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Dataset results
373 results for “stochastic”
Data from ``Developments in Stochastic Coupled Cluster Theory: The initiator approximation and application to the Uniform Electron Gas''
<p>We describe further details of the Stochastic Coupled Cluster method and a diagnostic of such calculations, the shoulder height, akin to the plateau found in Full Configuration Interaction Quantum Monte Carlo. We describe an initiator modification to Stochastic Coupled Cluster Theory and show that initiator calculations can be extrapolated to the unbiased limit. We apply this method to the 3D 14-electron uniform electron gas and present complete basis set limit values of the CCSD and previously unattainable CCSDT correlation energies for up to $r_s=2$, showing a requirement to include triple excitations to accurately calculate energies at high densities.</p>
Supplement_Stochastic_Reconstruction_and_Interpolation_of_Precipitation_Fields_Using_Combinded_Information_CML_and_RG
<p>This file includes the synthetic dataset used in the study "Stochastic Reconstruction and Interpolation of Precipitation Fields Using Combined Information of Commercial Microwave Links and Rain Gauges", including the full synthetic reference fields (VR_precip_h_30052013_02062013.nc) as well as the used synthetic rain gauge (precip_VRObs_dry+wet_30052013_02062013_Pgr1mm.csv) and commercial microwave links (VROBS_MWL_selection_Pgr1mm.csv) input data.</p> <p>The simulated ensembles of possible realizations of the precipitation fields are stored for the synthetic (simulation_VR) and real (simulation_realworld) world dataset. A corresponding description of the grid can be found in gridinfo.txt.</p>
SUPPORTING INFORMATION FOR: Stochastic dynamic mass spectrometric 3D structural analysis of caffeine metabolites
<p>Supporting information for the entitled contribution.</p><p>It contains:</p><p>Static quantum chemical and high accuracy molecular dynamics computational data on protomers, tautomers, zwitterions, and isotopomers of caffeine (CAFF), paraxanthine (PARAXAN), theobromine (THEOBR), theophylline (THEOPH), and guanine (GUA), uric acid (UA), and xantine (XAN), as well as their derivatives.</p><p>The content includes data on characteristic parent and product ions of the analytes in ion mobility spectrometric and mass spectrometric experimental conditions. Tautomers, charge transfer processes, and intramolecular rearrangement; if any, are accounted for considering. </p><p>Molecular mechanics/molecular dynamics data are shown as *.txt files. </p><p>High accuracy molecular dynamics includes adiabatic computations using Born-Oppenheimer approach.</p><p>High accuracy static ground state and transition state computations use M062X/SDD level of theory. </p><p>Figures in color, illustrating the entitled contribution shown as *.pdf files.</p><p>The experimental ion mobility spectrometry and mass spectrometry data are according to reference [1].</p><p>[1] H. Sepman, A. Kruve, S. Tshepelevitsh, H. Hupatz, Experimental IMS and MS/MS data of caffeine metabolites (2022). Zenodo, [https://doi.org/10.5281/zenodo.6637393][ https://zenodo.org/record/6637393] (Accessible for 04.04.2022.)</p><p>They have been used and processed via the following software:</p><p>[2] ProteoWizard 3.0.11565.0 (2017) [https://proteowizard.sourceforge.io/download.html];</p><p>[3] mMass 5.0.0 [http://www.mmass.org/download/old.php];</p><p>[4] AMDIS 2.71 (2012) software [https://chemdata.nist.gov/mass-spc/amdis/downloads/AMDIS_Installer-17.zip]; and</p><p>[5] NIST Search Software 2.0 [https://chemdata.nist.gov/dokuwiki/lib/exe/fetch.php?media=chemdata:nist17:nist17demo.zip], respectively.</p><p> </p><p> </p>
Data Release: "A New Probe of Gravitational Parity Violation Through (Non-)Observation of the Stochastic Gravitational-Wave Background"
<p>This dataset contains the results presented in "<strong>A New Probe of Gravitational Parity Violation Through (Non-)Observation of the Stochastic Gravitational-Wave Background</strong>" (<a href="https://arxiv.org/abs/2312.12532">arXiv:2312.12532</a>).</p> <p>The code used to generate this data can be found in the repository <a href="https://github.com/tcallister/stochastic-birefringence/">https://github.com/tcallister/stochastic-birefringence/</a>. This repository includes <a href="https://github.com/tcallister/stochastic-birefringence/tree/main/data">jupyter notebooks</a> that can be used to open, explore, and plot the files contained in this data set. Additional information about reproducing and/or using this dataset can be found in <a href="https://tcallister.github.io/stochastic-birefringence/">our associated documentation</a>.</p> <p>Further notes:</p> <ul> <li>The file <em>o1o2o3_mass_c_iid_mag_iid_tilt_powerlaw_redshift_result.json</em>, used for figure generation, was published by the LIGO Scientific Collaboration, Virgo Collaboration, and KAGRA Collaboration in support of the paper "<a href="https://arxiv.org/abs/2111.03634">The population of merging compact binaries inferred using gravitational waves through GWTC-3</a>" (see <a href="../record/5655785">https://zenodo.org/record/5655785</a>).</li> <li>The file <em>matlab_orfs.dat</em> is created via running the script <a href="https://github.com/tcallister/stochastic-birefringence/blob/main/input/generate_matlab_orfs.m">generate_matlab_orfs.m</a>, which requires a local installation of LIGO matapps tools to rerun.</li> </ul>
Data release: "The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background"
<p>This data release contains the data to reproduce the results of "<strong>The metallicity dependence and evolutionary times of merging binary black holes: Combined constraints from individual gravitational-wave detections and the stochastic background</strong>" (<a href="https://arxiv.org/abs/2310.17625">arXiv:2310.17625</a>, published version <a href="https://iopscience.iop.org/article/10.3847/1538-4357/ad3d5c">here</a>).</p> <p>The code that was used to generate this data can be found on <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference">this GitHub repository</a>. Jupyter notebooks to reproduce the figures of the paper are also included, and can be found <a href="https://github.com/kevinturbang/bbh_gwb_time_delay_inference/tree/main/figures">here</a>.</p>
Stochastic Navier-Stokes dataset for probablisitic forecasting
<p>This is the Stochastic Navier-Stokes dataset generated and used in our recent paper: <a href="https://arxiv.org/abs/2403.13724">https://arxiv.org/abs/2403.13724</a>. </p>
Dataset of "Stochastic multi-observables inversion for 3D thermochemical structure of lithosphere in spherical coordinates"
<p>### Introduction</p> <p>This repository contains source codes and raw data files associated with the manuscript entitled "<strong>Stochastic multi-observables inversion for 3D thermochemical structure of a lithosphere in spherical coordinates</strong>" by Yi Zhang (yizhang-geo@zju.eud.cn) and Xu Yixian at the School of Earth Sciences, Zhejiang University, Hangzhou 310027, China. Please see the copyright file before you use the enclosed files.</p> <p>Please check former versions for more code and data files.</p> <p> </p> <p>### Abbreviations</p> <p>* **GCTL**: Geophysical Computational Tools & Library (<a href="http://sanqian.synology.me:8418/zhangyiss/gctl">http://sanqian.synology.me:8418/zhangyiss/gctl</a>);</p> <p>* **GIST**: Geophysical Inversions under the Spherical coordinates using Tetrahedral meshes (see former versions of this repository).</p> <p> </p> <p>### Files</p> <p>* CNTS_DATA: data files for the field application.</p>
Simulation for "Asymptotic behavior of a time-inhomogeneous stochastic system in the harmonic potential "
<p>The code and the videos are related to the article T. Cavallazzi, and E. Luirard, « Asymptotic behavior of a time-inhomogeneous stochastic system in the harmonic potential ».</p> <p> </p>
Scale-dependent species-area relationship: niche-based versus stochastic processes in a typical subtropical forest
<p><span>Determining the patterns and drivers of the small-scale species-area relationship (SAR) is crucial for improving our understanding of community assembly and biodiversity patterns. Niche-based and stochastic processes are two principal categories of mechanisms potentially driving SARs. However, their relative importance has rarely been quantified rigorously owing to scale-dependence and the simplified niche volumes often used. </span></p> <p><span>In a fully mapped, 24-ha plot of a typical subtropical forest, we built the SARs and well-defined niche-hyper-volumes of a broad range of environmental variables at scales of 10 - 70 m (cell sizes). We then simulated passive sampling and partitioned the variances of the SAR slopes to disentangle the two contrasting mechanisms.</span></p> <p><span>We found that the small-scale SAR best followed a power-law relationship, consistent with large-scale SARs. The SAR slope declined with increasing scale; it was lower than expected under passive sampling at scales below 30 m and higher at larger scales. Environmental niches explained more (39%-64%) of the slope at larger scales, exceeding 50% at scales > 30 m, and these niches always captured the majority of the structured slopes. Environmental position (environmental mean values) effects were steady in absolute strength across scales and explained most (98%-68%) of the niche effect, but this proportion decreased with increasing scale. The effect of environmental heterogeneity increased with spatial scales, starting to rise at the 30 m scale after controlling for environmental position. Excluding soil properties from analyses strongly reduced these niche effects, highlighting the importance of soils for structuring the small-scale SAR. There was also substantial stochasticity in the SAR slopes, which was only partially explained by passive sampling.</span></p> <p><span>Synthesis: Our results show that the small-scale SAR in the studied subtropical forest follows a power-law, exhibits a scale shift in slope at 30 m, and is strongly shaped by niche effects that are dominated by environmental position relative to heterogeneity. However, soil heterogeneity controls the increase of niche effect and shift in the SAR slope with increasing spatial scales. Hence, edaphic factors can be responsible for scale-dependence in small-scale SARs, thereby linking small-scale and large-scale SARs. </span></p>
3D Optical Stochastic Cooling Data
<p>Example data and processing/analysis for 3D Optical Stochastic Cooling at Fermilab's IOTA ring</p>
Data from: A stochastic generative model for citation networks among academic papers
<p>We propose a stochastic generative model to represent a directed graph constructed by citations among academic papers, where nodes and directed edges represent papers with discrete publication time and citations respectively. The proposed model assumes that a citation between two papers occurs with a probability based on the type of the citing paper, the importance of cited paper, and the difference between their publication times, like the existing models. We consider the out-degrees of citing paper as its type, because, for example, survey paper cites many papers. We approximate the importance of a cited paper by its in-degrees. In our model, we adopt three functions: a logistic function for illustrating the numbers of papers published in discrete time, an inverse Gaussian probability distribution function to express the aging effect based on the difference between publication times, and an exponential distribution (or a generalized Pareto distribution) for describing the out-degree distribution. We consider that our model is a more reasonable and appropriate stochastic model than other existing models and can perform complete simulations without using original data. In this paper, we first use the Web of Science database and see the features used in our model. By using the proposed model, we can generate simulated graphs and demonstrate that they are similar to the original data concerning the in- and out-degree distributions, and node triangle participation. In addition, we analyze two other citation networks derived from physics papers in the arXiv database and verify the effectiveness of the model.</p>
Fig. 1 in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 1. Amalgamation of stochastic maps. Vertical bars are tree branches, their segments are mapped character states. The amalgamation of the stochastic map S1{0,1} and S2{0,1} yields the map S1,2{00,01,11,10}.
Fig. 3 in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 3. Amalgamation of stochastic maps corresponding to the characters of legs from Hymenoptera phylogeny (S7, S8, S9) into one 'leg character' (SL); see also Fig. 2.
Dataset for publication 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'
<p>This dataset includes all data and information on how to reproduce the results and the figures of the paper 'Reconnecting Stochastic Methods with Hydrogeological Applications: Uncertainty Analysis and Risk Assessment for the Design of Optimal Monitoring Networks'.</p>
Stochastic risk in the east coast of Africa
<p>These files provide the final products of the stochastic risk assessment performed for the east coast of Africa, and can be used to reproduce the figures from our paper. <br><br>If you have used this dataset, please cite our paper: <br>"<em>Benito, I., Aerts, J.C.J.H., Eilander, D., Ward, P.J., and Muis,S., 2024. Stochastic coastal flood risk modelling for the east coast of Africa."</em> <br><br><br></p>
Dataset related to article "Polynomial Chaos Expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data"
<p>Dataset related to simulations described in article:</p> <p>Polynomial Chaos Expansion of SAR and temperature increase variability in 3 T MRI due to stochastic input data. Article citation: Bottauscio et al 2024 <em>Phys. Med. Biol.</em> <a href="https://doi.org/10.1088/1361-6560/ad5070" target="_blank" rel="noopener">https://doi.org/10.1088/1361-6560/ad5070</a></p>
Dataset: Stochastic Conformance Checking Based On Expected Sub-Trace Frequency
<p>This contains the dataset and experimental results for the submission "Stochastic Conformance Checking Based On Expected Sub-Trace Frequency"</p>
Codes: An early warning indicator trained on stochastic disease-spreading models with different noises
<p>This dataset contains the training data (Version V1) and all the codes (Version V2) of the paper entitled "An early warning indicator trained on stochastic disease-spreading models with different noises." </p> <p>Time series and corresponding residuals from white noise (equation 2.5), environmental noise (equation 2.8), and demographic noise (equation 2.9) are stored in the training_data_WhiteN, training_data_EnvN, and training_data_DemN folders, respectively. All residuals of the time series are contained in the training_resids folder, which also includes labels and groups of the training data. For details on the data generation process, please refer to section 3.1 in the paper. </p>
Data and code for, "Predicting self-assembly of sequence-controlled copoly- mers with stochastic sequence variation"
Open the record for dataset details and reuse information.
Correlation analysis for paper "Energy Storage Profit Risk under Stochastic Fuel Prices"
<p>This is a supplementary data for reproducibility.</p>
ScienceDex guides
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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.