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5,805 results for “Data model”
The Lorenz63 model data
<p>The Lorenz63 model data used to identify the diurnal predictability barrier,</p>
Data from:Identification of landscape features influencing gene flow: how useful are habitat selection models?
Understanding how dispersal patterns are influenced by landscape heterogeneity is critical for modelling species connectivity. Resource selection function (RSF) models are increasingly used in landscape genetics approaches. However, because the ecological factors that drive habitat selection may be different from those influencing dispersal and gene flow, it is important to consider their explicit assumptions. We calculated pairwise genetic distances among 301 Alaskan Dall's sheep (Ovis dalli dalli) using an intensive sampling effort and 15 microsatellite loci. We used multiple regression of distance matrices to assess the correlation of pairwise genetic distance and landscape resistance derived from an RSF, and combinations of landscape features hypothesized to influence dispersal. Dall's sheep gene flow was positively correlated with steep slopes, moderate peak normalized difference vegetation indices (NDVI), and open land cover. Whereas RSF covariates were significant in predicting genetic distance, the RSF model itself was not significantly correlated with Dall's sheep gene flow, suggesting that certain habitat features important seasonally (rugged terrain, mid-range elevation) were not influential to breeding dispersal. This work underscores that consideration of both habitat selection and landscape genetics models in developing conservation strategies will ensure resources are managed to meet both the immediate survival needs of a species and allow for long-term genetic connectivity.
Data from: Accounting for genetic differences among unknown parents in microevolutionary studies: how to include genetic groups in quantitative genetic animal models
Quantifying and predicting microevolutionary responses to environmental change requires unbiased estimation of quantitative genetic parameters in wild populations. 'Animal models', which utilize pedigree data to separate genetic and environmental effects on phenotypes, provide powerful means to estimate key parameters and have revolutionized quantitative genetic analyses of wild populations. However, pedigrees collected in wild populations commonly contain many individuals with unknown parents. When unknown parents are non-randomly associated with genetic values for focal traits, animal model parameter estimates can be severely biased. Yet, such bias has not previously been highlighted and statistical methods designed to minimize such biases have not been implemented in evolutionary ecology. We first illustrate how the occurrence of non-random unknown parents in population pedigrees can substantially bias animal model predictions of breeding values and estimates of additive genetic variance, and create spurious temporal trends in predicted breeding values in the absence of local selection. We then introduce 'genetic group' methods, which were developed in agricultural science, and explain how these methods can minimize bias in quantitative genetic parameter estimates stemming from genetic heterogeneity among individuals with unknown parents. We summarize the conceptual foundations of genetic group animal models and provide extensive, step-by-step tutorials that demonstrate how to fit such models in a variety of software programs. Furthermore, we provide new functions in r that extend current software capabilities and provide a standardized approach across software programs to implement genetic group methods. Beyond simply alleviating bias, genetic group animal models can directly estimate new parameters pertaining to key biological processes. We discuss one such example, where genetic group methods potentially allow the microevolutionary consequences of local selection to be distinguished from effects of immigration and resulting gene flow. We highlight some remaining limitations of genetic group models and discuss opportunities for further development and application in evolutionary ecology. We suggest that genetic group methods should no longer be overlooked by evolutionary ecologists, but should become standard components of the toolkit for animal model analyses of wild population data sets.
Data from: False exclusion: a case to embed predator performance in classical population models
We argue that predator-prey dynamics, a cornerstone of ecology, can be driven by insufficiently-explored aspects of predator performance that are inherently prey-dependent: i.e., these have been falsely excluded. Classical -Lotka Volterra based- models tend to only consider prey-dependent ingestion rate. We highlight three other prey-dependent responses and provide empirically-derived functions to describe them. These functions introduce neglected nonlinearities and threshold behaviours into dynamic models leading to unexpected outcomes: specifically, as prey abundance increases predators: 1) become less efficient at using prey; 2) initially allocate resources towards survival and then allocate resources towards reproduction; and 3) are less likely to die. Based on experiments using model-zooplankton, we explore consequences of including these functions in the classical structure and show they alter qualitative and quantitative dynamics of an empirically-informed, generic predator-prey model. Through bifurcation analysis, our revised structure predicts: 1) predator extinctions, where the classical structure allows persistence; 2) predator survival, where the classical structure drives predators towards extinction; and 3) greater stability through smaller amplitude of cycles, relative to the classical structure. Then, by exploring parameter space, we show how these responses alter predictions of predator-prey stability and competition between predators. Based on our results, we suggest that classical assumptions about predator responses to prey abundance should be re-evaluated.
Data from: Joint prediction of multiple quantitative traits using a Bayesian multivariate antedependence model
Predicting organismal phenotypes from genotype data is important for preventive and personalized medicine as well as plant and animal breeding. Although genome-wide association studies (GWAS) for complex traits have discovered a large number of trait- and disease-associated variants, phenotype prediction based on associated variants is usually in low accuracy even for a high-heritability trait because these variants can typically account for a limited fraction of total genetic variance. In comparison with GWAS, the whole-genome prediction (WGP) methods can increase prediction accuracy by making use of a huge number of variants simultaneously. Among various statistical methods for WGP, multiple-trait model and antedependence model show their respective advantages. To take advantage of both strategies within a unified framework, we proposed a novel multivariate antedependence-based method for joint prediction of multiple quantitative traits using a Bayesian algorithm via modeling a linear relationship of effect vector between each pair of adjacent markers. Through both simulation and real-data analyses, our studies demonstrated that the proposed antedependence-based multiple-trait WGP method is more accurate and robust than corresponding traditional counterparts (Bayes A and multi-trait Bayes A) under various scenarios. Our method can be readily extended to deal with missing phenotypes and resequence data with rare variants, offering a feasible way to jointly predict phenotypes for multiple complex traits in human genetic epidemiology as well as plant and livestock breeding.
Data from: A structured population model suggests that long life and post-reproductive lifespan promote the evolution of cooperation
Social organization correlates with longevity across animal taxa. This correlation has been explained by selection for longevity by social evolution. The reverse causality is also conceivable but has not been sufficiently considered. We constructed a simple, spatially structured population model of asexually reproducing individuals to study the effect of temporal life history structuring on the evolution of cooperation. Individuals employed fixed strategies of cooperation or defection towards all neighbours in a basic Prisoner׳s Dilemma paradigm. Individuals aged and transitioned through different life history stages asynchronously without migration. An individual׳s death triggered a reproductive event by one immediate neighbour. The specific neighbour was chosen probabilistically according to the cumulative payoff from all local interactions. Varying the duration of pre-reproductive, reproductive, and post-reproductive life history stages, long-term simulations allowed a systematic evaluation of the influence of the duration of these specific life history stages. Our results revealed complex interactions among the effects of the three basic life history stages and the benefit to defect. Overall, a long post-reproductive stage promoted the evolution of cooperation, while a prolonged pre-reproductive stage has a negative effect. In general, the total length of life also increased the probability of the evolution of cooperation. Thus, our specific model suggests that the timing of life history transitions and total duration of life history stages may affect the evolution of cooperative behaviour. We conclude that the causation of the empirically observed association of life expectancy and sociality may be more complex than previously realized.
Data from: A mathematical model of marine bacteriophage evolution
To explore how particularities of a cell-virus system affects viral evolution, we formulate a mathematical model of marine bacteriophage evolution. The intrinsic simplicity of real-life phage-bacteria systems allows to have a reasonably simple model. The model constructed in this paper is based upon Beretta-Kuang model of bacteria-phage interaction. Compared to the Beretta-Kuang model, the model assumes the existence of a multitude of viral variants which correspond to continuously distributed phenotypes. It is noteworthy that this model does not include any explicit law or mechanism of evolution; instead it is assumed, in agreement to the principles of Darwinian evolution, that evolution in this system can occur as a result of random mutations and natural selection. Simulations with a leaner fitness landscape (which is chosen for the convenience of demonstration only) show that a pulse-type traveling wave moving towards increasing Darwinian fitness appears in the phenotype space. This implies that the overall fitness of a viral quasispecies steadily increasing in time. That is, the simulations demonstrate that for an uneven fitness landscape random mutations combined with a mechanism of natural selection lead to the Darwinian evolution. It is noteworthy that in this system the speed of propagation of this wave (and hence the rate of evolution) is not constant but varies, depending on the current viral fitness and the abundance of susceptible bacteria.
Data from: The effects of model choice and mitigating bias on the ribosomal tree of life
Deep-level relationships within Bacteria, Archaea, and Eukarya as well as the relationships of these three domains to each other require resolution. The ribosomal machinery, universal to all cellular life, represents a protein repertoire resistant to horizontal gene transfer, which provides a largely congruent signal necessary for reconstructing a tree suitable as a backbone for life's reticulate history. Here, we generate a ribosomal tree of life from a robust taxonomic sampling of Bacteria, Archaea, and Eukarya to elucidate deep-level intra-domain and inter-domain relationships. Lack of phylogenetic information and systematic errors caused by inadequate models (that cannot account for substitution rate or compositional heterogeneities) or improper model selection compound conflicting phylogenetic signals from HGT and/or paralogy. Thus, we tested several models of varying sophistication on three different datasets, performed removal of fast-evolving or long-branched Archaea and Eukarya, and employed three different strategies to remove compositional heterogeneity to examine their effects on the topological outcome. Our results support a two-domain topology for the tree of life, where Eukarya emerges from within Archaea as sister to a Korarchaeota/Thaumarchaeota (KT) or Crenarchaeota/KT clade for all models under all or at least one of the strategies employed. Taxonomic manipulation allows single-matrix and certain mixture models to vacillate between two-domain and three-domain phylogenies. We find that models vary in their ability to resolve different areas of the tree of life, which does not necessarily correlate with model complexity. For example, both single-matrix and some mixture models recover monophyletic Crenarchaeota and Euryarchaeota archaeal phyla. In contrast, the most sophisticated model recovers a paraphyletic Euryarchaeota but detects two large clades that comprise the Bacteria, which were recovered separately but never together in the other models. Overall, models recovered consistent topologies despite dataset modifications due to the removal of compositional bias, which reflects either ineffective bias reduction or robust datasets that allow models to overcome reconstruction artifacts. We recommend a comparative approach for evolutionary models to identify model weaknesses as well as consensus relationships.
Data from: Model sensitivity and use of the comparative finite element method in mammalian jaw mechanics: mandible performance in the Gray Wolf
Finite Element Analysis (FEA) is a powerful tool gaining use in studies of biological form and function. This method is particularly conducive to studies of extinct and fossilized organisms, as models can be assigned properties that approximate living tissues. In disciplines where model validation is difficult or impossible, the choice of model parameters and their effects on the results become increasingly important, especially in comparing outputs to infer function. To evaluate the extent to which performance measures are affected by initial model input, we tested the sensitivity of bite force, strain energy, and stress to changes in seven parameters that are required in testing craniodental function with FEA. Simulations were performed on FE models of a Gray Wolf (Canis lupus) mandible. Results showed that unilateral bite force outputs are least affected by the relative ratios of the balancing and working muscles, but only ratios above 0.5 provided balancing-working side joint reaction force relationships that are consistent with experimental data. The constraints modeled at the bite point had the greatest effect on bite force output, but the most appropriate constraint may depend on the study question. Strain energy is least affected by variation in bite point constraint, but larger variations in strain energy values are observed in models with different number of tetrahedral elements, masticatory muscle ratios and muscle subgroups present, and number of material properties. These findings indicate that performance measures are differentially affected by variation in initial model parameters. In the absence of validated input values, FE models can nevertheless provide robust comparisons if these parameters are standardized within a given study to minimize variation that arise during the model-building process. Sensitivity tests incorporated into the study design not only aid in the interpretation of simulation results, but can also provide additional insights on form and function.
Data from: Modelled three-dimensional suction accuracy predicts prey capture success in three species of centrarchid fishes
Prey capture is critical for survival, and differences in correctly positioning and timing a strike (accuracy) are likely related to variation in capture success. However, an ability to quantify accuracy under natural conditions, particularly for fishes, is lacking. We developed a predictive model of suction hydrodynamics and applied it to natural behaviours using three-dimensional kinematics of three centrarchid fishes capturing evasive and non-evasive prey. A spheroid ingested volume of water (IVW) with dimensions predicted by peak gape and ram speed was verified with known hydrodynamics for two species. Differences in capture success occurred primarily with evasive prey (64–96% success). Micropterus salmoides had the greatest ram and gape when capturing evasive prey, resulting in the largest and most elongate IVW. Accuracy predicted capture success, although other factors may also be important. The lower accuracy previously observed in M. salmoides was not replicated, but this is likely due to more natural conditions in our study. Additionally, we discuss the role of modulation and integrated behaviours in shaping the IVW and determining accuracy. With our model, accuracy is a more accessible performance measure for suction-feeding fishes, which can be used to explore macroevolutionary patterns of prey capture evolution.
Data from: A dynamic model of facilitation on environmental stress gradients
Theories based on competition for resources in animals and other non-sessile organisms rarely consider the role of facilitative interactions. Yet these interactions are important for community assembly, especially under stressful environments (e.g. the stress-gradient hypothesis, SGH). To make an explicit link between species interaction theory and SGH patterns, I used a classic resource competition model promoting coexistence between a beneficiary and its facilitator sharing a common resource along a stress gradient. I compared model outcomes for two fundamentally different mechanisms of facilitation (alleviation of resource vs. non-resource stress), and also tested the effect of a reciprocal cost of facilitation from the beneficiary. I then tested model's biological relevance using experimental data from two tuber moth species (Lepidoptera, Gelechiidae) for which facilitation in resource access was previously established. Simulation outcomes revealed that both the mode of facilitation and the incorporation of facilitation costs affected the shape of the facilitation-stress relationship. These predictions are in line with current SGH observations and experiments on both plants and animals and reconcile the frequently reported variability of this relationship in nature. Moreover, a sensitivity analysis of model's parameters confirmed the robustness of the modelling framework to uncover the mechanisms responsible for observed species interaction–stress patterns. Finally, when parameterized with tuber moth demographic data, model's results corresponded to observed interaction outcomes along resource stress gradients. Overall, having a common model for plants and animals may simplify assumptions in SGH studies, allow contrasting the shapes of different consumer-resource relationships and specifying the conditions that favour one type of interaction outcome over another.
Data from: Sperm competition games: a general model for pre-copulatory male-male competition
Reproductive males face a trade-off between expenditure on pre-copulatory male-male competition – increasing the number of females that they secure as mates – and sperm competition – increasing their fertilisation success with those females. Previous sperm allocation models have focused on scramble competition in which males compete by searching for mates and the number of matings rises linearly with pre-copulatory expenditure. However, recent studies have emphasised contest competition involving pre-copulatory expenditure on armaments, where winning contests may be highly dependent on marginal increases in relative armament level. Here we develop a general model of sperm allocation that allows us to examine the effect of all forms of pre-copulatory competition on sperm allocation patterns. The model predicts that sperm allocation decreases if either the 'mate-competition loading', a, or the number of males competing for each mating, M, increases. Other predictions remain unchanged from previous models: (i) expenditure per ejaculate should increase and then decrease, and (ii) total post-copulatory expenditure should increase, as the level of sperm competition increases. A negative correlation between a and M is biologically plausible, and may buffer deviations from the previous models. There is some support for our predictions from comparative analyses across dung beetle species and frog populations.
Data from: A national-scale model of linear features improves predictions of farmland biodiversity
1. Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically-relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies. 2. We assessed whether a novel spatial dataset mapping linear and woody linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites respectively. 3. Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. 4. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems, and also can potentially be used to assess habitat connectivity. 10-Mar-2017
Data from: Optimized modelling and application of exhaust gas recirculation performance evaluation of turbocharged diesel engine
Aimed at the problem of turbocharged diesel engine EGR (exhaust gas recirculation) performance evaluation and optimal EGR rate determination, especially in some cases with poor data because of the limit of conditions, an optimized multi-objective grey decision method based on grey theory and entropy weight is proposed. The internal combustion pressure ,fuel consumption rate, NOX ,CO and smoke degree were selected as the decision target, and the initial decision-making model was established based on traditional grey situation theory. According to the characteristics and optimization requirements of EGR, The optimal compromise between combustion and emission performance is proposed to transform into the decision-making target weight problem , then a subjective weight optimization method based on expert scoring and grey relational analysis is proposed. In addition, the entropy weight method was used to solve the subjective and objective weight vector and the optimized multi-objective grey situation decision making model was established, which can not weaken the human error of subjective empowerment, but also fully explore the intrinsic objective relationship of the evaluation index parameters. At last, an optimization simulation platform for EGR performance evaluation based MATLB/GUIDE is designed and established. The results show that the optimization simulation platform can effectively improve the efficiency of simulation calculation, which is more convenient for practical engineering applications; the optimized method can successfully solve the EGR performance evaluation and optimal EGR rate determination under different working conditions, the decision result is reasonable and consistent with the prevailing principle of the EGR determining, which provide a new research ideas for EGR performance optimization.
Data from: High invasion potential of Hydrilla verticillata in the Americas predicted using ecological niche modeling combined with genetic data
Ecological niche modeling is an effective tool to characterize the spatial distribution of suitable areas for species, and it is especially useful for predicting the potential distribution of invasive species. The widespread submerged plant Hydrilla verticillata (hydrilla) has an obvious phylogeographical pattern: Four genetic lineages occupy distinct regions in native range, and only one lineage invades the Americas. Here, we aimed to evaluate climatic niche conservatism of hydrilla in North America at the intraspecific level and explore its invasion potential in the Americas by comparing climatic niches in a phylogenetic context. Niche shift was found in the invasion process of hydrilla in North America, which is probably mainly attributed to high levels of somatic mutation. Dramatic changes in range expansion in the Americas were predicted in the situation of all four genetic lineages invading the Americas or future climatic changes, especially in South America; this suggests that there is a high invasion potential of hydrilla in the Americas. Our findings provide useful information for the management of hydrilla in the Americas and give an example of exploring intraspecific climatic niche to better understand species invasion.
Data from: Modelling social care provision in an agent-based framework with kinship networks
Current demographic trends in the UK include a fast-growing elderly population and dropping birth rates, and demand for social care amongst the aged is rising. The UK depends on informal social care -- family members or friends providing care -- for some 50% of care provision. However, lower birth rates and a graying population mean that care availability is becoming a significant problem, causing concern amongst policy-makers that substantial public investment in formal care will be required in decades to come. In this paper we present an agent-based simulation of care provision in the UK, in which individual agents can decide to provide informal care, or pay for private care, for their loved ones. Agents base these decisions on factors including their own health, employment status, financial resources, relationship to the individual in need, and geographical location. Results demonstrate that the model can produce similar patterns of care need and availability as is observed in the real world, despite the model containing minimal empirical data. We propose that our model better captures the complexities of social care provision than other methods, due to the socioeconomic details present and the use of kinship networks to distribute care amongst family members.
Data from: A hierarchical distance sampling model to estimate abundance and covariate associations of species and communities
Distance sampling is a common survey method in wildlife studies, because it allows accounting for imperfect detection. The framework has been extended to hierarchical distance sampling (HDS), which accommodates the modelling of abundance as a function of covariates, but rare and elusive species may not yield enough observations to fit such a model. We integrate HDS into a community modelling framework that accommodates multi-species spatially replicated distance sampling data. The model allows species-specific parameters, but these come from a common underlying distribution. This form of information sharing enables estimation of parameters for species with sparse data sets that would otherwise be discarded from analysis. We evaluate the performance of the model under varying community sizes with different species-specific abundances through a simulation study. We further fit the model to a seabird data set obtained from shipboard distance sampling surveys off the East Coast of the USA. Comparing communities comprised of 5, 15 or 30 species, bias of all community-level parameters and some species-level parameters decreased with increasing community size, while precision increased. Most species-level parameters were less biased for more abundant species. For larger communities, the community model increased precision in abundance estimates of rarely observed species when compared to single-species models. For the seabird application, we found a strong negative association of community and species abundance with distance to shore. Water temperature and prey density had weak effects on seabird abundance. Patterns in overall abundance were consistent with known seabird ecology. The community distance sampling model can be expanded to account for imperfect availability, imperfect species identification or other missing individual covariates. The model allowed us to make inference about ecology of species communities, including rarely observed species, which is particularly important in conservation and management. The approach holds great potential to improve inference on species communities that can be surveyed with distance sampling.
Data from: The sampling and estimation of marine paleodiversity patterns: implications of a Pliocene model
Data that accurately capture the spatial structure of biodiversity are required for many paleobiological questions, from assessments of changing provinciality and the role of geographic ranges in extinction and originations, to estimates of global taxonomic or morphological diversity through time. Studies of temporal changes in diversity and global biogeographic patterns have attempted to overcome fossil sampling biases through sampling standardization protocols, but such approaches must ultimately be limited by available literature and museum collections. One approach to evaluating such limits is to compare results from the fossil record with models of past diversity patterns informed by modern relationships between diversity and climatic factors. Here we use present-day patterns for marine bivalves, combined with data on the geologic ages and distributions of extant taxa, to develop a model for Pliocene diversity patterns, which is then compared with diversity patterns retrieved from the literature as compiled by the Paleobiology Database (PaleoDB). The published Pliocene bivalve data (PaleoDB) lack the first-order spatial structure required to generate the modern biogeography within the time available (<3 Myr). Instead, the published data (raw and standardized) show global diversity maxima in the Tropical West Atlantic, followed closely by a peak in the cool-temperate East Atlantic. Either today's tropical West Pacific diversity peak, double that of any other tropical region, is a purely Pleistocene phenomenon—highly unlikely given the geologic ages of extant genera and the topology of molecular phylogenies—or the paleontological literature is such a distorted sample of tropical Pliocene diversity that current sampling standardization methods cannot compensate for existing biases. A rigorous understanding of large-scale spatial and temporal diversity patterns will require new approaches that can compensate for such strong bias, presumably by drawing more fully on our understanding of the factors that underlie the deployment of diversity today.
Data from: Evolution of female multiple mating: a quantitative model of the "sexually-selected sperm" hypothesis
Explaining the evolution and maintenance of polyandry remains a key challenge in evolutionary ecology. One appealing explanation is the sexually-selected sperm (SSS) hypothesis, which proposes that polyandry evolves due to indirect selection stemming from positive genetic covariance with male fertilization efficiency, and hence with a male's success in post-copulatory competition for paternity. However, the SSS hypothesis relies on verbal analogy with 'sexy-son' models explaining co-evolution of female preferences for male displays, and explicit models that validate the basic SSS principle are surprisingly lacking. We developed analogous genetically-explicit individual-based models describing the SSS and 'sexy-son' processes. We show that the analogy between the two is only partly valid, such that the genetic correlation arising between polyandry and fertilization efficiency is generally smaller than that arising between preference and display, resulting in less reliable co-evolution. Importantly, indirect selection was too weak to cause polyandry to evolve in the presence of negative direct selection. Negatively-biased mutations on fertilization efficiency did not generally rescue runaway evolution of polyandry unless realized fertilization was highly skewed towards a single male, and co-evolution was even weaker given random mating-order effects on fertilization. Our models suggest that the SSS process is, on its own, unlikely to generally explain the evolution of polyandry.
Data from: Validating two-dimensional leadership models on three-dimensionally structured fish schools
Identifying leader-follower interactions is crucial for understanding how a group decides where or when to move, and how this information is transferred between members. Although many animal groups have a three-dimensional structure, previous studies investigating leader-follower interactions have often ignored vertical information. This raises the question whether commonly used two-dimensional leader-follower analyses can be used justifiably on groups that interact in three dimensions. To address this we quantified the individual movements of banded tetra fish (Astyanax mexicanus) within shoals by computing the three-dimensional trajectories of all individuals using a stereo-camera technique. We used these data firstly to identify and compare leader-follower interactions in two and three dimensions, and secondly to analyse leadership with respect to an individual's spatial position in three dimensions. We show that for 95% of all pairwise interactions leadership identified through two-dimensional analysis matches that identified through three-dimensional analysis, and we reveal that fish attend to the same shoalmates for vertical information as they do for horizontal information. Our results therefore highlight that three-dimensional analyses are not always required to identify leader-follower relationships in species that move freely in three-dimensions. We discuss our results in terms of the importance of taking species' sensory capacities into account when studying interaction networks within groups.
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.