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9,152 results for “Networking”
Dataset for "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market"
<p>This is the dataset for the manuscript "Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market" submitted to the journal PLOS ONE. </p>
"Thermodynamic limits on general far-from-equilibrium molecular templating networks" Supporting Examples Code
<p>Supporting example Mathematica codes for article</p>
TZO - Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network
<p>Data files hosted on Figshare for "Working memory performance in glioma patients is associated with functional connectivity between the right dorsolateral prefrontal cortex and default mode network".</p> <p>Contains functional connectivity matrices, clinical variables, and working memory performance scores of 45 glioma patients.</p>
Do patients of different craniofacial patterns have different volumes of the upper airways? A systematic review with network meta-analysis
<p>Dataset for all analyses</p>
Dysfunctions-of-Multiscale-Dynamic-Brain-Functional-Networks-in-Subjective-Cognitive-Decline
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Data from: Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes
<p>The file "Zarco-Perello et al Temperate Reef Fish Trophic Guilds Complete Diet Dataset.xlsx" contains several spreadsheet tabs related to the analyses carried out in the paper: <i><strong>Refining the trophic diversity, ecological network structure, and bottom-up importance of prey groups for temperate reef fishes: </strong></i><a href="https://doi.org/10.32942/X2CC97">https://doi.org/10.32942/X2CC97</a></p><p>All analyses, with the exception of the network calculations, of the study were carried out in the computer software R. The code is contained in the file "Zarco-Perello et al Temperate Reef Fish Trophic Ecology.R". For trophic network analyses we used the computer program Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&amp;pre=&amp;suf=&amp;sa=0">(Bastian et al. 2009).</a></p><p><strong>DATASET DESCRIPTION</strong></p><p><strong>Region of Study</strong></p><p>The region of study encompasses all the temperate reefs of south-western Australia (SWA). Extending along ~1600 km of coast, from Jurien Bay Marine Park (30° 18.6 S, 115° 0.1 E) to the Recherche Archipelago Nature Research (33° 53.7 S, 123° 52.3 E; supplementary Fig. S1), the temperate reefs of SWA are distributed across the Leeuwin and Houtman biogeographical ecoregions <a href="https://sciwheel.com/work/citation?ids=1796477&pre=&suf=&sa=0">(Spalding et al. 2007)</a>, conforming approximately ⅓ of the total distribution of temperate Australia, known as the Great Southern Reef <a href="https://sciwheel.com/work/citation?ids=4498783&pre=&suf=&sa=0">(Bennett et al. 2016).</a></p><p><strong>Species Composition</strong></p><p>The species composition of the metacommunity of temperate reef fishes of the region was obtained from a total of 4589 underwater visual surveys conducted across 206 reefs in 12 locations by the Reef Life Survey (RLS) citizen science program, and the Australian Temperate Reef Collaboration (ATRC, with support from the Department of Biodiversity Conservation and Attractions; https://www.atrc.au) from 1997 to 2021.</p><p><strong>Trophic Information</strong></p><p>All fish species listed in the RLS-ATRC database were classified in trophic guilds based on collected diet information from studies of gut content analyses in SWA, or other Australian and international regions in the absence of local information. A total of 298 fish species composed the metacommunity. For every species, we obtained diet information from the scientific literature reported on Fishbase <a href="https://sciwheel.com/work/citation?ids=10423542&pre=&suf=&sa=0">(Froese and Pauly 2019)</a> and through the search engine Scopus using the search terms: TS = (<i>name of species</i>* OR *<i>common name of species</i>*) AND TS = (diet OR *stomach content* OR *gut content* OR consump* OR herbi* OR predat* OR feeding). Diet information consisted of the average proportions of food items represented as the number of items (%N), percent volume (%V), or biomass (%W) in a population of each species. Preference was given to diet studies conducted in the region of study and those presenting biomass proportions. Species that lacked diet information globally were assigned diet proportions based on phylogenetically related species with similar size and habitat preferences based on the Fish Tree of Life <a href="https://sciwheel.com/work/citation?ids=10720381&pre=&suf=&sa=0&dbf=0">(Chang et al. 2019)</a>.</p><p><i><< The tab "Guilds Complete Diet Dataset" contains all the diet information (stomach content proportions) and its sources for all fish species considered in the study >></i></p><p><strong>Trophic guilds classification</strong></p><p>To quantify the diversity of trophic guilds and identify important fish consumers of specific groups of prey, we classified the fish species into trophic guilds performing a multi-step cluster analysis. Firstly, species were grouped into main trophic guilds using the mutually exclusive major categories of prey items. The diet proportions in these categories were used to create a dissimilarity matrix among species based on the Bray-Curtis linkage method using the function <i>vegdist</i> of the R package Vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, which was used to run a sequential divisive hierarchical cluster analysis using the function <i>diana</i> (divisive analysis) of the R package Cluster <a href="https://sciwheel.com/work/citation?ids=15165291&pre=&suf=&sa=0">(Maechler et al. 2022)</a>. Subsequently, because there are mismatches in the resolution of diet identification between species belonging to different trophic levels (<i>e.g.</i> the diets of herbivorous fish tend to have higher resolution on macrophytes, while carnivorous species tend to have higher resolution on animal prey), species within each identified main trophic guild were subject to a cluster analysis with higher definition of prey items to identify groups of species with diet specializations using sequential agglomerative hierarchical cluster analysis based on Ward's Method and Bray-Curtis or Euclidean dissimilarity matrix <a href="https://sciwheel.com/work/citation?ids=205080&pre=&suf=&sa=0">(Pineda‑Munoz and Alroy 2014)</a>.</p><p>The stomach content of most scarid species (parrotfish; Labridae: Scarinae) is very difficult to identify due to their pharyngeal mill, which grinds all food items to indiscernible particles. However, they are well identified as a special group that ingest detritus and algae by scraping the reef substrate with their specialized fused teeth. Thus, for the sake of differentiating their trophic guild, the proportions of diet for species of parrotfish was arbitrarily defined based on field observations as sediment and detritus (90%) and short filamentous algae (10%) <a href="https://sciwheel.com/work/citation?ids=11332249&pre=&suf=&sa=0&dbf=0">(Bonaldo et al. 2014)</a>. Additionally, cleaner fish and false cleaners are a special group of fishes that are difficult to group by diet given that they feed on prey that could be identified as zooplankton or zoobenthos, while in fact true cleaners forage, at least in part, on parasitic invertebrates attached to bigger fish, in addition to fish skin and scales <a href="https://sciwheel.com/work/citation?ids=13921938&pre=&suf=&sa=0">(Grutter 1997)</a>; thus, given their particular trophic ecology these labrid and blenny species were arbitrarily grouped in the major trophic group "fish cleaners" for the subsequent specialized trophic group classifications.</p><p>Visual analysis of the differences in multidimensional space between trophic guilds was done with Non-metric Multidimensional Scaling based on the dissimilarity matrix calculated for clustering using the function <i>metaMDS</i> of the R package vegan (reported in supplementary materials; <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>. Statistical significance in dietary differences among major and specialized trophic guilds (diet proportions ~ trophic guilds) was tested with permutational analysis of variance (PERMANOVA) using the function <i>adonis2 </i>of the R package vegan <a href="https://sciwheel.com/work/citation?ids=7457489&pre=&suf=&sa=0">(Oksanen et al. 2022)</a>, followed by pairwise comparisons using the function <i>pairwise.adonis2</i> of the R package pairwiseAdonis <a href="https://sciwheel.com/work/citation?ids=15190336&pre=&suf=&sa=0">(Martinez 2017)</a>.</p><p><i><< The tabs in the dataset called "Major Guilds Diet Data", "Herbivores Diet Data", "Cleaners Diet Data", "Zoobenthivores Diet Data, "Zooplanktivores Diet Data", and "Piscivores Diet Data" are the datasets with selected diet categories for each guild without "unidentified diet items" and standardized to 100 proportion which were used for the classification of each major trophic guild into specialized trophic guilds. >></i></p><p><strong>Trophic Network Links Between Specialized Guilds</strong></p><p>The trophic links between fishes and their invertebrate and macrophyte prey groups were identified by our trophic guild classification (Other Guilds Links tab in dataset); however, the trophic role of piscivores is faced with what here we called a "matrioshka paradox", because to know their links with other guilds, we must first know the trophic links of their prey. Moreover, this is not straightforward because the highest taxonomic identification of piscivorous prey is usually limited to family level, which could belong to multiple trophic guilds. This paradox is usually not explicitly stated in the literature, and it is unclear how trophic links have been drawn in previous studies without performing detailed quantitative trophic classifications. Here we estimated the trophic links between piscivorous guilds and the rest of fish guilds by (i) assigning each fish family identified in the diets of piscivorous fishes into their respective specialized guilds based in our trophic classification, (ii) pooling their diet proportions into each specialized trophic guilds they could belong to, (iii) standardizing values by number of species in each piscivorous guild, and (iv) dividing by the total sum of diet proportions to estimate their potential predation (0-100%) on other trophic guilds in the trophic network. Trophic links that had pooled diet proportions with values <5% were discarded for clarity of the network (Piscivores Trophic Links tab in dataset). This information was joined with the trophic information from non-piscivorous trophic guilds and formatted as a list of nodes (guilds and prey groups), and links between nodes (source-target) to create the trophic network of the entire temperate reef fish metacommunity (Nodes Network List and Edges Network Lisk tabs in dataset). All network analyses were done using the computer program for network visualization and analyzes Gephi v0.1 <a href="https://sciwheel.com/work/citation?ids=15257446&pre=&suf=&sa=0">(Bastian et al. 2009)</a>.</p><p><i><< The tabs "Piscivores Trophic Links" and "Other Guilds Links" are datasets containing the calculations of the links between specialized trophic links for Piscivores and other guilds respectively used to create the data of the tabs "Nodes Network List" and "Edges Network List" to create the trophic network of the system of study. >></i></p><p><i><< The tab "Herbivory, Omnivory and Carnivory" contains diet proportion data of all fish species of the study formated to build the barplot (Fig. 4) in the manuscript showing the distribution of consumption of macrophytes, invertebrates and fishes >></i></p><p> </p>
Dipper Throated Optimization with Deep Convolutional Neural Network-based Crop Classification on Remote Sensing Image Analysis
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A Systematic Review on the Use of Multi Agent Coordination for Teleprotection Relays in Electrical Distribution Networks
<p>Data set for review on the coordination of relays.</p>
LDA_NEURAL_NETWORK_PATIENT_DATA
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Replication package for "Measuring Diffusion over a Large Network"
<p>The package contains data sets and codes to replicate the tables and figures in He and Song (forthcoming), "Measuring Diffusion Over a Large Network" Review of Economic Studies. Detailed instructions to obtain some of the raw data are given in the package.</p> <p> </p>
THE IMPORTANCE OF DIVIDING SOCIAL NETWORKS INTO COMMUNITIES IN THE ECONOMY
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High-order correction of persistent batch effects in correlation networks
<p>This upload contains data to reproduce the results and figures in our work High-order correction of persistent batch effects in correlation networks. The code is publicly available on <a href="https://github.com/QuackenbushLab/cobra-experiments">GitHub</a>.</p>
PhD Thesis - Emergence of cortical network motifs
<p>Annotated representative video of mice performing sensorimotor task.</p>
Ischia cGNSS Network - Rinex data quality control (2020)
<p>For each cGNSS station, the file contains a summary report with information about Rinex observation data. For each day, the summary line shows the following information:</p><ol><li>cGNSS station name (Name),</li><li>the start time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date) and GNSS week (Week),</li><li>the end time of the window, the time format is year (Y), month (M), day (D), hour and minutes (Hour), day of year (DOY), modified Julian date (M J Date), and GNSS week (Week),</li><li>the start and end times of the window (time format is year month day hour min),</li><li>window time laps (Hrs),</li><li>observation interval (OI),</li><li>the number of possible observations (#expt) above the elevation mask,</li><li>the number of complete observations (#obs),</li><li>the ratio of complete to possible observations as a percent (DCP),</li><li>the RMS "multipath combinations" values MP1 and MP2, in meters, limited by the elevation mask (MP1, MP2) rounded to two decimal points,</li><li>cycle slips (CS),</li><li>the ratio of complete observations to cycle slips (obs/CS).</li></ol><p>A full description of cGNSS network is reported in:<br>- De Martino P, Dolce M, Brandi G, Scarpato G, Tammaro U (2021). The Ground Deformation History of the Neapolitan Volcanic Area (Campi Flegrei Caldera, Somma–Vesuvius Volcano, and Ischia Island) from 20 Years of Continuous GPS Observations (2000–2019). Remote Sensing. 13(14):2725. doi:10.3390/rs13142725.</p><p>Please cite this when using the dataset</p>
test data for Cardiologist-level interpretable knowledge-fused deep neural network for automatic arrhythmia diagnosis
<p>Companion python scripts are available in: https://github.com/xin-gou/automatic-ecgdiagnosis</p>
MODELING OF TELECOMMUNICATION NETWORKS BASED ON FUZZY LOGIC TECHNOLOGY
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Accurate Identification of Polyps in Screening Colonoscopies using Convolutional Neural Networks
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MUSDB18-HQ Test Set Inference Outputs for Models from "A Generalized Bandsplit Neural Network for Cinematic Audio Source Separation"
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Analogue Memristive Devices based on La2NiO4+δ as Synapses for Spiking Neural Networks
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Data for the publication "Mechanical communication within the microtubule through network-based analysis of tubulin dynamics"
<p>Repository containing all the necessary data to replicate the study "Mechanical communication within the microtubule through network-based analysis of tubulin dynamics", published in Biomechanics and Modeling in Mechanobiology (https://doi.org/10.1007/s10237-023-01792-5).</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.