Find research datasets worth reusing

opencc-by-4.0Apr 2020View details →

Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits

Dataset associated with the manuscript "Individual-based plant-pollinator networks are structured by phenotypic and microsite plant traits" (Arroyo-Correa et al. 2020), including plant-pollinator interactions, individual plant attributes and the plant polygon map created with drone flights.

opencc-by-4.0Apr 2020View details →

Data from "Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques"

DATA FILES from the study below: <a href="https://www.biorxiv.org/content/10.1101/603530v1">Behavioral flexibility is associated with changes in structure and function distributed across a frontal cortical network in macaques</a> Jérôme Sallet, MaryAnn P Noonan, Adam Thomas, Jill X O’Reilly, Jesper Anderson, Georgios KPapageorgiou, Franz X Neubert, Bashir Ahmed, Jackson Smith, Andrew H Bell, Mark J Buckley, LéaRoumazeilles, Steven Cuell, Mark E Walton, Kristine Krug, Rogier B Mars, Matthew FS Rushworth bioRxiv 603530; doi: <a href="https://doi.org/10.1101/603530">https://doi.org/10.1101/603530</a> *.nii.gz files could be opened with FSLeyes -<a href="https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)">https://fsl.fmrib.ox.ac.uk/fsl/fslwiki/FSLeyes)</a> Dara are also available from : https://www.jeromesallet.org/data-ofc-reversal-learning

opencc-zeroJun 2014View details →

Consensus between pipelines in structural brain networks

Weighted brain network matrices for all subjects reconstructed using each pipeline. Filename letters denote the combination of pipeline stages used to reconstruct the matrix, whereas the number represents the subject. Pipeline stages are described in filename_key.txt. The brain region names and parcellation labels corresponding to the network node row/column are in X_region_names.txt and X_parcellation_labels.txt, where X is the atlas abbreviation described in filename_key.txt.

opencc-by-4.0Nov 2023View details →

Dataset from the paper entitled "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics"

Dataset from the paper entitled  "Complex structure of molten FLiBe (2 LiF – BeF2) examined by experimental neutron scattering, X-ray scattering, and deep neural network-based molecular dynamics". These data include experimental total scattering measurements and molecular dynamics simulations on the molten structure of FLiBe.

opencc-by-4.0Nov 2023View details →

Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"

This upload contains the Datasets and code to generate the results of the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities".The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. The code folder contains the code to generate the results, and to generate the figures of the manuscript.

opencc-zeroMar 2024View details →

Fast-slow traits predict competition network structure and its response to resources and enemies

Plants interact in complex networks but how network structure depends on resources, natural enemies, and species resource-use strategy remains poorly understood. Here, we quantified competition networks among 18 plants varying in fast-slow strategy, by testing how increased nutrient availability and reduced foliar pathogens affected intra- and inter-specific interactions. Our results show that nitrogen and pathogens altered several aspects of network structure, often in unexpected ways due to fast and slow-growing species responding differently. Nitrogen addition increased competition asymmetry in slow-growing networks, as expected, but decreased it in fast-growing networks. Pathogen reduction made networks more even and less skewed because pathogens targeted weaker competitors. Surprisingly, pathogens and nitrogen dampened each other's effect. Our results show that plant growth strategy is key to understanding how competition responds to resources and enemies, a prediction from classic theories that has rarely been tested by linking functional traits to competition networks.

opencc-zeroApr 2024View details →

Data from: emergence of structure in plant-pollinator networks: low floral resource constrains network specialisation

Specialisation enhances the efficiency of plant-pollinator networks through the exchange of conspecific pollen transfer for floral resources. Floral resources form the currency of plant-pollinator interactions, but the understanding of how floral resources affect the structure of plant-pollinator networks remains modest. Previous theory predicts that optimally foraging animal species will specialise to improve resource acquisition under high resource availability. Although floral resource availability depends on both the plant production and animal consumption of the resources, previous work has assumed that production and availability to be equivalent. This potentially may have led to erroneous inferences on the effect of resource availability on specialisation. We develop a mutualistic Lotka-Volterra consumer-resource model to investigate the influence of floral resource availability on plant-pollinator network structure. The model incorporates animal adaptive foraging behaviour, floral resource dynamics, and density-dependent dynamics. Specialisation, nestedness and modularity of simulated networks generated from the model under a wide range of parameters were explained using the Generalised Linear Model. We found that the distinction between floral resource dynamics and plant density dynamics was necessary for partial specialisation of plant-pollinator networks. This is because floral resource dynamics constraint animal preference due to its depletion by animal species. Floral resource abundance had a positive effect on network specialisation, but animal density had a negative effect on network specialisation. Floral resource dynamics thus play key roles on the structure of plant-pollinator network, distinctive from plant species density dynamics.

opencc-zeroDec 2021View details →

Group composition of individual personalities alters social network structure in experimental populations of forked fungus beetles

Social network structure is a critical group character that mediates the flow of information, pathogens, and resources among individuals in a population, yet little is known about what shapes social structures. In this study, we experimentally tested whether social network structure depends on the personalities of group members. Replicate groups of forked fungus beetles (Bolitotherus cornutus) were engineered to include only members previously assessed as either more social or less social. We found that individuals behaved consistently across social contexts, exhibiting repeatable numbers of interactions and numbers of partners. At the group level, networks composed of more social individuals had higher interaction rates, higher tie density, higher global clustering, and shorter average shortest paths than those composed of less social individuals. We highlight group composition of personalities as a source of variance in group traits and a potential mechanism by which networks could evolve.

opencc-zeroApr 2024View details →

Structure and dynamics of enterovirus genotype networks

Like all biological populations, viral populations exist as networks of genotypes connected through mutation. Mapping the topology of these networks and quantifying population dynamics across them is crucial to understanding how populations adapt to changes in their selective environment. The influence of mutational networks is especially profound in viral populations which rapidly explore their mutational neighborhoods via high mutation rates. Using a novel single-cell sequencing method, scRNAseq-Enabled Acquisition of mRNA and Consensus Haplotypes Linking Individual Genotypes and Host Transcriptomes (SEARCHLIGHT), we captured and assembled viral haplotypes from hundreds of individual infected cells to reveal the complexity of viral populations. We obtained these genotypes in parallel with host cell transcriptome information, enabling us to link host cell transcriptional phenotypes to the genetic structures underlying virus adaptation. Our examination of these structures reveals the common evolutionary dynamics of enterovirus populations and illustrates how viral populations reach through mutational 'tunnels' to span evolutionary landscapes and maintain connection with multiple adaptive genotypes simultaneously.

opencc-by-4.0Jul 2024View details →

Decentralized Applications Network Structure

This dataset encompasses the network structure of decentralized applications (dApps) mainly deployed in the Ethereum blockchain and other platforms such as Binance, Optimism, Polygon, Astar, Shiden, and Thundercore. Each dApp's network structure is represented through a CSV which includes the following information: File: The name of the Solidity file Source_Contract: The name of the contract that calls the Target contract. Target_Contract: The name of the contract that is called by the Source contract. Source_Function: The name of the function that calls the Target function. Target_Function: The name of the function called by the Source function. Chain: The chain of function calls.

Fig. 4 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 4. Results of the Geneland analysis. A: Bar plot of posterior probability density according to the number of clusters; B: posterior probability maps for the delimited clusters.

Fig. 3 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 3. Pairwise FST between localities of Telmatobius pefauri obtained using mitochondrial (A) and microsatellite (B) data. The colour scale corresponding to the values of FST is shown to the right of each matrix. Significant (Bonferroni corrected) comparisons showing p <0.05, p <0.01 and p <0.001 are denoted by *, ** and ***, respectively.

Fig. 2 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 2. Median-joining network based on the fragment of the analysed control region. Table 1. Indices of mitochondrial diversity, nuclear diversity, and inbreeding coefficients (FIS) by locality

Fig. 1 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 1. Study area, distribution of Telmatobius pefauri. Localities, 1: Socoroma (Socoroma River); 2: Murmuntani; 3: Copaquilla; 4: Chapiquiña; 5: Belén; 6: Lupica; 7: Saxamar. Localities 2 and 3 belong to the Seco River drainage; localities 4–7 belong to the Tignamar River drainage. Basin limits are indicated with dashed lines. The inset map shows the study area (highlighted by a red box) in relation to South America. SAAD = South American Arid Diagonal.

Fig. 5 in Drainage Network Morphology Influences Population Structure and Gene Flow of the Andean Water Frog (Anura: Telmatobiidae) of the Atacama Desert, Northern Chile.

Fig. 5. Scatter plot for the first two principal components obtained in the Principal Components Analysis using SSR data.

opencc-by-4.0Jul 2024View details →

Supplementary material for publication "Multi-Echelon Inventory Optimization in Supply Chain Networks: Exploring Network Structures and Predictive Modeling"

<div> <div> <div> This dataset collects different supply chain network structures generated artificially. We present four types of networks: Serial, Convergent, Divergent, and General, each type consisting of 20,000 individual instances. All 80,000 network instances generated are available to researchers and practitioners in Excel. The repository consists of separate files for each network instance consisting of each network inventory data, node connections, and a visual representation. </div> </div> </div>

opencc-by-4.0Nov 2011View details →

Figure 5. ANN Structure 4.-Impact of Ethnic Group on Human Emotion Recognition Using Backpropagation Neural Network

For classification purpose of the emotions, we use ANN of supervised learning based on backpropagation algorithm. Backpropagation neural network architecture is used with its standards learning function with 28 inputs representing the extracted features and 6 outputs representing 6 emotions, happy, sad, angry, fear, shame and disgust. the emotions. We have also a hidden layer with 16 nodes selected after various trails to obtain the best results. The used ANN is depicted in Figure 5.