the resulting fracture path was preserved in the core sample.</p> <p><strong>Core Location in the M03 Cube Sample:</strong></p> <ul> <li><strong>Z:</strong> 12.5 cm</li> <li><strong>South-North:</strong> 12.5 cm</li> <li><strong>West-East:</strong> 11.5 cm to 1.36 cm (Coring direction)</li> </ul> <p>This spatial information specifies the exact location and orientation of the core extraction within the M03 cube sample.</p> <h4><strong>CT-scan instrument details:</strong></h4> <p>The M03 sample was analyzed using an X-ray micro-CT scanner (RX-Solutions Ultratom) under consistent scanning protocols and parameters. A reflective 230 kV microfocus X-ray source (Hamamatsu L10801) equipped with a 0.2 mm thick copper filter, a tungsten cathode, and a tungsten target was employed for the imaging process. The scans were conducted with a voltage of 120 kV and a current intensity of 80 mA.</p> <p>The volume data acquisition was performed in continuous helical mode, ensuring complete coverage of the sample’s height. For sample M03, 6 full rotations were executed, with 1312 projections captured for each 360° rotation, allowing for highly precise volume reconstruction. The X-ray beam attenuation was recorded by an XL Varex Paxscan 2530HE plane detector with a resolution of 2176 x 1792 pixels, and an exposure time of 0.50 seconds per projection.</p> <p>The acquired projections were processed using RX-Solutions X-act software with Filtered Backprojection to reconstruct a corrected volume. This reconstruction yielded approximately 9000 slices in 16-bit TIFF format, with voxel dimensions of 10 x 10 x 10 microns, providing detailed insights into the internal structure of the sample.</p> <h4><strong>Key Features:</strong></h4> <ul> <li> <p><strong>Fracture Characteristics</strong>: The fracture observed in the CT-scans represents a residual opening that remains post-fracturation. It is entirely contained within the core, showcasing the internal fracture geometry resulting from the hydraulic fracturing process.</p> </li> <li> <p><strong>CT-Scan Details</strong>: The CT-scans were taken perpendicular to the fracture surface, offering a detailed cross-sectional view of the fracture at different depths. This orientation is critical for accurately capturing the fracture morphology and allows for the reconstruction of the fracture surface in 3D.</p> </li> <li> <p><strong>Material Information</strong>: The core sample is composed of Molasse de Villarlod Sandstone, a sedimentary rock which is porous (18% porosity) and permeable. This material choice is relevant for studying fracture closure subjected to the leak-off of the fluid inside the porous medium.</p> </li> <li> <p><strong>Experimental Context</strong>: The radial hydraulic fracture experiment aimed to simulate the propagation of hydraulic fracture and its closure due to the leakage of fluid inside fracture into the porous medium. The dataset provides valuable insights into fracture propagation patterns, surface roughness, and the effects of fluid-driven fractures in porous media.</p> </li> </ul> <h4><strong>Applications:</strong></h4> <p>This dataset is particularly valuable for researchers and engineers involved in:</p> <ul> <li>Fracture mechanics and surface characterization</li> <li>3D reconstruction and visualization of fracture surfaces</li> <li>Surface roughness analysis</li> <li>Hydraulic fracturing studies</li> <li>Geomechanical modeling</li> </ul> <h4><strong>File Structure:</strong></h4> <p>The dataset is organized into zip-folder contains .tif images corresponding to different depths within the core. Each tif-image is a CT-scan for that specific depth, labeled according to their position along the fracture path.</p> <h4><strong>Processing code:</strong></h4> <p>Follow the <strong>URL repository</strong> in the software section to access to the code for processing these images and reconstructing the fracture surfaces.</p> <p><strong>Acknowledgment:</strong></p> <p>We would like to extend our deepest thanks to Gary Perrenoud, Albert Taureg, and Lionel Pittet, the technical specialists of the PIXE platform at École Polytechnique Fédérale de Lausanne (EPFL). Their expertise and support in operating the CT-scan machine were important to the success of this research. We greatly appreciate their dedication and the high-quality work they provided.</p> <p><strong>Contact and Support:</strong></p> <p>Email:</p> <p>Brice Lecampion: brice.lecampion@epfl.ch</p> <p>Mohsen Talebkeikhah: m.talebkeikhah@gmail.com</p>
ScienceDex guides
Understand access before you commit
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.