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6,452 results for “applicative”
Needs of plant health laboratories and applicability of the horizontal proficiency testing approach based on the questionnaire answers
<p>Data collected in the framework of work package 5 of the Valitest project. They correspond to the needs and expectation concerning proficiency assessment expressed by plant health laboratories during a survey conducted online in 2019.</p>
Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties
<p>This dataset provides data described and used in the article submitted to Journal of Advances in Modeling Earth Systems "Snow equi-temperature metamorphism described by a phase-field model applicable on micro-tomographic images: prediction of microstructural and transport properties".</p> <p>It contains .csv files with different properties computed on outputs of the model Snow3D simulating equi-temperature metamorphism. This micro-scale model was used here with experimental micro-tomographic snow images as input and returns series of 3-D images of snow showing features of equi-temperature metamorphism at different time steps as output.</p> <p>In this dataset, you will find two types of files:</p> <p>- the microstructural properties (density, specific surface area, covariance lengths, mean curvature) computed on the simulated images at different time steps.</p> <p>- the transport properties (effective conductivity, normalizes effective vapor diffusion coefficient, permeability) of the simulated images at different time steps.</p> <p>Finally, metadata_simulations.csv gather the information relative to the simulations.</p>
Dataset for "A 21 m Operation Range RFID Tag for "Pick to Light" Applications with a Photovoltaic Harvester"
<p>In the paper, a novel Radio-Frequency Identification (RFID) tag for “pick to light” applications is presented. The proposed tag architecture shows the implementation of a novel voltage limiter and a supply voltage (VDD) monitoring circuit to guarantee a correct operation between the tag and the reader for the “pick to light” application. The feasibility to power the tag with different photovoltaic cells is also analyzed, showing the influence of the illuminance level (lx), type of source light (fluorescent, LED or halogen) and type of photovoltaic cell (photodiode or solar cell) on the amount of harvested energy. Measurements show that the photodiodes present a power per unit package area for low illuminance levels (500 lx) of around 0.08 μW/mm<sup>2</sup>, which is slightly higher than the measured one for a solar cell of 0.06 μW/mm<sup>2</sup>. However, solar cells present a more compact design for the same absolute harvested power due to the large number of required photodiodes in parallel. Finally, an RFID tag prototype for “pick to light” applications is implemented, showing an operation range of 3.7 m in fully passive mode. This operation range can be significantly increased to 21 m when the tag is powered by a solar cell with an illuminance level as low as 100 lx and a halogen bulb as source light.</p> <p>This dataset contains some of the data gathered during the experimental work developed and used in the paper.</p>
L'achat de produits de contrefaçon en France : une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 415, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est le comportement d’achat de produits de contrefaçon.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 3 variables de signalétique, 7 variables de segmentation, le comportement d’achat (2 items : fréquence et récence), l’intention comportementale (2 items dont 1 d’identité personnelle ; cf. infra pour la justification théorique), les croyances sur les bénéfices attendus (9 items), l’attitude (3 items), les croyances sur les freins perçus (12 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Intention de parler positivement des LGBT chez les jeunes en France. Une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 372, année : 2020). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’intention Intention de parler positivement des LGBT lors d'une discussion. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 1 item de connaissance objective, 1 item d’engagement dans le questionnaire, 7 variables de segmentation, l’intention comportementale (2 items dont 1 d’identité personnelle ; cf. infra pour la justification théorique), les croyances sur les bénéfices attendus (9 items), l’attitude (3 items), les croyances sur les freins perçus (7 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items) et 5 variables de signalétique. L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 7 points.</p>
La consommation de contenus sur Netflix chez les jeunes en France : une application de la théorie du comportement planifié.
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 325, année 2020, février-mars). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est la consommation de contenus sur Netflix. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 3 variables de segmentation, le comportement de l’individu (2 items : fréquence et récence), l’intention comportementale (2 items dont 1 d’identité personnelle ; cf. infra pour la justification théorique), les croyances sur les bénéfices attendus (13 items), l’attitude (3 items), les croyances sur les freins perçus (9 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 2 variables de signalétique (sexe et âge). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
Intention de soutenir la légalisation du cannabis chez les jeunes en France : une application de la théorie du comportement planifié.
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 434, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’intention de soutenir la légalisation du cannabis lors d'une discussion. La population mère : les jeunes de 17 à 25 ans.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 4 variables de signalétique, l’intention comportementale (2 items dont 1 d’identité personnelle), les croyances sur les bénéfices attendus (14 items), l’attitude (3 items), les croyances sur les freins perçus (7 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), les comportements de consommation de cannabis et les modalités de la légalisation (7 items). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
E-commerce et covid 19 en France. Une application de la théorie du comportement planifié
<p>Cette base de données est issue d’une enquête quantitative par questionnaire (n= 378, année 2021). Elle est construite sur la base de la théorie du comportement planifié. La variable dépendante est l’achat de biens et/ou de services par internet.</p> <p><em>Contenu de la base de données</em></p> <p>Le questionnaire comprend les mesures suivantes : 2 variables de segmentation (piratage et type d’achat), le comportement de l’individu (2 items : fréquence et récence), l’impact du Covid-19 sur la fréquence d’achat (1 item), l’intention comportementale (2 items dont 1 d’identité personnelle), les croyances sur les bénéfices attendus (11 items), l’attitude (3 items), les croyances sur les freins perçus (13 items), la perception de contrôle sur le comportement (2 items), les normes descriptives (2 items), les normes injonctives (2 items), 3 variables de signalétique (sexe, âge et CSP). L’administration étant réalisée en ligne, la base de données comprend également une variable « temps de saisie » du questionnaire qui pourra servir à épurer la base. Toutes les variables à échelle sont mesurées en 6 points.</p>
early developmental milestones from a crowd-based application
<p>Dataset for a research paper on developmental milestone data from a crowd-authored tracking application. </p>
BIM applications
<p>We present here the result of the survey of BIM applications. The survey was based on the software table presented in the <a href="https://bimdictionary.com/">BIM Dictionary</a> for Clash Detection and the software tables of the <a href="https://brasil.cbic.org.br/acervo-publicacao-coletanea-bim">CBIC BIM Collection</a>. The websites of BIM application developers were consulted. Brazilian developers were contacted to provide data. We have added information on the availability of educational licenses.</p> <p>BIM applications were categorized by the following fields:</p> <ol> <li> <p>SOFTWARE: application name;</p> </li> <li> <p>VENDOR/DEVELOPER: name of seller and developer;</p> </li> <li> <p>TECHNICAL FUNCTION: categorization adopted by buildingSMART for software implementation (<a href="https://technical.buildingsmart.org/resources/software-implementations/">https://technical.buildingsmart.org/resources/software-implementations/</a>).</p> </li> <li> <p>SUBGATEGORY: from the categorization indicated above, where General is adopted (when the application is aimed to all disciplines as architectural, structures or building systems), name of the discipline (building structure or systems) or a specific application (Augmented Reality; Design Review, Project Collaboration, Budgeting, Construction Planning ...);</p> </li> <li> <p>VENDOR DESCRIPTION: description of the application by the developer;</p> </li> <li> <p>CLOUD OR LOCAL: if the license is installed locally or in the cloud:</p> </li> <li> <p>EDUCATIONAL LICENCE: Whether educational licenses are available and</p> </li> <li> <p>LINK TO OFFICIAL PAGE: URL of the online location.</p> </li> </ol>
Visual-inertial input datasets for SLAM applications containing extreme and human-like motion patterns
<p>Recorded datasets in compressed rosbag format, which contain visual and IMU sensor information that are bearing high resemblance to the movement of a human player with a handheld AR-capable device.</p> <p>For machine learning training and validation tasks, separate dataset are available containing motion patterns in a wide range from steady camera image to extremely challenging movements.</p>
Dataset: Systematic Mapping Study on the Development and Application of Sentiment Analysis Tools in Software Engineering
<p>Update: We updated the data set in March 2022 by adding newly published papers and by providing more insights on how we analyzed them. Details can be found in the file " SEnti-SMS.xlsx".</p> <p>----------</p> <p>Update: The updated version (-v2) contains the results of one more snowballing iteration and extracted information on the accuracy of the used methods.</p> <p>----------</p> <p>In 2020, we conducted a systematic literature review to explore the development and application of sentiment analysis tools in software engineering.</p> <p>Information on the execution of the SLR, its scope, the search string, etc. are presented in the paper linked below.</p> <p> </p> <p> </p>
Raw Metrics and Rankings for "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing"
<p>These datasets accompany the article published in <em>Remote Sensing </em>entitled: "Exploratory Analysis on Pixelwise Image Segmentation Metrics with an Application in Proximal Sensing".</p> <p>For each of the three segmentation models presented in the paper (DTSM, SVM and CIVE) two types of datasets are included: </p> <ul> <li><strong>Raw Metrics: </strong>the raw evaluations for each image returned by each of the 12 evaluation metrics. </li> <li><strong>Rankings:</strong> the ranking of each image in the dataset based on its raw evaluation. This dataset has been created by sorting in ascending order the dissimilarity metrics (GCE and HDD) and descending order the similarity metrics (all the other metrics). </li> </ul> <p>The datasets are in Excel (.xlsx) format and can be easily loaded in R and used to reproduce the results presented in the article.</p>
AI-TAM: a model to investigate user acceptance and collaborative intention in human-in-the-loop AI applications
<p>More and more frequently, digital applications make use of Artificial Intelligence (AI) capabilities<br> to provide advanced features; on the other hand, human-in-the-loop approaches are on the<br> rise to involve people in AI-powered pipelines for data collection, results validation and decision making.<br> Does the introduction of AI features affect user acceptance? Does the AI result quality<br> affect people’s willingness to use such applications? Does the additional user effort required in<br> human-in-the-loop mechanisms change the application adoption and use?<br> This study aims to provide a reference approach to answer those questions. We propose a model<br> that extends the Technology Acceptance Model (TAM) with further constructs explicitly related to<br> AI – user trust in AI and perceived quality of AI output, from explainable AI (XAI) literature – and<br> collaborative intention – willingness to contribute to AI pipelines.<br> We tested the proposed model with an application for car damage claim reporting with AI-powered<br> damage estimation for insurance customers. The results showed that the XAI related factors have<br> a strong and positive effect on behavioral intention, perceived usefulness, and ease of use of the<br> application. Moreover, there is a strong link between behavioral intention and collaborative intention,<br> indicating that indeed human-in-the-loop approaches can be successfully adopted in final user<br> applications.</p> <p>Users were invited to test the interactive prototype of the BumpOut application and to report the given car accident from start to finish. These are the two interactive prototypes experienced by users:</p> <ul> <li> <p><a href="https://bit.ly/bo-prototype-flawlessAI">FlawlessAI-Group prototype</a></p> </li> <li> <p><a href="https://bit.ly/bo-prototype-failingAI">FailingAI-Group prototype</a></p> </li> </ul> <p> </p> <p>This study is shared as a research object adopting the <a href="https://www.researchobject.org/ro-crate/1.0/">RO-Crate</a> specification.</p>
Profile of microbial changes after the application of different phosphate forms
<p>This data set includes the results of the application of different P forms that may be present at different rates in REFLOW fertilizers on soil microbial activities, composition and biomass.</p> <p> </p> <p>The dataset is under embargo until the corresponding publication becomes available.</p>
Profile data of the ADMIRE project use case applications
<p>HPC application traces from the use cases in the ADMIRE project. They were collected with TAU monitoring tools.</p> <p>Applications traced are described at: https://www.admire-eurohpc.eu/UseCases/</p> <p>- <em>Application 1: <a href="http://meteo.uniparthenope.it/">Monitoring and Modelling Marine, weather and Air quality</a> </em></p> <p>- <em>Application 2: Car-Parrinello molecular dynamic simulation of large molecules and small proteins </em></p> <p><em>- Application 3: Simulation of large scale turbulent flow.</em></p> <p><em>- Application 4: Continental-scale land cover mapping with scalable and automatic deep learning frameworks.</em></p> <p><em>- Application 5: Super-resolution imaging using Opera microscopy and SRRF/ImageJ software. </em></p> <p><em>- Application 6: Software Heritage Management & Indexing.</em></p> <p> </p> <p> </p> <p> </p>
A consolidated database of police-reported motor vehicle traffic accidents in the United States for actuarial applications
<p>This database is related to "A CONSOLIDATED DATABASE OF POLICE-REPORTED MOTOR VEHICLE TRAFFIC ACCIDENTS IN THE UNITED STATES FOR ACTUARIAL APPLICATIONS" (Araiza Iturria C.A., Hardy M., Marriott P.).</p> <p>Author Information</p> <p> A. Author<br> Name: Carlos Andrés Araiza Iturria<br> Email: caraizai@uwaterloo.ca<br> <br> B. Co-author<br> Name: Mary Hardy<br> Email: mary.hardy@uwaterloo.ca</p> <p> C. Co-author<br> Name: Paul Marriott<br> Email: pmarriott@uwaterloo.ca<br> <br> Institution: University of Waterloo<br> Address: 200 University Ave W, Waterloo, ON N2L 3G1</p> <p><br> Funding granted by the Natural Sciences and Engineering Research Council of Canada. Hardy: RGPIN-2018-03754, Marriott: RGPIN-2020-04015.</p> <p>The Python scripts to create the database can be directly accessed through related identifiers in this page.</p> <p>Parameter estimates along with their 90% confidence intervals from the 20 multinomial logistic regressions can be seen through related identifiers in this page.</p> <p> </p>
OpenFOAM cases of the paper "CFD modeling of pressure drop through an OCP server for data center applications"
<p>This dataset contains the<em> underling data</em> for the paper "<em>CFD modeling of pressure drop through an OCP server for data center applications</em>” in Energies Journal.</p> <p>Numerical simulations are performed using open-source CFD code OpenFOAM. Features of the numerical model described in the paper can be summarized as:</p> <ul> <li> A hexahedra (hex) and split-hex mesh was created using <em>snappyHexMesh</em> utility from the STL (Stereolithography) model of the server</li> <li><em>Allrun</em> script runs steady-state simulations for different inlet flow rates given in the <em>flowrates</em> file.</li> <li><em>k-omegaSST</em> turbulence closure model is used</li> <li>Convective and diffusive fluxes are computed using second-order accurate numerical schemes.</li> <li>Resultant matrices are solved using <em>GAMG</em> and <em>smoothSolver</em> methods.</li> </ul> <p>New libraries are developed for the detection of whether the solution reaches state-state and for the calculation of average pressures at the inlet and outlet of the server. These libraries need to be download via the following link and compiled before the cases run: </p> <p><a href="https://github.com/DSTECHNO/OCPFoam">https://github.com/DSTECHNO/OCPFoam</a></p> <p><strong>active_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow flow over Leopard V3.1 model of OCP server neglecting fan blades.</p> <p><strong>inactive_Server.tar.gz:</strong> OpenFOAM files and scripts for the steady-state simulation of turbulent flow over Leopard V3.1 model of OCP server considering fan blades.</p> <p><strong>Results_active_Server.tar.gz:</strong> Simulation results for active server.</p> <p><strong>Results_inactiveServer.tar.gz: </strong>Simulation results for inactive server.</p>
Dataset for: Application of Machine Learning for the Spatial Analysis of Binaural Room Impulse Responses
<p>This repository contains supplementary material for the paper titled `Application of Machine Learning for the Spatial<br> Analysis of Binaural Room Impulse Responses' Available at: <a href="http://dx.doi.org/10.3390/app8010105">dx.doi.org/10.3390/app8010105</a> . These programs and audio files are distributed in the hopes that they will prove useful under the Creative Commons Attribution 4.0, with no warranty; or the implied warranty of merchantability or fitness for a particular problem. Please give appropriate credit for use of the material provided in this repository back to the author. </p> <p>In order to use the MatLab code the Auditory Toolbox by Malcolm Slaney [1] and the Cochleagram function distributed by Bin Gao [2] are required.</p> <p>The python scrips require the following Python libraries to be installed: Numpy[3], SciPy[4] and Tensorflow [5].</p> <p>The MatLab code was tested using MatLab R2017a on a Computer running windows 7.</p> <p>The python code was tested using Python 3.2.5, using an anaconda Python environment - in windows command line.</p> <p>--</p> <p>The repository contains:</p> <p>Folders:</p> <p><br> 1.) neg90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the -90° rotation neural network.</p> <p>2.) pos90 - This folder contains the gaussian normalisation parameters stored as text files and the weights and biases for the trained neural network - these are all for the +90° rotation neural network.</p> <p>3.) testData - this folder contains pre-generated test data for the different binaural dummy head microphones, speaker, and signal type combinations.</p> <p>Python Scripts:</p> <p><br> 1.) AnalyseDoA.py - A python script that can be run to test the neural network using the pre-generated test data - running the script will allow the user to input the binaural dummy head, speaker, and signal type. The important variables generated by this script are DoA - the direction of arrival for each signal in the feature vector, and yDiff - the difference between the predicted DoA and the expected direction of arrival</p> <p>2.) DirectionAnalysis.py - This python file contains a set of function that are used to define the neural network, and run it. The function called DoAPrediction takes the feature vector generated by the MatLab code as its input argument, these features will then be passed to the neural network, and the output of this function is the direction of arrival predicted by the neural network for each signal. The functions: DoAAnalysis_neg90 and DoAAnalysis_pos90 are called by the DoAPrediction function, these functions create the neural network using the NN function, import the weights and biases, and passes the feature matrix (provided as input) through the neural network - the output of these functions are the predicted direction of arrival.</p> <p>MatLab files:</p> <p><br> 1.) runAnalysis.m - This MatLab script analyses the dataset provided as part of this repository. Users can change the variables head ('KEMAR' or 'KU100'), signalType ('directSound' or 'reflection'), and speaker ('EquatorD5' or 'Genelec8030'). This script will produce the gaussian normalised feature vector and expected direction of arrival for all signals with the defined head, signal type, and speaker combination. These variables are then saved in .mat files so they can be imported by the python scripts.</p> <p>2.) BinauralModelCochlea.m - This MatLab function analyses a given binaural signal and outputs the interaural cross-correlation, interaural level difference, interaural time difference, the cochlea output for the left and right channel and the centre frequencies of the gammatone filter band. The input variables are: IR - the signal to be analysed, N - the number of gammatone filters, freqLow - the lowest centre frequency of the gammatone filter bank (centre frequency of the first gammatone filter), and freqHigh - the highest centre frequency of the gammatone filter bank (the centre frequency of the Nth gammatone filter). This function requires Malcolm Slaney's Auditory Toolbox [1] and Bin Gao's Cochleagram function [2] in order to work.</p> <p>3.) generateFeatureVector.m - This MatLab function generates a feature vector from an input binaural signal x, and a version of the signal captured after the binaural dummy head has been rotated by either +90° or -90° degree (variables xPos90 and xNeg90 respectively). If the sampling frequency (Fs) isn't 44100, the signals are resampled to be at 44100. This file also contains a function 'gaussianNormalisationTestData' which gaussian normalises the data using the mean and standard deviation calculated from the data used to train the neural networks - the mean and standard deviation values are stored in the folder GMParams in the pos90 and neg90 folders.</p> <p>4.) generateTestData.m - This MatLab function analyses the included binaural dataset, it takes the input variables: head - the binaural dummy head used for the measurements either 'KEMAR' or 'KU100', speaker - the speaker used for the measurements either 'EquatorD5' or 'Genelec8030', and signalType - the type of signal being analysed either 'directSound' or 'reflection'.</p> <p>Text files:</p> <p><br> 1.) noLayers.txt - a text file containing the number of layers used when training the neural network - with the current version of the code the neural network contains only 1 layer.</p> <p>2.) README.txt - Read me file containing information about the repository.</p> <p>Audio files:</p> <p><br> This repository contains 1152 binaural signals half of which are direct sounds segmented from a binaural room impulse responses and the other half are reflections segmented from binaural room impulse responses (detailed in the paper this material supports) the direct sounds are recorded at angles from 0° to 357.5° in steps of 2.5° and the reflections are recorded at angles of 1° to 358.5° in steps of 2.5°. In the paper only recordings relating to signals recorded with the Equator D5 are analysed.</p> <p>The combination of audio files include:</p> <p>1.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 2.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Equator D5 speaker<br> 3.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 4.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Equator D5 speaker<br> 5.) 144 direct sound recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 6.) 144 reflection recordings captured with the KEMAR 45BC binaural dummy head microphone and the Genelec 8030 speaker<br> 7.) 144 direct sound recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker<br> 8.) 144 reflection recordings captured with the KU100 binaural dummy head microphone and the Genelec 8030 speaker</p> <p>The files are stored using the following file naming convention:<br> head_Test3_speaker_signalType_000_0_Degrees.wav - where _000_0 defines the azimuth direction of arrival so for example for a direct sound measured with the KEMAR unit and the Genelec8030 at 5 degrees would be 'KEMAR_Test3_Genelec8030_directSound_005_0Degrees.wav' and for a reflection measured with the KU100 and the Equator D5 at 298.5 degrees would be 'KU100_Test3_EquatorD5_reflection_298_5Degrees.wav'</p> <p>--</p> <p>Bibliography:<br> [1] Slaney, M. (1998). Auditory Toolbox. Palo Alto, CA. [Online]. Available: https://engineering.purdue.edu/~malcolm/interval/1998-010/ [Accessed: Oct. 27, 2017]</p> <p>[2] Gao, B. (2014). Cochleagram and IS-NMF2D for Blind Source Separation. [Online] Available: http://uk.mathworks.com/matlabcentral/fileexchange/48622-cochleagram-and-is-nmf2d-for-blind-source-separation?focused=3855900&tab=function [Accessed: Oct. 27, 2017]</p> <p>[3] NumFocus. (n.d.). NumPy. [Online]. Available: http://www.numpy.org/ [Accessed: Oct. 27, 2017]</p> <p>[4] SciPy. (n.d.). SciPy. [Online]. Available: https://www.scipy.org/ [Accessed: Oct. 27, 2017]</p> <p>[5] Google. (n.d.). TensorFlow. [Online] Available: https://www.tensorflow.org/ [Accessed: Oct. 27, 2017]</p> <p>--</p> <p>All code and audio produced by: Michael Lovedee-Turner, PhD candidate in Music Technology at the Audio Lab, Department of Electronic Engineering, University of York</p> <p>Contact: mjlt500@york.ac.uk</p>
Simulated application load on a Kubernetes system based on Human traffic pattern
<p>Contained here is the dataset generated using the tool based on the report 'A Dynamic Kubernetes Load Generation Solution Mimicking Human Traffic Pattern'. The tool was created to fill in the gap of having to generate artificial load within Kubernetes infrastructure. This tool could then be used as a stable testing framework to determine whether the Autoscaling solutions in place can handle the expected load pattern or not. It can also be used as a standard benchmarking tool to test various algorithms that aim to improve the already existing autoscaling solutions. The tool is primarily for IaaS (Infrastructure As A Service) providers, who have to deal with applications as a black box and are unaware of the scaling needs of the application. Some of the data generated in this dataset were made in reference to existing datasets obtained from Google Dataset of different containers. The reference dataset will also be uploaded in the near future. </p> <p>The dataset contains CSV files, which have the name of the pod, average CPU usage, average memory usage, and the timestamp of when the data was collected. These measurements were taken directly from the Prometheus service which uses using Kubernetes metrics server to scrape the data from the pods. <br>The `avg_cpu_usage` is the average CPU percent of the total CPU available the application utilized over a 1-minute interval window. While the `avg_memory_usage` is the average memory utilized in bytes over 1 1-minute interval window. The CSV file names also mention the period of time from when the measurements were taken. Any date and time within the dataset are in the CEST timezone. All of the CSV files in the dataset have the same layout. </p> <p>The datasets were collected from a Kubernetes node in a VM, with the following specifications,<br>- Intel core (Broadwell, no TSX, IBRS), 2 cores @ 2.594 GHzkb<br>- 4GiB of memory<br>- Ubuntu 22.04.4 LTS x86_64<br>- Kernel: 5.15.0-105-generic</p> <p>The names of the file itself specify the type of application load generated. The file names can be divided into segments separated by `__`. The last two segments signify the start time of the measurement data, and the end time of the measurement data respectively. The optional segment before the mentioned two segments signifies the container from the original input dataset that was used as a reference to produce the given data.</p> <p>Similarly, there is an optional tag of `CPU`, `mem`, or `test` at the beginning of the file name. This signifies the type of application load that was used to generate the data. `CPU` would mean the application was created by using options on the tool that used CPU stressing algorithms from `ng-stress`. This was used to better mimic a CPU-intensive application. Similarly, `mem` primarily makes use of a test algorithm that stresses memory. `test` is a special option making use of a combination of tests to have a balanced outcome. </p> <p>In the real world, the applications are never static, and between each run, with the same parameters, the application will always show some variations. To account for these variations, we added some inherent randomness in the number of concurrent connections and the amount of time a virtual user waits to make a request. There are some measurements where these measurements were frozen, which makes the class,<br>- fixed-load_fixed-sleep <br>- random-load_fixed-sleep<br>- fixed-load_random-sleep</p> <p>measurements.<br>The default class of (random-load_random-sleep) is every other measurement not marked by these tags in their name.</p> <p>The dataset itself was collected over a period of 3 days, from 25-03-2024 to 28-03-2024. Each dataset generally contains 34 min of simulation data, with the first and last 2 minutes without any traffic being directed to stabilize the system. The actual 30 min of data corresponds to the daily behavioral pattern as shown in a 30-day period.</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.