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9,152 results for “Networking”
The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading
<p>Dataset for the paper "The dynamical formation of ephemeral groups on networks and their effects on epidemics spreading "</p> <p>- NullModels_NoOverlap.xlsx : Index of data files and summary of results for the Null Model and No Overlap Analyses</p> <p>- EphemeralGroups.xlsx: Index of data files and summary of results of a selection of tested configurations for #G=(1,10,100,1000). The corresponding zip files contain the data files for each #G</p>
Dataset: Model-based Cognitive Communications for Low-power Wireless Networks
<p>Dataset: Model-based Cognitive Communications for Low-power Wireless Networks</p>
dataset where a Convolutional Neural Network controls a part of the TEM optics and OAM sorter
<p>Here are uploaded the data collected when we succesfully connected a Convolutional Neural Network to the Holo-TEM in Julich. In particular the CNN controls a part of the TEM optics (beam shift x and y, C3 intensity) and the OAM sorter (the Sorter1 and Sorter 2 bias, in particular it is able to change the S1 bias to perfectly match the phase profile generated from the second sorting element).</p>
Study of terminological subsystems of modern school textbooks in Russian with the help of word embedding models Word2Vec and neural networks
<p>The reported study was funded by RFBR, project number 19-29-14032 mk.</p>
Supplementary material 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
ChickenEgg-SCNM
Figure 3 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 3 Choropleth map for production of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model.
Figure 2 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 2 Choropleth map for total consumption of the product "Eggs" (quantity in tons per year) in Germany on NUTS-3 level created by the visualisation script of the attached FSKX model .
Figure 1 from: Ganas P, Fuhrmann M, Filter M (2021) A network model of the egg supply chain in Germany implemented as a FSKX compliant object. Food Modelling Journal 2: e74171. https://doi.org/10.3897/fmj.2.74171
Figure 1 Simplified schematic representation of the food supply chain network in Germany illustrating actors (indicated by boxes) and transport processes (indicated by arrows) according to the dynamic freight flow model from Balster and Friedrich (2019). *Regarding Warehouse and Store: the respective 28 brands are implemented as aggregated and as individual actors in the "egg supply chain network model".
Vortex control in superconducting Corbino geometry networks
<p>Supplemental Material</p>
synthetic climate data used for Controlled Abstention Network (CAN) development
<p>The synthetic climate data used in two papers to develop Controlled Abstention Netoworks. The data is approximately 720Mb, saved as a .mat file. The data is from Mamalakis et al. (2021) - with citation given below. </p> <p>Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes: Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset, submitted to Environmental Data Science, 11/2021, preprint available https://arxiv.org/abs/2103.10005.</p> <p>The code that uses this data can be accessed here:</p> <p>Elizabeth Barnes, & Randal J. Barnes. (2021). eabarnes1010/controlled_abstention_networks: (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.5750222</p> <p>The publications associated with this data are posted on arxiv (but will soon be published in JAMES):</p> <ul> <li> <p><strong>Barnes, Elizabeth A. </strong>and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for regression problems, accepted to <em>JAMES</em> 11/2021. Preprint available at <a href="https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F2104.08236&sa=D&sntz=1&usg=AFQjCNENUTEbOS90QNROchwcMQDDpJsrjQ">https://arxiv.org/abs/2104.08236</a></p> </li> <li> <p><strong>Barnes, Elizabeth A. </strong>and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for classification problems, accepted to <em>JAMES</em> 11/2021. Preprint available at <a href="https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F2104.08281&sa=D&sntz=1&usg=AFQjCNHUMJPchhqprYXHRAzb2HA4USVvUw">https://arxiv.org/abs/2104.08281</a></p> </li> </ul>
Network topology and patch connectivity affect dynamics in experimental and model metapopulations
<p>Biological populations are rarely isolated in space and instead interact with others via dispersal in metapopulations. Theory predicts that network connectivity patterns can have critical effects on network robustness, as certain topologies, such as scale-free networks, are more tolerant to disturbances than other patterns. However, at present, experimental evidence of how these topologies affect population dynamics in a metapopulation framework is lacking. We used experimental metapopulations of the aquatic protist <i>Paramecium tetraurelia</i> to determine how network topology influences occupation patterns. We created metapopulations engineered to be comparable in linkage density, but differing in their degree distribution. We compared random networks to scale-free networks by evaluating local population occupancy and abundance throughout 18-30 protist generations. In parallel, we used simulations to explore differences in patch occupation patterns among topologies. Under one scenario, random metapopulations of P. tetraurelia reached higher abundance and higher occupancy (proportion of occupied patches) compared to scale-free systems in both experimental and simulated systems, while in the other both types of metapopulations performed similarly. Increasing patch degree (i.e., number of connections per patch) reduced the probability of extinction of local populations in both types of networks. We suggest the interaction between colonization/extinction rates and network topology alters the likelihood of rescue effects which results in differential patterns of occupancy and abundance in metapopulations.</p>
Supporting data to "Open-source, low-cost, in-situ turbidity sensor for river network monitoring"
<p>This folder contains the Supporting Dataset that is part of the Manuscript "Open-source, low-cost, in-situ turbidity sensor for river network monitoring."</p>
Dataset for "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"
<p># 20171103.rbn_pskreporter_wspr_data.csv<br> #<br> # This data file contains the amateur radio spot data used in the manuscript:<br> # "First Observations of Large Scale Traveling Ionospheric Disturbances Using Automated Amateur Radio Receiving Networks"<br> # by Nathaniel A. Frissell W2NAF, Stephen R. Kaeppler AD0AE, Diego F. Sanchez KD2RLM, Gareth W. Perry KD2SAK,<br> # William D. Engelke AB4EJ, Philip J. Erickson W1PJE, Anthea J. Coster, J. Michael Ruohoniemi, and Joseph B. H. Baker<br> #<br> # Observations are provided courtesy of:<br> # 1. The Weak Signal Propagation Reporting Network (WSPRNet, https://www.wsprnet.org/)<br> # Operated by Bruce Walker W1BW<br> #<br> # 2. The Reverse Beacon Network (RBN, http://www.reversebeacon.net/)<br> # Operated by Nick Sinanis F5VIH/SV3SJ, David Pascoe KM3T, Mark Glenn K7MJG, Peter Smith N4ZR, Felipe Ceglia PY1NB/CT7ANO, and Dick Williams W3OA<br> #<br> # 3. PSKReporter (https://pskreporter.info/)<br> # Operated by Philip Gladstone N1DQ<br> #<br> # Explanation of Data Columns:<br> # freq: Frequency [kHz]<br> # ut: Time of Observation in Universal Time (UT)<br> # source: Observation Network (WSPRNet, RBN, or PSKReporter)<br> # tx_grid: Transmitter Maidenhead Gridsquare<br> # rx_grid: Receiver Maindenhead Gridsquare<br> # tx_lat: Transmitter Latitude (deg)<br> # tx_long: Transmitter Longitude (deg)<br> # rx_lat: Receiver Latitude (deg)<br> # rx_long: Receiver Longitude (deg)<br> # dist_Km: Ground-Level Great Circle Distance between Transmitter and Receiver (km)<br> # md_lat: Midpoint between Transmitter and Receiver Latitude (deg)<br> # md_long: Midpoint between Transmitter and Receiver Longitude (deg)<br> # slt_mid: Solar Local Time of Midpoint (hr)<br> #<br> # This datafile is a subset of the full WSPRNet/RBN/PSKReporter data, filtered with the following parameters:<br> # ut min: 2017-11-03 00:00:00<br> # ut max: 2017-11-03 23:59:59<br> # freq min: 14000.382<br> # freq max: 14280.0<br> # md_lat min: 20.004737453151876<br> # md_lat max: 54.99977112001497<br> # md_long min: -129.99845895196913<br> # md_long max: -60.008771378901315<br> # dist_Km min: 0.0<br> # dist_Km max: 19861.14355003741</p>
Figure 3 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 3 The DRYvER three-step workflow embedded within 7 Work Packages (WP) and the four main attributes of the DRYvER consortium (red ovals).
Figure 2 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 2 It shows how DRYvER will use this cyclic model as a structured loop embedded in a meta-system perspective to guide adaptive management of DRNs. DRYvER will translate climate projections into changes in flow intermittence patterns at multiple scales, including that of the strategically-selected focal DRNs. This physical setting will then be used to implement a dynamic meta-system perspective to understand the cascading changes in biodiversity, ecosystem functions and ecosystem services. This knowledge will be integrated to develop a multi-criteria decision framework combining scientific, management, socio-economic, legislative barriers and leverages to promote an adaptive management of DRNs.
Figure 4 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 4 DRYvER focal DRNs located in highly contrasted EU and CELAC biogeographic and climatic settings (red points), chosen to span the expected natural variability of drying processes and associated DRN responses.Mediterranean ecoregion: the Genal network, in Andalucía (Spain, Mediterranean climate), a dry region heavily impacted by climate change, where most rivers are already affected by drying (contact partner: UB);Alpine ecoregion: the Albarine network, in the Southern Jura (France, temperate climate), a region mildly impacted by climate change (the Albarine network is part of a national LTER project and monitored since 2006) (contact partner: INRAE);Continental ecoregion: the Velička network, in Morava (Czech Republic, continental climate), a region heavily impacted by climate change where many perennial rivers are shifting towards intermittent flow (contact partner: MU);Balkanic ecoregion: the Krka network, in the Dinaric Karst (Croatia, Mediterranean climate), a region where most rivers are already drying and heavily impacted by climate change (contact partner: UZ);Pannonian ecoregion: the Bükkösdi-víz network, in the Mecsek (Hungary, continental climate), a region moderately impacted by climate change, where DRNs are becoming common (contact partner: UP);Boreal ecoregion: the Vantaanjoki network, Helsinki-Uusimaa Region (Finland, boreal climate), region moderately impacted by climate change, where flow intermittence is currently rare (contact partner: SYKE);Pacific Lowlands: the Cube network, in the Andean-Choco region (Ecuador, tropical climate), a region where drying is very seasonal and increasing in duration and frequency (contact partner: USFQ);Central High Andes ecoregion: the Rio Chico network in the Sucre region (Bolivia, semi-arid climate), a dry area prone to desertification where political conflicts emerge due to water scarcity (contact partner: USFX);Caatinga ecoregion: the Jaguaribe network, in the Northeast Semiarid region (Brazil, semi-arid climate), the driest region in Brazil, already heavily impacted by climate change (contact partner: UFC).
Figure 1 from: Datry T, Allen D, Argelich R, Barquin J, Bonada N, Boulton A, Branger F, Cai Y, Cañedo-Argüelles M, Cid N, Csabai Z, Dallimer M, de Araújo JC, Declerck S, Dekker T, Döll P, Encalada A, Forcellini M, Foulquier A, Heino J, Jabot F, Keszler P, Kopperoinen L, Kralisch S, Künne A, Lamouroux N, Lauvernet C, Lehtoranta V, Loskotová B, Marcé R, Martin Ortega J, Matauschek C, Miliša M, Mogyorósi S, Moya N, Müller Schmied H, Munné A, Munoz F, Mykrä H, Pal I, Paloniemi R, Pařil P, Pengal P, Pernecker B, Polášek M, Rezende C, Sabater S, Sarremejane R, Schmidt G, Senerpont Domis L, Singer G, Suárez E, Talluto M, Teurlincx S, Trautmann T, Truchy A, Tyllianakis E, Väisänen S, Varumo L, Vidal J-P, Vilmi A, Vinyoles D (2021) Securing Biodiversity, Functional Integrity, and Ecosystem Services in Drying River Networks (DRYvER). Research Ideas and Outcomes 7: e77750. https://doi.org/10.3897/rio.7.e77750
Figure 1 Phases of flowing and drying alternate annually in the naturally intermittent Albarine River (France), a focal DRN of DRYvER. About half of EU's river channels now flow intermittently and this fraction is increasing. Photos: T. Datry.
NicheNet 2.0. Ligand-Target matrix and Networks
<p>NicheNet 2.0. ligand-target matrix and networks for both mouse and human</p>
Data for: Local contributions to beta-diversity in urban pond networks: implications for biodiversity conservation and management
<p><b>Aim:</b> An understanding of how biotic communities are spatially organised is necessary to identify and prioritize habitats within landscape-scale biodiversity conservation. Local Contribution to Beta diversity (LCBD) identifies individual habitats that make a significant contribution to beta-diversity and may have important practical implications, particularly for conservation of habitat networks. In this study, we develop and apply a conservation prioritisation approach based on LCBD in aquatic invertebrate communities from 132 ponds.</p> <p><b>Location:</b> Five urban settlements in the UK: Halton, Loughborough, Stockport, Birmingham, Huddersfield.</p> <p><b>Methods:</b> We partition LCBD into richness difference (nestedness: <sub>RichDiff</sub>LCBD) and species replacement (turnover: <sub>Repl</sub>LCBD) and identify key environmental variables driving LCBD. We examine LCBD at two scales relevant to conservation planning: within urban settlements and nationally across the UK.</p> <p><b>Results:</b> Significant differences in LCBD values were recorded among the five settlements. In four of the five urban settlements studied, pond sites with the greatest LCBD values typically showed high replacement values. Significant LCBD sites and sites with high taxonomic diversity together supported more of the regional species pool (70%-97%) than sites with high taxonomic diversity alone (54% to 94%) or what could be protected by the random selection of sites. LCBD was significantly associated with vegetation shading, surface area, altitude and macrophyte cover.</p> <p><b>Main conclusions:</b> Conservation prioritisation that incorporates LCBD and sites with high taxonomic diversity improves the effectiveness of conservation actions within pond habitat networks, ensures site supporting high biodiversity are protected, and provides a method to define a spatial network of protected sites. Identifying new, effective conservation approaches, particularly in urban areas where resources may be scarce and conflicts regarding land use exist, is essential to ensure biodiversity is fully supported and detrimental anthropogenic effects are reduced.</p>
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</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.