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105
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ShareScore release 0.9.0
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105 results for “temporal networks”
Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution
<p>This dataset contains the whistler-mode hiss waves obtained from the Van Allen Probes. It is accompanied by the manuscript "<span>Imbalanced regressive neural network model for whistler-mode hiss waves: spatial and temporal evolution". </span></p>
"Temporal Network Dataset of OSS Programming Language Ecosystems" reproducibility package
<p>Reproducibility package for the paper submission "Temporal Network Dataset of OSS Programming Language Ecosystems" to the Mining Software Repositories 2022 conference. Contains the dataset, extracted 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>
Temporal overlays of the co-word analysis network on Ecotourism, Sustainable Tourism and Nature Based Tourism (1986-2022): Map and network for VOSViewer visualization
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Technical Report and Simulation Results for the Paper "It's About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities"
<p>Archiving the data and the technical report corresponding to our publication "It’s About Time: On Optimal Virtual Network Embeddings under Temporal Flexibilities".<br> The data was previously publicly available at https://net.t-labs.tu-berlin.de/~stefan/tvnep.html.</p>
Temporal Network Benchmark Data
<p>We release a benchmark dataset for the evaluation and development of temporal network embedding methods that leverage such fundamental graph time-series representations. The benchmark consists of 26 temporal networks from a variety of different application domains. We also release the graphs in each of the different time-series representations as separate files that can be used for benchmarking purposes. This includes the $\tau$-graph time-series that decomposes the edge stream into a time-series of graphs based on an application time-scale such as 1 day, as well as the $\epsilon$-graph time-series that decomposes the edge stream into a time-series of graphs such that each graph has a constant number of edges. </p> <p>For a direct comparison between the $\tau$-graph and $\epsilon$-graph time series approaches, we require an equal number of graphs for each, that is, $|\mathcal{G}_{\tau}| = |\mathcal{G}_{\epsilon}|$. For this, we first fix $\tau$ to be an application time-scale (such as 1 hour, 1 day, and so on) and use this $\tau$ to derive a $\tau$-graph time-series $\mathcal{G}_{\tau}$. We then set the number of edges per snapshot in the $\epsilon$-graph time-series as $\epsilon = \ceil{\tfrac{|E|}{|\mathcal{G}_{\tau}|}}$, which results in an equal number of snapshots as desired.</p> <p>For each of the 26 temporal networks, we release a variety of temporal granulaties. For instance, for email-EU-core network, we have a directory called email-EU-core, inside that directory consists of the raw temporal network (edge stream) called email-EU-core.edges, which is a simple comma-delimited edge list where the first and second column are node ids, whereas the third is the timestamp of the edge. Furthermore, there are two other directories inside email-EU-core, namely, 1day and 1week, which indicates the temporal granulatiy, and inside either of those directories are two more directories called tau and epsilon, which contain the edge list files for each of the different graph time-series representations.</p>
Video simulations for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Videos of the comparison between numerical and deep learning simulations for test datasets 1, 2, and 3 for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p>
Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks"
<p>Raw datasets for paper "Rapid Spatio-Temporal Flood Modelling via Hydraulics-Based Graph Neural Networks".</p> <p>The zip folder comprises 4 subfolders (DEM, WD, VX, VY), containing the elevation, water depths in time, and velocities (in x and y directions) in time for all training and testing simulations. The overview.csv file provides the runtime of the numerical model on each different simulation, identified by its id.</p> <p>The simulations ids are divided as follows:</p> <p>- 1-80: Training and validation</p> <p>- 501-520: Testing dataset 1</p> <p>- 10001-10020: Testing dataset 2</p> <p>- 15001-15020: Testing dataset 3</p>
Data from: Dendritic network structure and dispersal affect temporal dynamics of diversity and species persistence
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Data from: Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants
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Data from: Microsites of seed arrival: spatio-temporal variations in complex seed-disperser networks
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Data from: Temporal variation in plant-pollinator networks from seasonal tropical environments: higher specialization when resources are scarce
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Data from: Evaluating the sensitivity of process domains for logjams to spatial and temporal sample size in river networks of the Southern Rockies, USA
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Data from: Temporal dynamics of direct reciprocal and indirect effects in a host-parasite network
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Data from: High temporal variability in the occurrence of consumer–resource interactions in ecological networks
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Data from: Rich do not rise early: spatio-temporal patterns in the mobility networks of different socio-economic classes
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Data from: Invariant antagonistic network structure despite high spatial and temporal turnover of interactions
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Data from: The multilayer temporal network of public transport in Great Britain
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Temporal flexibility of gene regulatory network underlies a novel wing pattern in flies
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Data from: The temporal dimension in individual-based plant pollination networks
The pollination success of animal-pollinated plants depends on the temporal coupling between flowering schedules and pollinator availability. Within a population, individual plants exhibiting disparate flowering schedules will be exposed to different pollinators when the latter exhibit temporal turnover. The temporal overlap between individual plants and pollinators will result in a turnover of interactions, which can be analyzed through a network approach. We have explored the temporal dynamics of individual-based plant networks resulting from pairwise similarities in pollinator composition. During two flowering seasons, we surveyed the phenology and pollinator fauna of the individual plants from a population of Erysimum mediohispanicum (Brassicaceae). We analyzed the topology of these networks by means of their modularity, clustering, and core–periphery structure. These metrics are related to network functional properties such as cohesion, transitivity and centralization respectively. Afterwards, we analyzed the influence of each pollinator functional group on network topology. We found that network topology varied widely over time as a consequence of the differences in plant phenology and the idiosyncratic and contextual effect of pollinators. When integrating all temporary networks, the network became cohesive (non modular), transitive (locally clusterized), and centralized (core–periphery topology). These topologies could entail important consequences for plant reproduction. Our results highlight the importance of considering the entire flowering season and the necessity of making comprehensive temporal sampling when trying to build reliable interaction networks.
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.
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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.