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9 results for “link layer”
Application of hyperbolic geometry of multiplex networks under layer link-based attacks
<p>As real multilayer networks, we consider four networks. The multilayer networks are converted to multiplex networks by assuming that all layers have the same number of nodes (the maximum number of nodes of all layers). Explanation of these networks is as follow:</p> <ol> <li><em>CS-Aarhus_multiplex [1]</em> : The first network used in this study is a 5-layer multiplex network, named CS-Aarhus_multiplex, which has 61 nodes and 620 edges. The multiplex social network consists of five kinds of online and offline relationships (Facebook, Leisure, Work, Co-authorship, Lunch) between the employees of the Computer Science department at Aarhus.</li> <li><em>Data_malaria_PLOSCompBiology_2013 [2]</em>: The second network is a 9-layer multiplex network, which consists of 307 nodes and 35306 edges. Networks of recombinant antigen genes from the human malaria parasite P. falciparum. Each of the 9 networks shares the same set of vertices but has different edges, corresponding to the 9 highly variable regions (HVRs) in the DBLa domain of the var protein. Nodes are var genes, and two genes are connected if they share a substring whose length is statistically significant.</li> <li>VICKERS CHAN 7th-GRADERS [3] : The third network is a 3-layer multiplex network, called VICKERS CHAN 7th-GRADERS, which includes 29 nodes and 740 edges. The data were collected by Vickers from 29 seventh-grade students in a school in Victoria, Australia. Students were asked to nominate their classmates on several relations including the three layers.</li> </ol> <p> 4. FAO MULTIPLEX TRADE NETWORK [4]: The fourth network is a 364-layer multiplex network, which contains 214 nodes and 318346 edges. We consider different types of trade relationships among countries, obtained from FAO (Food and Agriculture Organization of the United Nations)</p>
Data from: Link prediction in real-world multiplex networks via layer reconstruction method
Networks are invaluable tools to study real biological, social and technological complex systems in which connected elements form a purposeful phenomenon. A higher resolution image of these systems shows that the connection types do not confine to one but to a variety of types. Multiplex networks encode this complexity with a set of nodes which are connected in different layers via different types of links. A large body of research on link prediction problem is devoted to finding missing links in single-layer (simplex) networks. In recent years, the problem of link prediction in multiplex networks has gained the attention of researchers from different scientific communities. Although most of these studies suggest that prediction performance can be enhanced by using the information contained in different layers of the network, the exact source of this enhancement remains obscure. Here, it is shown that similarity w.r.t. structural features (eigenvectors) is a major source of enhancements for link prediction task in multiplex networks using the proposed Layer Reconstruction Method and experiments on real-world multiplex networks from different disciplines. Moreover, we characterize how low values of similarity w.r.t. structural features result in cases where improving prediction performance is substantially hard.
Keys from the Sky: A First Exploration of Physical-Layer Security Using Satellite Links
<p>Datasets containing the UBX files used for the evaluation in <a href="https://arxiv.org/abs/2010.07194">arXiv:2010.07194</a>.</p> <p>The .ubx files stem from a ublox ZED-F9P and can be converted to the RINEX .obs format with the open source software rtklib.<br> These contain the used carrier phase observables in an easily parseable format.</p> <p><br> The binary .ubx files contain additional signal information (lock time, satellite ephemeris, etc.) and can be viewed with u_center.</p>
Dataset linked to manuscript entitled "Changes in Arctic Stratification and Mixed Layer Depth Cycle, A Modeling Analysis" by Hordoir et al.
<p>This dataset allows the re-create the fingures showing changes in Arctic stratification and mixed layer depth, as in the manuscript. Additional information can be obtained by email.</p>
Data from: Link prediction in real-world multiplex networks via layer reconstruction method
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Linking evapotranspiration, boundary-layer processes and atmospheric moisture using isotope tracer modeling and data
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Datasets for "Contrasting responses of local climate to the perturbation of atmospheric boundary layer winds linked to land–atmosphere interactions"
<p>Datasets for paper "<strong>Contrasting responses of local climate to the perturbation of atmospheric boundary layer winds linked to land–atmosphere interactions"</strong></p>
Timing of Germ Layer Initiation Linked to Spatially Patterned Onset of Genome Activation
GEO Series GSE201874. Xenopus laevis. 106 samples. Type: Expression profiling by high throughput sequencing.
High fluid shear stress induces Hippo/YAP pathway in articular cartilage superficial layer cells: a potential mechanistic link to 0steoarthritis
GEO Series GSE289374. Rattus norvegicus. 6 samples. Type: Expression profiling by high throughput sequencing.
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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.