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9,113 results for “Sampling”
Stable Modeling on Resource Usage Parameters of MapReduce Application-Figure 7. Minimum sampling time of MapReduce applications on various response variable
<p>Pi application needs the smallest sampling time. The remaining applications need the similar minimum sampling time. Overall, the stable regression models on memory usage as response show the least need for sampling time. The results show that various applications have different minimum sampling time to get stable. The application which performs more read/write operations shows larger sampling time need.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 5. Image down sample
<p>Our proposal is to store each column and row bits count in a separate file and used that to reproduce the image using genetic algorithm.If we take 10% of an image size and the row and column image hamming bit count our total size will be approximately below 15% of the actual image size. We proposed a method to reproduce original image from using this 15% information.</p>
A Genetic Algorithm Approach to Regenerate Image from a Reduce Scaled Image Using Bit Data Count-Figure 8. Sample Data Extraction for a 20*20 size image
<p>As in figure 7 we are storing the extra data which is look like figure 8. Where a 20*20 size image of alphabet ‘A’ data has been stored. When we regenerate image, we are using these data.</p>
Fluid samples from gingival pockets
<p>Fluid samples from their deepest gingival pocket from a series of hospitalized neurosurgical patients undergoing preoperative dental screening (n=60). The patients were asked whether they brushed their teeth two times a day, once a day and less than every day. For further information or re-use please contact the authors.</p>
Sample ACC Data Set for LOFAR Station IE613 LBA whole sky observation
<p>This is a large sample of data from Station IE613 suitable for use with <a href="https://github.com/creaneroDIAS/beamModelTester">beamModelTester</a> and <a href="https://github.com/2baOrNot2ba/iLiSA">iLiSA</a>. To use with these systems, transfer it to a directory of the form :</p> <p><em>{STN_ID}_YYYYMMDD_HHMMSS_rcu{RCU_MODE}<em>dur{DURATION}</em>{SOURCE}_acc</em><br> e.g. IE613_20180406_091321_rcu3_dur85635_CasA_acc</p>
Randomly sampled coefficients for synchrotron radiative transfer in the Stokes basis, power law model, computed by rimphony, for consumption by neurosynchro
<p>This directory contains a training set of 22 million randomly-sampled radiative transfer coefficients generated by <a href="https://github.com/pkgw/rimphony/">rimphony</a>, suitable for use with the <a href="https://github.com/pkgw/neurosynchro/">neurosynchro</a> package. These coefficients can be used for numerical radiative transfer of synchrotron emission in the Stokes basis with a package such as <a href="https://github.com/jadexter/grtrans/">grtrans</a>.</p> <p>In this particular dataset, coefficients were computed using a model of a power law electron distribution isotropic in pitch angle. The input parameters, which were sampled randomly in a three-dimensional space, are:</p> <ul> <li><em>s</em>, the harmonic number, dimensionless, sampled logarithmically between 5 and 50,000,000.</li> <li><em>theta</em>, the angle between the ray path and the local magnetic field, measured in radians, sampled linearly between 0.001 and π/2 (namely, 1.5707963267948966).</li> <li><em>p</em>, the power-law index of the energetic electrons, dimensionless, sampled linearly between 1.5 and 7.</li> </ul> <p>The coefficients were computed on Harvard’s Odyssey cluster using Git commit <a href="https://github.com/pkgw/rimphony/commit/772161ebda0217b8c1ccb8ce3801ad9dc3701a4f">772161</a> of rimphony. A total of about 5,000 CPU hours were used, with 500 processes running for about 10 hours each. There are 2,748,835 data rows in total. The data are provided in their original format, split among 500 files, so that smaller subsamples of the data may be loaded easily. A README.md file provides more detailed information.</p>
Sacurine toy dataset - 2 samples / 2 blanks
<p><strong>Objective: </strong></p> <p>Influence of age, body mass index, and gender on the urine metabolome</p> <p><strong>Cohort: </strong></p> <p>183 employees from CEA</p> <p><strong>LC-HRMS: </strong></p> <p>LTQ-Orbitrap (negative ionization mode)</p>
Sample single frequency plots of various channels against Altitude and Azimuth for LOFAR single station
<p>Sample of outputs indicating the variation in stokes and linear polarisation channels for LOFAR single station</p>
Laboratory Dataset on Self-ignition of Biochar samples
<p>The file attached contains a complete set of experimental data from biochar self-heating ignition of cubic basket experiments for a range of feedstock materials (Softwood, Wheat pellets and rice husks) produced in a kiln reactor at temperatures ranging between 350 and 800 °C. The experiments are all oven-basket experiments, and were carried out in a thermostatically controlled oven with thermocouples for measuring the ambient and sample temperatures. The data reported includes the dates of experiments, volume of samples being tested, oven ambient temperature, reactor production temperature, bulk density of the sample and if the sample ignited or not. There are 3 worksheets included, one per feedstock type. This data is in support of the journal paper:</p> <p>F. Restuccia, O. Masek, R. M. Hadden, G. Rein, <strong>Quantifying self-heating ignition of biochar as a function of feedstock and </strong>the pyrolysis reactor temperature, <em>Fuel 236 (</em>2019), pp 201-213 <a href="https://doi.org/10.1016/j.fuel.2018.08.141">https://doi.org/10.1016/j.fuel.2018.08.141</a>.</p>
Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits
<p><strong>Introduction</strong></p> <p>These are the <em>Summary Level-data</em> as presented in:</p> <p>"Genome-wide association meta-analysis of 30,000 samples identifies seven novel loci for quantitative ECG traits". Eur J Hum Genet. 2019 Jan 24. doi: 10.1038/s41431-018-0295-z.<em> [Epub ahead of print]</em></p> <p>If you use these data please cite the corresponding manuscript, which can be downloaded here: <a href="http://em.rdcu.be/wf/click?upn=lMZy1lernSJ7apc5DgYM8eFz0euOx0-2B13Abimi4Sb0A-3D_2NNavOiAD9A7CPFnsa04dGla3sU002fLfkDtL-2FhGlad0GuoM-2B3OlDb0C5GiEhwIvtH7ba4KKF45ipTOFodx6CqvVvoP2GQ992sPGoV9ZPWIe04tUd8-2BGWey0In0TXPII5zK-2Bfp8Wk9TpEqEcSd-2BEmywqZc8o5TW4xGPXZqmchfUH8chy3P4SEtpzHXMG1LwsIYrKfwegqTXG85RAJPr-2B21Tk9SobtpvFs0frMkJ4ekKsl33ryoZfFPk1byjQunJYn4-2BB0iqMgGs6cXv0AOgAxg-3D-3D">https://rdcu.be/bh8mu</a>. When you have any questions or comments regarding this study or these files, please contact me via:</p> <p>Jessica van Setten, PhD | <em>Department of Cardiology, University Medical Center Utrecht, Utrecht University</em> | j.vansetten [at] umcutrecht [dot] nl</p> <p> </p> <p><strong>Files and description</strong></p> <p>There are four files available:</p> <ol> <li>RR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for RR interval</li> <li>PR_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for PR interval</li> <li>QT_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QT interval</li> <li>QRS_summary_Sept2018.txt.gz - gzipped file containing all the (unfiltered) meta-analysis results for QRS duration</li> </ol> <p>All these files have the same lay-out and are gzipped. The reference used for meta-analysis of GWAS was Genome of the Netherlands v4. </p> <ul> <li><em>SNP</em> - variantID (rsID), please note that few hundred variants do not have an rsID, but are NA instead. These can still be identified by chromosome and position.</li> <li><em>CHR</em> - chromosome numbers [1-22 and X].</li> <li><em>POS</em> - base pair position, hg19 / build37.</li> <li><em>CODED_ALLELE</em> - coded allele, <em>i.e.</em> the effect allele, as represented (and harmonized) across cohorts. Note that this is not necessarily the minor allele.</li> <li><em>NON_CODED_ALLELE</em> - the other allele, <em>i.e.</em> the non-effect allele.</li> <li><em>CODED_ALLELE_FREQ</em> - coded allele frequency, <em>i.e.</em> the effect allele frequency. Note that this is not necessarily the minor allele frequency.</li> <li><em>BETA</em> - beta from the fixed-effects model.</li> <li><em>SE </em>- standard error from the fixed-effects model.</li> <li><em>P </em>- P-value from the fixed-effects model.</li> <li><em>NEAREST_GENE</em> - the gene closest to the respective variant.</li> </ul> <p> </p>
Supporting data for "Snap happy: camera traps are an effective sampling tool when compared to alternative methods"
<p>Author recommendations and response ratios extracted from studies comparing camera traps to another survey method. These data underlie the analyses in a the journal article 'Snap happy: camera traps are an effective sampling tool when compared to alternative methods', published in the journal Royal Society Open Science (https://doi.org/10.1098/rsos.181748). </p>
A synthetic sample of short-cadence solar-like oscillators for TESS
<p>These are the simulated lightcurves for a synthetic sample of short-cadence solar-like oscillators as they might be observed by the Transiting Exoplanet Survey Satellite (TESS), as presented by Ball et al. (2018), "A synthetic sample of short-cadence solar-like oscillators for TESS". </p> <p>Each zip file contains the FITS lightcurves and mode data for stars observed in one sector, starting in the southern ecliptic hemisphere. The CSV file contains a selection of data contained in the FITS headers.</p> <p>For more detail, read the paper on <a href="https://arxiv.org/abs/1809.09108">arXiv</a>.</p> <p>Note that the FITS headers incorrectly report the units of the white noise level as <span class="math-tex">\(\mathrm{ppm}\)</span> rather than <span class="math-tex">\(\mathrm{ppm}\cdot\mathrm{hr}^{1/2}\)</span>. The white noise level excludes any systematic component, which should be added in quadrature if desired.</p>
Mocap gait motion samples - Optical marker trajectories
<p>=======================<br> <strong>Summary</strong><br> =======================<br> This database gathers a set of optical marker-based motion capture (mocap) tracking samples. Each sample corresponds to a single acquisition containing the 3D trajectories of a set of markers along a continuous interval of time.</p> <p>Keywords: Motion capture, mocap, Gait analysis, marker tracking.</p> <p>=======================<br> <strong>Data description</strong><br> =======================<br> Several persons wearing small reflective balls (markers) were asked to walk normally while recorded using motion capture cameras. These cameras, previously calibrated, are thought to detect the 2D pixel image position of the markers against the background thanks to IR lightning they are provided with. The XYZ position can be afterwards recovered by means of photogrammetric methods, whereas the tracking is kept across the frames. This data set contains the raw 3D trajectories of the aforementioned markers along the time.</p> <p>The set of markers is the layout proposed by Kadaba, Ramakrishnan, and Wootten, from the Helen Hayes Hospital. More details can be found in 'Dynamics of Human Gait' from Vaughan et al. Check out 'HH_marker_arrangement.png' for a visual description on marker placement.</p> <p>This database was built on purpose to serve as ground truth for the doctoral research "Cloud Point Labeling in Optical Motion Capture Systems", written by Juan L. Jiménez B. and supervised by Prof. Manuel Graña, from the Computational Intelligence Group, University of the Basque Country (UPV/EHU), and expected to be presented by the beginning of 2019.</p> <p>This work has been partially supported by the EC through project CybSPEED funded by the H2020 MSCA-RISE grant agreement Num. 777720.</p> <p>=======================<br> <strong>Equipment</strong><br> =======================<br> The following elements were taken to harvest the data:<br> - Six (6) motion capture cameras model S250e from Natural Point Inc. ( https://optitrack.com/ );<br> - Fifteen (15) passive-reflective optical markers;<br> - One computer running motion capture software from s.t.t.;<br> - A number of other minor accessories as camera wall mounts, Ethernet wires, adhesive tape, coffee and time.</p> <p>=======================<br> <strong>Technical details</strong><br> =======================<br> Database size: 71 recordings between 1.5 and 7 seconds long, summing up more than 20.000 frames<br> Exercise: regular gait motion at different paces<br> People: 14 different persons of different ages and shapes were recorded. No gait pathologies openly acknowledged.<br> Number of markers: 15 (Helen Hayes marker system)<br> Native camera resolution: 800x800 pixels<br> Sampling frequency: 100Hz<br> Cartesian units: millimeters<br> Right handed reference frame, +Y vertical upwards, ground height at Y=~0</p> <p>=======================<br> <strong>Data base format</strong><br> =======================<br> The database is composed by 71 different files, one per gait sequence. Each file is written in plain ASCII, comma separated values (CSV) format. Each line represents an instant of time (frame) containing the 3D Cartesian positions of all the 15 markers with to 2 mantissa digits and dot symbol as decimal separator. A triple 'nan nan nan' instead of actual numbers stands for marker occlusion and consequently no measured position can be given. The coordinates are numerically arranged as follows:<br> <br> X1; Y1; Z1; X2; Y2; Z2; ... Xi; Yi; Zi; ... X15; Y15; Z15;<br> <br> ... where 'i' refers to the i-th marker among the following list:</p> <p> 1- Right ASIS<br> 2- Left ASIS<br> 3- Sacrum<br> 4- Right Femoral wand<br> 5- Left Femoral wand<br> 6- Right Femoral epicondyle<br> 7- Left Femoral epicondyle<br> 8- Right Tibial wand<br> 9- Left Tibial wand<br> 10- Right Malleolus<br> 11- Left Malleolus<br> 12- Right Heel<br> 13- Left Heel<br> 14- Right Metatarsal head II<br> 15- Left Metatarsal head II</p> <p>=======================<br> <strong>Privacy statement</strong><br> =======================<br> The personal privacy of the volunteers, adults of 25 and over, remains protected since no confidential data has been collected at any time.</p> <p>The authors count on the express written consent of the company to use the gathered data in the scope of academic research and as ground truth for the aforementioned PhD Thesis, including its publication under the specified license terms.</p> <p><br> =======================<br> <strong>Further information</strong><br> =======================<br> If you find any mistake or experience any trouble in the interpretation of the data, please do not hesitate to contact us.</p> <p> </p>
SUEWS Sample Input Dataset
<p>Sample input dataset for SUEWS model v2018b</p>
Market Research - What's become... of new entrants in research workflows and scholarly communication? ('sample V2')
<p>Market Research - What’s become... of new entrants in research workflows and scholarly communication?</p> <p>Full report to be published as preprint on OSF Preprints shortly.</p> <p>To systematically create a list of the new entrants, a range of approaches and sources were used. This resulted in a long list (see: <a href="https://zenodo.org/record/2530048#.XDnfnFxKjIV">https://zenodo.org/record/2530048#.XDnfnFxKjIV</a>).</p> <p>The ‘long list’ was shortened to a sample of 120 independent for-profit startups through various filtering exercises, described in the full report. For the sample, three questions were investigated: 1. Did they still exist (independently) in 2018? 2. If so, how were they funded and how were they doing? 3. If they were acquired by 2018, by whom and when were they taken over?</p> <p>This file contains the ‘sample’ and answers to the research questions per 31 December 2018.</p>
Dataset of confocal microscopy stacks from plant samples - ImageJ SurfCut: a user-friendly, high-throughput pipeline for extracting cell contours from 3D confocal stacks
<p>This data set contains confocal stacks from <em>Arabidopsis thaliana </em><em>35S::GFP-MBD</em> light grown hypocotyl as well as propidium iodide stained cotyledon pavement cells and shoot apical meristem. This is the test dataset for the Fiji macro SurfCut (https://github.com/sverger/SurfCut; 10.5281/zenodo.2635737)</p> <p> </p> <p><strong>Material and methods:</strong></p> <p>Plant material and growth conditions</p> <p><em>Arabidopsis thaliana </em>wild type Col-0 and the microtubule reporter line <em>GFP-MBD</em> (WS-4, (Marc et al. 1998) were used. Seeds were cold treated for 48 hr to synchronize germination. Plants were then grown in a phytotron at 20°C, in a 16 hr light/8 hr dark cycle on solid Murashige and Skoog medium (MS medium, Duchefa, Haarlem, the Netherlands) with 0.8% agar, 1% sucrose, and no vitamin.</p> <p> </p> <p>Confocal microscopy</p> <p>Cell contour staining in the case of PC_PI_Col0_(1-8).tif and SAM_PI_Col-0.tif was performed by staining the cell wall with Propidium Iodide (PI). Plants were immersed in 0.2 mg/ml propidium iodide (PI, Sigma-Aldrich) for 10 min and washed with water prior to imaging. For imaging, samples were either placed on a solid agar medium and immersed in water, or placed between glass slide and coverslip separated by 400 μm spacers to prevent tissue crushing. Images were acquired using a Leica TCS SP8 confocal microscope, equipped with a water immersion objective (HCX IRAPO L 25x/0.95 W). PI excitation was performed using a 552 nm solid-state laser and fluorescence was detected at 600–650 nm. GFP excitation was performed using a 488 nm solid-state laser and fluorescence was detected at 495–535 nm. Stacks of 1024x1024 pixels (pixel size of 0.363 x 0.363 micron) optical section were generated with a Z interval of 0.5 μm.</p> <p> </p> <p><strong>File list:</strong></p> <p>Light grown hypocotyl, <em>GFP-MBD</em> reporter line:</p> <p>- Hypocotyl_GFP-MBD.tif</p> <p>Cotyledon’s pavement cells, PI staining:</p> <p>- PC_PI_Col0_1.tif</p> <p>- PC_PI_Col0_2.tif</p> <p>- PC_PI_Col0_3.tif</p> <p>- PC_PI_Col0_4.tif</p> <p>- PC_PI_Col0_5.tif</p> <p>- PC_PI_Col0_6.tif</p> <p>- PC_PI_Col0_7.tif</p> <p>- PC_PI_Col0_8.tif</p> <p>Shoot apical meristem, PI staining:</p> <p>- SAM_PI_Col-0.tif</p> <p> </p> <p><strong>Reference:</strong></p> <p>Marc, Jan, Cheryl L. Granger, Jennifer Brincat, Deborah D. Fisher, Teh-hui Kao, Andrew G. McCubbin, and Richard J. Cyr. 1998. “A GFP–MAP4 Reporter Gene for Visualizing Cortical Microtubule Rearrangements in Living Epidermal Cells.” <em>The Plant Cell</em> 10 (11): 1927–39. https://doi.org/10.1105/tpc.10.11.1927.</p>
Sample datasets for Transposon insertion sequencing analysis tutorial
<p>The dataset contains five files:</p> <ol> <li>Tnseq-Tutorial-reads.fastqsanger.gz - A subset of TnSeq reads published in `Santiago, M., Matano, L. M., Moussa, S. H., Gilmore, M. S., Walker, S., & Meredith, T. C. (2015). A new platform for ultra-high density Staphylococcus aureus transposon libraries. <em>BMC Genomics</em>, <em>16</em>(1), 1–18. http://doi.org/10.1186/s12864-015-1361-3`</li> <li>condition_barcodes.fasta - Set of barcodes to separate reads from different experimental conditions</li> <li>construct_barcodes.fasta - Set of barcodes to separate reads from different transposon constructs</li> <li>staph_aur.fasta : Genome file for <em>Staphylococcus aureus </em></li> <li>staph_aur.fasta : Annotation file for <em>Staphylococcus aureus </em></li> </ol>
Sample frequency list for use with BeamModelTeser
<p>This is a list of low-RFI frequencies at LOFAR Station SE607 for use with the sample observation in the beamModelTester tutorial.</p>
Open Access Finding Tools Study Sample
<p>We used five tools - Google Scholar, Lazy Scholar, Open Access Button, Unpaywall, and Kopernio - to search for open access versions of 1,000 articles to see how successful each one was. This dataset shows our results. See the ReadMe file for more detail.</p>
Single-crystal X-ray diffractometry data for a sample of NiCl₂-dppe collected on beamline I19-2 at Diamond Light Source
<p>Single-crystal X-ray diffractometry data for a sample of [1,2-Bis(diphenylphosphino)ethane]dichloronickel(II) (NiCl<sub>2</sub>-dppe, [(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>PCH<sub>2</sub>CH<sub>2</sub>P(C<sub>6</sub>H<sub>5</sub>)<sub>2</sub>]NiCl<sub>2</sub>).</p> <p>Data collected at Diamond Light Source I19-2 on 2015-05-18, publicly available for users to test data reduction routines. Data are known to produce good merging statistics and final refinements.</p> <p>The sample was prepared as follows:<br> Nickel chloride (II) hexahydrate (1 g, 2 mmol) was heated under vacuum to produce anhydrous nickel chloride (II) with a visible colour change from green to yellow. The resulting solid was taken up in ethanol (5 ml) and added to 1,2-bis(dimethylphosphine)ethane (dppe) (0.837 g, 2 mmol) in ethanol (10 ml). The solution was refluxed for 3 hour after which the solvent was evaporated. The small red crystals were purified by recrystallisation in acetone (70% yield).</p> <p>The sample was held at an approximate temperature of 150 K and the illuminating beam had a wavelength of 0.68890 Å (17.997 keV). The detector was held at 2θ = 25° throughout.</p> <p>Inventory of data:<br> <strong>010_Ni_dppe_Cl_2_150K01</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 160°.<br> <strong>010_Ni_dppe_Cl_2_150K02</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = 40°.<br> <strong>010_Ni_dppe_Cl_2_150K03</strong> — 130° ω scan, 0.4° images, 0.4s per image, 325 images; κ = 45°, φ = -80°.<br> <strong>010_Ni_dppe_Cl_2_150K04</strong> — 198° ω scan, 0.4° images, 0.4s per image, 495 images; κ = 0°, φ = -80°.</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.