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3,688 results for “Computer”
Cloud Computing Badge
<p>The Cloud Computing Badge can be used for presentations, courses, ...</p>
Data supporting "A real-time, scalable, fast and resource-efficient decoder for a quantum computer"
<p>Data includes the circuits (stim_circuits.zip) used to create samples to benchmark CC decoder across different noise rates and code sizes. The resulting accuracy and cycle data is in fpga_accuracy_data.csv. The memory footprint (in KB) of the algorithm for different code sizes is in fpga_memory_data.csv.</p> <p>Weights of syndromes for different noise rates for both phenomenological and circuit-level noise at distance d=23 and d=21 are in noise_rate_sampling_full_d23.csv and noise_rate_sampling_full_d21.csv respectively.</p>
Supplementary Material - Women's Journey in STEM Education in Brazil: A Rapid Review on Engineering and Computer Science
<p>Supplementary Material for the Rapid Review - Women's Journey in STEM Education in Brazil in Engineering and Computer Science courses</p>
Computing integrated activities scored for programming concepts
<p>Educators across disciplines are implementing lessons and activities that integrate computing concepts into their curriculum to broaden participation in computing. Out of myriad important introductory computing skills, it is unknown which—and to what extent—these concepts are included in these integrated experiences, especially when compared to concepts commonly taught in introductory computer science courses. Thus, it is unclear how integrated computing activities serve the goal of broadening participation in computing. To address this deficit, we compiled a database of 81 integrated computing activities, constructed a framework of fundamental programming concepts, and scored each activity in the database for the presence of each concept. The dataset also includes different activity features, including discipline, programming language, student age, and duration of activity. </p>
Deep Gradient Reinforcement learning for Music Improvisation in cloud computing framework
<p><span>The improvised music is further rendered in the MIDI format. The Bach Chorales dataset with six different attributes relevant to musical compositions is employed in implementing the present research. The model was set up in a containerised cloud environment and controlled for smooth load distribution. Five different parameters, such as pitch frequency (PF), standard pitch delay (SPD), average distance between peaks (ADP), note duration gradient (NDG) and pitch class gradient (PCG) are leveraged to assess the quality of the improvised music.</span></p>
Cryogenic quantum computer control signal generation using high-electron-mobility transistors data
<p>Data generated for the publication "Cryogenic quantum computer control signal generation using high-electron-mobility transistors"</p>
Trained Potentials for Article "Computationally Efficient Machine-Learned Model for GST Phase Change Materials via Direct and Indirect Learning"
<p>We provide 8 files here to get started using our trained potentials:</p> <p>1) *.yaml files for each trained potential. These are the outputs of the PACE training process.</p> <p>2) *.yace files for each trained potential. These are read by LAMMPS to use the trained potential. They can be obtained from the *.yaml files using the command line command: "pace_yaml2yace *.yaml".</p> <p>3) GST_config.data -- a starting configuration of GST to be read by LAMMPS. This configuration contains 504 atoms at density 5.85 g/cm^3.</p> <p>4) sample.inp -- a sample LAMMPS input file using the trained potentials. This currently uses "ACE-Indir2.yace" to run the starting configuration "GST_config.data" for 10 ps at 1200 K. When run, it outputs a log file "test.log" and a dump file "test.dump". The choice of trained potential can be changed in the "pair_coeff" section.</p>
Atmospheric Response Matrices (ARMs) computed with AtRIS in: The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates
<p>Ionization and Dose Atmospheric Response Matrices (ARMs) computed with the Atmspheric Interaction Radiation Simulator (AtRIS) and used in the following publication: <br>The Atmospheric Influence on Cosmic-Ray-Induced Ionization and Absorbed Dose Rates.<br><br>All files are in txt format.<br>Files ending in '_energy_bins.txt', contain the informations about the energy binning of the primary particles used in the AtRIS simulations. <br>Files ending in '_ioni.txt', contain the ionization ARMs computed with the above mentioned energy binning.<br>Files ending in '_dose.txt', contain the ICRU water sphere dose ARMs computed with the above mentioned energy binning.</p>
Eight Reasons to Prioritize Brain-Computer Interface Cybersecurity
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Input data of the multi-patch geometries used in: A. Farahat, H. M. Verhelst, J. Kiendl, M. Kapl, Isogeometric analysis for multi-patch structured Kirchhoff–Love shells, Computer Methods in Applied Mechanics and Engineering 411 (2023) 116060 DOI: 10.1016/j.cma.2023.116060
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Input data of the mixed meshes used in: J. Grošelj, M. Kapl, M. Knez, T. Takacs, V. Vitrih, C1-smooth isogeometric spline functions of general degree over planar mixed meshes: The case of two quadratic mesh elements, Applied Mathematics and Computation 460 (2024) 128278; DOI: 10.1016/j.amc.2023.128278
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Supplementary Information for "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" - Computational Data
<p>This is part of the external Supplementary Information covering the molecular dynamics simulations section for our publication "Biocatalytic Ether Lipid Synthesis by an Archaeal Glycerolprenylase" by Felix Kaspar et al., freely available as a preprint from ChemRxiv ( ).</p> <p>The .zip file contains raw data, processed data and metadata for the <strong>computational results</strong>. This includes files for the MD simulations and the preparations of the structures. </p> <p>Raw data regarding the experimental results are stored in a separate zenodo entry (<a href="../doi/10.5281/zenodo.10559443">https://zenodo.org/doi/10.5281/zenodo.10559443</a>) by Felix Kaspar. This includes UV, flourescence, NMR data, as well as data results of crystallization screens and reports on MPLC runs. </p>
Comparing LLM-Generated Tips and Expert-Created Tips in Quantum Computing Education
<p>This dataset includes the anonymized data from two studies with the goal to evaluate if LLM-generated tips can be used instead of expert-created tips to help students answer quantum computing questions. <br>For the main study (<em>main_test_anonymous.csv</em>) a between-subject design was used to quizz participants of the QUIKSTART 2024 summer school, giving them four multiple-choice quantum physics questions and one tip per question. Each participant was assigned one of four conditions represented by the combination of "creator" and "labeled_as" column in the csv file. In addition we asked to rate quality, correctness and helpfulness for each tip and to rate the perceived difficulty of the question. We removed demographics for anonymities sake.</p> <p>Additionally, we conducted a study directly comparing the LLM-generated and expert-created tips (<em>tip_eval_anonymous.csv</em>). Where we let experts and students rate the tips helpfulness, correctness, if they gave away the answer and if they pointed to relevant concepts. Furthermore, participants had to decide for each question which tip they preferred and were able to leave a comment to give their reasoning. We removed demographics for anonymities sake.<br><br>These datasets were evaluated in the paper "LLM-Generated Tips Rival Expert-Created Tips in Helping Students Answer Quantum-Computing Questions" by Lars Krupp, Jonas Bley, Isacco Gobbi, Alexander Geng, Sabine Müller, Sungho Suh, Ali Moghiseh, Arcesio Castaneda Medina, Valeria Bartsch, Artur Widera, Herwig Ott, Paul Lukowicz, Jakob Karolus, Maximilian Kiefer-Emmanouilidis</p>
The dataset of the manuscript "GPU-HADVPPM4HIP V1.0: higher model accuracy on China's domestically GPU-like accelerator using heterogeneous compute interface for portability (HIP) technology to accelerate the piecewise parabolic method (PPM) in an air quality model (CAMx V6.10)"
<p><strong>bcfile.zip:</strong> the clean boundary condition files.</p> <p><strong>CAMxv6x_cpp.zip: </strong>the source code of CAMx-HIP version which coupled with HIP-HADVPPM scheme.</p> <p><strong>data.zip:</strong> final data tables used to plot figures.</p> <p><strong>emisfile.zip: </strong>the emission files.</p> <p><strong>icfile.zip:</strong> the clean initial condition files.</p> <p><strong>tuvfile.zip </strong>and <strong>o3mapfile.zip:</strong> the photolysis files.</p> <p><strong>outputfile.zip:</strong> the computation results outputted by CAMx model for Fortran version on the Intel Xeon E5-2682 v4 CPU, CUDA version on the NVIDIA K40m and V100 clusters, and HIP version on the China' s domestically heterogeneous cluster A.</p> <p><strong>wrfcamx.zip:</strong> the meteorological files.</p> <p><strong>offline_test_cuda.zip: </strong>the advection module code written in CUDA C language</p> <p><strong>offline_test_fortran.zip:</strong> the advection module code written in Fortran language</p> <p><strong>offline_test_hip.zip: </strong>the advection module code written in HIP C language</p>
Segmented primary phases of Al-alloy EN AW-2618A in the T61 state using synchrotron computed tomography
<p><span>This video shows the primary phases of the aluminum alloy EN AW-2618A in the T61 state measured by synchrotron computed tomography. </span></p> <p><span>Further information is provided in the file content.pdf. </span></p>
FAIRmat Tutorial 14: Developing schemas and parsers for FAIR computational data storage using NOMAD-Simulations
<p><a href="https://nomad-lab.eu"><u>NOMAD</u></a> is an open-source, community-driven data infrastructure, focusing on materials science data. Originally built as a repository for data from DFT calculations, the NOMAD software can automatically extract data from the output of a large variety of simulation codes. Our previous computation-focused tutorials (<a href="https://fairmat-nfdi.github.io/AreaC-Tutorial-CECAM-2023/"><u>CECAM workshop</u></a>, <a href="https://fairmat-nfdi.github.io/AreaC-Tutorial10_2023/"><u>Tutorial 10</u></a>, and <a href="https://www.fairmat-nfdi.eu/events/fairmat-tutorial-7/tutorial-7-materials"><u>Tutorial 7</u></a>) have highlighted the extension of NOMAD’s functionalities to support advanced many-body calculations, classical molecular dynamics simulations, and complex simulation workflows. <br>But how can you utilize this infrastructure and associated suite of tools if your simulation code or method is not yet supported? <strong>This tutorial will provide foundational knowledge for customizing NOMAD to fit the specific needs of your computational research project</strong>. The following provides an outline of the major topics that will be covered:</p> <ul> <li>Introduction to the NOMAD software and repository</li> <li>Working with the NOMAD-Simulations schema plugin</li> <li>Extending NOMAD-Simulations to support custom methods and outputs</li> <li>Creating parser plugins from scratch</li> <li>Extra: Interfacing complex simulation and analysis workflows with NOMAD</li> </ul> <p><strong>Disclaimer:</strong> NOMAD is being continuously developed based on input and feedback from the scientific community. Hence the features, services or interface may have changed since the time of recording of this video. For up-to-date information please consult our latest tutorials and the NOMAD documentation <a href="https://nomad-lab.eu/prod/v1/docs/">https://nomad-lab.eu/prod/v1/docs/</a></p>
Cone beam computed tomography dataset of a knee phantom
<p>This is a cone beam computed tomography dataset of a custom-made knee phantom. The data was collected with Planmeca Viso G7 scanner. A total of 500 projections were collected. 100 kV with 80 mAs was used during the measurement. The data is stored in a mat-file that can be easily accessed in MATLAB, GNU Octave, Python, Julia, C/C++ and other languages. Only the flat field correction has been preapplied to the projection images, no other corrections have been done.</p> <p>Included are the FOV size, size of one detector pixel, the flat value, number of columns and rows in each projection image, the offset value for the object, the radians for the rotation of the panel (yaw/roll), the projection images themselves, the projection angles, the source to center of rotation and source to panel distances and the coordinates for the source and the center of the panel for each projection.</p> <p>The OMEGA software includes an example on how to use this dataset (all CBCT examples) in MATLAB/Octave and Python.</p>
Data for Unifying thermochemistry concepts in computational heterogeneous catalysis
<p>Data and Jupyter notebooks for the preprint "<span>Unifying</span> <span>thermochemistry</span> <span>concepts</span> <span>in</span> <span>computational</span><br><span>heterogeneous catalysis</span>"</p>
Raw Data to the article Mlynsky et al., J. Chem. Theory Comput. 2023
<p>Raw data to the article "Mlynsky, V., Kuhrova, P., Stadlbauer, P., Krepl, M., Otyepka, M., Banas, P., & Sponer, J. (2023). Simple adjustment of intranucleotide base-phosphate interaction in the ol3 amber force field improves RNA simulations. <em>Journal of Chemical Theory and Computation</em>, <em>19</em>(22), 8423-8433". See readme.txt file for additional info. The upload now contains the original JCTC article and its Supporting Information.</p>
Evaluation of orthodontically induced external root resorption following orthodontic treatment using Cone Beam Computed Tomography (CBCT): a systematic review and meta-analysis
<p>Datasets for all analyses performed in the paper.</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.