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2,208 results for “coupling”
ROMS-PISCES coupled implementation for the Marquesas (Run Sed5 - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed2.5 - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed2.5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed5 - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Ref - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Ref - no sediment) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Ref - Y4)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Ref - no sediment) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Biosope - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Biosope) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
ROMS-PISCES coupled implementation for the Marquesas (Run Sed2.5 - Y5)
<p>These data are the average outputs of the ROMS-PISCES implementation (Run Sed2.5) used in the paper: Raapoto et al. 2019 "Role of iron in the remarkable Marquesas island mass effect" </p>
The prediction data analyzed in "Seasonal Arctic sea ice prediction using a newly developed fully coupled regional model with the assimilation of satellite sea ice observations"
<p>The outputs of seasonal predictions with the new modeling system analyzed in the article including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Near surface air temperature (T2) </p>
Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane
<p>This Dataset contains primary data used for the publication "Cdc42 couples T cell receptor endocytosis to GRAF1-mediated tubular invaginations of the plasma membrane" published online on 04. November 2019<br> doi:10.3390/cells8111388</p> <p><strong>Abstract:</strong> T cell activation is immediately followed by internalization of the T cell receptor (TCR).<br> TCR endocytosis is required for T cell activation, but the mechanisms supporting removal of TCR<br> from the cell surface remain incompletely understood. Here we report that TCR endocytosis is<br> linked to the clathrin-independent carrier (CLIC) and GPI-enriched endocytic compartments<br> (GEEC) endocytic pathway. We show that unlike the canonical clathrin cargo transferrin or the<br> adaptor protein Lat, internalized TCR accumulates in tubules shaped by the small GTPase Cdc42<br> and the Bin/amphiphysin/Rvs (BAR) domain containing protein GRAF1 in T cells. Preventing<br> GRAF1-positive tubules to mature into endocytic vesicles by expressing a constitutively active<br> Cdc42 impairs the endocytosis of TCR, while having no consequence on the uptake of transferrin.<br> Together, our data reveal a link between TCR internalization and the CLIC/GEEC endocytic route<br> supported by Cdc42 and GRAF1.</p> <p> </p> <p>Data are organised in compressed (.zip) folders entitled as the corresponding Figures in the publication.</p> <p>Programs we recommend to view the files are:<br> .fcs files: FlowJo software v10 (Tree Star, Ashland, OR, USA)<br> .lif files: LAS X v3 (Leica Microsystems, Wetzlar, Germany)<br> .pzfx files: Prism v7 software (GraphPad, San Diego, CA, USA)</p> <p> </p> <p>In case this Dataset is updated, new version will be available with doi:10.5281/zenodo.3545842</p>
Inputdata for NorESM2.1.0 compset N1850 (fully coupled) at f19_tn14
<p>Compset longname is</p> <p>1850_CAM60%NORESM_CLM50%BGC-CROP_CICE%NORESM-MIP6_MICOM%ECO_MOSART_SGLC_SWAV_BGC%BDRDDMS</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>
Fig. 2 in The role of cladocerans in green and brown food web coupling
Fig. 2. Mean and standard error for values of δ13C and δ15N for the three lagoons analyzed.
Fig. 1 in The role of cladocerans in green and brown food web coupling
Fig. 1. Map of the sampling locations. Font: PEREIRA, Jaime Luiz Lopes, 2021.
Bigwig files for paper "STK19 is a transcription-coupled repair factor that participates in UVSSA ubiquitination and TFIIH loading"
<p>Bigwig files for paper "STK19 is a transcription-coupled repair factor that participates in UVSSA ubiquitination and TFIIH loading". </p>
FIGURE 7 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 7. Average error with respect to the number of deformation modes used.
FIGURE 6. First 12 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 6. First 12 modes of the skull in case 2 (with the web of beams in the model).
FIGURE 3 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 3. Locations of the nine nodes at which the stresses were evaluated.
FIGURE 5. First 12 in Coupling finite element analysis and multibody system dynamics for biological research
FIGURE 5. First 12 modes of the skull in case 1 (without web of beams in the model).
Dataset of simulated room impulse responses in three coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <p>Georg Götz, Teodors Kerimovs, Sebastian J. Schlecht, and Ville Pulkki. Dynamic late reverberation rendering using the common-slope model. In Proceedings of the AES 6th International Conference on Audio for Games, Tokyo, Japan, April 2024.</p> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset includes room acoustic simulations conducted with the hybrid simulation suite Treble, using a transition frequency of approximately 750 Hz between wave-based and GA simulation. We simulated the coupled room geometry depicted in the file room_geometry2.pdf. The orange × indicates the source position, and receivers were uniformly distributed on the xy-plane with 0.3 m resolution. Each room has a height of 3 m and exhibits a uniform absorption distribution. Room R2 is the most reverberant with an absorption coefficient similar to concrete (αR2 = 0.01), whereas R1 and R3 are significantly less reverberant with αR1 = 0.2 and αR3 = 0.1, respectively.</p> <p>The dataset also includes the common-slope analysis results for the omnidirectional responses and also for the sector-based analysis as described in the paper. Please also refer to the following paper for more details on the common-slope analysis:</p> <blockquote> <p>Georg Götz, Sebastian J. Schlecht, and Ville Pulkki. Common-slope modeling of late reverberation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, Vol. 31, pp. 3945–3957, September 2023. doi: <a href="https://doi.org/10.1109/TASLP.2023.3317572" target="_blank" rel="noopener">10.1109/TASLP.2023.3317572</a></p> </blockquote> </div> </div> </div> </div> </div> </div>
Flow Cytometry Data from "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing"
<p>This record contains the flow cytometry data from the manuscript "Bacterial cell surface characterization by phage display coupled to high-throughput sequencing."</p> <p>Files are in <a href="https://docs.flowjo.com/flowjo/advanced-features/fj-acs/">Archive Cytometry Standard (ACS) format</a> . Each <code>.acs</code> file is a zip container which holds both the raw <code>.fcs</code> files and a FlowJo workspace (<code>.wsp</code>) file.</p> <p>Keywords in the workspace file identify which primary antibody (<code>primary</code>) was used and which cell genotype (<code>strain</code>) was used for each sample. The workspace also encodes the gating scheme and compensation matrix applied to each sample. Plots in the manuscript are exported from Layout views in the workspace.</p>
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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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.