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310
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310 results for “State Model”
Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
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Data from: Estimating range expansion of wildlife in heterogeneous landscapes: a spatially explicit state-space matrix model coupled with an improved numerical integration technique
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Validation of the predictive accuracy of health-state utility values based on the Lloyd model for metastatic or recurrent breast cancer in Japan
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Data from: Population dynamics of an Arctiid caterpillar-tachinid parasitoid system using state-space models
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Data from: Mechanisms of resilience: empirically quantified positive feedbacks produce alternate stable states dynamics in a model of a tropical reef
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Data from: Fine-scale population dynamics in a marine fish species inferred from dynamic state-space models
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Data from: State-space modelling of geolocation data reveals sex differences in the use of management areas by breeding northern fulmars
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Data from: Alternative tree-cover states of the boreal ecosystem: a conceptual model
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Data from: Modeling and mapping the probability of occurrence of invasive wild pigs across the contiguous United States
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
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SnowClim v1.0: High-resolution snow model and data for the western United States
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The results of model learning on base an annotated text, compiled on the basis of the English-language news feed of the Yuri Gagarin State Technical University of Saratov
<p>The results of model learning on base an annotated text, compiled from the English-language news feed of the Yuri Gagarin State Technical University of Saratov.</p> <p>This file can be used in conjunction with Data for Model Learning on base OPENNLP DOI 10.5281/zenodo.3550016</p> <p> </p> <p> </p>
Datasets used for Multi-omics integration using Deep Learning and other state-of-the-art regression models
<p>This repository link contains the LIHC files that were downloaded using TCGA Assembler 2 and used in the publication for benchmarking DL and other state-of-the-art regression models.</p> <p>The contents are as follows.</p> <p>Gene level CNA , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC__genome_wide_snp_6__GeneLevelCNA.txt">LIHC__genome_wide_snp_6__GeneLevelCNA.txt</a>"</p> <p>DNA Methylation data around 1500 bp around TSS (450K) , filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_Methylation450__SingleValue__TSS1500__Both.txt">LIHC_Methylation450__SingleValue__TSS1500__Both.tx</a>t"</p> <p>RNASeq data, filename= "<a href="https://zenodo.org/api/files/15943ba8-5f3d-4397-8eb2-98ed85693b79/LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt">LIHC_RNASeq__illuminahiseq_rnaseqv2__GeneExp.txt</a>"</p>
Data from: Applied use of alternate stable state modeling in restoration ecology
<p>The concept of alternate stable states is important in ecological theory and models, but the application and implementation of these models have the potential to make significant future advances in the field of patterned landscapes. The bi-stable, ridge and slough landscape is a central feature of Everglades restoration and provides an important opportunity to test stable state theory with multistate transition models. We used these models to estimate environmental parameters associated with state changes (water depths, edaphic factors, etc.) to develop a quantitative method to measure resilience and stability. The multistate model indicates that long-term, local hydrology (15-year mean maximums and 15-year mean amplitude) and edaphic factors control the local scale shifts between ridge and slough states. We show that multistate models can provide hydrologic envelopes for managers, produce a tool to help assess future water management scenarios, and address issues of sustainability, resilience, and restoration for any bi-stable system.</p>
Bayesian modeling of the equation-of-state for liquid iron in Earth's outer core
<p>Input data of Matsumura et al.</p>
Data from: Control of entropy in neural models of environmental state
Humans and animals construct internal models of their environment in order to select appropriate courses of action. The representation of uncertainty about the current state of the environment is a key feature of these models that controls the rate of learning as well as directly affecting choice behaviour. To maintain flexibility, given that uncertainty naturally decreases over time, most theoretical inference models include a dedicated mechanism to drive up model uncertainty. Here we probe the long-standing hypothesis that noradrenaline is involved in determining the entropy, and thus flexibility, of neural models. Pupil diameter, which indexes neuromodulatory state including noradrenaline release, predicted increases (but not decreases) in entropy in a neural state model encoded in human medial orbitofrontal cortex, as measured using multivariate functional MRI. Activity in anterior cingulate cortex predicted pupil diameter. These results provide evidence for top-down, neuromodulatory control of entropy in neural state models.
Data from: A multi-state dynamic occupancy model to estimate local colonization-extinction rates and patterns of co-occurrence between two or more interacting species
1. Although ecology is rife with theory that explores how multiple species co-occur through space and time, the field lacks robust statistical models to parameterize this theory with empirical data, particularly when species are detected imperfectly and data are collected as a time-series. 2. We address this need by developing an occupancy model that estimates local colonization and extinction rates for two or more interacting species when data are collected across multiple sampling occasions. This model estimates how community composition at a site may change across sampling occasions by assuming the latent occupancy state is a categorical random variable. We used a multinomial-logit model to parameterize species-specific parameters and pairwise interactions between species, both of which can be made a function of covariates. These transition probabilities between community states can then be converted to occupancy or co-occurrence probabilities to determine how community composition varies along an environmental gradient or through time. 3. As an example, we estimate patterns of co-occurrence between coyote (Canis latrans), Virginia opossum (Didelphis virginiana), and raccoon (Procyon lotor) in Chicago, Illinois, USA with data from a multi-year camera trapping study. Models with pairwise interactions between species greatly out performed models that assumed independence between species. Opossum and raccoon, for example, were far less likely to go extinct in habitat patches where coyotes were present. 4. Community composition at a site depends on species interactions and the local environment. Our model can separate such effects by estimating the underlying processes that define species occurrence patterns. As a result, our model can more explicitly quantify a wide range of ecological dynamics and therefore be used to empirically test ecological theory, such as estimating priority effects at a site or turnover rates between species, both of which can be made to vary as a function of covariates.
Data from: State-space reduction and equivalence class sampling for a molecular self-assembly model
Direct simulation of a model with a large state space will generate enormous volumes of data, much of which is not relevant to the questions under study. In this paper, we consider a molecular self-assembly model as a typical example of a large state-space model, and present a method for selectively retrieving 'target information' from this model. This method partitions the state space into equivalence classes, as identified by an appropriate equivalence relation. The set of equivalence classes H, which serves as a reduced state space, contains none of the superfluous information of the original model. After construction and characterization of a Markov chain with state space H, the target information is efficiently retrieved via Markov chain Monte Carlo sampling. This approach represents a new breed of simulation techniques which are highly optimized for studying molecular self-assembly and, moreover, serves as a valuable guideline for analysis of other large state-space models.
Controlling Business Object States in Business Process Models to Support Compliance
<p>Business process model</p>
Wildfire Risk Assessment for Strategic Forest Management in the Southern United States: a Bayesian Network Modeling Approach
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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.