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105 results for “temporal networks”
Prediction model of the temporal dynamics of severe pest cashew Anacampsis phytomiella using artificial neural networks
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"Constructing Temporal Networks of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Constructing Temporal Networks of OSS Programming Language Ecosystems". Contains the dataset, extracted and external metrics, and all scripts used for the construction and the analysis done in the paper.</p> <p> </p> <p>Anonymised for the double-blind review process.</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
Dataset for Embedded Temporal Convolutional Networks for Essential Climate Variables Forecasting
<p>The dataset contains time series of surface soil moisture at three locations, namely Idaho, Indiana, and Oklahoma.</p> <p>For each location, the folder contains multiple gif images, one for each year, each containing 12 frames corresponding to monthly averages.</p> <p>More information at </p> <p>https://github.com/gtsagkatakis/ETCN</p>
Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks
<p>Dataset of bike-sharing Demand Prediction model based on Spatio-Temporal Graph Convolutional Networks</p>
Data from: Colourful network: Pair-bonding temporal dynamics involve sexual signals and impact reproduction
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Data from:Spatio-temporal networks: reachability, centrality and robustness
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Analysis of temporal patterns in animal movement networks
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Data from: Urbanization and the temporal patterns of social networks and group foraging behaviours
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Temporal and spatial changes in benthic invertebrate trophic networks along a salinity gradient
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Data from: Temporal scale-dependence of plant-pollinator networks
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Bat-flower interaction networks in Caatinga reveal generalized associations and temporal stability
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Data for: Thermal vulnerability in a mountain stream network: Temporal, spatial, and biological data
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Data from: Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes
We analyse the urban mobility in the cities of Medellín and Manizales (Colombia). Each city is represented by six mobility networks, each one encoding the origin-destination trips performed by a subset of the population corresponding to a particular socio-economic status. The nodes of each network are the different urban locations whereas links account for the existence of a trip between two different areas of the city. We study the main structural properties of these mobility networks by focusing on their spatio-temporal patterns. Our goal is to relate these patterns with the partition into six socio-economic compartments of these two societies. Our results show that spatial and temporal patterns vary across these socio-economic groups. In particular, the two datasets show that as wealth increases the early-morning activity is delayed, the midday peak becomes smoother and the spatial distribution of trips becomes more localized.
Data from: Temporal variation in plant-pollinator networks from seasonal tropical environments: higher specialization when resources are scarce
The temporal dynamics of plant phenology and pollinator abundance across seasons should influence the structure of plant-pollinator interaction networks. Nevertheless, such dynamics are seldom considered, especially for diverse tropical networks. Here, we evaluated the temporal variation of four plant-pollinator networks in two seasonal ecosystems in Central Brazil (Cerrado and Pantanal). Data were gathered on a monthly basis over one year for each network. We characterized seasonal and temporal shifts in plant-pollinator interactions, using temporally discrete networks. We predicted that the greater floral availability in the rainy season would allow for finer partitioning of the floral niche by the pollinators, i.e., higher specialization patterns as previously described across large spatial gradients. Contrary to these expectations, we found that dry season networks, although characterized by lower floral resource richness and abundance, showed higher levels of network-wide interaction partitioning (complementary specialization and modularity). For nestedness and species level indices, though, this between-seasons difference was not consistent. Reduced resource availability in the dry season may promote higher interspecific competition among pollinators leading to reduced niche overlap, thus explaining the increase in specialization. Importantly, we also show that targeted data collection during peak flowering generates higher estimates of network specialization. Hence, depending on the period of data collection, different structural values for the networks of interactions may be found. Synthesis: Our study suggests that networks of tropical environments have structural properties that vary according to seasons, which should be taken into account in the description of the complex systems of interactions between plants and their pollinators in these areas.
Data from: The multilayer temporal network of public transport in Great Britain
Despite the widespread availability of information concerning public transport coming from different sources, it is extremely hard to have a complete picture, in particular at a national scale. Here, we integrate timetable data obtained from the United Kingdom open-data program together with timetables of domestic flights, and obtain a comprehensive snapshot of the temporal characteristics of the whole UK public transport system for a week in October 2010. In order to focus on multi-modal aspects of the system, we use a coarse graining procedure and define explicitly the coupling between different transport modes such as connections at airports, ferry docks, rail, metro, coach and bus stations. The resulting weighted, directed, temporal and multilayer network is provided in simple, commonly used formats, ensuring easy access and the possibility of a straightforward use of old or specifically developed methods on this new and extensive dataset.
Data from: Invariant antagonistic network structure despite high spatial and temporal turnover of interactions
Recent work has suggested that emergent ecological network structure exhibits very little spatial or temporal variance despite changes in community composition. However, the changes in network interactions associated with turnover in community composition have seldom been assessed. Here we examine whether changes in ecological networks are best detected by standard emergent network metrics or by assessing internal network changes (i.e. interaction and composition turnover). To eliminate possible spatial or phylogenetic effects, that in large-scale studies may obscure mechanisms structuring networks and interactions, we sampled multiple antagonistic (plant-herbivore) networks for a single diverse plant family (the Restionaceae) in the hyperdiverse Cape Floristic Region. These are the first plant-herbivore networks constructed for this global biodiversity hotspot. We found invariant emergent network structure despite considerable changes in insect and plant composition across communities over time and space. In contrast, there was high interaction turnover between networks. Seasonally, this was driven by turnover in insect species and insect host switching. Spatially, this was driven by simultaneous turnover in plant and insect species, suggesting that many insects are host specific or that both groups exhibit parallel responses to environmental gradients. Spatial interaction turnover was also driven by turnover in plants, showing that many insects can utilise multiple (possibly closely related) hosts and this may create divergent selection gradients that promote insect speciation. Thus we show highly variable interaction fidelity, despite invariant emergent network structure. We suggest that evaluating internal network changes may be more effective at elucidating the processes structuring networks, and many fine-scale changes may be obscured when only calculating emergent network metrics.
Temporal Networks - Football, Handball, Basketball
<p>This is a collection of datasets used for research in the field of Temporal Networks</p> <p> </p> <p>We have first used these datasets in the following publication:</p> <p>O.Kostakis, N.Tatti, A.Gionis, "Discovering recurrent activity in temporal networks", Data Mining and Knowledge Discovery, Special Issue in Sports Analytics, 2016.</p> <p> </p> <p>In summary, this collection contains three different datasets. The first is data about all matches in the 1996-'97 English Premier League. The second dataset contains a temporal network corresponding to team-passing activity of a handball team. Finally, the third dataset contains play-by-play information for 1101 basketball matches of the 2014-'15 NBA season. Within each folder, you will find a separate README file for each dataset.</p> <p> </p> <p>Disclaimer:</p> <p>We do not claim to have produced or own the data. We do not claim the correctness of the data.</p> <p>We provide the data only for reasons related to Research, including but not limited to research reproducibility.</p>
Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity
<p><strong>Dataset for the publication: 'Reproducible network changes occur in a mouse model of temporal lobe epilepsy but do not correlate with disease severity '</strong><br><strong>Rigoni et al. 2023, Neurobiology of Disease, doi: <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.nbd.2023.106382" target="_blank" rel="noreferrer noopener"><span>https://doi.org/10.1016/j.nbd.2023.106382</span></a></strong></p> <p><strong>Dataset description</strong></p> <p><em>Data\data2publish\sub- </em>: 50 epochs of raw epicranial EEG data (31 x 8001 x 50, channels x time x n_epochs,<em> </em>Fs=4k Hz). The epochs are available for 29 mice, on different sessions (ses-d0, ses-d28, ses-d29) depending on the animal </p> <p><em>Data\data2publish\EA_info.xlsx</em>: number of epileptiform activities automatically detected for each animal at d28 and d29</p> <p><em>Data\data2publish\derivatives\eeg_preprocessing: </em>results of the script A_EEG_preprocessing.m, for each animal and session</p> <p><em>Data\data2publish\derivatives\elec_layout: </em>different layouts used to plot results. Mouse_layout_modif is the one used in Fig 4</p> <p><em>Data\data2publish\derivatives\network_metrics</em>_<em>wpli: </em>results of network analyses (script C_network_analyses.m)</p> <p><em>Data\data2publish\derivatives\wpli</em>: connectivity matrices (30 x 30) obtained with the script B_connectivity_wpli.m for each animal, in each session, for each frequency band wit</p> <p><strong>Code for analyses available here: </strong> <a href="https://github.com/IsottaR/ir_mice_project_Zenodo">https://github.com/IsottaR/ir_mice_project_Zenodo </a></p> <p>Abbreviations:</p> <p>EEG= electroencephalography</p>
Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks: dataset
<p>This repository contains the data accompanying the paper "<em>Learning the intrinsic dynamics of spatio-temporal processes through Latent Dynamics Networks</em>", by Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dedè and Alfio Quarteroni.</p> <p>The associated codes are available in the repository <a href="https://github.com/FrancescoRegazzoni/LDNets">https://github.com/FrancescoRegazzoni/LDNets</a></p>
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.