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1,600 results for “input”
Input data for Coalispr
<p>Datasets to illustrate the use of <a href="https://coalispr.codeberg.page/README.html" target="_blank" rel="noopener">Coalispr</a>, a Python tool to clean up (small) RNA sequencing results. Coalispr can visualize over 100 bedgraphs in one panel and helps to retrieve read counts from associated alignment files without reliance on reference features like GTF annotations. The archives contain bedgraphs (also in processed form), reference data, bam-alignment files for counting, and descriptions for experiments, ncRNAs and genes.</p>
Input files and codes for Gasparini, Forte, and Barnhart, ESURF, 2024
<p>This repository contains all the input files or codes, depending on the model, that were used in the study described in Gasparini et al., (2024). There are three separate folders that contain the required files to run the Landlab, TTLEM, and CHILD numerical experiments. The code used to create all the figures is also contained in this repository. Finally, any initial grids required to run any of the codes are contained in the data folder.</p> <p>Gasparini, N.M., Forte, A.M., and Barnhart, K.R., 2024, Short Communication: Numerically simulated time to steady state is not a reliable indicater of landsdcape response time, Earth Surface Dynamics.</p>
FRACTESUS_UC_S355_T0_MCT_input
<p>Fractesus project. Fracture test mini-CT. Master curve input S355J2. UC. </p>
FRACTESUS_UC_S460_T0_MCT_input
<p>Fractesus project. Fracture test mini-CT. Master curve input S460M. UC. </p>
Idiothetic representations are modulated by availability of sensory inputs and task-demands in hippocampal-septal circuit
<p>This is the dataset underlying the results presented in the article titled: "Idiothetic representations are modulated by availability of sensory inputs and task-demands in hippocampal-septal circuit". Data is sub-divided per brain regions: CA1, CA3, and lateral septum (LS). Then each region is subdivided by mouse number, and finally by experiment (day). Each experiment contains two main files: ms.mat (miniscope data) and behav.mat (behavior data). These two streams are not sampled at the same frequency and should thus be interpolated during analysis (timestamps are available for both streams). Each .mat files can be opened with Matlab or with any HDF5 reader (e.g. in H5py Python).</p>
Data from: Riverine transport and nutrient inputs affect phytoplankton communities in a coastal embayment
<p>1. Rivers often transport phytoplankton to coastal embayments and introduce nutrients that can enrich coastal plankton communities. We investigated the effects of the Nottawasaga River on the nearshore (i.e., within 500 m of shore) phytoplankton composition along a 10 km transect of Nottawasaga Bay, Lake Huron in 2015 and 2016. Imaging flow cytometry was used to identify and enumerate algal taxa, which were resolved at sizes larger than small nanoplankton (i.e., > 5 mm). Multivariate analysis (perMANOVA and RDA) and a dilution model were used to examine how nutrients and the transport of algal taxa affected community composition in the bay.</p> <p>2. Sampling stations with different percentages of river water had significantly different phytoplankton communities. Phytoplankton community composition was also strongly associated with nutrients, including total phosphorus, which also varied with the percentage of river water. The majority of the 51 phytoplankton taxa identified in 2016 had numerical abundances in the bay that could be explained simply by the dilution of incoming river water.</p> <p>3. Phytoplankton transported from the river had a higher proportion of "edible-sized" cells (< 30 mm), particularly in summer when colonial cyanobacteria were numerically dominant in the bay. Six taxa were more abundant than expected from the dilution of river water and included some cyanobacteria with late summer maxima. Five of the taxa that were transported from the river were less abundant than expected in the bay.</p> <p>4. Whereas impacts of fertilization due to the characteristically higher nutrient concentration in the river are to be expected, the strong and highly correlated effects of transport within the narrow coastal band of this study largely concealed any distinct fertilization effects.</p> <p>5. Riverine inputs may strongly influence the near-shore assemblage of phytoplankton in oligotrophic embayments in large lakes, creating hotspots for productivity, species turnover and trophic dynamics.</p>
Thalamic input to motor cortex facilitates goal-directed action initiation
<p>Data set for: Takahashi N, Moberg S, Zolnik TA, Catanese J, Sachdev RNS, Larkum ME, Jaeger D (2021) Thalamic input to motor cortex facilitates goal-directed action initiation. <em>Current Biology</em> (DOI: <a href="https://doi.org/10.1016/j.cub.2021.06.089">https://doi.org/10.1016/j.cub.2021.06.089</a>) </p> <p>The file named "Takahashi_Data&Code.zip" is a zipped version of a folder "Takahashi_Data&Code", which contains the data analyzed in the study along with the Matlab code used to generate the published figures. To access the data and the code, first unzip the file. Then add the folder with subfolders to the Matlab path. Before running the code (e.g., "PlotData_Fig1.m"), load the related Matlab data file (e.g., "Data_Pharmacology_Fig1.mat") in Workspace. Each code plots the results used in the corresponding figure.</p>
EWilson_MITgcm_SPG_input_files_v001
<p>Upload of input files for MITgcm described in the following study:</p> <p>Wilson EA, Thompson AF, Stewart AL, Sun S. Bottom-up control of subpolar gyres and the overturning circulation in the Southern Ocean. Journal of Physical Oceanography. Submitted.</p>
The configurations, inputs and outputs of the EFDC model for all simulated episodes
<p>TAIHU EFDC.zip includes two file folders named 2015 and 2018. The 2015 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2015. The 2018 file folder is the configurations, inputs and outputs of the EFDC model for the numerical experiment named EFDC of 2018.</p>
Input Data for "Assembly and Analysis of Cell-Scale Membrane Envelopes"
<p>Input structures for a manuscript, along with selected output data and structures. This directory structure contains a cut-down copy of the directories used to generate the simulation data and the analysis. In order to make this fit into the 50GB Zenodo limit, it was constructed with the following tar command: `tar -zcvf protocellmodeling.tar.gz --exclude="*BAK" --exclude="*#" --exclude="*xtc" --exclude="*gro" --exclude="*trr" --exclude="*js" --exclude="*[0-9].out" --exclude="*old" --exclude="*dcd" --exclude="*tmp" --exclude="*xst" --exclude="*edr" --exclude="*state_prev.cpt" --exclude="*.o[0-9]*" cgDracula`, which intentionally excludes large files. The full 4.8TB dataset that includes trajectories is available upon request.</p> <p>The data is split into multiple subdirectories and largely undocumented, however here are the highlights:</p> <ul> <li>The <strong>Analysis</strong> subdirectory is where the analysis in the paper lives. All other directories are related to building or running systems.</li> <li><strong>getsources.py</strong> in the main directory is the script that downloads the initial structure from MemProtMD.</li> <li><strong>transform.py</strong> builds the initial protein models from MemProtMD.</li> <li><strong>vesiclebuilder.py</strong> builds the lipid ball.</li> <li><strong>protpatchplacer.py</strong> sets up the ultra-coarse grained simulation, which is in the <strong>supercg</strong> directory.</li> <li><strong>movepatches.py</strong> takes the results from the ultra-coarse grained simulation, and builds the protein ball.</li> <li><strong>gendx.tcl</strong> generates the density maps from the protein ball.</li> <li>This is used in <strong>lipids/picklipids.py</strong>, which cuts out the pieces of the lipid that need to be removed.</li> <li>The water is added to the system with <strong>addwater/quicksolvate.sh</strong></li> <li>The system is ionized by <strong>ionize.py</strong></li> <li>And a topology is written by <strong>writetop.py</strong></li> </ul>
The corticospinal tract primarily modulates sensory inputs in the mouse lumbar cord (Raw data)
<p>Raw data of the eLife 2021 article</p> <p><strong>The corticospinal tract primarily modulates sensory inputs in the mouse lumbar cord.</strong></p> <p>Authors:</p> <p><strong>Yunuen Moreno-Lopez<sup>1</sup>, Charlotte Bichara<sup>1</sup>, Gilles Delbecq, Philippe Isope, Matilde Cordero-Erausquin</strong></p> <p>Methods are described in the article. Data is organized by figure.</p>
Input Data for "Protein Function Prediction for newly sequenced organisms"
<p>The input sequence files in FASTA format and the detailed list of all organisms excluded when testing each specific bacterium.</p>
Simulation input files: Cargo Release from Non-enveloped Viruses and Virus-like nanoparticles: Capsid Rupture or Pore Formation
<p>Simulation input files, and code for modification to simulation software for article:</p> <p>Cargo Release from Non-enveloped Viruses and Virus-like nanoparticles: Capsid Rupture or PoreFormation</p> <p>Lukáš Sukeník,†,‡</p> <p>Liya Mukhamedova,†</p> <p>Michaela Procházková,†</p> <p>Karel Škubník,†</p> <p>Pavel Plevka,†</p> <p>and Robert Vácha∗,†,‡,¶</p> <p>†CEITEC – Central European Institute of Technology, Masaryk University, Kamenice753/5, 625 00 Brno, Czech Republic</p> <p>‡Department of Condensed Matter Physics, Faculty of Science, Masaryk University,Kotl ́aˇrsk ́a 267/2, 611 37 Brno, Czech Republic</p> <p>¶National Centre for Biomolecular Research, Faculty of Science, Masaryk University,Kamenice 5, 625 00 Brno, Czech Republic</p> <p>E-mail: robert.vacha@mail.muni.cz</p>
SeisSol input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake published in Ulrich et al. (2021)
<p>This dataset contains the input files of the dynamic rupture scenarios of the 2004 Sumatra-Andaman earthquake presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.<br> </p> <p><strong>supermucNG_launch_script.sh</strong>: batch script for running a dynamic rupture earthquake scenario on Supermuc NG (LRZ).<br> <br> <strong>parameters_base_slab2.par, parameters_stronger_slab2.par, parameters_weaker_slab2.par</strong>: main parameter file for the base (resp. stronger, resp. weaker sediments) scenario.<br> <br> <strong>Sumatra_material_base_slab2.yaml, Sumatra_material_stronger_slab2.yaml, Sumatra_material_weaker_slab2.yaml</strong>: easi/yaml files describing the rock elastic and visco-plastic properties for each scenario.<br> It calls <strong>Sumatra_rhomulambda.yaml</strong> for the rock elastic properties and <strong>Sumatra_initial_stress_slab2.yaml</strong> for the stress tensor spatial variations.<br> <strong>Sumatra_fault_slab2.yaml</strong>: easi/yaml file describing the spatially variable on-fault parameters for the 3 main scenarios.<br> <strong>lithostaticStress_gamma.yaml</strong>: easi/yaml file describing the variations with depth of the lithostatic pressure, and specifying the pore fluid pressure ratio.<br> <br> <strong>Sumatra_fault_slab2_1d.yaml, Sumatra_initial_stress_slab2_1d.yaml, Sumatra_material_base_slab2_1d.yaml</strong>: easi/yaml files specific to the alternative scenario, which adopts a 1D PREM velocity structure.<br> <strong>Sumatra_fault_slab2_novar.yaml, Sumatra_initial_stress_slab2_novar.yaml, Sumatra_material_base_slab2_novar.yaml</strong>: easi/yaml files specific to the alternative dynamic rupture earthquake scenario in which no regional prestress variations are considered.<br> <br> <strong>Sumatra_slab2_layers_fixed.xdmf, Sumatra_slab2_layers_fixed</strong>: mesh file.</p>
GeoClaw input files of the 2004 Sumatra-Andaman tsunami scenarios published in Ulrich et al. (2021)
<p>This dataset contains the input files of the GeoClaw scenarios of the 2004 Sumatra-Andaman tsunami presented in:</p> <p>Ulrich, T., Gabriel, A. A., Madden, E. H. (2021). Stress, rigidity and sediment strength control megathrust earthquake and tsunami dynamics. doi: 10.31223/osf.io/s9263.</p> <p><strong>runconverterLMU_WGS84.sh</strong>: contains all the steps to transform a SeisSol surface output to a Geoclaw tt3 file.<br> It uses the displacement converter of Samoa to rasterize a SeisSol surface output to NetCDF.<br> See <strong>README_build_displacement-converter.txt</strong> for the procedure to download and build the displacement converter.<br> Note that the SAMPLER (<a href="https://github.com/SeisSol/SAMPLER">https://github.com/SeisSol/SAMPLER</a>) will replace the displacement-converter in the future.<br> <br> <strong>convert_geographic_SeisSol_geom.py</strong>: to transform the geometry array of a SeisSol surface output file to the geocentric coordinate system (latitude, longitude).<br> <strong>tapperNetcdf.py</strong>: to apply a Hanning window on a NetCDF file. This prevents sharp displacement discontinuities at the limits of the region of imposed displacements, which could generate spurious waves.<br> <strong>convert_netcdf_tt3.py</strong>: to convert a NetCDF displacement file to the tt3 format (GeoClaw).<br> <br> The GeoClaw simulations require the following files:<br> <br> <strong>displacement_tt3_files.tar.gz</strong> : rasterized input files in tt3 format for 4 earthquake scenarios.<br> <strong>gebco_2019_n25.0_s-21.0_w55.0_e110.0.nc</strong>: input bathymetry and topography data in NetCDF format downloaded from https://www.gebco.net/.<br> <strong>setrun.py</strong> which defines the simulation parameters.<br> a Makefile, plateform specific see e.g. https://github.com/clawpack/geoclaw/blob/master/examples/tsunami/chile2010/Makefile<br> <strong>setplot_fig4.py</strong>: configures GeoClaw for generating outputs for figure 4. <br> <strong>setplot_animation.py</strong>: configures GeoClaw for generating outputs for the supplementary animations. <br> GeoClaw simulations are run with `make .plots`.</p>
Abundance decline in the avifauna of the European Union reveals global similarities in biodiversity change: Input datasets & species results
<p>This archive contain the two input datasets of bird population estimates and trend estimates underpinning the journal article: <strong>Abundance decline in the avifauna of the European Union reveals global similarities in biodiversity change. </strong>It also contains the species level results obtained from the Bayesian hierarchical model described in section 2.2.1 of the paper.</p>
Emergence and radiation of distemper viruses in terrestrial and marine mammals - Input files, bash and R codes for analysing PDV and CDV sequence data
<p><span>Canine distemper virus (CDV) and phocine distemper virus (PDV) are major pathogens to terrestrial and marine mammals. Yet little is known about the timing and geographical origin of distemper viruses and to what extent it was influenced by environmental change and human activities. To address this, we i) performed the first comprehensive time-calibrated phylogenetic analysis of the two distemper viruses; ii) mapped distemper antibody and virus detection data from marine mammals collected between 1972-2018; iii) and compiled historical reports on distemper dating back to the 18<sup>th</sup> century. We find that CDV and PDV diverged in the early 17<sup>th</sup> century. Modern CDV strains last shared a common ancestor in the 19<sup>th</sup> century with a marked radiation during the 1930s-50s. Modern PDV strains are of more recent origin, diverging in the 1970s-80s. Based on the compiled information on distemper distribution, the diverse host range of CDV and basal phylogenetic placement of terrestrial morbilliviruses, we hypothesize a terrestrial CDV-like ancestor giving rise to PDV in the North Atlantic. Moreover, given the estimated timing of distemper origin and radiation, we hypothesize a prominent role of environmental change such as the Little Ice Age, and human activities like globalisation and war in distemper virus evolution. </span></p>
Advanced Optimal Sensor Placement for Kalman-based multiple-input estimation
<p>Data set for journal publication "Advanced Optimal Sensor Placement for Kalman-based input estimation".</p>
Progeny Project Gromacs input and trajectories of a C12E6 surfactant model at the vacuum-water interface
<p>This is gromacs 2021.2 input and output for an all-atom C12E6 surfactant molecule at the water-vacuum<br> interface with TIP4P-ew and SPC/E water models.</p>
Thermodynamic model input files
<p>PerpleX (6.7.2) input files for P-X modelling of Mars mantle, olivine and orthopyroxene systems. Contains refined olivine solution model parameters O(fei), Wad(fei) and Ring(fei) to fit experiments of Katsura and Ito (1989) and Fei et al. (1991); other contents either were pre-existing or irrelevant parameterizations. </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.