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5,805 results for “Data model”
Data from: A method for mapping morphological convergence on three-dimensional digital models: the case of the mammalian saber-tooth
<p>Morphological convergence can be assessed through a variety of statistical methods. None of the methods proposed to date enable the visualization of convergence. All are based on the assumption that the phenotypes either converge, or do not. However, between species, morphologically similar regions of a larger structure may behave differently. Previous approaches do not identify these regions within the larger structures or quantify the degree to which they may contribute to overall convergence. Here we introduce a new method to chart patterns of convergence on three-dimensional models, deployed with the R function conv.map. The convergence between pairs of models is mapped onto them to visualize and quantify the morphological convergence. We applied conv.map to a well-known case study, the saber-tooth morphotype which has evolved independently among distinct mammalian clades, from placentals to metatherians. Although previous authors have concluded that saber-tooths kill using a stabbing 'bite' to the neck, others have presented different interpretations for specific taxa, including the iconic Smilodon and Thylacosmilus. Our objective was to identify any shared morphological features among the saber-tooths that may underpin similar killing behaviours. From a sample of 49 placental and metatherian carnivores, we found stronger convergence among saber-tooths than for any other taxa. We found that the morphological convergence is most apparent in the rostral and posterior parts of the cranium. The extent of this convergence implies similarity in function among these phylogenetically distant species. In our view this function is most likely the killing of relatively large prey by a stabbing bite.</p>
Data to replicate: Forecasting community reassembly using climate-linked spatio-temporal ecosystem models
Ecosystems are increasingly impacted by human activities, altering linkages among physical and biological components. Spatial community reassembly occurs when these human impacts modify the spatial overlap between system components, and there is need for practical tools to forecast spatial community reassembly at landscape scales using monitoring data. To illustrate a new approach, we extend a generalization of empirical orthogonal function (EOF) analysis, which involves a spatio-temporal ecosystem model that approximates coupled physical, biological, and human dynamics. We then demonstrate its application to five trophic levels for the eastern Bering Sea by fitting to multiple, spatially unbalanced datasets measuring physical characteristics (temperature measurements and climate-linked forecasts), primary producers (spring and fall size-fractionated chlorophyll-a), secondary producers (copepods), juveniles (age-0 walleye pollock), adult consumers (five commercially important fishes), human activities (seasonal fishing effort), and mobile predators (seabirds). We identify the spatial niche for each ecosystem component, as well as dominant modes of variability that are highly correlated with a known bottom-up driver of dynamics. We then measure spatial overlap between interacting variables (using Schoener's-D) and identify that age-0 pollock have decreased spatial overlap with copepods and increased overlap with adult pollock during warm years, and also that adult pollock have increased overlap with arrowtooth flounder and decreased overlap with catcher-processor fishing effort during these warm years. Given the warming conditions that are projected for the coming decade, the model forecasts increased prey and competitor overlap involving adult pollock (between age-0 pollock, adult pollock and arrowtooth flounder) and decreased overlap with the copepod forage base and with the catcher-processor fishery during future warming. We recommend that joint species distribution models be extended to incorporate "ecological teleconnections" (correlations between distant locations arising from known mechanisms) arising from behavioral adaptation by mobile animals as well as passive advection of nutrients and planktonic juvenile stages.
Data from: Abundance data applied to a novel model invertebrate host sheds new light on parasite community assembly in nature
<ol> <li>Understanding how environmental drivers influence the assembly of parasite communities, in addition to how parasites may interact at an infracommunity level, are fundamental requirements for the study of parasite ecology. Knowledge of how parasite communities are assembled will help to predict the risk of parasitism for hosts, and model how parasite communities may change under variable conditions. However, studies frequently rely on presence-absence data and examine multiple host species or sites, metrics which may be too coarse to characterise nuanced within-host patterns.</li> <li>We utilised a novel host system, the freshwater mussel <i>Anodonta anatina</i>, to investigate the drivers of community structure and explore parasite interactions. In addition, we aimed to highlight consistencies and inconsistencies between presence-absence and abundance data.</li> <li>Our analysis incorporated 14 parasite taxa and 720 replicate infracommunities. Using redundancy analysis, a joint species distribution model and a Markov random fields approach, we modelled the impact of both host-level and environment-level characteristics on parasite structure, as well as parasite-parasite correlations after accounting for all other factors. This approach was repeated for both the presence and abundance of all parasites.</li> <li>We demonstrated that the regional species pool, individual host characteristics (mussel length and gravidity) and predicted parasite-parasite interactions are all important but to varying degrees across parasite species, suggesting that applying generalities to parasite community construction is too simplistic. Further, we showed that presence-absence data fails to capture important density-dependent effects of parasite load for parasites with high abundance, and in general performs poorly for high-intensity parasites.</li> <li>Host and parasite traits, as well as broader environmental factors, all contribute to parasite community structure, emphasising that an integrated approach is required to study community assembly. However, care must be taken with the data used to infer patterns, as presence-absence data may lead to incorrect ecological inference.</li> </ol>
Simulation Data of a Model with Ionospheric Effects for Global 21-cm Experiments
<p>The files contain integrated antenna temperature of two different models in binary.</p>
Data from: Nanotransfection-based vasculogenic cell reprogramming drives functional recovery in a mouse model of ischemic stroke
<p>Ischemic stroke causes vascular and neuronal tissue deficiencies that could lead to significant functional impairment and/or death. Although progenitor-based vasculogenic cell therapies have shown promise as a potential rescue strategy following ischemic stroke, current approaches face major hurdles. Here we used fibroblasts nanotransfected with <em>Etv2</em>, <em>Foxc2</em>, and <em>Fli1</em> (<em>EFF</em>), to drive reprogramming-based vasculogenesis, intracranially, as a potential therapy for ischemic stroke. Perfusion analyses suggest that intracranial delivery of <em>EFF</em>-nanotransfected fibroblasts led to a dose-dependent increase in perfusion 14 days post-injection. MRI and behavioral tests revealed ~70% infarct resolution and up to ~90% motor recovery for mice treated with <em>EFF</em>-nanotransfected fibroblasts. Immunohistological analysis confirmed increases in vascularity and neuronal cellularity, as well as reduced glial scar formation in response to treatment with <em>EFF</em>-nanotransfected fibroblasts. Altogether, our results suggest that vasculogenic cell therapies based on nanotransfection-driven (i.e., non-viral) cellular reprogramming represent a promising strategy for the treatment of ischemic stroke.</p>
Data from: New forms of structure in ecosystems revealed with the Kuramoto model
<p>This is the general Mathematica code for the article, "New forms of structure in ecosystems revealed with the Kuramoto model" by Vandermeer, Hajian-Forooshani, Medina and Perfecto. All results presented in the article can be obtained by running the code with appropriate parameter settings.</p>
Comparative analysis of angiogenesis models: MATLAB data files
<p>This data set contains the MATLAB files that were used to generate figures located in the article "Comparative analysis of angiogenesis models" (J. Math. Biol, in press). The article's abstract may be found below. </p> <p>Although discrete approaches are increasingly employed to model biological phenomena, it remains unclear how complex, population-level behaviours in such frameworks arise from the rules used to represent interactions between individuals. Discrete-to-continuum approaches, which are used to derive systems of coarse-grained equations describing the mean-field dynamics of a microscopic model, can provide insight into such emergent behaviour. Coarse-grained models often contain nonlinear terms that depend on the microscopic rules of the discrete framework, however, and such nonlinearities can make a model difficult to mathematically analyse. By contrast, models developed using phenomenological approaches are typically easier to investigate but have a more obscure connection to the underlying microscopic system. To our knowledge, there has been little work done to compare solutions of phenomenological and coarse-grained models. Here we address this problem in the context of angiogenesis (the creation of new blood vessels from existing vasculature). We compare asymptotic solutions of a classical, phenomenological "snail-trail" model for angiogenesis to solutions of a nonlinear system of partial differential equations (PDEs) derived via a systematic coarse-graining procedure (Pillay, 2017). For distinguished parameter regimes corresponding to chemotaxis-dominated cell movement and low branching rates, both continuum models reduce at leading order to identical PDEs within the domain interior. Numerical and analytical results confirm that pointwise differences between solutions to the two continuum models are small if these conditions hold, and demonstrate how perturbation methods can be used to determine when a phenomenological model provides a good approximation to a more detailed coarse-grained system for the same biological process.</p>
Data from: Testing gradual and speciational models of evolution in extant taxa: the example of ratites
Ever since Eldredge and Gould proposed their model of punctuated equilibria, evolutionary biologists have debated how often this model is the best description of nature and how important it is compared to the more gradual models of evolution expected from natural selection and the neo-Darwinian paradigm. Recently, Cubo proposed a method to test whether morphological data in extant ratites are more compatible with a gradual or with a speciational model (close to the punctuated equilibrium model). As shown by our simulations, a new method to test the mode of evolution of characters (involving regression of standardized contrasts on their expected standard deviation) is easier to implement and more powerful than the previously proposed method, but the Mesquite module CoMET (aimed at investigating evolutionary models using comparative data) performs better still. Uncertainties in branch length estimates are probably the largest source of potential error. Cubo hypothesized that heterochronic mechanisms may underlie morphological changes in bone shape during the evolution of ratites. He predicted that the outcome of these changes may be consistent with a speciational model of character evolution because heterochronic changes can be instantaneous in terms of geological time. Analysis of a more extensive dataset confirms his prediction despite branch length uncertainties: evolution in ratites has been mostly speciational for shape-related characters. However, it has been mostly gradual for size-related ones.
Data from: A hidden Markov model to identify and adjust for selection bias: an example involving mixed migration strategies
An important assumption in observational studies is that sampled individuals are representative of some larger study population. Yet, this assumption is often unrealistic. Notable examples include online public-opinion polls, publication biases associated with statistically significant results, and in ecology, telemetry studies with significant habitat-induced probabilities of missed locations. This problem can be overcome by modeling selection probabilities simultaneously with other predictor–response relationships or by weighting observations by inverse selection probabilities. We illustrate the problem and a solution when modeling mixed migration strategies of northern white-tailed deer (Odocoileus virginianus). Captures occur on winter yards where deer migrate in response to changing environmental conditions. Yet, not all deer migrate in all years, and captures during mild years are more likely to target deer that migrate every year (i.e., obligate migrators). Characterizing deer as conditional or obligate migrators is also challenging unless deer are observed for many years and under a variety of winter conditions. We developed a hidden Markov model where the probability of capture depends on each individual's migration strategy (conditional versus obligate migrator), a partially latent variable that depends on winter severity in the year of capture. In a 15-year study, involving 168 white-tailed deer, the estimated probability of migrating for conditional migrators increased nonlinearly with an index of winter severity. We estimated a higher proportion of obligates in the study cohort than in the population, except during a span of 3 years surrounding back-to-back severe winters. These results support the hypothesis that selection biases occur as a result of capturing deer on winter yards, with the magnitude of bias depending on the severity of winter weather. Hidden Markov models offer an attractive framework for addressing selection biases due to their ability to incorporate latent variables and model direct and indirect links between state variables and capture probabilities.
Data from: Conspicuous plumage does not increase predation risk: a continent-wide test using model songbirds
The forces shaping female plumage color have long been debated but remain unresolved. Females may benefit from conspicuous colors but are also expected to suffer costs. Predation is one potential cost, but few studies have explicitly investigated the relationship between predation risk and coloration. The fairy-wrens show pronounced variation in female coloration and reside in a wide variety of habitats across Australasia. Species with more conspicuous females are found in denser habitats, suggesting that conspicuousness in open habitat increases vulnerability to predators. To test this, we measured attack rates on 3D printed models mimicking conspicuously colored males and females, and dull females, in eight different fairy-wren habitats across Australia. Attack rates were higher in open habitats, and at higher latitudes. Contrary to our predictions, dull female models were attacked at similar rates to the conspicuous models. Further, the probability of attack in open habitats increased more for both types of female model than for the conspicuous male model. Across models, the degree of contrast (chromatic and achromatic) to environmental backgrounds was unrelated to predation rate. These findings do not support the long-standing hypothesis that conspicuous plumage, in isolation, is costly due to increased attraction of predators. Our results indicate that conspicuousness interacts with other factors in driving the evolution of plumage coloration.
Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
Data from: Microhabitat selection in the common lizard: implications of biotic interactions, age, sex, local processes, and model transferability among populations
Modeling species' habitat requirements are crucial to assess impacts of global change, for conservation efforts and to test mechanisms driving species presence. While the influence of abiotic factors has been widely examined, the importance of biotic factors and biotic interactions, and the potential implications of local processes are not well understood. Testing their importance requires additional knowledge and analyses at local habitat scale. Here, we recorded the locations of species presence at the microhabitat scale and measured abiotic and biotic parameters in three different common lizard (Zootoca vivipara) populations using a standardized sampling protocol. Thereafter, space use models and cross-evaluations among populations were run to infer local processes and estimate the importance of biotic parameters, biotic interactions, sex, and age. Biotic parameters explained more variation than abiotic parameters, and intraspecific interactions significantly predicted the spatial distribution. Significant differences among populations in the relationship between abiotic parameters and lizard distribution, and the greater model transferability within populations than between populations are in line with effects predicted by local adaptation and/or phenotypic plasticity. These results underline the importance of including biotic parameters and biotic interactions in space use models at the population level. There were significant differences in space use between sexes, and between adults and yearlings, the latter showing no association with the measured parameters. Consequently, predictive habitat models at the population level taking into account different sexes and age classes are required to understand a specie's ecological requirements and to allow for precise conservation strategies. Our study therefore stresses that future predictive habitat models at the population level and their transferability should take these parameters into account.
Data from: Avoidable errors in the modeling of outbreaks of emerging pathogens, with special reference to Ebola
As an emergent infectious disease outbreak unfolds, public health response is reliant on information on key epidemiological quantities, such as transmission potential and serial interval. Increasingly, transmission models fit to incidence data are used to estimate these parameters and guide policy. Some widely used modelling practices lead to potentially large errors in parameter estimates and, consequently, errors in model-based forecasts. Even more worryingly, in such situations, confidence in parameter estimates and forecasts can itself be far overestimated, leading to the potential for large errors that mask their own presence. Fortunately, straightforward and computationally inexpensive alternatives exist that avoid these problems. Here, we first use a simulation study to demonstrate potential pitfalls of the standard practice of fitting deterministic models to cumulative incidence data. Next, we demonstrate an alternative based on stochastic models fit to raw data from an early phase of 2014 West Africa Ebola virus disease outbreak. We show not only that bias is thereby reduced, but that uncertainty in estimates and forecasts is better quantified and that, critically, lack of model fit is more readily diagnosed. We conclude with a short list of principles to guide the modelling response to future infectious disease outbreaks.
Data from: Cats under cover: habitat models indicate a high dependency on woodlands by Atlantic Forest felids
Four Neotropical small and medium felids—the ocelot, jaguarundi, margay and southern tiger cat—have overlapping geographic distributions in the endangered Atlantic Forest. Local studies show that these felids avoid areas with high human impact, but the three smaller ones use human-modified areas more frequently than do ocelots. To understand how landscape changes affect the habitat distribution of these four felids in the Atlantic Forest of Argentina, we used maximum entropy models to analyze the effect of environmental and anthropogenic factors. We estimated niche breadth and overlap among these felids. The conversion of the native forest to land uses without trees was the most important variable that determined the habitat distribution of the four species. For all four species the optimal habitat covered less than 1/3 of the study area and corresponds mainly to the native forest areas. Nearly 50 percent of these areas had some level of protection. The niche width was higher for the small felids than for ocelots. Niche overlap was high for all species pairs, but higher among the small felids and lower for each of these with the ocelot. The four felids were negatively affected by native forest loss, with ocelots being more sensitive than the smaller felids. The conversion of unprotected forest areas to other types of land uses would imply a greater habitat loss for these felids. The protection of current remnants of Atlantic Forest in Argentina is important for the long-term conservation of the four felids.
Data from: Life table invasion models: spatial progression and species-specific partitioning
Biological invasions are increasingly being considered important spatial processes that drive global changes, threatening biodiversity, regional economies, and ecosystem functions. A unifying conceptual model of the invasion dynamics could serve as a useful tool for comparison and classification of invasion processes involving different species across large geographic ranges. By dividing these geographic ranges that are subject to invasions into discrete spatial units we here conceptualize the invasion process as the transition from pristine to invaded spatial units. We use California cities as the spatial units and a long-term database of invasive tropical tephritids to characterize the invasion patterns. A new life-table method based on insect demography, including the progression model of invasion stage transition and the species-specific partitioning model of multispecies invasions, was developed to analyze the invasion patterns. The progression model allows us to estimate the probability and rate of transition, for individual cities, from pristine to infested stages and subsequently differentiate first year of detection from detection recurrences. Importantly, we show that the interval of invasive tephritid recurrence in a city declines with increasing invasion stages of the city. The species-specific partitioning model revealed profound difference in invasion outcome depending on which tephritid species was first detected (and then locally eradicated) in the early stage of invasion. Taken together, we discuss how these two life-table invasion models can cast new light on existing invasion concepts; in particular, on formulating invasion dynamics as the state transition of cities and partitioning species-specific role during multispecies invasions. These models provide a new set of tools for predicting the spatiotemporal progression of invasion and providing early warnings of recurrent invasions for efficient management.
Data from: Development of a protocol for environmental impact studies using causal modelling
1- The global issue of water scarcity caused by climate change and human utilisation highlights the importance of an efficient assessment of water quality in freshwater systems. One of the challenges facing water management in environmental impact studies is the difficulty of inferring causality in complex systems. Traditional water assessment methods are inadequate because they are challenged to separate natural variation from the effect of human activities. 2- Knowing the causal structure of a complex ecosystem will enable managers to identify key anthropogenic, climate and flow drivers of water quality, and make informed decisions about interventions that improve water and environmental quality. In this study, I show how causal modelling can facilitate decision making for water treatment plant managers to improve their environmental management of this valuable resource. 3- Models built using causal modelling techniques, including structural equation modelling and the principles of Bayesian Networks, were utilised for management decision making purposes. The discharge load values were manipulated in the models to predict the effect of a potential intervention, e.g. treatment plant upgrade, on the values of the water quality variables in the creek. That is, water quality variables were predicted when an imaginary or counterfactual situation was imposed on the models. 4- This study showed that there would not be any observable effect of effluent on macroinvertebrate communities if the discharge loads of chlorophyll a, total organic carbon, total phosphorus, nitrate, and conductivity were reduced to 0.1 of observed values. The concentrations of environmental variables in the creek would return to their baseline levels when their corresponding discharge loads in the effluent were halved or divided by 10. 5- Based on the findings of this study, managers in the field of environmental impact studies can predict the response of a system in the presence of potential interventions under complex and uncertain conditions. The implementation of such techniques offers great promise in the wider field of environmental management where accounting for multiple factors structuring ecosystems in necessary to adequately represent causality.
Data from: Influence of device accuracy and choice of algorithm for species distribution modelling of seabirds: a case study using black-browed albatrosses
Species distribution models (SDM) based on tracking data from different devices are used increasingly to explain and predict seabird distributions. However, different tracking methods provide different data resolutions, ranging from < 10m to >100km. To better understand the implications of this variation, we modeled the potential distribution of black-browed albatrosses Thalassarche melanophris from South Georgia that were simultaneously equipped with a Platform Terminal Transmitter (PTT) (high resolution) and a Global Location Sensor (GLS) logger (coarse resolution), and measured the overlap of the respective potential distribution for a total of nine different SDM algorithms. We found slightly better model fits for the PTT than for GLS data (AUC values 0.958±0.048 vs. 0.95±0.05) across all algorithms. The overlaps of the predicted distributions were higher between device types for the same algorithm, than among algorithms for either device type. Uncertainty arising from coarse-resolution location data is therefore lower than that associated with the modeling technique. Consequently, the choice of an appropriate algorithm appears to be more important than device type when applying SDMs to seabird tracking data. Despite their low accuracy, GLS data appear to be effective for analyzing the habitat preferences and distribution patterns of pelagic species.
Data from: Modeling seasonal surface temperature variations in secondary tropical dry forests
Secondary tropical dry forests (TDFs) provide important ecosystem services such as carbon sequestration, biodiversity conservation, and nutrient cycle regulation. However, their biogeophysical processes at the canopy-atmosphere interface remain unknown, limiting our understanding of how this endangered ecosystem influences, and responds to the ongoing global warming. To facilitate future development of conservation policies, this study characterized the seasonal land surface temperature (LST) behavior of three successional stages (early, intermediate, and late) of a TDF, at the Santa Rosa National Park (SRNP), Costa Rica. A total of 38 Landsat-8 Thermal Infrared Sensor (TIRS) data and the Surface Reflectance (SR) product were utilized to model LST time series from July 2013 to July 2016 using a radiative transfer equation (RTE) algorithm. We further related the LST time series to seven vegetation indices which reflect different properties of TDFs, and soil moisture data obtained from a Wireless Sensor Network (WSN). Results showed that the LST in the dry season was 15–20 K higher than in the wet season at SRNP. We found that the early successional stages were about 6–8 K warmer than the intermediate successional stages and were 9–10 K warmer than the late successional stages in the middle of the dry season; meanwhile, a minimum LST difference (0–1 K) was observed at the end of the wet season. Leaf phenology and canopy architecture explained most LST variations in both dry and wet seasons. However, our analysis revealed that it is precipitation that ultimately determines the LST variations through both biogeochemical (leaf phenology) and biogeophysical processes (evapotranspiration) of the plants. Results of this study could help physiological modeling studies in secondary TDFs.
Data from: The plover neurotranscriptome assembly: transcriptomic analysis in an ecological model species without a reference genome
We assembled a de novo transcriptome of short-read Illumina RNA-Seq data generated from telencephalon and diencephalon tissue samples from the Kentish plover, Charadrius alexandrinus. This is a species of considerable interest in behavioural ecology for its highly variable mating system and parental behaviour, but it lacks genomic resources and is evolutionarily distant from the few available avian draft genome sequences. We assembled and identified over 21 000 transcript contigs with significant expression in our samples, showing high homology to exonic sequences in avian draft genomes. From these, we identified >31 000 high-quality SNPs and > 2500 simple sequence repeats (SSRs). We also analysed expression patterns in our data to identify potential candidate genes related to differences in male and female behaviour, identifying over 200 nonoverlapping putative autosomal transcripts that show significant expression differences between males and females. Gene ontology analysis revealed that female-biased transcripts were significantly enriched for cerebral functions related to learning, cognition and memory, and male-biased transcripts were mostly enriched for terms related to neural function such as neuron projection and synapses. This data set provides one of the first de novo transcriptome assemblies from non-normalized short-read next-generation data and outlines an effective strategy for measuring sequence and expression variability simultaneously without the aid of a reference genome.
Data from: Occupancy models for data with false positive and false negative errors and heterogeneity across sites and surveys
False positive detections, such as species misidentifications, occur in ecological data, although many models do not account for them. Consequently, these models are expected to generate biased inference. The main challenge in an analysis of data with false positives is to distinguish false positive and false negative processes while modeling realistic levels of heterogeneity in occupancy and detection probabilities without restrictive assumptions about parameter spaces. Building on previous attempts to account for false positive and false negative detections in occupancy models, we present hierarchical Bayesian models that utilize a subset of data with either confirmed detections of a species' presence (CP model) or both confirmed presences and confirmed absences (CACP model). We demonstrate that our models overcome the challenges associated with false positive data by evaluating model performance in Monte Carlo simulations of a variety of scenarios. Our models also have the ability to improve inference by incorporating previous knowledge through informative priors. We describe an example application of the CP model to quantify the relationship between songbird occupancy and residential development, plus we provide instructions for ecologists to use the CACP and CP models in their own research. Monte Carlo simulation results indicated that, when data contained false positive detections, the CACP and CP models generated more accurate and precise posterior probability distributions than a model that assumed data did not have false positive errors. For the scenarios we expect to be most generally applicable, those with heterogeneity in occupancy and detection, the CACP and CP models generated essentially unbiased posterior occupancy probabilities. The CACP model with vague priors generated unbiased posterior distributions for covariate coefficients. The CP model generated unbiased posterior distributions for covariate coefficients with vague or informative priors, depending on the function relating covariates to occupancy probabilities. We conclude that the CACP and CP models generate accurate inference in situations with false positive data for which previous models were not suitable.
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.