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3,688
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3,688 results for “Computer”
EEG Data for: "Composing only by thought: novel application of the P300 brain-computer interface"
<p>EEG Data for: "Composing only by thought: novel application of the P300 brain-computer interface". See the file "Information about the Dataset.pdf" for further information.</p>
Dataset of "Tracking Urban Human Activity from Mobile Phone Calling Patterns" PLOS Computational Biology paper
<p>This are the dataset file for "Tracking Urban Human Activity from Mobile Phone Calling Patterns", to be published in PLOS Computational Biology.</p> <p>The files contain probability distributions of finding a first, last, or any call as a function of time, derived from anonymized call detail records for a 12 months period in the year 2007 from a mobile phone service provider in a European country. The first data file contains the data obtained fom 30 different cities. the second for the six most populated cities, splitting the data into different age and gender groups.</p> <p>Details in README files.</p>
Discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes
<p>Data generated for the paper "The application of a high-order discontinuous Galerkin time-domain method for the computation of electromagnetic resonant modes" published in Applied Mathematical Modelling. It includes:</p> <ul> <li>Meshes</li> <li>Time domain signals</li> <li>Spectra</li> <li>Figures</li> </ul>
Dataset used in "Steady state visual evoked potential (SSVEP) based brain-computer interface (BCI) performance under different perturbations" 2018, PLOS ONE
<p>This file contains the segmented EEG data in offline and online conditions. Please read the readme.doc file for the detailed explanation.</p>
Revisiting the paradigm of reaction optimization in flow with a priori computational reaction intelligence
<p>The use of micro/meso-fluidic reactors has resulted in both new scenarios for chemistry and new requirements for chemists. Through flow chemistry, large-scale reactions can be performed in drastically reduced reactor sizes and reaction times. This obvious advantage comes with the concomitant challenge of re-designing long-established batch processes to fit these new conditions. The reliance on experimental trial-and-error to perform this translation frequently makes flow chemistry unaffordable, thwarting initial aspirations to revolutionize chemistry. By combining computational chemistry and machine learning, we have developed a model that provides predictive power tailored specifically to flow reactions. We show its applications to translate batch to flow, to provide mechanistic insight, to contribute reagent descriptors, and to synthesize a library of novel compounds in excellent yields after executing a single set of conditions.</p>
Extended computing integrated curricula scored for K-12 CS standards
<p>Integrated computing curricula combine learning objectives in computing with those in another discipline, like literacy, math, or science, to give all students experience with computing, typically before they must decide whether to take standalone CS courses. One goal of integrated computing curricula is to provide an accessible path to an introductory computing course by introducing computing concepts and practices in required courses. This paper analyzed integrated computing curricula to determine which CS practices and concepts they teach and how extensively and, thus, how they prepare students for later computing courses. The authors conducted a content analysis to examine primary and lower secondary (i.e., K-8) curricula that are taught in non-CS classrooms, have explicit CS learning objectives (i.e., CS+X), and that took 5+ hours to complete. Lesson plans, descriptions, and resources were scored based on frameworks developed from the K-12 CS Framework, including programming concepts, non-programming CS concepts, and CS practices. The results found that curricula most extensively taught introductory concepts and practices, such as sequences, and rarely taught more advanced content, such as conditionals. Students who engage with most of these curricula would have no experience working with fundamental concepts, like variables, operators, data collection or storage, or abstraction in the context of a program. While this focus might be appropriate for integrated curricula, it has implications for the prior knowledge that students should be expected to have when starting standalone computing courses.</p>
Dataset, Model Statistics, and 3D designs for "From Eyes to Cameras: Computer Vision for High-Throughput Liquid-Liquid Separation"
<p>Dataset, model statistics, and 3D design of high throughput platform associated with HeinSight3.0. </p> <p> </p> <p>Pre-print: https://chemrxiv.org/engage/chemrxiv/article-details/65e5481f9138d231619c1879</p> <p> </p> <p>The code and model of HeinSight3.0 can be found at (https://doi.org/10.5281/zenodo.11053915)</p>
Data for "High-Throughput Computational Evaluation of Low Symmetry Pd2L4 Cages to Aid in System Design"
<p>In the following subdirectories are the input and output of Gaussian calculations + structures from screening for this publication:</p> <p>chemrxiv: <a href="https://chemrxiv.org/engage/chemrxiv/article-details/60c758e0ee301c7eadc7b7df">10.26434/chemrxiv.14604294</a></p> <p>Published: <a href="https://onlinelibrary.wiley.com/doi/10.1002/anie.202106721">10.1002/anie.202106721</a></p> <p>Previously uploaded in 10.5281/zenodo.8432296 and <a href="https://github.com/andrewtarzia/citable_data" rel="noopener noreferrer">https://github.com/andrewtarzia/citable_data</a></p> <p>Software repository: <a href="https://github.com/andrewtarzia/unsymm_match">github.com/andrewtarzia/unsymm_match</a></p> <p>screening_structures directory:</p> <ul> <li>contains the structures from xTB optimisation that were used in ranking in structures.tar.gz as `.mol` files</li> <li>all_cage_results.txt contains their properties for ranking</li> </ul> <p>single_point_dft directory:</p> <ul> <li>contains the structures and output of single point DFT calculations</li> <li>during the revision process, we confirmed (based on reviewer suggestions) our DFT validation results using ORCA 4.2.1 with PBE0 and B97-3c in the gas phase. These results were consistent with our previous ones, so were not added to the manuscript. But are useful for future work! <ul> <li>These results are in the s_orca directory.</li> </ul> </li> </ul> <p>free_energy_calculations directory:</p> <ul> <li>during the revision process, it was suggested to calculate the free energies using the xTB method (low-cost) and compare that to the total energies we use.</li> <li>the script `run_gfn2_free_energy.py` in the unsymm_match code repository does this for top candidate ligands using the stko.XTB class. <ul> <li>for each structure, the free energy is output to a .fey file.</li> </ul> </li> </ul>
Towards quantum utility for NMR quantum simulation on a NISQ computer
<p>Included are CSV files to reproduce the plots from the publication "Towards quantum utility for NMR quantum simulation on a NISQ computer" and a jupyter notebook script for plotting. If all files are saved in the same folder it should work immediately. Plotly library is used for plotting.</p>
Supplementary materials: "Synthesizing Particle-in-Cell Simulations Through Learning and GPU Computing for Hybrid Particle Accelerator Beamlines"
<p>Data archive for stage 3 of manuscript for PASC24. See Readme stage 3.txt for more details.</p> <p> </p> <p>This work was supported by the Laboratory Directed Research and Development Program of Lawrence Berkeley National Laboratory under U.S. Department of Energy Contract No. DE-AC02-05CH11231 and by LLNL under Contract DE-AC52-07NA27344. This material is based upon work supported by the U.S. Department of Energy, OFfice of Science, Office of High Energy Physics, General Accelerator R&D (GARD), under contract number DE-AC02-05CH11231. This material is based upon work supported by the CAMPA collaboration, a project of the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research and Office of High Energy Physics, Scientific Discovery through Advanced Computing (SciDAC) program. This research was supported by the Exascale Computing Project (17-SC-20-SC), a joint project of the U.S. Department of Energy's Office of Science and National Nuclear Security Administration, responsible for delivering a capable exascale ecosystem, including software, applications, and hardware technology, to support the nation's exascale computing imperative.This research used resources of the National Energy Research Scientific Computing Center, a DOE Office of Science User Facility supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC02-05CH11231 using NERSC award HEP-ERCAP0023719.</p>
FunShield4Med e-learning module on Computational Toxicology
<p>This is a two-module presentation about the use of computational methods in toxicology providing the audience with an introduction of molecular modelling techniques applied to toxicology assessment (first module, theoretical part). Taking advantage of selected case studies targeting ochratoxin A, interested persons are learning how in silico methods can be broadly applied to investigate the toxicodynamics and toxicokinetic of mycotoxins (second module, e-learning part), supporting the early stage of risk assessment (i.e. the hazard identification and characterization). The two modules are explaining in depth and step-by-step the procedure used and the underpinning rationale and base of knowledge to make anyone virtually able to reproduce such kind of analysis.</p>
Dataset on computational and experimental external dose rates from nuclear medicine patients.
<p><strong>A journal paper published in EJNMMI Physics details the method to create the data, which support also the results described in the paper. The paper describes the development and validation of an advanced computational framework for the assessment of external dose rates from nuclear medicine patients. </strong></p>
Data for: A linear response framework for simulating bosonic and fermionic correlation functions on quantum computers
<p>Response functions are a fundamental aspect of physics; they represent the link between experimental observations and the underlying quantum many-body state. However, this link is often under-appreciated, as the Lehmann formalism for obtaining response functions in linear response has no direct link to experiments. Within the context of quantum computing, and by using a linear response framework, we restore this link by making the experiment an inextricable part of the quantum simulation. This method can be frequency- and momentum-selective, avoids limitations on operators that can be directly measured, and is ancilla-free. As prototypical examples of response functions, we demonstrate that both bosonic and fermionic Green's functions can be obtained, and apply these ideas to the study of a charge-density-wave material on {\emph{ibm\_auckland}}. The linear response method provides a robust framework for using quantum computers to study systems in physics and chemistry.</p>
Dataset for the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection"
<p>Datasets to accompany the article "Efficient Computation of Magnetic Polarizability Tensor Spectral Signatures for Object Characterisation in Metal Detection". Written by J. Elgy and P. D. Ledger. </p> <p>The datasets include data files, meshes, and code for recreating the results from the paper. This requires the open source MPT-Calculator software available at <a href="http://github.com/MPT-Calculator/MPT-Calculator">https://github.com/MPT-Calculator/MPT-Calculator</a> (v.1.5.0).</p> <p>J. Elgy and P. D. Ledger gratefully acknowledge the financial support received from EPSRC in the form of grant EP/V009028/1.</p>
Supplementary Material - Uncovering the Effects of Quantum Computing on Software Engineering: A Systematic Mapping
<p>This is the supplementary material regarding: “On the Influence of Quantum Computing on Software Engineering: A Systematic Mapping”, the idea being to make it possible to reproduce the methodology applied in the Systematic Mapping (SM) used in the study. It consists of:</p> <ul> <li>Spreadsheet with the data of the papers selected in each step of the filtering phase of the SM development</li> <li>Fluxogram detailing the Methodology of the SM step-by-step<strong></strong></li> </ul> <h3><strong>Objectives</strong></h3> <p>The main objective of the SM was to provide a detailed analysis on how quantum computing has affected four software engineering topics: Software Testing and Quality; Software Reengineering/Modernization; Software Modeling and Software Processes and Development Platforms</p> <p>Based on the results of such Mapping, four research questions were defined realted to the quantum software engineering topic:</p> <ul> <li> <p>RQ1: <span>How has QC impacted software testing and qual</span><span>ity? This RQ aims to identify proposed methods for </span><span>testing quantum software and topics related to quantum </span><span>metrics and bugs.</span></p> </li> <li> <p>RQ2: How has QC influenced software reengineering and modernization practices? This study seeks to analyze the treatment of reverse engineering and refactorings within this context.</p> </li> <li> <p>RQ3: How is quantum software modeled? This research aims to investigate the impact of QC on modeling languages and the granularity level used.</p> </li> <li> <p>RQ4: How have development processes and platforms been shaped by QC? The goal is to assess the current maturity of processes and platforms for QC.</p> </li> </ul> <h3><strong>Preparation</strong></h3> <p>In this part we outline the resources, digital libraries, the search string and inclusion and exclusion criteria used in this SM on the influence of quantum computing on software engineering.</p> <p>The platform used to manage the articles and define the search string and inclusion and exclusion criteria was Parsifal. In this process articles were sourced from : ACM Digital; IEEE Digital Library; Science Direct and Scopus.</p> <p>A search string was employed in each digital library, adapted to the syntax of each of them, while encompassing the time frame from 01/2018 to 06/2023, with the Base Search String being:</p> <p><strong>(“Software Model” OR “Software Engineering” OR “Software Development” OR “Software Lifecycle” OR “Software Development Methodologies” OR “Software Project Management” OR “Testing” OR “Design Pattern” OR “Reengineering” OR “Reverse Engineering” OR “Metrics” OR “Service-Oriented”) AND (“Quantum Software” OR “Quantum Programming” OR “Quantum Computing” OR “Quantum Software Development” OR “Quantum Subroutine” OR “Quantum Program”)</strong></p> <p>Where the upper part includes terms related to software engineering and the lower part covers the quantum terms.</p> <p>Another important task was to define inclusion and exclusion criteria for filtering the studies, being them:</p> <p><strong>Inclusion Criteria</strong></p> <ul> <li> <p>The study is published in English.</p> </li> <li> <p>The content of the study is related to the topic being analyzed.</p> </li> </ul> <p><strong><strong>Exclusion Criteria</strong></strong></p> <ul> <li> <p>The study is not available</p> </li> <li> <p>The study is duplicated</p> </li> <li> <p>The study is not primary study (surveys, systematic reviews/mappings, talks, proceedings, etc)</p> </li> <li> <p>The content of the study is not related to the theme or it is too superficial.</p> </li> <li> <p>The content of the study was updated in a next more-complete version. When the authors published a new and more complete version of the same content, we remove the previous paper.</p> </li> </ul> <h3><strong>Conduction</strong></h3> <p>During the conduction of the papers filtering process there were four stages to which the set of papers were applied to until reaching the final set:</p> <ul> <li> <p>First stage: Removal of every paper that was duplicated, leaving only one instance of each paper.</p> </li> <li> <p>Second stage: Removal of papers that are not a primary study.</p> </li> <li> <p>Third stage: Removal of papers which keywords, title and abstract are not related to the topics of the Systematic Mapping;</p> </li> <li> <p>Fourth stage: Removal of papers whose content is not related to the topics of the Systematic Mapping.</p> </li> </ul>
Computing the Committor with the Committor: an Anatomy of the Transition State Ensemble
<p>The study of the kinetic bottlenecks that hinder the rare transitions between long-lived metastable states is a major challenge in atomistic simulations. We propose a method to explore the transition state ensemble, which is the distribution of configurations that the system passes as it translocates from one metastable basin to another. We base our method on the committor function and the variational principle to which it obeys. We find its minimum through a self-consistent procedure that starts from information limited to the initial and final states. Right from the start, our procedure allows sampling very many transition state configurations. With the help of the variational principle, we perform a detailed analysis of the transition state ensemble, ranking quantitatively the degrees of freedom mostly involved in the transition and enabling for a systematic approach for the interpretation of simulation results and the construction of efficient physics-informed collective variables.</p>
Inhibitors and reactivators of acetylcholinesterase - structural and computational insight [dataset]
<div> <p>The dataset comprises:<br>i) AlphaFold predictions for acetylcholinesterase<br>ii) MD simulations input files <br>iii) PyMOL sessions from AQUA-DUCT calculations</p> </div>
Computed data for "Unveiling the Multielectron Acceptor Properties of π‐Expanded Pyracylene: Reversible Boat to Chair Conversion"
<p>Computed structures and TD-DFT raw data as discussed in the article "Unveiling the Multielectron Acceptor Properties of π‐Expanded Pyracylene: Reversible Boat to Chair Conversion" published in JACS, <span>https://doi.org/10.1021/jacs.4c02314</span></p>
Computational Data for "Synthesis of (1-silyl)allylboronates by KOtBu-catalyzed ring-opening gem-silylborylation of cyclopropenes"
<p>Computational data used for the article "Synthesis of (1-silyl)allylboronates by KOtBu-catalyzed ring-opening gem-silylborylation of cyclopropenes". This Includes the computational output files of geometries, optimizations, and frequency calculations of all mentioned structures.</p>
Cost Management in Cloud Computing: a study on financial waste and correction strategies
<p>This project explores the identification and mitigation of financial wastage in Cloud Computing (CC) operations by professionals in the field, examining the alignment of their strategies with FinOps guidelines. Through a survey distributed via social media and messaging apps to CC professionals, the study engaged 28 participants from various companies, sizes, and sectors. It revealed that these professionals have encountered cost-related issues in cloud applications, particularly financial wastage. The investigation aimed to understand the prevalence of financial wastage in CC and evaluate the effectiveness of the adopted practices in addressing these inefficiencies within the framework of FinOps principles.</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.