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149 results for “network connectivity”
LAGOS-US NETWORKS v1.0: Data module of surface water networks characterizing connections among lakes, streams, and rivers in the conterminous U.S
Knowing the degree of surface water connectivity among aquatic ecosystems can help scientists better understand and predict the movement of materials and biota across ecosystems. Methods to quantify surface water networks that include lake and stream connections at broad spatial scales are rare because it is difficult to balance accurate estimates of surface water connectivity and computational challenges. The LAGOS-US NETWORKS (NETS) module contains surface connectivity metrics for lake networks across the conterminous United States. We applied a graph theory approach to identify lake networks (i.e. a set of lakes connected by streams either upstream, downstream, or both) created from the medium resolution NHD lakes, streams, and rivers and subsequently derive surface water connectivity metrics for lakes and networks. Using this approach, we created a total of 898 networks that include 86,511 lakes. The NETS module includes a table with metrics for connections between lakes (both upstream and downstream), dams, network position, and whole networks. NETS also includes a flow table and bidirectional and unidirectional distance tables that provide the distances between every pair of connected lakes.
Connectivity networks for Acropora corals on the GBR to investigate split spawning
<p>Connectivity networks for Acropora corals on the GBR to investigate split spawning.</p> <p>If using these outputs please cite the article:</p> <p>Hock K, Doropoulos C, Gorton R, Condie SA, Mumby PJ. (2019). <strong>Split spawning increases robustness of coral larval supply and inter-reef connectivity</strong>. Nature Communications <strong>10</strong>, 3463.</p> <p>Link to the paper:</p> <p>https://rdcu.be/bOW1x</p> <p> </p> <p> </p> <p> </p>
Dataset: Environmental Impact on the Long-Term Connectivity and Link Quality of an Outdoor LoRa Network
<p>This repository contains the long-term connectivity and link quality dataset collected on <a href="https://chirpbox.github.io/">ChirpBox</a> over 4 months (May -- September 2021) in the city of Shanghai, China. </p> <p>In addition to the dataset itself, we provide evaluation scripts for data analysis and visualization, in order to facilitate data exploration and re-use. To make it clear how to use the scripts, we provide a <em>Jupyter notebook -- </em> <strong>dataset.ipynb</strong> for dataset visualization.</p> <p><strong>List of files:</strong></p> <ol> <li><em>dataset_03052021_15092021.csv</em> <ul> <li>The dataset includes LoRa connectivity and link quality, as well as environmental information, collected from May 3 to September 15, 2021.</li> </ul> </li> <li><em>data_analysis.py</em> <ul> <li>The script for dataset analysis and visualization. One can use the functions in this script to derive network-level statistics (e.g., in terms of average number of correctly-exchanged packets), link-level statistics (e.g., in terms of SNR, RSS, and PRR), and node-level statistics(e.g., in terms of number of neighbours and temperature evolution over time).</li> </ul> </li> <li><em>metadata_processing.py</em> <ul> <li>The script for pre-processing metadata into CSV files. One can use the functions in this script to convert metadata for each measurement saved in TXT and JSON formats to CSV files that include attributes such as link quality, connectivity, and environmental information, an example of which is <strong>dataset_03052021_15092021.csv</strong>.</li> </ul> </li> <li><em>dataset.ipynb </em> <ul> <li>The Jupiter notebook contains examples of visualization and metadata pre-processing of datasets with functions in <strong>data_analysis.py</strong> and <strong>metadata_processing.py</strong>.</li> </ul> </li> <li><em>topology_map.png</em> <ul> <li>The node deployment map used to create topology figures. A usage example is <strong>Figure 1</strong> shown in the notebook <strong>dataset.ipynb</strong>.</li> </ul> </li> <li><em>dataset_metadata.zip</em> <ul> <li>The dataset metadata is stored in TXT and JSON formats. Among them, link quality, connectivity and on-board sensor data are stored in TXT files and weather information are stored in JOSN files.</li> </ul> </li> <li><em>README.md</em> <ul> <li>The README.md explains all the files in this repository and gives some examples of how to use the provided scripts to analyze the dataset.</li> </ul> </li> </ol>
Ascending arousal network connectivity during recovery from traumatic coma
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Leveraging on Digital Signage Networks to Bring Connectivity to IoT Devices
<p>This dataset contains the open data related to the research paper:</p> <p><br /> J. David de Hoz, Jose Saldana, Julián Fernández-Navajas, José Ruiz-Mas, Rebeca Guerrero Rodríguez, Félix de Jesús Mar Luna, Raúl Iván Herrera González, "Leveraging on Digital Signage Networks to Bring Connectivity to IoT Devices," Telcon UNI 2015, Lima, Peru, Oct. 2015.</p> <p><br /> This work has been partly financed by CONACYT (PEI 682/2014); Servicios d TI de Durango S.A. de C.V.; Ateire S.A.C., and de ER H2020 Wi 5 project (Grant Agreement no: 644262). </p> <p><br /> The name of each of the files indicates the figure of the paper: for example, "figure_15.csv" includes the information used to generate the figure 15. In some cases, ".csv" and ".xlsx" files are provided, but they include the same information.</p> <p><br /> In the "measurements" folder, the results are provided, and also the scripts used to obtain them.</p>
Dataset: Connectivity and rigidity percolation of cytoskeletal networks.
<p>Dataset containing information for "Connectivity and rigidity percolation of cytoskeletal networks."</p> <p>File: Fig1A_MEDYAN_Unbranched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_b: number of free branchers</p> <p>File: Fig1A_ODE_Unbranched_timeseries_motor_333_linker_1500_tmax_10000.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> p: Number of plus ends<br> G: Number of free G-actin<br> M: Number of free motors</p> <p>File: Fig1A_Unbranched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, no branchers.<br> The simulations can be found in the Simulations_Unbranched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> Simulation: Simulation ID</p> <p>File: Fig1B_Branched_MEDYAN.csv<br> Description:<br> Species concentrations in MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> The simulations can be found in the Simulations_Branched folder<br> Columns:<br> Last_Timestep: Measured timestep<br> N_Motors: Number of motors<br> N_Linkers: Number of linkers<br> chem_path: Path of the simulation<br> AD: Number of unbound G-actins<br> BD: Number of unbound branchers<br> MD: Number of unbound motors<br> LD: Number of unbound linkers<br> FA: Number of bound F-actin monomers<br> PA: Number of plus ends<br> MA: Number of minus ends<br> LA: Number of bound linkers<br> MOA: Number of bound motors<br> BA: Number of bound branchers<br> Simulation: Simulation ID</p> <p>File: Fig1B_MEDYAN_Branched_timeseries_motor_333_linker_1500_tmax_122.csv<br> Description:<br> Average MEDYAN simulations in a 1um3 box with 333 motors and 1500 linkers, 300 branchers.<br> Columns:<br> Last_Timestep: Last time step of the simulations<br> N_Motors: Total number of motors in the simulation<br> N_Linkers: Total number of linkers in the simulation<br> Simulation: Number of simulations<br> M_p: Number of plus ends<br> M_m: Number of minus ends<br> M_c: Number of free binding sites<br> M_M: Number of free motors<br> M_L: Number of free linkers<br> M_pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> M_cMc: Number of bound motors<br> M_cLc: Number of bound linkers<br> M_G: Number of free G-actin<br> M_B: total number of branchers<br> M_cBm: number of bound branchers<br> M_b: number of free branchers</p> <p>File: Fig1B_ODE_Branched_timeseries_motor_333_linker_1500_tmax_10000_v2.csv<br> Description:<br> Chemical kinetics calculations for transient concentrations of motor, linker and brancher for equivalent MEDYAN simulations of a 1um3 box with 333 motors and 1500 linkers, and 300 branchers.<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> p: Number of plus ends<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of minus ends<br> M: Number of free motors<br> G: Number of free G-actin<br> B: Number of free branchers</p> <p>File: Fig1C_Ps_timeseries_unbranched.csv<br> Description:<br> Flory-Stockmayer results for unbranched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio</p> <p>File: Fig1D_Ps_timeseries_branched.csv<br> Description:<br> Flory-Stockmayer results for branched chemical kinetics calculations<br> Columns:<br> pm: Number of plus ends connected to minus ends (polymerized F-actin)<br> L: Number of free linkers<br> cMc: Number of bound motors<br> cBm: Number of bound branchers<br> cLc: Number of bound linkers<br> c: Number of free binding sites<br> m: Number of free minus ends<br> p: Number free of plus ends<br> G: Number of free G-actin<br> M: Number of free motors<br> B: Number of free branchers<br> P0: Probability that an F-actin monomer is connected to another one on its plus end<br> P1: Probability that an F-actin monomer is connected to another one on its minus end<br> P2: Probability that an F-actin monomer is connected to another one on its binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Nb: Average number of bonds per F-actin monomer<br> Nn: Mean cluster size<br> Nw: Mean weighted cluster size<br> Ratio: Nw/Nn Ratio<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site</p> <p>File: Fig2_Two-step.csv<br> Description:<br> Representative steady state concentrations for a non-cooperative two-step model of linker binding.<br> Columns:<br> Fc: Concentration of free binding sites<br> FcL: Concentration of linkers bound to a single binding site<br> FcLFc: Concentration of linkers bound to a pair of binding sites<br> L: Concentration of unbound linkers<br> Fc0: Total concentration of binding sites<br> L0: Total concentration of linkers</p> <p>File: Fig3_two_step_heatmap.csv<br> Proportion of the concentration of crosslinks to the concentration of total binding sites as a function of the linker binding equilibrium constant<br> Description:<br> 2D matrix, where the columns indicate the linker binding constant multiplied by the total concentration of binding sites, the rows indicate the total concentration of linkers per binding site , and the value corresponds to the total number of linkers bound to two binding sites divided by the total concentration of binding sites.</p> <p>File: Fig5A_Ps_unbranched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher.</p> <p><br> File: Fig5B Ps_branched.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher.<br> </p> <p>File: Fig6_Ps_Branched_var.csv<br> Description:<br> 2D matrix, where the columns indicate the proportion of branchers to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher or motors.</p> <p><br> File: Fig7B_Ps_unbranched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model without brancher. The clusters are defined here as F-actin monomers connected by linkers, and without including motor connections.</p> <p>File: Fig7D_Ps_branched_linkeronly.csv</p> <p>Description:<br> 2D matrix, where the columns indicate the proportion of motors to actin, the rows indicate the proportion of linkers to actin , and the value corresponds to the probability that an F-actin monomer is in a finite cluster using the chemical kinetics model with brancher. The clusters are defined here as F-actin monomers connected by linkers or branchers, and without including motor connections.</p> <p>File: Fig9_data.csv<br> Description:<br> Minimum motor concentration to reach rigidity percolation as a function of the linker concentration for systems with and without brancher, considering both linker and motor connections or just motor connections and for different values of linker rigidity. The motor and linker concentrations are measured as the proportion of linkers or motors to actin.<br> Columns:<br> L: linker concentration <br> "M (unbranched, linkers and motors, bcLc=0)"<br> "M (unbranched, linkers and motors, bcLc=1)"<br> "M (unbranched, linkers and motors, bcLc=2)"<br> "M (unbranched, linkers and motors, bcLc=3)"<br> "M (unbranched, linkers and motors, bcLc=4)"<br> "M (unbranched, linkers and motors, bcLc=5)"<br> "M (unbranched, linkers and motors, bcLc=6)"<br> "M (branched, linkers and motors, bcLc=0)"<br> "M (branched, linkers and motors, bcLc=1)"<br> "M (branched, linkers and motors, bcLc=2)"<br> "M (branched, linkers and motors, bcLc=3)"<br> "M (branched, linkers and motors, bcLc=4)"<br> "M (branched, linkers and motors, bcLc=5)"<br> "M (branched, linkers and motors, bcLc=6)"<br> "M (unbranched, linkers only, bcLc=0)"<br> "M (unbranched, linkers only, bcLc=1)"<br> "M (unbranched, linkers only, bcLc=2)"<br> "M (unbranched, linkers only, bcLc=3)"<br> "M (unbranched, linkers only, bcLc=4)"<br> "M (unbranched, linkers only, bcLc=5)"<br> "M (unbranched, linkers only, bcLc=6)"<br> "M (branched, linkers only, bcLc=0)"<br> "M (branched, linkers only, bcLc=1)"<br> "M (branched, linkers only, bcLc=2)"<br> "M (branched, linkers only, bcLc=3)"<br> "M (branched, linkers only, bcLc=4)"<br> "M (branched, linkers only, bcLc=5)"<br> "M (branched, linkers only, bcLc=6)"</p> <p>File: FigS1_data.csv<br> Description:<br> Connectivity percolation as a function of the probabilities that an F-actin monomer site is bound to another F-actin.<br> Columns:<br> ppm: probability that an F-actin monomer plus end is connected to another F-actin monomer minus end<br> pcc: probability that an F-actin monomer binding site is connected to another F-actin monomer binding site<br> Pcm: probability that an F-actin monomer binding site is connected to another F-actin monomer minus end<br> Qp: Probability that an F-actin monomer is not connected to an infinite cluster through the plus end<br> Qm: Probability that an F-actin monomer is not connected to an infinite cluster through the minus end<br> Qc: Probability that an F-actin monomer is not connected to an infinite cluster through the binding site<br> Ps: Probability that an F-actin monomer is not connected to an infinite cluster<br> Percolated: Whether the system is percolated or not.</p> <p>File: simulations.tar.gz<br> Description: Contains the MEDYAN simulations used for figure 1. Each folder contains an individual simulation, with the following files:<br> systeminput.txt: Contains the input for the system conditions and settings<br> chemistryinput.txt: Contains the input for the chemical species<br> chemistry.traj: Output trajectory containing number of species in the simulations<br> snapshot.traj: Output trajectory containing the coordinates of the species.<br> For more information please reference the MEDYAN user guide and reference:<br> K Popov, JE Komianos and GA Papoian (2016) MEDYAN: Mechanochemical Simulations of Contraction and Polarity Alignment in Actomyosin Networks. PLoS Comput Biol 12(4): e1004877. doi:10.1371/journal.pcbi.1004877</p>
Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks
<p>This Data set contain 29 table of weighted interaction network between plants (columns) and frugivore birds from the Brazilian Atlantic Forest used in the manuscript "Forest cover and connectivity have pervasive effects on the maintenance of evolutionary distinct interactions in seed dispersal networks" published in Oikos Journal.</p>
Data and scripts for: Green turtles highlight connectivity across a regional marine protected area network in West Africa
<p>Data derivates and analysis scripts (in R) used for the paper on analyzing green turtle MPA coverage and connectivity in West Africa.</p>
Maps of ecosystem multifunctionality and ecological connectivity for identifying Green Infrastructure networks in the European Alps
<p>High resolution raster datasets (20 meters) containing the results of an ecological connectivity and an ecosystem multifunctionality assessment for identifying Green Infrastructure networks in 10 pilot regions of the European Alps, modelled as part of the LUIGI Interreg Alpine Space project. Pilot regions include: department of Isère (FR), departments of Savoie and Haute-Savoie (FR), Munich Metropolitan Region (DE), Central Area of Salzburg (AT), South Burgenland (AT), Goriška region (SI), South Tyrol (IT), canton of Grisons (CH), Metropolitan City of Milan (IT), and Metropolitan City of Turin (IT). For a preview of the data and the results available for each pilot region <a href="https://www.alpine-space.org/projects/luigi/en/project-results/d.t1.2.1-pilot-regions-policy-briefs">click here</a></p> <p>Further information on the LUIGI project is available at: <a href="https://www.alpine-space.org/projects/luigi/en/home">https://www.alpine-space.org/projects/luigi/en/home</a></p> <p><a href="https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf">https://webassets.eurac.edu/31538/1661510408-luigi-wp1-technical-annex-mapping-a-green-infrastructure-network-in-the-alpine-space.pdf </a></p> <p>The datasets include:</p> <ul> <li>a map for ecosystem service-based multifunctionality calculated out of the average of 11 standardized ecosystem service indicators: water provision, crop potential, timber production, fodder provision, pollination potential, carbon sequestration, nitrogen retention, natural hazard mitigation, runoff retention, outdoor recreation, and landscape aesthetics.</li> <li>a map of the modelled Ecological Network composed of core areas and ecological corridors. Corridors are modelled for medium-large forest mammal species and represent least-cost pathways connecting core areas. Different classes indicate areas with different levels of current ecological connectivity starting from core areas to areas in cities or anthropized land with no connectivity. Modeled corridors are presented in two classes to mirror different levels of prioritization and management actions.</li> <li>a map of the resistance of the landscape to the movement of forest mammal species. The landscape resistance raster has been developed by reclassifying and aggregating a high resolution (5m) land use and land cover map. Resistance values have been determined in relation to the naturalness of different land use and land cover classes. In this context, land use or landscape resistance is intended as the opposite of habitat suitability.</li> </ul>
Hypothetical landscapes to evaluate connectivity metrics of protected area networks.
<p>This repo contains the raw datasets (as GIS shapefiles) useful to evaluate connectivity metrics of protected area networks. Please suggest if additional landscapes could be added that would be useful to evaluate an additional class or characteristic of protected area networks. They were created using Google Earth Engine script: <strong><a href="https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160">https://code.earthengine.google.com/d1a8dfa3202ac8b4e55657bd3b5a1160</a>.</strong></p> <p>Two shapefiles are provided: (1) ProNet_connectivity_library_L1_26pa -- this contains polygons that represent the size and shape of protected areas (PAs); (2) ProNet_connectivity_library_L1_26pae -- this contains polylines that represent "edges" that do not represent any protected area but denotes that two PAs are connected. Note that these landscapes are fictitious, and represented at the global origin (i.e. 0.0 degrees latitude and 0.0 degrees longitude) -- and are quite small so zooming in will be required to see them in GIS software.</p>
BRAIN Journal-Evolving Spiking Neural Networks for Control of Artificial Creatures-Figure 10: A typical neuron and postsynaptic connections with weights and delays
<p>First, an initial random population of creatures is generated where the neural networks of the creatures are coded as chromosomes, as shown in Figure 10a and Figure 10b. Each chromosome consists of four parts: A1, A2, A3 and A4. Each part consists of N segments for N neurons of a typical neural network structure. The first part, A1, denotes a, b, c and d parameters of neurons Izhikevich model (discussed in (1) and (2)). Each segment of A2 shows postsynaptic weights and connections for corresponding neuron and each segment of A3 indicates postsynaptic delays of theconnections. Segment A4 shows postsynaptic neurons that are connected to corresponding neuron, as shown in Figure 10b.</p>
Dataset for "Adaptive connectivity control in networked multi-agent systems: A distributed approach"
<div> <div>Dataset accompanying the paper "<em>Adaptive connectivity control in networked multi-agent systems: A distributed approach</em>" by M. Krizmancic and S. Bogdan submitted to PLOS ONE journal on April 30, 2024.</div> <div> </div> <div> <div> <div>Contains:</div> <ul> <li>Vector images of the figures presented in the paper.</li> <li>Data files containing the values used to build the figures.</li> </ul> <p>Detailed information and instructions are available in the README file within the dataset.</p> </div> </div> </div>
Code and Data for the Study "A User-Centric Model of Connectivity in Street Networks"
<p>This resource contains the code and results used in the paper:</p> <p>Corcoran, P. and R. Lewis (Pending) “A User-Centric Model of Connectivity in Street Networks”</p> <p>Please consult <strong>UserGuide.pdf</strong> for further information. </p>
Classification of Phonocardiograms with Convolutional Neural Networks-Figure 4. a) Sparse Connectivity, b) Shared Weights (Convolutional Neural Networks (LeNet), 2018)
<p>A CNNs are biologically inspired variants of a multilayer perceptron. CNNs establish a built-in local correlation by applying a local link model between the neurons of adjacent layers. As shown in Figure 4a, the inputs of the hidden units in the m-layer are obtained from a subset of the units having the built-in areas in the m-1 layer. This ensures that a number of layers arrive consecutively, resulting in a filtration. It can encode 5 features such as a neuron in the m+1 hidden layer. On CNNs, each filter hi is repeated on the entire image surface. These repeated units form a feature map that shares the weight and bias parameters. 3 hidden units of the same feature map are shown in Figure 4b. The weights shown by parallel lines in the figure were also limited as same. To learn such shared parameters, the gradient method can be used with only a small modification to the original parameters. The sum of the inverse gradients of the shared parameters equals the gradient of the shared weights.</p>
Expanding China's protected areas network to enhance resilience of climate connectivity
<p>The code used for analysis, mammalian species occurrence points, prediction accuracy(AUC), and prediction distribution.</p> <p>Specifically,</p> <p>The main nodes are named "network efficiency.R" and "network efficiency_random.R", which can be run in RStudio software (download at https://posit.co/), and the files "hfp_expanded.csv" and "EN_length_expanded.net" are example data.</p> <p>"Terrestrial mammalian species occurrence points.csv" is the latitude and longitude coordinates of occurrence records.</p> <p>"AUC_prediction accuracy of Maxent.xlsx" is the prediction accuracy of each species in the Maxent model.</p> <p>"all_species_maxent.zip" is the composite terrestrial mammalian species distribution of equal weight overlapping the spatial distribution of each species ("each_species_maxent.zip").</p> <p> </p> <p> </p>
Data and code from: River network connectivity reductions dominate declines in the richness of plateau fish species under climate change in the upper Yangtze River Basin
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Data and code from: Determinants of species’ centrality in spatially-connected plant-frugivore networks
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Audible Networks: Connecting Texts through Music in 16th-Century Swiss Printed Ballads
<p>In recent years, researchers of early modern print culture, particularly those concerned with news circulation, have increasingly embraced network analysis to account for the flow of information across different regions and media. Even though most of these studies focus on people or cities as nodes and hubs in networks of news distribution, interrelations on a textual level, such as networks of co-citation, have received some attention as well. These approaches, as well as examples of textual network analysis in media history of more recent periods, can serve as inspiration for the study of early modern printed ballads.<br> Like in other printed objects, connections between different ballads can be discerned on a textual level, i.e. as adaptations of existing lyrics, quotation or the combination of several ballads in one print. However, and perhaps obviously, ballads can also be associated on a musical level. The practice of using already existing, popular melodies as a basis for a new text – commonly referred to as “contrafactum” – has repeatedly been shown to be relevant not only as a mnemonic device, but as a means for alluding to themes of existing songs. Previous studies have suggested that this technique was often consciously employed by the authors of songs in order to add an additional layer of meaning.<br> This paper will present some early deliberations within the framework of an ongoing PhD project on political ballads of the 16th-century Swiss Confederation. The project as a whole examines songs from a perspective of media history, investigating their role in constructing and transmitting ideas and imaginations about diplomatic relations between the confederates. Using the example of “contrafactum-relations”, the paper will explore how a methodology inspired by network analysis might be useful in this endeavour. Based on an initial corpus of around 150 printed ballads (containing both original songs and reprints or adaptations of earlier songs) printed in Switzerland between 1530 and 1600, it will provide a visual representation of the connection between the different songs as a network in which individual printed songs function as nodes and melodies as edges. This will allow for the identification of particularly influential melodies, chains of “musical references” and clusters of songs which share the same melodies. These results, in turn, may be put in relation to the subject matter of the songs in question and compared to text-based networks, thus not only providing visual and quantitative evidence of the practice of contrafactum, but giving insight into the mechanisms by which information is transmitted through this particular medium.</p>
Non-random network connectivity comes in pairs: Code & generated data to reproduce results and figures of the article
<p>Complete research code and generated data for the article to reproduce the figures and computations referenced.</p> <p>Please visit https://non-random-connectivity-comes-in-pairs.github.io/ for documentation of the code.</p>
Keynote: Bringing Reinforcement learning Into Radio Light Network for Massive Connections
<blockquote><p>3GPP standardization has been progressing at an astonishingly rapid phase, where Release 15 and Release 16 have set the foundations of the 5G system, while Release 17 provides enhancements and optimizations to enable support for further use cases. In parallel to 5G standardization efforts, several initiatives worldwide endeavour to drive and support the evolution of smart networks and services. Among others Europe is establishing the <i>Joint Undertaking on Smart Networks and Services</i> in the frame of the Horizon Europe programme for research and innovation. Other initiatives are complementing the European initiative, such as <i>Secure 5G & Beyond Act</i> in the U.S., <i>roadmap towards 6G </i>in Japan, <i>MSIT 6G programme</i> in S. Korea, and <i>MIIT 6G programme</i> in China.</p></blockquote><blockquote><p>The workshop will provide an opportunity for reflection and discussion about requirements and architectural considerations for future generations of mobile systems. The focus will be on presenting version 4.0 of the Architecture white paper developed by the 5G PPP architecture working group. It will allow to move from 5G and beyond towards a fully-fledged 6G architecture.</p></blockquote>
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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.