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3,688 results for “Computer”
GHTraffic: A Dataset for Reproducible Research in Service-Oriented Computing
<p>This is the latest version of the GHTraffic project. The main aim is to model a variety of transaction sequences to reflect more complex service behaviour.</p> <p>It has two editions: Small (S) and Large (L) where the records were created by selecting the same repositories as the original Small and Large datasets. The newest S dataset contains records from <a href="https://github.com/google/guava">google/guava</a> repository. The L dataset contains records from eight repositories (i.e., <a href="https://github.com/twbs/bootstrap">twbs/bootstrap</a>, <a href="https://github.com/symfony/symfony">symfony/symfony</a>, <a href="https://github.com/docker/docker">docker/docker</a>, <a href="https://github.com/Homebrew/homebrew">Homebrew/homebrew</a>, <a href="https://github.com/rust-lang/rust">rust-lang/rust</a>, <a href="https://github.com/kubernetes/kubernetes">kubernetes/kubernetes</a>, <a href="https://github.com/rails/rails">rails/rails</a>, and <a href="https://github.com/angular/angular.js">angular/angular.js</a>). </p> <p>The entire data generation process is quite similar to the original GHTraffic design. But it incorporates minor changes to the process of synthetic data generation where it uses a random date after successfully posting a resource to make up the request and response for all of the HTTP methods. It also adds yet another subset of unsuccessful transactions by stipulating requests before resource creation is successful.</p> <p>This results in a far more dynamic series of transactions to named resources.</p> <p>Scripts used for datasets construction are accessible from the <a href="https://bitbucket.org/tbhagya/ghtraffic-version-2.0.0">repository</a>.</p>
Hartree-Fock on a superconducting qubit quantum computer
<p>The simulation of fermionic systems is among the most anticipated applications of quantum computing. Here, we performed several quantum simulations of chemistry with up to one dozen qubits, including modeling the isomerization mechanism of diazene. We also demonstrated error-mitigation strategies based on N-representability which dramatically improve the effective fidelity of our experiments. Our parameterized ansatz circuits realized the Givens rotation approach to non-interacting fermion evolution, which we variationally optimized to prepare the Hartree-Fock wavefunction. This ubiquitous algorithmic primitive is classically tractable to simulate, yet still generates highly entangled states over the computational basis, which allowed us to assess the performance of our hardware and establish a foundation for scaling up correlated quantum chemistry simulations.</p>
Raw Data to "Can Small Polyaromatics Describe Their Larger Counterparts for Local Reactions? A Computational Study on the H-Abstraction Reaction by an H-Atom from Polyaromatics"
<p>This data includes the Turbomole input files (without molecular orbitals) and output files (under <strong> DFT_and_CC_*tar</strong>) as well as the output of the xTB calculations (under <strong>xtb*tar</strong>) for different reactive sites of the polyaromatics (PAHs) studied in the related publication, for the reaction:<br> C<sub>X</sub>H<sub>Y</sub> + H -> C<sub>X</sub>H<sub>Y-1</sub> + H<sub>2</sub><br> <br> <strong>File structure:</strong></p> <ul> <li>The xtb*tar contains the xTB (xTB version 6.3.1) optimized structures for the parameterizations <strong>GFN0</strong>, <strong>GFN1</strong> and <strong>GFN2</strong>. The parameter set is indicated by the folder name "<strong>BACK_XTB_NATIVE_GFN0</strong>", "<strong>BACK_XTB_NATIVE_GFN1</strong>" and "<strong>BACK_XTB_NATIVE_GFN2</strong>"<br> </li> <li>The DFT calculations are carried out at the <strong>TPSSh-D3/TZVP </strong>level and they are directly under the subdirectories of dft*tar folders (C*H*) and are used to calculate the data under <strong>Figure 3</strong> and <strong>4</strong> of the related publication, as well as to calculate partition functions which are listed under <strong>freeh.out</strong>,<strong> </strong>the output of the freeh program of Turbomole.<br> </li> <li>The coupled cluster calculations are always located under the subdirectories of the DFT calculations.<br> </li> <li>The subdirectories <strong>PNO-CC.tz</strong> are <strong>PNO-CCSD/cc-pVTZ</strong> calculations and the output files are named as 'pnoccsd.out.tpno.7' or 'pnoccsd.out.tpno.8', where 7 and 8 stands for the PNO selection thresholds of 10<sup>-7 </sup>and 10<sup>-8</sup>, which are used to produce the data under <strong>Table 11</strong> of the related publication.<br> </li> <li>The subdirectories <strong>CC.atz.f12</strong>, <strong>CC.dz</strong>, and <strong>CC.tz</strong> under<strong> </strong>C6H6* and C10H8* correspond to <strong>R(O)HF-CCSD(F12*)(T)/aug-cc-pVTZ</strong>, <strong>R(O)HF-CCSD(T)/cc-pvDZ </strong>and <strong>R(O)HF-CCSD(T)/cc-pvTZ </strong>calculations respectively, which is used for the calculation of the data under <strong>Table</strong> <strong>10</strong> of the related publication.<br> </li> <li>The subdirectories <strong>UHF-CC.atz.f12</strong> under C6H6-* and C10H8-* include <strong>UHF-CCSD(F12*)(T)/aug-cc-pVTZ </strong>calculations for the transition states and the products. The subdirectories <strong>CC.atz.f12 </strong>under C6H6 and C10H8 include <strong>RHF-CCSD(F12*)(T)/aug-cc-pVTZ</strong> calculations for the reactants benzene and naphthalene.<strong> </strong>These are used to calculate the data under <strong>Table 9</strong> in the related publication<br> </li> <li>The subdirectories <strong>rij.grid_m3.scfconv_7</strong> under C14H10* includes the DFT calculations with m3 integration grid and RI-J approximation. Under these, the subfolders <strong>(UHF-)PNO-CC.atz.f12</strong> include the <strong>PNO-(UHF-)CCSD(F12*)(T)/aug-cc-pVTZ</strong> calculations which are used to produce the data under <strong>Table 9</strong> in the related publication.<br> </li> <li>The subdirectories <strong>(UHF-)PNO-CC.atz.f12</strong> under C6H6* and C10H8* include<strong> PNO-(UHF-)CCSD(F12*)(T)/aug-cc-pVTZ</strong> calculations which are used for<strong> PNO threshold selection</strong> for the UHF-CCSD(F12*)(T) calculations (<strong>Table S2 </strong>of the Supporting Information of the related publication).</li> </ul>
input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"
<p>PLUMED input and TS ensemble for "Converging experimental and computational views of the knotting mechanism of the smallest knotted protein"</p>
Effect of space diffuser on flow characteristics of a centrifugal pump by computational fluid dynamic analysis
<p><span>Achieving an optimal configuration of the diffuser is indispensable for high pump performances. In this work, a numerical study on diffuser configuration is conducted for a high pump performance using a computational fluid dynamics code, and the effects of the wrap angle and the relative position of the diffuser vane to the impeller on pump performances are included. The results indicate that the modified diffuser with a suitable wrap angle may improve the pump hydraulic efficiency and the head by approximately 4% and 8%, respectively, while a suitable position of the diffuser vane can enhance the pump head by more than 4%. Meanwhile, the pressure recovery coefficient and the local Euler head of the diffuser are adopted to evaluate the diffuser performance. For a high pump performance, the local Euler head of the diffuser has a peak value at the leading edge with the change rate of zero along the meridian streamline, meaning that no blade loading at the leading edge of the diffuser guarantees a better match between the impeller and the diffuser.</span></p>
Pitfalls of Computed Tomography 3D Reconstruction Models in Cranial Nonmetric Analysis
<p>Many studies in the literature have highlighted the utility of virtual 3D databanks as a substitute for real skeletal collections and the important application of radiological records in personal identification. However, none have investigated the accuracy of virtual material compared to skeletal remains in nonmetric variant analysis using 3D models. The present study investigates the accuracy of 20 computed tomography (CT) 3D reconstruction models compared to the real crania, focusing on the quality of the reproduction of the real crania and the possibility to detect 29 dental/cranial morphological variations in 3D images. An interobserver analysis was performed to evaluate trait identification, number, position, and shape. Results demonstrate a false bone loss in 3D models in some cranial regions, specifically the maxillary and occipital bones in 85% and 20% of the samples. Additional analyses revealed several difficulties in the detection of cranial nonmetric traits in 3D models, resulting in incorrect identification in circa 70% of the traits. In particular, pitfalls included the detection of erroneous position, error in presence/absence rates, in number, and in shape. The lowest percentages of correct evaluations were found in traits localized in the lateral side of the cranium and for the infraorbital suture, mastoid foramen, and crenulation. The present study highlights important pitfalls in CT scan when compared with the real crania for nonmetric analysis. This may have crucial consequences in cases where 3D databanks are used as a source of reference population data for nonmetric traits and pathologies and during bone-CT comparisons for identification purposes.</p>
Framework for performing experiments on IBM Quantum Computers
<p>The data used in my B.Sc. thesis.</p>
Data set for validation of a Python script for computation of Protein-Ligand Interaction Fingerprints
<p><strong>1. Data set for for validation of the Protein-Ligand Interaction Fingerprints, which includes examples of protein structures (original PDB and equilibrated) and molecular dynamics trajectories (equilibration and ligand dissociation generated using Random Acceleration MD simulations, RAMD)</strong></p> <p><strong>mdifp_validation_data.tar.gz - </strong>archive that contains benchmark dataset for evaluation of the protein-ligand IFP protocol (PDB structures of protonated complexes, ligands, and MOL2 files of ligands) published in D. B. Kokha, B. Doser, S. Richter, F. Ormersbach, X. Cheng, R. C. Wade "A Workflow for Exploring Ligand Dissociation from a Macromolecule: Efficient Random Acceleration Molecular Dynamics Simulation and Interaction Fingerprints Analysis of Ligand Trajectories" J. Chem. Phys. <strong>153</strong>, 125102 (2020); <a href="https://doi.org/10.1063/5.0019088">https://doi.org/10.1063/5.0019088</a></p> <p>(2020) <a href="https://arxiv.org/abs/2006.11066">arXiv:2006.11066</a> </p> <p><strong>2YKI </strong>- protein-ligand complex , PDB ID 2YKI<br> - 2yki_MOE.pdb complex with hydrogen added and energy minimized using MOE software (https://www.chemcomp.com/)<br> - ligand_2yki_MOE.mol2 and ligand_2yki_MOE.pdb - ligand structure with hydrogens prepered by MOE software (https://www.chemcomp.com/)</p> <p><strong>6EI5</strong> - MD trajectory of the protein-ligand complex generated from PDB ID 6EI5<br> - ref-min.pdb minimized structure<br> - ref.prmtop topology file<br> - moe.mol2 - ligand structure in mol2 format<br> - amber2namd2.dcd generated MD trajectory </p> <p><strong>SAD_3-RAMD-03-2020.pkl </strong>- a pkl dataset with IFPs generated from RAMD dissociation trajectory of the complex PDB ID: 5LQ9 (trajectories from the paper Front. Mol. Biosci., 2019 DOI:10.3389/fmolb.2019.00036)</p> <p><strong>HSP90_Gromacs.zip </strong>- an archive that contains three pkl data sets of protein-ligand IFPs (for three HSP90 complexes; PDB ID: 5J64, 5J86, 5LQ9) generated from RAMD dissociation trajectories simulated using new Gromacs-RAMD engine (https://github.com/HITS-MCM/gromacs-ramd)</p> <p>The rest of the files contains data obtained from simulation of the complex of <strong>GPCR muscarinic receptor M2 (PDB ID:4MQT);</strong> immersed in a mixed membrane: 50% CHL, 30% POPC, 20% POPE) with a small molecule agonist iperoxo. <br> - <strong>IXO.pdb and moe.mol2 </strong>- PDBand MOL2 structure of iperoxo<br> - <strong>AMBER_eq.tar.gz</strong> - structure of the equilibrated complex generated using AMBER software<br> -<strong> NAMD_eq.tar.gz </strong>- two equilibration trajectories in dcd format generated using NAMD software <br> - <strong>RAMD_eq.tar.gz </strong>- dissociation tarjectoris of iprtoxo from the M2 protein generated from the last snapshot of two NAMD equilibration trajectories (for each case 2 RAMD dissociaiton trajectories are available) </p> <p>( *csv files were added erroneously and do not belong to the project)</p>
Co-authoring graphs of research teams in a laboratory in computer science
<p>Our aim is to study inter-organisational collaborations initiated by researchers in their research activity. We considered the co-authoring graph involving at least researchers from LORIA (<a href="https://www.loria.fr/fr/">https://www.loria.fr/fr/</a>), a French laboratory in computer science.</p> <p>The dataset is collected from the open French archive HAL (<a href="https://data.archives-ouvertes.fr/">https://data.archives-ouvertes.fr/</a>).</p> <p>Each file encodes (in <a href="http://www.graphviz.org/about/">DOT</a>) the co-authoring graph of a team of LORIA. A node represents a researcher, two nodes are linked only if the corresponding researchers published together over the three considered years 2017, 2018 and 2019. An affiliation attribute is attached to each considered node.</p> <p>The name of researchers and the teams as well as the HAL:id are anonymised. Only affiliations remain the same.</p>
Computational code for CCV metrics and two empirical datasets (D. dichotoma and S. viridis)
<p>The zipped file contains: 1) the self-explained R script for conducting multi-scale grid sampling and calculating various CCV metrics presented in the paper, 2) the users' guide for the R code, 3) abundance of <em>D. dichotoma</em> in sampling quadrats of the Changjiang water reservoir in Zhongshan City of the Guangdong Province of China, and 4) <em>S. viridis</em> in sampling quadrats of the highland barley cropland Rikaze City of Tibet, China.</p>
Dataset for: Buffer Management for Trust Computation in Resource-constrained IoT Networks
<p>Dataset for Buffer Management for Trust Computation in Resource-constrained IoT Networks</p>
Computing a correlation length scale from MFLL-OCO2 CO2 differences, and accounting for correlated errors when assimilating OCO-2 data
<p>This dataset contains code and data used in 'A new exponentially-decaying error correlation model for assimilating OCO-2 column-average CO<sub>2</sub> data, using a length scale computed from airborne lidar measurements' by David F. Baker, Emily Bell, Kenneth J. Davis, Joel F. Campbell, Bing Lin, and Jeremy Dobler, submitted to Geoscientific Model Development.</p> <p>In particular, the MATLAB script used to compute the autocorrelation spectrum of Multi-functional Fiber Laser LiDAR (MFLL) and Orbiting Carbon Observatory (OCO-2) column CO<sub>2</sub> differences (in Section 2.2 of the paper) is given here as file</p> <p>comp_MFLL_OCO2_autocorrl_spectrum.m</p> <p>along with the needed MFLL and OCO-2 CO<sub>2</sub> data for each of the six flights analyzed (as described in Section 2.1 of the paper) in files:</p> <p>20160727_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20160805_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20170215_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20170308_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20171022_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20171027_mfll_averaged_L1_RA_GMAO_ACTadj.h5<br> 20160727_oco_averaged_B9_GMAO_ACTadj.h5<br> 20160805_oco_averaged_B9_GMAO_ACTadj.h5<br> 20170215_oco_averaged_B9_GMAO_ACTadj.h5<br> 20170308_oco_averaged_B9_GMAO_ACTadj.h5<br> 20171022_oco_averaged_B9_GMAO_ACTadj.h5<br> 20171027_oco_averaged_B9_GMAO_ACTadj.h5</p> <p>The MFLL data given here was downloaded in late 2018 in the form of L1b files (calibrated radiances), as described in Bell et al (2020).</p> <p>In the second part of the paper, different error correlation models are presented and applied to the averaging of OCO-2 column CO<sub>2</sub> data. The original bias-corrected OCO-2 data, in the form of daily OCO-2 version 10 "Lite" files, have been from obtained from NASA's GES DISC data repository, here:<br> https://disc.gsfc.nasa.gov/datasets/OCO2_L2_Lite_FP_10r/summary?keywords=OCO2_L2_Lite_FP</p> <p>The bias-corrected column CO<sub>2</sub> retrievals, their uncertainties, and other parameters needed for this analysis were extracted from these<br> "Lite" files and saved to daily files, which have been packaged up in the following compressed tarball:<br> OCO2_XCO2_2014_2020.tar.gz</p> <p>These daily files are read in and averaged across 2-second and 10-second spans (as described in Section 3.5 of the paper), using the different error correlation models outlined in the paper. The code that implements these averages is given in the following two FORTRAN programs:</p> <p>Make_OCO2_2sec_averages.f90<br> Make_OCO2_10sec_averages.f90</p> <p>which need the following list of days having good OCO-2 data:</p> <p>OCO2_dates.txt</p> <p>Program "Make_OCO2_2sec_averages.f90" averages the OCO-2 data across a 2-second (~13.5 km long) span along the groundtrack, collapsing the relatively thin data swath into a one-dimensional data record, upon which the one-dimensional averaging models describe in Sections 3.1 and 3.2 of the paper may be applied. Program "Make_OCO2_10sec_averages.f90" implements these averaging models, which average the 2-second averages across longer, 10-second (~67.5 km) spans. Please see the manuscript for more information on the data and methods provided here.</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-8 week</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-6 week</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-control group</p>
Datasets for "Insight into ductular reaction in obstructive biliary disease from a three-dimensional perspective using ex vivo X-ray phase contrast computed tomography"
<p>Phase-contrast CT of BDL rats liver-4 week</p>
Computational epitope map of SARS-CoV-2 spike protein
<p>Dataset accompanying the publication "Map of SARS-CoV-2 spike epitopes not shielded byglycans" published in XYZ.</p> <p> </p> <p>The dataset contains:</p> <p>1. raw epitope screening scores (README file attached in the archive)</p> <p>2. structure and GROMACS topology and input files for two systems:</p> <p> - 4x SARS-CoV-2 spike protein, glycosylated<br> - 4x SARS-CoV-2 spike protein, non-glycosylated</p> <p> </p>
FIGURE 95 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 95. FMNH PR2081, Tyrannosaurus rex. Right femur in posterior (A), lateral (B), anterior (C), and medial (D) views. Left femur in posterior (E), lateral (F), anterior (G), and medial (H) views. I, right femur, proximal view. J, Right femur, distal view. Scale = 30 cm; abbreviations in Appendix 1. Photographs by J. Weinstein.
FIGURE 39 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 39. FMNH PR2081, Tyrannosaurus rex. Horizontal CT slice through braincase, 1 cm ventral to floor of endocranial cavity. Note hollow nature of parasphenoid rostrum and complex of recesses within basioccipital. Abbreviations in Appendix 1.
FIGURE 15 in Osteology of Tyrannosaurus rex: insights from a nearly complete skeleton and high-resolution computed tomographic analysis of the skull
FIGURE 15. FMNH PR2081, Tyrannosaurus rex. Sutural relationships between nasal, lacrymal, and maxilla above the antorbital fenestra. Left lateral view. See Appendix 1 for abbreviations. Photographs by J. Weinstein.
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.