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864 results for “numerative”
Probing temperature-responsivity of microgels and its interplay with a solid surface by superresolution microscopy and numerical simulations
<p>This dataset supports the publication ' Probing temperature-responsivity of microgels and its interplay with a solid surface by super resolution microscopy and numerical simulations' published on ACS Nano. DOI: https://doi.org/10.1021/acsnano.2c07569</p>
Defining Categorical Reasoning of Numerical Feature Models with Feature-Wise and Variant-Wise Quality Attributes
<p><strong>To watch it in Youtube:</strong></p> <p><a href="https://youtu.be/Uq2qtb4_K2U">https://youtu.be/Uq2qtb4_K2U</a></p> <p><strong>This is a pre-print, please access and cite the published version:</strong></p> <p><a href="https://doi.org/10.1145/3503229.3547057">https://doi.org/10.1145/3503229.3547057</a></p> <p>Automatic analysis of variability is an important stage of <em>Software Product Line</em> (SPL) engineering. Incorporating quality information into this stage poses a significant challenge. However, quality-aware automated analysis tools are rare, mainly because in existing solutions variability and quality information are not unified under the same model.</p> <p>In this paper, we make use of the <em>Quality Variability Model</em> (QVM), based on <em>Category Theory</em> (CT), to redefine reasoning operations. We start defining and composing the six most common operations in SPL, but now as quality-based queries, which tend to be unavailable in other approaches. Consequently, QVM supports interactions between variant-wise and feature-wise quality attributes. As a proof of concept, we present, implement and execute the operations as lambda reasoning for CQL IDE -- the state-of-the-art CT tool.</p>
Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"
<p>Software repository for the reproduction of the test cases from</p> <p><strong>Thomas Richter, Rolf Ulrich, Markus Janczyk:</strong> <em>Diffusion models with time-dependent parameters: "An analysis of computational effort and accuracy of different numerical methods"</em></p> <p>This software is used in particular for the reproducibility of the results.</p> <p>However, the algorithms can also be used directly for own purposes. If you have any questions about possibly necessary adaptations, please contact thomas.richter@ovgu.de.</p> <p>Parts of this repository</p> <p>General setup</p> <p><strong>Python</strong> collects all Python script. Here, <strong>Python/PythonTools</strong> are several internal functions, e.g. the realizations of KFE and random walks. <strong>Python/results</strong> and <strong>Python/pics</strong> are the directories where the results (figures and text-files) are put.</p> <p><strong>C++</strong> collects the C++ scripts.</p> <p>Case I</p> <p>Reproduces Case I of the paper (time-independent)</p> <ul> <li>Python/TestCase1.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase1.cc</strong> (as reference solution) and by <strong>Python/TestCase1-Plot.py</strong></p> <ul> <li>C++/testcase1.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase1.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase1.py</strong> for computing errors.</p> <ul> <li>Python/TestCase1-Plot.py</li> </ul> <p>produces Fig. 6 of the paper. It requires the outputs of <strong>Python/TestCase1.py</strong> and <strong>C++/testcase1.cc</strong></p> <p>Case II</p> <p>Reproduces Case II of the paper (time-dependent thresholds and drift)</p> <ul> <li>Python/TestCase2.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE. It produces output in <strong>Python/pics</strong> and <strong>Python/results</strong>. These results will be used in <strong>C++/testcase2.cc</strong> (as reference solution) and by <strong>Python/TestCase2-Plot.py</strong></p> <ul> <li>C++/testcase2.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase2.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase2.py</strong> for computing errors.</p> <ul> <li>Python/TestCase2-Plot.py</li> </ul> <p>produces Fig. 7 of the paper. It requires the outputs of <strong>Python/TestCase2.py</strong> and <strong>C++/testcase2.cc</strong></p> <ul> <li>Python/TestCase2-AdjustRandomWalks.py</li> </ul> <p>runs simulations to reproduce Fig. 11 of the paper and implements the modification of the random walk strategy to limit oscillations.</p> <p>Case III</p> <p>Reproduces Case III of the paper (dependency of the accuracy on the derivative of the drift)</p> <ul> <li>Python/TestCase3.py</li> </ul> <p>runs the test-case with random walks, integral equation method and with KFE for a fixed discretization but with different values of the drift tau. It produces first part of Fig. 8.</p> <ul> <li>C++/testcase3.cc</li> </ul> <p>runs the stochastic Euler simulation. Script is started by <strong>C++/run-testcase3.sh</strong>. It reads in the reference solution generated by <strong>Python/TestCase3.py</strong> for computing errors.</p> <ul> <li>Python/TestCase3-Plot.py</li> </ul> <p>produces second part of Fig. 8. Depends on the output of <strong>Python/TestCase3.py</strong></p> <p>Case IV</p> <p>Reproduces Case IV of the paper (accuracy and efficiency for Dirac initial data)</p> <ul> <li>Python/TestCase4.py</li> </ul> <p>runs the test-case with random walks, integral equation and with KFE for a refined discretizations.</p> <ul> <li>Python/TestCase4-Plot.py</li> </ul> <p>produces Fig. 9. Depends on the output of <strong>Python/TestCase4.py</strong></p> <ul> <li>Python/TestCase4-showsolution.py</li> </ul> <p>Solves with the KFE and plots the solution as surface plot over time and space variable. This skript is used to create Fig. 10 of the paper. Problem parameters and discretization can be adjusted at the top of the script. To test the different stabilization strategies, one can either adjust the value of theta, or one activates Rannacher time-marching by commenting in the marked lines in the skript PythonTools/kfe.py, here in kfe_ale(..)</p> <p>Data Fitting</p> <p>Python scripts to fit the KFE model to the Data published by Rolf Ulrich et al. in</p> <p><strong>R. Ulrich, H. Schröter, H. Leuthold, T. Birngruber</strong> <em>Automatic and controlled stimulus processing in conflict tasks: Superimposed diffusion processes and delta functions.</em>Cognitive Psychology, 78 , 148–174</p> <ul> <li>Python/DataFitting-Simon.py</li> </ul> <p>runs the parameter fitting for the Simon task and produces data for Fig. 9 and Table 1.</p> <ul> <li>Python/Eriksen-Fletcher.py</li> </ul> <p>runs the parameter fitting for the Eriksen Fletcher task and produces data for Fig. 9 and Table 2.</p> <p>Installation & running the examples</p> <p>Python</p> <p>The python skripts can just be started. Just note that they depend on each other, i.e.: <strong>Python/TestCase1.py</strong> produces a reference solution that is required by <strong>C++/testcase1.cc</strong> and the results of both are needed in <strong>Python/TestCase1-Plot.py</strong></p> <p>The scripts only depend on standard packages like numpy or scipy and all Python environments should work. One suggestion is to use Spyder as part of Anaconda.</p> <p>C++</p> <p>The C++-programs are not intended for performing the simulations in a stand-alone application. Instead, the SDE is simulated for a given number of trials <strong>N_tr</strong> and a given time step <strong>dt</strong> and this simulation is repeated <strong>64</strong> times in order to estimate the average error. It should however be simple to use the scripts as basis for an efficient parallel simulation tool that uses multithreading.</p> <p>Configuration</p> <p>The C++ test cases must be compiled. The test cases are set up to use <strong>cmake</strong>. We suggest the following (in a Linux-environment or on a Mac using homebrew or MacPorts):</p> <ol> <li>Create a directory for compilation, e.g. <strong>C++/bin</strong> now called the <strong>bin-dir</strong></li> <li>In the <strong>bin-dir</strong> calls cmake by <strong>cmake ..</strong> (adjust the path, if the <strong>bin-dir</strong> is not a subdirectory of the <strong>C++-dir</strong>.</li> <li>Several options can be adjusted. In <strong>C++/bin</strong> call <strong>ccmake .</strong> to make all necessary changes.</li> </ol> <p>If you change the location of the <strong>bin-dir</strong> you will have to modify the run-scripts <strong>run-testcase[123].sh</strong>.</p> <p>Compilation</p> <p>Initially and whenever you change the code, the programs must be re-compiled</p> <ol> <li>In <strong>C++/bin</strong> just call <strong>make</strong></li> </ol> <p>Running the examples</p> <p>The programs are started in <strong>C++</strong>. For each of the test-case there is a skript to start the program.</p> <ol> <li>In <strong>C++</strong> call <strong>sh ./run-testcase1.sh</strong> (or <strong>sh ./run-testcase2.sh</strong>, etc.)</li> </ol> <p>Each script will start the programs several times. For <strong>Case I</strong>, <strong>Case II</strong> and <strong>Case IV</strong> the simulation is started on a sequence of finer and finer discretizations, for <strong>Case III</strong> the value of <em>tau</em> will be changed.</p> <p>The scripts store the output in <strong>C++/results</strong>. Old outputs will be overwritten! Further, the scripts read information about the reference solution from <strong>Python/resuts</strong>.</p> <p>The C++ programs use multithreading the OpenMP. If you do not specify the number of threads to be used, all available threads are taken including all hyperthreads. This is usually not efficient it is therefore advisable to set the number of threads by hand, e.g. by calling</p> <p><strong>export OMP_NUM_THREADS=8</strong></p> <p>before calling the run-scripts.</p> <p>License Information</p> <p>Initially the software has been written Thomas Richter, Otto-von-Guericke University Magdeburg, Germany in 2022, 2023 (thomas.richter@ovgu.de)</p> <p>You are free to use the scripts under the <em>Creative Commons Attribution 4.0 License</em>.</p>
Postdiction Competition - Sera.ta - Seismic Response of Masonry Cross Vaults: Shaking table tests and numerical validations
<p>Once the blind prediction has been completed (DOI 10.5281/zenodo.7624666), new data are given to the research groups to perform the post diction simulations. The main objectives are:</p> <ul> <li>to calibrate the numerical model based on the dynamic properties;</li> <li>to emprove the capability of different modelling/analysis approaches and better justify the numerical assumptions.</li> </ul> <p>Below you can find the data set for the post diction analysis namely:</p> <ul> <li>Unstrengthened specimen results: </li> <li>Strengthened specimen results: </li> <li>Results of mix mortars used for the TRM technique:</li> </ul> <p> </p>
Precessing binary-black-hole numerical relativity catalogue (complete data release)
<p>This page contains the minimal data release associated with the catalogue presented in <a href="https://dcc.ligo.org/DocDB/0186/P2300054/001/catalogue.pdf">A catalogue of precessing black-hole-binary numerical-relativity simulations</a>. This catalogue contains 80 single-spin precessing black-hole-binary configurations. </p> <p>The content of the data release is described <a href="https://data.cardiffgravity.org/bam-catalogue/">here</a>, along with instructions on how to parse the data.</p>
Data for Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms
<p>This is a reference case setup to re-generate all the data for the paper titled "Numerical Study on the Impact of Large Air Purifiers, Physical Distancing, and Mask Wearing in Classrooms"<br> </p>
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 in The Red Data Book Waterbirds In The Coastal Wetlands Of The Azov-Black Sea Region Of Ukraine - The Results Of The August Counts 2018 And 2021
Fig. 2. The most numerous RDB wetland birds species, recorded during August Counts 2018 and 2021 (bar chart — amount of birds, line chart — number of sites were species was recorded).
Dataset for: "Micromagnetic Tomography: Numerical Libraries"
<p>This dataset contains the codes and Jupyter notebooks to reproduce the results of the publication <em>Micromagnetic Tomography: Numerical Libraries. </em></p> <p>This data is hosted in the Github repository: <a href="https://github.com/Micromagnetic-Tomography/paper-2023-mmt-numerical-libraries">https://github.com/Micromagnetic-Tomography/paper-2023-mmt-numerical-libraries</a></p> <p> </p>
Numerical Modeling of Asteroid Impact in 2D Cylindrical Coordinates
<p>Additional material and code for the Bachelor Thesis "Numerical Modeling of Asteroid Impact in 2D Cylindrical Coordinates" </p>
Numerical study of the one-dimensional Holstein model using the momentum-space hierarchical equations of motion method
<p>Data on the finite-temperature current-current correlation function of the one-dimensional Holstein model. Data are obtained using the newly developed momentum-space hierarchical equations of motion (HEOM) method. Details on the method development, as well as on the model parameters, will be given as a supplementary material to a journal publication that will be deposited on arXiv. Folders Regime* contain temporal evolution of the current-current correlation function (j_j_real_time.txt), diffusion constant (diffusion_constant.txt), diffusion exponent (diffusion_exponent.txt), and the electron's spread (delta_x.txt). They also contain frequency profiles of the Fourier transformed current-current correlation function (j_j_real_frequency.txt) and dynamical mobility (dynamical_mobility.txt).</p>
Supplementary codes and datasets for "Efficient numerical method for reliable upper and lower bounds on homogenized parameters"
<p>This repository supports L. Gaynutdinova, M. Ladecký, A. Nekvinda, I. Pultarová, and J. Zeman, <em>Efficient numerical method for reliable upper and lower bounds on homogenized parameters</em> (first announced as the arXiv preprint <a href="http://arxiv.org/abs/2208.09940">2208.09940</a>).</p> <p>In particular, it contains MATLAB source files for reproducing the results presented in Examples 1 and 2 of the manuscript for discretizations with <span class="math-tex">\(N_1 = N_2 = N_3 = 6, 12, 24\)</span>. For other parameters, the code needs to be modified manually.</p> <p>The most recent version of the codes is available in the <a href="https://gitlab.com/ul_bounds_homog_CTU/3d-fem-homogenized-parameters">GitLab repository</a>.</p>
Supporting Dataset for "Reducing a tropical cyclone weak-intensity bias in a global numerical weather prediction system"
<p>This archive supports the submission of "Reducing a tropical cyclone weak intensity bias in a global numerical weather prediction system" to Monthly Weather Review. It contains model configurations, the software used to create ensemble perturbations, the software used to compute the diagnostics discussed in the text, and the software use to plot figures.</p> <p>After downloading, the contents can be extracted using:</p> <blockquote> <p>tar -xzf idealtc1_archive-1.tgz</p> </blockquote>
Direct Numeric Simulation dataset archives
<p>Cloud droplets are analyzed using DNS data. Let's first discuss the many types of cloud representation and the cloud's structure before getting into the data. Later, the World Meteorological Organization adopted this classification, and eleven cloud genera are now recognized according to height and appearance. The middle clouds in the center, low clouds in the bottom panel, and high clouds in the top panel. We used simulated data of cumulus-type low clouds. Fair weather clouds that resemble white cotton balls are present. Clouds have a number of effects on the atmosphere, including the hydrological cycle and the radiation imbalance.</p>
Data for: Direct numerical simulation surface layer and IR-based measurements
<p>This is the primary dataset used in a manuscript in the process of being submitted to JTECH. The bulk of the dataset is a direct numerical simulation (DNS) of an open channel flow with a shear-free surface. The DNS includes scalar, velocity, and divergence fields over 181 realizations. From the scalar field, another vector field was generated by a method called feature image velocimetry that successively cross-correlates the scalar fields to derive the underlying velocity field. The two velocity fields are then compared by their spectra, one from the DNS and the other derived from the scalar field.</p>
Numerical model and natural river data for the timescale analysis of meandering channel migration
<p>This is the archive of the numerical model and river centerline data used for analyzing the timescale related to meandering channel migration, which is tied to the manuscript submitted to Journal of Geophysical Research: Earth Surface: Li, Y., and Limaye, A. B., Timescale of the morphodynamic feedback between planform geometry and lateral migration of meandering rivers.</p> <p>Running this model needs a MATLAB® software environment. The model can be launched by the wrapper scripts saved under the folder "software code/example wrappers". The wrapper script called "wrapper01a_channelOnly_runModel.m" is used to generate all model simulations in this study.</p>
Thomson problem in the disk: sup- plemental material (numerical data)
<p>Supplemental material of the paper "Thomson problem in the disk". It contains the minimum energy configurations for the Thomson problem in the disk" for selected number of charges, the largest one being N=40886</p>
H2020 ENODISE: VKI Numerical Aeroacoustic Database of Configuration B1
<p>This database represents the resultant data from a hybrid methodology consisting of aerodynamic simulations using uRANS and acoustic propagation simulations using FEM. The simulation of both the aerodynamic installation effects and the acoustic installation effects is available. The data from the aerodynamic simulations is given in the<em> .cgns </em>format and are in the <em>Aerodynamic_Results</em> folder. The resulting acoustic directivities from these sources using the FEM-Source Mode method are stored in the <em>Acoustic_Results</em> folder as <em>.csv </em>files. Both folders have a README file available with more details.</p>
H2020 ENODISE: VKI and SISW Numerical Aeroacoustic Database of Configuration C
<p>The hybrid aeroacoustic methodology prediction findings produced by VKI and SISW for configuration C within the scope of the European project ENODISE are contained in this database. In this setup, contra-rotating propellers with and without a shroud are investigated. Simulations of single rotors with and without the shroud are also included. The measurements made at the anechoic facility ALCOVES at VKI (https://zenodo.org/record/7966211) and further simulations made by ONERA (https://zenodo.org/record/8191815) can be compared to the findings provided here . </p> <p>A description of the numerical set up and database file structure are included in the PDF: <em>VKI-SISW_Simulation_Methodology_and_File_Organization_Config_C.pdf</em></p> <p><br> </p>
ONERA Numerical Database Configuration A1 (static propeller)
<p>This database contains the numerical results obtained by ONERA on the configuration A1, using ZDES mode 2 and ZDES mode 3 methods. The ZDES mode 3 approach offers an explicit resolution of the turbulent scales present in the outer region of the flat plate boundary layer and their interaction with the propeller.</p>
Numerical response of predator to prey: Dynamic interactions and population cycles in Eurasian lynx and roe deer
<p>The dynamic interactions between predators and their prey have two fundamental processes; numerical and functional responses. Numerical response is defined as predator growth rate as a function of prey density or both prey and predator densities [dP/dt = f(N, P)]. Functional response is defined as the kill rate by an individual predator being a function of prey density or prey and predator densities combined. Although there are relatively many studies on the functional response in mammalian predators, numerical response remains poorly documented. We studied numerical response of Eurasian lynx (<em>Lynx lynx</em>) to various densities of its primary prey species, roe deer (<em>Capreolus</em> <em>capreolus</em>), and to itself (lynx). We exploited an unusual natural situation, spanning three decades where lynx, after a period of absence in central and southern Sweden, during which roe deer populations had grown to high densities, subsequently recolonized region after region, from north to south. We divided the study area into seven regions, with increasing productivity from north to south. We found strong effects of both roe deer density and lynx density on lynx numerical response. Thus, both resources and intraspecific competition for these resources are important to understand the lynx population dynamic. We built a series of deterministic lynx–roe deer models and applied them to the seven regions. We found a very good fit between these Lotka-Volterra-type models and the data. The deterministic models produced almost cyclic dynamics or dampened cycles in five of the seven regions. Thus, we documented population cycles in this large-predator-large-herbivore system, which is rarely done. The amplitudes in the dampened cycles decreased towards the south. Thus, the dynamics between lynx and roe deer became more stable with increasing carrying capacity for roe deer, which is related to higher productivity in the environment. This increased stability could be explained by variation in predation risk, where human presence can act as prey refugia, and by a more diverse prey guild that will weaken the direct interaction between lynx and roe deer.</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.