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9,152 results for “Networking”
Really a nontraded commodity? A look at the international potato trade network (dataset and R code)
<p>This archive contains code and data for a social network analysis of international potato trade that was published at https://perspectivesandforesight.wordpress.com/2012/11/08/really-a-nontraded-commodity-a-look-at-the-international-potato-trade-network/ on 08 November, 2012.</p> <p>It can serve as a reference for understanding the analysis, and as a basis for replication of the results as well as for carrying out more detailed analyses of international potato trade.</p> <p>The following information and data is included:</p> <p>- The R code used for the social network analysis of international potato trade.</p> <p>- Data files with bilateral matrices of global trade in fresh, frozen and seed potatoes.</p> <p>- Data files with supplementary data used for the analysis.</p> <p>- A copy of the original blog post that was written using the data and code provided herewith.</p>
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>
A questionnaire of community networks
<p>The result of a questionnaire on community networks, as studied in https://zenodo.org/record/7450</p>
Improving Network Efficiency with Simplemux
<p>This dataset contains the open data related to the research paper:</p> <p>Jose Saldana, Ignacio Forcen, Julian Fernandez-Navajas, Jose Ruiz-Mas, "Improving Network Efficiency with Simplemux,'' IEEE CIT 2015, International Conference on Computer and Information Technology, 26-28 October 2015 in Liverpool, UK.</p> <p>This work has been partially financed by the EU H2020 Wi-5 project (Grant Agreement no: 644262), and European Social Fund in collaboration with the Government of Aragon.</p> <p><strong><em>Paper Abstract</em></strong>—The high amount of small packets currently transported by IP networks results in a high overhead, caused by the significant header-to-payload ratio of these packets. In addition, the MAC layer of wireless technologies makes a non-optimal use of airtime when packets are small. Small packets are also costly in terms of processing capacity. This paper presents Simplemux, a protocol able to multiplex a number of packets sharing a common network path, thus increasing efficiency when small packets are transported. It can be useful in constrained scenarios where resources are scarce, as community wireless networks or IoT. Simplemux can be seen as an alternative to Layer-2 optimization, already available in 802.11 networks. The design of Simplemux is presented, and its efficiency improvement is analyzed. An implementation is used to carry out some tests with real traffic, showing significant improvements: 46% of the bandwidth can be saved when compressing voice traffic; the reduction in terms of packets per second in an Internet trace can be up to 50%. In wireless networks, packet grouping results in a significantly improved use of air time.</p>
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>
facebook social network graph
<p>This dataset is also contained in http://snap.stanford.edu/data/egonets-Facebook.html, but it is converted to one by one single graphs in this dataset.</p> <p>Graph Example:</p> <p># graph_id, graph_activity, number_of_vertices, number_of_edges</p> <p>vertex_1， vertex_2, ... , vertex_n</p> <p>vertex_1 vertex_k edge_label vertex_2 vertex_m edge_label, ..., vertex_i, vertex_j, edge_label</p>
The Business of Satirical Prints in Late-Georgian England: network graphs
<p>These figures support research published in Chapter 8 of James Baker, <em>The Business of Satirical Prints in Late-Georgian England</em> (London: Palgrave, 2017). It contains figures made by James Baker in February 2013. These are figures 8.13-8.20 of <em>The Business of Satirical Prints</em>. Figures generated in Gephi using data deposited at http://dx.doi.org/10.5281/zenodo.49548. All errors are the fault of the author.</p>
Accompanying dataset for: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks
<p>This is the accompanying data and code for the publication [Markovitch & Krasnogor: Predicting Species Emergence in Simulated Complex Pre-Biotic Networks] containing the full set of 10,000 lognormal networks studied, their network communities and the compotype species observed during simulations with the GARD model. Details are given in the aforementioned paper. Please see also: http://ico2s.org/</p> <p>This work was funded by the UK's Engineering and Physical Sciences Research Council (EPSRC) under projects (EP/J004111/2) "Towards a Universal Biological-Cell Operating System (AUdACiOuS)" and (EP/N031962/1) "Synthetic Portabolomics: Leading the way at the crossroads of the Digital and the Bio Economies"</p> <p> </p>
A Queueing Network Model for Performance Prediction of Apache Cassandra
<p>The dataset consists in several csv files containing Cassandra and ScyllaDB performance.</p> <p>The experiments are organized in folders. There are three main folders containing:<br> - Cassandra 4 nodes: the files related to the Cassandra experiments conducted on a cluster composed of four nodes.<br> - ScyllaDB 4 nodes: The files related to the ScyllaDB experiments conducted on a cluster composed of four nodes. <br> - Cassandra QUORUM variant: the simulation data where a different kind of QUORUM is implemented in Cassandra.<br> <br> "Cassandra 4 nodes" and "ScyllaDB 4 nodes" include some subfolders, each one containing the files of the Consistency Level applied for those experiments. Each experiment is composed by three files (data*.csv) with the data reported by Yahoo! Cloud System Benchmark (YCSB) in the end of the experiment execution. Each folder contains also a sim.csv file with the data gathered from the simulation of the model inside Java Modeling Tool.</p> <p>The data*.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Number of Read requests<br> -Overall Read Response Time<br> -95 percentile Read Response Time<br> -99 percentile Read Response Time<br> -99.9 percentile Read Response Time<br> <br> Differently, the sim.csv files are composed by:<br> -Number of threads or clients<br> -Overall Throughput<br> -Overall Read Response Time</p>
Gilby et al PLoS ONE Bayesian Belief Network input data
<p>Gilby et al PLoS ONE Bayesian Belief Network input data. Data used to educate relationships between nodes in Bayesian Belief Network for coral reef condition relative to management intervenations on coral reefs in Moreton Bay, Queensland, Australia.</p>
Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz
<p>Data to figure 7 in the related publication:</p> <p>Scattering parameter (input port voltage reflection coefficient) of transmit network of NQR probehead from 40 MHz to 140 MHz</p>
Averaged results of blood flow simulations with discrete RBC tracking for microvascular networks
<p>The dataset contains the results for blood flow simulations in 3 cerebral micorvascular networks.The microvascular networks are from the mouse parietal cortex (Blinder et al., 2013) and embedded in a tissue volume of approximately 1 cubic mm. For the blood flow simulations we used a numercial model with discrete tracking of RBCs which is described in Schmid et al., 2017.</p> <p>For each network the following data are provided:<br> - Microvascular network with averaged flow and pressure field, as well as averaged values for the distribution and motion of red blood cells (RBCs).<br> - RBC trajectories describing the motion of individual RBCs through the microvascular networks.<br> - The data is stored as a graph, i.e. vertices connected by edges.<br> - Details regarding the simulation parameters can be found in Schmid et al., 2017.<br> - Data format (pickle - files containing python dictonairies).<br> <br> <strong>Microvascular networks:</strong><br> <strong>edgesDict.pkl:</strong> dictionary with edge related data (dictionary keys: flow [um^3/ms], diameter [um], tuple [-], httBC [-], nkind [-], length [um], htt [-], nRBC [-], diameters [um], points [um])<br> <strong>verticesDict.pkl:</strong> dictionary with vertex related data (dictionary keys: pressure [mmHg], coordinates [um], pBC [mmHg])</p> <p>Additional comments on dictionary keys:<br> - pBC: pressure boundary conditions. 'None' for internal nodes. Assigned based on the hierarchical boundary conditions approach (see Schmid et al. 2017 for details)<br> - tuple: connectivity of graph, tuple of vertices<br> - httBC: tube hematocrit boundary conditions. 'None' for internal nodes. Constant value assigned.<br> - nkind: integere to describe the vessel type. 0: pial artery, 1: pial venule, 2: descending arteriole, 3: ascending venule, 4: capillaries, 5: unknown<br> - htt: tube hematocrit<br> - nRBC: number of red blood cells<br> - points: list of tortuous vessel coordinates per edge<br> - diameters: local diameter measurements associated to the 'points' key.</p> <p><br> <strong>RBC trajectories:</strong><br> <strong>RBC_trajectories.pkl: </strong>dictonary for each RBC with relevant tracking data (dictionary key: RBC index). The relavant tracking data per RBC is stored in another dictionary with the following keys: edges, lengths, times, pressure, nkindsMod, RBCleft</p> <p>Additional comments on dictionary keys per RBC:<br> - RBCleft: bool to indicate that RBC left the computational domain<br> - edges: edge indices through which the RBC moves on its way through the vasculature<br> - pressure: pressure [mmHg] values at the nodes along the RBC trajectory<br> - times: time [ms] the RBC spends in the respective edge segment<br> - nkindsMod: nkind at the nodes along the RBC trajectory <br> - lengths: cummulative length travelled [um]</p>
Figure 9. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 9. - Information flows between EU BON and LTER Europe, as envisaged on the 3rd EU BON Stakeholder Roundtable in Granada on 9-11 December 2015.
Figure 8. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 8. - The patchiness of survey coverage in Europe illustrated by the distribution map of Plantago lanceolata taken from GBIF in 2016. This species is one of the commonest and most widespread in Europe, it should occur in almost all areas of this map, but in fact the data traces out the borders of countries and area who have published data on GBIF.
Figure 6. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 6. - Individual Metacat instances can be connected to DataOne which replicates public files. Thus the data is still available if a single instance goes offline.https://search.dataone.org/#data/page/0
Figure 5. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 5. - PPBio has installed a Metacat instance for their researchers to upload and make publicly available the results of work related to biodiversity in the Western Amazon.https://ppbiodata.inpa.gov.br/metacatui/
Figure 4. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 4. - The public data repository provided by the Knowledge Network for Biocomplexity (KNB).https://knb.ecoinformatics.org/#data/page/0
Figure 3. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 3. - The implementation of Darwin Core Archive in Plazi to transfer treatment data. Observation data described with Darwin Core terms.
Figure 1. from: Data sharing tools adopted by the European Biodiversity Observation Network Project - Research Ideas and Outcomes 2: e9390 (31 May 2016) https://doi.org/10.3897/rio.2.e9390
Figure 1. - ARPHA consists of two integrated workflows: in ARPHA-XML, the manuscript is written and processed via the ARPHA Writing Tool, and in ARPHA-DOC, the manuscript is submitted and processed as document file(s).
Dataset supplementing Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. Annals of the New York Academy of Sciences. 1339, 138-153.
<p>Data supplementing the paper Marx, S., Gruenhage, G., Walper, D., Rutishauser, U., Einhäuser, W. (2015). Competition with and without priority control: linking rivalry to attention through winner-take-all networks with memory. <em>Annals of the New York Academy of Sciences. 1339, </em>138-153. doi: 10.1111/nyas.12575 The files can be freely used for scientific purposes, provided this reference is appropriately cited.</p> <p>Files contain the behavioral data, the model can be found at https://doi.org/10.5281/zenodo.573026</p> <p> </p> <p>The following files are contained in this folder:</p> <p>dataExp1.mat contains the data of experiment 1</p> <p>The variables durationLeft and durationRight contain 5 x 6 x 6 cell arrays with the dominance durations for the left and right grating, respectively. Dimensions are subject x contrast level left x contrast level right.</p> <p><br> dataExp2.mat contains the data of experiment 2</p> <p>Variables buttonStart, buttonEnd and whichButton contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain the start time and end time of each button press, and which button (1/2) was pressed, respectively.</p> <p>Variables presStart and presEnd contain 3x4x5 (contrast levels x blank duration levels x subjects) cell arrays that contain start and end of each blank period. All time stamps refer to the onset of the first blanking trial (end of continuous presentation)</p> <p>Variable prevPerz contains the percept (button) that was pressed at the end of the continuous presentation period.</p> <p><br> figure3_human.m, figure4_human.m and figure6_human.m exemplify the usage of the data by re-plotting the figures containing human data of the aforementioned paper</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.