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1,721 results for “network data”
Data from: A dedicated network for social interaction processing in the primate brain
Primate cognition requires interaction processing. Interactions can reveal otherwise hidden properties of intentional agents, such as thoughts and feelings, and of inanimate objects, such as mass and material. Where and how interaction analyses are implemented in the brain is unknown. Using whole-brain functional magnetic resonance imaging in macaque monkeys, we discovered a network centered in the medial and ventrolateral prefrontal cortex that is exclusively engaged in social interaction analysis. Exclusivity of specialization was found for no other function anywhere in the brain. Two additional networks, a parieto-premotor and a temporal one, exhibited both social and physical interaction preference, which, in the temporal lobe, mapped onto a fine-grain pattern of object, body, and face selectivity. Extent and location of a dedicated system for social interaction analysis suggest that this function is an evolutionary forerunner of human mind-reading capabilities.
Data from: Does detection range matter for inferring social networks in a benthic shark using acoustic telemetry?
Accurately estimating contacts between animals can be critical in ecological studies such as examining social structure, predator–prey interactions or transmission of information and disease. While biotelemetry has been used successfully for such studies in terrestrial systems, it is still under development in the aquatic environment. Acoustic telemetry represents an attractive tool to investigate spatio-temporal behaviour of marine fish and has recently been suggested for monitoring underwater animal interactions. To evaluate the effectiveness of acoustic telemetry in recording interindividual contacts, we compared co-occurrence matrices deduced from three types of acoustic receivers varying in detection range in a benthic shark species. Our results demonstrate that (i) associations produced by acoustic receivers with a large detection range (i.e. Vemco VR2W) were significantly different from those produced by receivers with smaller ranges (i.e. Sonotronics miniSUR receivers and proximity loggers) and (ii) the position of individuals within their network, or centrality, also differed. These findings suggest that acoustic receivers with a large detection range may not be the best option to represent true social networks in the case of a benthic marine animal. While acoustic receivers are increasingly used by marine ecologists, we recommend users first evaluate the influence of detection range to depict accurate individual interactions before using these receivers for social or predator–prey studies. We also advocate for combining multiple receiver types depending on the ecological question being asked and the development of multi-sensor tags or testing of new automated proximity loggers, such as the Encounternet system, to improve the precision and accuracy of social and predator–prey interaction studies.
Data from: Speciation network in Laurasiatheria: retrophylogenomic signals
Rapid species radiation due to adaptive changes or occupation of new ecospaces challenges our understanding of ancestral speciation and the relationships of modern species. At the molecular level, rapid radiation with successive speciations over short time periods—too short to fix polymorphic alleles—is described as incomplete lineage sorting. Incomplete lineage sorting leads to random fixation of genetic markers and hence, random signals of relationships in phylogenetic reconstructions. The situation is further complicated when you consider that the genome is a mosaic of ancestral and modern incompletely sorted sequence blocks that leads to reconstructed affiliations to one or the other relative, depending on the fixation of their shared ancestral polymorphic alleles. The laurasiatherian relationships among Chiroptera, Perissodactyla, Cetartiodactyla, and Carnivora present a prime example for such enigmatic affiliations. We performed whole-genome screenings for phylogenetically diagnostic retrotransposon insertions involving the representatives bat (Chiroptera), horse (Perissodactyla), cow (Cetartiodactyla), and dog (Carnivora), and extracted among 162,000 preselected cases 102 virtually homoplasy-free, phylogenetically informative retroelements to draw a complete picture of the highly complex evolutionary relations within Laurasiatheria. All possible evolutionary scenarios received considerable retrotransposon support, leaving us with a network of affiliations. However, the Cetartiodactyla–Carnivora relationship as well as the basal position of Chiroptera and an ancestral laurasiatherian hybridization process did exhibit some very clear, distinct signals. The significant accordance of retrotransposon presence/absence patterns and flanking nucleotide changes suggest an important influence of mosaic genome structures in the reconstruction of species histories.
Irradiance monitoring network data
<p>The data.tar.gz archive contains data from an irradiance monitoring network in Tucson, Arizona for the period 2014-04-05 to 2014-06-30. It includes a sensor metadata csv, csv files for the measurements on each day, and csv files for the clearsky-profiles for each sensor on each day. This data was used to make short-term forecasts of solar irradiance.</p>
Irradiance monitoring network data and wind motion vectors
<p>The data.tar.gz archive contains data from an irradiance monitoring network in Tucson, Arizona for the period 2014-04-05 to 2014-06-30. It includes a sensor metadata csv, csv files for the measurements on each day, csv files for the clearsky-profiles for each sensor on each day, and a time-series of the expected wind motion vectors obtained from a numerical weather model. This data was used to make short-term forecasts of solar irradiance.</p>
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>
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>
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>
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>
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>
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>
Social Network Data for Jones, The Decline and Fall of the Assyrian Court Scholar
Open the record for dataset details and reuse information.
Input Data for A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks
<p>Training datasets for the manuscript A Fast Surrogate Model for 3D-Earth Glacial Isostatic Adjustment using Tensorflow (v2.8.0) Artificial Neural Networks. Two separate datasets are contained for training the ANNs: the 3D-spherically-symmetric (SS) rate-of-change of relative sea level (ROCRSL) and the 3D-SS rate of change of radial displacement (ROCRAD) as a function of SS profiles. Two other datasets contain RSL projections from the explicit (i.e. Seakon 3D - Seakon SS + NMSS ) model and the NMSS model, labelled Seakon_plus_NMSS_RSL and NMSS respectively.</p> <p>Filenames denote the structure of the SS profile: </p> <p>???_?.??_??.*.csv = LT_UMV_LMV.*.{csv,nc}<br> </p> <p>LT = elastic lithosphere thickness (km)</p> <p>UMV = upper mantle viscosity (1E21 Pa s)</p> <p>LMV = lower mantle viscosity (1E21 Pa s)</p> <p>i.e. 96_0.5_10.seakon_S40RTS_lr18-SS.rrad.roc.r360x180.P5.density_wSSRRADROC.csv.bz2 has the SS profile</p> <p>96km elastic lithosphere, 0.5E21 Pa s upper mantle viscosity, 10E21 Pa s lower mantle viscosity</p> <p> </p> <p>The columns of the input files are as follows:</p> <p>LT, UMV, LMV, longitude, latitude, time(t=0), ice(t=0), SS_ROC_RSL (t=0), time(t=-1), ice(t=-1), time(t=-2), ice(t=-2), time(t=-3), ice(t=-3), time(t=-4), ice(t=-4), 3D-SS_ROC_RSL(t=0)</p> <p>units for the above are as follows:</p> <p>km, 1E21 Pas, 1E2 Pas, degrees east (0->360), degrees (-180->180), days since 2000, m, mm/year, days since 2000, m, days since 2000, m, days since 2000, m, days since 2000, m, mm/year</p> <p>where 'days since 2000' assumes exactly 365.25 days per year.</p>
Data of decomposition, topology, and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes
<p>Data of decomposition, topology and the corresponding graphs of Random Woody Crown Networks with size from 10 to 248 nodes</p>
Statistical Analysis of Feature-based Molecular Networking Results from Non-Targeted Metabolomics Data
<p>This folder contains the following used for the publication:</p><ul><li>MASSIVE Repositories: MSV000082312 and MSV000085786. This contains the original data in both .raw and .mzxml formats.</li><li>MZmine 3 files: The feature table (SD_BeachSurvey_GapFilled_quant.csv), the associated mgf file, the batch file (.xml) used for MZmine 3 to obtain the feature table, the mgf file for SIRIUS annotations (SD_BeachSurvey_SIRIUS_fixed.mgf)</li><li>SIRIUS and CANOPUS summary files (.tsv files)</li><li>FBMN Result files</li></ul>
Branching tidal creek networks morphology data set
<p>Data and codes used in the paper entitled "The origin of branching in tidal creek networks".</p>
Data and code for Fitzgerald et al: MDD seeded co-expression networks
<p>Below is a decription of the data and code supplied within this repository related to Fitzgerald et al "Astrocyte fatty acid metabolism as a driver of risk for major depressive disorder"</p> <table> <tbody> <tr> <td>Generated data </td> </tr> <tr> <td>Data</td> <td>About</td> <td> </td> </tr> <tr> <td>All_GTEx_DLPFC_networks.RData</td> <td>Non-thresholded coexpression summary statistics for MDD risk genes in GTEx frontal cortex</td> <td> </td> </tr> <tr> <td>my_big_negative_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R < -0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td>my_big_positive_GTEx_DLPFC_list.RData</td> <td>"All_GTEx_DLPFC_networks.Rdata" data filtered to those genes with R > 0.5 and FDR < 0.05</td> <td> </td> </tr> <tr> <td> </td> <td> </td> <td> </td> </tr> <tr> <td>Generated code</td> </tr> <tr> <td>File</td> <td>About</td> <td>Related figure</td> </tr> <tr> <td>Chang_bootstrap.R</td> <td>Bootstrapping of coexpression networks in the Chang et all data for comparing FADS1 coexpressed genes across disease states</td> <td>Fig 4G</td> </tr> <tr> <td>CMC_QC.R</td> <td>Quality control for the common mind consortium data for validation of coexpression networks</td> <td>Supp</td> </tr> <tr> <td>Cont_vs_MDD_modscores.R</td> <td>Generating module scores in snRNA-seq data</td> <td>Fig 4E</td> </tr> <tr> <td>FADS1_clustering.R</td> <td>Clustering of snRNA-seq data using genes coexpressed with FADS1</td> <td>Fig 5</td> </tr> <tr> <td>gene_analysis.sh</td> <td>Annotation of GWAS summary statistics using Hi-C data</td> <td>Fig 2A</td> </tr> <tr> <td>gene_set_analysis.sh</td> <td>GWAS enrichment analysis using MAGMA</td> <td>Fig 6C</td> </tr> <tr> <td>GTEx_coexp_networks.R</td> <td>Generating seeded coexpression networks for MDD risk genes in the GTEx dataset</td> <td>Fig 2B</td> </tr> <tr> <td>GTEx_QC_1.R</td> <td>Filtering of the GTEx dataset</td> <td>NA</td> </tr> <tr> <td>GTEx_QC_2.R</td> <td>Normalisation and regression of technical covariates from the GTEx data</td> <td>NA</td> </tr> <tr> <td>Labonte_et_al_QC.R</td> <td>Quality control, filtering and regression of technical covariates from the Labonte et al dataset</td> <td>Fig 4F</td> </tr> <tr> <td>Milo_analysis.R</td> <td>Neighbourhood based analysis for differentially abundant nuclei between control and MDD nuclei</td> <td>Fig 5H</td> </tr> <tr> <td>Nagy_et_al_astro_subsetting.R</td> <td>Subsetting astrocytes from the full Nagy et al snRNA-seq dataset </td> <td>NA</td> </tr> <tr> <td>Network_analysis.R</td> <td>To generate and analyse a graph of coexpression networks</td> <td>Fig 3E</td> </tr> <tr> <td>NicheNet.R</td> <td>For a NicheNet analysis to infer patterns of cell-cell communication</td> <td>Fig 6F</td> </tr> <tr> <td>Vizium_analysis.R</td> <td>Processing spatial RNA-seq data and generating cell scores for spatial inference of identified cell states</td> <td>Fig 5F</td> </tr> </tbody> </table>
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.