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52 results for “Multi‐scale modelling”
Data of A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths
<pre>Data related to the publication (we would be grateful if you could cite the paper in the case in which you are using the data) title = "A recurrent neural network-accelerated multi-scale model for elasto-plastic heterogeneous materials subjected to random cyclic and non-proportional loading paths", journal = "Computer Methods in Applied Mechanics and Engineering", pages = " 113234", year = "2020", issn = "0045-7825", doi = "https://doi.org/10.1016/j.cma.2020.113234", author = "Wu, Ling and Nguyen, Van Dung and Kilingar, Nanda Gopala and Noels, Ludovic"</pre>
Extreme model exploration of a multi-scale simulation of tumor growth
<p>The dataset comprises the output of several simulations of a model of tumor growth with different parameter values. The model is a multi-scale agent-based model of a tumor spheroid that is treated with periodic pulses of the cytokine tumor necrosis factor (TNF). The multi-scale model simulates processes including i) the diffusion, uptake, and secretion of molecular entities such as oxygen, or TNF; ii) the mechanical interaction between cells; and iii) cellular processes including cell life cycle, cell death models, signal transduction.</p> <p>The multi-scale model was implemented and simulated using the PhysiBoSS framework (Letort et al. 2019). The dataset corresponds to different simulations trajectories obtained for alternative parameter values. The parameter explored are: i) the decay rate of the TNF after it binds the cell; ii) the TNF binding rate; and iii) the TNF secretion rate by NFkB activated cells.</p> <p>The dataset includes 48 different combinations of parameters. Each simulation is stored in a folder instance_[0-9]+ which includes the PhysiBoSS standard output files (<a href="https://github.com/gletort/PhysiBoSS/wiki">https://github.com/gletort/PhysiBoSS/wiki</a>). The root folder also includes other settings, logs, and outputs as well as the binary used to run the simulation.</p>
Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
1. Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. 2. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time scales, e.g. step lengths obtained from GPS tags every hour, dive depths obtained from time-depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi-stream and multi-scale data is lacking. 3. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple, connected behavioural processes, each of which operates at the time scale at which the corresponding variables were observed. 4. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time scales, and in particular help to draw a much more comprehensive picture of an animal's movement patterns, e.g. with regard to long-term vs. short-term movement strategies. 5. The suggested approach is illustrated in two real-data applications, where we jointly model i) coarse-scale horizontal and fine-scale vertical Atlantic cod (Gadus morhua) movements throughout the English Channel, and ii) coarse-scale horizontal movements and corresponding fine-scale accelerations of a horn shark (Heterodontus francisci) tagged off the Californian coast.
Data from: Multi-scale model of regional population decline in little brown bats due to white-nose syndrome
The introduced fungal pathogen Pseudogymnoascus destructans is causing decline of several species of bats in North America, with some even at risk of extinction or extirpation. The severity of the epidemic of white-nose syndrome caused by P. destructans has prompted investigation of the transmission and virulence of infection at multiple scales, but linking these scales is necessary to quantify the mechanisms of transmission and assess population-scale declines. We build a model connecting within-cave disease dynamics of little brown bats to regional scale dispersal, reproduction, and disease spread, including multiple plausible mechanisms of transmission. We parameterize the model using the approach of plausible parameter sets, by comparing stochastic simulation results to statistical probes from empirical data on within-cave prevalence and survival, as well as between-cave spread across a region. Our results are consistent with frequency-dependent transmission between bats, support an important role of environmental transmission, and show very little effect of dispersal among colonies on metapopulation survival. The model also offers a generalizable method to assess hypotheses about cave-to-cave transmission and to identify gaps in knowledge about key processes, and could be expanded to include additional mechanisms or bat species as research on this detrimental fungus progresses.
Data for "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry"
<p>Neural and FEM E-field simulation data generated for Aberra AS, Wang R, Grill WM, Peterchev AV. (2023). "Multi-scale model of axonal and dendritic polarization by transcranial direct current stimulation in realistic head geometry". <i>Brain Stimulation</i>. Dataset includes:</p><ul><li><i>cell_data/ </i>- Coordinates and morphology information for all model neurons</li><li><i>nrn_sim_data/</i> - Polarization data from NEURON simulations for all 25 model neurons included in the study, either in response to uniform E-field or tDCS.</li><li><i>layer_data/ - </i>surface meshes used for placing and orienting neuron models and corresponding sampling grids for CNNs</li><li><i>simnibs/</i> - E-field simulation and head mesh data generated within SimNIBS simulation environment</li></ul><p>To generate figures using these data, use the matlab code stored in the tDCSsim_Aberra2023 repository (https://github.com/Aman-A/tDCSsim_Aberra2023) </p>
Multi-scale soil moisture data and process-based modeling reveal the importance of lateral groundwater flow in a subarctic catchment
<p>Hydrological data measured in Lompolonjängänoja (LJO) catchment and used in Nousu et al.</p> <p> </p> <p>ET_fluxes.csv<br>- Eddy-covariance based, daily evapotranspiration (ET) fluxes [mm/d] at Kenttärova (NFOR) and Lompolojänkkä (NWET) stations</p> <p>GW_levels.csv<br>- Observed groundwater levels [m] relative to the ground surface measured around the LJO catchment</p> <p>Q_runoff.csv<br>- Observed specific discharge [mm/d] at the LJO catchment outlet</p> <p>THETA_kenttarova.csv<br>- Automatically measured soil moisture (i.e. volumetric water content [m3/m3]) around Kenttärova stations</p> <p>THETA_spatial.csv<br>- Manually measured soil moisture (i.e. volumetric water content [m3/m3]) around the LJO catchment</p>
A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamics
<p>Dataset related to an accepted manuscript:</p> <p>A Multi-Scale Spatial Model of Hepatitis-B Viral Dynamic. Cangelosi Q., Means S., Ho H. PLOS One</p> <p>Dataset documented in a ReadMe file.</p>
A multi-species benchmark for training and validating large scale mass spectrometry proteomics machine learning models
<p>This is a de novo sequencing benchmark dataset derived from nine<br>publicly available mass spectrometry datasets. There are two versions<br>of the benchmark: main and balanced. The balanced version randomly<br>eliminates some spectra associated with some species in order to<br>create a smaller, more evenly balanced dataset. Also provided are two<br>zip files containing the raw data as well as intermediate results.<br>Details about how the benchmark was created are provided in an<br><a href="../records/13653420">associated zenodo release</a>, which contains the source code as well as a<br>manuscript describing the benchmark.</p> <p>This release fixes a bug that incorrectly detected shared peptides <br>between different species. It also includes the annotated spectra in <br>mzSpecLib format.</p> <p> </p>
Multi-scale Modeling of Sleep Behaviors in Social Networks
ClinicalTrials.gov study NCT02846077. IPD Sharing: UNDECIDED. Countries: 1. Publications: 4.
Multi-scale Modeling of Breast Conserving Therapy
ClinicalTrials.gov study NCT02310711. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
Ultrasound Large-scale Model in Thyroid Field: A Multi-center Study on Medical-Engineering Integration
ClinicalTrials.gov study NCT07296575. IPD Sharing: NO. Countries: 1. Publications: 3.
Data from: Very high resolution digital elevation models: are multi-scale derived variables ecologically relevant?
Open the record for dataset details and reuse information.
Data from: Multi-scale habitat modeling identifies spatial conservation priorities for mainland clouded leopards (Neofelis nebulosa)
Open the record for dataset details and reuse information.
Data from: Multi-scale, numerical modeling of spatio-temporal signaling in cone phototransduction
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Data from: Joint modelling of multi-scale animal movement data using hierarchical hidden Markov models
Open the record for dataset details and reuse information.
Data from: Multi-scale model of regional population decline in little brown bats due to white-nose syndrome
Open the record for dataset details and reuse information.
Predictive multi-scale occupancy models at range-wide extents: effects of habitat and human disturbance on distributions of wetland birds
<p><span><i>Aim:</i> Predicting distributions is fundamental to ecology, yet hindered by spatially-restricted sampling, scale-dependent relationships, and detection error associated with field surveys. Predictive species distribution models (SDMs) are nonetheless vital for conservation of many species. We developed a framework for building predictive SDMs with multi-scale data, and used it to develop range-wide breeding-season SDMs for 14 marsh bird species of concern.</span></p> <p><span><i>Location: </i>USA.</span></p> <p><span><i>Methods: </i>We built SDMs using data from range-wide surveys conducted over 14 years, and habitat and disturbance covariates measured at multiple spatial scales. We built hierarchical occupancy models that included heterogeneity in detectability during sampling, and used Bayesian model selection to regulate model complexity (covariates and scales) based explicitly on spatial predictive abilities. We thus integrated model selection for optimizing out-of-sample prediction, range-wide sampling over broad conditions, multi-scale analyses and scale-optimization, and species-specific detectability for a suite of wide-ranging species. </span></p> <p><span><i>Results: </i>Distributions of marsh birds were affected by local wetland conditions, but also by agricultural, urban, and hydrologic disturbances operating from local scales (100 – 500 m) to the watershed level. Variables measuring human disturbances improved prediction for most species, and every species was affected by attributes at > 1 scale. Five species showed evidence for continental-scale range contraction during the study.</span></p> <p><span><i>Main conclusions: </i>We demonstrate how hierarchical occupancy models can be optimized for prediction across a species' range at the extent of a continent while also accounting for imperfect detection, and thus describe a generalizable approach that can be used for any species. We provide the first data-driven, empirical SDMs built at the range-wide extent for most of our 14 study species and demonstrate that previous studies focused on local distributions and the effects of fine-scale wetland vegetation missed important broad-scale drivers of occupancy for marsh birds. </span></p>
Data from: Using multi-response models to investigate pathogen coinfections across scales: insights from emerging diseases of amphibians
1.Associations among parasites affect many aspects of host-parasite dynamics, but a lack of analytical tools has limited investigations of parasite correlations in observational data that are often nested across spatial and biological scales. 2.Here we illustrate how hierarchical, multiresponse modeling can characterize parasite associations by allowing for hierarchical structuring, offering estimates of uncertainty, and incorporating correlational model structures. After introducing the general approach, we apply this framework to investigate coinfections among four amphibian parasites (the trematodes Ribeiroia ondatrae and Echinostoma spp., the chytrid fungus Batrachochytrium dendrobatidis, and ranaviruses) and among >2000 individual hosts, 90 study sites, and five amphibian host species. 3.Ninety-two percent of sites and 80% of hosts supported two or more pathogen species. Our results revealed strong correlations between parasite pairs that varied by scale (from among hosts to among sites) and classification (microparasite versus macroparasite), but were broadly consistent across taxonomically diverse host species. At the host-scale, infection by the trematode R. ondatrae correlated positively with the microparasites, B. dendrobatidis and ranavirus, which were themselves positively associated. However, infection by a second trematode (Echinostoma spp.) correlated negatively with B. dendrobatidis and ranavirus, both at the host- and site-level scales, highlighting the importance of differential relationships between micro- and macroparasites. 4.Given the extensive number of coinfecting symbiont combinations inherent to natural systems, particularly across multiple host species, multiresponse modeling of cross-sectional field data offers a valuable tool to identify a tractable number of hypothesized interactions for experimental testing while accounting for uncertainty and potential sources of co-exposure. For amphibians specifically, the high frequency of co-occurrence and coinfection among these pathogens – each of which is known to impair host fitness or survival – highlights the urgency of understanding parasite associations for conservation and disease management.
Data from: Multi-scale model of CRISPR-induced coevolutionary dynamics: diversification at the interface of Lamarck and Darwin
The CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) system is a recently discovered type of adaptive immune defense in bacteria and archaea that functions via directed incorporation of viral and plasmid DNA into host genomes. Here, we introduce a multi-scale model of dynamic coevolution between hosts and viruses in an ecological context that incorporates CRISPR immunity principles. We analyze the model to test whether and how CRISPR immunity induces host and viral diversification and the maintenance of many coexisting strains. We show that hosts and viruses coevolve to form highly diverse communities. We observe the punctuated replacement of extant strains, so that populations have very low similarity compared over the long term. However in the short term, we observe evolutionary dynamics consistent with both incomplete selective sweeps of novel strains and the recurrence of previously rare strains. Coalitions of multiple dominant host strains are predicted to arise because host strains can have nearly identical immune phenotypes mediated by CRISPR defense albeit with different genotypes. We close by discussing how our explicit eco-evolutionary model of CRISPR immunity can help guide efforts to understand the drivers of diversity seen in microbial communities where CRISPR systems are active.
Supplementary material to "Food and habitats requirements of the Scops Owl (Otus scops) in Switzerland revealed by very high-resolution multi-scale models"
<p><strong>Abstract</strong></p> <p>In Europe, agricultural practices have progressively evolved towards high productivity leading either to the intensification of productive and accessible areas or to the abandonment of less profitable sites. Both processes have led to the degradation of semi-natural habitats like extensive grasslands, threatening species such as the Eurasian Scops Owl <em>Otus scops</em> that rely on extensively managed agricultural landscapes. In this work, we aimed to assess the habitat preferences of the Scops Owl using habitat suitability models combined with a multi-scale approach. We generated a set of multi-scale predictors, considering both biotic and abiotic variables, built on two newly developed vegetation management and orthopteran abundance models. To select the variables to incorporate in a ‘best multi-scale model’, we chose the best spatial scale for each variable using univariate models and by calculating their relative importance through multi-model inference. Next, we built ensembles of small models (ESMs) at 10 different scales from 50 to 1000 m, and an additional model with each variable at its best scale (‘best multi-scale model’). The latter performed better than most of the other ESMs and allowed the creation of a high-resolution habitat suitability map for the species. Scops Owls showed a preference for dry sites with extensive and well-structured habitats with 30–40% bush cover, and relied strongly on semi-extensive grasslands covering at least 30% of the surface within 300 m of the territory centre and with high orthopteran availability near the centre (50-m radius), revealing a need for good foraging grounds near the nest. At a larger spatial scale within a radius of 1000 m, the habitat suitability of Scops Owls was negatively related to forest cover. The resulting ESM predictions provide valuable tools for conservation planning, highlighting sites in need of particular conservation efforts together with offering estimates of the percentage of habitat types and necessary prey abundance that could be used as targets in future management plans to ensure the persistence of the population.</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.