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9,152 results for “Networking”
Train and Test Dataset for a Neural Network for the task of RSA Segmentation in the context of a Master thesis 2024
<p>The data corresponds to the work of the master thesis "3D Segmentation of Plant Roots from MRI Images for Enhanced Automated Root Tracing using Deep Neural Networks" by Daniel Weißen from the 24/07/2024</p> <p>train_data.tar<br>- contains the training and validation data used for the DNN<br> - data_gmm contains the soil noise data based on a Gaussian Mixture Model<br> - data_FS contains the soil noise data based on the Fourier Synthesis approach described in the thesis</p> <p>test_data.tar<br>- contains the test data which is manually labeled</p> <p>segmentations.tar<br>- contains the segmentations of the trained SwinUNETR-V2+ on the real test MRIs<br> - fs_model_output: contains the Fourier Synthesis trained NNs segmentation<br> - fmm_model_output: contains the GMM trained NNs segmentation<br> - threshold_output: contains the Dice optimized threshold labeling</p> <p>FS_checkpoint.ckpt, GMM_checkpoint.ckpt<br>- contains the checkpoint for the trained SwinUNETR-V2+ on the Fourier synthesis, and GMM soil noise respectively</p>
A Framework for Improving Social Inclusion using Network Analysis and IoT-based Contact Tracing, Dataset and Source Code
Open the record for dataset details and reuse information.
Data for constructing China's co-invention network across 371 cities and depicting the regional context of 19 city-regions under COVID-19
<p>co-inventions.xlsx contains the application records of China's invention patents with more than one applicants in 371 cities from 2005 to 2021.</p> <p>determinants.xlsx contains statistical data depicting the socioeconomic background, innovation conditions, and COVID-19 pandemic severity of 19 city-regions in China.</p>
Modeling fluid flow in ship systems for controller tuning using an artificial neural network
<p>Dataset used to develop ANN NARX models</p>
Fig. 5 Haplotype networks for datasets IV in Dugesia hepta and Dugesia benazzii (Platyhelminthes: Tricladida): two sympatric species with occasional sex?
Fig. 5 Haplotype networks for datasets IV (a Dunuc12) and III (b Cox1). Haplotypes are depicted as individual circles which are proportional to their abundancy (number of sequences), highlighted in a white square. Mutations are either depicted with black bars or black triangles when the number of mutations between linked haplotypes is equal to or exceeds a certain
Fig. 3 Median-joining haplotype network obtained for 867 in Diversification and evolutionary history of brush-tailed mice, Calomyscidae (Rodentia), in southwestern Asia
Fig. 3 Median-joining haplotype network obtained for 867 bp of mitochondrial Cyt b of the genus Calomyscus. Circle size is relative to haplotype frequency; black circles represent extinct or unsampled
FIGURE 2. Median joining network genes showing 95 in Taxonomic status of the black fly Prosimulium italicum Rivosecchi (Diptera: Simuliidae) based on genetic evidence
FIGURE 2. Median joining network genes showing 95% probability linkages among 28 haplotypes of COI and COII genes obtained from 29 individuals of P. hirtipes and P. italicum. Mutational changes are represented by ticks across network connections. The area of each circle is proportional to the number of individuals sharing the haplotype.
Dataset for "Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study"
<p>The dataset (EPANET file) which accompanies the publication </p> <p>Vrachimis, S. G., Eliades, D. G., and Polycarpou, M. M.: Real-Time Hydraulic Interval State Estimation for Water Transport Networks: a Case Study, Drink. Water Eng. Sci., 2018</p>
A collection of public transport network data sets for 25 cities
<p>This dataset describes the public transport networks of 25 cities across the world in multiple easy-to-use data formats. These data formats include network edge lists, temporal network event lists, SQLite databases, GeoJSON files, and General Transit Feed Specification (GTFS) compatible ZIP-files.<br> <br> The source data for creating these networks has been published by public transport agencies according to the GTFS data format. To produce the network data extracts for each city, the original data have been curated for errors, filtered spatially and temporally and augmented with walking distances between public transport stops using data from OpenStreetMap. <br> <br> Cities included in this dataset version: Adelaide, Belfast, Berlin, Bordeaux, Brisbane, Canberra, Detroit, Dublin, Grenoble, Helsinki, Kuopio, Lisbon, Luxembourg, Melbourne, Nantes, Palermo, Paris, Prague, Rennes, Rome, Sydney, Toulouse, Turku, Venice, and Winnipeg.</p> <p>Contrary to the version 1.0 of this data set, this version (1.2) does not include the cities of Antofagasta and Athens, for which non-commercial usage of the data is not allowed.<br> <br> Contrary to previous versions of the data set (1.0 and 1.2), in this version (1.2) the temporal filtering of the data has been slightly adapted, so that the daily and weekly data extracts cover all trips departing between from 03 AM on Monday to 03 AM on Tuesday (daily extract) or 03 AM of the Monday next week (weekly extract). Additionally, a temporal network extract covering a full week of operations has been added for each city.<br> <br> Documentation of the data can be found in the Data Descriptor article published in Scientific Data: http://doi.org/10.1038/sdata.2018.89 <br> When using this dataset, please cite also the above-mentioned paper.</p>
Selected CO2 Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub>data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
A Performance Evaluation of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau
<p>The data is associated with the paper titled "A Performance Evaluation of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau" which is published in Journal of Atmospheric and Oceanic Technology with the doi: 10.1175/JTECH-D-17-0144.1. The abstract is as follows:</p> <p>A systematic evaluation of the performance of the World Wide Lightning Location Network (WWLLN) over the Tibetan Plateau is conducted using data from the Cloud-to-Ground Lightning Location System (CGLLS) developed by the State Grid Corporation of China for 2013–15 and lightning data from the satellitebased Tropical Rainfall Measuring Mission (TRMM) Lightning Imaging Sensor (LIS) for 2014–15. The average spatial location separation magnitudes in the midsouthern Tibetan Plateau (MSTP) region between matched WWLLN and CGLLS strokes and over the whole Tibetan Plateau between matched WWLLN and LIS flashes were 9.97 and 10.93 km, respectively. The detection efficiency (DE) of the WWLLN rose markedly with increasing stroke peak current, and the mean stroke peak currents of positive and negative cloud-toground (CG) lightning detected by theWWLLNin the MSTP region were 62.43 and256.74 kA, respectively. The duration, area, and radiance of the LIS flashes that were also detected by the WWLLN were 1.27, 2.65, and 4.38 times those not detected by the WWLLN. The DE of the WWLLN in the MSTP region was 9.37% for CG lightning and 2.58% for total lightning. Over the Tibetan Plateau, the DE of the WWLLN for total lightning was 2.03%. In the MSTP region, theCGflash data made up 71.98% of allWWLLNflash data. Based on the abovementioned results, the ratio of intracloud (IC) lightning to CG lightning in the MSTP region was estimated to be 4.05.</p>
Nanopublication dump from server network
<p>Dump of 10803231 nanopublications from the server network, retrieved on 5 April 2018.</p>
Discovering and explaining the learning process of neural networks: A study on EEG data. The animated visualizations.
<p>In the paper 'Discovering and explaining the learning process of neural networks: A study on EEG data' frames of animated visualizations are presented. Here, the full animations are made available so they can be used for interpretation.</p>
FIGURE 4. Neighbour Joining network derived from a in Two new species of blue-eyed Trimma (Pisces; Gobiidae) from New Guinea
FIGURE 4. Neighbour Joining network derived from a BOL analysis of the COI gene of the Trimma haplogroups associated with T. blematium and T. meityae. Image by R. Winterbottom.
F I G U R E 6 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 6 Euclidean distances moved by Tetrahymena individuals depending on densities in our entire dataset. Across all replicates of all landscapes (patches from different landscapes types highlighted by different symbols; see legend) we find positively densitydependent movement. The solid lines represent fits of the averaged linear mixed model (red: dendritic landscapes; blue: linear landscapes) and the shaded area shows 95% confidence intervals (see Table 4 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 4 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 4 Comparison of variation in population densities between linear and dendritic networks at day 15 of the experiment. The solid line represents the difference between inter-quartile range (IQR) over median population densities of linear and dendritic landscapes. The distribution (grey) represents the distribution of the differences between IQR over median population densities of 200,000 random re-samplings for our data. As we theoretically expect the dendritic landscapes to be more variable we can perform a one-sided test which gives a probability of p =.047 of our observed difference between IQR to median ratios to be larger than zero. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 3 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 3 Fit of theoretical expectations to the distribution of Tetrahymena population densities depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) for day 15. Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape, distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal red and blue lines visualise fits of the theoretically expected distribution of population densities to data from the dendritic and linear networks assuming network specific dispersal rates (d) and carrying capacities (K). White squares show fits of the theoretically expected distribution of population densities assuming the same d and K values for both network types. Shaded areas, respectively, error bars, show 95% confidence intervals of the fits. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 2 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 2 Distribution of Tetrahymena population densities depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape, distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 2 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 1 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 1 Median population densities (in thousands of individuals) of Tetrahymena in corresponding dendritic (a) and linear (b) landscapes at the end of the experiment (day 15) and across the three replicate landscapes. In these landscapes, outer nodes are labelled "O," inner and central nodes are labelled "I" and "C", respectively. [Colour figure can be viewed at wileyonlinelibrary.com]
F I G U R E 5 in Dispersal in dendritic networks: Ecological consequences on the spatial distribution of population densities
F I G U R E 5 Euclidean distances moved by Tetrahymena individuals depending on network type (linear versus dendritic networks), network position (central versus inner versus outer nodes) and time (days 0, 8 and 15). Violin plots show the overall distribution of the data, the white point gives the median, and the solid black line the 25% and 75% percentiles, respectively. Given the network structure (Figure 1) and the three replicates per landscape distributions include N = 18 (9, 3) measurements for outer (inner, central) nodes of dendritic networks and N = 6 (6, 18) measurements for outer (inner, central) nodes of linear landscapes. Horizontal lines visualise back-transformed parameter estimates of the averaged linear mixed effects model and shaded areas show 95% confidence intervals (see Table 3 for model selection results). [Colour figure can be viewed at wileyonlinelibrary.com]
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.