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3,688 results for “Computer”
Computational Studies of Substrate Transport and Specificity in a Phospholipid Flippase
<p>MD trajectories of all-atom and CG simulations of PI4P activated E2P state of the Drs2p-Cdc50p complex.</p>
Search-and-Rescue From Drones With Computer Vision
<p>Unmanned aerial vehicles (UAVs), most commonly known as drones, are increasingly used as a technological support tool for search-and-rescue (SAR) operations (and post-disaster area explorations as well). UAVs equipped with high-resolution cameras and embedded, yet powerful GPUs, in fact, can provide an effective and efficient aid to emergency rescue operations, mainly because locating victims, which may be unconscious or injured, as much fast as possible, is crucial to improve their chance of survival. In particular, the use of drones that are able to automatically detect people in the scenes can increase detection rate, while reducing rescue time. In this repository, we provide a new dataset specifically conceived for SAR operations from drones with computer vision. As it is small-sized, the dataset is currently intended for testing and evaluation purposes only. The main aim of the repository is to encourage contributions on this intriguing topic. In particular, any contribution to make the dataset bigger is welcome.</p>
PLOS Comput. Biol. "Biophysically detailed mathematical models of multiscale cardiac active mechanics": datasets
<p>This repository contains the data accompanying the PLOS Computational Biology paper "<em>Biophysically detailed mathematical models of multiscale cardiac active mechanics</em>", by Francesco Regazzoni, Luca Dedè and Alfio Quarteroni.</p> <p>It contains the following datasets:</p> <ul> <li><strong>steady_state.csv</strong>: steady-state active tension for constant calcium concentration and sarcomere length (Figs. 11, 12, 13 ,14).</li> <li><strong>isometric_twitches.csv</strong>: active tension transients in isometric conditions (Figs. 15, 16, 17).</li> <li><strong>force_velocity_relationship.csv</strong>: force-velocity relationship at different calcium concentrations and sarcomere lenghts (Fig. 18).</li> <li><strong>fast_transient_response.csv</strong>: tension-elongation curve after a fast step in length (Fig. 19).</li> </ul> <p>CSV headers refer to the following variables (and measure units):</p> <ul> <li><strong>Ca</strong> (<em>μM</em>): intracellular calcium concentration.</li> <li><strong>SL</strong> (<em>μm</em>): sarcomere length.</li> <li><strong>active_tension</strong> (<em>kPa</em>): active tension.</li> <li><strong>Delta_L</strong> (<em>nm/hs</em>): step length.</li> <li><strong>velocity</strong> (<em>hs/s</em>): shortening velocity.</li> <li><strong>time</strong> (<em>s</em>): time.</li> </ul>
Computed results for Bayesian genome scale modelling temperature effect on yeast metabolism
<p>This repository contains the computed results for reproducing the figures in the manuscript "Li G., et al. Bayesian genome scale modelling identifies thermal determinants of yeast metabolism". The scripts can be found in Github (<a href="https://github.com/Gangl2016/BayesianGEM">https://github.com/Gangl2016/BayesianGEM</a>)</p>
Computational Implementation of "Uncoupling electrokinetic flow solutions", published in Mathematical Geosciences
<p>This dataset includes Python and Mathematica scripts used to generate figures, and images used in the Mathematical Geosciences (MG) manuscript "Uncoupling Electrokinetic Flow Solutions" by Kuhlman and Malama (2020).</p> <p>Python scripts implementing eigenvalue uncoupling approach for differential equations governing 1D cylindrically symmetric electrokinetic flow problem (i.e., flow to a pumping well).</p> <ol> <li>mpmath python script (recombine-expint.py) implementing Theis "type curve" solution for an infinite domain (Figures 1-3 in MG manuscript)</li> <li>fipy python script (compare-via-fipy.py) and plotting script (plot_fipy_results.py) showing a finite-volume fully coupled solution for a similar finite domain for comparison against eigenvalue uncoupling approach (Figure 4 in MG manuscript). Also includes two shell scripts for driving python scripts for a variety of inputs.</li> </ol> <p>mathematica script (periodic-1D-steady-state-type-1.nb) for solving the algebra associated with the governing equations and plotting figures for analytical solution of periodically driven 1D solution (i.e., laboratory sinusoidal streaming potential and electroosmosis; Figures 4-9 in MG manuscript).</p> <p> </p>
Computational modelling of metal soap formation in historical oil paintings: the influence of fatty acid concentration and nucleus geometry on the induced chemo-mechanical damage.
<p>Metal soap formation is one of the most wide-spread degradation mechanisms observed in historical oil paintings, affecting works of art from museum collections worldwide. Metal soaps develop from a chemical reaction between metal ions present in the pigments and saturated fatty acids, which are released by the oil binder. The presence of large metal soap crystals inside paint layers or at the paint surface can be detrimental for the visual appearance of artworks. Moreover, metal soaps can possibly trigger mechanical damage, ultimately resulting in flaking of the paint. This paper departs from a recently proposed computational model to predict chemo-mechanical degradation in historical oil paintings, as presented in Eumelen et al. (J Mech Phys Solids 132:103683, 2019). The model describes metal soap formation and growth, which are phenomena that are driven by the diffusion of saturated fatty acids and proceed by a nucleation process from a crystalline nucleus of small size. This results into a chemically-induced strain in the paint, which may promote crack nucleation and propagation. The proposed model is here used to investigate the effects of saturated fatty acid concentration and initial nucleus geometry on the amount of chemo-mechanical damage generated. Numerical simulations show that both factors have a marginal influence on the growth rate of the metal soap crystal, but play a significant role on the extent of fracture induced in the paint.</p>
Coarse-grained Near-global Aqua-planet Simulation with Computed Dynamical Tendencies
<p>This dataset includes the coarse-grained 3D state of the near-global CRM simulations (NG-Aqua). The simulation is run at a 4km resolution using the System for Atmospheric Modeling (SAM)</p> <p>A dataset derived from the same simulation is included at the <a href="https://dx.doi.org/10.5281/zenodo.1226370">10.5281/zenodo.1226370</a>. This current posting supplements this dataset with the dynamical tendencies for total water and liquid-ice potential temperature, respectively given by FQT and FSLI. These are computed by initializing SAM run at a 160km resolution with the coarse-grained fields from NG-Aqua; evolving the state forward for 10 30 second time steps; saving the output; and finally computing the difference with the initial condition.</p> <p>This netCDF dataset is split into several part files for more robust uploading/downloading. To download this data, download each "part" file, and combine them with the "cat" linux command:</p> <pre><code>cat noBlur.nc.part?? > noBlur.nc</code></pre> <p>If using this with the uwnet code repository, you should then move this file to "data/processed/training/noBlur.nc", creating that folder if necessary.</p>
X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats
<p>X-ray computed tomography of bedded halite and halite crystals from the Bonneville Salt Flats, Utah. </p>
GECCO Industrial Challenge 2019 Dataset: A water quality dataset for the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition at the Genetic and Evolutionary Computation Conference 2019, Prague, Czech Republic.
<p>Dataset of the 'Internet of Things: Online Event Detection for Drinking Water Quality Control' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 13th-17th 2019, Prague, Czech Republic</p> <p> </p> <p>The task of the competition was to develop an anomaly detection algorithm for a water- and environmental data set.</p> <p> </p> <p>Included in zenodo: </p> <p>1. Original train dataset of water quality data provided to participants (identical to gecco2019_train_water_quality.csv)</p> <p>2. Call for Participation</p> <p>3. Rules and Description of the Challenge</p> <p>4. Resource Package provided to participants</p> <p>5. The complete dataset, consisting of train, test and validation merged together (gecco2019_all_water_quality.csv)</p> <p>6. The test dataset, which was used for creating the leaderboard on the server (gecco2019_test_water_quality.csv)</p> <p>7. The train dataset, which participants had available for training their models (gecco2019_train_water_quality.csv)</p> <p>8. The validation dataset, which was used for the end results for the challenge (gecco2019_valid_water_quality.csv)</p> <p> </p> <p>The challenge required the participants to submit a program for event detection. A training dataset was available to the participants (gecco2019_train_water_quality.csv). During the challenge the participants were able to upload a version of their program to out online platform, where this version was scored against the testing dataset (gecco2019_test_water_quality.csv), thus an intermediate leaderboard was available. To avoid overfitting against this dataset, at the end of the challenge, the end result was created from scoring with the validation dataset (gecco2019_valid_water_quality.csv). </p> <p>Train, Test, Validation dataset are from the same measuring station and are in chronological order. So the timestamps from the test dataset begin directly after the train timestamps, while the validation timestamps begin directly after the test timestamps. </p> <p> </p> <p>The competition was organized by:</p> <p>F. Rehbach, S. Moritz, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Thüringer Fernwasserversorgung and IMProvT research project</p> <p> </p> <p> </p> <p>Internet of Things: Online Event Detection for Drinking Water Quality Control</p> <p> </p> <p>Description:</p> <p>For the 8th time in GECCO history, the SPOTSeven Lab is hosting an industrial challenge in cooperation with various industry partners. This years challenge, based on the 2018 challenge, is held in cooperation with "Thüringer Fernwasserversorgung" which provides their real-world data set. The task of this years competition is to develop an anomaly detection algorithm for the water- and environmental data set. Early identification of anomalies in water quality data is a challenging task. It is important to identify true undesirable variations in the water quality. At the same time, false alarm rates have to be very low.</p> <p><br> Competition Opens: End of January/Start of February 2019<br> Final Submission: 30 June 2019</p> <p>Official webpage:</p> <p><a href="https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php">https://www.th-koeln.de/informatik-und-ingenieurwissenschaften/gecco-challenge-2019_63244.php</a></p> <p> </p>
Data set for the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD"
<p>Data set to accompany the article "Efficient Computation of the Magnetic Polarizability Tensor Spectral Signature using POD" written by B.A. Wilson (Swansea University) and P.D. Ledger (Keele University)</p>
Intrinsische Motivation von Schülerinnen und Schülern beim Physical Computing im Informatikunterricht
<p>Der KIM-Fragebogen wurde genutzt, um die intrinsische Motivation von Schülerinnen und Schülern bezüglich des Physical-Computing-Unterrichts zu erheben. Anschließend wurde der Fragebogen verwendet um zu untersuchen, welche Physical-Computing-Tätigkeiten besonders positiv auf die intrinsische Motivation wirken können. Zusätzlich wurde in offenen Fragen erhoben, welche Tätigkeiten die Schülerinnen und Schüler im Unterricht besonders mochten. </p>
Computational atomic coordinate files for Quantification of Ni-N-O bond angles and NO activation by X-ray emission spectroscopy
<p>Geometry optimized coordinates and other atomic coordinate files in xyz format used to calculate X-ray emission spectra of beta-diketiminate nickel nitrosyl complexes.</p>
Electrical & Computer Engineering: An International Journal (ECIJ)
<p>Electrical & Computer Engineering: An International Journal (ECIJ)</p> <p>ISSN : 2201-5957</p> <p>http://wireilla.com/engg/ecij/index.html</p> <p>CALL FOR PAPERS</p> <p>Submission Deadline : February 06, 2021</p> <p>Contact Us : ecijjournal@wireilla.com</p>
Evaluation results for When a Computer Cracks a Joke: Automated Generation of Humorous Headlines
<p>Evaluation results for the paper:</p> <p>Alnajjar, K., & Hämäläinen, M. (2021) When a Computer Cracks a Joke: Automated Generation of Humorous Headlines. In<em> The Proceedings of the Twelfth International Conference on Computational Creativity, ICCC’21</em>.</p> <p>The table has the aggregated evaluation results for each evaluation question. The left and right columns are the title before and after the replacement word. The replacement column show the humorous word and original column the word that existed in the headline before the replacement. The system column indicates whether the humorous headline was produced by our system or by a human.</p>
Data for: Scalable and Live Trace Processing with Kieker Utilizing Cloud Computing
<p>Knowledge of the internal behavior of applications often gets lost over the years. This circumstance can arise, for example, from missing documentation. Application-level monitoring, e.g., provided by Kieker, can help with the comprehension of such internal behavior. However, it can have large impact on the performance of the monitored system. High-throughput processing of traces is required by projects where millions of events per second must be processed live. In the cloud, such processing requires scaling by the number of instances.</p> <p>In this paper, we present our performance tunings conducted on the basis of the Kieker monitoring framework to support high-throughput and live analysis of application-level traces. Furthermore, we illustrate how our tuned version of Kieker can be used to provide scalable trace processing in the cloud.</p> <p>This is the dataset containing the results of our conducted benchmarks.</p>
The Software Sustainability Institute's Collaborations Workshop 2015 (CW15) attendees computational tools dataset
<p>Contains the question, raw data, and cleaned data for producing the most used software word cloud for those who attended the Software Sustainability Institute's Collaborations Workshop 2015 (CW15) held at the Oxford e-Research Institute, Oxford, UK from 25-27 March 2015</p>
Genomic DNA transposition induced by human PGBD5 (Accompanying Scripts for Computational Analyses)
<p>We include the bash scripts, python code and command-line parameters used to prepare, map, and analyze NGS sequencing data generated from a modified version of flanking sequence exponential anchored (FLEA) PCR to identify genomic insertion locations of a transposable element reporter construct. In summary, we map these reads to a hybrid genome consisting in the human reference hg19 and the reporter plasmid, identify reads that span the insertion breakpoint and thus recover the genomic insertion loci. </p> <p>Please find more details in the supplied README. </p> <p> </p> <p>This collection of scripts corresponds to the data analysis in the following publication:</p> <p>Genomic DNA transposition induced by human PGBD5</p> <p>Anton Henssen, Elizabeth Henaff, Eileen Jiang, Amy Eisenberg, Julianne R. Carson, Camila Villasante, Mondira Ray, Eric Still, Melissa Burns, Jorge Gandara, Cedric Feschotte, Christopher E. Mason, Alex Kentsis</p> <p> </p> <p> </p>
Demonstrative simulations of L-PEACH: a computer-based model to understand how peach trees grow
<p>L-PEACH is a computer-based model that simulates source-sink interactions, architecture and physiology of peach trees (Allen et al., 2005, 2006, 2007). The model integrates important concepts related to water transport and carbon assimilation, distribution, and use within the tree (DeJong et al., 2011). L-PEACH is able to simulate crop yield responses to commercial practices such as fruit thinning (Lopez et al., 2008) and pruning (Smith et al., 2008) and could be useful for making fruit growers understand how to optimize these operations. In this work we present several demonstrative simulations of L-PEACH to complement the existing references about L-PEACH and demonstrate its value to study, understand and teach how trees grow (DeJong et al., 2008).</p> <p>The FIRST SIMULATION corresponds with the version of L-PEACH that runs on a daily time-step (L-PEACH-d) (Lopez et al., 2008, 2010). The simulation shows the growth of a peach tree over three years. The color of the stem indicates the direction of the movement of carbon within the tree (white indicates no flux of carbon, increasing apical flux of carbon from light yellow to red, and increasing basal flux of carbon from light blue to deep purple) (see details of colors in Allen et al., 2005). During this simulation the tree was stopped during the dormant season between years and the trees were pruned by the model operator in a manner that is similar to how trees would be pruned when growing in an orchard. Also during the first year of tree growth, grafting is simulated by cutting the tree back in early spring and allowing the tree to grow again as it would in a tree nursery. After this first year the tree is cut back to a single trunk in the same manner as is commonly done when a tree is transplanted from a tree nursery to a commercial fruit orchard.</p> <p>In the SECOND SIMULATION a detailed section of the tree was selected to better appreciate the realism of leaf and fruit growth and in the THIRD SIMULATION we show how to prune a peach tree to a V-system. Responses to pruning were modelled based on the concept of apical dominance as described in Smith et al. (2008) and Lopez et al. (2008).</p> <p>Subsequent simulations correspond to the last version of the L-PEACH model that includes a xylem circuit so that the diurnal water potential of each organ could be simulated along with its physiological functioning and growth. Sub-models for leaf transpiration, soil water potential and the soil-plant interface were also incorporated to provide the driving force and pathway for water flow. In the FOURTH SIMULATION we presented the effect of different irrigation treatments (control irrigation and drought irrigation) on tree development, growth and fruit yield (Da Silva et al., 2011; 2014). L-PEACH-h was also use to illustrate the effect of severity of pruning in tree growth (FIFTH SIMULATION). We tested three levels of pruning: soft, control, and hard. The simulation indicates how trees that received hard pruning are able to recover a similar tree size than control and soft pruned trees due to the generation of vigorous shoots in response to hard pruning.</p> <p>The SIXTH SIMULATION was generated to demonstrate that L-PEACH can be also used to simulate the effect of size-controlling rootstock in tree growth (Da Silva et al., 2015). In this simulation we compared tree growth with a standard rootstock (Control) and a size-controlling rootstock (Rootstock) by reducing the hydraulic conductance of the ‘rootstock” piece (base of the trunk) by 50% in the size-controlling rootstock to simulate a reduction in vessel diameters and consequently reduced hydraulic conductance in that part of the tree. After four years of simulated growth, the virtual tree on the dwarfing rootstock was substantially smaller than the virtual tree on the control rootstock.</p> <p>What you can’t see in the movies is that the L-PEACH model calculates the distribution of light in the tree canopy as the tree grows and the rate of photosynthesis in each leaf during a simulated day or hour (depending on whether the daily or hourly models are used for the simulation). Then the distribution and use of photo-assimilates are calculated by the methods described in the papers cited below. The simulations are based on real environmental input data (light, temperature, day length, etc. collected from a real weather station located near a peach orchard) and development of tree architecture is based on developmental principles governing tree growth and detailed measurements of shoots of peach trees (see references).</p> <p><em><strong>Description of files</strong></em></p> <p>Simulation 1: L-PEACH-d over three years of growth.</p> <p>Simulation 2: Detailed growth of leaves and fruit using L-PEACH.</p> <p>Simulation 3: Pruning L-PEACH-d to a v-system.</p> <p>Simulation 4: Control irrigation vs. Drought irrigation using L-PEACH-h.</p> <p>Simulation 5: Reactions to soft, control and hard pruning using L-PEACH-h.</p> <p>Simulation 6: Simulating the effect of size-controlling rootstock using L-PEACH-h.</p>
Supplementary Material: Computational Study of Quasi-2D Liquid State in Free Standing Platinum, Silver, Gold, and Copper Monolayers
<p>Supplementary files for <em>Condensed Matter</em> <strong>2016</strong>, <em>1</em>(1), 1; doi:10.3390/condmat1010001; http://www.mdpi.com/ 2410-3896/1/1/1.</p> <p>Captions:</p> <p><strong>Video S1.</strong> (Pt 2400 K 5 ps) 5 ps Molecular Dynamics Movie of Pt Freestanding Monolayer at 2400 K. </p> <p><strong>Video S2.</strong> (Ag 1050K 6 ps) 6 ps Molecular Dynamics Movie of Ag Freestanding Monolayer at 1050 K.<br /> <br /> <strong>Video S3.</strong> (Au 1600K 4ps) 4 ps Molecular Dynamics Movie of Au Freestanding Monolayer at 1600 K.<br /> <br /> <strong>Video S4.</strong> (Cu 1400K 3ps) 3 ps Molecular Dynamics Movie of Cu Freestanding Monolayer at 1400 K. </p>
Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading
<p>Ex-situ X-ray CT fatigue testing data sets published as a data in brief:</p> <p>"<em>Ex-situ X-ray computed tomography data for a non-crimp fabric based fibre composite under fatigue loading</em>", Data in brief, 2017, doi.org/10.1016/j.dib.2017.10.074.</p> <p>Together with the following article:</p> <p>K. M. Jespersen and L. P. Mikkelsen, “Three dimensional fatigue damage evolution in non-crimp glass fibre fabric based composites used for wind turbine blades,” <em>Compos. Sci. Technol. </em> (In press), 2017, 10.1016/j.compscitech.2017.10.004.</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.