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51 results for “Hidden states”
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
<p><span><span><span><span><span><span><span><span><span><span>Ancestral state reconstruction is not only a fundamental tool for studying trait evolution, but also very useful for predicting the unknown trait values (hidden states) of extant species. A well-known problem in ancestral and hidden state predictions is that the uncertainty associated with predictions can be so large that predictions themselves are of little use. Therefore, for meaningful interpretation of predicted traits and hypothesis testing, it is prudent to accurately assess the uncertainty of the predictions. Commonly used constant-rate Brownian motion (BM) model fails to capture the complexity of tempo and mode of trait evolution in nature, making predictions under the BM model vulnerable to lack-of-fit errors from model misspecification. Using empirical data (mammalian body size and bacterial genome size), we show that the distribution of residual Z-scores under the BM model is neither homoscedastic nor normal as expected. Consequently, the 95% confidence intervals (CIs) of predicted traits are so unreliable that the actual coverage probability ranges from 33% (strongly permissive) to 100% (strongly conservative). Alternative methods such as BayesTraits and StableTraits that allow variable rates in evolution improve the predictions but are computationally expensive. Here we develop RasperGade, a method of ancestral and hidden state prediction that uses the Levy process to explicitly model gradual evolution, pulsed evolution and time-independent variation. Using the same empirical data, we show that RasperGade outperforms both BayesTraits and StableTraits and is orders-of-magnitude faster. Our results suggest that, when predicting the ancestral and hidden states of continuous traits, the tempo and mode of evolution should always be assessed and the quality of confidence estimates should always be examined.</span></span></span></span></span></span></span></span></span></span></p>
Three syntactic probing tasks for hidden states
<p>This is a dataset for syntactic probes, used in the article "What does Chinese BERT learn about syntactic knowledge?"</p>
Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement
Open the record for dataset details and reuse information.
Modeling pulsed evolution and time-independent variation improves the confidence level of ancestral and hidden state predictions
Open the record for dataset details and reuse information.
Single-molecule two- and three-colour FRET studies reveal a hidden transition state in SNARE disassembly by NSF
<p>Raw Data for the NC manuscript</p>
Supplementary material 1 from: Ferreira EM, Valerio F, Medinas D, Fernandes N, Craveiro J, Costa P, Silva JP, Carrapato C, Mira A, Santos SM (2022) Assessing behaviour states of a forest carnivore in a road-dominated landscape using Hidden Markov Models. In: Santos S, Grilo C, Shilling F, Bhardwaj M, Papp CR (Eds) Linear Infrastructure Networks with Ecological Solutions. Nature Conservation 47: 155-175. https://doi.org/10.3897/natureconservation.47.72781
Figures S1–S3
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>
FIG. 8 in Hidden Diversity in the Mountain Chorus Frog (Pseudacris brachyphona) and the Diagnosis of a New Species of Chorus Frog in the Southeastern United States
FIG. 8. The distribution (solid gray line) and mean (gray dashed line) of D and I (A, B) niche overlap estimates obtained from MaxEnt models, originating from randomly selecting occurrences (and associated environmental values) in the Southern population. Asymmetric background tests were used to compare these distributions with the empirical estimate from the Northern population (black dashed line). The same procedure was carried out for the reciprocal comparison, Southern vs. Northern (C, D). In both cases, the empirical estimates significantly differed from the randomized distributions, suggesting the absence of niche overlap.
FIG. 6 in Hidden Diversity in the Mountain Chorus Frog (Pseudacris brachyphona) and the Diagnosis of a New Species of Chorus Frog in the Southeastern United States
FIG. 6. Maximum entropy species distribution models (SDMs) for all P. brachyphona (A), Northern (B), and Southern clades (C). Models represent average probability of occurrence based on ten replicates. We applied a 10th percentile threshold rule to the predicted suitability, thus gray pixels in these maps represent suitable habitat. Hollow circles show the location of occurrence records used in the models, and the dashed line indicates the position of the Tennessee River.
Hidden state foraging task, interleaved barrier manipulation
<p>Behavioural dataset of hidden state foraging task</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.