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2,208 results for “coupling”
Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration - model outputs
<p>These tar file are associated with an article submitted to Geoscientific Model Development under identification number gmd-2021-413 (https://www.geoscientific-model-development.net): Assessment of the sea surface temperature diurnal cycle in CNRM-CM6-1 based on its 1D coupled configuration<br> By A. Voldoire, R. Roehrig, H. Giordani, R. Waldman, Y. Zhang, S. Xie, MN Bouin</p> <p>3 files correspond to code components that can be distributed freely</p> <p>- surfex.tgz for the surfex v8.0 distributed under a Cecill-C License</p> <p>- oasis-mct-3.0.tgz for oasis-mct3.0 distributed under a GNU General Public License</p> <p>- nemo_v3.6.tgz for the nemo, distibuted under a Cecill-C License</p> <p>These three components are mainly fortran codes.</p> <p>The last file "<a href="https://zenodo.org/api/files/e94922e7-22eb-445b-acc3-03e11ca1af6b/CNRM-CM6-1D_published_experiments.tgz?versionId=2460ec63-89f5-415f-9613-1954f048b238">CNRM-CM6-1D_published_experiments.tgz </a>" contains all model outputs that have been used in this article. These model outputs are in netcdf format and organized by experiment.</p>
The Regional Coupled Suite (RCS): application of a flexible regional coupled modelling framework to the Indian region at km-scale
<p>Supporting data for figures in GMD draft paper: The Regional Coupled Suite (RCS): application of a flexible regional coupled modelling framework to the Indian region at km-scale.</p>
The prediction data analyzed in the article: "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018"
<p>The outputs of seasonal predictions with the Coupled Arctic Prediction System version 1 analyzed in the article, "An improved regional coupled modeling system for Arctic sea ice simulation and prediction: a case study for 2018", including:</p> <p>Sea ice concentration (SIC)</p> <p>Sea ice thickness (SIT)</p> <p>Sea surface temperature (SST)</p> <p>Ice mass budget diagnostics</p> <p>Accumulated downward shortwave radiation at the surface (ASWDN)</p> <p>Accumulated downward longwave radiation at the surface (ALWDN)</p> <p>Near surface air temperature (T2) </p> <p>Temperature and salinity profile of the upper ocean under sea ice </p>
An Improved Coupled Data Assimilation System with a CGCM Using Multi-Timescale High Efficiency EnOI-Like Filtering
<p>Coupled data assimilation (CDA) combining coupled models and observations plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. However, traditional CDA algorithms while being expensive lack sufficient representation of multi-scale background flows. Here, a Multi-timeScale High-Efficiency Approximate EnKF (MSHea-EnKF) has been implemented in the global fully coupled climate model of the Geophysical Fluid Dynamics Laboratory. It consists of stationary, low-frequency, and high-frequency filters constructed from the timeseries of a single model solution, with improved representation for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnKF is evaluated in a biased twin experiment framework with synthetic “observations” produced by the other coupled model, Community Earth System Model, and a three-decade coupled analysis experiment with real observations. Results show that while computationally costing only a small fraction of traditional ensemble CDA, the MSHea-EnKF significantly improves the assimilation quality due to better representation of the slow-varying background flows in the filtering. The coupled analysis of MSHea-EnKF also improves the estimation of the Atlantic meridional overturning circulation, including better standard deviation distribution and mass transport at the Rapid section. These results of MSHea-EnKF on prevalent resolution coupled model with high computational efficiency promises its further applications to high-resolution coupled model data assimilation and reanalysis which will greatly advance our understanding for seamless weather-climate analysis and predictions.Coupled data assimilation (CDA) combining coupled models and observations plays a critical role in climate studies by producing a four-dimensional estimation of Earth system states. However, traditional CDA algorithms while being expensive lack sufficient representation of multi-scale background flows. Here, a Multi-timeScale High-Efficiency Approximate EnKF (MSHea-EnKF) has been implemented in the global fully coupled climate model of the Geophysical Fluid Dynamics Laboratory. It consists of stationary, low-frequency, and high-frequency filters constructed from the timeseries of a single model solution, with improved representation for low-frequency background error statistics and enhanced computational efficiency. The MSHea-EnKF is evaluated in a biased twin experiment framework with synthetic “observations” produced by the other coupled model, Community Earth System Model, and a three-decade coupled analysis experiment with real observations. Results show that while computationally costing only a small fraction of traditional ensemble CDA, the MSHea-EnKF significantly improves the assimilation quality due to better representation of the slow-varying background flows in the filtering. The coupled analysis of MSHea-EnKF also improves the estimation of the Atlantic meridional overturning circulation, including better standard deviation distribution and mass transport at the Rapid section. These results of MSHea-EnKF on prevalent resolution coupled model with high computational efficiency promises its further applications to high-resolution coupled model data assimilation and reanalysis which will greatly advance our understanding for seamless weather-climate analysis and predictions.</p>
Decay by ectomycorrhizal fungi couples soil organic matter to nitrogen availability
<p>Interactions between soil nitrogen (N) availability, fungal community composition, and soil organic matter (SOM) regulate soil carbon (C) dynamics in many forest ecosystems, but context dependency in these relationships has precluded general predictive theory. We found that ectomycorrhizal (ECM) fungi with peroxidases decreased with increasing inorganic N availability across a natural inorganic N gradient in northern temperate forests, whereas ligninolytic fungal saprotrophs exhibited no response. Lignin-derived SOM and soil C were negatively correlated with ECM fungi with peroxidases and were positively correlated with inorganic N availability, suggesting decay of lignin-derived SOM by these ECM fungi reduced soil C storage. The correlations we observed link SOM decay in temperate forests to tradeoffs in tree N nutrition and ECM composition, and we propose SOM varies along a single continuum across temperate and boreal ecosystems depending upon how tree allocation to functionally distinct ECM taxa and environmental stress covary with soil N availability.</p>
Data files for "Hybrid quantum-classical approach for coupled-cluster Green's function theory"
<p>Source code and data files for the manuscript "Hybrid quantum-classical approach for coupled-cluster Green's function theory."</p> <p>Reference: Quantum 6, 675 (2022); https://doi.org/10.22331/q-2022-03-30-675.</p> <p>Title: Hybrid quantum-classical approach for coupled-cluster Green's function theory</p> <p>Authors: Trevor Keen, Bo Peng, Karol Kowalski, Pavel Lougovski, and Steven Johnston.</p> <p>Abstract: The three key elements of a quantum simulation are state preparation, time evolution, and measurement. While the complexity scaling of dynamics and measurements are well known, many state preparation methods are strongly system-dependent and require prior knowledge of the system’s eigenvalue spectrum. Here, we report on a quantum-classical implementation of the coupled-cluster Green’s function (CCGF) method, which replaces explicit ground state preparation with the task of applying unitary operators to a simple product state. While our approach is broadly applicable to a wide range of models, we demonstrate it here for the Anderson impurity model (AIM). The method requires a number of T gates that grow as $O(N^5)$ per time step to calculate the impurity Green’s function in the time domain, where N is the total number of energy levels in the AIM. For comparison, a classical CCGF calculation of the same order would require computational resources that grow as $O(N^6)$ per time step.</p>
SMART Radar and WSR-88D Data Associated with "Mobile Radar Observations of Hurricanes at Landfall. Part II: Convectively Coupled Vortex Rossby Waves"
<p>The data contained in this archive are associated with "Mobile Radar Observations of Hurricanes at Landfall. Part II: Convectively Coupled Vortex Rossby Waves" in review in the <em>Journal of the Atmospheric Sciences</em>. Two sets of data associated with Hurricanes Isabel (2003) and Matthew (2016) are contained. Each subset of data contains the raw radar files that contribute to the manuscript in cfradial netCDF format.</p> <p>A readme file in included that describes the variables and format of the radar volume files. Questions about the dataset may be directed to addisonalford@ou.edu, drdoppler@ou.edu, or gordon.carrie-1@ou.edu.</p>
Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors - Scripts & Source data
<p>Source data and scripts to reproduce the figures that are part of the publication "Single cell imaging of ERK and Akt activation dynamics and heterogeneity induced by G protein-coupled receptors".</p> <p>Journal of Cell Science (2022) 135, jcs259685, DOI: 10.1242/jcs.259685</p> <p> </p> <p>An earlier version of this work is published as a preprint: "Heterogeneity and dynamics of ERK and Akt activation by G protein-coupled receptors depend on the activated heterotrimeric G proteins", DOI: <a href="https://doi.org/10.1101/2021.07.27.453948">10.1101/2021.07.27.453948</a></p>
Freshwater viral metagenome assembled genomes (vMAGs) used for vContact2 analysis in publication Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments
<p>This dataset contains all freshwater viruses that were mined from publicly available data in an effort to provide biogeographical context to viral communities identified from the Columbia River. These two files include data from:</p> <p>1) East River, CO (PRJNA579838)</p> <p>2) A previous study from the Columbia River, WA (PRJNA375338)</p> <p>3) Prairie Potholes, ND (PRJNA365086)</p> <p>4) Amazon River (PRJNA237344)</p> <p> </p> <p>Manuscript title Genome-resolved metaproteomics decodes the microbial and viral contributions to coupled carbon and nitrogen cycling in river sediments</p>
Dataset for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study"
<p># Data and code for "Ultra-strong coupling of a single molecule to a plasmonic nanocavity: A first-principles study," M. Kuisma, B. Rousseaux, K.M. Czajkowski, T.P. Rossi, T. Shegai, P. Erhart, T.J. Antosiewicz, ACS Photonics, doi:10.1021/acsphotonics.2c00066 (2022).</p> <p><br> ## Contents</p> <p>* *data-{type}/*: reproducible data<br> * *src/*: input scripts</p> <p><br> ## Description of the data</p> <p>The data are stored in directories *data-{type}/*. The contents of the directories<br> can be reproduced with the included input scripts.</p> <p>The data are organized in subdirectories *data-{type}/{system}/* corresponding to<br> the considered nanoparticle-molecule systems and simulation type:</p> <p>* data-fd: free energy calculations done with the finite difference mode<br> * data-lcao: strong coupling calculations done with LCAO mode<br> * data-d3: DFT-D3 calculations</p> <p>The contents of each subdirectory are:</p> <p>* *data-{fd,lcao,d3}/{system}/structure.xyz*: physical atomic structure<br> * *data-lcao/{system}/td-x/dm.dat*: delta-kick-induced time-dependent dipole moment<br> * *data-lcao/{system}/td-x/dm_abs_Lorentz_0.100.dat*: photoabsorption spectrum</p> <p>The spectrum plots in the article correspond to the first (x values) and<br> second (y values) columns of the spectrum files.</p> <p>## Reproduction of the data</p> <p>The data was produced using the Python scripts in *src/*,<br> Python version 3.7.3, GPAW version 20.1.0, libxc version 4.3.4,<br> ASE version 3.20.0, NumPy version 1.16.2, and SciPy version 1.2.1.</p> <p>The calculation of the data of a system consists of<br> the following steps (in *bash* shell with, e.g., system=rlx-ico-Al147`):</p> <p>1. Ground-state calculation:<br> * Copy the gs folder to a data-lcao/{system} folder<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Select the Poisson Solver in the settings.py file by commenting out / uncommenting:<br> * for single particles or molecules use poissonsolver = PoissonSolver(eps=eps, remove_moment=9)<br> * otherwise comment out the above line and uncomment the last 8 lines<br> * Submit the gs.py calculation as appropriate for the particular system<br> 2. Time-propagation calculation:<br> * Requires finished ground-state calculation<br> * Set up parellel calculaton parameters as necessary for the computing infrastructure (parallel.py)<br> * Submit the td.py calculation as appropriate for the particular system<br> 3. Spectrum calculation:<br> * Requires finished time-propagation calculation (30 fs propagation)<br> * Run the `$ python spec.py` script</p> <p>Note that the example python scripts use variables STARTTIME and WALLTIME to define<br> allocated compuing time in HPC environments. The `WALLTIME` and `STARTTIME` environment<br> variables defined in *submit.sbatch* are required for a clean exit of the calculation<br> within the allocated time.</p> <p>If the ground-state or time-propagation calculations do not finish within the<br> allocated time, the same *gsc.py* or *tdc.py* scripts can be (re)run to continue<br> the calculation.</p>
Assimilation of NASA's Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system
<p>Data Analysis Scripts and Post-Processed Model Data for a case study using assimilation of ASO Snow Data into the NASA LIS/WRF-Hydro Model. </p> <p>Manuscript Citation:</p> <p>Lahmers T. M., S. V. Kumar, D. Rosen, A. L Dugger, D. Gochis, J. A. Santanello, C. Gangodagamage<sup>,</sup> and R. Dunlap,<strong> </strong>2020: Assimilation of NASA’s Airborne Snow Observatory snow measurements for improved hydrological modeling: A case study enabled by the coupled LIS/WRF-Hydro system,<em>Water Resour. Res.,</em></p>
Dataset for "Oxidase-type C-H/C-H coupling using an isoquinoline-derived organic photocatalyst"
<p>We have uploaded the corresponding raw and processed data for each figure from main text and supporting information. We also uploaded the raw data of NMR and HR-MS for characterization of the organic products.</p>
Storyline data used in the paper "The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging"
<p>We provide the storyline data (in NetCDF format) used in the paper: “The July 2019 European heatwave in a warmer climate: Storyline scenarios with a coupled model using spectral nudging” published in Journal of Climate. The data is structured in four .tar.gz files (Preindustrial, Present, 2 and 4 K warmer climates) containing all variables used in this each climate. The data from the five ensemble members (E1 to E5) have been included separately in 3-months files.</p> <p>Atmospheric variables (Files are named as: {variable}_E{ensemble member}_{starting month}{year}.nc:</p> <ul> <li> <p>Latent heat flux (ahfl)</p> </li> <li> <p>Sensible heat flux (ahfs)</p> </li> <li> <p>Monthly Global Mean 2m Temperature (GMTT2mMonthly)</p> </li> <li> <p>Maximum 2m Temperature (t2max)</p> </li> <li> <p>Mean 2m Temperature (t2mean)</p> </li> <li> <p>Minimum 2m Temperature (t2max)</p> </li> <li> <p>Soil Wetness (ws)</p> </li> </ul> <p> Only for present climate:</p> <ul> <li> <p>850 hPa Temperature (T850)</p> </li> <li> <p>Total Cloud Cover (TCC)</p> </li> <li> <p>500 hPa Geopotential Height (Z500)</p> </li> </ul> <p>Five layers soil moisture (Only for present climate, Files are named as: From20172019in2017Climatessp370{ensemble member}_{year}{starting month}.01_jsbid.nc) </p> <p>Oceanic variables (from FESOM, Files are named as: {variable}_E{ensemble member}_{year}{starting month}01.nc:</p> <ul> <li> <p>Sea Ice Concentration (SIC)</p> </li> <li> <p>Sea Surface Temperature (SST)</p> </li> </ul> <p><strong>Please, note that FESOM uses an unstructured mesh.</strong></p>
IIb-RAD-seq coupled with random forest classification indicates regional population structuring and sex-specific differentiation in salmon lice (Lepeophtheirus salmonis)
<p><span>The aquaculture industry has been dealing with salmon lice problems forming serious threats to salmonid farming. Several treatment approaches have been used to control the parasite. Treatment effectiveness must be optimized, and the systematic genetic differences between sub-populations must be studied to monitor louse species and enhance targeted control measures. We have used IIb-RAD sequencing in tandem with a random forest classification algorithm to detect the regional genetic structure of the Norwegian salmon lice and identify important markers for sex differentiation of this species. We identified 19428 single nucleotide polymorphisms (SNPs) from 95 individuals of salmon lice. These SNPs, however, were not able to distinguish differential structure of lice populations. Using the random forest algorithm, we selected 91 SNPs important for geographical classification and 14 SNPs important for sex classification. The geographically important SNP data substantially improved the genetic understanding of the population structure and classified regional demographic clusters along the Norwegian coast. </span><span>We also uncovered SNP markers that could help determine the sex of the salmon louse. </span><span>A large portion of the SNPs identified to be under directional selection were also ranked highly important by random forest. According to our findings, there is a regional population structure of salmon lice associated with the geographical location along the Norwegian coastline.</span></p>
Coupling Novel Soil Moisture-Suction Sensors and UAV Photogrammetry Technology to the Performance of Highway Embankments
<p>The movement of water plays a critical role in the mechanical performance and service life of transportation infrastructure, especially for pavement subgrades and highway embankments consisting of high-plasticity, expansive soils that saturate and ultimately lead to infrastructure distress. Shallow slides along highway embankments are ubiquitous across Region 6 because long-term wetting and drying cycles considerably weaken these compacted soils. In the aftermath of heavy rains, pore-water pressures increase to a critical threshold such that a failure occurs. The implications of embankment failures range from repeated maintenance repairs to long-term road closures. A comprehensive approach to model highway embankments comprising of laboratory testing, setup, and field data collection using unmanned aerial vehicles has been proposed in this study.</p>
Low-frequency anharmonic couplings in bromoform revealed from 2D Raman-THz spectroscopy: From the liquid to the crystalline phase
<p>Raw data for "Low-frequency anharmonic couplings in bromoform revealed from 2D Raman-THz spectroscopy: From the liquid to the crystalline phase"</p>
Dataset for the paper "Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry" DOI: 10.1021/acscatal.2c00609
<p>The data in the attached spreadsheet was used to produce the figures in the paper</p> <p>Authors:Guohui Zhang, Youxin Cui, Anthony Kucernak</p> <p>Title:Real-Time in Situ Monitoring of CO2 Electroreduction in the Liquid and Gas Phases by Coupled Mass Spectrometry and Localized Electrochemistry</p> <p>Journal:ACS Catalysis</p> <p>DOI:10.1021/acscatal.2c00609</p> <p>Please cite the above reference if you wish to use this data</p> <p>DOI of data:10.5281/zenodo.6526651</p>
A B cell actomyosin arc network couples integrin co-stimulation to mechanical force-dependent immune synapse formation
<p>B-cell activation and immune synapse (IS) formation with membrane-bound antigens are actin-dependent processes that scale positively with the strength of antigen-induced signals. Importantly, ligating the B-cell integrin, LFA-1, with ICAM-1 promotes IS formation when antigen is limiting. Whether the actin cytoskeleton plays a specific role in integrin-dependent IS formation is unknown. Here we show using super-resolution imaging of mouse primary B cells that LFA-1: ICAM-1 interactions promote the formation of an actomyosin network that dominates the B-cell IS. This network is created by the formin mDia1, organized into concentric, contractile arcs by myosin 2A, and flows inward at the same rate as B-cell receptor (BCR): antigen clusters. Consistently, individual BCR microclusters are swept inward by individual actomyosin arcs. Under conditions where integrin is required for synapse formation, inhibiting myosin impairs synapse formation, as evidenced by reduced antigen centralization, diminished BCR signaling, and defective signaling protein distribution at the synapse. Together, these results argue that a contractile actomyosin arc network plays a key role in the mechanism by which LFA-1 co-stimulation promotes B-cell activation and IS formation.</p>
Supplementary Tables for the article"One-to-one Coupling between Southern Ocean Productivity and Antarctica Climate"
<p>Supplementary Tables for the article"One-to-one Coupling between Southern Ocean Productivity and Antarctica Climate".</p> <p>Table S1. Tie points in the age model of Site U1537; </p> <p>Table S2. Age-depth model and uncertainties at Site U1537;</p> <p>Table S3. Natural gamma radiation data at Site U1537;</p> <p>Table S4. Color reflectance component b* data at Site U1537;</p> <p>Table S5. Ca counts data at Site U1537;</p> <p>Table S6. Magnetic susceptibility data at Site U1537.</p>
Data for Vibrational Couplings between Protein and Co-factor in Bacterial Phytochrome Agp1revealed by 2D-IR Spectroscopy
<p>2D-IR data for the bacteriophytochrome Agp1 in the Pr and Pfr states </p>
ScienceDex guides
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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.