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105 results for “temporal networks”
Linking temporal changes in species composition and biomass in a globally distributed grassland experiment: The Nutrient Network
Global change drivers, such as anthropogenic nutrient inputs, are increasing globally. Nutrient deposition simultaneously alters plant biodiversity, species composition, and ecosystem processes like aboveground biomass production. These changes are underpinned by species extinction, colonization, and shifting relative abundance. Here, we use the Price equation to quantify and link the contributions of species that are lost, gained, or that persist to change in aboveground biomass in 59 experimental grassland sites. Under ambient (control) conditions, compositional and biomass turnover was high, and losses (i.e., local extinctions) were balanced by gains (i.e. colonization). Under fertilization, the decline in species richness resulted from increased species loss and from decreases in species gained. Biomass increase under fertilization resulted mostly from species that persist, and to a lesser extent from species gained. Drivers of ecological change can interact relatively independently with diversity, composition, and ecosystem processes and functions such as aboveground biomass due to the individual contributions of species lost, gained, or persisting.
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Learning Naturalistic Temporal Structure in the Posterior Medial Network
Open the record for dataset details and reuse information.
Diffusion in Temporal Networks: London Tube passenger flows
<p>This video shows the diffusion of visitation probabilities of a random walk in a temporal network that has been constructed based on time-stamped data on passenger flows in the London Tube network.</p> <p>The left panel shows a random walk based on the empirical temporal network. This temporal network exhibits non-Markovian characteristics as shown in [1]. The right panel shows a random walk based on a a shuffled version of the same network, in which all order correlations are destroyed. This corresponds to a Markovian temporal network in which all time-respecting path statistics correspond to what is expected based on the static, time-aggregated network.</p> <p>This video is an illustrative supporting animation for the following paper:</p> <p><em>[1] Ingo Scholtes, Nicolas Wider, René Pfitzner, Antonios Garas, Claudio Juan Tessone and Frank Schweitzer: </em><strong>Causality-driven</strong><strong> slow-down and speed-up of diffusion in non-Markovian temporal networks, </strong>Nature Communications, Vol. 5, Article 5024, September 24, 2014</p>
Data and code corresponding to the article "Interaction network structure explains species temporal persistence in empirical plant-pollinator communities"
<p>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".</p><p>The database comprises two files containing the abundances of plants and pollinators, and one containing the interaction networks among plants and pollinators. </p><p>The code folder contains the code to generate the results, and to generate the figures of the manuscript. </p>
Temporal Analysis of Knowledge Networks
<p>This dataset contains 64 topic networks extracted from the English Wikipedia's internal link network. Every topic network contains 19 snapshots, which are taken on March 1st of each year from 2002 to 2020. More detailed information are available at: https://github.com/XikunHuang/TAKN.</p>
Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network
<p>Supporting information, datasets, and R and JavaScript code for the the National Ecological Observatory Network’s Airborne Observation Platform (AOP) sampling design and publication, <em>"Spanning Scales: The Airborne Spatial and Temporal Sampling Design of the National Ecological Observatory Network"</em></p>
Temporal and spatial changes in benthic invertebrate trophic networks along a taxonomic richness gradient
<p>Species interactions underlie most ecosystem functions and are important for understanding ecosystem changes. Representing one type of species interaction, trophic networks were constructed from biodiversity monitoring data and known trophic links to assess how ecosystems have changed over time. The Baltic Sea is subject to many anthropogenic pressures, and low species diversity makes it an ideal candidate for determining how pressures change food webs. In this study, we used benthic monitoring data from 20 years (1980-1989 and 2010-2019) from the Swedish coast of the Baltic Sea and Skagerrak to investigate changes in benthic invertebrate trophic interactions. We constructed food webs and calculated fundamental food web metrics evaluating network horizontal and vertical diversity, as well as stability that were compared over space and time. Our results show that the west coast of Sweden (Skagerrak) suffered a reduction in benthic invertebrate biodiversity by 32 % between the 1980's and 2010's, and that the number of links, generality of predators, and vulnerability of prey, have been significantly reduced. The other basins (Bothnian Sea, Baltic Proper and Bornholm Basin) do not show any significant changes in species richness or consistent significant trends in any food web metrics investigated, demonstrating resilience at a lower species diversity. The decreased complexity of the Skagerrak food webs indicates vulnerability to further perturbations and pressures should be limited as much as possible to ensure continued ecosystem functions.</p>
Fig. 3. Minimum spanning network for Haemoproteus and Plasmodium mitochondrial DNA cytochrome b in Spatial, temporal, molecular, and intraspecific differences of haemoparasite infection and relevant selected physiological parameters of wild birds in Georgia, USA
Fig. 3. Minimum spanning network for Haemoproteus and Plasmodium mitochondrial DNA cytochrome b haplotypes detected in four species of passerines from Georgia (USA). Circles are drawn proportional to the frequency at which haplotypes were observed. Color represents the host species from which haplotypes originated: red for Northern Cardinal (Cardinalis cardinalis), blue for Indigo Bunting (Passerina cyanea), yellow for White-throated Sparrow (Zonotrichia albicollis), and grey for Tufted Titmouse (Baeolophus bicolor). A single mutation separates nodes unless explicitly indicated by number. Letters within each node refer to Table 8 which indicates the haplotype name, sampling location, and other factors associated with hosts.
Community-Based Event Detection in Temporal Networks
<p>This dataset contains daily tweets ids during April 2013. This is provided to facilitate reproducibility of results presented in the following paper:</p> <p>Pablo Moriano, Jorge Finke, and Yong-Yeol Ahn. "Community-Based Event Detection in Temporal Networks." Scientific Reports 9, 4358, 2019. DOI: <a href="https://doi.org/10.1038/s41598-019-40137-0">https://doi.org/10.1038/s41598-019-40137-0</a></p> <p>These data are provided for non-commercial purposes only. If you use this dataset for research, please be sure to cite the above paper.</p>
Dog neuroimaging data from: Action observation reveals a network with divergent temporal and parietal cortex engagement in dogs compared to humans
<p>Action observation is a fundamental pillar of social cognition. Neuroimaging research has revealed a human and non-human primate action observation network (AON) encompassing fronto-temporo-parietal areas with links to the species’ imitation tendencies and relative lobe expansion. Dogs (Canis familiaris) have good action perception and imitation skills and a less expanded parietal than temporal cortex, but their AON remains unexplored. We conducted a functional MRI study with 28 dogs and 40 humans and found functionally analogous involvement of somatosensory and temporal brain areas of both species’ AONs and responses to transitive and intransitive action observation in line with their imitative skills. Employing a functional localizer, we also identified functionally analogous agent-responsive areas within both species’ AONs. However, activation and task-based functional connectivity measures suggested significantly less parietal cortex involvement in dogs than in humans. These findings advance our understanding of the neural bases of action understanding and the convergent evolution of social cognition, with analogies and differences resulting from similar social environments and divergent brain expansion, respectively.</p> <p>This data set contains:</p> <ul> <li>raw functional neuroimaging data of <em>N</em> = 28 dogs</li> <li>structural scans of the same dogs including brain masks & skull-stripped versions</li> <li>eventfiles containing the condition names, onsets and durations for each dog and task run</li> </ul> <p>Data of the comparative human neuroimaging sample will be made available by the first author upon reasonable request.</p> <p>Please also visit our Open Science Framework project site for group-level imaging data of both species (https://osf.io/z479k/).</p>
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
<p>Current ecological research and ecosystem management call for improved understanding of the abiotic drivers of community dynamics, including temperature effects on species interactions and biomass accumulation. Allometric trophic network (ATN) models, which simulate material (carbon) transfer in trophic networks from producers to consumers based on mass-specific metabolic rates, provide an attractive framework to study consumer-resource interactions from organisms to ecosystems. However, the developed ATN models rarely consider temporal changes in some key abiotic drivers that affect e.g. consumer metabolism and producer growth. Here, we evaluate how temporal changes in carrying capacity and light-dependent growth rate of producers and in temperature-dependent mass-specific metabolic rate of consumers affect ATN model dynamics, namely seasonal biomass accumulation, productivity and standing stock biomass of different trophic guilds, including age-structured fish communities. Our simulations of the pelagic Lake Constance (LC) food web indicated marked effects of temporally changing abiotic parameters on seasonal biomass accumulation of different guild groups, particularly among the lowest trophic levels (primary producers and invertebrates). While the adjustment of average irradiance had a minor effect, increasing metabolic rate associated with 1–2˚C temperature increase led to a marked decline of larval (0-year age) fish biomass, but to a substantial biomass increase of 2- and 3-year-old fish that were not predated by ≥4-year-old top predator fish, European perch. However, when averaged across the 100 simulation years, the inclusion of seasonality in abiotic drivers caused only minor changes in standing stock biomasses and productivity of different trophic guilds. Our results demonstrate the potential of introducing seasonality in and adjusting the average values of abiotic ATN model parameters to simulate temporal fluctuations in food-web dynamics, which is an important step in ATN model development aiming to e.g. assess potential future community-level responses to ongoing environmental changes.</p>
Flood water depth prediction with Convolutional Temporal Attention Networks
<p>SwissFlood dataset from Flood water depth prediction with Convolutional Temporal Attention Networks paper.</p>
Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"
<p>Datasets for Paper "BenchTemp: A General Benchmark for Evaluating Temporal Graph Neural Networks"<br> URL: https://github.com/qianghuangwhu/benchtemp</p> <p>Openreview: https://openreview.net/forum?id=rnZm2vQq31</p> <p><br> There are 19 (15+4) benchmark temporal graph datasets:<br> reddit,<br> wikipedia,<br> mooc,<br> lastfm,<br> enron,<br> SocialEvo,<br> uci,<br> CollegeMsg,<br> TaobaoSmall,<br> CanParl,<br> Contacts,<br> Flights,<br> UNtrade,<br> USLegis,<br> UNvote,</p> <p>DGraphFin,</p> <p>TaobaoLarge,</p> <p>YoutubeReddit,</p> <p>YoutubeRedditLarge</p> <p> </p> <p><br> Each dataset has three files:<br> 1. ml_{data_name}.csv - the csv file of the Temporal Graph.</p> <p>This file have five columns with properties:</p> <p>'u': the id of the user.<br> 'i': the id of the item.<br> 'ts': the timestamp of the interaction (edge) between the user and the item.<br> 'label': the label of the interaction (edge).<br> 'idx': the index of the interaction (edge).<br> For example:</p> <p>,u,i,ts,label,idx<br> 0,1,2,0.0,0.0,1<br> 1,1,3,0.0,0.0,2<br> 2,1,4,0.0,0.0,3<br> 2. ml_{data_name}.npy - the edge features corresponding to the interactions (edges) in the the Temporal Graph..</p> <p>3. ml_{data_name}_node.npy - the initialization node features of the Temporal Graph.</p>
Temporal and spatial changes in benthic invertebrate trophic networks along a taxonomic richness gradient
Open the record for dataset details and reuse information.
Effects of temporal abiotic drivers on the dynamics of an allometric trophic network model
Open the record for dataset details and reuse information.
Functional, temporal, and spatial complementarity in mammal-fungal spore networks enhances mycorrhizal dispersal following forest harvesting
Open the record for dataset details and reuse information.
Temporal and spatial changes in benthic invertebrate trophic networks along a salinity gradient
<p>Species interactions underlie all ecosystem goods and services and are important for understanding ecosystem changes. Representing one type of species interaction, trophic networks are able to be constructed from biodiversity monitoring data and known trophic links to understand how ecosystems have changed over time. The Baltic Sea is subject to high anthropogenic pressures, and its low species diversity makes it an ideal candidate for understanding how pressures change food webs. In this study, we used benthic monitoring data from 20 years (1980-1989 and 2010-2019) from the Swedish coast of the Baltic Sea and Skagerrak to investigate changes in benthic invertebrate trophic interactions. We constructed food webs and calculated traditional food web metrics that we compared over space and time. Our results show that the west coast of Sweden (Skagerrak) showed a reduction in benthic invertebrate biodiversity by 40% between the 1980's and 2010's, and that the number of links, linkage diversity, generality of predators, and vulnerability of prey have been significantly reduced. However, connectance has not significantly changed in the Skagerrak. The other basins (Bothnian Sea, Baltic Proper and Bornholm Basin) do not show any consistent significant trends in any food web metrics investigated, demonstrating resilience at a lower species diversity. The decreased complexity of the Skagerrak food webs indicates vulnerability to further perturbations and pressures should be limited as much as possible to ensure continued ecosystem functions.</p>
Data: Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy
<p>We make available all the brain network data, and metadata of 83 patients and 29 healthy controls included in our study.</p> <p>Nishant Sinha, Natalie Peternell, Gabrielle M. Schroeder, Jane de Tisi, Sjoerd B. Vos, Gavin P. Winston, John S. Duncan, Yujiang Wang, and Peter N. Taylor "<em>Focal to bilateral tonic-clonic seizures are associated with widespread network abnormality in temporal lobe epilepsy.</em>" Epilepsia 2021 <em>doi:10.1111/epi.16819</em>.</p> <p>Methodological details on MRI acquisition and data processing are provided in our manuscript. We request users to kindly cite our article and data appropriately.</p>
Bat-flower interaction networks in Caatinga reveal generalized associations and temporal stability
<p>Seasonal variation in precipitation regimes influences species composition and plant-animal interactions. Such temporal variation is especially relevant in the Brazilian Caatinga, the largest Seasonally Dry Tropical Forest in South America, where bat pollination is unusually frequent in comparison with other tropical plant communities. Here, we describe seasonal and annual variations of the interaction networks between nectarivorous bats and flower species in the Caatinga. Five species of nectar-feeding bats interacted with 30 plant species. Nectarivorous bats showed high levels of interaction overlap, which contributed to ecological generalization (low specialization and modularity) and lack of nestedness in the interaction networks. This pattern was consistent across seasons and years. Chiropterophilous and non-chiropterophilous plants were equally important components of the interaction network. The generalized interaction patterns found may be a necessary condition for the persistence of nectarivorous bats and their specialized plants in the environmentally harsh and variable Caatinga. The underappreciated generalized interactions of bats with plants calls for studies testing the effectiveness of bats in pollinating the plants they visit, including those not typically categorized as "bat-flowers".</p>
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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.