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1,582 results for “manuscript”
Supplemental files for the manuscript "Chromosome assembly of large and complex genomes using multiple references"
<p>This archive contains supplemental files for the manuscipt: "Chromosome assembly of large and complex genomes using multiple references".</p> <p>It contains assemblies generated by Ragout and RACA as well as evaluation scripts that were used in our analysis.</p> <p>Each subdirectory contains an additional README file with details.</p> <p>Please note that some intermediate files were deleted in the interest of saving space. If you need access to those files or having issues with reproducing our results, don't hesitate to contact Mihkail Kolmogorov: fenderglass@gmail.com</p>
Data for the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'
<p> This is the data used by the manuscript named 'Soft X-ray imaging of the magnetosheath and cusps under different solar wind conditions: MHD simulations'.</p> <p> The uploaded data is the X-ray intensity data for all the five cases studied in the manuscritpt. 'Casen' (n=1, 2, 3, 4, and 5) in the name of each data file indicates the case number, and 'sat pointX' (X=A, B, C, D) show the satellite positions analyzed in the manuscript. </p> <p> The data can be read by IDL using the following program statments:</p> <p>openr,lun,datai,/get_lun<br> xgse=0. & ygse=0. & zgse=0.<br> readf,lun,xgse,ygse,zgse ;;;;(satellite position in the GSE coordinate)<br> xsat=0. & ysat=0. & zsat=0.<br> readf,lun,xsat,ysat,zsat ;;;;(satellite position in the GSM coordinate)<br> xpoint=0. & ypoint=0. & zpoint=0.<br> readf,lun,xpoint,ypoint,zpoint ;;;;(satellite pointing of SXI, aim point)<br> nthtmax=0L & nphimax=0L<br> readf,lun,nthtmax,nphimax ;;;;(number of the tht and phi grids)<br> thti=fltarr(nthtmax) & phii=fltarr(nphimax)<br> readf,lun,thti,format='(e14.6)' ;;;;(the tht grids)<br> readf,lun,phii,format='(e14.6)' ;;;;(the phi grids)<br> Pxraytp=fltarr(nthtmax,nphimax)<br> readf,lun,Pxraytp,format='(e14.6)' ;;;;(X-ray intensity)<br> close,lun<br> free_lun,lun</p>
Supporting dataset for GimmeMotifs manuscript and analysis
<p>Dataset to reproduce analysis. Details can be found at https://github.com/vanheeringen-lab/gimme-analysis/tree/master/cluster_motifs</p>
Data for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"
<p><strong>Data repository for manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing"</strong></p> <p>Here we describe the data stored in this archive, which has been used for the manuscript "Mechanisms and Impacts of a Partial AMOC Recovery Under Enhanced Freshwater Forcing", submitted to Geophysical Research Letters. This data archive includes two primary folders, one containing the CESM data and one containing the 2D model data. Within each of those folders are appropriately titled subfolders for the different model simulations and variables, in accordance with the descriptions provided in the manuscript. The following provides a description of the data within each folder: </p> <p><br> ----------------------------------------<br> ------------- CESM DATA --------------<br> ----------------------------------------<br> The CESM data folder has been split into four subfolders, one for each simulation: Control simulation (CESM_controlrun), 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun), the longer repeat of the 0.1 Sv freshwater flux simulation (CESM_0pt1Sv_FWFrun_repeat), and the 0.15 Sv freshwater flux simulation (CESM_0pt15Sv_FWFrun). Within each of these can be found folders for each of the 6 variables we have used: 4D temperature fields (TEMP), 4D salinity fields (SALT), 4D potential density fields (PD), 3D MOC fields (MOC), 3D mixed layer depth fields (HMXL), and 2D meridional heat transport fields (N_HEAT). Each CESM data file (one per monthly time step) is saved in netcdf format (.nc4), containing all appropriate dimensional data and descriptive meta data. Note that only the data used in the manuscript has been stored (e.g. only MOC data is provided for the CESM_0pt15Sv_FWFrun, while all fields have been provided for the CESM_0pt1Sv_FWFrun). </p> <p>----------------------------------------<br> ------------ 2D MODEL DATA -----------<br> ----------------------------------------<br> As described in the manuscript, the 2D model has been run using various choices of input parameter and freshwater flux. For each selection of parameter choice a new control run is first required, from which a set of experiments is then initiated. The 2D model data folder therefore contains 2 main subfolders, one containing the control run data for each of set of parameter choices (control_runs),and one containing the freshwater perturbation experiments for each set of parameter choices (fwp_change_exps). Within those, each control and experiment folder is titled according to the choices of vertical viscosity (kvd), Southern Ocean wind stress perturbation (txp), and the freshwater perturbation (fwp); when absent from the title, the default value for that variable is used (i.e. kvd=1e4 m2/s; txp=0.2 N/m2). The naming convention for the Southern Ocean wind stress is minpt07 for 0.13 N/m2 (i.e. minus 0.07) and pt07 for 0.27 N/m2. </p> <p>Model output data has been provided in ascii format for: the 2D Eulerian AMOC fields (MOC_EUL.dat.dat), 2D Quasi-Lagrangian fields (MOC_QLag.dat), 2D Salinity fields (salt2D.dat), 2D temperature fields (temp2d.dat), 2D density fields (rho.dat), the latitude values (ytdeg.dat; ydeg.dat) and depth values (zt.dat, zw.dat), and the timeseries values for a number of variables (timeseries.dat): The timeseries.dat data has 19 columns, of which the first 13 are useful: </p> <p> column: description:<br> 1 time (year)<br> 2 mean basin temperature (deg.C)<br> 3 mean basin salinity (psu)<br> 4 mean basin density (kg/m3) <br> 5 kinetic energy (KE) density<br> 6 potential energy (PE)<br> 7 mean surface heat flux<br> 8 mean surface salinity flux<br> 9 minimum meridional overturning streamfunction (Sv)<br> 10 maximum meridional overturning streamfunction (Sv)<br> 11 minimum advective poleward heat transport (PW)<br> 12 maximum advective poleward heat transport (PW)<br> 13 MOC (Sv) = max(psi) in the north Atlantic</p> <p>The final subfolder in the 2D model directory is titled 'additional_data': The first folder contains the snapshot data (fwp025_snapshots; one subfolder for each snapshot) for the experiment run with 25 cm/s freshwater flux as shown in Fig. S6, in which the data is stored in the same format as described above. The second folder contains the tendency terms as shown in Fig. S7 (fwp025_tendency; one subfolder per tendency term for each of the control and 25 cm/s freshwater flux experiment), in which the data is provided in ascii format for the 2D tendency term and the depth and latitude values. </p>
Dataset used in manuscript: "Monolayer and thin h–BN as substrates for electron spectro-microscopy analysis of plasmonic nanoparticles "
<p>This file contains raw data for the manuscript:<br> "Monolayer and thin h–BN as substrates for electron spectro-microscopy analysis of plasmonic nanoparticles"<br> Tizei LHG et al, Applied Physics Letters 113, 231108 (2018).</p> <p>The data is electron energy loss spectroscopy (EELS) hyperspectral images of gold nanotriangles on different substrates.</p> <p>Data can be opened and manipulated using Hyperspy (www.hyperspy.org), Numpy and Matploplib libraries available in Python 3. The file formats used were HSPY (based HDF5 open standard) and MSA.</p> <p>Each folder contains the following data for all the triangles used in the manuscript:</p> <p>1) One annular dark field image of the triangle in HSPY format;<br> 2) One spectrum image aligned (the zero-loss speak is set to 0 eV) in HSPY format;<br> 3) Three spectra, one for each tip, already after deconvolution (20 steps using a home-made script in Digital Micrograph) in MSA format;<br> 4) The zero-loss spectrum used for the deconvolution of the data in MSA format;</p> <p>The file names have a specific format to facilite scripting:</p> <p>1) finishes with "Calibrated.hspy";<br> 2) finishes with "aligned.hspy";<br> 3) finishes with "TipX.msa" where X is 1, 2 or 3;<br> 4) finishes with "Summed.msa";</p> <p>Data acquisition parameters are described in the manuscript: Tizei LHG et al APL 113, 231108 (2018).</p>
The reference index files used for RNA-seq workflow benchmark in CWL-metrics manuscript
<p>The reference files used in the RNA-Seq workflow benchmark in the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
The output and the log files from RNA-Seq workflow benchmark for CWL-metrics manuscript
<p>The output files and log files generated by the workflow executions for RNA-Seq workflow benchmark by CWL-metrics, from the manuscript "Accumulating computational resource usage of genomic data analysis workflow to optimize cloud computing instance selection" (https://doi.org/10.1101/456756).</p>
Dataset manuscript "Does it Help to Feel your Body? Evidence is Inconclusive that Interoceptive Accuracy and Sensibility Help Cope with Negative Experiences
<p>In four studies (total <em>N</em> = 534), we examined the moderating impact of Interoceptive Accuracy (i.e., IAcc, as measured with the heartbeat counting task) and Interoceptive Sensibility (IS, assessed via questionnaire) on negative affect, following social exclusion or after receiving negative feedback. Results from an integrative data analysis combining the four studies confirmed that the manipulations were successful at inducing negative affect. However, no significant interaction between mood induction (control versus negative affect induction) and interoception on mood measures was observed, and this was true both for objective (i.e., IAcc) and subjective (i.e., IS) measures of interoception. Hence, previous conclusions on the moderating impact of interoception in the relationship between mood induction and self-reported mood were neither replicated nor generalized to this larger sample. We discuss these findings in light of theories of emotion regulation as well as recent concerns raised about the validity of the heartbeat counting task. </p>
Supplementary materials for the manuscript entitled "Comprehensive Identification, Phylogenetic Analysis and Expression Profiling of Multicopper Oxidase Genes in Maize"
<p><strong>Table S1. </strong>Gene IDs and names of maize and Arabidopsis MCOs</p> <p><strong>Figure S1.</strong> Phylogenetic tree of protein sequences of multicopper oxidase (MCO) of maize and Arabidopsis with rectangular layout. Branches are not cladogram-transformed. Shown in nodes are bootstrap support. The tree is polar layout, and branches are cladogram-transformed. Maize IDs are labeled in brown. Clades representative of<em> SKS</em>, <em>LAC</em> and <em>AAO</em> are labeled in blue, red and green, respectively. The tree is reconstructed by PhyML with 1000 bootstrap replicates.</p> <p><strong>Figure S2. </strong>Chromosome map depicting location of maize <em>MCO</em>s, with gene IDs shown in the map. Figure legend is the same as Figure 2.</p> <p><strong>Figure S3. </strong>Phylogenetic tree of maize and Arabidopsis <em>AAO</em>s. The trees are rectangular layout. The tree is reconstructed by PhyML with 1000 bootstrap replicates. Shown in nodes are bootstrap support.</p>
Data used in manuscript Spatial modelling of local-scale biogenic and anthropogenic carbon dioxide emissions in Helsinki
<p>This data set includes data used to develop and evaluate carbon dioxide emission modelling component in the Surface Urban Energy and Water balance Scheme (SUEWS). The data files are:</p> <ol> <li>CO2_Model_Parameter_Fitting.zip contains m-files (Matlab) used to calculate parameters for photosynthesis modelling <ul> <li>F_pho_data.mat includes meteorological and EC data used to fit photosynthesis model parameters in Kumpula</li> <li>FitKumpulaData.m calculates the model parameters in Kumpula</li> <li>FitViikkiData.m calculates the model parameters in Viikki</li> <li>Other m-files needed by the above two codes</li> </ul> </li> <li>Data.zip contains measured data used to develop and evaluate SUEWS <ul> <li>KumpulaData2012.txt and TorniData2012.txt include eddy covariance data measured at the two sites in Helsinki</li> <li>SMEARIII_meteorology_2016MM_30.m meteorological data used to fit model parameters in Viikki street trees (see 00 ReadMe_SMEARIII_Meteorology.TXT for details)</li> <li>Viikki_SWC_2016.txt measured soil moisture from Viikki in 2016</li> <li>Kumpula_2016_HH_RLAI6_Output.out is SPP output used to fit model parameters in Viikki street trees</li> </ul> </li> <li>SUEWS_EC_Site_Model_runs: SUEWS input and output files for Kumpula and Torni model runs</li> <li>SpatialRun_input.zip: SUEWS input files for the spatial model run</li> <li>spatmatHel_final.mat: SUEWS output files for spatial model run in mat-format</li> </ol>
Data for manuscript: The orbital anisotropy profiles of nearby globular clusters from Gaia Data Release 2
<p>We upload the data used in our paper here so that our results may be reproduced. We include the dataset of stars that survive our cuts, the profiles we plot, and the manual points selected as part of our CMD cut. See the paper for details. The first version of this paper is published on the arXiv with ID: arXiv:1903.11070. </p>
Source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice"
<p>This repository contains the source ELISA data for the manuscript "Restrained expansion of the recall germinal center response as biomarker of protection for influenza vaccination in mice" currently under review by PLOS ONE.</p> <p>It supports the following figures:</p> <p>Fig 4A: rHA ELISA data miniHA study.xlsx<br> Fig 4B: Competition ELISA data miniHA study.xlsx<br> S7 Fig: Competition ELISA data POC study.xlsx</p> <p> </p> <p>Files include Raw OD's per plate and reported values analysis. </p> <p> </p>
Simulation Results for CAUSE Manuscript
<p>Simulation results for evaluating CAUSE, an MR method</p> <p>https://www.biorxiv.org/content/10.1101/682237v3</p> <p>https://jean997.github.io/cause/simulations.html</p>
Data for "Why Study Gene Ratios" manuscript
<p>This upload includes the pre-processed data used for the "Why Study Gene Ratios" manuscript.</p>
Data file with manuscript titled 'A Structurally Validated Sequence Alignment of 497 Human Protein Kinase Domains'
<p>The files used in different analysis reported in the manuscript titled - 'A Structurally-Validated Multiple Sequence Alignment of 497 Human Protein Kinase Domains' are shared at two locations. Following is a brief description of these files.</p> <p>Location - https://github.com/DunbrackLab/Kinases<br> 1. HMM profile files - HMM files for each of the nine groups computed separately labeled as Groupname.hmm, like AGC.hmm<br> 2. HMM profile file - HMM file computed from the full alignment including all the sequences - Human-PK.hmm<br> 3. Score files - HMM scores of each kinase sequence against all the groupwise HMMs both for iteration1 (HMM-iter1-scores-tables.txt) and iteration2 (HMM-iter1-scores-tables.txt)<br> 4. Jalview session file - Kinase alignment with sequences colored by secondary structure information from PDB file if the structure is known; or predicted secondary structure if the experimental structure is not known. The file could be opened in Jalview - kinases-PDB-SSPred.jvp</p> <p>Location - https://zenodo.org/record/3445533<br> 1. The file contains list of residue pairs aligned in pairwise structural alignments of 272 human protein kinases which were used as a benchmark in the study. The alignments were created by FATCAT and optimized by SE program.</p>
Data accompanying the Dragomir et al 2019 manuscript
<p>Processed imaging data accompanying the Dragomir et al 2019 manuscript.</p> <p>The easiest way to read the files is using the JLD2 library in Julia (tested with Julia 1.2, see https://github.com/portugueslab/Dragomir-et-al-2019-modelfit)</p> <p>JLD2 files are compatible with HDF5, so they can be opened with libraries in other programming languages (e.g. h5py in Python)</p> <p>rois.zip contains all traces from all cells extracted from the imaging data</p> <p>rois_fit.jld2 contains the fitting results from all ROIs (also those excluded from visualization because of poor fit)</p> <p>mask_binary.jld2 is a mask from which it can be determined which coordinates are inside the brain</p>
Data set related to the manuscript "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors"
<p>Graphical files in the agr format for all the figures in the main text of the manuscript entitled "On the development of an original mesoscopic model to predict the capacitive properties of carbon-carbon supercapacitors" (<a href="https://doi.org/10.1016/j.electacta.2019.135022">10.1016/j.electacta.2019.135022</a>).</p>
Data set of manuscript entitled "Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense GNSS network during 2013–2016" submitted to the Journal of Geophysical Research: Solid Earth
<p>This data set was used for manuscript entitled “Spatiotemporal evolution of long- and short-term slow slip events in the Tokai region, central Japan, estimated from a very dense Global Navigation Satellite Systems (GNSS) network during 2013–2016” submitted to the Journal of Geophysical Research: Solid Earth. This data set includes 1 figure file, 1 station list and 26 numerical data files. Figure and numerical data are locations of GNSS stations and GNSS time series used in our submitted manuscript, respectively.</p> <p>Figure file maned “location_of_station.png” shows locations of GNSS stations used in our submitted manuscript. Blue dots denote a continuous GNSS network named GEONET was installed by the Geospatial Information Authority of Japan, and red triangles denote continuous GNSS stations constructed by the Japanese University Consortium for GPS Researchers (JUNCO) and operated by the Earthquake Research Institute at the University of Tokyo and allied universities.</p> <p>The coordinates of JUNCO station are collected in a file named “site_junco.bl”. Description of each column is as follows:</p> <p>1. Column 1: Longitude in degree.</p> <p>2. Column 2: Latitude in degree.</p> <p>3. Column 3: Station name.</p> <p>Numerical data is GNSS time series, corresponds to the corrected time series in our submitted manuscript, observed for the period between 1 January 2013 and 31 January 2016. A complete description of data set is found in our submitted manuscript. Description of each column is as follows:</p> <p> </p> <p>1. Column 1: Days since 31 December 2012.</p> <p>2. Column 2: East displacement in cm</p> <p>3. Column 3: North displacement in cm</p> <p>4. Column 4: Vertical displacement in cm</p> <p>5. Column 5: Standard deviation of east displacement in cm</p> <p>6. Column 6: Standard deviation of north displacement in cm</p> <p>7. Column 7: Standard deviation of vertical displacement in cm</p> <p> </p> <p>The numerical data in this data set includes only the 26 JUNCO stations data. Numerical data files are named by the regularity of the combination of the 4 characters station name and extension “.dat”.</p>
Supplementary data for the manuscript "Thermodynamic properties of isoprene and monoterpene derived organosulfates estimated with COSMOtherm"
<p>.cosmo and .energy files of various isoprene and monoterpene derived organosulfates and methyl bisulfate (neutral and deprotonated), IEPOX (neutral) and hydrated sodium (cation).</p>
Data set for the manuscript 'Studying the different coupling regimes for a plasmonic particle in a plasmonic trap'
<p>This repository includes data set and Matlab scripts, which support the manuscript entitled 'Studying the different coupling regimes for a plasmonic particle in a plasmonic trap', published in Optics Express. We include the data set necessary to reproduce the results of the paper in the 'RawData.zip' file. We provide Matlab scripts and functions in the 'PostProcessing.zip' file to process the raw data. We also attach HTML documents explaining the data and how we process them. </p> <p><strong>Raw data visualization with python.html</strong>: This is the first HTML file containing all the information to understand and visualize the raw data. It is generated by Jupyter Notebook, and it includes python scripts to visualize the raw data.</p> <p><strong>Post-processing raw data using Matlab.html</strong>: This is the second HTML file, which gives you a guideline to the data processing routines with the explanations of the Matlab scripts and functions. </p> <p><strong>RawData.zip</strong>: the data set used to produce the results in the manuscript. </p> <p><strong>PostProcessing.zip</strong>: Matlab scripts and functions for data post-processing.</p> <p><strong>python.zip</strong>: python files</p> <p>Note: This version update includes the additional data set for the revision of the manuscript. </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.