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1,721 results for “network data”
Data from: Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish
According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance.
Data from: Why are some plant-pollinator networks more nested than others?
1. Empirical studies have found that the mutualistic interactions forming the structure of plant-pollinator networks are typically more nested than expected by chance alone. Additionally, theoretical studies have shown a positive association between the nested structure of mutualistic networks and community persistence. Yet, it has been shown that some plant-pollinator networks may be more nested than others, raising the interesting question of which factors are responsible for such enhanced nested structure. 2. It has been argued that ordered network structures may increase the persistence of ecological communities under less predictable environments. This suggests that nested structures of plant-pollinator networks could be more advantageous under highly seasonal environments. While several studies have investigated the link between nestedness and various environmental variables, unfortunately, there has been no unified answer to validate these predictions. Here, we move from the problem of describing network structures to the problem of comparing network structures. We develop comparative statistics, and apply them to investigate the association between the nested structure of 59 plant-pollinator networks and the temperature seasonality present in their locations. 3. We demonstrate that higher levels of nestedness are associated with a higher temperature seasonality. We show that the previous lack of agreement came from an extended practice of using standardized measures of nestedness that cannot be compared across different networks. 4. Importantly, our observations complement theory showing that more nested network structures can increase the range of environmental conditions compatible with species coexistence in mutualistic systems, also known as structural stability. This increase in nestedness should be more advantageous and occur more often in locations subject to random environmental perturbations, which could be driven by highly changing or seasonal environments. This synthesis of theory and observations could prove relevant for a better understanding of the ecological processes driving the assembly and persistence of ecological communities.
Data from: A model to identify urban traffic congestion hotspots in complex networks
The rapid growth of population in urban areas is jeopardizing the mobility and air quality worldwide. One of the most notable problems arising is that of traffic congestion. With the advent of technologies able to sense real-time data about cities, and its public distribution for analysis, we are in place to forecast scenarios valuable for improvement and control. Here, we propose an idealized model, based on the critical phenomena arising in complex networks, that allows to analytically predict congestion hotspots in urban environments. Results on real cities' road networks, considering, in some experiments, real- traffic data, show that the proposed model is capable of identifying susceptible junctions that might becomes hotspots if mobility demand increases.
Data from: Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance
Neuronal activity in sensory and fronto-parietal (FP) areas underlies the representation and attentional control, respectively, of sensory information maintained in visual working memory (VWM). Within these regions, beta/gamma phase-synchronization supports the integration of sensory functions, while synchronization in theta/alpha bands supports the regulation of attentional functions. A key challenge is to understand which mechanisms integrate neuronal processing across these distinct frequencies and thereby the sensory and attentional functions. We investigated whether such integration could be achieved by cross-frequency phase synchrony (CFS). Using concurrent magneto- and electroencephalography, we found that CFS was load-dependently enhanced between theta and alpha–gamma and between alpha and beta-gamma oscillations during VWM maintenance among visual, FP, and dorsal attention (DA) systems. CFS also connected the hubs of within-frequency-synchronized networks and its strength predicted individual VWM capacity. We propose that CFS integrates processing among synchronized neuronal networks from theta to gamma frequencies to link sensory and attentional functions.
Data from: Black Queen evolution and trophic interactions determine plasmid survival after the disruption of conjugation network
Mobile genetic elements such as conjugative plasmids are responsible for antibiotic resistant phenotypes in many bacterial pathogens. The ability to conjugate, the presence of antibiotics and ecological interactions all have a notable role in the persistence of plasmids in bacterial populations. Here, we set out to investigate the contribution of these factors when the conjugation network was disturbed by a plasmid-dependent bacteriophage. Phage alone effectively caused the population to lose plasmids, thus rendering them susceptible to antibiotics. Leakiness of the antibiotic resistance mechanism allowing Black Queen evolution (i.e. race to the bottom) was a more significant factor over antibiotic concentration (lethal vs sub-lethal) in determining plasmid prevalence. Interestingly, plasmid loss was also prevented by protozoan predation. These results show that the outcome of attempts to re-sensitize bacterial communities by disrupting the conjugation network are highly dependent on ecological factors and resistance mechanisms.
Data from: Using network approaches to enhance the analysis of cross-linguistic polysemies
Since long it has been noted that cross-linguistically recurring polysemies can serve as an indicator of conceptual relations, and quite a few approaches to model and analyze such data have been proposed in the recent past. Although – given the nature of the data – it seems natural to model and analyze it with the help of network techniques, there are only a few approaches which make explicit use of them. In this paper, we show how the strict application of weighted network models helps to get more out of cross-linguistic polysemies than would be possible using approaches that are only based on item-to-item comparison. For our study we use a large dataset consisting of 1252 semantic items translated into 195 different languages covering 44 different language families. By analyzing the community structure of the network reconstructed from the data, we find that a majority of the concepts (68%) can be separated into 104 large communities consisting of five and more nodes. These large communities almost exclusively constitute meaningful groupings of concepts into conceptual fields. They provide a valid starting point for deeper analyses of various topics in historical semantics, such as cognate detection, etymological analysis, and semantic reconstruction.
Data from: Estrogen receptor alpha distribution and expression in the social neural network of monogamous and polygynous Peromyscus
In microtine and dwarf hamsters low levels of estrogen receptor alpha (ERa) in the bed nucleus of the stria terminalis (BST) and medial amygdala (MeA) play a critical role in the expression of social monogamy in males, which is characterized by high levels of affiliation and low levels of aggression. In contrast, monogamous Peromyscus males display high levels of aggression and affiliative behavior with high levels of testosterone and aromatase activity. Suggesting the hypothesis that in Peromyscus ERa expression will be positively correlated with high levels of male prosocial behavior and aggression. ERa expression was compared within the social neural network, including the posterior medial BST, MeA posterodorsal, medial preoptic area (MPOA), ventromedial hypothalamus (VMH), and arcuate nucleus in two monogamous species, P. californicus and P. polionotus, and two polygynous species, P. leucopus and P. maniculatus. The results supported the prediction, with male P. polionotus and P. californicus expressing higher levels of ERa in the BST than their polygynous counter parts, and ERa expression was sexually dimorphic in the polygynous species, with females expressing significantly more than males in the BST in both polygynous species and in the MeA in P. leucopus. Peromyscus ERa expression also differed from rats, mice and microtines as in neither the MPOA nor the VMH was ERa sexually dimorphic. The results supported the hypothesis that higher levels of ERa are associated with monogamy in Peromyscus and that differential expression of ERa occurs in the same regions of the brains regardless of whether high or low expression is associated with social monogamy. Also discussed are possible mechanisms regulating this differential relationship.
Data from: Topology of tree-mycorrhizal fungus interaction networks in xeric and mesic Douglas-fir forests
1. From the phytocentric perspective, a mycorrhizal network (MN) is formed when the roots of two or more plants are colonized by the same fungal genet. MNs can be modelled as interaction networks with plants as nodes and fungal genets as links. The potential effects of MNs on facilitation or competition between plants are increasingly recognized, but their network topologies remain largely unknown. This information is needed to understand the ecological significance of MN functional traits. 2. The objectives of this study were to describe the interaction network topologies of MNs formed between two ectomycorrhizal fungal species, Rhizopogon vesiculosus and R. vinicolor, and interior Douglas-fir trees at the forest stand scale, identify factors leading to this structure and to contrast MN structures between forest plots with xeric versus mesic soil moisture regimes. 3. Tuberculate mycorrhizas were sampled in six 10 × 10 m plots with either xeric or mesic soil moisture regimes. Microsatellite DNA markers were used to identify tree and fungal genotypes isolated from mycorrhizas and for comparison with reference tree boles above-ground. 4. In all six plots, trees and fungal genets were highly interconnected. Size asymmetries between different tree cohorts led to non-random MN topologies, while differences in size and connectivity between Rhizopogon species-specific subnetwork components contributed towards MN nestedness. Large mature trees acted as network hubs with a significantly higher node degree compared to smaller trees. MNs representing trees linked by R. vinicolor genets were mostly nested within larger, more highly connected R. vesiculosus-linked MNs. 5. Attributes of network nodes showed that hub trees were more important to MN topology on xeric than mesic sites, but the emergent structures of MNs were similar in the two soil moisture regimes. 6. Synthesis. This study suggests MNs formed between interior Douglas-fir trees and R. vesiculosus and R. vinicolor genets are resilient to the random loss of participants, and to soil water stress, but may be susceptible to the loss of large trees or fungal genets. Our results regarding the topology of MNs contribute to the understanding of forest stand dynamics and the resilience of forests to stress or disturbance.
Data from: Modeling the internet of things, self-organizing and other complex adaptive communication networks: a cognitive agent-based computing approach
Background: Computer Networks have a tendency to grow at an unprecedented scale. Modern networks involve not only computers but also a wide variety of other interconnected devices ranging from mobile phones to other household items fitted with sensors. This vision of the "Internet of Things" (IoT) implies an inherent difficulty in modeling problems. Purpose: It is practically impossible to implement and test all scenarios for large-scale and complex adaptive communication networks as part of Complex Adaptive Communication Networks and Environments (CACOONS). The goal of this study is to explore the use of Agent-based Modeling as part of the Cognitive Agent-based Computing (CABC) framework to model a Complex communication network problem. Method: We use Exploratory Agent-based Modeling (EABM), as part of the CABC framework, to develop an autonomous multi-agent architecture for managing carbon footprint in a corporate network. To evaluate the application of complexity in practical scenarios, we have also introduced a company-defined computer usage policy. Results: The conducted experiments demonstrated two important results: Primarily CABC-based modeling approach such as using Agent-based Modeling can be an effective approach to modeling complex problems in the domain of IoT. Secondly, the specific problem of managing the Carbon footprint can be solved using a multiagent system approach.
Data from: Reticulate evolutionary history and extensive introgression in mosquito species revealed by phylogenetic network analysis
The role of hybridization and subsequent introgression has been demonstrated in an increasing number of species. Recently, Fontaine et al. (Science, 347, 2015, 1258524) conducted a phylogenomic analysis of six members of the Anopheles gambiae species complex. Their analysis revealed a reticulate evolutionary history and pointed to extensive introgression on all four autosomal arms. The study further highlighted the complex evolutionary signals that the co-occurrence of incomplete lineage sorting (ILS) and introgression can give rise to in phylogenomic analyses. While tree-based methodologies were used in the study, phylogenetic networks provide a more natural model to capture reticulate evolutionary histories. In this work, we reanalyse the Anopheles data using a recently devised framework that combines the multispecies coalescent with phylogenetic networks. This framework allows us to capture ILS and introgression simultaneously, and forms the basis for statistical methods for inferring reticulate evolutionary histories. The new analysis reveals a phylogenetic network with multiple hybridization events, some of which differ from those reported in the original study. To elucidate the extent and patterns of introgression across the genome, we devise a new method that quantifies the use of reticulation branches in the phylogenetic network by each genomic region. Applying the method to the mosquito data set reveals the evolutionary history of all the chromosomes. This study highlights the utility of 'network thinking' and the new insights it can uncover, in particular in phylogenomic analyses of large data sets with extensive gene tree incongruence.
Data from: How the online social networks are used: dialogs-based structure of MySpace
Quantitative study of collective dynamics in online social networks is a new challenge based on the abundance of empirical data. Conclusions, however, may depend on factors such as user's psychology profiles and their reasons to use the online contacts. In this study, we have compiled and analysed two datasets from MySpace. The data contain networked dialogues occurring within a specified time depth, high temporal resolution and texts of messages, in which the emotion valence is assessed by using the SentiStrength classifier. Performing a comprehensive analysis, we obtain three groups of results: dynamic topology of the dialogues-based networks have a characteristic structure with Zipf's distribution of communities, low link reciprocity and disassortative correlations. Overlaps supporting 'weak-ties' hypothesis are found to follow the laws recently conjectured for online games. Long-range temporal correlations and persistent fluctuations occur in the time series of messages carrying positive (negative) emotion; patterns of user communications have dominant positive emotion (attractiveness) and strong impact of circadian cycles and interactivity times longer than 1 day. Taken together, these results give a new insight into the functioning of online social networks and unveil the importance of the amount of information and emotion that is communicated along the social links. All data used in this study are fully anonymized.
Data from: Network structure and local adaptation in coevolving bacteria-phage interactions
Numerous theoretical and experimental studies have investigated antagonistic coevolution between parasites and their hosts. Although experimental tests of theory from a range of biological systems are largely concordant regarding the influence of several driving processes, we know little as to how mechanisms acting at the smallest scales (individual molecular and phenotypic changes) may result in the emergence of structures at larger scales, such as coevolutionary dynamics and local adaptation. We capitalized on methods commonly employed in community ecology to quantify how the structure of community interaction matrices, so called 'bipartite networks', reflected observed coevolutionary dynamics, and how phages from these communities may or may not have adapted locally to their bacterial hosts. We found a consistent nested network structure for two phage types, one previously demonstrated to exhibit arms race coevolutionary dynamics and the other fluctuating coevolutionary dynamics. Both phages increased their host ranges through evolutionary time, but we found no evidence for a trade off with impact on bacteria. Finally, only bacteria from the arms race phage showed local adaptation, and we provide preliminary evidence that these bacteria underwent (sometimes different) molecular changes in the wzy gene associated with the LPS receptor, while bacteria coevolving with the fluctuating selection phage did not show local adaptation and had partial deletions of the pilF gene associated with Type IV pili. We conclude that the structure of phage-bacteria interaction networks is not necessarily specific to coevolutionary dynamics, and discuss hypotheses for why only one of the two phages was, nevertheless, locally adapted.
Data from: Linking topological structure and dynamics in ecological networks
Interaction networks are basic descriptions of ecological communities and are at the core of community dynamics models. Knowledge of their structure should enable us to understand dynamical properties of ecological communities. However, the relationships between dynamical properties of communities and qualitative descriptors of network structure remain unclear. To improve our understanding of such relationships, we develop a framework based on the concept of strongly connected components, which are key structural components of networks necessary to explain stability properties such as persistence and robustness. We illustrate this framework for the analysis of qualitative empirical food webs and plant-plant interaction networks. Both types of networks exhibit high persistence (on average, 99% and 80% of species, respectively, are expected to persist) and robustness (only 0.2% and 2% of species are expected to disappear following the extinction of a species). Each of the networks is structured as a large group of interconnected species accompanied by much smaller groups that most often consist of a single species. This low-modularity configuration can be explained by a negative modularity-stability relationship. Our results suggest that ecological communities are not typically structured in multispecies compartments and that compartmentalization decreases robustness.
Data from: Context-dependency and anthropogenic effects on individual plant-frugivore networks
Anthropogenic activities, such as grazing by domestic animals, are considered drivers of environmental changes that may influence the structure of interaction networks. The study of individual-based networks allows testing how species-level interaction patterns emerge from the pooled interaction modes of individuals within populations. Exponential random graph models (ERGMs) examine the global structure of networks by allowing the inclusion of specific node (i.e. interacting partners) properties as explanatory covariates. Here we assessed the structure of individual plant-frugivore interaction networks and the ecological variables that influence the mode of interactions under different land-use (grazed vs ungrazed protected areas). We quantified the number of visits, the number of fruits removed per visit and the interaction strength of mammal frugivore species at each individual tree. Additionally we quantified ecological variables at the individual, microhabitat, neighborhood and habitat scales that generated interaction network structure under the different land uses. Individual plant-frugivore networks were significantly modular in both land uses but the number of modules was higher in the grazed areas. We found interaction networks for grazed and ungrazed lands were structured by phenotypic traits of individual trees, by the microhabitat beneath the tree canopy and were affected by habitat modifications of anthropogenic origin. The neighborhood surrounding each individual plant influenced plant-frugivore interactions only at the grazed-land trees. We conclude that anthropogenic land uses influence the topological patterns of plant-frugivore networks and the frugivore visitation to trees through modification of both habitat complexity and the ecological traits underlying interactions between individual plants and frugivore species.
Data from: Nonlinear growth: an origin of hub organization in complex networks
Many real-world networks contain highly connected nodes called hubs. Hubs are often crucial for network function and spreading dynamics. However, classical models of how hubs originate during network development unrealistically assume that new nodes attain information about the connectivity (for example the degree) of existing nodes. Here, we introduce hub formation through nonlinear growth where the number of nodes generated at each stage increases over time and new nodes form connections independent of target node features. Our model reproduces variation in number of connections, hub occurrence time, and rich-club organization of networks ranging from protein–protein, neuronal and fibre tract brain networks to airline networks. Moreover, nonlinear growth gives a more generic representation of these networks compared with previous preferential attachment or duplication–divergence models. Overall, hub creation through nonlinear network expansion can serve as a benchmark model for studying the development of many real-world networks.
Data from: Hydrologic controls on river network connectivity
This study proposes a probabilistic approach for the quantitative assessment of reach- and network-scale hydrological connectivity as dictated by river flow space-time variability. Spatial dynamics of daily streamflows are estimated based on climatic and morphological features of the contributing catchment, integrating a physically based approach that accounts for the stochasticity of rainfall with a water balance framework and a geomorphic recession flow analysis. Ecologically meaningful minimum stage thresholds are used to evaluate the connectivity of individual stream reaches, and other relevant network-scale connectivity metrics. The framework allows a quantitative description of the main hydrological causes and the ecological consequences of water depth dynamics experienced by river networks. The analysis shows that the spatial variability of local-scale hydrological connectivity is strongly affected by the spatial and temporal distribution of climatic variables. Depending on the underlying climatic settings and the critical stage threshold, loss of connectivity can be observed in the headwaters or along the main channel, thereby originating a fragmented river network. The proposed approach provides important clues for understanding the effect of climate on the ecological function of river corridors.
Data from: Consequences of plant invasions on compartmentalization and species' roles in plant–pollinator networks
Compartmentalization—the organization of ecological interaction networks into subsets of species that do not interact with other subsets (true compartments) or interact more frequently among themselves than with other species (modules)—has been identified as a key property for the functioning, stability and evolution of ecological communities. Invasions by entomophilous invasive plants may profoundly alter the way interaction networks are compartmentalized. We analysed a comprehensive dataset of 40 paired plant–pollinator networks (invaded versus uninvaded) to test this hypothesis. We show that invasive plants have higher generalization levels with respect to their pollinators than natives. The consequences for network topology are that—rather than displacing native species from the network—plant invaders attracting pollinators into invaded modules tend to play new important topological roles (i.e. network hubs, module hubs and connectors) and cause role shifts in native species, creating larger modules that are more connected among each other. While the number of true compartments was lower in invaded compared with uninvaded networks, the effect of invasion on modularity was contingent on the study system. Interestingly, the generalization level of the invasive plants partially explains this pattern, with more generalized invaders contributing to a lower modularity. Our findings indicate that the altered interaction structure of invaded networks makes them more robust against simulated random secondary species extinctions, but more vulnerable when the typically highly connected invasive plants go extinct first. The consequences and pathways by which biological invasions alter the interaction structure of plant–pollinator communities highlighted in this study may have important dynamical and functional implications, for example, by influencing multi-species reciprocal selection regimes and coevolutionary processes.
Data from: Temporal stability in a male African elephant social network
Social animals live in complex and variable socio-ecological environments where individuals adapt their behaviour to local conditions. Recently, there have been calls for studies of animal social networks to take account of temporal dynamics in social relationships as these have implications for the spread of information and disease, group cohesion, and the drivers of sociality, and there is evidence that maintaining stable social relationships has fitness benefits. It has recently been recognised that male elephants form strong social bonds with other males. The nature of these relationships, and thus network structure, may vary over time in response to environmental conditions and as individuals age. Using social network analysis, we examine the stability of relationships and network centrality in a population of male African elephants. Our results suggest that males may maintain stable social relationships with others over time. Older males show greater stability in network centrality than younger males, suggesting younger males face uncertainty in transitioning to adult society. For elephants, where older individuals function as social repositories of knowledge, maintaining a social network underpinned by older males could be of particular importance.
Data from: Abundance drives broad patterns of generalisation in plant-hummingbird pollination networks
Abundant pollinators are often more generalised than rare pollinators. This could be because abundant species have more chance encounters with potential interaction partners. On the other hand, generalised species could have a competitive advantage over specialists, leading to higher abundance. Determining the direction of the abundance-generalisation relationship is therefore a 'chicken-and-egg' dilemma. Here we determine the direction of the relationship between abundance and generalisation in plant-hummingbird pollination networks across the Americas. We find evidence that hummingbird pollinators are generalised because they are abundant, and little evidence that hummingbirds are abundant because they are generalised. Additionally, most patterns of species-level abundance and generalisation were well explained by a null model that assumed interaction neutrality (interaction probabilities defined by species relative abundances). These results suggest that neutral processes play a key role in driving broad patterns of generalisation in animal pollinators across large spatial scales.
Data from: Large-scale functional networks identified from resting-state EEG using spatial ICA
Several methods have been applied to EEG or MEG signals to detect functional networks. In recent works using MEG/EEG and fMRI data, temporal ICA analysis has been used to extract spatial maps of resting-state networks with or without an atlas-based parcellation of the cortex. Since the links between the fMRI signal and the electromagnetic signals are not fully established, and to avoid any bias, we examined whether EEG alone was able to derive the spatial distribution and temporal characteristics of functional networks. To do so, we propose a two-step original method: 1) An individual multi-frequency data analysis including EEG-based source localisation and spatial independent component analysis, which allowed us to characterize the resting-state networks. 2) A group-level analysis involving a hierarchical clustering procedure to identify reproducible large-scale networks across the population. Compared with large-scale resting-state networks obtained with fMRI, the proposed EEG-based analysis revealed smaller independent networks thanks to the high temporal resolution of EEG, hence hierarchical organization of networks. The comparison showed a substantial overlap between EEG and fMRI networks in motor, premotor, sensory, frontal, and parietal areas. However, there were mismatches between EEG-based and fMRI-based networks in temporal areas, presumably resulting from a poor sensitivity of fMRI in these regions or artefacts in the EEG signals. The proposed method opens the way for studying the high temporal dynamics of networks at the source level thanks to the high temporal resolution of EEG. It would then become possible to study detailed measures of the dynamics of connectivity.
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.