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1,600 results for “input”
Input data files for simulation: top_hat_cg_supg
Input data files for simulation: top_hat_cg_supg
Action potential dependent sIPSCs from juvenile (P21-30) C57BL/6J male mice from CA1 pyramidal neurons receiving input from PV+ interneurons
<p>species : Mus musculus</p> <p>sex : male</p> <p>strain : C57BL/6J</p> <p>age : post-natal day 21-30</p> <p>Voltage-clamp recordings of GABAA spontaneous inhibitory post-synaptic currents (sIPSCs) were obtained from juvenile (P21-30) C57BL/6J male mice and recorded from hippocampal CA1 pyramidal neurons receiving input from parvalbumin positive (PV+) interneurons. The dataset contains 614 individual events recorded from 3 different neurons (expC1-C3).</p>
Action potential dependent sIPSCs from juvenile (P21-30) C57BL/6J male mice CA1 pyramidal neurons receiving input from PV+ and CCK+ interneurons
<p>species : Mus musculus</p> <p>sex : male</p> <p>strain : C57BL/6J</p> <p>age : post-natal day 21-30</p> <p>Voltage-clamp recordings of GABAA spontaneous inhibitory post-synaptic currents (sIPSCs) were obtained from juvenile (P21-30) C57BL/6J male mice and recorded from hippocampal CA1 pyramidal neurons receiving input from parvalbumin positive and cholecystokinin positive (CCK+) interneurons. The dataset contains 2130 individual events recorded from 3 different neurons (expD1-D3).</p>
POPC/Cholesterol (70:30) lipid membrane, 303K, Charmm36 force field through the use of Gromacs input files, simulation files and 100 ns trajectory for openMM simulation engine v7
<p>The starting structure was obtained from CHARMM-GUI Membrane Builder v1.7 (http://www.charmm-gui.org/) online tool. [1]</p> <p>All runs were performed with openMM simulation engine v7 and CHARMM36 additive force field parameters obtained from CHARMM-GUI input files [1]. Specifically, Gromacs file format provided by [1] was specifically used for this simulation.</p> <p>Conditions: T=303, 84 POPC and 36 Cholesterol molecules, 4800 tip3p waters, 100ns trajectory (preceded with equilibration).</p> <p>These data were originally obtained for the nmrlipids.blospot.fi project.</p> <p>Find more details at nmrlipids.blospot.fi and https://github.com/NMRLipids/nmrlipids.blogspot.fi</p> <p>[1] CHARMM-GUI Input Generator for NAMD, GROMACS, AMBER, OpenMM, and CHARMM/OpenMM Simulations Using the CHARMM36 Additive Force Field, J. Lee et al.<strong>,</strong> JCTC,<strong> </strong>DOI: 10.1021/acs.jctc.5b00935</p>
RAPID input files corresponding to the Mississippi River Basin using the NHDPlus v2 Dataset
<p><strong>Corresponding peer-reviewed publication</strong></p> <p>This dataset corresponds to RAPID input files that were used in study published by Journal of American Water Resources Association (JAWRA):</p> <p>Tavakoly, A. A., A. D. Snow, C. H. David, M. L. Follum, D. R. Maidment, and Z.-L. Yang, (2016) "Continental-Scale River Flow Modeling of the Mississippi River Basin Using High-Resolution NHDPlus Dataset", Journal of the American Water Resources Association (JAWRA) 1-22. DOI: 10.1111/1752-1688.12456</p> <p><em>Please cite the aforementioned article and the dataset herein, when using of any of these files in this dataset.</em></p>
Input catalogue of JASMINE (extended window)
<p>This data set contains the sources on the extended window of the Galactic Center Survey (GCS, -3.1<l<3.1 & |b|<1.8) of JASMINE that have Hw apparent magnitudes between 9.5 and 14.5 magnitudes. In total, this table contains 2,772,883 sources.</p> <p>The table contains also the crossmatch with Gaia as well as the propagated positions and errors 12 years into the future of DR5-like uncertanties (we scaled the DR3 errors following the pygaia scaling relations).</p> <p>For more details, refer to Ramos et al. 2024 (SPIE proceedings)</p>
Mesh and large input files of SeisSol models of the Kahramanmaraş earthquake doublet published in Jia et al. (2023) and Gabriel et al. (2023)
<div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div> <div>This dataset contains mesh and large input files for SeisSol models of the Kahramanmaraş earthquake doublet, as published in Jia et al. (2023) and Gabriel et al. (2023). The rest of the setup can be found at: <a href="https://github.com/Thomas-Ulrich/Turkey-Syria-Earthquakes/" target="_new" rel="noopener">GitHub - Turkey-Syria Earthquakes</a>. <ul> <li> <p><strong>Turkey78_75_dip70_3</strong>: The mesh file.</p> </li> <li> <p><strong>Turkey78_75_dip70.smd</strong>: The SimModeler file used to generate the mesh.</p> </li> <li> <p><strong>Turkey78_75_dip70_3.xml</strong>: Contains the mesh and analysis attributes required to generate it using PUMGen and SimModelerLib.</p> </li> <li> <p><strong>Turkey78_75_dip70_3.xmdf</strong>: A helper file for opening the mesh in ParaView.</p> </li> <li> <p><strong>StressChange_filt_v7_joint_ASAGI.nc</strong>: The stress change from the kinematic model used in Jia et al.'s simulations.</p> </li> <li> <p><strong>Turkey_31M_o5_el_ev1_2500_s2_05_al065_R056_resampled_stress_change_coarse.nc</strong> and<br><strong>Turkey_31M_o5_el_ev1_2500_s2_05_al065_R056_resampled_stress_change_fine.nc</strong>: These files represent the stress change from the first event for the second event simulation in Jia et al.'s model. They were generated from the output files of the first event simulation, as described here: <a href="https://github.com/Thomas-Ulrich/Turkey-Syria-Earthquakes/tree/main/SeisSolSetupHeterogeneities/asagi_file" target="_new" rel="noopener">SeisSol Setup Heterogeneities - ASAGI Files</a>.</p> </li> </ul> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div> </div>
DynQual input example: Rhine basin
<p>This repository contains all required input datasets for running the dynamical water quality routing model (DynQual) at 5 arc-min spatial resolution with a daily timestep for the Rhine basin.</p> <p>This input data is provided to provide users with a self-contained example for running DynQual, with the primary purpose of allowing users to increase their familiarity with the model setup.</p> <p>The model code (and user manual) that is also required for running this example setup are available at: https://github.com/UU-Hydro/DYNQUAL</p> <p> </p>
Example input and output files for the virus variant caller pipeline
<p><code>example_config_files.zip</code>: reference annotations for HSV-1 strain 17 and config files describing the input files.</p> <p><code>example_bam_files.zip:</code> alignment files (in BAM format) for two replicates each of the ICP0 and vhs knockout mutants.</p> <p><code>example_fastq_files.zip</code>: read sequences (in FASTQ format) for two replicates each of the ICP0 and vhs knockout mutants.</p> <p><code>example_output.zip</code>: resulting output files</p> <p> </p> <p>After downloading and unpacking the zip-archives, <strong>adjust the constants in the Watchdog workflow</strong> as follows:</p> <p><code><const name="REFERENCE">/path/to/chrHsv1_s17.fa</const></code><br><code><const name="REFERENCE_SNPS">/path/to/reference_snps.txt</const></code><br><code><const name="CONFIG">/path/to/config_strains_HSV1.txt</const></code><br><code><const name="GTF">/path/to/HSV1_Annotation_chrHsv1_s17.gtf</const></code><br><code><const name="samples">/path/to/example_samples.txt</const></code><br><code><const name="replicates">/path/to/example_replicates.txt</const></code></p> <p>where <code>/path/to/</code> is the directory the content of the archive zip-archives is located in.</p> <p>Furthermore, change <code>/path/to/</code> in the <code>example_replicates.txt</code> and <code>example_samples.txt</code> files to the directory the content of the archive zip-archives is located in.</p>
Ensemble experiment to investigate Antarctic meltwater input under global warming by GFDL CM2.1
<p>Dataset for "Non-monotonic responses of Atlantic Meridional Overturning Circulation to Antarctic meltwater forcing" submitted to GRL.</p><p>GW indicates global_warming experiments without meltwater input whereas MW is with meltwater input.</p><p>atlantic, global, surface_density, integrated_density.mat is preprocessed ocean dataset (structure) by MATLAB.</p>
Scripts, inputs and outputs of Module 3 BN analysis and impact assessment regarding Deliverable D6.3 "Performance and Impact assessment" in the IP4MaaS project
<p>All the materials, inputs, models, and scripts that are used in the performance assessment toolbox (IP4MaaS project, Deliverable D6.3 Performance and Impact Assessment) are available in the attached folder. </p> <p>The attached folder contains the following:</p> <p>Module 3_ BN analysis_Graphs and weights (the inputs, weights, graphs, and scripts of Bayesian Network analysis per each IP4MaaS demo site).</p> <p>The scripts have been designed for easy adoption in other projects with similar end goals. While the scripts operate on the codified representation of the traveller profiles, functionalities, and service providers, there is no restriction on the type of codification used. The only factor that the scripts assume is the order of the variables introduced for codification. That is the traveller profile variable (wherever applicable), followed by the Functionality variable, followed by the Service Provider variable. As long as the order is maintained, the scripts self-analyze the number of variables representing the traveller profiles, functionalities, and service providers, and perform calculations (USI, Effectiveness) and analysis (Regression and BN), Hence, in a similar framework, the scripts may be used for performing data analysis on a large data set with no restriction on the number of functionalities, service providers or subsets of traveller profiles. Documented Scripts can be found in a downloadable ZIP file here. </p>
Input data for boreal tip analysis code
<p>Input data for analysis code of boreal tip repository.<br>Includes central European geographical subsets of CRU TS, MOD44B, GLC2000, MPI-ESM and CHELSA-TraCE21k data products as well as paths for download of complete data sets.</p>
Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023
<p>Molecular dynamics trajectories, GROMACS input files, and analysis code from "Rational optimization of a transcription factor activation domain inhibitor" by Basu et. al, Nature Structural & Molecular Biology, 2023</p> <p> </p> <p> </p>
ADAPT Global Solar Magnetic Maps - 2010 Sep 18-20 (w/ & w/o farside active region input)
<p>ADAPT (Air Force Data Assimilative Photospheric flux Transport) model global solar magnetic maps using HMI magnetograms with ("wfar") and without ("orig") estimated farside active region flux, for the 3 day period: September 18-20, 2010. The farside emergence of NOAA AR11109 (approximately on 18sep2010, based on STEREO observations of farside) is estimated by modeling the HMI vector observation on the east-limb (i.e., at a CMD of approximately -61.5 degrees) back ~6 days. </p>
Model version, input data, results, and processing scripts for the Speizer et al. zero emissions transport paper
<p>Includes the files needed to run the GCAM scenarios, analyze the outputs, and produce the figures for the Speizer et al. zero emissions transport paper.</p>
Assessment of the Effect of a Test Setup on the Input Impedance Measurement of Cables (Dataset)
<p>The dataset includes the source of all measured and simulated data utilized in the paper titled<i> "Assessment of the Effect of a Test Setup on the Input Impedance Measurement of Cables".</i><br>Simulation data is available in ".xlsx" format, whereas measurement data is provided in ".s1p" format. </p>
SURFEX-MEGAN inputs
<p>ISOP.zip includes the isoprene emission potential data used in the paper.</p><p>SURFEX<i>physiographic</i>maps includes all needed physiograĥic inputs to run SURFEX (more details can be found in the SURFEX website https://www.umr-cnrm.fr/surfex/spip.php?page=recherche&recherche=LSPLIT_PATCH)</p>
SURFEX-MEGAN inputs
<p>ISOP includes the isoprene emission potential data used in the paper. SURFEXphysiographicmaps includes all needed physiograĥic inputs to run SURFEX (more details can be found in the SURFEX website https://www.umr-cnrm.fr/surfex/spip.php?page=recherche&recherche=LSPLIT_PATCH). </p><p>SURFEX technical and scientific documentation are also provided.</p>
Mechanical Comparison of Arrangement Strategies for Topological Interlocking Assemblies: Abaqus input files
<p>Topological Interlocking assemblies are arrangements of blocks kinematically constrained by a fixed frame, such that all rigid body motions of each block are constrained only by its permanent contact with other blocks and the frame. In the literature several blocks are introduced that can be arranged into different interlocking assemblies.<br><br>In this study we investigate the influence of arrangement on the overall structural behaviour of the resulting interlocking assemblies.<br>This is performed using the Versatile Block, as it can be arranged in three different doubly periodic ways given by wallpaper symmetries.<br><br>Our focus lies on the load transfer mechanisms from the assembly onto the frame.<br>For fast a priori evaluation of the assemblies we introduce a combinatorial model called Interlocking Flows.<br><br>To investigate our assemblies from a mechanical point of view we conduct several finite element studies. These reveal a strong influence of arrangement on the structural behaviour, for instance, an impact on both the point and amount of maximum deflection.<br>The results of the finite element analysis are in very good agreement with the predictions of the Interlocking Flow model.</p>
Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution
<p>Replication package for the paper "Learning by Viewing: Generating Test Inputs for Games by Integrating Human Gameplay Traces in Neuroevolution" </p><p> </p><p>Although automated test generation is common in many programming domains, games still challenge test generators due to their heavy randomisation and hard-to-reach program states. Neuroevolution combined with search-based software testing principles has been shown to be a promising approach for testing games, but the co-evolutionary search for optimal network topologies and weights involves unreasonably long search durations. Humans, on the other hand, tend to be quick in picking up basic gameplay. In this paper, we therefore aim to improve the evolutionary search for game input generators by integrating knowledge about human gameplay behaviour. To this end, we propose a novel way of systematically recording human gameplay traces, and integrating these traces into the evolutionary search for networks using traditional gradient descent as a mutation operator. Experiments conducted on eight diverse Scratch games demonstrate that the proposed approach reduces the required search time from five hours down to only 30 minutes on average.</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.