Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
2,208
datasets available to search
ShareScore release 0.7.1
Dataset results
2,208 results for “coupling”
A Dataset for detecting Change Coupling and Structural Dependencies.
<p><strong>Introduction</strong></p> <p>This repository hosts the results of runs evaluating the first developments of prototype tool for detecting Change Coupling and Structural Dependencies in the context of cyber-physical-systems(CPS). This tool analyzes the projects' code repository history and uses rule mining to detect logistical couplings, it also analyzes the source code to detect which of those changes have additional structural dependencies. </p> <p> </p> <p>The two datasets are the result from the analysis of two popular GitHub CPS projects:</p> <ul> <li>Eclipse Concierge (Java) — a small-footprint implementation of the OSGi Core Specification optimized for mobile and embedded devices.<sup>1</sup></li> <li>PX4 — a flight control solution for drones, that also contains a Drone Middleware Platform, providing drivers and middleware to run drones <sup>2</sup></li> </ul> <p>The datasets were generated by running a change coupling and structural dependencies analyzer, that is a prototype under development. The resulting raw data is available under the <code>project_results/Proj_Name</code> folder. Additionally, in the folder <code>notebooks </code>the user can find examples of how to easily query the functionality offered by the visualization and analytics libraries (in folder <code>analytics)</code>.</p> <p> </p> <p>[1] https://github.com/eclipse/concierge</p> <p>[2] https://github.com/PX4/PX4-Autopilot</p>
Data Analysis files for "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity"
<p>Data Analysis for the paper "Dissipative Quantum Feedback in Measurements Using a Parametrically Coupled Microcavity", which is published in PRX Quantum <strong>3</strong>, 020309 (2022).</p>
Geosci. Model Dev. paper data for Flipo et al., "Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data"
<p>Data and associated user guide, as part of the paper :</p> <p>Flipo N., Gallois N., Schuite J. Regional coupled surface-subsurface hydrological model fitting based on a spatially distributed minimalist reduction of frequency-domain discharge data, Geoscientific Model Development.</p> <p>In consistency with the “Code and data availability” sub-section of the paper, all data necessary for the reproduction of<br> Figs. 7, 8c, 8d, 9, 10 and 11 are here provided.</p>
Data for Li et al., Coupling remote sensing and particle tracking to estimate trajectories in large water bodies, International Journal of Applied Earth Observation and Geoinformation, 2022
<p>This data set contains four parts:</p> <p>1) compressed folder with input parameters and results for the hydrodynamic model</p> <p>2) compressed folder with input parameters and results for the particle tracking</p> <p>3) compressed folder with satellite data </p> <p>4) code used in the article for hydrodynamic model, particle tracking and image processing</p> <p>Each folder contains a readme file,</p>
Data for article "Theory of superconductivity mediated by Rashba coupling in incipient ferroelectrics"
<p>Ab initio frozen-phonon splitting of the electronic bands, normalized with the polar displacement. The data is plotted in figure 3 (b) of the article Phys. Rev. B <strong>105</strong>, 224503 (2022). </p>
Intermolecular interactions in G protein-coupled receptor allosteric sites at the membrane interface from molecular dynamics simulations and quantum chemical calculations
<p>Allosteric modulators are called to be promising candidates in G protein-coupled receptor (GPCR) drug development by displaying target selectivity and fewer side effects. Among the allosteric sites known to date, extrahelical cavities represent an uncharacteristic binding location that raises many questions about the ligand interactions and stability; the binding site structure, and how all of these are affected by lipid molecules. In this work, we analyze the dynamics and interactions in the PAR2, C5aR1, and GCGR receptors unbound and bound to allosteric modulators at the receptor-lipid interface using molecular dynamics simulations in three lipid compositions. In addition, we performed quantum chemical calculations to further explore electrostatic interactions and the strength of atom pairwise contacts in the stabilization of the ligand-receptor complexes. We show that besides classical hydrogen bonds weak polar interactions such as O-HC, O-Br, and S-HC contacts and aromatic interactions contribute to the binding of allosteric modulators at the extrahelical sites in the middle of the membrane. The allosteric cavities are open and detectable in various membrane compositions but not always predicted as druggable. The availability of polar atoms for interactions in such cavities can be assessed by water molecules from the simulations. Although ligand-lipid interactions are weak, the lipid tails play a role in sizing and shaping the large part of the allosteric cavity. </p> <p>You will find the following files:</p> <ul> <li>Input files of the equilibration and production protocols of MD simulations (MD_simulations_inputs.zip)</li> <li>Input files and coordinate files of F-SAPT and NCIPLOT calculations (quantum_chemical_coordiates_inputs.zip)</li> </ul>
Dataset for the manuscript "Collective spin waves in RKKY interlayer-coupled Ni80Fe20/Ru/ Ni80Fe20 nanowire arrays"
<p>These are the dataset relative to paper entitled "<strong><span>Collective spin waves in RKKY interlayer-coupled </span></strong><strong><span>Ni</span></strong><strong><sub><span>80</span></sub></strong><strong><span>Fe</span></strong><strong><sub><span>20</span></sub></strong><strong><span>/Ru/</span></strong><strong><span> </span></strong><strong><span>Ni</span></strong><strong><sub><span>80</span></sub></strong><strong><span>Fe</span></strong><strong><sub><span>20 </span></sub></strong><strong><span>nanowire arrays</span></strong><strong>"</strong></p>
Extended Room Transition Dataset (ERTD) of room impulse responses in coupled rooms
<p>This dataset accompanies the publication</p> <blockquote> <div> <div> <div> <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> </div> </div> </div> </blockquote> <div> <div> <div> <p> </p> <div> <div> <div> <p>The dataset contains finite-difference time-domain (FDTD) simulations. It extends the Room Transition Dataset (RTD) by McKenzie et al (doi: <a href="https://doi.org/10.5281/zenodo.4636068" target="_blank" rel="noopener">10.5281/zenodo.4636068</a>). More precisely, it extends the “meeting room to hallway” transition, by additional receiver positions in both rooms. The ERTD contains 2833 RIRs in total. For all RIRs, the sound source position was chosen according to the “source in meeting room, no line-of-sight” (NLOS) configuration of the RTD. While the RTD only features receiver positions on a straight line between the rooms, the Extended Room Transition dataset samples the entire room geometry using a uniform grid with 0.2 m resolution. All simulations are performed on the horizontal plane at 1.55 m height.</p> <p>We simulated two variants of the dataset. In variant ERTD1, the wall absorption properties were assigned to approximately match the corresponding measurements. The absorption coefficient was assigned uniformly to all walls in the respective rooms, where the meeting room walls exhibited an absorption coefficient alphaR1 = 0.12, while the hallway was modeled less absorbent with alphaR2 = 0.042. In variant ERTD2, a significantly higher absorption coefficient alphaR3 = 0.48 is assigned to the right wall of the hallway, while all other surfaces are modeled analogous to ERTD1. We use ERTD2 in our evaluation to demonstrate that the common-slope model can also be used in environments with very non-uniform absorption distributions.</p> <p> </p> <p>___________</p> <div> <div> <div> <p>We acknowledge the computational resources provided by the Aalto Science-IT project.</p> </div> </div> </div> </div> </div> </div> </div> </div> </div>
Dataset for Paper Titled "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations"
<div>The attached two files were used for analysis in the paper "First assessment of cloud-land coupling in LASSO Large-Eddy Simulations." The NetCDF file included planetary boundary layer heights derived from lidar and radiosondes. The CSV file detailed the model configurations for selected case days in simulation sets ID1-5.</div> <div> <div> <p> </p> </div> </div>
Supporting data for "A 2x2 quantum dot array with controllable inter-dot tunnel couplings"
<p>Supporting data and analysis scripts for all figures in in "A 2x2 quantum dot array with controllable inter-dot tunnel couplings", arXiv:1802.05446 (preprint)</p> <p>This dataset contains a readme file, a data file in hdf5 format containing all the relevant datasets, and a python script file that can be used to access, analyse and plot the datasets in the data file, reproducing the plots in the figures presented in the manuscript.</p>
Data and code for "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling"
<p>The data includes atomic structures, time-dependent dipole moments, and photoabsorption spectra of the systems modeled and analyzed in the article "Strong plasmon-molecule coupling at the nanoscale revealed by first-principles modeling" by Tuomas P. Rossi, Timur Shegai, Paul Erhart, and Tomasz J. Antosiewicz.</p> <p>The input scripts for reproducing the data are also included. The time-dependent density-functional theory calculations use the LCAOTDDFT module of <a href="https://wiki.fysik.dtu.dk/gpaw/">the GPAW code</a>, and the atomic structures are created with <a href="https://wiki.fysik.dtu.dk/ase/">the ASE code</a>.</p> <p>See <em>README.md</em> in the archive for a detailed description.</p>
Data from "Predicting Global Ground Geoelectric Field With Coupled Geospace and Three‐Dimensional Geomagnetic Induction Models"
<p>Data presented in http://dx.doi.org/10.1029/2018SW001859 excluding the first and last hours which were determined to contain partially unphysical results and probably should not be used.</p> <p>Each file contains the external ground magnetic field components calculated on 5x5 degree geographic grid and the results of induction modeling using 1d and 3d ground conductivity models: total ground magnetic field components, horizontal ground electric field components. Times are given in UTC.</p> <p>To reproduce Figure 8 in above reference use:</p> <p> </p> <p>import numpy<br> import matplotlib.pyplot<br> data = numpy.load('2006-12-14T22:58:00.npz') # or 2006-12-14T22_58_00.npz<br> matplotlib.pyplot.colorbar(<br> matplotlib.pyplot.imshow(<br> data['B_3D_north'],<br> cmap = matplotlib.pyplot.get_cmap('bwr'),<br> vmin = -800, vmax = 800,<br> extent = (-180, 180, -90, 90)<br> ),<br> format = '%.0f', fraction = 0.02, pad = 0.03<br> )<br> matplotlib.pyplot.show()</p> <p> </p> <p>and substitute B_3D_east, E_3D_north, etc. for the different panels.</p>
On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations - data release
<p>This release contains all data and metadata used to prepare the publication <a href="https://arxiv.org/abs/2405.05765"><em>On the mixing between flavor singlets in lattice gauge theories coupled to matter fields in multiple representations</em> [2405.05765].</a> </p> <p>If you encounter difficulties downloading the large files, we recommend using <a href="../records/11142962">zenodo-get</a>. This provides a command-line downloader for any Zenodo record. For unstable connections we recommend using it with the -w flag to generate a list all files in this Zenodo record. This can then be used with tools such as <a href="https://www.gnu.org/software/wget/">wget</a> to resume partial downloads as</p> <p><code>zenodo_get RECORD_ID_OR_DOI -w - | xargs wget -c<br>zenodo_get RECORD_ID_OR_DOI </code></p> <p>(The second line ensures that the downloads completed correctly, and that the md5 hashes match)<br><br>Further details are given in the file README.md.</p> <p>The work of EB and BL is supported in part by the EPSRC ExCALIBUR programme ExaTEPP (project EP/X017168/1). The work of EB, BL, MP, and FZ has been supported by the STFC Consolidated Grant No. ST/X000648. The work of EB has also been supported by the UKRI Science and Technology Facilities Council (STFC) Research Software Engineering Fellowship EP/V052489/1. The work of NF has been supported by the STFC Consolidated Grant No. ST/X508834/1. The work of DKH was supported by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2017R1D1A1B06033701). The work of DKH was further supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (2021R1A4A5031460). The work of JWL is supported by IBS under the project code, IBS-R018-D1. The work of HH and CJDL is supported by the Taiwanese MoST grant 109-2112-M-009-006-MY3 and NSTC grant 112-2112-M-A49-021-MY3. The work of CJDL is also supported by Grants No. 112-2639-M-002-006-ASP and No. 113-2119-M-007-013. The work of BL and MP has been further supported in part by the STFC Consolidated Grant No. ST/T000813/1.<br>BL and MP received funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation program under Grant Agreement No.~813942. The work of DV is supported by STFC under Consolidated Grant No. ST/X000680/1.</p> <p>Numerical simulations have been performed on the DiRAC Extreme Scaling service at the University of Edinburgh, and on the DiRAC Data Intensive service at Leicester.<br>The DiRAC Extreme Scaling service is operated by the Edinburgh Parallel Computing Centre on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk). This equipment was funded by BEIS capital funding via STFC capital grant ST/R00238X/1 and STFC DiRAC Operations grant ST/R001006/1. DiRAC is part of the National e-Infrastructure</p>
Maintenance of Convectively Coupled Kelvin waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing (Code and Data)
<p>This is the dataset and code for generating all figures for the journal article named "Maintenance of Convectively Coupled Kelvin Waves: Relative Importance of Internal Thermodynamic Feedback and External Momentum Forcing," The article was written by Mu-Ting Chien and Daehyun Kim and submitted to Geophysical Research Letters in 2024.</p>
Data supplement for "Localized states in coupled Cahn-Hilliard equations"
<p>Data set and plotting codes for all figures of "Localized states in coupled Cahn-Hilliard equations". Additionally, it includes Matlab codes for numerical path continuation and python codes for time simulation which can be used to reproduce the data.</p> <p>For more information, please see the included README.md</p>
Coupled Hydrological and Thermal Models of Rockwall Permafrost
<p>This dataset contains forcing data and selected output of coupled thermal and hydrological simulations applied to a high-elevated rockwall site (the Aiguille du Midi, 3842 m asl, Mont Blanc massif, France).</p> <p>All data are provided as .shp and .shx for display, as well as a .dbf file for quick reading. They are made of 5 columns, whose:</p> <ul> <li>« Node » is the Node ID,</li> <li>« X » is the position (in m) on the x axis,</li> <li>« Y » is the position (in m) on the y axis,</li> <li>« xINIT » is the calculated value for the parameter indicated in the file name(head, temperature, etc.)</li> <li>« Time » is the time step at which the value is calculated.</li> </ul> <p>The model output are gathered according to various cases studies of saturation and water flows. The model settings of the various cases studies are outlined in this file but more details about the mathematical approach and modeling settings and strategy are provided in the study to which the dataset belongs and which was submited for the first time to Journal of Geophysical Research: Earth Surface in July 2020.</p> <ul> <li><strong><em>SaFl </em></strong>corresponds to a saturated with forced water flows case study by assuming a constant recharge and discharge.</li> <li><strong><em>SaNF</em></strong> is a saturated case study with no water flows.</li> <li><strong><em>uSFl</em></strong> corresponds to an unsaturated case study with forced water flows in selected fractures only.</li> <li><strong><em>uSLF</em></strong> is unsaturated with limited water flows.</li> </ul> <p>For <strong><em>SaFl</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at various time step after of transient simulations (1550 AD, 2000 AD, 2015 AD, 2030 AD) such as displayed in Figures S2 and S3.</li> <li>Hydraulic heads (m) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 6 and S3.</li> <li>The ice bulk volumetric fraction at various time step after of transient simulations (2000 AD, 2015 AD, 2030 AD) such as in Figure S3.</li> <li>Temperature (°C) after initialization (0 AD) and at various time steps of the transient simulations (1850 AD, 2000 AD, 2015 AD, 2030 AD and 2100 AD) such as in Figure 4 and S3.</li> </ul> <p>For <strong><em>SaNF </em></strong>the following output are provided:</p> <ul> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> <li>Hydraulic heads (m) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 6.</li> </ul> <p>For <strong><em>uSFl </em></strong>the following output are provided:</p> <ul> <li>Hydraulic heads (m) and temperature (°C) at 2100 AD such as in Figure 4 and 6.</li> <li>Saturation in 852 AD, 853 AD and 854 AD such as in Figure 5.</li> </ul> <p>For <strong><em>uSLF</em></strong> the following output are provided:</p> <ul> <li>Darcy flux (m.s<sup>-1</sup>) at 1550 AD and 2015 AD such as in Figure S2</li> <li>Hydraulic heads (m) after initialization (0 AD) and at 1850 AD and 2100 AD) such as in Figure 6.</li> <li>Temperature (°C) after initialization (0 AD) and for 1850 AD and 2100 AD such as in Figure 4.</li> </ul> <p>In addition, the temperature and hydrological (hydraulic head changes for the unsaturated cases studies only) forcing data (« Input_data_boundary_conditions.csv ») are provided. This last file contains:</p> <ul> <li><em>A to D</em>: the surface points extracted along the 4-m resolution DEM transect (Horizontal (X) position in m, Vertical (Y) position (elevation in m)) and MARST map (for the period 1961-1990: MARST<sub>init</sub>) as illustrated on Fig. S1, together with the adjusted MARST to run the model initialization simulations.</li> <li><em>F to I</em>: the surface points taken on for forcingthe model at its upper boundary and in between which the forcing data were interpolated.</li> <li><em>L to BH</em>: Data used for transient simulations with the time in year (<em>L</em>), the MARST anomaly applied to the adjusted MARST (<em>M</em>), the time in days (<em>N</em>), the MARST value applied at each surface node from 1 to 23 (<em>O </em>to <em>AK</em>), as well as the changes in head values applied at at each surface node from 1 to 23 (<em>AL </em>to <em>BH</em>) for the concerned simulations.</li> </ul> <p> </p> <p>More information can be made available by contacting Florence Magnin or Jean-Yves Josnin at <a href="mailto:florence.magnin@univ-smb.fr"><em>florence.magnin@univ-smb.fr</em></a><em> </em>or <a href="mailto:jean-yves.josnin@univ-smb.fr"><em>jean-yves.josnin@univ-smb.fr</em></a></p>
Data accompanying the article "Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework"
<p><em>Amonthly_files.tar.gz</em> contains the gridded monthly averaged quantities used in the manuscript Arctic sea ice mass balance in a new coupled ice-ocean model using a brittle rheology framework" for each year between 2000 and 2018.</p> <p>Files containing "simba" in their name contain quantities related to the sea ice mass balance (volume of melt/growth...)</p> <p>Files containing "icemod" in their name contain other quantities related to sea ice properties (thickness, concentration...)</p> <p>In case information is missing, do not hesitate to contact guillaume.boutin@nersc.no , heather.regan@nersc.no or einar.olason@nersc.no</p> <p>This research has been funded by the Norwegian Research Council (Nansen Legacy: grant no. 27673, FRASIL: grant no. 263044, and ARIA: grant no. 302934), JPI Climate and JPI Oceans (MEDLEY project, under agreement with the Norwegian Research Council, grant no 316730), and by Copernicus Marine Environment Monitoring Service (CMEMS) WIzARd project. CMEMS is implemented by Mercator Ocean in the framework of a delegation agreement with the European Union<br> Copernicus Marine Environment Monitoring Services (contract no.<br> 69), and the European Space Agency through the Cryosphere Virtual Laboratory (CVL, grant no. 4000128808/19/I-NS).</p>
Advanced open source data formats for geometrically and physically coupled systems - examples
<p>Some model files to describe a geometrically and physically coupled PDE and a ODE system, arising from discretization in space. The files correspond to:</p> <ul> <li>a simplified two component problem with coupling and an FMU in <strong>withFMU.json</strong></li> <li>the corresponding ODE in <strong>io_withFMU_FECoupled.json</strong></li> <li>a PDE model of a complete machine in <strong>ictimt_coupledModel.zip</strong>. This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> <li>the corresponding discrete model in <strong>ictimt_feCoupled.zip </strong>This model belongs to https://doi.org/10.17973/MMSJ.2021_7_2021072</li> </ul> <p> </p> <p> </p>
Soil microbial community coupling network in response to diversified crop rotations
<p>Dataset of manuscript entitled “Soil microbial community coupling network in response to diversified crop rotations”. This manuscript includes the results of WP3 from the SOFT project (ref. 890874).</p>
Noncanonical electromechanical coupling paths in cardiac hERG potassium channel (semi-binary contact maps)
<p>Matrices of the semi-binary contact maps of the following open and closed systems: WT, A527L, A614G, L524R, L529H, L532H, T425L, T618L, W563L.</p> <p>The residue numbering is not the official one because the first residues (397) of hERG (PAS domain) were not included in our simulations so that each subunit comprizes 466 residues. Moreover, the four subunits were numbered consecutively. The official numbering of a residue can be easily recovered. The general rule is:</p> <p>official residue - 397 = our residue</p> <p>For example, the official T425 corresponds to T28 in the first subunit (425-397), T494 in the second subunit (425-397+466), T960 in the third subunit (425-397+466+466), and T1426 in the fourth subunit (425-397+466+466+466).</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.