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9,152 results for “Networking”
Data from: Experimental species removals impact the architecture of pollination networks
Mutualistic networks are key for the creation and maintenance of biodiversity, yet are threatened by global environmental change. Most simulation models assume that network structure remains static after species losses, despite theoretical and empirical reasons to expect dynamic responses. We assessed the effects of experimental single bumblebee species removals on the structure of entire flower visitation networks. We hypothesized that network structure would change following processes linking interspecific competition with dietary niche breadth. We found that single pollinator species losses impact pollination network structure: resource complementarity decreased, while resource overlap increased. Despite marginally increased connectance, fewer plant species were visited after species removals. These changes may have negative functional impacts, as complementarity is important for maintaining biodiversity–ecological functioning relationships and visitation of rare plant species is critical for maintaining diverse plant communities.
Data from: A convolutional neural network for detecting sea turtles in drone imagery
1. Marine megafauna are difficult to observe and count because many species travel widely and spend large amounts of time submerged. As such, management programs seeking to conserve these species are often hampered by limited information about population levels. 2. Unoccupied aircraft systems (UAS, aka drones) provide a potentially useful technique for assessing marine animal populations, but a central challenge lies in analyzing the vast amounts of data generated in the images or video acquired during each flight. Neural networks are emerging as a powerful tool for automating object detection across data domains and can be applied to UAS imagery to generate new population-level insights. To explore the utility of these emerging technologies in a challenging field setting, we used neural networks to enumerate olive ridley turtles (Lepidochelys olivacea) in drone images acquired during a mass-nesting event on the coast of Ostional, Costa Rica. 3. Results revealed substantial promise for this approach; specifically, our model detected 8% more turtles than manual counts while effectively reducing the manual validation burden from 2,971,554 to 44,822 image windows. Our detection pipeline was trained on a relatively small set of turtle examples (N=944), implying that this method can be easily bootstrapped for other applications, and is practical with real-world UAS datasets. 4. Our findings highlight the feasibility of combining UAS and neural networks to estimate population levels of diverse marine animals and suggest that the automation inherent in these techniques will soon permit monitoring over spatial and temporal scales that would previously have been impractical.
Figure 1 in First characterization of a taxonomically well-resolved trophic network composed by host plants and gall midges (Diptera: Cecidomyiidae) in the Neotropical region
Figure 1. Bipartite network of host plants and gall midge species at the Restinga of Barra de Maricá (Maricá, RJ, Brazil). Lower bars represent host plant species and upper bars represent gall-midge species; grey bars represent interactions. Bar thickness is proportional to the number of interactions of each species. ble interactions. The observed connectance (C = 0.028) decades), which enhances the sampling of rarer interacwas lower than expected from null model values (Null tions, and consequently increases the specialization of C = 0.032 ± 0.001, p <0.001). Similarly, the observed num- the network. However, this fact reinforces the relevance ber of links per species (L = 0.608) also was lower than of the observed patterns, because even with such a long expected by chance (Null L = 0.710 ± 0.020, p <0.001). sampling, only species-specific plant-galling interactions The observed modularity for plant-galling network was were registered. very high (M = 0.958), but did not differ from null model The structure of the network formed by the gall midgvalues (Null M = 0.959 ± 0.001, p> 0.05). Robustness ob- es and their host plants proved to be highly specialized. served was relatively low (R = 1.343), but was higher than The connectance observed in the present study (2.8%) expected by chance (Null R = 1.334 ± 0.232, p <0.001). was low as compared to other plant-phytophagous networks (review in Araújo et al., 2015). However, comparing with other networks of galling arthropods, the val- DISCUSSION ue observed here was higher than observed by Araújo
Data from: Planning for climate change through additions to a national protected area network: implications for cost and configuration
<p>Expanding the network of protected areas is a core strategy for conserving biodiversity in the face of climate change. Here we explore the impacts on reserve network cost and configuration associated with planning for climate change in the United States using networks that prioritize areas projected to be climatically suitable for 1,460 species both today and into the future, climatic refugia, and areas likely to facilitate climate-driven species movements. For 14% of the species, networks of sites selected solely to protect areas currently climatically suitable failed to provide climatically suitable habitat in the future. Protecting sites climatically suitable for species today and in the future significantly changed the distribution of priority sites across the U.S.—increasing relative protection in the northeast, northwest, and central U.S. Protecting areas projected to retain their climatic suitability for species cost 59% more than solely protecting currently suitable areas. Including all climatic refugia and 20% of areas that facilitate climate-driven movements increased the cost by another 18%. Our results indicate that protecting some types of climatic refugia may be a relatively inexpensive adaptation strategy. Moreover, although addressing climate change in conservation plans will have significant implications for the configuration of networks, the increased cost of doing so may be relatively modest.</p> <p> </p>
Ecological mechanisms explaining interactions within plant-hummingbird networks: morphological matching increases towards lower latitudes
<p><a name="ab4">Interactions between species are influenced by different ecological mechanisms, such as morphological matching, phenological overlap, and species abundances</a>. <a name="Ab1"></a><a name="ab5">How</a> these mechanisms explain interaction frequencies across environmental gradients remains poorly understood. Consequently, we also know little about the mechanisms that drive the geographical patterns in network structure, such as complementary specialization and modularity. Here, we use data on morphologies, phenologies and abundances to explain interaction frequencies between hummingbirds and plants at a large geographic scale. <a name="ab2">For 24 quantitative networks sampled throughout the Americas, we found that</a> the tendency of species to interact with morphologically matching partners contributed to specialized and modular network structures. Morphological matching best explained interaction frequencies in networks found closer to the equator and in areas with low temperature seasonality. When comparing the three ecological mechanisms within networks, we found that both morphological matching and phenological overlap generally outperformed abundances in the explanation of interaction frequencies. Together, these findings provide insights into the ecological mechanisms that underlie geographical patterns in resource specialization. Notably, our results highlight morphological constraints on interactions as a potential explanation for increasing resource specialization towards lower latitudes.</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>
Implementing large genomic SNP datasets in phylogenetic network reconstructions: a case study of particularly rapid radiations of cichlid fish
<p><span><span><span><span><span><span><span><span><span><span><span>The Midas cichlids of the <i>Amphilophus</i> <i>citrinellus </i>spp<i>.</i> species complex from Nicaragua, are an extraordinary adaptive rapid radiation (<24,000 years old; 13 described species). These cichlids are a very challenging group to infer its evolutionary history in phylogenetic analyses, due to the apparent prevalence of ILS, as well as past and current gene flow. Assuming solely a vertical transfer of genetic material from an ancestral lineage to new lineages is not appropriate in many cases of genes transferred horizontally in nature. Recently developed methods to infer phylogenetic networks under such circumstances might be able to circumvent these problems. These models accommodate not just incomplete lineage sorting, but also gene flow, under the multispecies network coalescent model (MSNC), processes that are at work in young, hybridizing, and/or rapidly diversifying lineages. There are currently only a few programs available that implement MSNC for estimating phylogenetic networks. Here, we present a novel way to incorporate single nucleotide polymorphism (SNP) data into the currently available PhyloNetworks program. Based on simulations, we demonstrate that SNPs can provide enough power to recover the true phylogenetic network. Moreover, our approach results in a faster algorithm compared to the original pipeline in PhyloNetworks, without losing power. We also applied our new approach to infer the phylogenetic network of Midas cichlid radiation. We implemented the most comprehensive genomic dataset to date (RADseq dataset of 679 individuals and >37K SNPs from 19 ingroup lineages) <span><span>and present estimated phylogenetic networks for this extremely young and fast-evolving radiation of cichlid fish. </span></span>We demonstrate that the MSNC is more appropriate than the multispecies coalescent alone for the analysis of this rapid radiation. </span></span></span></span></span></span></span></span></span></span></span></p>
Class 97 Network Rail 97302
Class 97 Netword Rail 97302 Modelled in Blender 3.1.2 **** Not Presented as Game Ready **** If you are into your trains, checkout this youtube channel. It's been so helpful to me with my models. https://www.youtube.com/channel/UCRqa3Y2GVMKLyoRmZ80Ep9w Source: Objaverse 1.0 / Sketchfab
Data and Codes for "Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime" by Pahlavan et al. (2023)
<p>This is part of the code and data related to the paper entitled Explainable Offline-Online Training of Neural Networks for Parameterizations: A 1D Gravity Wave-QBO Testbed in the Small-data Regime, available at https://arxiv.org/abs/2309.09024.</p><p>The original sources of the codes are the v1.0.0 version of open source software EnsembleKalmanProcesses.jl for EKI analysis, accessible at zenodo.org/records/7806813, and the \emph{qbo1d} code for the 1D-QBO model simulations, accessible at github.com/DataWaveProject/qbo1d.git.</p>
Southeastern Australian rescued observational climate network, 1788–1859
<p>Historical meteorological observations for southeastern Australia, covering 1788–1859. The dataset contains digitised versions of 38 sources of historical temperature, rainfall and pressure information for the southeastern Australian region. It also contains monthly and seasonal anomalies of southern and eastern SEA climate variability for 1788 to 1859. This dataset was developed as part of the South Eastern Australian Recent Climate History project (SEARCH, www.climatehistory.com.au).</p>
Data for Secure communication in IP-based wireless sensor networks via a trusted gateway publication
<p>This archive file contains the raw data obtained from Contiki sensor nodes during Cooja experiments in the folders e2e, terminate, terminate_1st and plaintext.</p> <p>The archive accompagnies the IEEE ISSNIP 2015 publication titled "Secure communication in IP-based wireless sensor networks via a trusted gateway" by Floris Van den Abeele, Tom Vandewinckele, Jeroen Hoebeke, Ingrid Moerman and Piet Demeester.</p> <p><br /> Also included is the data_parser python script that converts the raw data into CSV files that are parseable by R. The script contains the definitions of the contents of the raw data files.<br /> Finally, the R scripts that use the CSV files to generate the plots from the paper are also included.</p>
Neutralization Data and Aligned ENV Sequences for Predicting Antibody Affinities using Artificial Neural Networks
<p>Sample file with neutralization data (IC<sub>50</sub>) for different antibodies and viral strains, adapted from J. Huang, G. Ofek, L. Laub, M. K. Louder, N. A. Doria-Rose, N. S. Longo, H. Imamichi, R. T. Bailer, B. Chakrabarti, S. K. Sharma, S. M. Alam, T. Wang, Y. Yang, B. Zhang, S. A. Migueles, R. Wyatt, B. F. Haynes, P. D. Kwong, J. R. Mascola, and M. Connors, “Broad and potent neutralization of HIV-1 by a gp41-specific human antibody.,” <em>Nature</em>, vol. 491, no. 7424, pp. 406–12, Nov. 2012.</p> <p> </p> <p>Aligned ENV sequences downloaded from the HIV Sequence Database (www.hiv.lanl.gov/content/sequence/HIV/mainpage.html). There are 4907 sequences and the alignment length is 1369.</p>
Interactive Tagging Networks (Following/Followers and Tags on 1 million Twitter Users)
<p><strong>Abstract</strong> (our paper)</p> <p>How do users behave if they can tag each other in social networks? In this paper, we answer this question by studying the interactive tagging network constructed by Twitter lists. Twitter lists can be regarded as the tagging process; a user (i.e., tagger) creates a list with a name (i.e., tag) and adds other users (i.e., tagged users) into the list. This tagging network is by nature different from the resource tagging networks (e.g., Flickr and Delicious) because users on this network can tag each other. We address the following research questions: (RQ1) What is the common patterns and the difference between the interactive tagging network and the resource tagging networks? (RQ2) Do users tag each other on the interactive tagging network? And if so, to what extent? (RQ3) What is the difference between the two types of relationships on Twitter: who-tags-whom and who-follows-whom? By quantitatively studying million-scale networks, we found the pervasive patterns across the different tagging networks, and the interactive patterns within the interactive tagging network. This study sheds light on the underlying characteristics of the interactive tagging network, which is relevant to the social scientists and the system designers of the tagging systems.</p> <p><strong>Data</strong></p> <p>twitter.seed.users:<br> The first column is the user id, and the second column is the json of the user objects on Twitter. This is the set of 1 million seed users to collect the following data.</p> <p>twitter.tagging.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id.</p> <p>twitter.tagging-out-going-from-seed-users.network:<br> The first column is the source user id (from user id), the second column is the destination user id (to user id), the third column is the tag (<em>i.e.</em> slug or list name), and the fourth column is the list id. This is only the out-going edges from the seed users, <em>i.e.</em>, this is a subset of twitter.tagging.network.</p> <p>twitter.following.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id).</p> <p>twitter.following-closed-seed-users.network:<br> The first column is the source user id (from user id), and the second column is the destination user id (to user id). This is not used in the following publication paper, but will be useful in other studies.</p> <p><strong>Publication</strong></p> <p>This data set was created for our study. If you make use of this data set, please cite:<br> Yuto Yamaguchi, Mitsuo Yoshida, Christos Faloutsos, Hiroyuki Kitagawa. Patterns in Interactive Tagging Networks. <em>Proceedings of the Ninth International AAAI Conference on Web and Social Media (ICWSM-15)</em>. pp.513-522, 2015.<br> http://www.aaai.org/ocs/index.php/ICWSM/ICWSM15/paper/view/10556</p> <p><strong>Code</strong></p> <p>Our code outputting experiment results made available at:<br> https://github.com/yamaguchiyuto/icwsm15</p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang and G. Casale, accepted for presentation at MAMA workshop 2015. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Mouse and Human Co-expression maps and supplementary material for: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels"
<p>Co-expression maps of the human and mouse species derived from microarray data for the first release of the GeneFriend tool.</p> <p>The two co-expression maps have been compared in order to discern similarities and differences between the two species. The results have been described in the manuscript titled: "A comparison of human and mouse gene co-expression networks reveals conservation and divergence at the tissue, pathway and disease levels".</p> <p>The supplementary material of the manuscript have also been included in this repository.</p> <p> </p>
Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data
<p>The data part of this release support the results presented in the paper <br /> "Maximum Likelihood Estimation of Closed Queueing Network Demands from Queue Length Data", by W. Wang, G. Casale, A. Kattepur and M. Nambiar, accepted for presentation at ICPE 2016. </p> <p>When referring to the dataset or scripts please cite the paper above. </p>
Anonymized Instagram network data from Amsterdam and Copenhagen, Pajek format
<p>Networks of reciprocated recognition (mutual liking and/or commenting) among Instagram users in Amsterdam and Copenhagen, on the basis of data collected over a twelve-week period in 2015. User names are hashed to anonymize the data.</p>
Interactive Citation Networks for Texas Archaeology
<p>This dataset includes the JavaScript used to generate two interactive citation networks. IntNetFig1 includes the network filtered by the giant component and nodes sized by OutDegree, and IntNetFig2 is the same network filtered by a degree of two, nodes sized by Eigenvector Centrality and colored by modularity class.</p>
Texas Archaeology Publications--Network Data
<p>This dataset was harvested from Scopus using the search "Texas AND archaeology." These data were then used to generate a citation network used to explain how the work of Texas archaeologists articulates with one another. </p>
Spatial Tournament Data for Complex Networks Non Deterministic Size - MSc Dissertation
<p>A data set that contains the results of various spatial tournaments, for the iterated prisoner's dilemma. Three different types of complex networks have been used as topology for the spatial tournaments. Small world, random and complete networks.</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.