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373
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Dataset results
373 results for “stochastic”
Data for "The impact of mass-dependent stochasticity at cosmic dawn"
<p>Data for for the mass-dependent stochasticity model described in Gelli, Mason & Hayward 2024, ApJ<br><br></p> <p>The "LF/" folder contains the UV luminosity functions for the mass-dependent UV scatter model for z = 5-20. The two columns are:</p> <p>- UV magnitude Muv [mag]</p> <p>- number density log10_phi [/mag/Mpc^3]</p> <p> </p> <p>The "SFRD/" folder contains the redshift evolution of the UV luminosity density (obtained by integrating the UVLF down to Muv=-17) and the star formation rate density (derived using [Madau+99](https://iopscience.iop.org/article/10.1086/306975)). The columns are:</p> <p>- redshift z</p> <p>- luminosity density log10_rhoUV [erg/s/Hz/Mpc^3]</p> <p>- star formation rate density log10_SFRD [Msun/yr/Mpc^3]</p>
Data for "Earthquake nucleation and slip behavior altered by stochastic normal stress heterogeneity"
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Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"
<p>Datasets for reproducing the results in "True random number generators with flicker noise: stochastic model, min-entropy calculation and online test"</p> <p>Includes scripts for generating raw results, postprocessing scripts, as well as scripts/notebooks for generating plots for the manuscript</p>
Summary of results from numerical experiments described in research paper "A New Simheuristic Approach for Stochastic Runway Scheduling"
<p>This file provides a summary of results from the numerical experiments described in our research paper, "A New Simheuristic Approach for Stochastic Runway Scheduling".</p>
Dataset for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme"
<p>Datasets for "Global Simulation of the Madden–Julian Oscillation With Stochastic Unified Convection Scheme". The global simulation outputs (climatologies and daily anomalies), calculated RMM indexes, and the results of the budget analysis are included.</p>
A basic community dynamics experiment: Disentangling deterministic and stochastic processes in structuring ecological communities
<p>Community dynamics are governed by two opposed processes: species sorting, which produces deterministic dynamics leading to an equilibrium state, and ecological drift, which produces stochastic dynamics. Despite a great deal of theoretical and empirical work aiming to demonstrate the predominance of one or the other of these processes, the importance of drift in structuring communities and maintaining species diversity remains contested. Here we present the results of a basic community dynamics experiment using floating aquatic plants, designed to measure the relative contributions of species sorting and ecological drift to community change over about a dozen generations. We found that species sorting became overwhelmingly dominant as the experiment progressed, and directed communities towards a stable equilibrium state maintained by negative frequency-dependent selection. The dynamics of any particular species depended on how far its initial frequency was from its equilibrium frequency, however, and consequently the balance of sorting and drift varied among species.</p>
Data and scripts from: Experimental evidence of size-selective harvest and environmental stochasticity effects on population demography, fluctuations, and nonlinearity
<p class="MsoNormal">Theory and analyses of fisheries datasets indicate that harvesting can alter population structure and destabilize nonlinear processes, which increases population fluctuations. We conducted a factorial experiment on the population dynamics of <em>Daphnia magna</em> in relation to size-selective harvesting and stochasticity of food supply. Harvesting and stochasticity treatments both increased population fluctuations. Timeseries analysis indicated that fluctuations in control populations were nonlinear, and nonlinearity increased substantially in response to harvesting. Both harvesting and stochasticity induced population juvenescence, but harvesting did so via depletion of adults whereas stochasticity increased the abundance of juveniles. A fitted fisheries model indicated that harvesting shifted populations towards higher reproductive rates and larger-magnitude damped oscillations that amplify demographic noise. These findings provide experimental evidence that harvesting increases nonlinearity of population fluctuations and that both harvesting and stochasticity increase population variability and juvenescence.</p>
Determinants of Smallholder Farmers Technical Efficiency of Bread Wheat Production and Implications of Seed Recycling in Ethiopia: The Stochastic Frontier Approach
<p>This is a survey data gathered from two districts of East Gojam Zone, Ethiopia for a study entitled with "Determinants of Smallholder Farmers Technical Efficiency of Bread Wheat Production and Implications of Seed Recycling in Ethiopia: The Stochastic Frontier Approach"</p>
Dataset for "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites"
<p>SCAM5 and LES outputs from "Optimization and Evaluation of Stochastic Unified Convection Using Single-Column Model Simulations at Multiple Observation Sites". Includes simulation outputs of stochastic UNICON and original UNICON. The LES intercomparison data of DYCOMSRF01 is available at https://gcss-dime.giss.nasa.gov/pub/DYCOMS-II/GCSS7-RF01/gcss7.nc, and the data of CGILS is available at http://www.atmos.washington.edu/~bloss/CGILS2data.tar.</p>
Dataset for paper Pavel Perezhogin, Laure Zanna, Carlos Fernandez-Granda "Generative data-driven approaches for stochastic subgrid parameterizations in an idealized ocean model" submitted to JAMES.
<p>The dataset consists of the directory tree of .zarr archives. See <a href="https://github.com/m2lines/pyqg_generative/blob/master/Google-Colab/dataset.ipynb">Github repository</a> for the description of the dataset.</p> <p>The directory tree is:</p> <pre><code>├── eddy │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires ├── jet │ ├── 48 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 64 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ ├── 96 │ │ ├── gauss │ │ ├── hires-gauss │ │ ├── hires-sharp │ │ ├── lores │ │ └── sharp │ └── hires</code></pre> <ul> <li>Every individual dataset is a <code>.zarr</code> <a href="https://zarr.readthedocs.io/en/stable/">archive</a></li> <li><code>eddy/jet</code> - configuration of the pyqg; eddy is default; See <a href="https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2022MS003258">Ross2022</a> for description</li> <li><code>hires.zarr</code> - high-resolution simulation at 256x256 grid</li> <li><code>48/64/96</code> - resolution of the coarse models</li> <li><code>lores.zarr</code> - low-resolution simulation</li> <li><code>gauss.zarr</code>, <code>sharp.zarr</code> - training datasets for prediction of subgrid forcing obtained with Gaussian or Sharp filters</li> <li><code>hires-gauss.zarr</code>, <code>hires-sharp.zarr</code> - high-resolution simulation projected onto coarse grid with Gaussian or Sharp filters</li> </ul> <p>The directory tree is split into small tar.gz files each representing a separate .zarr archive. Download any required parts of the dataset and unpack with:</p> <p><strong>tar -xf *.tar.gz </strong></p> <p><strong>The directory tree will be restored automatically!</strong></p>
Laser Ablation Inductively Coupled Plasma Mass Spectrometric Quantification of Isotope Trace Elements in Human Carcinoma Tissue - Stochastic Dynamics and Theoretical Analysis (SUPPORTING INFORMATION)
<p>Supporting information file of publication [https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4334866]. </p>
First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems by a fractional moments-based mixture distribution approach
<p>First-passage probability estimation of high-dimensional nonlinear stochastic dynamic systems is a significant task to be solved in many science and engineering fields, but remains still an open challenge. The present paper develops a novel approach, termed ‘fractional moments-based mixture distribution’, to address such challenge. This approach is implemented by capturing the extreme value distribution (EVD) of the system response with the concepts of fractional<br> moment and mixture distribution. In our context, the fractional moment itself is by definition a high-dimensional integral with a complicated integrand. To efficiently compute the fractional moments, a parallel adaptive sampling scheme that allows for sample size extension is developed using the refined Latinized stratified sampling (RLSS). In this manner, both variance reduction and parallel computing are possible for evaluating the fractional moments. From the knowledge<br> of low-order fractional moments, the EVD of interest is then expected to be reconstructed. Based on introducing an extended inverse Gaussian distribution and a log extended skew-normal distribution, one flexible mixture distribution model is proposed, where its fractional moments are derived in analytic form. By fitting a set of fractional moments, the EVD can be recovered via the proposed mixture model. Accordingly, the first-passage probabilities under different<br> thresholds can be obtained from the recovered EVD straightforwardly. The performance of the proposed method is verified by three examples consisting of two test examples and one engineering problem.</p>
Supporting data for "Studying stochastic systems biology of the cell with single-cell genomics data"
<p>The dataset tar.gz files contain the raw unspliced and spliced count matrices generated by <em>kallisto</em>|<em>bustools</em> 0.26.0 from six datasets.</p> <p>The GVP_2023...zip file is a mirror of the related GitHub repository.</p>
10,000 stochastic prediction RMMs generated by SMP of MJO-Net
<p>The numpy array has shape of (710, 10000, 70, 6). It contains information of 710 MJO events. Each MJO event has 70-length 10,000 stochastic MJO RMMs predictions.</p> <p>Assume this numpy array named MJO_Data.</p> <p>To extract the 1,000 46-day MJO stochastic predictoins for 710 MJO events, use:</p> <p>MJO_Pred = MJO_Data[:, :, 10:, 2:4]</p> <p>To extract the paired 46-day ECMWF real-time predictoin for 710 MJO events, use:</p> <p>MJO_ECMWF = MJO_Data[:, :1, 10:, 4:6]</p> <p>To extract the paired 46-day MJO RMMs observation, use:</p> <p>MJO_OB = MJO_Data[:, :1, 10:, :2]</p>
Dataset for Correct-by-Design Control of Parametric Stochastic Systems
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Stochastic Resonance Mattress (Physiological Interventions) and Biomarkers for Enhancing Neonatal Health
ClinicalTrials.gov study NCT01643057. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Application of Whole-body Vibration With Stochastic Resonance in Frail Elderly: The Effects on Postural Control
ClinicalTrials.gov study NCT01543243. IPD Sharing: Not stated. Countries: 1. Publications: 2.
Stochastic Resonance Stimulation Effect on Gait Stability in Parkinson Disease
ClinicalTrials.gov study NCT06829342. IPD Sharing: UNDECIDED. Countries: 1. Publications: 0.
Effects of Stochastic Whole-body Vibration Training on Balance and Executive Functions
ClinicalTrials.gov study NCT06629298. IPD Sharing: NO. Countries: 1. Publications: 1.
Data from: The evolution of male-biased dispersal under the joint selective forces of inbreeding load, and demographic and environmental stochasticity
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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.