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817 results for “use of devices”
Results from simulated drift trajectories of driting Fish Aggregating Devices used in Figures for paper Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles
<p>Escalle et al., (2024) Simulating drifting fish aggregating device trajectories to identify potential interactions with endangered sea turtles<br><span><a href="../doi/10.5281/zenodo.10815559">https://zenodo.org/doi/10.5281/zenodo.10815559</a></span></p> <p>Results data description<br>The above doi contains simulation output data generated from passive drift simulations of drifting FADs in the Pacific ocean. We refer the reader to the main text of the paper for details and definitions of the different zones and simulation experiments.</p> <p>All data files are matrices stored in comma-delimited files (.csv), where the first provides the column names of the matrix, and the first column provides the row names. The first cell in row 1, column 1 is simply a placement value that describes the nature of the matrix. </p> <p>When this value is a hash symbol (#), it denotes that the matrix represents a spatial density of virtual particles under a particular drift scenario and over a particular period of time. Column names provide the latitude indices of each cell, and row names are the longitude indices. Values are the proportion of all particules in the domain that passed through this cell, during the drift-time window of this results file (see below).</p> <p>When the first cell value is a hash followed by a code (e.g. # EqZ), it denotes that the file represents a connectivity matrix between the zones given by the code (e.g. Equatorial Zones EqZ or Fishing Zones FZ) and defined in the row names of the matrix, and the turtle zones defined by the column names. Values are the proportion of particules beginning the zone defined by the row name at the start of the simulation, which are now present in the zone defined by the column name.</p> <p>Files name follow a convention describing the deployment and drift-time scenario they represent.</p> <p>For spatial density matrices:<br>[Origin Zones]_[Subset]_Density_[Drift Time]_....csv</p> <p>For connectivity/transition matrices:<br>[Origin Zones]_[Drift Time]_[Number of Simulations Run]_TransitionMatrix_....csv</p>
Superconducting flip-chip devices using indium microspheres on Au-passivated Nb or NbN as under-bump metallization layer
<p>Data used for figures in "Superconducting flip-chip devices using indium microspheres on Au-passivated Nb or NbN as under bump metallization layer" by A. Paradkar et al. <a href="https://doi.org/10.1063/5.0235266">Appl. Phys. Lett. <strong>126</strong>, 022601 (2025)</a></p>
Hybrid Market- and Community-Driven Strategy for Repurposing Devices for Medical Use
<p>There is a split in today's technological development between traditional proprietary-based and open community-driven development. Open development ( open science / open software / open hardware ) often loses steam when capital and long-term commitment is required. Proprietary-based development solves the issue by attracting capital, but the need minimize and compensate risks leads to secrecy and a focus on developed economies.<br> <br> Here I propose a strategy to reconcile the two worlds. I focus on the scenario of off-the-shelf devices that can be repurposed for meeting needs in the medical field, as it can lower the typically high initial capital required in the medical device sector while motivating community involvement. The strategy is contingent on the owner of the initial technology not being deprived of its intellectual property rights.<br> <br> This is a work in progress that will be updated through my own involvement and that of any interested bodies.</p>
CAD file and chromatographic data for determination of diclofenac in wastewater using a 3D printed immunosorbent device
<p>CAD file of the 3D-printed device (in FreeCAD) and chromatographic data for determination of diclofenac in wastewater associated to Fig. S5 (in CSV) of the paper "A 3D printed spinning cup-shaped device for immunoaffinity solid-phase<br> extraction of diclofenac in wastewaters" published in Microchimica Acta 2022 (DOI:10.1007/s00604-022-05267-9.)</p>
Lecture of physiological activity with EEG techniques in two groups of students using a BCI device
<p>Currently, there is a great interest in knowing more in detail what happens in a student's mind in the classroom. This dataset seeks to help those researchers who want to perform data analytics of student behavior in a classroom.</p> <p>This dataset has the peculiarity that it has been built inside a classroom in an authentic learning situation wherein a face-to-face model a teacher teaches his class in front of a traditional group.</p> <p>The data set achieved seeks to provide support for the analysis of learning activities to identify the good and bad practices and the availability of students to appropriate new thoughts. In addition, these results also serve as support to generate new research through other learning scenarios, whether traditional or distance learning, where the results of the physiological activity of the brain can be used to compare the didactics that produce the best results in the students and the areas of opportunity in the teaching processes.</p>
Noise data for the study of consonant-in-noise discrimination using an auditory model with different speech-based decision devices
<p>The data stored in this repository correspond to two sets of 5000 speech-shaped noises (SSN) that were used in the conference paper titled "Consonant-in-noise discrimination using an auditory model with different speech-based decision devices" by the same authors, presented in the DAGA conference in Vienna, Austria, on 17/08/2021. </p> <p>The two zip files (<strong>osses2021c_S01</strong> and <strong>osses2021c_S02</strong> for participants S01 and S02, respectively) have following structure:</p> <ul> <li><strong>NoiseStim-SSN</strong>: Folder containing the 5000 noises</li> <li><strong>Results</strong>: Results of the listening experiment collected using the fastACI toolbox (https://github.com/aosses-tue/fastACI).</li> </ul> <p>To obtain similar results for other participants the same experiment has to be run using the fastACI toolbox. For instance, to collect new data for participant 'S03', you have to input the following command in MATLAB:</p> <pre><code class="language-bash">fastACI_experiment('speechACI_varnet2013','S03','SSN');</code></pre>
SIMCor - Demo of fast-to-evaluate surrogate models used for device implantation
<p>This video illustrates a fast to evaluate model for TAVI device implantation based on isogeometric analysis. The tissue is modelled in a rigid manner, meaning that no deformation can be caused by the device. The device deployment is modelled using an expanding cylinder and assessing the contacts between device and tissue. This approach allows rapid evaluation of the risk of paravalvular leakage for different device orientations.</p>
Laser Beam Characterization Data Related to Testing Different Near-Infrared Laser Therapy Devices Used in Sports Medicine
<p>This dataset contains laser beam characterization data collected for different near-infrared laser therapy devices that are used in sports medicine. Data were collected between September 2021 and December 2022 at the Department of Anatomy II (Neuroanatomy) of the Ludwig-Maximilians University of Munich, Germany. Laser therapy devices were characterized using a thermal power sensor, a photodiode, and a beam profiling camera (for details, see [1]). Two different laser therapy devices were investigated (Dolorclast High Power Laser, Electro Medical Systems; Cube 4 Med, Eltech K-laser s.r.l.). The measurements were repeated for three units of each laser therapy device (EMS-1, EMS-2, EMS3; K-1, K-2, K-3). The dataset contains raw data files from each sensor that include text (TXT) files, comma-separated-values (CSV) formatted files, and raw camera recordings in binary format (BINARY.BGDATA). The data was used to create figures that were submitted to the journal MDPI Biomedicines [1]. The figures in [1] were created with Python in a Jupyter Notebook, which is also included in this dataset.</p> <p><strong>2023-02-17 Update: The manuscirpt has been published in MDPI Biomedicines [2]. If you make any use of this dataset, please cite [2].</strong></p> <p>[1] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Several Units of the Same Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Preprints</em> <strong>2023</strong>, <em>2023010251</em>, https://doi.org/10.20944/preprints202301.0251.v1.</p> <p>[2] Kaub, L.; Schmitz, C. Spread of the Optical Power Emission of Three Units Each of Two Different Laser Therapy Devices Used in Sports Medicine, Which Cannot Be Assessed by the Users, Shown by Means of High-Fidelity Laser Measurement Technology. <em>Biomedicines</em> <strong>2023</strong>, <em>11</em>, 585. https://doi.org/10.3390/biomedicines11020585</p>
LiftWEC deliverable 3.6 - Part I: Dataset from 3D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains numerical simulation results obtained from 3D-validation studies of the high-fidelity RANS model employed in the LiftWEC project. The case identifiers (ID) correspond to the case numbering employed in the experimental reference cases defined by École Centrale de Nantes. It is highly recommended to read the corresponding project reports on numerical modelling (D3.6) and on experimental modelling (D4.5, D4.6, D4.7, D4.8) which are also available in the LiftWEC community on zenodo (https://zenodo.org/communities/liftwec/).</p> <p>The cases comprise simulations of a rotor at constant velocity in calm water and regular waves in full 3D simulations. It further includes 2D simulation results of a rotor at constant rotational velocity in irregular waves and at variable velocity in monochromatic waves.</p> <p>All loads in the data set are given in force per unit span length (N/m), torque and power output is given as values per unit span as well. Wave elevation data up and down-wave of the rotor is given in (m).</p> <p> </p> <p> </p> <p> </p>
LiftWEC deliverable 3.6 - Part II: Dataset from numerical simulations of full-scale LiftWEC device using a high-fidelity RANS model
<p>This dataset contains the numerical simulation results for a full-scale LiftWEC device in regular and irregular wave conditions. The regular wave cases occur at static pitch and fixed rotational velocity. The parameters used for these cases are described in the corresponding *CaseParameters.csv file. These simulations were used to derive a first estimate of the maximum conversion efficiency of the rotor from wave power to shaft power. A detailed description of the employed numerical model and the case setup can be found in LiftWEC deliverable 3.6 Hydrodynamic Validation of Final Design, which was also uploaded to the LiftWEC community on zenodo. In accordance with the coordinate system definition used in the validation case, a relative phase angle of 270° degree corresponds to foil1 at 3 o'clock position while the wave crest passes over the rotor axis position. Rotation is clockwise, Rotor rotates in the direction of orbital wave particle velocities.</p> <p>The control reference case describes the first test case of a cyclorotor in irregular waves under active control of angular velocity and foil pitch. This case is also documented in deliverable D3.6. The case files can be used to recreate wave conditions and motion signal. The load-file can be used to validate the obtained tangential and radial forces on foil 1 as well as the total power output over time. More information on the control model can be found in the corresponding project deliverables D5.X.</p> <p>All loads given as values per unit span length (e.g. for forces [N/m]).</p>
LiftWEC deliverable 3.3 - Dataset from 2D validation simulations of LiftWEC device using a high-fidelity RANS model
<p>This dataset contains results obtained from numerical simulations of the LiftWEC model scale device in a two-dimensional setting. The simulations were done based on the experimental validation campaign conducted in the scope of the LiftWEC project and documented in deliverables D4.2, D4.3 and D.4. The corresponding experimental datasets are also available within the LiftWEC community on zenodo.</p> <p>The numerical setup as well as a presentation and discussion of obtained results is available in LiftWEC deliverable D3.3 Tool Validation and Extension report, which also contains information on the potential flow model. All forces presented in this document are given as forces per unit span length. As the 2D RANS model was found to be rather sensitive to high fluctuations at this preliminary investigation stage, results are presented as mean forces and force fluctuations at rotation period, analysed by means of an FFT post-processing routine. The case identifiers correspond to the case numbering employed in the experimental model tests.</p>
Comparison Study of Two Supraglottic Airway Devices Used for Patients Under General Anesthesia
ClinicalTrials.gov study NCT01001078. IPD Sharing: Not stated. Countries: 1. Publications: 18.
Comparison of Tissue Oxygenation Measurement Using Multimodal Devices
ClinicalTrials.gov study NCT05784103. IPD Sharing: NO. Countries: 1. Publications: 2.
Study of Inhaler Device Attributes Investigating Critical and Overall Errors, Ease of Use, and Preference Between a Number of Inhaler Devices
ClinicalTrials.gov study NCT02184624. IPD Sharing: YES. Countries: 2. Publications: 1.
Effect of Mentoring on Endotracheal Tube Exchange Using a New Device
ClinicalTrials.gov study NCT02763189. IPD Sharing: YES. Countries: 1. Publications: 1.
The Use of Assistive Gait Devices Can Reduce the Risk of Falls in Patients With Neuromuscular Diseases Following a Training Period.
ClinicalTrials.gov study NCT07072676. IPD Sharing: NO. Countries: 1. Publications: 6.
Pain Control for Intrauterine Device Placement Using Paracervical Block
ClinicalTrials.gov study NCT02219308. IPD Sharing: NO. Countries: 1. Publications: 16.
Efficacy and Safety Study of Intranasal Administration of 100, 200, and 400 μg of Fluticasone Propionate Using a Novel Bi-directional Device
ClinicalTrials.gov study NCT01624662. IPD Sharing: YES. Countries: 1. Publications: 3.
AccuCath™ Intravenous (IV) Device Used by Vascular Access Team (VAT)
ClinicalTrials.gov study NCT01937195. IPD Sharing: NO. Countries: 1. Publications: 1.
Norditropin NordiFlex® Device Compared to the Device Previously Used by Patients or Parents
ClinicalTrials.gov study NCT01245374. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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.