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2,208 results for “coupling”
The Role of Sacks Sentence Completion Test in a Couple with Borderline Personality Disorder and Narcissistic Personality Disorder: A Case Study
<p> </p> <p><span>Abstract</span></p> <p><span>Background: A voluminous amount of material has been written on individual diagnostic indicators of borderline disorder and narcissistic personality disorder from a psychopathological perspective. However, very few have explored when a narcissist and borderline personality forms a bond or a marriage link. This research aims to show that personality psychopathology is primarily interpersonal at its foundation, and it can be well reflected using Sacks sentence completion test.</span></p> <p><span>Methodology: A Case study of a couple with borderline personality disorder and narcissistic personality disorder from a psychiatric hospital OPD at Jaipur. <span> </span>International personality disorder examination (IPDE) DSM IV, diagnostic interview schedule and Sacks sentence completion test have been administered. The couple clinical report was qualitatively interpreted using content analysis and </span><span>phenomenological approach.</span><span> </span></p> <p><span>Results indicated that couple with borderline personality disorder and narcissistic personality disorder leads to dysfunctional adult relationship. </span></p> <p><span>Conclusion: Lack of secure base from parental figures in childhood is reflected in terms connecting with the partner. </span></p>
Data for: Intervalley coherence and intrinsic spin-orbit coupling in rhombohedral trilayer graphene
<p>Data files and data fitting code for manuscript "Intervalley coherence and intrinsic spin-orbit coupling in rhombohedral trilayer graphene." Analysis files Figure3.ipynb and Extracting_lambda.ipynb generate processed data for Fig 3 and extract values of lambda (spin orbit coupling strength) respectively. </p>
NMR data for "Application of a Hydrophobic Polyglutamate Bearing a Triphenylphosphine Group for the Orientation of Pharmaceutically Active Compounds and the Measurement of Residual Dipolar Couplings"
<p>NMR raw data for the work titled:</p> <p>"Application of a Hydrophobic Polyglutamate Bearing a Triphenylphosphine Group for the Orientation of Pharmaceutically Active Compounds and the Measurement of Residual Dipolar Couplings"</p> <p>The archive consists of NMR spectra of all compounds synthesized and the NMR spectra used for the determination of RDCs (isotropic and anisotropic).</p>
Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"
<h1>Online data of "Strong hole-photon coupling in planar Ge for probing charge degree and strongly correlated states"</h1> <p>DOI: https://doi.org/10.1038/s41467-024-54520-7</p> <h2>Authors</h2> <ul> <li>Franco De Palma</li> <li>Fabian Oppliger</li> <li>Wonjin Jang</li> <li>Stefano Bosco</li> <li>Marián Janík</li> <li>Stefano Calcaterra</li> <li>Georgios Katsaros</li> <li>Giovanni Isella</li> <li>Daniel Loss</li> <li>Pasquale Scarlino</li> </ul> <h2>Description</h2> <p>The data for all figures in the main text can be found in csv files in ASCII format in the corresponing folders. For Figures 4-6, the panels are numbered from top to bottom.</p>
Simulation cases of a lab-scale wet-operated stirred media mill using coupled CFD-DEM
<p>Simulation cases described in the article, "Coupled CFD-DEM simulation of pin-type wet stirred media mills using immersed boundary approach and hydrodynamic lubrication force", DOI: <a href="https://doi.org/10.1016/j.powtec.2024.120060" rel="nofollow">https://doi.org/10.1016/j.powtec.2024.120060</a></p> <p><strong>Pre-requisites:</strong> LIGGGHTS, OpenFOAM-6, cfdemCoupling, and their corresponding dependencies, Python (>3.6)</p> <p>*The versions of simulation softwares used in the simulation cases are taken from Institute for Particle Technology's (iPAT) GitLab repository: <a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a></p> <p>To run the simulations in this repository, one should first install the pre-requisites i.e., LIGGGHTS, OpenFOAM-6 and cfdemCoupling. The repositiries can be found at Institute for Particle Technology's GitLab (<a href="https://git.rz.tu-bs.de/partikeltechnik/" rel="nofollow">https://git.rz.tu-bs.de/partikeltechnik/</a>) if not, they shall be requested.</p> <p>Running the simulations in the repositories includes, generation of the cases in "Base_Cases_Init", using the "generateCases.py" file (Python3), then run the "variables_Modify.py" file. Running of the "jobfile_Modify.py" and "jrun.py", sequentially, will submit the simulations to a HPC cluster. After the successful run of these simulations, the cases in the folders "Base_Cases_Stable" and "Base_Cases_Stable_Lubrication" can be launched in the same manner as described above, i.e., sequentially running "generateCases.py", "variables_Modify.py", "jobfile_Modify.py" and "jrun.py" (one needs to check if the corresponding restart files are existing in the Base_Cases_Stable*/Base_Case_Stable/Restart folder, which are generated from the "Base_Cases_Init" runs). Following this, the cases in "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" can be run using the same method as described above (one needs to check if the corresponding restart files are existing in the Base_Cases_Run*/Base_Case_Run/Restart folder, which are generated from the "Base_Cases_Stable" runs). After successfully running of the simulations the python file "generateAndRunPostFiles.py", in each of the corresponding "Base_Cases_Run_800_um", "Base_Cases_Run_1100_um", and "Base_Cases_Run_Lubrication" folders should be run.</p> <p> </p> <p> </p> <p> </p> <p><strong>Description:</strong> This repository provides the simulation cases to generate and run the simulation cases of the "stirred media mill" (MiniCeR). The simulations are setup to couple the CFD and DEM via two-way coupling and the corresponding files in the "Run" folder contain the post-processing scripts to extract the "collision/stress energies" and assemble them into a "collision/stress energy distribution". The simulations are setup in three stages, namely, "Init", "Stable", and "Run". The combinations of operating settings can be easily modified and the respective cases can be generated using the python scripts in the corresponding repositories. The scripts to run the simulations on the HPC-cluster systems are also added.</p> <p><strong><em>a. Init:</em></strong> This stage is to initialize the system with the particles. Three insertion faces are used to generate and insert the required number of particles (calculated according to their size and filling degree) into the system. The "base case" folder contains the necessary DEM scripts of the case setup and the required CAD (geometry) files. The python script "generateCases.py" generates the requested simulation cases according to the specified operating settings. It uses the help of "MakeCases.sh". The "variables_Modify.py" file modifies the variables in the generated folders of the simulation cases to alter the operation setting values. The "jobfile_Modify.py", and the "jrun.py" are used to modify the cluster job files and run the submit the simulation jobs onto the cluster, respectively.</p> <p><strong><em>b. Stable:</em></strong> This is the first stage couples the CFD and DEM. The restart files generated in the "Init" stage are used to start the coupling and run for a specified time. It follows the similar system as init, i.e., to generate the cases and modify the variables, but with additional generation and modifications in the CFD folder i.e., the mesh generation, etc. The simulations are launched in the same way as described above and the corresponding restart files are extracted.</p> <p><strong><em>c. Run:</em></strong> This second stage of the coupling of CFD and DEM launches the srabilized system and extracts the collision energies and stores them in ".txt" files which are postprocessed later to assemble the stress energy distribution. The post-processing to extract the stress energy distribution is done using the "Stress_Energy_Calculation.py" and "generateAndRunPostFiles.py", which generate corresponding folders of post-processing in each of the corresponding case folders.</p>
Thermal coupling mode in mantle-outer core convection predicted from an ultra-high-resolution numerical simulation of two-layer convection with a large viscosity contrast
<p>Movie of temperature and velocity fields in the highly viscous layer (HVL) and the low-viscosity layer (LVL) (left panels) and the close-up views focusing on the interior of the LVL (right panels). The viscosity contrast between the HVL and LVL is 10<sup>4</sup>.</p>
Coupling nitrogen removal and watershed management to improve global lake water quality
<p>Updated dataset and code for the article "Coupling nitrogen removal and watershed management to improve global lake water quality" after removing deep lakes.</p>
Source data for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling"
<div> <p>This repository contains data and source code for the publication "Ultra-dispersive resonator readout of a quantum-dot qubit using longitudinal coupling."</p> </div>
Supplemental materials to "A quasi-2D model of convectively coupled vortices"
<p>math_derivation_note: A hand-written note of key mathematical steps, mostly about section 4 and Appendix C. </p> <p>quasi-2D model.zip: The package of the quasi-2D model code.</p> <p>postprocess_code_quasi2D.zip: The package of the postprocessing codes and intermediate files (.mat) related to the quasi-2D simulations.</p> <p>postprocess_code_CM1.zip: The package of the postprocessing codes and intermediate files (.mat) related to the CM1 simulation.</p> <p>Group_dh.avi: The Group-dh experiments with varying convective intermittency (dh/H). The first, second, and third column shows Group-dh-1, Ref, and Group-dh-2. Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l</em>=30 km) normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_dL.avi: The Group-<em>l</em> experiments with varying convective filter length <em>l.</em> The first, second, and third column shows Ref (<em>l</em>=30 km), Group-<em>l</em>-1 (<em>l</em>=45 km), and Group-<em>l</em>-2 (<em>l</em>=60 km). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_fE.avi: The Group-fE experiments with varying Coriolis parameter f and Ekman number E<em>.</em> They differ in the strength of the rotational flow. The first, second, and third column shows Group-fE-1 (f=1e-5 1/s), Ref (f=1e-4 1/s), and Group-fE-2 (f=2e-4 1/s). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Group_eta.avi: The Group-eta experiments with varying mesoscale feedback parameter eta<em>.</em> They differ in the strength of the mesoscale feedback. The first, second, and third column shows Group-eta-1 (eta=0), Group-eta-2 (eta=1.2), and Group-eta-3 (eta=1.4). Only the first member of each experimental ensemble is shown. The first row shows the raw vorticity normalized by f. The second shows the Gaussian-filtered vorticity (with a length scale of <em>l=</em>30 km<em>)</em> normalized by f. The black contour is the zero-value contour of the Gaussian-filtered vorticity.</p> <p>Please contact Dr. Hao Fu (haofu@uchicago.edu) if you have any questions!</p>
Dataset for Super-Resolution Image Reconstruction based on Random-coupled Neural Network and EDSR
<p>This dataset folder contains the DIV2K public dataset, which is utilized for model training and comprises 900 high-quality, high-resolution images along with their corresponding low-resolution versions. Additionally, all pre-trained models used in the experiment and their associated test results are publicly available.</p> <p>The main directory is organized into two subfolders: one labeled "dataset," which houses the DIV2K dataset, and another named "Model_results," which contains the pre-trained models and their corresponding test outcomes. The Dataset folder includes the original DIV2K dataset (referred to as "DIV2K") as well as a channel-expanded dataset processed by the RCNN model (designated as "DIV2K-RCNN"). Within the Model_results folder, the Model_trained subfolder contains all pre-trained models employed during the experiment, while the Test_results subfolder holds the test results for each model.</p>
Waveguide coupled III-V photodiodes monolithically integrated on Si
<p>This dataset supports the study "Waveguide coupled III-V photodiodes monolithically integrated on Si", <a href="https://arxiv.org/ftp/arxiv/papers/2106/2106.00620.pdf">https://arxiv.org/ftp/arxiv/papers/2106/2106.00620</a>. The material here represents the raw data that measured in the experiments without further processing, which was subsequently performed using Origin Graphing.</p>
Supplementary data for structure-conditioned amino-acid couplings
<p>This dataset contains supplementary data for the work "Structure-conditioned amino-acid couplings: how contact geometry affects pairwise sequence preferences" and includes two files: a spreadsheet listing the CASP models used for structure evaluation and an archived directory containing the structure and energy files that make up "DB200K", the main dataset of interaction motifs and their structure-conditioned energies used in the published work.</p> <p>The spreadsheet, "CASP-models.xlsx", lists the CASP round (9, 10, etc.), target name, model ID, and GDT_TS score for each model included in the structure evaluation experiment (see Fig. 7 in the published work). All information was collected from the CASP website, predictioncenter.org. See the "CASP model evaluation" section in the Methods section of the published work for more information.</p> <p>The archived directory, "DB200K.tar.gz", contains the structures and structure-conditioned energies of 200,002 inter-residue contact motifs. Each motif has a structure and set of structure-conditioned energies for each of the three motif sizes considered in the published work: 1x1, 3x3, and 5x5. For each motif of each size, there are two corresponding files, both indexed by the motif size, PDB ID, and position pair (chain and residue numbers according to the PDB file). One file is a PDB file containing the motif's structure and the other is a text-based file listing the 400 structure-conditioned energies of the motif's interacting residue pair. Each of the 400 energies is indexed by the pair of three-letter amino-acid codes it corresponds to. The residue positions in the PDB file match those listed in the energy file. For details on how this database's contacts were selected, see the "Contact database creation" section in the Methods section of the published work; for details on how these energies were computed, see the "Structure-conditioned potentials" section.</p>
Supplementary Material for "Model-Free Analysis of Experimental Residual Diploar Couplings in Small Organic Compounds"
<p>NMR Spectra (CLIP-HSQC, perfectCLIP-HSQC, TSE-PSYCHEDELIC) of isopinocampheol in six alignment conditions.</p> <p>Simulation input (experimental RDC data in six alignment media, input geometries, keywords) and output files (simulation / geometry trajectories, alignment data, SECONDA analysis) for isopinocampheol runs with the TITANIA software.</p>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
Research Data supporting "Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization"
<p>Raw research data supporting the article E. Weyandt, L. Leanza, R. Capelli, G. M. Pavan, G. Vantomme, and E.W. Meijer, "Controlling the length of porphyrin supramolecular polymers via coupled equilibria and dilution-induced supramolecular polymerization".</p>
Data: Gradient expansions for the large-coupling strength limit of the Møller-Plesset adiabatic connection (arXiv:2111.13146)
<p>Data corresponding to the work <em>Gradient expansions for the large-coupling strength limit of the Møller-Plesset adiabatic connection</em> (https://arxiv.org/abs/2111.13146). </p> <p>The zip file contains:</p> <p>- Data for the functional E_el and W_1/2 for all the systems presented in the article (.csv files)</p> <p>- Minimising positions for all the systems presented in the article (.csv files)</p> <p>- Checkpoint Files for all the calculations done with PySCF 1.7.6.</p>
Data for "Impact of bulk-edge coupling on observation of anyonic braiding statistics in quantum Hall interferometers"
<p>This contains the data sets for the work "Impact of bulk-edge coupling on observation of anyonic braiding statistics in quantum Hall interferometers". Data is in CVS format. </p>
Dataset: "Vibronic Coupling in Spherically Encapsulated, Diatomic Molecules: Prediction of a Renner-Teller-like Effect for Endofullerenes"
<p>This datasets contains scripts and output files for the publication:<br> "Vibronic coupling in spherically encapsulated, diatomic molecules:<br> Prediction of a Renner-Teller-like effect for endofullerenes"<br> by<br> Andreas W. Hauser and Johann V. Pototschnig</p> <p>The zip file NO.zip contains the output files.<br> The zip file code.zip contains the python scripts.</p>
Representing surface heterogeneity in land-atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime - E3SM SCM data and code
<p>This dataset contains post-processed E3SM single-column model output and code used to produce the figures in the manuscript that we are targeting Geoscientific Model Development to submit. </p>
Photoactive Nickel Complexes in Cross-Coupling Catalysis
<p>ChemDraw figures to the minireview published in <em>Chem. Eur. J.</em> <strong>2021</strong>, <em>27</em>, 2770-2278; doi: <a href="https://chemistry-europe.onlinelibrary.wiley.com/doi/10.1002/chem.202003974"> 10.1002/chem.202003974</a>.</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
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.