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66 results for “stochastic modelling”
Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyvaskyla for 2030 and 2050
<p>******************* Please view the README.txt or README.md file for detailed documentation of data. ********************</p> <p>Title: Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model: Morphed hourly outdoor temperatures for Jyväskylä for 2030 and 2050</p> <p>Date of release: 25/11/2020</p> <p>Identifier: 10.5281/zenodo.4275759</p> <p>Permalink: http://dx.doi.org/10.5281/zenodo.4275759</p> <p>Associated publication: Hietaharju, P.; Louis, J.-N.; Pulkkinen, J.; Ruusunen, M. Long-term heat demand scenarios under climate change utilising a stochastic dynamic building stock model, <strong><em>Under Review</em></strong>, 2020.</p> <p>Suggested citation: Please reference the associated publication above when using any datasets or materials described in the README.txt and README.md files.</p> <p><br> Contact information: Jari Pulkkinen, University of Oulu, Oulu, Finland, jari.pulkkinen@oulu.fi; Jean-Nicolas Louis, University of Oulu, Oulu, Finland, jean-nicolas.louis@oulu.fi<br> </p> <p>Dates of data: 2030, 2050</p> <p>Type of data: Outdoor Temperature</p> <p>Geographic location: Jyväskylä</p> <p>Time resolution: hourly, full year</p> <p>Format: All data is stored in .csv files</p> <p>Number of files: 1 .zip --> 50 files + README.txt + README.md</p> <p>This directory contains the following datasets: A summary of all the files has been compiled and stored in the "README.txt" and "README.md" files</p> <p> </p> <p>Notifications:</p> <p>Contains modified Copernicus Climate Change Service (C3S) information [2018] and modified Finnish Meteorological Institute [2017,2019] information from etsin.fairdata.fi and from Open data repository (https://en.ilmatieteenlaitos.fi/open-data).</p> <p><br> Contains modified Climate One Building information [2019] (reference Lawrie L.K. and Crawley D.B. 2019) and Test Reference Year 2012 (TRY2012) information from Jylhä et al. [2011] and Jylhä et al. [2015] (Energy demand for the heating and cooling of residential houses in Finland in a changing climate).</p> <p>Contains modified Ruosteenoja et al. [2016] information.</p> <p>Other data and information sources are described in README.txt, README.md, references and on the associated publication.</p>
Dataset: Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model
<p>This dataset is used to reproduce the figures in the manuscript "Stochastic data-driven parameterization of unresolved eddy effects in a baroclinic quasi-geostrophic model"</p> <p>The figures can be created with the following Python scripts:</p> <p>.</p> <p> </p>
Analysis of correlation-based biomolecular networks from different omics data by fitting stochastic block models
<p><strong>Baum_et_al_2019_Supplementary_Figures.pdf: </strong>Supplementary Figures S1-S4. Legends are included under each figure.</p> <p><strong>sbm-for-correlation-based-networks-master.zip: </strong>Archived source code of R and Python functions for the analyses and example workflow description at time of publication. Files are maintained at https://gitlab.com/biomodlih/sbm-for-correlation-based-networks and https://gitlab.com/kabaum/sbm-for-correlation-based-networks.</p>
Slip model and dataset - A stochastic view of the 2020 Elazig Mw 6.8 earthquake (Turkey)
<p>This repository contains files describing the slip model and data published in "A stochastic view of the 2020 Elazig Mw 6.8 earthquake (Turkey)" by T. Ragon et al.</p> <p>The slip model has been inferred with a Bayesian approach (AlTar), assuming a complex fault geometry with triangular subfaults and layered crustal structure, and accounting for epistemic uncertainties.<br> The slip model and dataset are extensively described in the publication.</p> <p>Description of the files:<br> > Slip model<br> - elazig_sigma_dip.dat : Standard deviation of the slip in the along-strike direction<br> - elazig_sigma_stk.dat : Standard deviation of the slip in the along-dio direction<br> - elazig_slip.dat : Total slip amplitude <br> - elazig_slip_dip.dat : Slip amplitude in the along-dip direction<br> - elazig_slip_stk.dat : Slip amplitude in the along-strike direction <br> - elazig_slipdir.dat : Vectors for the rake<br> > Data<br> - elazig_A116_data_rect.dat : downsampled surface displacement for the Sentinel 1A asc. interferogram<br> - elazig_A182_data_rect.dat : downsampled surface displacement for the ALOS 2 asc. interferogram<br> - elazig_A182_po_data_rect.dat : downsampled surface displacement for the ALOS 2 pixel offset ascending track<br> - elazig_D077_data_rect.dat : downsampled surface displacement for the ALOS 2 dsc. interferogram<br> - elazig_D077_po_data_rect.dat : downsampled surface displacement for the ALOS 2 pixel offset descending track<br> - elazig_D123_data_rect.dat : downsampled surface displacement for the Sentinel 1A dsc. interferogram<br> - elazig_gps_data.dat : GPS data</p> <p>The format of all *slip* and *sigma* files at the exception of 'elazig_slipcenterll.dat' and 'elazig_slipdir.dat' is as follow:<br> > -Z[ slip amplitude ] # [subault index 1] [subfault index 2] 9999999 # [ strike slip amplitude] [dip slip amplitude] 0.0 <br> [longitude] [latitude] [depth of point 1]<br> [longitude] [latitude] [depth of point 2]<br> [longitude] [latitude] [depth of point 3]</p> <p>The format of all inteferograms and PO files is as follow:<br> > -Z[surface displacement in the LOS or azimuth direction]<br> [lon] [lat for NW corner]<br> [lon] [lat for NE corner]<br> [lon] [lat for SE corner]<br> [lon] [lat for SW corner]<br> [lon] [lat for NW corner]</p>
Stochastic modeling of sediment connectivity for reconstructing sand fluxes and origins in the unmonitored Se Kong, Se San, and Sre Pok tributaries of the Mekong River
<p>Sediment supply to rivers, subsequent fluvial transport, and the resulting connectivity on network-scales are often sparsely monitored or subject to major uncertainty. Hence, we propose to adopt stochastic modeling approaches for studying network sediment connectivity. We demonstrate such a stochastic approach for modeling sand connectivity in the major, poorly monitored Se Kong, Se San, and Sre Pok (3S) tributaries of the Mekong River. Specifically, we run many random initializations of the CASCADE modeling framework for sediment connectivity in a Monte Carlo approach in order to quantify how unknown properties of sediment sources translate into uncertainty regarding network sediment connectivity. We identify a reduced ensemble of model realizations that reproduces downstream observations of sediment transport. This ensemble presents an inverse stochastic approximation of the spatial distribution, magnitude, and variability of transport capacity, sediment flux, and bed material grain size in the entire network (i.e., upscaling point observations to the entire network). The approximated magnitude of sediment flux in each tributary is controlled by reaches of low transport capacity (“bottlenecks”). These “bottlenecks” limit the ability of the inverse stochastic approximation to predict sediment transport in the upper parts of the catchment but they allow a clear partitioning of sand deliveries from the 3S to the Mekong, with the Se Kong delivering less (1.9 Mt/yr) and coarser (median grain size: 0.4 mm) sand than the Se San (5.3 Mt/yr, 0.22 mm) and Sre Pok (11 Mt/yr, 0.19 mm).</p>
Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim
<p>Here is the dataset and code for the paper by Hu, X et al. (2023, under review). <span>Stochastic Modelling of Thin Mud Drapes inside Point Bar Reservoirs with ALLUVSIM-GANSim,</span> Water Resources Research.</p>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
<p>Integral projection models (IPMs) can estimate the population dynamics of species for which both discrete life stages and continuous variables influence demographic rates. Stochastic IPMs for imperiled species, in turn, can facilitate population viability analyses (PVAs) to guide conservation decision-making. Biphasic amphibians are globally distributed, often highly imperiled, and ecologically well-suited to the IPM approach. Herein, we present the first stochastic size- and stage-structured IPM for a biphasic amphibian, the U.S. federally threatened California tiger salamander (<em>Ambystoma</em> <em>californiense</em>; CTS). This Bayesian model reveals that CTS population dynamics show the greatest elasticity to changes in juvenile and metamorph growth and that populations are likely to experience rapid growth at low density. We integrated this IPM with climatic drivers of CTS demography to develop a PVA and examined CTS extinction risk under the primary threats of habitat loss and climate change. The PVA indicates that long-term viability is possible with surprisingly high (20–50%) terrestrial mortality, but simultaneously identified likely minimum terrestrial buffer requirements of 600–1000 m while accounting for numerous parameter uncertainties through the Bayesian framework. These analyses underscore the value of stochastic and Bayesian IPMs for understanding both climate-dependent taxa and those with cryptic life histories (e.g., biphasic amphibians) in service of ecological discovery and biodiversity conservation. In addition to providing guidance for CTS recovery, the contributed IPM and PVA supply a framework for applying these tools to investigations of ecologically-similar species.</p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
GRM: A Novel Stochastic Model for Real-time GNSS Tropospheric Delay Estimation
<p>The dataset includes the proposed RWPN model (Cal_rwpn_new.m) and related files. The model is built based on ERA5 ZWD products from 2010 to 2019, which can be accessed at (<a>ftp://ftp.gfz-potsdam.de/pub/home/GNSS/products/gfz-vmf1/</a>). The proposed GRM model can contribute greatly by providing an efficient RWPN value to real-time GNSS ZTD estimation with an accuracy improvement of over 10% compared to fixed RWPN results. In addition, GRM also shows the superiorities of saving computation cost significantly since a large volume of the ERA5-derived RWPN values is modeled with only several parameters.</p>
Ultrasound Stochastic Tomography simulation data for In-silico 2D Breast Phantom model with tumour
<p>An anatomically realistic numerical breast phantom model (with realistic acoustic properties of speed of sound, density, and attenuation coefficient of tissues) derived from [1] is presented with details of a ultrasound tomography experiment in simulation. Details of source wavelets, geometry of transducer set, observed data at each transducer for each shots are provided with phantom model.</p> <p>References</p> <p>[1] <a href="https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/">https://anastasio.bioengineering.illinois.edu/downloadable-content/oa-breast-database/</a></p>
Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
<ol> <li>How demographic factors lead to variation or change in growth rates can be investigated using life table response experiments (LTRE) based on structured population models. Traditionally, LTREs focused on decomposing the asymptotic growth rate, but more recently decompositions of annual 'realized' growth rates have gained in popularity.</li> <li>Realized LTREs have been used particularly to understand how variation in vital rates translates into variation in growth for populations under long-term study. For these, complete population models may be constructed by combining data in an integrated population model (IPM). IPMs are also used to investigate how temporal variation in environmental drivers affect vital rates. Such investigations have usually come down to estimating covariate coefficients for the effects of environmental variables on vital rates, but formal ways of assessing how they lead to variation in growth rates have been lacking. </li> <li>We extend realized LTREs in two ways. First, we further partition the contributions from vital rates into contributions from temporally varying factors that affect them. The decomposition allows us to compare the resultant effect on the growth rate of different environmental factors that may each act via multiple vital rates. Second, we show how realized growth rates can be decomposed into separate components from environmental and demographic stochasticity. The latter is typically omitted in LTRE analyses.</li> <li>We illustrate how to use the approach in an IPM for data from a 26-year study on northern wheatears (Oenanthe oenanthe), a migratory passerine bird breeding in an agricultural landscape. For this population, consisting of around 50–120 breeding pairs per year, we partition variation in realized growth rates into environmental contributions from temperature, rainfall, population density, and unexplained random variation via multiple vital rates, and from demographic stochasticity.</li> <li>The case study suggests that variation in first-year survival via the random component, and adult survival via temperature are two main factors behind environmental variation in growth rates. More than half of the variation in growth rates is suggested to come from demographic stochasticity, demonstrating the importance of this factor for populations of moderate size.</li> </ol>
Data: Applying stochastic and Bayesian integral projection modeling to amphibian population viability analysis
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The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics
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Data from: Partitioning variance in population growth for models with environmental and demographic stochasticity
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Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Inferring core processes using stochastic models of the geodynamo
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Stochastic-GENeSYS-MOD Japan: Model, technology, demand, and renewable data
<p>This zenodo-repository contains a stochastic version of GENeSYS-MOD, an application to the Japanese energy system, and the underlying data.</p>
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
Data for "A stochastic model of geomorphic risk due to episodic river aggradation and degradation"
<p>The code and the dataset can be read/run by using Matlab. The description as follows:<br>1. Dataset of riverbed measurement (long profile and water level gauge data), carbon dating data, and rainfall record in the Laonong River (Taiwan). The dataset are used for the model calibration and the model application. <br>2. The developed riverbed stochastic processing model and the maximum likelihood calibration model. </p> <p>Note: this new version includes the corrected Monte Carlo simulation code and a required Matlab function (fminsearchbnd.m) that was missing in the first version.</p>
Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
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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.