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6,452 results for “Application”
Data from: Continuous-time spatially explicit capture-recapture models, with an application to a jaguar camera-trap survey
<ol> <li>Many capture-recapture surveys of wildlife populations operate in continuous time but detections are typically aggregated into occasions for analysis, even when exact detection times are available. This discards information and introduces subjectivity, in the form of decisions about occasion definition.</li> <li>We develop a spatio-temporal Poisson process model for spatially explicit capture-recapture (SECR) surveys that operate continuously and record exact detection times. We show that, except in some special cases (including the case in which detection probability does not change within occasion), temporally aggregated data do not provide sufficient statistics for density and related parameters, and that when detection probability is constant over time our continuous-time (CT) model is equivalent to an existing model based on detection frequencies. We use the model to estimate jaguar density from a camera-trap survey and conduct a simulation study to investigate the properties of a CT estimator and discrete-occasion estimators with various levels of temporal aggregation. This includes investigation of the effect on the estimators of spatio-temporal correlation induced by animal movement.</li> <li>The CT estimator is found to be unbiased and more precise than discrete-occasion estimators based on binary capture data (rather than detection frequencies) when there is no spatio-temporal correlation. It is also found to be only slightly biased when there is correlation induced by animal movement, and to be more robust to inadequate detector spacing, while discrete-occasion estimators with binary data can be sensitive to occasion length, particularly in the presence of inadequate detector spacing.</li> <li>Our model includes as a special case a discrete-occasion estimator based on detection frequencies, and at the same time lays a foundation for the development of more sophisticated CT models and estimators. It allows modelling within-occasion changes in detectability, readily accommodates variation in detector effort, removes subjectivity associated with user-defined occasions, and fully utilises CT data. We identify a need for developing CT methods that incorporate spatio-temporal dependence in detections and see potential for CT models being combined with telemetry-based animal movement models to provide a richer inference framework.</li> </ol>
Data for Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications
<p>These data were generated for the Open-Acces Article :</p> <p>Kamerling, S.; Vuillerme, V.; Rodat, S. Solar Field Output Temperature Optimization Using a MILP Algorithm and a 0D Model in the Case of a Hybrid Concentrated Solar Thermal Power Plant for SHIP Applications. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3731. https://doi.org/10.3390/en14133731</p> <p>In these dataset, the data for the Case Study and the Sensitivity Analysis are available. Jupyter Notebooks for further process of these data are also available. The NoteBooks AnalyseHourlyValues, AnalyseDailyValues and AnalyseMonthlyValues allow for easy change of variable, whereas CaseStudyAnalysis is for one specific set of data. The AnalyseSets were created in order to analyse the influence of the optimization on the solar fraction of the different datasets.</p>
Nanosized Cu-SSZ-13 and Its Application in NH3-SCR. Dataset related to publication. Version: 1
<ul> <li><strong>Data type</strong>: tabular data, experimental measurements.</li> <li><strong>Data format</strong>: plain text (.txt), Bruker data (.spc and .par), Origin data (.opju)</li> <li><strong>Origin of the data</strong>: <ul> <li>Experimental spectroscopic measurements</li> <li>Computer modelling</li> </ul> </li> <li>The data were generated by a Bruker EMXmicro X-band spectrometer purchased on 2008</li> <li><strong>Information on data collection and elaboration</strong>: After collecting the data from the spectrometer, they were processed and analysed by using the Easyspin toolbox. Therefore the plain text data obtained from Easyspin were elaborated on Origin software.</li> <li><strong>Availability of the tools used to elaborate the data:</strong> Easyspin (5.2.28) and Origin 2019 software are required to read part of the data. The former is an open-source MATLAB toolbox (link to download it: http://www.easyspin.org/). The latter requires a license. The link for acquiring the Origin license is: https://www.originlab.com/index.aspx?go=Purchase/PricingAndOrdering&pid=1780 </li> </ul>
Data for: A Comparison of the Influence of Different Multi-Core Processors on the Runtime Overhead for Application-Level Monitoring
<p>Application-level monitoring is required for continuously operating software systems to maintain their performance and availability at runtime. Performance monitoring of software systems requires storing time series data in a monitoring log or stream. Such monitoring may cause a significant runtime overhead to the monitored system.</p> <p>In this paper, we evaluate the influence of multi-core processors on the overhead of the Kieker application-level monitoring framework. We present a breakdown of the monitoring overhead into three portions and the results of extensive controlled laboratory experiments with microbenchmarks to quantify these portions of monitoring overhead under controlled and repeatable conditions. Our experiments show that the already low overhead of the Kieker framework may be further reduced on multi-core processors with asynchronous writing of the monitoring log.</p> <p>Our experiment code and data are available as open source software such that interested researchers may repeat or extend our experiments for comparison on other hardware platforms or with other monitoring frameworks.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: A Benchmark Engineering Methodology to Measure the Overhead of Application-Level Monitoring
<p>Application-level monitoring frameworks, such as Kieker, provide insight into the inner workings and the dynamic behavior of software systems. However, depending on the number of monitoring probes used, these frameworks may introduce significant runtime overhead. Consequently, planning the instrumentation of continuously operating software systems requires detailed knowledge of the performance impact of each monitoring probe.</p> <p>In this paper, we present our benchmark engineering approach to quantify the monitoring overhead caused by each probe under controlled and repeatable conditions. Our developed MooBench benchmark provides a basis for performance evaluations and comparisons of application-level monitoring frameworks. To evaluate its capabilities, we employ our benchmark to conduct a performance comparison of all available Kieker releases from version 0.91 to the current release 1.8.</p> <p>This dataset supplements the paper and contains the raw experimental data as well as several generated diagrams for each experiment.</p>
Data for: Performance Benchmarking of Application Monitoring Frameworks
<p>Application-level monitoring of continuously operating software systems provides insights into their dynamic behavior, helping to maintain their performance and availability during runtime. Such monitoring may cause a significant runtime overhead to the monitored system, depending on the number and location of used instrumentation probes. In order to improve a system’s instrumentation and to reduce the caused monitoring overhead, it is necessary to know the performance impact of each probe.<br /> While many monitoring frameworks are claiming to have minimal impact on the performance, these claims are often not backed up with a detailed performance evaluation determining the actual cost of monitoring. Benchmarks can be used as an effective and affordable way for these evaluations. However, no benchmark specifically targeting the overhead of monitoring itself exists. Furthermore, no established benchmark engineering methodology exists that provides guidelines for the design, execution, and analysis of benchmarks.<br /> This thesis introduces a benchmark approach to measure the performance overhead of application-level monitoring frameworks. The core contributions of this approach are 1) a definition of common causes of monitoring overhead, 2) a general benchmark engineering methodology, 3) the MooBench micro-benchmark to measure and quantify causes of monitoring overhead, and 4) detailed performance evaluations of three different application-level monitoring frameworks. Extensive experiments demonstrate the feasibility and practicality of the approach and validate the benchmark results. The developed benchmark is available as open source software and the results of all experiments are available for download to facilitate further validation and replication of the results.</p> <p>This dataset supplements the thesis and contains the results of all experiments, including the raw result data, the results of additional experiments, and the configuration of our benchmarks.</p>
Porting Workflow for SAGrid applications (November 2014)
<p>This is the application porting workflow for new applications, using the continuous integration service. As of 15.12.2014</p>
Autonomic Provisioning and Application Mapping on Spot Cloud Resources
<p>1. Attached files: </p> <p>500_100_fmincon_0.95_1.mat <br /> Experiment with 500 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>500_100_heuristic_0.95_1.mat<br /> Experiment with 500 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_70_fmincon_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, exact algorithm.</p> <p>2000_70_heuristic_0.95_1.mat<br /> Experiment with 2000 users, 70ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.9_1.mat<br /> Experiment with 2000 users, 100ms max response time, 90% availability, exact algorithm.</p> <p>2000_100_heuristic_0.9_1.mat <br /> Experiment with 2000 users, 100ms max response time, 90% availability, our algorithm.</p> <p>2000_100_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>2000_100_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2000_100_fmincon_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, exact algorithm.</p> <p>2000_100_heuristic_0.999_1.mat <br /> Experiment with 2000 users, 100ms max response time, 99.9% availability, our algorithm.</p> <p>2000_300_fmincon_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, exact algorithm.</p> <p>2000_300_heuristic_0.95_1.mat <br /> Experiment with 2000 users, 300ms max response time, 95% availability, our algorithm.</p> <p>10000_100_fmincon_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, exact algorithm.</p> <p>10000_100_heuristic_0.95_1.mat <br /> Experiment with 10000 users, 100ms max response time, 95% availability, our algorithm.</p> <p>2. Data format:</p> <p>MATLAB data format, can be load from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure
<p>Data and scripts used to generate figures presented in the paper "Combining Analytical Modeling, Realistic Simulation and Real Experimentation for the Optimization of Monte-Carlo Applications on the European Grid Infrastructure" submitted to the Future Generation Computer Systems Journal.</p>
Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers (Data)
<p>Auxiliary materials for ICPE 2016 paper titled:<br /> "Communication Characterization and Optimization of Applications Using Topology-Aware Task Mapping on Large Supercomputers".</p> <p> </p>
Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS - Dataset
<p>These files are the dataset for the following paper "Classifying hot water chemistry: Application of MULTIVARIATE STATISTICS". Authors: Prihadi Sumintadireja<sup>1</sup>, Dasapta Erwin Irawan<sup>1</sup>, Yuano Rezky<sup>2</sup>, Prana Ugiana Gio<sup>3, </sup>Anggita Agustin<sup>1</sup></p>
Model-driven Application Refactoring to Minimize Deployment Costs in Preemptible Cloud Resources
<p>1. Attached files: </p> <p>This archive contains 1440 MATLAB files, each one containing the results of a single experiment.<br /> The name of each file follows the following format:</p> <p> A_B_C_null_D_0.95_E_F.mat</p> <p>Where the fields A, B, C, D, E, and F are described as follows.</p> <p>A: number of users.<br /> Considered values are: 2500, 5000, 10000, 20000, 40000.</p> <p>B: variation in the profile of the requests.<br /> Considered values are: ref, var1, var2, var3, var4, var5.<br /> ref -> reference experiment, no users are halved or doubled<br /> var1 -> users in class 1 halved, other users doubled <br /> var2 -> users in class 2 halved, other users doubled <br /> var3 -> users in class 3 halved, other users doubled <br /> var4 -> users in class 4 halved, other users doubled <br /> var5 -> users in class 5 halved, other users doubled </p> <p>C: variation of the replaceability set of the application server.<br /> Considered values are: ref, k1, k2, k3, k4, k5.<br /> ref -> reference experiment, no rates are halved or doubled<br /> k1 -> only possible substitution has rate k1 halved and other rates doubled<br /> k2 -> only possible substitution has rate k2 halved and other rates doubled <br /> k3 -> only possible substitution has rate k3 halved and other rates doubled <br /> k4 -> only possible substitution has rate k4 halved and other rates doubled <br /> k5 -> only possible substitution has rate k5 halved and other rates doubled </p> <p>D: variation of the design constraints.<br /> Considered values are: none, 4, 34, 234.<br /> none -> no components can be replicated<br /> 4 -> only the application server can be replicated<br /> 34 -> only the application server and the database server can be replicated<br /> 234 -> all the components can be replicated</p> <p>E: Optimization algorithm.<br /> Considered values are: norefactoring, replacement, reassignment, full<br /> norefactoring -> experiment with no refactorings<br /> replacement -> experiment with only replacement refactoring<br /> reassignment -> experiment with only reassignment refactoring<br /> full -> experiment with replacement and reassignment refactorings</p> <p>F: Experiment seed.<br /> Considered values are from 1 to 20</p> <p> </p> <p>2. Data format:</p> <p>MATLAB data format, can be loaded from MATLAB using the following command:</p> <p>results = load(filename);</p> <p>results is defined as a structure with the following fields:</p> <p>results.cost<br /> Type: scalar, positive real number.<br /> Desc: hourly cost in US dollars.</p> <p> <br /> results.time<br /> Type: scalar, positive real number.<br /> Desc: total time (in seconds) needed by the algorithm to compute the solution.</p> <p>results.evaluations<br /> Type: scalar, positive integer number.<br /> Desc: number of constraints evaluations needed by the algorithm to compute the <br /> solution.</p> <p><br /> results.d<br /> Type: matrix, non negative positive real number. <br /> Desc: association matrix between rented resources (columns) and application <br /> components (rows). The sum of all the elements of this matrix is equal to<br /> the ECUs used by the application.</p>
Datasets for paper: J. A. Cerón-Guzmán and E. León-Guzmán (2016), A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election. SocialCom 2016. DOI: 10.1109/BDCloud-SocialCom-SustainCom.2016.47
<p>Datasets for paper: J. A. Cerón-Guzmán and E. León-Guzmán (2016), A Sentiment Analysis System of Spanish Tweets and Its Application in Colombia 2014 Presidential Election. The 9th IEEE International Conference on Social Computing and Networking (SocialCom). DOI: 10.1109/BDCloud-SocialCom-SustainCom.2016.47</p>
RPL: a domain-specific language for designing and implementing parallel C++ applications
<p>Parallelising sequential applications is usually a very hard job, due to many different ways in which an application can be parallelised and a large number of programming models (each with its own advantages and disadvantages) that can be used. In this paper, we describe a method to semi-automatically generate and evaluate different parallelisations of the same application, allowing programmers to find the best parallelisation without significant manual reengineering of the code. We describe a novel, high-level domain-specific language, Refactoring Pattern Language (RPL), that is used to represent the parallel structure of an application and to capture its extra-functional properties (such as service time). We then describe a set of RPL rewrite rules that can be used to generate alternative, but semantically equivalent, parallel structures (parallelisations) of the same application. We also describe the RPL Shell that can be used to evaluate these parallelisations, in terms of the desired extra-functional properties. Finally, we describe a set of C++ refactorings, targeting OpenMP, Intel TBB and FastFlow parallel programming models, that semi-automatically apply the desired parallelisation to the application's source code, therefore giving a parallel version of the code. We demonstrate how the RPL and the refactoring rules can be used to derive efficient parallelisations of two realistic C++ use cases (Image Convolution and Ant Colony Optimisation).</p>
System Call Logs with Natural Random Faults: Experimental Design and Application
<p>In this work we present a large dataset for use in embedded systems research which has been gathered from a realistic development environment operating in the path of an accelerated neutron beam. The dataset contains traces with events from a real-time operating system as as user events from a safety critical application. All data is carefully timestamped and in human understandable form.</p>
Research data supporting "One-pot synthesis of multiple protein-encapsulated DNA flowers and their application in intracellular protein delivery"
<p>Research data supporting the publication:</p> <p>Eunjung Kim, Limor Zwi-Dantsis, Natalie Reznikov, Catherine S. Hansel, Shweta Agarwal, and Molly M. Stevens, <strong>One-Pot Synthesis of Multiple Protein-Encapsulated DNA Flowers and Their Application in Intracellular Protein Delivery, </strong>2017, Adv Mater,<strong> </strong>DOI:<strong> </strong>10.1002/adma.201701086.</p> <p> </p> <p> </p>
Application Domain of 5,000 GitHub Repositories
<p>We provide a manual classification of the application domain of 5,000 GitHub repositories (the most popular ones, by number of stars, on January, 2017).<br> <br> We classified each system in one of the following application domains:</p> <ul> <li><strong>Application software:</strong> systems that provide functionalities to end-users, like browsers and text editors (e.g., WordPress/WordPress and adobe/brackets).</li> <li><strong>System software:</strong> systems that provide services and infrastructure to other systems, like operating systems, middleware, and databases (e.g., torvalds/linux and mongodb/mongo).</li> <li><strong>Web libraries and frameworks</strong> (e.g., twbs/bootstrap and angular/angular.js).</li> <li><strong>Non-web libraries and frameworks</strong> (e.g., google/guava and facebook/fresco).</li> <li><strong>Software tools:</strong> systems that support development tasks, like IDEs, package managers, and compilers (e.g., Homebrew/homebrew and git/git).</li> <li><strong>Documentation:</strong> repositories with documentation, tutorials, source code examples, etc. (e.g., iluwatar/java-design-patterns).</li> </ul> <p>To cite the dataset, please use the following paper (which proposes and uses a first dataset version):</p> <p>Hudson Borges, Andre Hora, Marco Tulio Valente. <em>Understanding the Factors that Impact the Popularity of GitHub Repositories</em>. In 32nd IEEE International Conference on Software Maintenance and Evolution (ICSME), pages 334-344, 2016.</p>
Dataset for Application of Recommender Systems on Police Photo Lineup Assembling task
<p>For more information about the Recommender Systems for Police Photo Lineup project, please visit: http://www.ksi.mff.cuni.cz/~peska/lineup/paper.pdf</p> <p>The dataset consists of two parts:</p> <p><strong>The raw dataset </strong>contains visual and attribute-based desriptors of the list of candidate persons:</p> <p>- personsCB_IDs.csv: ordered list of persons IDs<br> - personsData.csv: raw attribute features of the persons<br> - personsCBVectors.csv: derived binary attribute-features with TF-IDF applied. The ordering of records is the same as in personsCB_IDs.csv<br> - personsVectors.csv: derived visual descriptors of persons' images. Probability layer of VGG-Face CNN was applied for this task. The ordering of records is the same as in personsCB_IDs.csv.</p> <p><strong>The implicit feedback dataset</strong> (feedbackDatasetCB-RSFeatures.csv) contains information received from the user-study on lineup assembling task performed by seven domain experts. This table has following structure:</p> <p>- evaluatorID;<br> - lineupID (id of the suspect);<br> - candidateID (person recommended to this particular suspect);<br> - calculated content-based similarity<br> - selection (1 if evaluator selected this candidate, 0 otherwise);<br> - substraction of CB features of the suspect and the candidate (this can be simply modified by accessing raw dataset to contain visual descriptors or any other combination of features)</p> <p> </p>
Magellan/M2FS Spectroscopy of Galaxy Clusters: Stellar Population Model and Application to Abell 267
<p>supplementary data products, including all sky-subtracted spectra from individual galaxies, as well as random draws from posterior PDFs for model parameters (see included README file)</p>
Dataset of "Synaptic function in two-contact devices for neuromorphic circuit applications"
<p><span>This dataset supports the article "Synaptic function in two-contact devices for neuromorphic circuit applications" </span></p> <p> </p> <p><span>Raw data for the article "Synaptic function in two-contact devices for neuromorphic circuit applications". For further details see the Readme.txt file.</span></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.