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115 results for “time-dependence”
Dataset: Environment effects on X-ray absorption spectra with quantum embedded real-time Time-dependent density functional theory approaches
<p>This dataset collects the outputs from real-time TDDFT simulation of X-ray absorption of halides in model systems, using the frozen density embedding (FDE) and block-orthogonalized Manby-Miller embedding (BOMME), as well as processing tools and scripts used to carry out the calculations.</p>
Dataset: Time-dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling positive matrix factorisation (PMF) window
<p>Uploaded igor pxp files are the data to generate all figures of the results from our publication in Atmospheric Chemistry and Physics with the name of <em>"Time dependent source apportionment of submicron organic aerosol for a rural site in an alpine valley using a rolling PMF window"</em> by Chen et al. (2021).</p> <p>This study deployed a novel and advanced source apportionment technique on a dataset measured in Magadino. Rolling PMF allows retrieving more realistic, time-dependent and detailed information of the organic aerosol sources. This work highlights the strength of the rolling PMF mechanism by comparing it with the results derived from conventional seasonal PMF. Overall, this comprehensive interpretation of chemical speciation monitor (ACSM) data could be a role model for similar analyses.</p>
Branching patterns in phylogenies cannot distinguish diversity-dependent diversification from time-dependent diversification
One of the primary goals of macroevolutionary biology has been to explain general trends in long-term diversity patterns, including whether such patterns correspond to an up-scaling of processes occurring at lower scales. Reconstructed phylogenies often show decelerated lineage accumulation over time. This pattern has often been interpreted as the result of diversity-dependent diversification, where the accumulation of species causes diversification to decrease through niche filling. However, other processes can also produce such a slowdown, including time-dependence without diversity-dependence. To test whether phylogenetic branching patterns can be used to distinguish these two mechanisms, we formulated a time-dependent, but diversity-independent model that matches the expected diversity through time of a diversity-dependent model. We simulated phylogenies under each model and studied how well likelihood methods could recover the true diversification mode. Standard model selection criteria always recovered diversity-dependence, even when it was not present. We correct for this bias by using a bootstrap method and find that neither model is decisively supported. This implies that the branching pattern of reconstructed trees contains insufficient information to detect the presence or absence of diversity-dependence. We advocate that tests encompassing additional data, e.g., traits or range distributions, are needed to evaluate how diversity drives macroevolutionary trends.
Tomographic X-ray data of time-dependent 3D cross phantom
<p>This is the documentation of the tomographic X-ray data of a dynamic cross phantom made available at http://www.fips.fi/dataset.php. The data can be freely used for scientific purposes with appropriate references to the data and to this document in http://arxiv.org/. The data set consists of (1) the X-ray sinogram with 16 or 30 time frames (depending on resolution) of 2D slices of the cross phantom, made by crossing aluminum and graphite sticks in melted candle wax and (2) the corresponding static and dynamic measurement matrices modeling the linear operation of the X-ray transform. Each of these sinograms was obtained from a measured 360-projection fan-beam sinogram by down-sampling and taking logarithms. The original (measured) sinogram is also provided in its original form and resolution.</p> <p>This new version contains added file <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_560x60.mat?versionId=1e53d43d-d999-435e-bb1c-8297cf32c984">DataStatic_560x60.mat </a> with 80 time frames and <a href="https://zenodo.org/api/files/5c00de09-bb57-4c9e-9beb-00c63358de3c/DataStatic_1120x60.mat?versionId=afded407-6c88-4880-b427-fe7fd09306f4">DataStatic_1120x60.mat </a> with 230 time frames. You can run these files with the code example 2, which computes a Tikhonov regularized reconstruction using conjugate gradient algorithm.</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>
Dataset: Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations
<p>This dataset collects the unprocessed (= outputs from calculations) results discussed in the paper titled "Core excitations and ionizations of uranyl in Cs2UO2Cl4 from relativistic embedded damped response time-dependent density functional theory and equation of motion coupled cluster calculations", by Wilken Aldair Misael and Andre Severo Pereira Gomes. It also contains the figures used in the manuscript.</p>
Branching patterns in phylogenies cannot distinguish diversity-dependent diversification from time-dependent diversification
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Real-time benchmark dynamics of the Ohmic Spin-Boson Model computed with Time-Dependent Variational Matrix Product States. (TDVMPS) coupling strength and temperature parameter space
<p>Data describing the complete propagators (maps) for the evolution of the Ohmic Spin-Boson Model are made available, here. Using a time-dependent variotnal matrix product states (TDVMPS) respresentation of the complete spin-environment wave function, non -perturbative results are presented over a wide range of coupling strengths, temperatures and initial conditions. The results in this repository are associated with the article: </p> <p>https://www.preprints.org/manuscript/202012.0016/v1 </p> <p>A mathematica notebook that allows the data to be visualised and manipulated is also provided. </p>
Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar
<p>This is digital research data corresponding to a published manuscript, Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar. Chemosphere (2024), 347, 140688. https://doi.org/10.1016/j.chemosphere.2023.140688. </p> <p>Common isotherm and kinetic models cannot describe the pH-dependent sorption of heavy metal cations by biochar. In this paper, we evaluated a pH-dependent, equilibrium/kinetic model for describing the sorption of cadmium (Cd), copper (Cu), nickel (Ni), lead (Pb), and zinc (Zn) by poultry litter-derived biochar (PLB). We performed sorption experiments across a range of solution pH, initial metal concentration, and reaction time. </p>
Data from: Higher-order species interactions cause time-dependent niche and fitness differences: experimental evidence in plant-feeding arthropods
<p><strong>trajectories.csv </strong>(the raw data)</p> <p><strong>id</strong>: replicate identifier<br><strong>variant</strong>: co-existence status ("competition" or "monoculture")<br><strong>day</strong>: day of experiment<br><strong>species</strong>: mite species ("CRM" or "WCM")<br><strong>n</strong>: population density</p> <p> </p> <p><strong>model.R</strong></p> <p>The R script with the GAMM fitted to the trajectory data (the GAMM model is saved as <strong>model.RData</strong>); also produces simulations from this model (saved as <strong>sim.csv</strong>).</p> <p> </p> <p><strong>model.RData</strong></p> <p>The GAMM for growth rates.</p> <p> </p> <p><strong>sim.csv</strong> (simulations from the GAMM)</p> <p><strong>day</strong>: day of experiment<br><strong>spec_var</strong>: combination of co-existence status ("competition" or "monoculture") and species ("CRM" or "WCM")<br><strong>X1:X1000</strong>: population densities simulated from the fitted GAMM (on the log scale)</p> <p> </p> <p><strong>NFD_over_time_monte_carlo_gam.py</strong></p> <p>Python script to compute niche and fitness differences. Takes <strong>sim.csv</strong> (densities over time for different instantiations) and <strong>model.RData</strong> (stores the GAMM from R for the growth rates) as input and generates the file <strong>Data_NFD_monte_carlo_multi_c.csv</strong> which stores the niche and fitness differences computed for these communities.</p> <p> </p> <p><strong>figures.R</strong></p> <p>The R script that produces Figures 2-4.</p> <p> </p> <p><strong>plot_biotic_model.py</strong></p> <p>Python code to generate the figures S3 and S4 showing the simulations of a biotic resource competition model. </p> <p> </p> <p><strong>plot_abiotic_model.py</strong></p> <p>Python code to generate the figures S1 and S2 showing simulations of an abiotic resource competition model. </p>
The data for Time-dependent Stellar Flare Models of Deep Atmospheric Heating
<p>This repository contains model output (within two .tar.gz files) and a pdf document (analysis_tools_mdwarfradyngrid-v1.0.pdf) that explains the contents and use. The models are described in Kowalski, A.F., Allred, J.C., & Carlsson, M. <em>Time-dependent Stellar Flare Models of Deep Atmospheric Heating, </em>published 2024 July 5 in <em>The Astrophysical Journal</em> Volume 969, Number 2 (DOI: <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad4148">10.3847/1538-4357/ad4148</a>).</p> <p>The Jupyter notebook demo, radyn_xtools_Demo.ipynb is included in the PyPI package installation that is described in analysis_tools_mdwarfradyngrid-1.0.pdf.</p>
Data for: An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation
<p>Data for publication: J. Roulet, J. Vanicek, An implicit split-operator algorithm for the nonlinear time-dependent Schrödinger equation, J. Chem. Phys. <strong>155</strong>, 204109 (2021).</p> <p>Contains the data for reproducing the figures in the abovementioned publication.</p>
Strong selective environments determine evolutionary outcome in time-dependent fitness seascapes
<p>The impact of fitness landscape features on evolutionary outcomes has attracted considerable interest in recent decades. However, evolution often occurs under time-dependent selection in so-called fitness seascapes where the landscape is under flux. Fitness seascapes are an inherent feature of natural environments, where the landscape changes owing both to the intrinsic fitness consequences of previous adaptations and extrinsic changes in selected traits caused by new environments. The complexity of such seascapes may curb the predictability of evolution. However, empirical efforts to test this question utilising a comprehensive set of regimes are lacking. Here we employed an in vitro microbial model system to investigate differences in evolutionary outcomes between time-invariant and -dependent environments, including all possible temporal permutations, with three subinhibitory antimicrobials and a viral parasite (phage) as selective agents. Expectedly, time-invariant environments caused stronger directional selection for resistances compared to time-dependent environments. Intriguingly, however, multidrug resistance outcomes in both cases were largely driven by two strong selective agents (rifampicin and phage) out of four agents in total. These agents either caused cross-resistance or obscured the phenotypic effect of other resistance mutations, modulating the evolutionary outcome overall in time-invariant environments and as a function of exposure epoch in time-dependent environments. This suggests that identifying strong selective agents and their pleiotropic effects is critical for predicting evolution in fitness seascapes, with ramifications for evolutionarily informed strategies to mitigate drug resistance evolution. </p>
AR12473 Time-Dependent Data-Driven Magnetofrictional Simulation
<p>This dataset is an excerpt of the time-dependent data-driven magnetofrictional modelling carried out in Price et al. (2020). This data is provided with a 6-hour cadence. For greater temporal resolution, please contact the authors. If you use this data, please cite the following publication:</p> <ul> <li>Price, D. J., Pomoell, J., & Kilpua, E. K. J. 2020, A&A, 644, A28 (doi: <a href="https://doi.org/10.1051/0004-6361/202038925">10.1051/0004-6361/202038925</a>)</li> </ul> <p>A compatible data-loader can be found, for example, in the <a href="https://github.com/pricedj/mafiat">MAFIAT repository</a> (Magnetic Field Analysis Tools, Price et al. 2022).</p>
Time-Dependent Probabilistic Tsunami Inundation Assessment Using Mode Decomposition to Assess Uncertainty for an Earthquake Scenario
<p>This is the dataset of the manuscript submitted to JGR Ocean (Feb 2021).</p>
HDF files for the time-dependent inversion of the 2016 moderate earthquakes along Chaman fault
<p>We use GAMMA software to process Sentinel-1 SLC data and generate InSAR data. After converting the data into UTM coordinates, we use LiCSBAS to perform the time-series analysis, and the resulting HDF files are imported to MATLAB to perform the time-dependent inversion. MATLAB source codes are available.</p> <p>Original Sentinel-1 SLC data are available from https://scihub.copernicus.eu/dhus.</p>
Sampling time-dependent artifacts in single-cell genomics studies: scRNA-seq data
<p>Robust protocols and automation now enable large-scale single-cell RNA and ATAC sequencing experiments and their application on biobank and clinical cohorts. However, technical biases introduced during sample acquisition can hinder solid, reproducible results, and a systematic benchmarking is required before entering large-scale data production. Here, we report the existence and extent of gene expression and chromatin accessibility artifacts introduced during sampling and identify experimental and computational solutions for their prevention.</p> <p>This repository contains the expression matrices and Seurat objects associated with the scRNA-seq data of the manuscript: "Sampling time-dependent artifacts in single-cell genomics studies" published in Genome Biology in 2020. The purpose of this repo is to share processed files and metadata for immediate access and reproducibility. The code to analyze it is thoroughly documented at the associated Github repository (https://github.com/massonix/sampling_artifacts).</p>
Time-dependent benefits of camouflage strategies on heterogeneous backgrounds
<p>Background-matching camouflage is a well-established strategy to reduce detection, but implementing this on heterogeneous backgrounds is challenging. For prey with fixed colour patterns, solutions include specialising on a particular visual microhabitat or adopting a compromise or generalist appearance, matching multiple backgrounds imperfectly. Existing studies suggest both approaches can succeed, but most consider relatively simple scenarios, where artificial prey appear against two backgrounds differing in a single visual characteristic. Here, we used computer-based search tasks with human participants to test the relative benefits of specialising and generalising for complex targets, displayed on either two or four types of naturalistic backgrounds. Overall, across two background types, specialisation was beneficial, though its success varied with search duration, such that generalist targets could outperform specialists in the first seconds of each search event. Against four different backgrounds, the initial cost to specialisation was greater, so specialists and generalists ultimately experienced similar survival. Generalists performed better when their patterning was a compromise between backgrounds that were more similar to each other than when backgrounds were more different, with similarity in luminance more relevant than pattern differences. Time-dependence in the relative success of these strategies suggests that predator search behaviour may affect optimal camouflage in real-world situations.</p>
The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS
<p>Companion data set to the paper "<strong>The Impact of the </strong><strong>Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</strong>" The 'GNSS_Stations' folder contains the GNSS stations used for 2011 <strong>M</strong>7.9 Ibaraki, 2011 <strong>M</strong>7.4 Iwate, 2011A <strong>M</strong>7.3 Miyagi and 2003 <strong>M</strong>8.3 Tokachi 2003 earthquakes. The 'Mesh' folder contains the fault geometry mesh files for the Japan Trench, including the GMSH files. The '3D_velocity_model' folder contains the 3D velocity model for the combined West and East domains of the 3D Japan Integrated Velocity Structure Model in a rfile format and the corresponding ifile format. The 'Ruptures' folder contains the projected rupture models for each earthquake on the Japan Trench mesh and the corresponding 100 realizations of the mean rupture models generated using FakeQuakes. If you use these data, please cite the associated publication:</p> <p>Fadugba, ­O. I., Sahakian, V. J., Diego Melgar, D., Rodgers, A. & Shimony, R. (2023). The Impact of the Three-Dimensional Structure of a Subduction Zone on Time-dependent Crustal Deformation Measured by HR-GNSS.</p>
Digital research data from: Evaluation of a pH- and time-dependent model for the sorption of heavy metal cations by poultry litter-derived biochar
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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.