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1,774 results for “Acceleration”
Dataset for acceleration measurements at the research bridge openLAB in Bautzen, Germany - Change of dynamic behavior in the concrete hardening process
<p>This data set was collected during the construction phase of the openLAB in Bautzen, Germany during the period from 22.01.2024 - 30.04.2024. It includes acceleration measurements and temperature measurements that record the dynamic behavior of the bridge over a period of 49 days (from 22.01.2024 to 11.03.2024; the remaining data cannot be uploaded due to the Zenodo upload restriction, but can be released on request). The detailed documentation of the data set can be found in the file "Bartels, Dunkel, Marx_2024_Documentation.pdf". The documentation describes the structure, the applied monitoring system and the collected data in detail.</p>
Dataset to: Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles
<p>This is the dataset to the published article "Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles" (DOI: 10.1016/j.ijhydene.2023.08.292) in which the degradation of two commercial PEM fuel cell stacks was analyzed. <strong>Please cite this publication if you use the dataset in a publication as follows</strong>:</p> <p>P. Thiele, Y. Yang, S. Dirkes, M. Wick, S. Pischinger, Realistic accelerated stress tests for PEM fuel cells: Test procedure development based on standardized automotive driving cycles, Int. J. Hydrogen Energy 52 (Part D) (2024) 1065–1080, https://doi.org/10.1016/j.ijhydene.2023.08.292.</p>
BRAIN Journal-Cursor Movement – a Valuable Indicator in Intelligent System Design-Figure 3. Average values in acceleration and clicks
<p>However, when looking at the median values of acceleration and clicks, a clear difference appears between the states: more clicks for relaxation, higher acceleration for stress (Figure 3). </p>
fetal movement acceleration data
<p>This dataset contains accelerometer-recorded fetal movement signals with 16 different pregnant women<br> The dataset contains signals from one accelerometer placed on the abdominal wall of the pregnant women.</p> <p>Note:<br> The file format is .mat, which is recognized by the Matlab software. <br> the "xxxxxx_bp" files contain the signals of the maternal perception push-button markers (the mother was required to press a push-button every time she felt one fetal movement), and the "xxxxxx_signal" files contain acceleration data from the 3 axes (x, y, and z).<br> Hardware settings: <br> accelerometer: ADXL355 from ANALOG DEVICES<br> Sampling frequency: 500Hz</p>
Accelerated mechanochemical bond scission and stabilization against heat and light in carbamoyloxime mechanophores
<p>Underpinning data of manuscript and Supplemental Information sorted after Figures and Tables. Additionally, coordinate files from computational investigations.</p>
AI for SDG Acceleration - NSG MasterClass in Cooperation with MGG-PRODIGEES
<p>The video contains the lecture by Dr Reevana Balmahoon on artificial intelligence as a tool for accelerating the achievement of the Sustainable Development Goals (SDGs). Her presentation was part of the joint conference “International Capacity Development for the Civil Service - The Sustainable Digitalisation Agenda”, May 5-8, 2024, Cape Town, South Africa. The conference was jointly organised by the German Institute of Development and Sustainability (IDOS) and the National School of Government of South Africa (The NSG) in the framework of the ‘Managing Global Governance (MGG) network and its PRODIGEES project on digitalisation towards sustainable development. Dr Balmahoon is an AI and extended reality research lead of the South African Council for Scientific and Industrial Research (CSIR). The lecture and ensuing discussion, with responses from Dr Sven Grimm (IDOS) and Serusha Govender (Chatham House), were part of the NSG Master Class Series.</p>
Supporting Information for Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations
<p>Supplementary material to accompany the manuscript "Accelerating Combustion Mechanism Discovery with Automated Uncertainty, Sensitivity, Thermodynamics, and Kinetics Calculations" by Sevy Harris and Richard H West.</p> <ul> <li>The software (mostly Python scripts) is in autoscience_workflow.zip. </li> <li>DFT results (Gaussian log files, Arkane input files, Arkane output files) for all species and reactions are in dft.zip</li> <li>RMG-built detailed kinetic models are in mechanisms.zip </li> <li>Additional plots and results (as described in the manuscript) are in supporting_information.pdf</li> </ul>
A Data-Driven Epigenetic Characterization of Morning Fatigue Severity in Oncology Patients Receiving Chemotherapy: Associations with Epigenetic Age Acceleration, Blood Cell Types, and Expression-Associated Methylation
<p>This dataset contains supplementary materials including the eCpG mapping analysis results and annotation. The manuscript has been accepted for publication at Cancer Medicine. Please cite both the paper as well as the DOI of this dataset if you make use of the data.</p>
Oscillations of Offshore Wind Turbines undergoing Installation II: Filtered and Integrated data - acceleration, velocity, displacement
<p>This is dataset is based on the raw measurement data from <a href="https://zenodo.org/record/5009061">https://zenodo.org/record/5009061</a></p> <p>The data included in the archives are the resampled and high-pass filtered accelerations as well as the velocity and displacement data.</p>
Raw plot data for: Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators
<p>Data for publication: K. Karandashev, J. Vanicek, Accelerating equilibrium isotope effect calculations. II. Stochastic implementation of direct estimators, J. Chem. Phys. <strong>151</strong>, 134116 (2019) <a href="https://doi.org/10.1063/1.5124995">https://doi.org/10.1063/1.5124995</a></p> <p>This data set contains the raw numerical data for reproducing Figures 1-8 in the publication.</p>
Dataset for Nature Geoscience: Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks
<p>This archive file contains datafiles used in the manuscript: 'Acceleration of a large deep-seated tropical landslide due to urbanisation feedbacks' [https://doi.org/10.1038/s41561-022-01073-3]. It contains historical aerial orthomosaics, UAS DSM and orthomosaics, shapefiles, deformation maps, etc. for Funu landslide.</p>
Matrix Approach to Accelerate Spin-Up of CLM5
<p>The spin-up problem hinders our ability to study some key issues in land carbon cycle modeling, such as sensitivity analysis. This work applies a new semi-analytical spin-up (SASU) framework to accelerate spin-up of Community Land Model matrix version 5. We evaluate the computational efficiency and steady-state consistency of ND, AD and SASU approaches using CLM5 at both one-site (Brazil) and the global scale. In addition, we test SASU in Parameter Perturbation Experiment (PPE) in CLM5.</p>
FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis calcium dataset
<p>The dataset was used in paper FIOLA: an accelerated pipeline for Fluorescence Imaging OnLine Analysis named as 1MP. The dataset was only used to test FIOLA motion correction performance.<br> The dataset was collected for the paper Sensory-driven enhancement of calcium signals in individual Purkinje cell dendrites of awake mice (link: https://pubmed.ncbi.nlm.nih.gov/24582958/) but never published before. Data was recorded in the left lobule of the cerebellum of an awake mouse using the calcium indicator GCaMP6f. GCaMP6f was selectively expressed in Purkinje cells via a combinatorial virus strategy (as explained in the paper).</p> <p>For other datasets used in paper FIOLA, check the original paper and sources they were published.</p> <p> </p> <p> </p>
Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"
<p>Dataset for "VHEE beam dosimetry at CERN Linear Electron Accelerator for Research under ultra-high dose rate conditions"</p> <p>Daniela Poppinga <em>et al</em> 2021 <em>Biomed. Phys. Eng. Express</em> 7 015012</p> <p>https://doi.org/10.1088/2057-1976/abcae5</p> <p> </p>
Data from: Accelerated high-throughput imaging and phenotyping system for small organisms
<p>Studying the complex web of interactions in biological communities requires large multifactorial experiments with sufficient statistical power. Automation tools reduce the time and labor associated with setup, data collection, and analysis in experiments that untangle these webs. We developed tools for high-throughput experimentation (HTE) in duckweeds, small aquatic plants that are amenable to autonomous experimental preparation and image-based phenotyping. We showcase the abilities of our HTE system in a study with 6,000 experimental units grown across 2,000 treatments. These automated tools facilitated the collection and analysis of time-resolved growth data, which revealed finer dynamics of plant-microbe interactions across environmental gradients. Altogether, our HTE system can run experiments with up to 11,520 experimental units and can be adapted for other small organisms.</p>
Accelerated exploration of multinary systems
<p>This repository contains the datasets produced from the characterizations of the quinary Nb-Ti-Zr-Cr-Mo, and predictions made by Machine Learning models.</p> <p><strong>Experimental work</strong></p> <p>Gradients of composition were characterized by:</p> <ul> <li>EDX for composition evaluation, with an error of 1% on atomic and mass composition</li> <li>nanoindentation for the measurement of the elastic modulus (E) and hardness (H)</li> <li>EBSD : from each map we extract the Confidence Index CI and Image Quality IQ that are indicator of crystallinity. CI is also used to define phase classes (0 for amorphous, 1 for crystalline)</li> <li>XRD: from each diffractogram we extract a phase class (0 for amorphous, 1 for crystalline): raw data are available in XRD.zip</li> </ul> <p>Different datasets are built:</p> <ul> <li>Raw_data associate to each composition the EBSD CI, IQ, EBSD phase class, and the elastic modulus (E) and hardness (H) computed by the software TestWork Analysis without any correction. For each composition, 5 measurement replications were performed. </li> <li>Raw_data_corrected contains the EBSD CI, IQ, EBSD phase class, and the 5 replications per compositions of E and H corrected through Oliver and Pharr model. </li> <li>Compo_E_H_threshold correspond to Raw_data_corrected in which we have thresholded values of E and H. We removed all composition such that E < 10 GPa and all H < 2 GPa, as they correspond to nanoindentation test failures. </li> <li>Compo_E_wo_outliers and Compo_H_wo_outliers: Dixon test allows to identify outliers on E replications and H replications, that are removed to give each dataset. Each composition is associated to replications of E or H that were not identified as outliers.</li> <li>Averaged_data: each composition is associated to EBSD CI, IQ, EBSD phase class, and with average values of E and H replications without outliers. </li> <li>Data_averaged_mechanical_model: add to previous data the other mechanical properties computed with Galanov model from E and H experimental results: relative characteristic size of the elastic-plastic zone under the indenter <span class="math-tex">\(x = \frac{b_s}{c}\)</span>, the constrain factor <span class="math-tex">\(C\)</span> – linking yield strength and hardness – and the ductility characteristic <span class="math-tex">\(\delta_H\)</span> – ratio of plastic deformation and total deformation. It also contains <span class="math-tex">\(\frac{E²}{H}\)</span>.</li> <li>Database_XRD: each composition is associated to phase class defined from XRD diffractograms</li> </ul> <p>The dataset_initial.zipl contains the experimental results with an initial 20-gradients sets which screen preferably the center of Nb-Ti-Zr-Cr-Mo. It contains all the kind of datasets.</p> <p>The dataset_adding_binaries.zip contains the experimental results for the initial 20-gradients + additional binary gradients Nb-Ti binary 1), Nb-Cr (binary 2) and Cr-Mo (binary 3). It contains the data without outliers, averaged data and XRD database.</p> <p><strong>Predictions of Machine Learning Models from experimental datasets</strong></p> <p>Machine Learning models are trained to predict properties from compositions: Random Forest (RF), Support Vector Machine (SVM) and Neural Network (NN) models.</p> <p>Model assessment (i.e. choosing best hyper-parameters for each model) was performed on Compo_E_wo_outliers for E prediction, Compo_H_wo_outliers for H prediction, and on Averaged_data and Database_XRD for phase prediction. Results of model trainings are given in ModelAssessment.tar.gz.</p> <p>The best model of RF, NN and SVM are trained on all datasets: results are given in Train_model_xx.tar.gz. Training the same model with datasets with more or less outliers for E and H predictions allows to see the effect of outliers on the results. </p> <p>The best models of RF and NN are then trained adding iteratively the binaries: results are in tarball Train_model_xx_adding_binaries.tar.gz</p> <p><strong><em>These tarball are to be used with PyTerK modules available <a href="https://gricad-gitlab.univ-grenoble-alpes.fr/garele/accelerated-exploration-of-multinary.git">here</a>. </em></strong></p> <p>The models then predict, for all atomic compositions of Nb-Ti-Zr-Cr-Mo, with 2%at steps, the associated properties:</p> <ul> <li>predictions_XX contain atomic compositions associated to predicted CI, IQ, EBSD phase class, XRD phase class, E, H, for each kind of model. </li> <li>Predictions_XX_mechanical_model contain the same data with other mechanical properties computed with Galanov model from E and H predictions: relative characteristic size of the elastic-plastic zone under the indenter <span class="math-tex">\(x = \frac{b_s}{c}\)</span>, the constrain factor <span class="math-tex">\(C\)</span> – linking yield strength and hardness – and the ductility characteristic <span class="math-tex">\(\delta_H\)</span> – ratio of plastic deformation and total deformation. It also contains <span class="math-tex">\(\frac{E²}{H}\)</span>.</li> </ul> <p>The prediction_initial.zip contains the predictions made for all the model families with initial datasets. </p> <p>The predictions_adding_binaries.zip the predictions made with the best model (determined with the initial dataset) trained with the initial dataset+ binaries</p>
Dataset: Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled Cluster Theory for GPU-accelerated Computer Architectures
<p>This dataset collects the data (outputs, coordinate files) for the calculations presented in the manuscript "Formulation and Implementation of Frequency-Dependent Linear Response Properties with Relativistic Coupled<br> Cluster Theory for GPU-accelerated Computer Architectures".</p>
Data from: Accelerating local extinction associated with very recent climate change
<p>Climate change has already caused local extinction in many plants and animals, based on surveys spanning many decades. As climate change accelerates, the pace of these extinctions may also accelerate, potentially leading to large-scale, species-level extinctions. We tested this hypothesis in a montane lizard. We resurveyed 18 mountain ranges in 2021–2022 after only ~7 years. We found rates of local extinction among the fastest ever recorded, which have tripled in the past ~7 years relative to the preceding ~42 years. Further, climate change generated local extinction in ~7 years similar to that seen in other organisms over ~70 years. Yet, contrary to expectations, populations at two of the hottest sites survived. We found that genomic data helped predict which populations survived and which went extinct. Overall, we show the increasing risk to biodiversity posed by accelerating climate change, and the opportunity to study its effects over surprisingly brief timescales.</p>
CHALO! 2.0: A Mobile Technology Based Intervention to Accelerate HIV Testing and Linkage to Preventive Treatment.
ClinicalTrials.gov study NCT04814654. IPD Sharing: YES. Countries: 1. Publications: 1.
Physical seed damage, not rodent’s saliva, accelerates seed germination of trees in a subtropical forest
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Allen Brain Atlas
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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.