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60 results for “Electromechanics”
Data and code for figures: Intrinsic Kerr amplification for microwave electromechanics
<p>This directory contains the datasets and code for generating the figures in the research article "Intrinsic Kerr amplification for microwave electromechanics", <em>Appl. Phys. Lett.</em> 124, 243503 (2024).</p>
Data and code for figures: Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing
<p>This directory contains the datasets and code (if applicable) for generating the figures in the research article "Kinetic Inductive Electromechanical Transduction for Nanoscale Force Sensing", Physical Review Applied 20, 024022 (2023).</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>
Clinical phenotypes in acute and chronic infarction explained through human ventricular electromechanical modelling and simulations
<p>This dataset includes the meshes, model parameters, and Alya executable binary for simulating acute and chronic stage post-myocardial infarct using Alya, to replicate the results in the article <a href="https://doi.org/10.7554/eLife.93002.1">https://doi.org/10.7554/eLife.93002.1</a></p> <p>For each scenario simulated, a baseline simulation folder is provide with all the required meshes, fields, and model parameters necessary to run an Alya simulation. An additional series of models with variability in ionic conductances is also included for each scenario under the folder <scenario>_pom/, under which 20 simulations are included. For each simulation, only the file describing the ionic conductance scaling factors (ventricular_cell.txt) are included, all other files required to run each particular simulation can be found in the <scenario>_baseline/ version. </p> <p>The file structure is as follows:</p> <ul> <li>Alya executable binary</li> <li>control_baseline</li> <li>control_pom</li> <li>75%_transmural_scar <ul> <li>acute <ul> <li>bz1_baseline</li> <li>bz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz2_baseline</li> <li>bz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>bz3_baseline</li> <li>bz3_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>chronic <ul> <li>rz1_baseline</li> <li>rz1_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> <li>rz2_baseline</li> <li>rz2_pom <ul> <li>pom_id_0</li> <li>...</li> <li>pom_id_19</li> </ul> </li> </ul> </li> <li>fast_pacing <ul> <li>alternans1</li> <li>alternans4</li> </ul> </li> </ul> </li> </ul> <p>The simulation files in alternans4/ was use to generate results Figure 6 of the accompanying article, and alternans1/ was used to generate results Figure 7. </p> <p>The Alya executable binary has been built on ARCHER2 with the following loaded modules:</p> <p>1) craype-x86-rome <br>2) libfabric/1.12.1.2.2.0.0<br>3) craype-network-ofi <br>4) perftools-base/22.12.0 <br>5) xpmem/2.5.2-2.4_3.30__gd0f7936.shasta <br>6) bolt/0.8 <br>7) epcc-setup-env <br>8) load-epcc-module <br>9) gcc/11.2.0 <br>10) craype/2.7.19 <br>11) cray-dsmml/0.2.2 <br>12) cray-mpich/8.1.23 <br>13) cray-libsci/22.12.1.1 <br>14) PrgEnv-gnu/8.3.3 <br>15) tk/8.6.13 <br>16) tcl/8.6.13 <br>17) cray-python/3.9.13.1<br>18) matplotlib/3.7.2<br><br>To replicate the study, access to an installation of the code in the Nord supercomputer can be requested to <a title="mailto:mariano@elem.bio" href="mailto:mariano@elem.bio">mariano@elem.bio</a></p>
Ground State Cooling of an Ultracoherent Electromechanical System
<p>This dataset corresponds to the processed data presented in our paper "Ground State Cooling of an Ultracoherent Electromechanical System", currently available as a preprint at https://arxiv.org/abs/2107.05552</p> <p>The dataset is composed of 10 json files comprising the data for each figure of the main texte and the supplementary information, as well as a Jupyter notebook comprising 10 cells, each for plotting a figure of the main text or the supplementary information of the manuscript from the provided json files. A comment at the beginning of the cell indicates which figure will be plotted.</p>
A Large Scale Fabrication of Graphene Based Nano-electromechanical Contact Switches With Ultra-low Pull-in Voltage [Dataset]
<p>Original data set for the 'A Large Scale Fabrication of Graphene Based Nano-electromechanical Contact<br> Switches With Ultra-low Pull-in Voltage' is uploaded.</p>
Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)
<p><strong>General information on the data set</strong></p> <p>The dataset was generated with two different measurement systems at the ZeMA testbed for electromechanical cylinders.</p> <p> </p> <p><strong>All relevant information can be found within the hdf5 file.</strong></p> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in MATLAB:</strong></p> <pre><code># available structures inside file dataset = 'axis11_2kHz_ZeMA_PTB_SI.h5'; h5disp(dataset) % general attributes about file attr = h5info(dataset).Attributes; project = jsondecode(attr(1,1).Value) person = jsondecode(attr(2,1).Value) publication = jsondecode(attr(3,1).Value) experiment = jsondecode(attr(4,1).Value)</code></pre> <p> </p> <p><strong>Example for reading out the metadata of the hdf5 file in Python:</strong></p> <pre><code>import h5py import json # open file h5file = h5py.File("axis11_2kHz_ZeMA_PTB_SI.h5", "r") # general attributes about file for key in h5file.attrs: print(key) val = json.loads(h5file.attrs[key]) for subkey, subval in val.items(): print(" ", subkey, " : ", subval) # available structures inside file h5file.visit(print) # proper exit h5file.close()</code></pre> <p> </p> <p><strong>Metadata output of the hdf5 file:</strong></p> <ul> <li><strong>For the dataset:</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/' Attributes: 'Project': '{ "fullTitle":"Metrology for the Factory of the Future", "acronym":"Met4FoF", "websiteLink":"www.met4fof.eu", "fundingSource":"European Commission (EC)", "fundingAdministrator":"EURAMET", "funding programme":"EMPIR", "fundingNumber":"17IND12", "acknowledgementText":"This work has received funding within the project 17IND12 Met4FoF from the EMPIR program co-financed by the Participating States and from the European Union's Horizon 2020 research and innovation program. The authors want to thank Clifford Brown, Daniel Hutzschenreuter, Holger Israel, Giacomo Lanza, Bj\u00f6rn Ludwig, and Julia Neumann fromPhysikalisch-Technische Bundesanstalt (PTB) for their helpful suggestions and support." }' 'Person': '{ "dc:author":[ "Tanja Dorst", "Maximilian Gruber", "Anupam Prasad Vedurmudi" ], "e-mail":[ "t.dorst@zema.de", "maximilian.gruber@ptb.de", "anupam.vedurmudi@ptb.de" ], "affiliation":[ "ZeMA gGmbH", "Physikalisch-Technische Bundesanstalt", "Physikalisch-Technische Bundesanstalt" ] }' 'Publication': '{ "dc:identifier":"10.5281/zenodo.5185953", "dc:license":"Creative Commons Attribution 4.0 International (CC-BY-4.0)", "dc:title":"Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit)", "dc:description":"The data set was generated with two different measurement systems at the ZeMA testbed. The ZeMA DAQ unit consists of 11 sensors and the SmartUp-Unit has 13 differentsignals. A typical working cycle lasts 2.8s and consists of a forward stroke, a waiting time and a return stroke of the electromechanical cylinder. The data set does not consist of the entire working cycles. Only one second of the return stroke of every 100rd working cycle is included. The dataset consists of 4776 cycles. One row represents one second of the return stroke of one working cycle.", "dc:subject":[ "dynamic measurement", "measurement uncertainty", "sensor network", "digital sensors", "MEMS", "machine learning", "European Union (EU)", "Horizon 2020", "EMPIR" ], "dc:SizeOrDuration":"24 sensors, 4776 cycles and 2000 datapoints each", "dc:type":"Dataset", "dc:issued":"2021-09-10", "dc:bibliographicCitation":"T. Dorst, M. Gruber and A. P. Vedurmudi : Sensor data set of one electromechanical cylinder at ZeMA testbed (ZeMA DAQ and Smart-Up Unit), Zenodo [data set], https://doi.org/10.5281/zenodo.5185953, 2021." }' 'Experiment': '{ "date":"2021-03-29/2021-04-15", "DUT":"Festo ESBF cylinder", "identifier":"axis11", "label":"Electromechanical cylinder no. 11" }'</code></pre> <p> </p> </li> <li><strong>Example for one sensor (BMA 280, acceleration) of the PTB SmartUp Unit (SUU) and one sensor of ZeMA DAQ (pressure):</strong> <pre><code>HDF5 axis11_2kHz_ZeMA_PTB_SI.h5 Group '/PTB_SUU' Group '/PTB_SUU/BMA_280' Group '/PTB_SUU/BMA_280/Acceleration' Attributes: 'qudt:hasQuantityKind': '[ "qudt:Acceleration", "qudt:Acceleration", "qudt:Acceleration" ]' 'misc': '{ "interpolation_scheme":"cubic" }' 'si:unit': '"\\metre\\second\\tothe{-2}"' 'sosa:madeBySensor': '"BMA 280"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration uncertainty", "Y acceleration uncertainty", "Z acceleration uncertainty" ]' Dataset 'qudt:value' Size: 4766x1000x3 MaxSize: 4766x1000x3 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '[ "X acceleration", "Y acceleration", "Z acceleration" ]' Group '/ZeMA_DAQ' Group '/ZeMA_DAQ/Pressure' Attributes: 'qudt:hasQuantityKind': '"qudt:Pressure"' 'sosa:madeBySensor': '"Festo VPPM"' 'si:unit': '"\\pascal"' 'rdf:type': '"qudt:Quantity"' Dataset 'qudt:standardUncertainty' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure uncertainty"' Dataset 'qudt:value' Size: 4766x2000 MaxSize: 4766x2000 Datatype: H5T_IEEE_F64LE (double) ChunkSize: [] Filters: none FillValue: 0.000000 Attributes: 'si:label': '"Pneumatic pressure"' 'misc': '{ "raw_data":false, "comment":"Converted from ADC values based on appropriate conversion." }'</code></pre> </li> </ul>
Supplementary videos for the "Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture" manuscript
<p>Supplementary videos for preprint manuscript: </p> <p><em>Remote-refocusing light-sheet fluorescence microscopy enables 3D imaging of electromechanical coupling of hiPSC-derived and adult cardiomyocytes in co-culture</em><br> Liuba Dvinskikh, Hugh Sparks, Liliana Brito, Kenneth T MacLeod, Sian E Harding, Christopher Dunsby<br> bioRxiv 2023.01.28.526043; doi: https://doi.org/10.1101/2023.01.28.526043</p> <p>All videos have been rendered with JPEG compression.</p> <p>Shortened video captions (Please see supplementary information document for full caption)<br> <strong>Video 1:</strong> 3D LSFM timelapse of hiPSC-CM undergoing spontaneous calcium transients. <br> <strong>Video 2:</strong> Widefield transillumination timelapse of hiPSC-CM and adult-CM <br> <strong>Video 3:</strong> Widefield fluorescence timelapse of hiPSC-CM and adult CM with synchronized spontaneous calcium transients. <br> <strong>Video 4a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 4b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients. <br> <strong>Video 5a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 5b</strong>: Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture without NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 6a</strong>: 3D LSFM timelapse of hiPSC-CM and adult-CM co-culture undergoing synchronized spontaneous transients in a sample treated with NBleb. <br> <strong>Video 6b:</strong> Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 1 co-culture with NBleb undergoing synchronized spontaneous transients. <br> <strong>Video 7a:</strong> 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture undergoing synchronized spontaneous transients in a sample without NBleb. <br> <strong>Video 7b: </strong>Depth-encoded MIPs of the 3D LSFM timelapse of hiPSC-CM and adult-CM day 0 co-culture without NBleb. </p> <p> </p>
Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Heart Electromechanics Model Using Gaussian Processes Emulators - Training Datasets
<p>This database contains all training datasets for the Gaussian processes emulators (GPEs) trained in the study entitled "Cell to Whole Organ Global Sensitivity Analysis on a Four-chamber Electromechanics Model Using Gaussian Processes Emulators", submitted to PLOS Computational Biology.</p> <p>Every folder contains two csv files:</p> <p>- parameters.csv: the rows are the samples and the columns represent the parameters that were varied in the analysis</p> <p>- outputs.csv: the rows are the samples and the columns represent the values for the output features simulated for each sample</p> <p>In ventricular_cell_model, there are four folders:</p> <p>- ionic: ToR-ORd model samples used to train GPEs to predict the ventricular calcium transient features</p> <p>- contraction_isometric_stretch1.0: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with no strain (or stretch 1.0).</p> <p>- contraction_isometric_stretch1.1: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isometric contractions with 0.1 strain (or stretch 1.1).</p> <p>- contraction_isotonic: ToR-ORd model coupled with the Land contraction model samples used to train GPEs to predict the ventricular active tension transient features. The simulations were isotonic.</p> <p>The folder atrial_contraction_model follows the same structure, but the ionic model was Courtemanche, used to represent an atrial rather than ventricular calcium transient.</p> <p>The folder tissue_electrophysiology contains the training dataset for the GPEs to predict total atrial and ventricular activation times with an Eikonal model.</p> <p>The folder passive_mechanics contains the training dataset for the GPEs to predict inflated volumes and mean atrial and ventricular fiber strains for a passive inflation.</p> <p>The folder CircAdapt contains the training dataset for the GPEs to predict four-chamber pressure and volume features with the CircAdapt ODE model.</p> <p>Finally, the folder fourchamber contains the samples generated with a 3D-0D four-chamber electromechanics model to predict pressure and volume biomarkers for cardiac function.</p> <p>The details about the model can be found in the original publication.</p>
Data for "Cavity electromechanics with parametric mechanical driving"
<p>Data as plotted in the Main Paper and Supplementary Material. Raw data of the main paper. In addition some python and labview analysis and fitting scripts, as well as origin files used for processing and plotting.</p>
Impact of Intraventricular Septal Fiber Orientation on Cardiac Electromechanical Function
<p>Data and materials regarding the submission of the paper. See Data Availability section and https://github.com/valeryozenne/Cardiac-Structure-Database/tree/master/Article-3</p>
Atrial Electromechanical Function in Endurance Athletes With and Without Atrial Fibrillation
ClinicalTrials.gov study NCT03305744. IPD Sharing: UNDECIDED. Countries: 1. Publications: 3.
Data from: Systematic computational assessment of atrial function impairment due to fibrotic remodeling in electromechanical properties
Open the record for dataset details and reuse information.
Weak signal enhancement by non-linear resonance control in a forced nano-electromechanical resonator
<p>All the data for figures shown in the article entitled "Weak signal enhancement by non-linear resonance control in a forced nano-electromechanical resonator"</p>
Enhanced polarization in epitaxially strained monoclinic potassium niobate for lead-free electromechanical applications
<p>Atomic structure files used to study the anisotropic structural index for ferroelectric properties of strained KNbO3 perovskite polymorphs.</p>
Large flux-mediated coupling in hybrid electromechanical system with a transmon qubit
<p>The dataset contains the script and measured data to reproduce the figures shown in the publication titled "Large flux-mediated coupling in hybrid electromechanical system with a transmon qubit" written by Tanmoy Bera, Sourav Majumder, Sudhir Kumar Sahu and Vibhor Singh</p>
Characterisation Data - Electromechanical Characterization
<p>Ref_fatigue_1</p>
Characterisation Data - Electromechanical Characterization
<p>CNTs_CF_fatigue_2</p>
Characterisation Data - Electromechanical Characterization
<p>Pristine_3_fatigue_B</p>
Characterisation Data - Electromechanical Characterization
<p>st_3</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.