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809 results for “Network Analysis”
DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks
<p>Dataset and code used in V Wåhlstrand-Skärström, et al, "DeepFRAP: Fast fluorescence recovery after photobleaching data analysis using deep neural networks", published in Journal of Microscopy. In this work, we develop a new approach for FRAP analysis based on deep neural networks. From a numerical FRAP model developed in previous work, we generate a very large set of realistic, simulated recovery curve data. The data is used for training deep neural network regression models for prediction of e.g. the diffusion coefficient. We compare the performance of the neural network estimation framework to conventional least squares estimation on simulated and <br> experimental data. Herein, the simulated FRAP data used for the training, validation, and test data sets, the experimental data, and the Matlab and Python/Tensorflow code are supplied.</p>
Glottis Analysis Tools - Deep Neural Networks
<p>Netron Overview Diagrams of Deep Neural Networks (DNNs) shipped with Glottis Analysis Tools (GAT) 2020.</p>
Dataset for a Mouse and Rat heart trancriptomic and co-expression network analysis
<ul> <li>mouse_heart_data and rat_heart_expression contain GEO expression matrix for mouse and rat experiments.</li> <li>gse_gsm_mouse.txt and gse_gsm_rat.txt contain experiment IDs and series IDs from GEO.</li> <li>heart_quantNormData_mouse.tsv and heart_quantNormData_rat.tsv contain normalised expression matrices.</li> <li>heart_quantNormData_mouse_1sd.tsv and heart_quantNormData_rat_1sd.tsv contain the normalised expression matrices restricted to genes with a standard deviation higher than 1.</li> <li>fileForSCHypeThreshold0.5_heart.txt and fileForSCHypeThreshold0.75_heart.txt are the input for SCHype. schype_output_0.5th_heart.nodes.txt, schype_output_0.5th_heart.edges.txt, schype_output_0.75th_heart.nodes.txt and schype_output_0.75th_heart.edges.txt are the outputs.</li> <li>geneLists.zip contains gene lists used for ontology analysis (ENSEMBL gene id)</li> </ul>
Data and code from Lamb et al., "Evaluating conservation units using network analysis: a sea duck case study"
<p>This file consists of data and code used to construct network models for scoters in North America and is associated with the manuscript "<strong>Evaluating conservation units using network analysis: a sea duck case study</strong>" published in Frontiers in Ecology and the Environment. </p><p> </p><p><strong>Continental scoter network mapping </strong>is the R script used to run analyses.</p><p> </p><p><strong>duck_nodes</strong> is the main datafile. Columns are organized as follows:</p><p>id - unique identifier</p><p>species - species from which the centroid was obtained (BLSC = black scoter, SUSC = surf scoter, WWSC = white-winged scoter)</p><p>stage - period of the annual cycle to which the centroid belongs (W = winter, B = breeding, S = spring staging, M = fall staging and molt, WM = winter migration, BM = breeding migration, MM = molt migration, SM = spring migration)</p><p>site - position of centroid within season (i.e., W1 = first site occupied during winter, W2 = second site occupied, etc.)</p><p>cycle - number of annual cycles following transmitter attachment (1 = first cycle after attachment, 2 = second cycle after attachment, etc.)</p><p>sex - sex of individual (M = male, F = female)</p><p>age - age of individual (HY = hatch year, SY = second year, TY = third year, ASY = after second year, ATY = after third year, AHY = after hatch year</p><p>capture_reg - general area where individual was captured</p><p>capture_subreg - specific region within capture region where individual was captured</p><p>lon - longitude of centroid</p><p>lat - latitude of centroid</p><p>duration - number of days spent at centroid</p><p>start - date of arrival at centroid</p><p>end - date of departure from centroid</p><p>jstart - Julian date of arrival at centroid</p><p>jend - Julian date of departure from centroid</p><p>season - season of annual cycle in which centroid occurred (W = winter, F = fall, B = breeding, S = spring)</p><p>year - calendar year in which centroid began</p><p>to - node in which centroid is grouped</p><p>from - node in which previous centroid is grouped (i.e., node in which indiviual was located before moving to present node)</p><p>to_sea - season of annual cycle in which centroid occurred</p><p>from_sea - season of annual cycle in which previous centroid occurred</p><p>type - movement type to centroid; the first letter represents the season (coded as in "season" column), and the second represents the nature of the movement (WD = dispersal within a season, M = migration among seasons)</p><p>type2 - same as "type", but with dispersal movements coded by the stage in which they occur (W = winter, B = breeding, SM = spring migration, WM = winter migration)</p><p>ew - capture location in eastern (east; Atlantic and Great Lakes) or western (west; Pacific) North America</p><p>count_ind_sp - total number of tracked individuals of the species represented by centroid</p><p>wt - base centroid weight (all centroids equal, deployments excluded)</p><p>wt_sp - species-adjusted centroid weight: for centroid <i>x</i> in species <i>s</i>, weight<i>x</i> = (<i>N </i>centroids) × (1 / (<i>n </i>centroids in <i>s</i>))</p><p>wt_dur - duration-adjusted centroid weight: for centroid <i>x</i>, weight<i>x</i> = (days at centroid location) × 365-1</p><p>wt_ind - individual-adjusted centroid weight: for centroid <i>x</i> in individual<i> j</i>, weight<i>x</i> = 1 / (<i>n </i>centroids in <i>j</i>)</p><p>wt_ind_sp - individual and species adjusted centroid weight: for centroid <i>x</i>, individual <i>j</i>, and species <i>s</i>, weight<i>x</i> = (<i>N </i>centroids / (<i>N </i>species * <i>n</i> individuals in <i>s</i>)) × (1 / (<i>n </i>centroids in <i>j</i>))</p><p>wt_cap - capture location adjusted centroid weight: for centroid <i>x</i> and capture location <i>c, </i>weight<i>x </i>= (<i>N </i>centroids / <i>N</i> capture locations) / <i>n</i> centroids in <i>c</i></p><p>wt_ew - east-west adjusted centroid weight: for for centroid <i>x</i> and region <i>r, </i>weight<i>x </i>= (<i>N </i>centroids / <i>N</i> regions) / <i>n</i> centroids in <i>r</i></p><p>wt_spew - species and east-west adjusted centroid weight: for centroid <i>x</i> species <i>s</i>, and region <i>r, </i>weight<i>x </i>= (<i>N </i>centroids / (<i>N</i> species × <i>N</i> regions)) / <i>n </i>centroids for species <i>s</i> in region <i>r</i></p><p>wt_indspew - individual, species, and east-west adjusted centroid weight: for centroid <i>x,</i> individual<i> j, </i>species <i>s</i>, and region <i>r, </i>weight<i>x </i>= <i>N </i>centroids / (<i>N</i> species × <i>N</i> regions × <i>n</i> centroids for individual <i>j </i>× <i>n</i> individuals for species <i>s</i> in region <i>r</i>)</p><p>wt_spewcap - species, east-west, and capture location adjusted centroid weight: for centroid <i>x,</i> species <i>s</i>, capture location <i>c, </i>and region <i>r, </i>weight<i>x </i>= <i>N </i>centroids / (<i>N</i> species × <i>N</i> regions × <i>n</i> centroids for species <i>s</i> in capture location <i>c </i>× <i>n</i> capture locations for species <i>s</i> in region <i>r</i>)</p>
Taxonomically revised dataset of acanthomorphic acritarch species of the Doushantuo Formation: dataset for rarefaction, NMDS, and network analysis
<p>This is supplementary material for the article entitled "Silicified microfossils from the Ediacaran Doushantuo Formation along a shelf margin-slope-basin transect in Hunan Province, South China, with stratigraphical implications" by Ouyang et al. The dataset contains (1) taxonomic revisions of published acanthomorph specimens from the Doushantuo Formation from 55 previous publications that provide clear, pulished, microfossil images and clearly designated stratigraphic horizons; and updated occurrence data of Doushantuo acanthomorphic acritarchs and certain sphaeromorphic taxa considered as stratigraphically useful (i.e., <em>Schizofusa zangwenlongii</em> Grey, 2005) based on the revised taxonomy, with each occurrence assigned to a specific fossil collection; (2) acritarch relative abundance data from this and six previous publications for rarefaction analysis in this study; (3) presence/absence data of the Doushantuo acritarchs (based on occurrence data in sheet 1) for NMDS and network analysis with R in this study; (4) species abbreviations in (3); (5) species loadings generated by the NMDS analysis and plotted in Fig. 41.3 of this study.</p>
Network visualisation of co-authorship analysis of countries
<p>VOSviewer mapping shows network visualisation of co-authorship analysis of countries for <span>focusing on MICP research in the context of hydrodynamics (1999-2024).</span></p>
Supplemental Information on the Weighted Gene Co-expression Network Analysis performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"
<p>Supplemental Information on the Weighted Gene Co-expression Network Analysis (WGNCA) performed for the work "Time-resolved oxidative signal convergence across the algae–embryophyte divide"</p> <p>The results are sorted by the three species analysed: the two algae <em><span>Zygnema circumcarinatum</span></em><span> SAG 698-1b (<em>Zygnema</em>) and <em>Mesotaenium endlicherianum </em></span><span>SAG 12.97 (<em>Mesotaenium</em>); and the bryophyte <em>Physcomitrium patens</em></span><span><em> </em>strain Gransden 2004 (<em>Physcomitrium</em>).</span></p>
Exploring Kv1.2 channel inactivation through MD simulations and network analysis
<p>MD equilibration trajectories of Kv1.2 WT and mutants in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file (.prmtop).</p>
Data and R files for the analysis of the innovative capacity and the network position of national manufacturing industries in world production
<p>Data and R files for the reproducibility of the results obtained in Kim and Ozaygen, Analysis of the innovative capacity and the network position of national manufacturing industries in world production.</p> <p>It also includes an R/Shiny application which runs at <a href="https://awekim.shinyapps.io/Manuf_shiny_R/">https://awekim.shinyapps.io/Manuf_shiny_R/ </a></p>
A Survival Analysis based Volatility and Sparsity Modeling Network for Student Dropout Prediction
<p>KDDCup15.rar and XuetangX.rar are the metadata used in the paper "A Survival Analysis based Volatility and Sparsity ModelingNetwork for Student Dropout Prediction". Both of them have be drawn from the largest MOOC platform in China, XuetangX (see https://www.xuetangx.com/). If any interested parties want to fetch the original dataset, they may follow the URLs bellow:</p> <p>KDDCup 2015 dataset is available at <a href="https://www.biendata.xyz/competition/kddcup2015/data/">https://www.biendata.xyz/competition/kddcup2015/data/</a>.</p> <p>XuetangX dataset is available at <a href="http://moocdata.cn/data/user-activity">http://moocdata.cn/data/user-activity</a>.</p>
Large-scale dataset for the analysis of outdoor-to-indoor propagation for 5G mid-band operational networks
<p>We present a comprehensive dataset of channel measurements, performed to analyze outdoor-to-indoor propagation characteristics in the mid-band spectrum identified for the operation of 5th Generation (5G) cellular systems. The dataset includes measurements of channel power delay profiles from two 5G networks operating in Band n78, i.e., 3.3--3.8 GHz. Such measurements were collected at multiple locations in a large office building in the city of Rome, Italy, by using the Rohde & Schwarz (R&S) network scanner TSMA6 for several weeks in 2020 and 2021. A primary goal of the dataset is to provide an opportunity for researchers to investigate a large set of 5G channel measurements, aiming at analyzing the corresponding propagation characteristics towards the definition and refinement of empirical channel propagation models.</p>
Understanding the Influence of Receptive Field and Network Complexity in Neural-Network-Guided TEM Image Analysis
<p>TEM images of Au nanoparticles of various sizes on ultra-thin carbon substrates and their corresponding labels for semantic segmentation. The images have a dataset label of "images" and the labels have a dataset label of "labels". </p>
Network analysis reveals that acute stress exacerbates gene regulatory responses of the gill to seawater in Atlantic salmon
<p>The transition from freshwater to seawater represents a physiological challenge for Atlantic salmon smolts preparing for downstream migration. Stressors occurring during downstream migration to the ocean impair the ability of smolts to maintain osmotic/ionic homeostasis in seawater. The molecular mechanisms underlying this interaction are not fully understood, especially at the organ level. We combined RNA-Seq with measures of whole-animal homeostasis to examine gene expression dynamics in the gills of smolts associated with impaired seawater tolerance after an aquaculture-related stressor. Smolts were given a 24 h seawater tolerance test before and after exposure to an acute handling/confinement stress. RNA-Seq followed by differential expression and weighted gene correlation network analysis (WGCNA) was used to quantify the transcriptional response of the gill to handling/confinement stress, seawater and their interaction. Exposure to acute stress was associated with a general stress response and impaired osmotic/ionic homeostasis in seawater. We identified gene networks in the gill exhibiting response to acute stress alone, seawater alone, and others exhibiting combined effects of both stress and seawater. Our findings indicate that acute handling/ confinement stress increases the intensity of seawater-related gene expression and suggest that increased investment in mechanisms related to ion transport may be part of a compensatory response to impaired seawater tolerance in smolts.</p>
Data from: Network analysis of sea turtle movements and connectivity: a tool for conservation prioritization
<p><strong>Aim</strong>: Understanding the spatial ecology of animal movements is a critical element in conserving long-lived, highly mobile marine species. Analysing networks developed from movements of six sea turtle species reveals marine connectivity and can help prioritize conservation efforts.</p> <p><strong>Location</strong>: Global.</p> <p><strong>Methods</strong>: We collated telemetry data from 1,235 individuals and reviewed the literature to determine our dataset's representativeness. We used the telemetry data to develop spatial networks at different scales to examine areas, connections, and their geographic arrangement. We used graph theory metrics to compare networks across regions and species and to identify the role of important areas and connections.</p> <p><strong>Results</strong>: Relevant literature and citations for data used in this study had very little overlap. Network analysis showed that sampling effort influenced network structure and the arrangement of areas and connections for most networks was complex. However, important areas and connections identified by graph theory metrics can be different than areas of high data density. For the global network, marine regions in the Mediterranean had high closeness while links with high betweenness among marine regions in the South Atlantic were critical for maintaining connectivity. Comparisons among species-specific networks showed that functional connectivity was related to movement ecology, resulting in networks composed of different areas and links.</p> <p><strong>Main conclusions</strong>: Network analysis identified the structure and functional connectivity of the sea turtles in our sample at multiple scales. These network characteristics could help guide the coordination of management strategies for wide-ranging animals throughout their geographic extent. Most networks had complex structures that can contribute to greater robustness, but may be more difficult to manage changes when compared to simpler forms. Area-based conservation measures would benefit sea turtle populations when directed towards areas with high closeness dominating network function. Promoting seascape connectivity of links with high betweenness would decrease network vulnerability.</p>
Research data: "Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe"
<p>This data set belongs to the paper Effects of Network Structures on the Production Planning in Closed-loop Supply Chains – A Case Study based Analysis for Lithium-ion Batteries in Europe in the International Journal of Production Economics (DOI). The BatPac model, as well as, the model for the economic assessment of the recycling route are not included. The needed data can be found in the file (name). Further, the BatPaC model can be gather from the website of Argonne National Laboratory and the assessment tool for the recycling routes via this DOI: 10.5281/zenodo.6500946.</p> <p> </p> <p>This work is part of the research project Recycling 4.0 (EFRE | ZW 6-85018080), which is funded by the European Regional Development Fund and managed by the development bank for the German federal state of Lower Saxony (NBank).</p>
CNN-based Network Application for Petrophysical Parameter Inversion: Sensitivity Analysis of Input-output Parameters and Network Architecture
<p>The uploaded file includes four groups of data. They have original elastic and reservoir parameters data, the k=5 and k=25 (k means the sampling interval in inline and crossline.), and testing dataset.</p>
Virtual Axle Detector based on Analysis of Bridge Acceleration Measurements by Fully Convolutional Network
<p>We recorded the measurement data used in the present study on a single-span steel trough railway bridge located on a long-distance traffic line in Germany. The bridge is 18.4 m long in total with a free span of 16.4 m. A total of 10 seismic uniaxial accelerometers of the type PCB-39B04 (PCB Synotech) with a sensitivity of 1000 mV/g (±10\%), a broadband resolution of 0.000003 gRMS, a measurement range of ±5 gpk and a frequency range of 0.06 to 450 Hz (±5\%) were installed. The measurements are triggered via the rising slope of the wheel load measuring point G1, the measurements from the ring buffer are stored from ten seconds before the trigger together with the 50 seconds long measurement after the triggering. The recorded signals thus all have a length of 60 seconds. All sensor signals were recorded with a sampling frequency of fs = 600 Hz using the catmanAP software and the CX22 data recorder connected to an MX1601B universal amplifier and an MX1616B strain gauge amplifier (all products are from HBK). </p>
Data from: Improving governance outcomes for water quality: insights from participatory social network analysis for chalk stream catchments in England
<p>Globally important chalk streams in England are in poor ecological health, in part due to inadequate water quality. Addressing this issue requires an understanding of the governance systems that surround water quality. The complexity and uncertainty inherent in hydrological systems has led to the emergence of integrated and adaptive forms of governance. In these multi-actor governance systems, the structure of the relationships between actors (the social network) has been shown to affect governance processes and outcomes.</p> <p>Using participatory social network analysis, we mapped and analysed the social networks for the River Test and River Itchen in Hampshire, UK, to identify actors and their roles, determine the network characteristics, and identify interventions to improve governance.</p> <p>Although the results suggest a well connected network of actors from the state, private sector and civil society, we find that decision making is not decentralised. Bureaucratic governance by central state actors dominates. However, trust in these central state actors and private actors in the networks is low, which undermines collaboration and co-ordination in the network.</p> <p>Devolving authority to local actors, building trust in the networks, and improving connections to important actors could help to improve governance outcomes for water quality.</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 1000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</p>
Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples
<p>Graph Neural Network dataset : mechanical stress analysis in networks of spherical pores - 6000 samples</p> <p>unzip, run data_set.py to see how to interact with the dataset using PyTorch Geometric toolbox</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.