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404 results for “Reference Data”
Reference data used in pufferfish pre-print experiments
<p>This holds the reference data used in the pufferfish pre-print. This includes a specific version of the human transcriptome, the human genome, and a collection of microbial genomes. These references are also available from different public repositories, but these versions are uploaded here to ease reproducibility of the manuscript's tests, and because the references were pre-processed to replace ambiguous nucleotides with valid ones.</p>
Training data for 'Reference based RADSeq ' tutorial (Galaxy Training Material)
<p>The data provided here are part of a Galaxy Training Network tutorial that analyzes RAD-seq data from a study published by Hohelnlohe et al., 2010 (DOI:10.1371/journal.pgen.1000862) to identify and type single nucleotide polymorphisms (SNPs) in each of 100 individuals from two oceanic and three freshwater populations and thus estimate genetic diversity and differentiation among populations. </p>
Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)
<p>This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>)</p> <p>Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019. </p> <p>Number of sample sites: 500</p> <p>Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos. </p> <p><br>Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a></p>
Figureȱ 4.ȱ Graphȱ plottingȱ skullȱ max.ȱ widthȱ againstȱ skullȱ totalȱ lengthȱ withoutȱ rhamphothecaȱ forȱ Imperialȱ Woodpecker Campephilus imperialis and Ivory-billed Woodpecker C. principalis, using data from Table 1. in The conundrum of an overlooked skeleton referable to Imperial Woodpecker Campephilus imperialis in the collection of the Natural History Museum at Tring
Figureȱ 4.ȱ Graphȱ plottingȱ skullȱ max.ȱ widthȱ againstȱ skullȱ totalȱ lengthȱ withoutȱ rhamphothecaȱ forȱ Imperialȱ Woodpecker Campephilus imperialis and Ivory-billed Woodpecker C. principalis, using data from Table 1.
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated. in Synopsis of the Snakes of the Philippines A Synthesis of Data from Biodiversity Repositories, Field Studies, and the Literature
Reference map illustrating the major Philippine faunal regions as defined by the Pleistocene Aggregate Island Complexes (PAICs). Selected island groups, such as the Babuyans, Batanes, the Romblon Island Group (RIG), and the Sulu Archipelago, are also indicated.
Reference data for "Limitations of alignment-free tools in total RNA-seq quantification"
<p>This repository contains reference data for "Limitations of alignment-free tools in total RNA-seq quantification"</p>
[opendc-sc18-dataset] A Reference Architecture for Datacenter Scheduling: Data Artifacts
<p>This release contains the data artifacts of the paper A Reference Architecture for Datacenter Scheduling presented at <a href="https://sc18.supercomputing.org/">Supercomputing 2018</a></p> <p>For the paper, experiments have been run on the following traces:</p> <ul> <li><strong>Askalon (W-Eng)</strong> - <code>askalon_workload_ee</code></li> <li><strong>Chronos (W-Ind)</strong> - <code>chronos_exp_noscaler_ca</code></li> </ul> <p>Each of the directories for the traces have the following structure:</p> <ul> <li><strong>/setup.txt</strong><br> This text file describes the trace used for the experiment in addition to the amount of times the experiment was repeated and the amount of warm-up experiments.</li> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter used in the experiments. Each item represents the identifiers of the resource (here, CPU type) to use in the machine. The available CPU types are (1) Intel i7 (4 cores, 4100 MHz) and (2) Intel i5 (2 cores, 3500 MHz).</li> <li><strong>/trace</strong><br> This directory contains the trace used in the simulation. The trace is stored in the Grid Workload Format. See the <a href="http://gwa.ewi.tudelft.nl/">Grid Workload Archive</a> for more information.</li> <li><strong>/data/experiments.csv</strong><br> A CSV file containing information of all simulations that have been run on the OpenDC platform for this experiment.</li> <li><strong>/data/job_metrics.csv</strong><br> A CSV file containing metrics (NSL, JMS, etc.) for each job that ran during the simulations.</li> <li><strong>/data/stage_measurements.csv</strong><br> A CSV file containing timing measurements for the scheduling stages that ran during the simulations.</li> <li><strong>/data/task_metrics.csv</strong><br> A CSV file containing metrics for each task that ran during the simulations.</li> <li> <p><strong>/data/tasks.csv</strong><br> A CSV file containing information about the tasks (submit time, runtime, etc.) that ran during the simulations as extracted from the traces.</p> <p>Additionally, we describe the format of each data file in the associated metadata file.</p> </li> </ul> <p><strong>Hardware</strong></p> <p>The hardware used for running the experiments is a MacBook Pro with a 2,9 GHz Intel Core i7 processor and 16 GB 2133 MHz LPDDR3 internal memory.</p> <p><strong>Reproduction</strong></p> <p>This section describes the instructions for reproducing the paper results using a provided Docker image. Please make sure you have <a href="https://www.docker.com/">Docker</a> installed and running.</p> <p>For reproduction, you will run the following experiments:</p> <ul> <li><code>askalon_workload_ee</code><br> This is the large experiment of the paper and will take approximately 4 hours to complete similar hardware.</li> <li><code>chronos_exp_noscaler_ca</code><br> This is the smaller experiment of the paper and will take approximately 5 minutes to complete on similar hardware.</li> </ul> <p>The Docker image <a href="https://hub.docker.com/r/atlargeresearch/sc18-experiment-runner/"><code>atlargeresearch/sc18-experiment-runner</code></a> can be used for running the experiments. A volume can be attached to the directory <code>/home/gradle/simulator/data</code> to capture the results of the experiments.</p> <p>Make sure you have, in your current working directory, the following files:</p> <ul> <li><strong>/setup.json</strong><br> This JSON file describes the topology of the datacenter and can be found in this archive at <code>askalon_workload_ee/setup.json</code>.</li> <li><strong>/askalon_workload_ee.gwf</strong><br> This file contains the trace for the Askalon workload. This file can be found in the archive at <code>askalon_workload_ee/trace/askalon_workload_ee.gwf</code>.</li> <li><strong>/chronos_exp_noscaler_ca.gwf</strong><br> This file contains the trace for the Chronos workload. This file can be found in the archive at <code>chronos_exp_noscaler_ca/trace/chronos_exp_noscaler_ca.gwf</code>.</li> </ul> <p>Then, you can start the Askalon experiments as follows:</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/askalon_workload_ee.gwf </code></pre> <p>The experiment runner can be configured with the following options</p> <ul> <li><strong>-r</strong>, <strong>--repeat</strong><br> The amount of times to repeat an experiment for each scheduler.</li> <li><strong>-w</strong>, <strong>--warm-up</strong><br> The amount of times to warm-up the simulator for each scheduler.</li> <li><strong>-p</strong>, <strong>--parallelism</strong><br> The number of experiments to run in parallel.</li> <li><strong>--schedulers</strong><br> The list of schedulers to test, separated by spaces. The following schedulers are available: <code>SRTF-BESTFIT</code>, <code>SRTF-FIRSTFIT</code>, <code>SRTF-WORSTFIT</code>, <code>FIFO-BESTFIT</code>, <code>FIFO-FIRSTFIT</code>, <code>FIFO-WORSTFIT</code>, <code>RANDOM-BESTFIT</code>, <code>RANDOM-FIRSTFIT</code>, <code>RANDOM-WORSTFIT</code>.</li> </ul> <p>After the Askalon experiments have been finished, you can start the Chronos experiments. <strong>Make sure</strong> you have a copy of the result files in your directory as the result files will be overwritten.</p> <pre><code>$ docker run -it --rm -v $(pwd):/home/gradle/simulator/data atlargeresearch/sc18-experiment-runner -r 32 -w 4 -s data/setup.json data/chronos_exp_noscaler_ca.gwf </code></pre>
Viral reference data for PathoLive
<p>Viral reference data for PathoLive including GI numbers and taxonomic information per sequence. Data taken from the viral part of the NCBI RefSeq downloaded on 2016-07-06.</p>
DIY Particle Detector: reference measurement data
<p>This dataset is released along the data analysis source code of <a href="https://github.com/ozel/DIY_particle_detector">github.com/ozel/DIY_particle_detector</a> and <a href="https://doi.org/10.5281/zenodo.3361755">10.5281/zenodo.3361755</a>.</p> <p>Since the binary files are large, they are not part of the github archive (but stored via github's LFS feature for large files).</p> <p>The files are in python's pickle format, created using python version 3.6.5, pandas module version 0.24.1 and numpy module version 1.14.3. </p> <p>The measurements are taken with the diode-based detector design detailed in the repository above besides the following two files, which are recorded with iPadPix (<a href="https://doi.org/10.1088/1748-0221/11/11/C11032">https://doi.org/10.1088/1748-0221/11/11/C11032</a>) for comparison:<br> 3hoursRadonBalloon_2019-02-10_14-43-21___2321___2-56.pkl<br> KCL_block_bare_2019-02-11_20-54-41___1083___1-03.pkl</p> <p> </p> <p> </p> <p> </p>
EEG data for generating reference rdFC patterns and sample rdFC patterns
<p>The correlation structure embedded in scalp EEG data is explored through three representative reference rdFC patterns which can be generated from the data in referenceEEG.txt. Three sample EEG data files, each of a triplet of electrodes, serve as examples for comparison with the reference rdFC patterns.</p>
FIGURE 3. 3.1 von Mises stresses, 3.2 von Mises strains, 3.3 Displacements, 3.4 von Mises stress relationship with reference value, 3.5 von Mises strain relationship with reference value and 3.6 in Insights into the controversy over materials data for the comparison of biomechanical performance in vertebrate
FIGURE 3. 3.1 von Mises stresses, 3.2 von Mises strains, 3.3 Displacements, 3.4 von Mises stress relationship with reference value, 3.5 von Mises strain relationship with reference value and 3.6 Displacement relationship with reference value in front of variation in the elastic modulus (E) in points P and Q.
FIGURE 5. 5.1 von Mises stresses, 5.2 von Mises strains, 5.3 Displacements, 5.4 von Mises stress relationship with reference value, 5.5 von Mises strain relationship with reference value and 5.6 in Insights into the controversy over materials data for the comparison of biomechanical performance in vertebrate
FIGURE 5. 5.1 von Mises stresses, 5.2 von Mises strains, 5.3 Displacements, 5.4 von Mises stress relationship with reference value, 5.5 von Mises strain relationship with reference value and 5.6 Displacement relationship with reference value in front of variation in the elastic modulus (E) in points P and Q.
Data from multi-sensor devices and reference station to monitoring urban air quality
<p>Data from electrochemical and optical sensors.</p> <h3>Files names</h3> <ul> <li>ECT01, ECT02, ECT06, ECT07 = device name</li> <li>ISSEP = reference station <ul> <li>"c" = calibration data</li> <li>"v" = validation data</li> </ul> </li> </ul> <h3>Variable description</h3> <table> <tbody> <tr> <td><strong>Electrochemical sensor</strong></td> <td><strong>Optical sensor</strong></td> <td><strong>Probe</strong></td> <td><strong>Reference</strong></td> </tr> <tr> <td> <p>AE = auxiliary electrode (mV)</p> <p>WE = working electrode (mV)</p> <p>N = temperature correction </p> <ul> <li>ch0 = CO sensor</li> <li>ch1 = OX sensor</li> <li>ch2 = NO2 sensor</li> <li>ch3 = NO sensor</li> </ul> </td> <td> <p>PM1, PM2.5 and PM10 in µg/m³</p> </td> <td> <p>Prs_mbar = pressure (mbar)</p> <p>Temp = temperature (°C)</p> <p>RH = relative humidity (%)</p> </td> <td> <p>DV30 = wind direction @ 30m (°)</p> <p>HR = relative humidity (%)</p> <p>NO, NO2, O3, PM10 and PM2.5 (µg/m³)</p> <p>Precipita = precipitation (mm)</p> <p>TC3 = temperature @ 3m (°C)</p> <p>VV30 = wind speed @ 30m (m/s)</p> </td> </tr> </tbody> </table> <p> </p>
Mirror of data from NOAA U.S. Climate Reference Network for Research Computing in Earth Science
<p>This is a mirror of data from the NOAA U.S. Climate Reference Network (https://www.ncei.noaa.gov/products/land-based-station/us-climate-reference-network).</p> <p>It was created because outbound FTP access is not allowed from some cloud-based JupyterHub setups.</p>
CheckM v1 reference data
<p><strong>Precalculated data files for CheckM v1.<br> <br> Manuscript:</strong><br> CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes.<br> https://genome.cshlp.org/content/early/2015/05/14/gr.186072.114</p> <p><strong>Installation instructions:</strong><br> https://github.com/Ecogenomics/CheckM/wiki/Installation#how-to-install-checkm.</p>
WorldCereal open global harmonized reference data repository (CC-BY-SA licensed data sets)
<p>Within the<strong> ESA funded</strong> WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY-SA license or a license similar to CC-BY-SA. See document "_In-situ-data-World-Cereal - license - CC-BY-SA.pdf" for an overview of the original data sets.</p>
WorldCereal open global harmonized reference data repository (CC-BY licensed data sets)
<p>Within the <strong>ESA funded </strong>WorldCereal project we have built an open harmonized reference data repository at global extent for model training or product validation in support of land cover and crop type mapping. Data from 2017 onwards were collected from many different sources and then harmonized, annotated and evaluated. These steps are explained in the harmonization protocol (10.5281/zenodo.7584463). This protocol also clarifies the naming convention of the shape files and the WorldCereal attributes (LC, CT, IRR, valtime and sampleID) that were added to the original data sets.</p> <p>This publication includes those harmonized data sets of which the original data set was published under the CC-BY license or a license similar to CC-BY. See document "_In-situ-data-World-Cereal - license - CC-BY.pdf" for an overview of the original data sets. </p>
BAM reference data: results of ASTM E139 -11 creep tests on a reference material of Nimonic 75 nickel-base alloy
<p>Results of creep tests on a certified reference material at T = 600°C and a tensile creep load of 160 MPa are provided. The raw data are available in ASCII format (*.lis files). <br> The file "Inhalt_Content_V1.1.pdf" contains further information about the files provided.<br> The evaluated results include the times to reach 2% and 4% creep strain, respectively, and the creep rate after 400 h.</p> <p>The tests were carried out in an accredited test laboratory. The calibrations of all measurands and test and measuring equipment are documented. The calibrations meet the requirements of the test procedure and are metrologically traceable.</p>
Reference data from the Pathomove simulation, for the manuscript "Novel pathogen introduction triggers rapid evolution in animal social movement strategies"
<p>This is a reference dataset of multiple runs of the 'Pathomove' simulation, to accompany the manuscript "Novel pathogen introduction rapidly alters the evolution of movement, restructuring animal societies". The datasets are in the form of R data objects saved as Rds files.</p> <p>This version of the data is intended to accompany a resubmission to <em>eLife</em>.</p>
Large-eddy simulation source code and data for (LS)2D reference publication in JAMES.
<p>This archive contains the MicroHH large-eddy simulation source code, the (LS)2D source code, and all simulation input and statistics, used for the publication:</p> <p><em>"The Benefits and Challenges of Downscaling a Global Reanalysis with Doubly-Periodic Large-Eddy Simulations" </em>by B.J.H. van Stratum et al.</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.