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3,690 results for “Computing”
A Systematic Review on Security Mechanisms for Serverless Computing
<p>This dataset was collected as part of developing and writing a systematic review on security mechanisms for Serverless Computing related to the article "A Systematic Review on Security Mechanisms for Serverless Computing." The considered articles were searched on the fifth of July 2024 and were limited to works published from 2018 forward, inclusively. The dataset comprises 3 CSV files, one with the final included articles and the other two with the excluded articles in each phase of the Screening process. The first line of each file has the titles of each column. For more details regarding the dataset contents, please refer to the README.md.</p>
Supplementary material: A variationally computed IR line list for the methyl radical CH3
<p>Supplementary material to the manuscript: A. Y. Adam, A. Yachmenev, S. N. Yurchenko, P. Jensen, A variationally computed IR line list for the methyl radical CH<sub>3</sub>, submitted</p> <p>Contains following files</p> <ol> <li><em><strong>ch3_pes_f90.txt</strong></em> - Fortran 90 routine for calculating potential energy values for CH<sub>3</sub>. Please rename to ch3_pes.f90 before compiling.</li> <li><em><strong>ch3_dms_f90.txt</strong></em> - Fortran 90 routine for calculating electric dipole moment values for CH<sub>3</sub>. Please rename to ch3_dms.f90 before compiling.</li> <li><em><strong>ch3_pes_inp.txt</strong></em> - an input file for ch3_pes.f90 containing the ab initio potential energy parameters of CH<sub>3</sub>. </li> <li><em><strong>ch3_dms_inp.txt</strong></em> - an input file for ch3_dms.f90 containing the ab initio dipole moment parameters of CH<sub>3</sub>.</li> <li><em><strong>12C-1H3__AYYJ.pf</strong></em> - Partition function of CH<sub>3</sub></li> <li><em><strong>12C-1H3__AYYJ.states.bz2</strong></em> - Labelled ro-vibrational states for CH<sub>3</sub></li> <li><em><strong>12C-1H3__AYYJ__00000-01000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 0-1000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__01000-02000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 1000-2000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__02000-03000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 2000-3000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__03000-04000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 3000-4000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__04000-05000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 4000-5000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__05000-06000.trans.bz2 </strong></em>- Transition file for CH<sub>3</sub>, 5000-6000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__06000-07000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 6000-7000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__07000-08000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 7000-8000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__08000-09000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 8000-9000 cm<sup>-1</sup></li> <li><em><strong>12C-1H3__AYYJ__09000-10000.trans.bz2</strong></em> - Transition file for CH<sub>3</sub>, 9000-10000 cm<sup>-1</sup></li> </ol> <p><br> <strong>Byte-by-byte description of .trans</strong> <br> -------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> -------------------------------------------------------------------------------<br> 1- 12 i12 --- N' Upper state ID<br> 14- 24 i12 --- N" Lower state ID<br> 27- 36 e10.4 s-1 A Einstein A-coefficient of the transition<br> -------------------------------------------------------------------------------</p> <p><br> <strong>Column-by-column description of states files: 12C-1H3__AYYJ.states.bz2</strong></p> <p>(QN=Quantum number)<br> ----------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> ----------------------------------------------------------------------<br> 1- 12 i12 --- i State ID, non-negative integer index, starting at 1<br> 13- 25 i12 cm-1 E State energy term value in cm<sup>-1</sup><br> 26- 32 i6 --- g Total state degeneracy<br> 33- 40 i7 --- J [0/85] J quantum number, the total angular momentum excluding nuclear and electronic spin<br> 41- 45 i5 --- G [1/6] Total symmetry in D3h(M), (1,2,3,4,5,6)=A1',A2',E',A1",A2",E"<br> 46- 51 i6 --- n1 A1', Normal mode quantum number (QN)<br> 52- 55 i4 --- n2 A1', Normal mode QN<br> 56- 59 i4 --- n3 E', Normal mode QN<br> 60- 63 i4 --- l3 E', Normal mode QN<br> 64- 67 i4 --- n4 E', Normal mode QN<br> 68- 71 i4 --- l4 E', Normal mode QN<br> 73- 76 i5 --- Gv Vibrational component symmetry in D3h(M)<br> 81- 83 i3 --- J [0/85] J is the total angular momentum excluding nuclear and electronic spin<br> 85- 87 i3 --- K [0/85] Projection of J on axis of molec. symmetry, in units of hbar<br> 90- 91 i2 --- Pr [0/1] Rotational parity tau, defined as (-1)<sup>tau</sup><br> 94- 95 i2 --- Gr Rotational component symmetry in D3h(M)<br> 98-102 f4.2 --- C^2 [0/1] Largest coefficient<br> 106-109 i4 --- n1 TROVE QN C-H local model stretch<br> 110-113 i4 --- n2 TROVE QN C-H local model stretch<br> 114-117 i4 --- n3 TROVE QN C-H local model stretch<br> 118-121 i4 --- n4 TROVE QN CHH local mode bend<br> 122-125 i4 --- n5 TROVE QN CHH local mode bend<br> 126-129 i4 --- n6 TROVE inversional QN<br> ----------------------------------------------------------------------</p> <p><strong>Byte-by-byte description of 12C-1H3__AYYJ.pf</strong><br> -------------------------------------------------------------------------------<br> Bytes Format Units Label Explanations<br> -------------------------------------------------------------------------------<br> 1- 9 f9.1 K T Temperature in Kelvin<br> 11- 25 f15.4 --- Q Parition funcion<br> -------------------------------------------------------------------------------</p> <p><br> <strong>Contact Information</strong></p> <p>Andrey Yachmenev<br> andrey.yachmenev@desy.de<br> Center for Free-Electron Laser Science, Deutsches Elektronen-Synchrotron DESY, <br> Notkestrasse 85, D-22607 Hamburg, Germany,</p> <p>Sergei Yurchenko<br> s.yurchenko@ucl.ac.uk<br> Department of Physics and Astronomy, University College London,<br> Gower Street, London WC1E 6BT, United Kingdom</p> <p> </p>
Figure 14 from: Jochum A, Prieto CE, Kampschulte M, Martels G, Ruthensteiner B, Vrabec M, Dörge DD, de Winter AJ (2019) Re-evaluation of Zospeum schaufussi von Frauenfeld, 1862 and Z. suarezi Gittenberger, 1980, including the description of two new Iberian species using Computer Tomography (CT) (Eupulmonata, Ellobioidea, Carychiidae). ZooKeys 835: 65-86. https://doi.org/10.3897/zookeys.835.33231
Figure 14 CT scans of Zospeumgittenbergeri sp. n. holotype (RMNH.MOL.234166). Scale bar: 500 µm.
Fig. 5 in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 5. Schematic overview of the reconstruction procedure. Image by HCMR micro-CT lab.
Fig. 6. Data processing from a in Micro-computed tomography for natural history specimens: a handbook of best practice protocols
Fig. 6. Data processing from a stack of 2D images to a 3D model. Image by MNHN.
FESOM 2.0 computation of meridional overturning and barotropic streamfunctions
<p>The dataset as well as the Python code related to the article "Simple algorithms to compute meridional overturning and barotropic streamfunction on unstructured meshes" submitted to GMD, 2019</p>
THEORETICAL AND PRACTICAL ASPECTS OF USING COMPUTER TECHNOLOGIES AND PROGRAMS WHEN CREATING THEMATIC DICTIONARIES IN ENGLISH AND UZBEK
<p><em><span>This article explores the theoretical and practical aspects of utilizing computer technologies and programs in the creation of thematic dictionaries, specifically focusing on English and Uzbek. The study examines the advantages, limitations, and methodological considerations of applying computer-aided techniques for collecting, analyzing, and organizing linguistic data in these two languages. The analysis highlights the unique challenges and opportunities presented by the linguistic characteristics of each language, as well as the evolving landscape of dictionary creation in the digital age. </span></em></p>
Towards practical privacy-preserving computation of clinical data
<p>Clinical data sets and implementation for the submission "Towards practical privacy-preserving computation of clinical data"</p>
Dataset for paper "Direct multiple shooting for computationally efficient train trajectory optimization" (Kouzoupis et al.)
Open the record for dataset details and reuse information.
Deep-Learned Broadband Encoding Stochastic Filters for Computational Spectroscopic Instruments
<p>Abstract</p> <p>Computational spectroscopic instruments with broadband encoding stochastic (BEST) filters allow the reconstruction of the spectrum at high precision with only a few filters. However, conventional design manners of BEST filters are often heuristic and may fail to fully explore the encoding potential of BEST filters. The parameter constrained spectral encoder and decoder (PCSED)—a neural network-based framework—is presented for the design of BEST filters in spectroscopic instruments. By incorporating the target spectral response definition and the optical design procedures comprehensively, PCSED links the mathematical optimum and practical limits confined by available fabrication techniques. Benefiting from this, a BEST-filter-based spectral camera presents a higher reconstruction accuracy with up to 30 times enhancement and better tolerance to fabrication errors. The generalizability of PCSED is validated in designing metasurface- and interference-thin-film-based BEST filters.</p> <p> </p> <p>Please refer to https://github.com/Hao-Laboratory/PCSED for the source code for data analysis and visualization.</p>
Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool
<p>We uploaded the database of the patients entrolled in the manuscript: Grassi, Roberto, Salvatore Cappabianca, Fabrizio Urraro, Vincenza Granata, Giuliana Giacobbe, Simona Magliocchetti, Diletta Cozzi, Roberta Fusco, Roberta Galdiero, Carmine Picone, Maria P. Belfiore, Alfonso Reginelli, Umberto Atripaldi, Ornella Picascia, Michele Coppola, Elio Bignardi, Roberta Grassi, and Vittorio Miele. 2021. "Evolution of CT Findings and Lung Residue in Patients with COVID-19 Pneumonia: Quantitative Analysis of the Disease with a Computer Automatic Tool" <em>Journal of Personalized Medicine</em> 11, no. 7: 641. https://doi.org/10.3390/jpm11070641</p>
Fatigue failure accumulated probability of an offshore structure as computed in initial condition and after considering additional information through Bayesian inference
<p>Fatigue failure accumulated probability of an offshore structure as computed in initial condition and after considering additional information through Bayesian inference</p>
Solving the multi-commodity flow problem using an evolutionary routing algorithm in a computer network environment
<p>The continued increase in Internet traffic requires that routing algorithms make the best use of all available network resources. Most of the current deployed networks are not doing so due to their use of single path routing algorithms. In this work we propose the use of a multipath capable routing algorithm using Evolutionary Algorithms (EAs) that takes into account all the traffic going over the network and the link capacities by leveraging the information available at the Software Defined Networks (SDN) controller. The use of such information ensures that no link is used beyond its capacity, eliminating network congestion. We use EAs as true multi-objective solvers to provide a set of valid routing solutions from a single run of the algorithm. Modifications to the Multipath TCP (MPTCP) protocol are proposed to overcome the multipath problems associated with TCP.</p>
A computational study on the energy efficiency of species production by single-pulse streamers in air
<p>This dataset contains the data used to generate the results presented in the article "A computational study on the energy efficiency of species production by single-pulse streamers in air". This dataset includes (1) the source code; (2) the reaction set and transport data file, which contain a list of included reactions, with transport coefficients and their reaction rate coefficients; (3) configuration files for running the simulations and (4) output log files, which contain information about the physics and numerical properties of the simulations.</p>
Trajectory files for "Where does the energy go during the interstellar NH3 formation on water ice? A computational study"
<p>CP2K trajectory files for the N+H, NH + H and NH2 + H reactions on the amorphous water ice surface</p>
Data for "Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions"
<p>Data used in "<em>Machine Learning Scoring Functions for Drug Discovery from Experimental and Computer-generated Protein-Ligand Structures: Towards Per-target Scoring Functions</em>"<br> by F. Pellicani, D. Dal Ben, A. Perali, S. Pilati</p> <p>If you use these data or the python script for your research or other activities, please cite the corresponding journal article.</p> <p> </p> <p>====================</p> <p>Uncompressing the zipped file <em>DataSFUnicam.zip</em> provies the following files and folders:</p> <p><br> <strong>DataSFUnicam/</strong></p> <p> </p> <p> ExperimentalDataPDBFiles/<br> <em>This folder contains 2408 .pdb files of experimental complex structures. The files are named with a univocal code corresponding to the protein-ligand complex.</em></p> <p> </p> <p> ExperimentalDataXLSXFile.xlsx<br> <em>This Excel file reports the experimental protein-ligand chemical information. In the sheet named “Foglio1”, the first column contains the univocal code of the protein-ligand complex, the second column contains the experimentally measured pK_d.</em></p> <p> </p> <p> SyntheticDataPDBFiles/<br> <em>This folder contains the .pdb files of the synthetic complex structures. The .pdb files are grouped in 17 folders according to just as many target proteins. The folders are named after the corresponding protein. Each folder contains the .pdb files for the best position of each protein-ligand pair according to the MOE docking score. The files are named with a univocal code.</em></p> <p> </p> <p> SyntheticDataXLSXFiles/<br> <em> The folder contains 17 Excel files with the chemical information of the synthetic protein-ligand complexes. The files are named after the corresponding target protein. In the sheet named “Foglio1” of each .xlsx file, the first column contains a univocal code of the protein-ligand complex in each conformation, the second column contains an auxiliary numerical code corresponding to the protein-ligand pair, the third column contains the experimentally measured pK_i, and the fourth column contains the docking score provided by the MOE software.</em></p> <p>====================</p> <p>USER GUIDE FOR THE PYTHON SCRIPT</p> <p>Download and uncompress the zipped file "<em>SFUnicam.zip</em>" with a command like "<em>unzip SFUnicam.zip</em>". </p> <p>The following file structure is created:</p> <p><em>SFUnicam/</em></p> <p> <em>ComplexToBePredictedFolder/4ey5_30.pdb <br> MaxAssMatrix.npy<br> my_model<br> devStndSynt.npy<br> mediaSynt.npy<br> UnicamSF13prot.py<br> README.txt</em><br> <br> The subfolder "<em>ComplexToBePredictedFolder/</em>" contains the example PDB file "<em>4ey5_30.pdb</em>".</p> <p>-) To execute the script "<em>UnicamSF13prot.py</em>", Python 3 should be installed with the following libraries and sublibraries:<br> <em>Keras:<br> Regularizers<br> Sequential (keras.models)<br> Conv1D, Dense, MaxPooling1D, GlobalMaxPooling1D, GlobalAveragePooling1D, AveragePooling1D (keras.layers)<br> Adam (keras.optimizers)<br> Numpy</em><br> <em>Tensorflow</em></p> <p>Operation:<br> -) Copy the .pdb file related to the protein-ligand complex whose affinity is to be predicted in the subfolder “<em>ComplexToBePredictedFolder/</em>”.<br> -) Make sure the following files are in the same folder where the python script is:<br> <em>MaxAssMatrix.npy<br> mediaSynt.npy<br> devStndSynt.npy<br> my_model</em><br> -) Run the code using Python 3 with a command like "<em>python3.x UnicamSF13prot.py</em>".<br> -) Enter the name of the protein-ligand PDB file whose affinity is to be predicted (excluding the extension ".pdb").<br> -) Read the predicted affinity from screen.<br> </p> <p> </p>
Computational data on "A Model for the Rapid Assessment of Solution-Structures for 24-Atom Macrocycles: The Impact of β-Branched Amino Acids on Conformation"
<p>The archive contains 5 different folders:<br> <br> 1. mdp_files, that contains all the input files for the minimization, equilibration and molecular dynamics run (in gromacs format);<br> 2. topologies, that contains the equilibrated configurations and topology of the 4 systems studied (in gromacs format);<br> 3. trajectories, that contains the coordinates of the macrocycles during the metadynamics calculations and the relevant metadynamics output files (hills file and energy file);<br> 4. plumed_input, that contains the input file for the metadynamics calculations.<br> 5. gaussian, that contains input and output for the single-point charge calculations.</p>
The effect of Computer Assisted Rehabilitation Environment (CAREN) in cognitive impairment and coping strategies in Parkinson's disease: a preliminary study
<p>Parkinson's disease is a neurodegenerative disorder characterized by different motor, vegetative, behavioral, and cognitive impairments, with worsening quality of life. Virtual reality devices have given promising results in neurorehabilitation as they can provide multisensory stimulation in a realistic environment. This study aims to test the efficacy of virtual reality training by using Computer Assisted Rehabilitation Environment in cognitive impairment in a sample of PD. 31 patients affected by PD were enrolled. All PD patients underwent 24 sessions of Computer Assisted Rehabilitation Environment training. The participants were assessed at baseline (T0) and after two months (T1). Our results suggested that Computer Assisted Rehabilitation Environment training may be effective in the cognitive and emotional domains, particularly by improving executive function, anxiety, and depressive symptoms. These changes have helped to improve self-efficacy and coping strategies. These results indicate greater cognitive and physical effort to overcome stressors. Our results show that Computer Assisted Rehabilitation Environment training was beneficial in improving cognitive functions. Longer duration training may be especially beneficial for patients with mild cognitive impairment. Our findings open the door to tailored personalized treatments based on the patient's motor and cognitive profiles.</p>
Sentiment analysis of media's political bias in micro-blogging - A computational framework for optimized recommendation systems
<p>This dataset contains tweets from four Pakistani news channels, namely DAWN, GEO, ARY, and 24News, collected using the twarc command line tool with a Twitter academic researcher account. The tweets were collected between Nov, 2015, and April 4, 2022, and relate to three major political parties in Pakistan, PTI, PMLN, and PPP through their official names in the relevant news channel page tweets. </p>
Metadane dla Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey
<p>Metadata for Energy-Efficient OFDM Radio Resource Allocation Optimization With Computational Awareness: A Survey</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.