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93 results for “joint model”
Data, code, models for "Weakly Supervised Semantic Segmentation for Joint Key Local Structure Localization and Classification of Aurora Image"
<p>Data, code and models for https://ieeexplore.ieee.org/document/8410588/</p>
Lithospheric structure and strength variations in Antarctica from joint modeling of elevation, geoid and seismic data
<p>These models include Moho depth, LAB depth and integrated lithospheric strength based on a 1D approach involving thermal analysis under local isostasy and a rheological method.</p>
Data for "Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"
<p>Data for "<strong>Development of a joint probabilistic rainfall-runoff model for high-to-extreme flow simulation and projection in a changing climate"</strong></p>
Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf
<p>The eastern North Pacific right whale (NPRW) is the most endangered population of whale and has been observed north of its core feeding ground in recent years with low sea ice extent. Sea ice and water temperature are important drivers for zooplankton dynamics within the whale's core feeding ground in the southeastern Bering Sea, seasonally forming stable fronts along the shelf that give rise to distinct zooplankton communities. A northward shift in NPRW distribution driven by changing distribution of prey resources could put this species at increased risk of entanglement and vessel strikes. We modeled the abundance of NPRW prey, <em>Calanus glacialis</em>, <em>Neocalanus</em>, and <em>Thysanoessa</em> species, using a dynamic biophysical food web model of nine zooplankton guilds in the Bering shelf zooplankton community during a period of warming (2006–2016). This model is unique from prior zooplankton studies from the region in that it includes density dependence, thereby allowing us to ask whether species interactions influence zooplankton dynamics. Modeling confirmed the importance of sea ice and ocean temperature to zooplankton dynamics in the region. Density-independent growth drove community dynamics while dependent factors were comparatively minimal. Overall, <em>Calanus</em> responded to environmental terms, with the strength and direction of response driven by copepodite stage. <em>Neocalanus</em> and <em>Thysanoessa</em> responses were weaker, likely due to their primary occurrence on the outer shelf. We also modeled the steady-state (equilibrium) abundance of <em>Calanus</em> in conditions with and without wind gusts to test whether advection of outer shelf species might disrupt steady-state dynamics of <em>Calanus</em> abundance; results did not support disruption. Given the annual fall sampling design, we interpret our results as follows: low ice-extent winters induced stronger spring winds and weakened fronts on the shelf, thereby advecting some outer shelf species into the study region; increased development rates in these warm conditions influenced the proportion of <em>C. glacialis</em> copepodite stages over the season. Residual correlation suggests missing drivers, possibly predators and phytoplankton bloom composition. Given the continued loss of sea ice in the region and projected continued warming, our findings suggest that <em>C. glacialis</em> will move northward, and thus, whales may move northward to continue targeting them.</p>
Scale-dependence of ecological assembly rules: insights from empirical datasets and joint species distribution modelling
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The impact of varying spatiotemporal scales on different joint species distribution models: A case study of pelagic fish species in the northwest Pacific Ocean
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Data from: Using joint species distribution modelling to identify climatic and non-climatic drivers of Afrotropical ungulate distributions
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Data from: Effectiveness of joint species distribution models in the presence of imperfect detection
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Joint species distribution modeling reveals a changing prey landscape for North Pacific right whales on the Bering shelf
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Scale dependency of joint species distribution models challenges interpretation of biotic interactions
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Data from: Joint spatial modeling of cluster size and density for a heavily hunted primate persisting in a heterogeneous landscape
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Model output data for "Negative density-dependent dispersal emerges from the joint evolution of density- and body condition-dependent dispersal strategies"
<p>Empirical studies have documented both positive and negative density-dependent dispersal, yet most theoretical models predict positive density dependence as a mechanism to avoid competition. Several hypotheses have been proposed to explain the occurrence of negative density-dependent dispersal, but few of these have been formally modeled. Here, we developed an individual based model of the evolution of density-dependent dispersal. This model is novel in that it considers the effects of density on dispersal directly, and indirectly through effects on individual condition. Body condition is determined mechanistically, by having juveniles compete for resources in their natal patch. We found that the evolved dispersal strategy was a steep, increasing function of both density and condition. Interestingly, although populations evolved a positive density-dependent dispersal strategy, the simulated metapopulations exhibited negative density-dependent dispersal. This occurred because of the negative relationship between density and body condition: high density sites produced low condition individuals that lacked the resources required for dispersal. Our model therefore generates the novel hypothesis that observed negative density-dependent dispersal can occur when high density limits the ability of organisms to disperse. We suggest that future studies consider how phenotype is linked to the environment when investigating the evolution of dispersal.</p>
Joint Autoregressive and Graph Models for Software and Developer Social Networks
<p>This zip contains three CSV files and one folder. This dataset contains information for the recent ten distributions.</p> <ul> <li><strong>developer_attributes.csv</strong>: There are seven columns in this file. "distro" (str) represents distribution name. "source" (str) denotes source package name. "person_id" (str) indicates developer identity. "closes" (int), "high" (int), "medium" (int), "low" (int) are the features.</li> <li><strong>source_bugs.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents the source package name. "bug_count" (int) denotes the number of bugs that source package has at a particular distribution.</li> <li><strong>source_sizes.csv</strong>: In this file, three columns are present. "distro" (str) represents the distribution name. "source" (str) represents source package name. "size" (int) denotes the size of the package.</li> <li><strong>Dependency folder:</strong> Within this folder, ten dependency lists are present. Each file contains two columns i.e "start" (str) and "target" (str). Both of them represent source packages. So, we read as the "start" source package depends on "target" source package. </li> </ul> <p>Here is the arxiv version of our paper: <a href="https://arxiv.org/abs/2101.08729">https://arxiv.org/abs/2101.08729</a>. </p> <p>Here is the portal link: <a href="https://sites.google.com/view/rima-hazra/swnet">https://sites.google.com/view/rima-hazra/swnet</a></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: Understanding co-occurrence by modelling species simultaneously with a Joint Species Distribution Model (JSDM)
A primary goal of ecology is to understand the fundamental processes underlying the geographic distributions of species. Two major strands of ecology – habitat modelling and community ecology – approach this problem differently. Habitat modellers often use species distribution models (SDMs) to quantify the relationship between species' and their environments without considering potential biotic interactions. Community ecologists, on the other hand, tend to focus on biotic interactions and, in observational studies, use co‐occurrence patterns to identify ecological processes. Here, we describe a joint species distribution model (JSDM) that integrates these distinct observational approaches by incorporating species co‐occurrence data into a SDM. JSDMs estimate distributions of multiple species simultaneously and allow decomposition of species co‐occurrence patterns into components describing shared environmental responses and residual patterns of co‐occurrence. We provide a general description of the model, a tutorial and code for fitting the model in R. We demonstrate this modelling approach using two case studies: frogs and eucalypt trees in Victoria, Australia. Overall, shared environmental correlations were stronger than residual correlations for both frogs and eucalypts, but there were cases of strong residual correlation. Frog species generally had positive residual correlations, possibly due to the fact these species occurred in similar habitats that were not fully described by the environmental variables included in the JSDM. Eucalypt species that interbreed had similar environmental responses but had negative residual co‐occurrence. One explanation is that interbreeding species may not form stable assemblages despite having similar environmental affinities. Environmental and residual correlations estimated from JSDMs can help indicate whether co‐occurrence is driven by shared environmental responses or other ecological or evolutionary process (e.g. biotic interactions), or if important predictor variables are missing. JSDMs take into account the fact that distributions of species might be related to each other and thus overcome a major limitation of modelling species distributions independently.
Data from: Testing species assemblage predictions from stacked and joint species distribution models
Aim: Predicting the spatial distribution of species assemblages remains an important challenge in biogeography. Recently, it has been proposed to extend correlative species distribution models (SDMs) by taking into account (a) covariance between species occurrences in so-called joint species distribution models (JSDMs) and (b) ecological assembly rules within the SESAM (spatially explicit species assemblage modelling) framework. Yet, little guidance exists on how these approaches could be combined. We, thus, aim to compare the accuracy of assemblage predictions derived from stacked and from joint SDMs. Location: Switzerland Taxon Birds, tree species Methods: Based on two monitoring schemes (national forest inventory and Swiss breeding bird atlas), we built SDMs and JSDMs for tree species (at 100m resolution) and forest birds (at 1km resolution). We tested accuracy of species assemblage and richness predictions on holdout data using different stacking procedures and ecological assembly rules. Results Despite minor differences, results were consistent between birds and tree species. Cross-validated species-level model performance was generally higher in SDMs than JSDMs. Differences in species richness and assemblage predictions were larger between stacking procedures and ecological assembly rules than between stacked SDMs and JSDMs. On average, predictions were slightly better for stacked SDMs compared to JSDMs, probabilistic stacks outperformed binary stacks, and ecological assembly rules yielded best predictions. Main conclusions: When predicting the composition of species assemblages, the choice of stacking procedure and ecological assembly rule seems more decisive than differences in underlying model type (SDM vs. JSDM). JSDMs do not seem to improve community predictions compared to SDMs or improve predictions for rare species. Still, JSDMs may provide additional insights into community assembly and may help deriving hypotheses about prevailing biotic interactions in the system. We provide simple rules of thumb for choosing appropriate modelling pathways. Future studies should test these preliminary guidelines for other taxa and biogeographic realms as well as for other JSDM algorithms.
Data from: Comparing the prediction of joint species distribution models with respect to characteristics of sampling data
Biotic interactions have been rarely included in traditional species distribution models, wherein Joint Species Distribution Models (JSDMs) emerge as a feasible approach to incorporate environmental factors and interspecific interactions simultaneously, making it a powerful tool for analyzing the structure and assembly processes of biotic communities. However, the predictability and statistical robustness of JSDMs are largely unknown because of the lack of research efforts for those newly developed models. This study systematically evaluated the performances of five JSDMs in predicting the occurrence and biomass of multiple species, with a particular focus on diverse characteristics of sampling data, including type of response variables, number of sampling sites, and the number of species included in models. In general, most models yielded satisfactory performances on fitting to observed data and on the estimation of environmental effects; however, they showed less well performances in evaluating species associations, and their predictability had large variations. The JSDMs showed inconsistent performances between the goodness-of-fit and predictability in cross-validation, and the Boral model was relatively robust than others. The predictability of JSDMs was less influenced by sample sizes and substantially improved by incorporating rare species. This study contributes to an appropriate model selection and application of JSDMs.
Joint particles model database
<p>Paper is </p> <p>Jichao Sun. Joint particles model database for discrete element numerical simulation. International Journal of Ground Sediment & Water, 2022, 16: 973-976, DOI: 10.5281/zenodo.6152695</p> <p>Password is in the last paragraph in the above paper.</p> <p>Password:Zhucuiling</p> <p>By downloading, you agree to cite this paper and database when using this database.</p>
Data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"
<p>Joint embedding data to publication "The performance of deep generative models for learning joint embeddings of single-cell multi-omics data"</p> <p>Code available at https://github.com/MTreppner/multiomics_dgms</p>
Geodetic Model of 2017 Kerman earthquake sequences estimated from joint inversion of InSAR and offset tracking techniques
<p>We upload here the inSAR and Pixel offset tracking results obtained based on Radar, Sentinel-1, PlanetScope, and Sentinel-2 satellite data. </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.