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1,721 results for “network data”
Data from: Does biological intimacy shape ecological network structure? A test using a brood pollination mutualism on continental and oceanic islands
Biological intimacy—the degree of physical proximity or integration of partner taxa during their life cycles—is thought to promote the evolution of reciprocal specialization and modularity in the networks formed by co‐occurring mutualistic species, but this hypothesis has rarely been tested. Here, we test this "biological intimacy hypothesis" by comparing the network architecture of brood pollination mutualisms, in which specialized insects are simultaneously parasites (as larvae) and pollinators (as adults) of their host plants to that of other mutualisms which vary in their biological intimacy (including ant‐myrmecophyte, ant‐extrafloral nectary, plant‐pollinator and plant‐seed disperser assemblages). We use a novel dataset sampled from leafflower trees (Phyllanthaceae: Phyllanthus s. l. [Glochidion]) and their pollinating leafflower moths (Lepidoptera: Epicephala) on three oceanic islands (French Polynesia) and compare it to equivalent published data from congeners on continental islands (Japan). We infer taxonomic diversity of leafflower moths using multilocus molecular phylogenetic analysis and examine several network structural properties: modularity (compartmentalization), reciprocality (symmetry) of specialization and algebraic connectivity. We find that most leafflower‐moth networks are reciprocally specialized and modular, as hypothesized. However, we also find that two oceanic island networks differ in their modularity and reciprocal specialization from the others, as a result of a supergeneralist moth taxon which interacts with nine of 10 available hosts. Our results generally support the biological intimacy hypothesis, finding that leafflower‐moth networks (usually) share a reciprocally specialized and modular structure with other intimate mutualisms such as ant‐myrmecophyte symbioses, but unlike nonintimate mutualisms such as seed dispersal and nonintimate pollination. Additionally, we show that generalists—common in nonintimate mutualisms—can also evolve in intimate mutualisms, and that their effect is similar in both types of assemblages: once generalists emerge they reshape the network organization by connecting otherwise isolated modules.
Data from: Seed-dispersal networks in tropical forest fragments: area effects, remnant species, and interaction diversity
<p>Seed dispersal interactions involve key ecological processes in tropical forests that help to maintain ecosystem functioning. Yet this functionality may be threatened by increasing habitat loss, defaunation and fragmentation. However, generalist species, and their interactions, can benefit from the habitat change caused by human disturbance while more specialized interactions mostly disappear. Therefore changes in the structure of the local, within fragment, networks can be expected. Here we investigated how the structure of seed-dispersal networks changes along a gradient of increasing habitat fragmentation. We analysed 16 bird seed-dispersal assemblages from forest fragments of a biodiversity-rich ecosystem. We found significant species-, interaction- and network-area relationships, yet the later was determined by the number of species remaining in each community. The number of frugivorous bird and plant species, their interactions, and the number of links per species decreases as area is lost in the fragmented landscape. In contrast, network nestedness has a negative relationship with fragment area, suggesting an increasing generalization of the network structure in the gradient of fragmentation. Network specialization was not significantly affected by area, indicating that some network properties may be invariant to disturbance. Still, the local extinction of partner species, paralleled by a loss of interactions and specialist-specialist bird-plant seed dispersal associations suggests the functional homogenization of the system as area is lost. Our study provides empirical evidence for network-area relationships driven by the presence/absence of remnant species and the interactions they perform.</p>
Data from: Ecological network inference from long-term presence-absence data
Ecological communities are characterized by complex networks of trophic and nontrophic interactions, which shape the dy-namics of the community. Machine learning and correlational methods are increasingly popular for inferring networks from co-occurrence and time series data, particularly in microbial systems. In this study, we test the suitability of these methods for inferring ecological interactions by constructing networks using Dynamic Bayesian Networks, Lasso regression, and Pear-son's correlation coefficient, then comparing the model networks to empirical trophic and nontrophic webs in two ecological systems. We find that although each model significantly replicates the structure of at least one empirical network, no model significantly predicts network structure in both systems, and no model is clearly superior to the others. We also find that networks inferred for the Tatoosh intertidal match the nontrophic network much more closely than the trophic one, possibly due to the challenges of identifying trophic interactions from presence-absence data. Our findings suggest that although these methods hold some promise for ecological network inference, presence-absence data does not provide enough signal for models to consistently identify interactions, and networks inferred from these data should be interpreted with caution.
Data from: Between-year changes in community composition shape species' roles in an Arctic plant-pollinator network
Inter-annual turnover in community composition can affect the richness and functioning of ecological communities. If incoming and outgoing species do not interact with the same partners, ecological functions such as pollination may be disrupted. Here, we explore the extent to which turnover affects species' roles --as defined based on their participation in different motifs positions-- in a series of temporally replicated plant-pollinator networks from high-Arctic Zackenberg, Greenland. We observed substantial turnover in the plant and pollinator assemblages, combined with significant variation in species' roles between networks. Variation in the roles of plants and pollinators tended to increase with the amount of community turnover, although a negative interaction between turnover in the plant and pollinator assemblages complicated this trend for the roles of pollinators. This suggests that increasing turnover in the future will result in changes to the roles of plants and likely those of pollinators. These changing roles may in turn affect the functioning or stability of this pollination network.
Data from: Network structure and the optimisation of proximity-based association criteria
<ol> <li>Animal social network analysis (SNA) often uses proximity data obtained from automated tracking of individuals. Identifying associations based on proximity requires deciding on quantitative criteria such as the maximum distance or the longest time interval between visits of different individuals to still consider them associated. These quantitative criteria are not easily chosen based on <i>a priori</i> biological arguments alone.</li> <li>Here we propose a procedure for optimising proximity-based association criteria in SNA, whereby different spatial and temporal criteria are screened to determine which combination detects more network structure. If we assume that biologically-relevant associations among individuals are non-random, and that proximity data are mostly influenced by those associations, then it is logical to select criteria that minimise random associations and show the underlying network structure more clearly.</li> <li>We first used simulations to evaluate which of four simple descriptors of network structure remain unbiased (i.e., do not change directionally) when reducing the number of observations, since unbiased descriptors are necessary for comparing the structure of networks using different association criteria. Then, using two of those descriptors (coefficient of variation of the strength of associations, and network entropy), and empirical proximity data from automated tracking of common waxbills (<i>Estrilda astrild</i>) in a mesocosm environment, we found that the structure-based optimisation procedure selected the most biologically-relevant combination of spatial and temporal proximity criteria, in the sense that those criteria were also the best at distinguishing between previously known social sub-groups of individuals.</li> <li>These results indicate that, provided that the assumptions for structure-based optimisation are met, this procedure can find the most biologically-relevant association criteria. Thus, under the condition that proximity data are shaped by non-random social associations, and if using adequate descriptors of network structure, structure-based optimisation may be a useful tool for SNA, particularly when <i>a priori</i> biological arguments are insufficient to inform the choice of proximity-based association criteria.</li> </ol>
Data from: Genetics-based interactions of foundation species affect community diversity, stability, and network structure
We examined the hypothesis that genetics-based interactions between strongly interacting foundation species, the tree Populus angustifolia and the aphid Pemphigus betae, affect arthropod community diversity, stability and species interaction networks of which little is known. In a 2-year experimental manipulation of the tree and its aphid herbivore four major findings emerged: (i) the interactions of these two species determined the composition of an arthropod community of 139 species; (ii) both tree genotype and aphid presence significantly predicted community diversity; (iii) the presence of aphids on genetically susceptible trees increased the stability of arthropod communities across years; and (iv) the experimental removal of aphids affected community network structure (network degree, modularity and tree genotype contribution to modularity). These findings demonstrate that the interactions of foundation species are genetically based, which in turn significantly contributes to community diversity, stability and species interaction networks. These experiments provide an important step in understanding the evolution of Darwin's 'entangled bank', a metaphor that characterizes the complexity and interconnectedness of communities in the wild.
Data from: Reconfiguration of functional brain networks and metabolic cost converge during task performance
<p>The ability to solve cognitive tasks depends upon adaptive changes in the organization of whole-brain functional networks. However, the link between task-induced network reconfigurations and their underlying energy demands is poorly understood. We address this by multimodal network analyses integrating functional and molecular neuroimaging acquired concurrently during a complex cognitive task. Task engagement elicited a marked increase in the association between glucose consumption and functional brain network reorganization. This convergence between metabolic and neural processes was specific to feedforward connections linking the visual and dorsal attention networks, in accordance with task requirements of visuo-spatial reasoning. Further increases in cognitive load above initial task engagement did not affect the relationship between metabolism and network reorganization but only modulated existing interactions. Our findings show how the upregulation of key computational mechanisms to support cognitive performance unveils the complex, interdependent changes in neural metabolism and neuro-vascular responses.</p>
Data from: Interaction rewiring and the rapid turnover of plant-pollinator networks
Whether species interactions are static or change over time has wide-reaching ecological and evolutionary consequences. However, species interaction networks are typically constructed from temporally aggregated interaction data, thereby implicitly assuming that interactions are fixed. This approach has advanced our understanding of communities, but it obscures the timescale at which interactions form (or dissolve) and the drivers and consequences of such dynamics. We address this knowledge gap by quantifying the within-season turnover of plant–pollinator interactions from weekly censuses across 3 years in a subalpine ecosystem. Week-to-week turnover of interactions (1) was high, (2) followed a consistent seasonal progression in all years of study and (3) was dominated by interaction rewiring (the reassembly of interactions among species). Simulation models revealed that species' phenologies and relative abundances constrained both total interaction turnover and rewiring. Our findings reveal the diversity of species interactions that may be missed when the temporal dynamics of networks are ignored.
Data from: Synchronized mating signals in a communication network: the challenge of avoiding predators while attracting mates
Conspicuous mating signals attract mates but also expose signalers to predators and parasites. Signal evolution, therefore, is driven by conflicting selective pressures from multiple receivers, both target and nontarget. Synchronization of mating signals, for example, is an evolutionary puzzle given the assumed high cost of reduced female attraction when signals overlap. Synchronization may be beneficial, however, if overlapping signals reduce attraction of nontarget receivers. We investigate how signal synchronization is shaped by the tradeoff between natural and sexual selection in two anuran species: pug-nosed tree frogs (<i>Smilisca sila</i>), in which males produce mating calls in near-perfect synchrony, and túngara frogs (<i>Engystomops pustulosus</i>), in which males alternate their calls. To examine the tradeoff imposed by signal synchronization, we conducted field and laboratory playback experiments on eavesdropping enemies (bats and midges) and target receivers (female frogs). Our results suggest that, while synchronization can be a general strategy for signalers to reduce their exposure to eavesdroppers, relaxed selection by females for unsynchronized calls is key to the evolution and maintenance of signal synchrony. This study highlights the role of relaxed selection in our understanding the origin of mating signals and displays.
Data from: Experimental species removals impact the architecture of pollination networks
Mutualistic networks are key for the creation and maintenance of biodiversity, yet are threatened by global environmental change. Most simulation models assume that network structure remains static after species losses, despite theoretical and empirical reasons to expect dynamic responses. We assessed the effects of experimental single bumblebee species removals on the structure of entire flower visitation networks. We hypothesized that network structure would change following processes linking interspecific competition with dietary niche breadth. We found that single pollinator species losses impact pollination network structure: resource complementarity decreased, while resource overlap increased. Despite marginally increased connectance, fewer plant species were visited after species removals. These changes may have negative functional impacts, as complementarity is important for maintaining biodiversity–ecological functioning relationships and visitation of rare plant species is critical for maintaining diverse plant communities.
Data from: A convolutional neural network for detecting sea turtles in drone imagery
1. Marine megafauna are difficult to observe and count because many species travel widely and spend large amounts of time submerged. As such, management programs seeking to conserve these species are often hampered by limited information about population levels. 2. Unoccupied aircraft systems (UAS, aka drones) provide a potentially useful technique for assessing marine animal populations, but a central challenge lies in analyzing the vast amounts of data generated in the images or video acquired during each flight. Neural networks are emerging as a powerful tool for automating object detection across data domains and can be applied to UAS imagery to generate new population-level insights. To explore the utility of these emerging technologies in a challenging field setting, we used neural networks to enumerate olive ridley turtles (Lepidochelys olivacea) in drone images acquired during a mass-nesting event on the coast of Ostional, Costa Rica. 3. Results revealed substantial promise for this approach; specifically, our model detected 8% more turtles than manual counts while effectively reducing the manual validation burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on a relatively small set of turtle examples (N=944), implying that this method can be easily bootstrapped for other applications, and is practical with real-world UAS datasets. 4. Our findings highlight the feasibility of combining UAS and neural networks to estimate population levels of diverse marine animals and suggest that the automation inherent in these techniques will soon permit monitoring over spatial and temporal scales that would previously have been impractical.
Data from: Planning for climate change through additions to a national protected area network: implications for cost and configuration
<p>Expanding the network of protected areas is a core strategy for conserving biodiversity in the face of climate change. Here we explore the impacts on reserve network cost and configuration associated with planning for climate change in the United States using networks that prioritize areas projected to be climatically suitable for 1,460 species both today and into the future, climatic refugia, and areas likely to facilitate climate-driven species movements. For 14% of the species, networks of sites selected solely to protect areas currently climatically suitable failed to provide climatically suitable habitat in the future. Protecting sites climatically suitable for species today and in the future significantly changed the distribution of priority sites across the U.S.—increasing relative protection in the northeast, northwest, and central U.S. Protecting areas projected to retain their climatic suitability for species cost 59% more than solely protecting currently suitable areas. Including all climatic refugia and 20% of areas that facilitate climate-driven movements increased the cost by another 18%. Our results indicate that protecting some types of climatic refugia may be a relatively inexpensive adaptation strategy. Moreover, although addressing climate change in conservation plans will have significant implications for the configuration of networks, the increased cost of doing so may be relatively modest.</p> <p> </p>
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Neutralization Data and Aligned ENV Sequences for Predicting Antibody Affinities using Artificial Neural Networks
<p>Sample file with neutralization data (IC<sub>50</sub>) for different antibodies and viral strains, adapted from J. Huang, G. Ofek, L. Laub, M. K. Louder, N. A. Doria-Rose, N. S. Longo, H. Imamichi, R. T. Bailer, B. Chakrabarti, S. K. Sharma, S. M. Alam, T. Wang, Y. Yang, B. Zhang, S. A. Migueles, R. Wyatt, B. F. Haynes, P. D. Kwong, J. R. Mascola, and M. Connors, “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody.,” <em>Nature</em>, vol. 491, no. 7424, pp. 406–12, Nov. 2012.</p> <p> </p> <p>Aligned ENV sequences downloaded from the HIV Sequence Database (www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). There are 4907 sequences and the alignment length is 1369.</p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang and G. Casale, accepted for presentation at MAMA workshop 2015. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang, G. Casale, A. Kattepur and M. Nambiar, accepted for presentation at ICPE 2016. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Anonymized Instagram network data from Amsterdam and Copenhagen, Pajek format
<p>Networks of reciprocated recognition (mutual liking and/or commenting) among Instagram users in Amsterdam and Copenhagen, on the basis of data collected over a twelve-week period in 2015. User names are hashed to anonymize the data.</p>
Texas Archaeology Publications--Network Data
<p>This dataset was harvested from Scopus using the search "Texas AND archaeology." These data were then used to generate a citation network used to explain how the work of Texas archaeologists articulates with one another. </p>
Spatial Tournament Data for Complex Networks Non Deterministic Size - MSc Dissertation
<p>A data set that contains the results of various spatial tournaments, for the iterated prisoner's dilemma. Three different types of complex networks have been used as topology for the spatial tournaments. Small world, random and complete networks.</p>
ScienceDex guides
Understand access before you commit
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.