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308 results for “Dynamic Network”
Graph neural network emulator for modeling of ice dynamics and calving in the Helheim Glacier, Greenland
<p>These files include the following codes and datasets for developing graph neural network (GNN) emulators for the Ice-sheet and Sea-level System Model (ISSM) for modeling ice sheet dynamics and calving in the Helheim Glacier, Greenland.</p> <ul> <li>ISSM_DGL_Helheim.py: Python file for training GNN models</li> <li>ISSM_CNN_Helheim.py: Python file for training convolutional neural network (CNN) models</li> <li>*.mat: Datasets of the ISSM transient simulation results</li> </ul>
Data for "CryoDRGN-ET: Deep reconstructing generative networks for visualizing dynamic biomolecules inside cells"
<p>Trained models weights, training parameters, sampled density maps, reconstructed density maps for featured classes, and plotting scripts are included for each of the following datasets and training runs:</p> <ul> <li><em>M. pneumoniae</em> ribosome, initial training run with all 18,466 particles, 1 tilt per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 10 tilts per particle</li> <li><em>M. pneumoniae</em> ribosome, training run with 16,655 filtered particles and 41 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, initial training run with all 119,031 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 93,281 filtered particles, 10 tilts per particle</li> <li><em>S. cerevisiae</em> ribosome, training run with 30,657 particles in the non-rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>ribosome, training run with 62,624 particles in the rotated state, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, initial training run with all 33,492 particles, 10 tilts per particle</li> <li><em>S. cerevisiae </em>fatty acid synthase, training run with all 5,239 filtered particles, 10 tilts per particle</li> </ul>
Speed Prediction in Large and Dynamic Traffic Sensor Networks
<p>Aggregated traffic sensor data from Fortaleza (Brazil) in 2014.</p> <p>Please cite the following paper when using the dataset:</p> <p>R.P. Magalhaes, F. Lettich, J.A. Macedo, F.M. Nardini, R. Perego, C. Renso, R. Trani., <strong>Speed prediction in large and dynamic traffic sensor networks</strong>, Information Systems (2019) 101444, <a href="https://doi.org/10.1016/j.is.2019.101444">https://doi.org/10.1016/j.is.2019.101444</a></p> <p>You can also check details regarding the dataset in the paper.</p>
Forecasting model of seasonal dynamics of boll weevil Anthonomus grandis grandis (Coleoptera: Curculionidae) in cotton crops using artificial neural networks
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Dataset for the paper "Network-Based Differential Abundance Analysis: Bridging Community Interactions and Host-Microbiome Dynamics."
<p>The files with extension rds are files that contain simulated data and the tsv files contain original data along with their meta data.</p>
ReaxANA: Analysis of Reactive Dynamics Trajectories for Reaction Network Generation
<p><span>ReaxFF simulations, QM calculation input/output files, and Jupyter notebooks for data analysis and visualization.</span></p>
Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions
1.Heterogeneity within pathogen species can have important consequences for how pathogens transmit across landscapes; however, discerning different transmission routes is challenging. 2.Here we apply both phylodynamic and phylogenetic community ecology techniques to examine the consequences of pathogen heterogeneity on transmission by assessing subtype specific transmission pathways in a social carnivore. 3.We use comprehensive social and spatial network data to examine transmission pathways for three subtypes of feline immunodeficiency virus (FIVPle) in African lions (Panthera leo) at multiple scales in the Serengeti National Park, Tanzania. We used FIVPle molecular data to examine the role of social organization and lion density in shaping transmission pathways and tested to what extent vertical (i.e., father and/or mother offspring relationships) or horizontal (between unrelated individuals) transmission underpinned these patterns for each subtype. Using the same data, we constructed subtype specific FIVPle co-occurrence networks and assessed what combination of social networks, spatial networks, or co-infection best structured the FIVPle network. 4.While social organization (i.e., pride) was an important component of FIVPle transmission pathways at all scales, we find that FIVPle subtypes exhibited different transmission pathways at within- and between-pride scales. A combination of social and spatial networks, coupled with consideration of subtype co-infection, was likely to be important for FIVPle transmission for the two major subtypes, but the relative contribution of each factor was strongly subtype specific. 5.Our study provides evidence that pathogen heterogeneity is important in understanding pathogen transmission, which could have consequences for how endemic pathogens are managed. Furthermore, we demonstrate that community phylogenetic ecology coupled with phylodynamic techniques can reveal insights into the differential evolutionary pressures acting on virus subtypes, which can manifest into landscape-level effects.
Dynamic rewiring of electrophysiological brain networks during learning
<p>This space is dedicated to the dataset used in the manuscript "Dynamic rewiring of electrophysiological brain networks during learning" under peer-reviewing process.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
Exploring spatiotemporal dynamics of flower visitor association pattern on two Avicennia mangroves: A network approach
<p>All the data sets used in the analyses of this study are provided here along with the <strong>R-Script.</strong><br> The datasets for foraging behaviour and Generalized linear mixed models will be provided upon request to the first or corresponding author of this article.</p> <p><strong>Please note that, in this R-script, we have shown the codes only for one dataset of respective analyses.</strong><br> <strong>PLEASE NOTE: In this R-script, there are two minor mistakes, as follows:<br> 1) Line no. 46<br> Present code: </strong><strong>nulls <- nullmodel(I_S.network, N=1000, method=3) ##(3=Vaznull) ## file name mistake<br> Correct code: nulls <- nullmodel(AO_Site, N=1000, method=3) ##(3=Vaznull)</strong></p> <p><strong>2) Line no. 55<br> Present code: AO_Site_V<-AM_Site[,-1] ##Omitting individual coloumn(species) ## file name mistake<br> Correct code: AO_Site_V<-AO_Site[,-1] ##Omitting individual coloumn(species)</strong></p> <ul> <li><strong>Description of the data set</strong></li> </ul> <p>Data explorers the plant-flower visitor network with spatiotemporal approaches. Here, AM denotes <em>Avicennia marina </em>and AO denotes <em>Avicennia officinalis. </em>For the overall site-visitor network (combining all years and all time frames) datasets are AO_Site and AM_Site.</p> <p>For the overall visiting time-visitor network (combining all years and all sites) the datasets are AO_Time and AM_Time</p> <p>For the site-visitor networks on the yearly scale, the datasets are AO_2016, AO_2017, AO_2018, AM_2016, AM_2017 and AM_2018.</p> <p>For the site-specific visiting time-visitor networks the datasets are AO_Satjelia, AO_Bali, AO_Sagar, AO_Bakkhali, AM_Satjelia, AM_Bali, AM_Sagar and AM_Bakkhali.</p>
Shifting Dynamics in International Trade Networks: A Longitudinal Analysis of Major Exporting Economies (1992-2020): A case of the US, China, India, Japan and South Korea
<p>This article aims to analyze the changes in trading networks by bringing out aspects of centrality and reciprocity among participating countries. The research considers the trade relations of five export countries, namely, The U.S., China, India, Japan, and South Korea from 1992 to 2020. We examine whether trade networks are truly becoming more bidirectional over the long term and how the political and economic crises that have occurred in the past 30 years affect the structure of trade networks. The article presents key findings on the changes in the network structure of international trade over the past 30 years. The network structure has become more reciprocal for most product categories, and the center of the trade network has shifted from the United States to China. The analysis also demonstrated that the center of the network has shifted from U to C for all product categories, except for raw materials. The study also highlights the significant impact of global events and crises on the international trade network structure, emphasizing the importance of considering geopolitical events and economic shocks in understanding trade dynamics.</p>
Data set for Dynamic Service Restoration of Distribution Networks with Volt-Var Devices, Distributed Energy Resources, and Energy Storage Systems
<p>Two power distribution systems are presented. The first system consists of 53 nodes and 61 branches, while the second system consists of 404 buses and 430 branches. Both distribution systems offer extensive applications in problems related to multi-time service restoration, Volt/Var devices, and distributed energy resource operation.</p>
Data of "Dissipative quantum many-body dynamics in (1+1)D quantum cellular automata and quantum neural networks"
<p>The uploaded files contain the data of the simulations presented in the figures in <a href="https://doi.org/10.48550/arXiv.2304.11209">https://doi.org/10.48550/arXiv.2304.11209</a>.</p>
Data from: Integrating variation in bacterial-fungal co-occurrence network with soil carbon dynamics
<p>Bacteria and fungi are core microorganisms in diverse ecosystems, and their cross-kingdom interactions are considered key determinants of microbiome structure and ecosystem functioning. However, how bacterial-fungal interactions mediate soil organic carbon (SOC) dynamics remains largely unexplored in the context of artificial forest ecosystems. Here, we characterized soil bacterial and fungal communities in four successive planting of Eucalyptus and compared them to a neighboring evergreen broadleaf forest. Carbon (C) mineralization combined with five C-degrading enzymatic activities was investigated to determine the effects of successive planting of Eucalyptus on SOC dynamics. Our results indicated that successive planting of Eucalyptus significantly altered the diversity and structure of soil bacterial and fungal communities and increased the negative bacterial-fungal associations. The bacterial diversity significantly decreased in all Eucalyptus plantations compared to the evergreen forest, while the fungal diversity showed the opposite trend. The ratio of negative bacterial-fungal associations increased with successive planting of Eucalyptus due to the decrease in SOC, ammonia nitrogen (NH4+−N), nitrate nitrogen (NO3−−N), and available phosphorus (AP). Structural equation modeling indicated that the potential cross-kingdom competition, based on the ratio of negative bacterial-fungal correlations, was significantly negatively associated with the diversity of total bacteria and keystone bacteria, thereby increasing C-degrading enzymatic activities and C mineralization.</p> <p>Synthesis and applications: Our results highlight the regulatory role of the negative bacterial-fungal association in enhancing the correlation between bacterial diversity and C mineralization. This suggests that promoting short-term successive planting in the management of Eucalyptus plantations can mitigate the impact of this association on SOC decomposition. Taken together, our study advances the understanding of bacterial-fungal negative associations to mediate carbon mineralization in Eucalyptus plantations, giving us a new insight into SOC cycling dynamics in artificial forests.</p>
Investigation of Brain Network Dynamics in Depression
ClinicalTrials.gov study NCT01931995. IPD Sharing: Not stated. Countries: 1. Publications: 1.
Data from: Colourful network: Pair-bonding temporal dynamics involve sexual signals and impact reproduction
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Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
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Data from: Non-native species spread in a complex network: the interaction of global transport and local population dynamics determines invasion success
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Estimating drivers and identifying uncertainties in smallmouth bass population dynamics in an invaded river network
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Data from: Linking social and spatial networks to viral community phylogenetics reveals subtype-specific transmission dynamics in African lions
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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.