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1,721 results for “network data”
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>
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>
Data series of seismic events for the article "Seismic monitoring using the telecom fiber network"
<p>The file "catalog.h5" contains the catalog of seismic events analyzed in the paper "Seismic monitoring using the telecom fiber network" by S. Donadello et al., Commun Earth Environ 5, 178 (2024) <a href="https://doi.org/10.1038/s43247-024-01338-2">https://doi.org/10.1038/s43247-024-01338-2</a> (formerly "Earthquake observatory with coherent laser interferometry on the telecom fiber network" on arXiv preprint).</p> <p>The reported data correspond to raw recordings, acquired by coherent interferometry techniques on a telecommunication fiber (see also <a href="doi.org/10.1109/TIM.2023.3288255">https://doi.org/10.1109/TIM.2023.3288255</a>), and decimated to a lower sampling rate.</p> <p>The catalog is organized as about 900 seismic events in the period between June 19th, 2021 and Sept. 26th, 2022, between Feb. 6th and March 23th 2023, and between Nov. 9th, 2022 and Nov. 18th, 2022, according to the criteria described in the paper.</p> <p>A detailed description of the ".h5" file format is provided in "h5_file_description.txt".</p> <p>A Python3 script "h5_cat_parser.py" for data interpretation in terms of standard python structures is provided.</p>
Data from: Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation
<p>Simulation data from Assessing Electrogenetic Activation via a Network Model of Biological Signal Propagation, doi: 10.3389/fsysb.2024.1291293</p> <p>There are 10 csv files per network type, each dataset contains the Timestep, Node, Strain, Inducer Concentration/Duration, and Node weights for the timecourse of the simulation. </p>
Data and Software: Upsampling Monte Carlo Reactor Simulation Tallies in Depleted SFR Assemblies using a Convolutional Neural Network
<p>Datasets and code used in upsampling OpenMC SFR simulation neutron flux tallies.</p>
Raw data for 'Long-baseline Quantum Sensor Network as Dark Matter Haloscope'
Open the record for dataset details and reuse information.
Data and codes: Automated estimation of bioturbation intensity and ichnodiversity from the core section image using convolutional neural network
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Supplementary data to BANMF-S: a blockwise accelerated non-negative matrix factorization framework with structural network constraints for single cell RNA-seq data imputation
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Data analysis file for the manuscript "Assessing the speed of individual bacteria dispersing on mycelial networks"
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Single cell gene expression data and gene regulatory network
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Simulation and experimental data of frequency domain and time domain optical signal measurements for optical network digital twins
<p>The dataset contains IQ optical constellation samples for 16-QAM optical connections. Data have been generated both experimentally and through simulations with a MATLAB-based simulator. Different configurations have been simulated: 62 lightpaths having a different number of spans and links and 4 soft-failures affecting a lightpath with increasing failure magnitude.</p>
synthetic climate data used for Controlled Abstention Network (CAN) development
<p>The synthetic climate data used in two papers to develop Controlled Abstention Netoworks. The data is approximately 720Mb, saved as a .mat file. The data is from Mamalakis et al. (2021) - with citation given below. </p> <p>Mamalakis, Antonios, Imme Ebert-Uphoff and Elizabeth A. Barnes: Neural Network Attribution Methods for Problems in Geoscience: A Novel Synthetic Benchmark Dataset, submitted to Environmental Data Science, 11/2021, preprint available https://arxiv.org/abs/2103.10005.</p> <p>The code that uses this data can be accessed here:</p> <p>Elizabeth Barnes, & Randal J. Barnes. (2021). eabarnes1010/controlled_abstention_networks: (v1.0.1). Zenodo. https://doi.org/10.5281/zenodo.5750222</p> <p>The publications associated with this data are posted on arxiv (but will soon be published in JAMES):</p> <ul> <li> <p><strong>Barnes, Elizabeth A. </strong>and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for regression problems, accepted to <em>JAMES</em> 11/2021. Preprint available at <a href="https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F2104.08236&sa=D&sntz=1&usg=AFQjCNENUTEbOS90QNROchwcMQDDpJsrjQ">https://arxiv.org/abs/2104.08236</a></p> </li> <li> <p><strong>Barnes, Elizabeth A. </strong>and Randal J. Barnes: Controlled abstention neural networks for identifying skillful predictions for classification problems, accepted to <em>JAMES</em> 11/2021. Preprint available at <a href="https://www.google.com/url?q=https%3A%2F%2Farxiv.org%2Fabs%2F2104.08281&sa=D&sntz=1&usg=AFQjCNHUMJPchhqprYXHRAzb2HA4USVvUw">https://arxiv.org/abs/2104.08281</a></p> </li> </ul>
Supporting data to "Open-source, low-cost, in-situ turbidity sensor for river network monitoring"
<p>This folder contains the Supporting Dataset that is part of the Manuscript "Open-source, low-cost, in-situ turbidity sensor for river network monitoring."</p>
Data for: Local contributions to beta-diversity in urban pond networks: implications for biodiversity conservation and management
<p><b>Aim:</b> An understanding of how biotic communities are spatially organised is necessary to identify and prioritize habitats within landscape-scale biodiversity conservation. Local Contribution to Beta diversity (LCBD) identifies individual habitats that make a significant contribution to beta-diversity and may have important practical implications, particularly for conservation of habitat networks. In this study, we develop and apply a conservation prioritisation approach based on LCBD in aquatic invertebrate communities from 132 ponds.</p> <p><b>Location:</b> Five urban settlements in the UK: Halton, Loughborough, Stockport, Birmingham, Huddersfield.</p> <p><b>Methods:</b> We partition LCBD into richness difference (nestedness: <sub>RichDiff</sub>LCBD) and species replacement (turnover: <sub>Repl</sub>LCBD) and identify key environmental variables driving LCBD. We examine LCBD at two scales relevant to conservation planning: within urban settlements and nationally across the UK.</p> <p><b>Results:</b> Significant differences in LCBD values were recorded among the five settlements. In four of the five urban settlements studied, pond sites with the greatest LCBD values typically showed high replacement values. Significant LCBD sites and sites with high taxonomic diversity together supported more of the regional species pool (70%-97%) than sites with high taxonomic diversity alone (54% to 94%) or what could be protected by the random selection of sites. LCBD was significantly associated with vegetation shading, surface area, altitude and macrophyte cover.</p> <p><b>Main conclusions:</b> Conservation prioritisation that incorporates LCBD and sites with high taxonomic diversity improves the effectiveness of conservation actions within pond habitat networks, ensures site supporting high biodiversity are protected, and provides a method to define a spatial network of protected sites. Identifying new, effective conservation approaches, particularly in urban areas where resources may be scarce and conflicts regarding land use exist, is essential to ensure biodiversity is fully supported and detrimental anthropogenic effects are reduced.</p>
Identification of species by combining molecular and morphological data using convolutional neural networks
<p>Integrative taxonomy is central to modern taxonomy and systematic biology, including behavior, niche preference, distribution, morphological analysis, and DNA barcoding. However, decades of use demonstrate that these methods can face challenges when used in isolation, for instance, potential misidentifications due to phenotypic plasticity for morphological methods, and incorrect identifications because of introgression, incomplete lineage sorting, and horizontal gene transfer for DNA barcoding. Although researchers have advocated the use of integrative taxonomy, few detailed algorithms have been proposed. Here, we develop a convolutional neural network method (morphology-molecule network [MMNet]) that integrates morphological and molecular data for species identification. The newly proposed method (MMNet) worked better than four currently available alternative methods when tested with 10 independent data sets representing varying genetic diversity from different taxa. High accuracies were achieved for all groups, including beetles (98.1% of 123 species), butterflies (98.8% of 24 species), fishes (96.3% of 214 species), and moths (96.4% of 150 total species). Further, MMNet demonstrated a high degree of accuracy (<i>></i>98%) in four data sets including closely related species from the same genus. The average accuracy of two modest subgenomic (single nucleotide polymorphism) data sets, comprising eight putative subspecies respectively, is 90%. Additional tests show that the success rate of species identification under this method most strongly depends on the amount of training data, and is robust to sequence length and image size. Analyses on the contribution of different data types (image vs. gene) indicate that both morphological and genetic data are important to the model, and that genetic data contribute slightly more. The approaches developed here serve as a foundation for the future integration of multimodal information for integrative taxonomy, such as image, audio, video, 3D scanning, and biosensor data, to characterize organisms more comprehensively as a basis for improved investigation, monitoring, and conservation of biodiversity.</p>
Data from: Spatial familial networks to infer demographic structure of wild populations
<p class="List1">In social species, reproductive success and rates of dispersal vary among individuals resulting in spatially structured populations. Network analyses of familial relationships may provide insights on how these parameters influence population-level demographic patterns. These methods have however rarely been applied to genetically-derived pedigree data from wild populations.</p> <p class="List1">Here we use parent-offspring relationships to construct familial networks from polygamous boreal woodland caribou (<i>Rangifer tarandus caribou</i>) in Saskatchewan, Canada, to inform recovery efforts. We collected samples from 933 individuals at 15 variable microsatellite loci along with caribou-specific primers for sex identification. Using network measures, we assess the contribution of individual caribou to the population with several centrality measures and then determine which measures are best suited to inform on the population demographic structure. We investigate the centrality of individuals from eighteen different local areas, along with the entire population.</p> <p class="List1">We found substantial differences in centrality of individuals in different local areas, that in turn contributed differently to the full network, highlighting the importance of analyzing networks at different scales. The full network revealed that boreal caribou in Saskatchewan form a complex, interconnected familial network, as the removal of edges with high betweenness did not result in distinct subgroups. Alpha, betweenness, and eccentricity centrality were the most informative measures to characterize the population demographic structure and for spatially identifying areas of highest fitness levels and family cohesion across the range. We found varied levels of dispersal, fitness and cohesion in family groups.</p> <p class="List1"><i>Synthesis and applications</i>: Our results demonstrate the value of different network measures in assessing genetically-derived familial networks. The spatial application of the familial networks identified individuals presenting different fitness levels, short and long-distance dispersing ability across the range in support of population monitoring and recovery efforts.</p>
Data from: Evolutionary transition from a single RNA replicator to a multiple replicator network
<p><span>In prebiotic evolution, self-replicating molecules are believed to have evolved into complex living systems by expanding their information and functions open-endedly. Theoretically, such evolutionary complexification could occur through successive appearance of novel replicators that interact with one another to form replication networks. Here we perform long-term evolution experiments of RNA that replicates using a self-encoded RNA replicase. The RNA diversifies into multiple coexisting host and parasite lineages, whose frequencies in the population initially fluctuate and gradually stabilize. The final population, comprising five RNA lineages, forms a replicator network with diverse interactions, including cooperation to help the replication of all other members. These results support the capability of molecular replicators to spontaneously develop complexity through Darwinian evolution, a critical step for the emergence of life.</span></p>
Data from: Anderson lab experiments from synthesizing the effects of spatial network structure on predator prey dynamics
<p>Predator-prey persistence is thought to be enhanced by spatial heterogeneity. Theory predicts that metacommunity size, spatial connectivity, network synchrony, predator identity, and productivity influence predator-prey persistence, through a variety of mechanisms such as statistical stabilization, colonization-extinction dynamics, and trophic interactions. However, comparative tests and synthesis of the multiple factors and mechanisms across different spatial networks are needed to understand which factors and mechanisms of spatial network structure promote predator-prey persistence. To address this gap between theory and empirical work, we synthesized data from 22 microcosm experiments of protist predator-prey communities differing the productivity, connectivity, and size of spatial habitat structure. Prey time to extinction was better explained by productivity and spatial factors than predator time to extinction. At the local and regional scale, metacommunity size and productivity had positive effects on prey occupancy, whereas connectivity negatively influenced prey occupancy. For predators, metacommunity size and connectivity had positive effects on predator occupancy, network synchrony had negative influences, and productivity showed a hump-shaped relationship with predator occupancy. Further, trophic interactions drove variation in the way species were spatially structured, where the strength and direction of predator and prey occupancy relationships varied among productivity levels and predator-prey combinations. In predator-prey interactions that were stronger, prey occupancy showed negative relationship with predator occupancy regardless of productivity. However, in predator-prey interactions that were weaker, prey occupancy was positively related to predator occupancy at low productivity, and this relationship disappeared at higher productivity treatments where prey occupancy was high regardless of predator occupancy. Predictions from metapopulation theory explained predator occupancy, while prey were better explained by trophic dynamics. Taken together, these results highlight that spatial network structure has a complex, spatially contingent relationship with predator-prey dynamics.</p>
Network origin-destination data
<p>The first sheet has the tonnage data for each of the 440 routes. Each of these routes is connecting some of the 72 segments, which are entered in Columns B and C.</p> <p>Name of the routes/segments in the original format are provided in the second sheet. Column C has the names of the segments.</p> <p>The third sheet has the cost of dredging each of the segments entered in Column A at different depths that are entered in Columns B to L</p> <p>The fourth sheet has the tonnage capacity of each route entered in Column A after dredging at different depths entered in Columns B to L</p> <p>The fifth sheet has the required number of vessels to meet the demand after dredging each of the routes entered in Column A at different depths entered in Columns B to L</p> <p>The sixth and seventh sheets have water routes entered in Column A that carries some demand to the destination and from the origin</p> <p>The eighth sheet has the freight demand on each OD entered in Column A</p> <p> </p>
Data of Symbolic Quantitative Verification of Quantized Neural Networks
<p>Experimental Results for the Symbolic Quantitative Verification of Quantized Neural Networks Paper</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.