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286 results for “mocking”
Dataset: Mock vs actual care
<p>Data attached to: Why is mock care not a good proxy for predicting hand contamination during patient care? <a href="https://doi.org/10.1016/j.jhin.2020.11.016">https://doi.org/10.1016/j.jhin.2020.11.016</a></p>
Mock Brick Stamp Test
Impression of a brick stamp found during the 2016 Cosa Excavations. (C16.15) The stamp is CIL XV.368. This mock-up was created with modelling clay and a carved bar of soap. I did not overlay the texture created with the model. Source: Objaverse 1.0 / Sketchfab
IMP simulated mock community data set
<p>This file contains the simulated mock (SM) metagenomic and metatranscriptomic dataset along with the original genomes used for simulation used within the article:</p> <p><strong>IMP: a reproducible pipeline for reference-independent integrated metagenomic and metatranscriptomic analyses</strong></p> <p>Shaman Narayanasamy<sup>†</sup>, Yohan Jarosz<sup>†</sup>, Emilie E.L. Muller, Cédric C. Laczny, Malte Herold, Anne Kaysen, Anna Heintz-Buschart, Nicolás Pinel, Patrick May, and Paul Wilmes<sup>*</sup></p> <p>Preprint: http://biorxiv.org/content/early/2016/02/10/039263</p> <p>The folder contains two subfolders (MG and MT), each containing the simulated metagenomic (MG) data and simulated metatranscriptomic (MT) data respectively. The files within these folders are in FASTQ format. The methods for generating these simulated data sets are described in the article.<br> </p> <p>The genomes and the resulting simulated metatranscriptomic data was generated and analysed within the article:</p> <p><strong>Comparison of assembly algorithms for improving rate of metatranscriptomic functional annotation</strong></p> <p>Albi Celaj, Janet Markle, Jayne Danska and John Parkinson; 2014; doi:10.1186/2049-2618-2-39</p> <p> </p> <p>It was provided upon request by the first author Albi Celaj, with permission to share the data. Please cite the aforementioned publication if this simulated metatranscriptomic data is used data is used.</p>
Code and data for RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project
<p>Model configuration code and post-processed data for simulations with SAM6.11.2 (Khairoutdinov and Randall, 2003) and CAM6 (https://github.com/ESCOMP/CESM/releases/tag/release-cesm2.1.3) needed to reproduce figures in the protocol paper for RCEMIP-II (Wing et al., 2023):</p> <p>Wing, A. A., Silvers, L. G., and Reed, K. A.: RCEMIP-II: Mock-Walker Simulations as Phase II of the Radiative-Convective Equilibrium Model Intercomparison Project, Geosci. Model Dev. Discuss. [preprint], https://doi.org/10.5194/gmd-2023-235, in review, 2023.</p> <p>SAM6.11.2 data (SAM6.11.2-lambda6000.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>crh_avg_$SST_$dT.mat: column relative humidity averaged over the short (y) dimension, as a function of x and time.</li> <li>mockwalker2048x128x74_3km_12s_$SST_$dT.nc: domain-averaged 0D (function of t) and 1D (function of z and t) data <ul> <li>The "long" simulations, which have a domain that is twice as long as normal, instead have files with names mockwalker4096x128x74_3km_12s_$SST_$dT.nc</li> <li>The "wide" simulations, which have a domain that is twice as wide as normal, instead have files with names mockwalker2048x256x74_3km_12s_$SST_$dT.nc</li> <li>The "longwide" simulations, which have a domain that is twice as long and twice as wide as normal, instead have files with names mockwalker4096x256x74_3km_12s_$SST_$dT.nc</li> </ul> </li> <li>SAM_CRM_MW_$SST_$dT_1D_cldfrac_avg.nc: domain cloud fraction profile (function of z and t) following cfv2 definition of Stauffer and Wing (2022)</li> </ul> </li> </ul> <p>SAM6.11.2 configuration files (SAM6.11.2-lambda6000-config.zip and SAM6.11.2-lambda6144.zip):</p> <ul> <li>lambda6000: simulations with wavelength 6000 km</li> <li>lambda6144: simulations with wavelength 6144 km</li> <li>Each simulation, for a given mean SST and delta SST has the following configuration files <ul> <li>snd: Initial sounding</li> <li>prm: Namelist parameters</li> <li>grd: Vertical grid</li> <li>domain.f90: Domain size and number of subdomains</li> <li>simpleocean.f90: SST specification</li> </ul> </li> </ul> <p>CAM6 data (CAM6.zip):</p> <ul> <li>Each simulation, for a given mean SST ($SST) and delta SST ($dT) has the following data files <ul> <li>MockWalk54_humidity_HCF_$dT_$SST.nc: column relative humidity averaged over 4 longitude points, as a function of latitude and time.</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rlut_avg.nc: domain-averaged longwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsut_avg.nc: domain-averaged upwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_0D_rsdt_avg.nc: domain-averaged downwelling shortwave flux at the top of the atmosphere</li> <li>CAM6_MockW_$dT_cos_$SST_3_yr_HCF_1D_cldfrac_avg.nc: domain-averaged cloud fraction profile (function of z and t)</li> </ul> </li> </ul> <p>CAM6 configuration files (CAM6-MW295dT1p25-config.tar, CAM6-MW300dT1p25-config.tar, CAM6-MW305dT1p25-config.tar): Contains model initialization and configuration files for simulations with delta SST = 1.25 K. Simulations with other delta SST values need only change the delta SST parameter. </p>
CGAII: Mock Data
<p>Mock datasets used in the article "Charting galactic accelerations II: how to 'learn' accelerations in the solar neighbourhood" by <a href="https://arxiv.org/abs/2112.07657">Naik, An, Burrage and Evans (2021)</a>.</p> <p>The code used to produce the five mock datasets is publicly available in the Github repository <a href="https://github.com/aneeshnaik/LocalFlows">aneeshnaik/LocalFlows</a>. The documentation there also gives further details about the contents of the datasets. As described there, the five directories here are to be downloaded, unzipped, and placed in the /data directory, e.g. LocalFlows/data/fiducial.</p>
RSV subgroup A direct RNA seq of infected MRC5 cells and mock infected MRC5 cells mRNA on nanopore
<p>Fastq files of mRNA sequenced by direct RNAseq on Oxford nanopore device and associated data for the paper</p>
Strainy: phasing and assembly of strain haplotypes from long-read metagenome sequencing - Real and mock datasets
<p>This repository contains the reads, assemblies, and references required to replicate the <strong>real and mock</strong> results presented in the paper: https://doi.org/10.1101/2023.01.31.526521</p>
In silico mock communities for evaluation of taxonomic profilers across prokaryotes and viruses
<p><em>In silico </em>mock communities generated with CAMISIM for benchmarking the performance of taxonomic profilers across prokaryotic (50 communities), eukaryotic (30 communities), and viral communities (10 communities) of the human microbiome. Metagenomes were generated using CAMISIM (Fritz et al., 2019), which simulates 2.1 Gb of Illumina 2 ×150 bp paired end reads with the default HiSeq 2500 error profile and a mean insert size of 200 bp. To assess profiling performance for a range of sequencing depths, the 50 <em>in silico</em> metagenomes were also rarefied with seqtk (-s100) to sequencing depths of 20, 5, 2, 1, 0.5, 0.25 and 0.1 million read pairs. Counts are provided for rarefied metagenomes.</p> <p><strong>Prokaryotic communities<br></strong>For prokaryotic benchmarking, 10 body site-representative prokaryotic metagenomes were simulated for each of the following five body sites: adult gut, infant gut, oral, skin, and vagina. Genome accession ids for prokaryotic species found in each human body site were identified from published literature (Bäckhed et al., 2015; Proctor et al., 2019; Saheb Kashaf et al., 2021). </p> <p>Adult Gut: pro_gut_adult.zip<br>Infant Gut: pro_gut_infant.zip<br>Oral: pro_oral.zip<br>Skin: pro_skin_1.zip, pro_skin_2.zip, pro_skin_3.zip<br>Vaginal: pro_vaginal.zip</p> <p>Downsized counts: </p> <p><strong>Eukaryotic communities<br></strong>30 eukaryotic <em>in silico</em> metagenomes comprising up to 200 randomly sampled genomes from a set of 113 eukaryotic species (See Supplementary Table 2 from the paper) corresponding to the eukaryotic species within both CHAMP and MetaPhlAn 4 (Blanco-Míguez et al., 2023) databases.</p> <p>Eukaryotic data is deposited here: <a href="https://doi.org/10.5281/zenodo.12090449" target="_blank" rel="noopener">doi: 10.5281/zenodo.12090449</a></p> <p><strong>Viral communities</strong><br>10 viral communities were simulated with 95% of the reads from bacteria and 5% of the reads originating from phages. Each community consisted of 200 randomly selected bacterial genomes from GTDB with species-level annotation and 200 viral genomes from the Gut Phage Database (GPD, Camarillo-Guerrero et al., 2021). </p> <p>Counts: phage_communities_counts.zip<br>FastQ, forward reads: camisimu_[1-10].fq.1.gz<br>FastQ, reverse reads: camisimu_[1-10].fq.2.gz</p> <p><strong>References</strong></p> <p>Bäckhed, F., Roswall, J., Peng, Y., Feng, Q., Jia, H., Kovatcheva-Datchary, P., et al. (2015). Dynamics and Stabilization of the Human Gut Microbiome during the First Year of Life. <em>Cell Host Microbe</em> 17, 690–703. doi: 10.1016/J.CHOM.2015.04.004</p> <p>Blanco-Míguez, A., Beghini, F., Cumbo, F., McIver, L. J., Thompson, K. N., Zolfo, M., et al. (2023). Extending and improving metagenomic taxonomic profiling with uncharacterized species using MetaPhlAn 4. <em>Nature Biotechnology 2023 41:11</em> 41, 1633–1644. doi: 10.1038/s41587-023-01688-w</p> <p>Camarillo-Guerrero, L. F., Almeida, A., Rangel-Pineros, G., Finn, R. D., and Lawley, T. D. (2021). Massive expansion of human gut bacteriophage diversity. <em>Cell</em> 184, 1098. doi: 10.1016/J.CELL.2021.01.029</p> <p>Fritz, A., Hofmann, P., Majda, S., Dahms, E., Dröge, J., Fiedler, J., et al. (2019). CAMISIM: Simulating metagenomes and microbial communities. <em>Microbiome</em> 7, 1–12. doi: 10.1186/S40168-019-0633-6/FIGURES/5</p> <p>Proctor, L. (2019). Priorities for the next 10 years of human microbiome research. <em>Nature 2021 569:7758</em> 569, 623–625. doi: 10.1038/d41586-019-01654-0</p> <p>Saheb Kashaf, S., Proctor, D. M., Deming, C., Saary, P., Hölzer, M., Mullikin, J., et al. (2021). Integrating cultivation and metagenomics for a multi-kingdom view of skin microbiome diversity and functions. <em>Nature Microbiology 2021 7:1</em> 7, 169–179. doi: 10.1038/s41564-021-01011-w</p>
FRB Mock Catalog and Reproduction Package for "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"
<h3>Quickstart: FRB Mock Catalog</h3> <p>A simulated 1-day catalog of one-off FRBs, that allows users to access the FRB population without installing the entire frbpoppy package. Download and unzip 1_Day_FRB_Sky_on_Earth.txt.zip (175 MB). This human and machine readable file contains 3.5E6 FRBs that are brighter than 0.01 Jy ms, the best limit in one-off FRB detection currently. The simulated catalog is produced by the perfect telescope in frbpoppy, free of selection effects, that observed 4pi of sky for 24 hrs, with minimum detectable fluence 0.01 Jy ms, for the best-fit no-delay SFR model. This file can be read using the accompanying jupyter notebook "starting_with_mock_catalog.ipynb".</p> <p>If you use this, please cite Wang & van Leeuwen 2024 (A&A), <a href="https://doi.org/10.1051/0004-6361/202450673">https://doi.org/10.1051/0004-6361/202450673</a></p> <h3>Reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin"</h3> <p>ReproductionPackage.zip is a basic reproduction package for the paper "Birth and Evolution of Fast Radio Bursts: Strong Population-Based Evidence for a Neutron-Star Origin" by Wang & van Leeuwen (2024).</p> <p> * arXiv: [<a href="https://arxiv.org/abs/2405.06281">2405.06281</a>] <br> * DOI: [<a href="https://doi.org/10.1051/0004-6361/202450673">10.1051/0004-6361/202450673</a>] </p> <h3>Installation</h3> <p>First pull or download and `frbpoppy` from <https://github.com/TRASAL/frbpoppy>.<br>Then download `ReproductionPackage.zip` and extract it starting in the frbpoppy/ base directory.<br>The scripts to produce the Figures are found in folder `frbpoppy/tests/markov_chain_monte_carlo/`.<br>The data used for these Figures resides in folder `frbpoppy/data/populations/mcmc/`.</p> <h3>Software</h3> <p>The methods and software packages used to produce the results are listed in the paper (including links to the relevant publications and/or packages):<br> FRBPOPPY: <https://github.com/TRASAL/frbpoppy><br> TRASAL: <https://github.com/TRASAL></p> <h3>Raw Data</h3> <p>The data are publicly available at<br> https://www.wis-tns.org/</p> <p> </p>
Power spectrum and reduced bispectrum of halo number-count mock catalogs
<p>Power spectrum (left) and reduced bispectrum (right) computed from sets of halo number-count mock catalogs (of 80 realizations each ) obtained from the calibration of BAM using the TkWEB model as described in . First rows show the mean in each case. The second row shows the ratio of to the reference (RTR) and the third row shows the variance in the respective statistics. The shaded area in the second rows denote the $5\%$ deviation with respect to unity.</p>
Abundace of mock haloes
<p>Cumulative halo abundance as a function of virial mass Mvir (left column) and velocity dispersion σv (right column) obtained in different cosmic-web types.</p>
Mock datasets for MPoL tutorials and tests
<p>`*.npz` and `*.asdf` files containing visibilities are in the TMS format (opposite that of CASA).</p> <p>logo_cube.noise.npz visibilities have been rescaled such that data - model / sigma follows the expected Gaussian envelope.</p> <p>HD 143006 continuum visibilities have flagged outliers removed and weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.</p> <p>AS 209 continuum visibilities have been averaged across frequency and have their weights rescaled such that the data - model / sigma follows the expected Gaussian envelope, for each spectral window.</p>
2-point statistics covariance with fewer mocks: covariances from various datasets and results of the fits
<p>A set of covariances from random poissonian samples, LRG and ELG, and lognormal mocks, used for plots in the relevant publication.</p>
A catalogue of Galactic GEMS: Globular cluster Extra-tidal Mock Stars
<p>The Globular cluster Extra-tidal Mock Star (GEMS) catalogue contains data for simulated extra-tidal stars and binaries created via three-body dynamical encounters in globular cluster cores. Using the particle-spray code Corespray (presented in Grondin et al. 2023a), we provide data for extra-tidal single stars and escaped recoil binaries for 159 globular clusters in the Milky Way. Sky positions, kinematics, stellar properties and escape information are provided for all simulated stars. This online catalogue is associated with Grondin et al. (2023b), which has been submitted to MNRAS and is available on arXiv. A README.txt file contains information on how to access the catalogue and describes the parameters within. If you use the GEMS catalogue, please cite Grondin et al. (2023b).</p>
This Was a Multinational Study Comparing the Efficacy and Safety of Two Medicines , Solifenacin Succinate and Mirabegron Taken Together, or Separately, or a Mock Treatment (Placebo) in Subjects With S
ClinicalTrials.gov study NCT01972841. IPD Sharing: YES. Countries: 42. Publications: 1.
iTYSA GIS-based flood preparedness wireframe and mock-up demonstration
<p>This file contains three items. First, the complete wireframes in Adobe XD format for the development of a GIS-Based mobile application for flood risk preparedness. Second, a video presenting the potential look and functioning of the iTYSA flood preparedness app with a mock-up created by Abby Muricho Onencan. The mock-up was created with Adobe XD and the demonstration was created with an inbuilt functionality for Adobe XD to develop videos. Finally there is a readme file.</p>
Inscriptions from Bodhgayā, unpublished mock-up. British Museum 1897,0528,0.15.
<p>Alexander Cunningham, Inscriptions from Bodhgayā, unpublished mock-up. Probably prepared in 1891. British Museum 1897,0528,0.15.</p>
Fish mock community with 41 species from 13 orders
<p>Tissue extracts of 41 North American fish species were obtained from the Ministère des Forêts, de la Faune et des Parcs (Québec). The selected species were chosen to represent a diversity of families within the Actinopterygii, and to include some species known to be present at the Experimental Lakes Area. Muscle or fin tissue was extracted using Qiagen Blood and Tissue kits and equimolarised to 15ng/µl. Library preparation and next-generation sequencing (NGS) was performed by equimolarising the DNA and combining two replicate mock community libraries which were then PCR amplified and sequenced. Sequencing was conducted using 2x300bp Illumina MiSeq at the McGill University and Génome Québec Innovation Centre, Montréal.<br> </p>
Efficacy of metabarcoding for identification of fish eggs evaluated with mock communities
<p>There is urgent need for effective and efficient monitoring of marine fish populations. Monitoring eggs and larval fish may be more informative that traditional fish surveys since ichthyoplankton surveys reveal the reproductive activities of fish populations, which directly impact their population trajectories. Ichthyoplankton surveys have turned to molecular methods (DNA barcoding & metabarcoding) for identification of eggs and larval fish due to challenges of morphological identification. In this study we examine the effectiveness of using metabarcoding methods on mock communities of known fish egg DNA. We constructed six mock communities with known ratios of species. In addition we analyzed two samples from a large field collection of fish eggs and compared metabarcoding results with traditional DNA barcoding results. We examine the ability of our metabarcoding methods to detect species and relative proportion of species identified in each mock community. We found that our metabarcoding methods were able to detect species at very low input proportions, however levels of successful detection depended on the markers used in amplification, suggesting that the use of multiple markers is desirable. Variability in our quantitative results may result from amplification bias as well as interspecific variation in mitochondrial DNA copy number. Our results demonstrate that there remain significant challenges to using metabarcoding for estimating proportional species composition; however the results provide important insights into understanding how to interpret metabarcoding data. This study will aid in the continuing development of efficient molecular methods of biological monitoring for fisheries management.</p>
Mock visibilities produced from ALMA Logo Cube
<p>CASA measurement sets produced from the ALMA Logo FITS Cube (https://doi.org/10.5281/zenodo.4460127) using CASA simobserve. Noiseless and Noisey measurement sets provided as tarred files.</p> <p>Sampled with the Cycle 7 43-7 antenna positions, at the location of HD 163296, for 3600s.</p>
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