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Dataset results
21 results for “Multi-state”
Dataset from "Matthieu Delescluse and Christophe Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29."
<p>The dataset (in HDF5 format) used in Delescluse and Pouzat (2006) Efficient spike-sorting of multi-state neurons using inter-spike intervals information Journal of Neuroscience Methods 150: 16-29. arXiv:q-bio/0505053. See this reference for recording details. Data collected by Matthieu Delescluse. Briefly, 4 channels (data sets Channel_0,1,2,3, organized in a group called 'ExtracellularData'; extracellular recordings along the Purkinje cell layer of a young rat cerebellar cortex slice) of a linear 'Michigan' (now Neuronexus) probe and a loose cell-attached recording (data set Reference, in group 'CellAttached') from one of the Purkinje cells that is also extracellularly recorded: a 'ground truth' for spike sorting algorithms. Each group has three attributes: SamplingRate, HighPass and LowPass. The last two are the filter settings used prior to A/D conversion. These attributes have identical values for the 5 traces (2 groups): the data were sampled at 15 kHz, high-passed at 300 Hz and low-passed at 5 kHz.</p>
Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets
<p>This archive contains the empirical data analysed in the paper 'Mark loss can strongly bias estimates of demographic rates in multi-state models: a case study with simulated and empirical datasets' by Touzalin et al. (https://doi.org/10.24072/pci.ecology.100416). The dataset is provided as a .Rdata file ('TLoss_GMdata.Rdata'), and full description of the content is provided in the file 'Readme_TLdata.csv'. All additional details are available in the main text (https://doi.org/10.24072/pci.ecology.100416) or in the supporting information (https://doi.org/10.5281/zenodo.10204538).</p>
Fig. 1 in Toxoplasma gondii exposure in arctic-nesting geese: A multi-state occupancy framework and comparison of serological assays
Fig. 1. Comparison of seroprevalence estimates for Ross's Geese and Lesser Snow Geese generated by naïve and multi-state occupancy estimators (seroprevalence = Ψ1 × Ψ2).
Multi-state modeling of the PhoQ two-component system
<p>This directory contains the input data, protocols and output model for the modeling of the PhoQ homodimer, using cysteine crosslinking and multi-state Bayesian modeling in IMP.</p> <p>For more information about how to reproduce this modeling, see https://salilab.org/phoq or the README file.</p>
Coexisting multi-states in catalytic hydrogen oxidation on rhodium - Supplementary Database 1
<p>Supplementary Database 1 to the associated article in Nature Communications (DOI: <a href="https://doi.org/10.1038/s41467-021-26855-y">10.1038/s41467-021-26855-y</a>) containing the raw data for the results shown in the display items. Experimental conditions, parameters and evaluation procedures are given in the corresponding figure captions and the Methods section of the associated article.</p>
A multi-state occupancy model to non-invasively monitor visible signs of wildlife health with camera traps that accounts for image quality
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A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables
<p>Supporting data related to manuscript 'DeepLNE++ leveraging knowledge distillation for accelerated multi-state path-like collective variables'</p>
FIGURE 4. Multi-state character analysis for 89 in Systematic study in Dipcadi ursulae (Asaparagaceae, Scillioideae) from Maharashtra, India
FIGURE 4. Multi-state character analysis for 89 characters using UPGMA Manhattan distance analysis DMAJ: D. montanum (Ajra), DBA: D. montanum (Badami), DKK: D. montanum (Anantapur), DKR: D. saxorum var. saxorum (Kanheri), DMR: D. saxorum var. saxorum (Manori), DUG: D. saxorum var. saxorum (Uttan), DMS: D. saxorum var. kanheriense (Manori), DKG: D. saxorum var. kanheriense (Kanheri), DUK: D. ursulae var. ursulae (Kas), DUJN: D. ursulae var. ursulae (Jungti), DUSW: D. ursulae var. ursulae (Sadawagapur), DUM: D. ursulae var. ursulae (Masai), DULP: D. ursulae var. ursulae (Pandavleni), DUDB: D. ursulae var. ursulae (Burondi), DMBH: D. ursulae var. alba (Mama-Bhanja), DULG: D. longiracemosum (Girnar), DJSN: D. janae-shrirangii (Naravan), DGRK: D. goaense (Goa), DCDS: D. concanense var. devrukhense (Devrukh), DCBK: D. concanense var.concanense (Brahamanwadi).
Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect
<p>Source data for: Multi-state Data Storage in a Two-dimensional Stripy Antiferromagnet Implemented by Magnetoelectric Effect</p>
Data from: Quantifying apart what belongs together: a multi-state species distribution modeling framework for species using distinct habitats
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Data from: Reliability modelling and analysis of a multi-state element based on a dynamic Bayesian network
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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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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.
Multi-state diel occupancy model
<p>Current methods to model species habitat use through space and diel time are limited. Development of such models is critical when considering rapidly changing habitats where species are forced to adapt to anthropogenic change, often by shifting their diel activity across space. We use an occupancy modeling framework to specify the multi-state diel occupancy model (MSDOM), which can evaluate species diel activity against continuous response variables which may impact diel activity within and across seasons or years. We used two case studies, fosa in Madagascar and coyote in Chicago, USA, to conceptualize the application of this model and to quantify the impacts of human activity on species spatial use in diel time. We found support that both species varied their habitat use by diel states—in and across years, and by human disturbance. Our results exemplify the importance of understanding animal diel activity patterns and how human disturbance can lead to temporal habitat loss. The MSDOM will allow more focused attention in ecology and evolution studies on the importance of the short temporal scale of diel time in animal-habitat relationships and lead to improved habitat conservation and management.</p>
Condition-based maintenance with imperfect inspection for a multi-state system subject to competing and hidden failures
<p>Data are used to draw figures and tables. </p>
Multi-state diel occupancy model
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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
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HiCrayon reveals distinct layers of multi-state 3D chromatin organization
GEO Series GSE255264. Homo sapiens. 91 samples. Type: Other; Third-party reanalysis.
A Multi-Cancer, Multi-State, Platform Study of Durvalumab (MEDI4736) and Oleclumab (MEDI9447) in Pancreatic Adenocarcinoma, Non-Small Cell Lung Cancer and Squamous Cell Carcinoma of the Head and Neck
ClinicalTrials.gov study NCT04262388. IPD Sharing: NO. Countries: 1. Publications: 0.
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.