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9,152 results for “Networking”
Data Set From Molisan Regional Seismic Network Events
<p>Abstract:</p> <p><em>After the earthquake occurred in Molise (Central Italy) on 31st October 2002<br /> (Ml 5.4, 29 people dead), the local Servizio Regionale per la Protezione Civile<br /> to ensure a better analysis of local seismic data, through a convention with<br /> the Istituto Nazionale di Geofisica e Vulcanologia (INGV), promoted the design<br /> of the Regional Seismic Network (RMSM) and funded its implementation. The 5<br /> stations of RMSM worked since 2007 to 2013 collecting a large amount of seismic<br /> data and giving an important contribution to the study of seismic sources<br /> present in the region and the surrounding territory. This work reports about<br /> the dataset containing all triggers collected by RMSM since July 2007 to March<br /> 2009, including actual seismic events; among them, all earthquakes events<br /> recorded in coincidence to Rete Sismica Nazionale Centralizzata (RSNC) of INGV<br /> have been marked with S and P arrival timestamps. Every trigger has been<br /> associated to a spectrogram defined into a recorded time vs. frequency domain.<br /> The main aim of this structured dataset is to be used for further analysis with<br /> data mining and machine learning techniques on image patterns associated to the<br /> waveforms.</em></p> <p>Link arXiv: http://arxiv.org/abs/1607.02607</p>
A data driven network approach to rank countries production diversity and food specialization
<p>This file explains the database, that can be freely downloaded, used in the work A data driven network approach to rank food complexity and countries production diversity?(arXiv:1606.01270v1) by Chengyi Tu, Joel Carr and Samir Suweis. If you use the data, please cite the article. The file OrganizeData.xlsx includes bipartite adjacency matrix, both binary (b) and weighted (w) in tons of food, describing the food commodities produced (country-product), imported (country-import) and exported (country-export) by the considered countries from the year 1992 to the year 2011. GDP data are easily available from UN (http://data.un.org/Data.aspx?d=WDI&f=Indicator_Code%3ANY.GDP.MKTP.CD) or worldbank (http://data.worldbank.org/indicator/NY.GDP.MKTP.CD) website. From these data the results on the Minimum Spanning Forest and on the country fitness and food specialization can be reproduced.</p>
Temporal Networks - Football, Handball, Basketball
<p>This is a collection of datasets used for research in the field of Temporal Networks</p> <p> </p> <p>We have first used these datasets in the following publication:</p> <p>O.Kostakis, N.Tatti, A.Gionis, "Discovering recurrent activity in temporal networks", Data Mining and Knowledge Discovery, Special Issue in Sports Analytics, 2016.</p> <p> </p> <p>In summary, this collection contains three different datasets. The first is data about all matches in the 1996-'97 English Premier League. The second dataset contains a temporal network corresponding to team-passing activity of a handball team. Finally, the third dataset contains play-by-play information for 1101 basketball matches of the 2014-'15 NBA season. Within each folder, you will find a separate README file for each dataset.</p> <p> </p> <p>Disclaimer:</p> <p>We do not claim to have produced or own the data. We do not claim the correctness of the data.</p> <p>We provide the data only for reasons related to Research, including but not limited to research reproducibility.</p>
Expression Data from "A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension"
<p>Normalized expression data (PCL files) and labeled PCL files (LPCL) that contain the WGCNA coexpression module assignment from Taroni, et al. A novel multi-network approach reveals tissue-specific cellular modulators of fibrosis in systemic sclerosis, pulmonary fibrosis, and pulmonary arterial hypertension. <em>bioRxiv</em>. doi: 10.1101/038950</p> <p>See also Gene Expression Omnibus under the following accession numbers: GSE9285, GSE32413, GSE45485, GSE59785, GSE76806, GSE76807, GSE76808, GSE48149, GSE68698, GSE19617, and GSE22356. </p>
FIGURE 7. Haplotype parsimony networks for ITS1 in A comparative study of populations of Ectopleura crocea and Ectopleura ralphi (Hydrozoa, Tubulariidae) from the Southwestern Atlantic Ocean
FIGURE 7. Haplotype parsimony networks for ITS1+5.8S (A) and COI (B) for the different populations of Ectopleura crocea and Ectopleura ralphi of the Southwestern Atlantic Ocean. Black circles for the Argentine haplotypes, white ones for Brazilian localities; the sizes of the circles are proportional to the number of localities sharing the haplotype; small black circles represent hypothetical or unsampled haplotypes. Lines connecting circles represent one substitution step. Note that A BO/MP samples differ from JU/PA/BO only by an indel of three base pairs (AAT/– – –). Locality codes are represented inside the circles as MA: Macaé, JU: Juréia, PA: Paraná, BO: Bombas, MP: Mar del Plata.
Data from: An adaptable toolkit to assess commercial fishery costs and benefits related to marine protected area network design
<p>Data generated by the BESTMPA package as part of Daigle et al. (2015). The R package is available at https://github.com/remi-daigle/BESTMPA and also archived at https://zenodo.org/record/259997</p> <p>The entire directories 'BESTMPA_results/' (3 volumes) and 'BESTMPA_sensitivity/' (X volumes) has been compressed into volumes to facilitate upload/download. Extracting files from a single volume will be difficult, please download all volumes from the directory of choice.</p> <p>Daigle RM, Monaco CJ and Baldridge AK. An adaptable toolkit to assess commercial fishery costs and benefits related to marine protected area network design. <em>F1000Research</em> 2015, <strong>4</strong>:1234 https://f1000research.com/articles/4-1234</p>
FIGURE 3. Haplotype network for P in Phylogeny and genetic variation within the widely distributed Bluntnose Minnow, Pimephales notatus (Cyprinidae), in North America
FIGURE 3. Haplotype network for P. notatus. Median-joining haplotype network for the mitochondrial (cytb) gene examined for P. notatus. Each circle represents a different haplotype; circle sizes are proportional to the number of individuals possessing a particular haplotype. Major drainages are represented by different colors. Small red dots represent missing (unsampled or extinct) haplotypes. Lines between circles represent one mutational step, and numbers presented are the number of mutations between haplotypes. Dotted-square-lines depict two major lineages resolved in mtDNA phylogenetic analyses. Dotted-circle depicts a subclade represented in detail (inset). One specimen from P. tenellus was included for comparisons.
FIGURE 3. Median-joining network showing relationships among PTN12 in Revision of the water-holding frogs, Cyclorana platycephala (Anura: Hylidae), from arid Australia, including a description of a new species
FIGURE 3. Median-joining network showing relationships among PTN12 haplotypes of C. occidentalis (blue), C. platycephala eastern (black), C. platycephala northern (red) and C. verrucosa (orange). Haplotypes are designated numerically followed by the number of individuals observed with that haplotype in italics. Numbers along branches indicate the number of inferred mutations.
Data for arXiv:1702.04117: "The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone"
<p>This dataset contains the raw confocal image stacks of the osteocyte lacuno-canalicular network in woven bone from mouse and fibrolamellar bone from sheep as described in "The Small World of Osteocytes: Connectomics of the Lacuno-Canalicular Network in Bone" (https://arxiv.org/abs/1702.04117). See the methods section of the manuscript for further detail. MATLAB code to reproduce the data plots in the figures can be downloaded from https://github.com/phi-max/OCY_connectomics.</p>
Iberian Neolithic networks
<p>To reproduce the paper "Iberian Neolithic networks: the rise and fall of the Cardial world" here you can download GIS information, cultural data and radiocarbon dataset.</p> <p>R code was made by C.Michael Barton (Arizona State University) and we've upload ob behalf of the author.</p>
FIGURE 1 in LepNet: The Lepidoptera of North America Network
FIGURE 1. Geographic distribution of the 27 partner institutions in LepNet and their digitization targets for specimen label data (blue) and specimen images (red).
FIGURE 2 in LepNet: The Lepidoptera of North America Network
FIGURE 2. Existing (black) and expected (blue, yellow, and green) number of records to be produced by LepNet for the 3 major groups and 15 families of Lepidoptera that are most common in the collections of the partner institutions. Existing records were obtained from the SCAN and iDigBio data portals.
HUMANE Survey for REVEAL project: Human-Machine Network Characterisation
<p>The objective of the HUMANE project is to provide a framework and method for classifying and analysing the characteristics of<br> such networks (collectively called HumanMachine Networks (HMNs)), inform design and policy making, and provide<br> recommendations for R&I actions. In order to perform these tasks, HUMANE is revisiting current or recent ICT projects and<br> performing surveys in order to collect feedback about the implications of HMNs and recommendations for design practices.</p> <p>In this survey we focus on the REVEAL project (http://revealproject.eu) as an example of a HMN that provides advanced content<br> aggregationfilteringcuration and recommendation tools for making a higher level analysis of social media possible, enabling<br> users to reveal hidden ‘modalities’ such as reputation, influence or credibility of information. In the REVEAL use case, journalists<br> interact with information sources, the public and other journalists, and use the developed tools for revealing information with<br> respect to social media contributors (source of the information), content (the information itself) and context.</p> <p> </p>
HUMANE Survey for REVEAL project: Implications and Design Solutions for Human-Machine Network
<p>The objective of the HUMANE project is to provide a framework and method for classifying and analysing the characteristics of<br> such networks (collectively called HumanMachine Networks (HMNs)), inform design and policy making, and provide<br> recommendations for R&I actions. In order to perform these tasks, HUMANE is revisiting current or recent ICT projects and<br> performing surveys in order to collect feedback about the implications of HMNs and recommendations for design practices.</p> <p>In this survey we focus on the REVEAL project (http://revealproject.eu) as an example of a HMN that provides advanced content<br> aggregationfilteringcuration and recommendation tools for making a higher level analysis of social media possible, enabling<br> users to reveal hidden ‘modalities’ such as reputation, influence or credibility of information. In the REVEAL use case, journalists<br> interact with information sources, the public and other journalists, and use the developed tools for revealing information with<br> respect to social media contributors (source of the information), content (the information itself) and context.</p>
Gene regulatory networks for 38 human tissues
<p>We reconstructed gene regulatory networks for 38 tissues from the Genotype-Tissue Expression project (GTEx), and used these networks to investigate gene expression and regulation across these tissues. In the RData file, we share the following objects:</p> <p>- <strong>edges</strong>: an 19,476,492 by 3 data.frame including three columns: TF (the transcription factor's gene symbol), Gene (Ensembl ID), Prior (whether an edge is canonical (1) or non-canonical (0)).<br> <br> - <strong>exp</strong>: a 30,243 by 9,435 matrix including normalized expression data for each sample.<br> <br> - <strong>expTS</strong>: a 30,243 by 38 matrix including, for each gene and each tissue, information on whether the gene is expressed in a tissue-specific manner in that tissue (1) or not (0).<br> <br> - <strong>genes</strong>: a 30,243 by 4 data.frame that includes annotation information (Symbol) for Ensembl gene IDs (Name). This data.frame also includes information on whether genes are also transcription factors (AlsoTF), with options: no, yes/motif (TF with a known DNA-binding motif) yes/nomotif (TF without a known DNA-binding motif). In addition, the multiplicity of the gene (Multiplicity) is given.<br> <br> - <strong>net</strong>: a 19,476,492 by 38 matrix that includes edge weights for each tissue. Edge order corresponds to edge order in the the object "edges".<br> <br> - <strong>netTS</strong>: a 19,476,492 by 38 matrix that includes information of whether edges are specific to a tissue (1) or not (0).<br> <br> - <strong>samples</strong>: a 9,435 by 2 data.frame that includes sample identifiers (matching the identifiers in "exp") and the tissue to which these samples belong.</p>
An Online Paleoclimate Data Assimilation with a Deep Learning-based Network
<p>OnlinePDA_zenodo.rar (files compressed with RAR compression) includes:</p> <p>1. Directory 'Code' contains the <em>LIM</em>.<em>m</em> and <em>NET</em>.<em>m</em> for the examination of predictive skills of the surrogate models; the <em>Exp_LIM</em>.<em>m</em> and <em>Exp_NET</em>.<em>m</em> for the reconstruction of SAT during the preindustrial period (851-1850 CE) and the instrumental period(1880-2000CE), for both pseudoproxy experiments and real proxy experiments; </p> <p>2. Directory 'Data' contains the necessary data in the <strong>model</strong>, <strong>prior</strong>, <strong>proxy</strong>, and <strong>obs</strong> directory used to perform the resconstruction. e.g. surrogate models, prior samples, pseudoproxy and real proxy, and instrumental data; <em>Figure</em>*.<em>m and</em> <em>CERMSE</em>*.<em>mat</em> provide the necessary code and data for plotting Figures 3-10</p>
Learning topological states from randomized measurements using variational tensor network tomography
<p>Dataset for paper <strong>Learning topological states from randomized measurements using variational tensor network tomography.</strong><br>The numerical code can be found at the repo: https://github.com/teng10/tn-shadow-qst</p>
Code and data for "An integrated microwave neural network for broadband computation and communication"
<div> <div> </div> </div> <div> <div> <div> <div> <div> <div> <p>This repository contains code and data used in the presentation of results in the article "An integrated microwave neural network for broadband computation and communication". The contents of the zipped files are:</p> <ul> <li><strong>Spectrum Analyzer Outputs (Datasets and ML scripts for digital emulation, radar and signal encoding classification.zip)</strong>: Reduced-bandwidth outputs used to train the backend for results presented in Figs. 3 and 4 and Supplementary Fig. 3.</li> <li><strong>Simulation Code (Coupled mode simulation of integrated MNN.zip) </strong>: For modeling the coupled MNN system shown in Fig. 2 and Extended Figs. 4 and 5.</li> <li><strong>Radar Signal Simulation (Training data and code for simulating dynamic targets in simulated airspace.zip)</strong>: Code to simulate received baseband signals from radar targets.</li> </ul> <p>Each folder contains readme files on how to run the code and analyze data.</p> <p>Please install a recent Python release (https://www.python.org/downloads/) and a recent release of MATLAB (https://www.mathworks.com/help/install/) to run the code. No non-standard hardware is required. </p> </div> </div> </div> </div> </div> </div>
Products developed through the "What About Model Data?, Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network" project
This dataset includes products developed through the "What About Model Data? Determining Best Practices for Preservation and Replicability, EarthCube Research Coordination Network (RCN)" project. Products include: 1) a rubric worksheet to assist researchers in deciding what simulation output needs to be preserved in a trusted, community repository to communicate knowledge and satisfy publisher and funder requirements, 2) instructions on how to use the rubric worksheet, which include reference use cases, and 3) outputs and presentations from the three project workshops.
Replication data and analysis code for the article "Insect-habitat-plant interaction networks provide guidelines to mitigate the risk of transmission of Xylella fastidiosa to grapevine in Southern France"
<p>This deposit contains the dataset used in the article "Insect-habitat-plant interaction networks provide guidelines to mitigate the risk of transmission of Xylella fastidiosa to grapevine in Southern France" in the form of a RData file, directly loadable in R, as well as the Rmd script used to analyse the data and produce the figures.</p>
ScienceDex guides
Understand access before you commit
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.