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9,152 results for “Networking”
Daily profiles (2030) of load of an urban electricity distribution network form Portugal - ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in an urban area as in 2030. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 10 kV and 60 kV, has 207 nodes (99 with consumption) and 206 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly.</p>
Daily profiles (2020) of load of an urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in an urban area as in 2020. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 10 kV and 60 kV, has 221 nodes (104 with consumption) and 220 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. The auxiliary flexibility files comprise 4 typical days (business day of summer, Sunday of summer, business day of winter and Sunday of winter) gathering the upward and downward active power of flexibility at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each flexibility file should be used together with the respective load data file.</p>
Daily profiles (2040) of load and generation of a semi-urban electricity distribution network from Portugal – ATTEST project
<p>This dataset was prepared under the framework of the ATTEST project, financed by the European Commission with grant number 864298. The dataset contains information about a real Portuguese Distribution test network, totally anonymized, located in a semi-urban area as in 2040. A network file (including grid topology, nodes, generators, consumption, all of which connected by power lines or transformers) and auxiliary files are provided. The network file (MatPower format) includes a “snapshot” or “steady-state” at a given time with a converged power flow solution. The grid, which is operated at 15 kV, 30 kV and 60 kV, has 229 nodes (118 with consumption) and 229 branches. The auxiliary load data comprises 12 typical days representing the combination of each season and the type of day (business day, Saturday, Sunday) gathering the active and reactive consumption at each node of the network in intervals of 15 minutes. In addition, a “read me” file called “Manual” includes technical detailed information about how to read the data properly. For the sake of coherence, each generation file should be used together with the respective load data file.</p>
Supplementary material 1 from: Del Rio-Hortega L, Martín-Forés I, Castro I, De Miguel JM, Acosta-Gallo B (2022) Network-based analysis reveals differences in plant assembly between the native and the invaded ranges. NeoBiota 72: 157-181. https://doi.org/10.3897/neobiota.72.72066
Table S1. Species ID for each country and type of species
Network Analysis to Identify Critical Links for Relief Activities During Extreme Weather Events
<p>As one of the principal lifeline systems, transportation networks are crucial for evacuation and delivering essential resources and services during the response and recovery phases of extreme weather events and must remain intact to enhance regional resiliency. The conventional evaluation measures that estimate the vulnerability or criticality of road network based on travel time or link volumes do not capture the community impacts due to disruptions. This study seeks to develop a framework to evaluate road network infrastructure criticality during extreme weather events by introducing measures that evaluate the vulnerability of roads users, rather than the physical aspects of link importance. The research develops an innovative approach that integrates three important concepts including hurricane evacuation behavior, community impacts, and road criticality to identify the critical links. Results show that the critical links for vulnerable populations during evacuation do not always align with conventional link-based measures. This highlights the importance of using a performance measure that takes the social vulnerability of road users into consideration when identifying the criticality of a road network and planning for fortification of links to avoid irreversible consequences for vulnerable population groups. Furthermore, decision-making that considers the risks to different communities may lead to a more effective distribution of resources and help support a timely and safe evacuation from disaster events by strengthening the preservation of critical infrastructure links.</p>
Infrastructure Networks within NFDI: Survey among Institutions partnering with NFDI consortia
<p>This dataset is part of Base4NFDI's proposal to acquire funding for developing NFDI-wide basic services.</p> <p>Base4NFDI rolled out a survey to quantitatively illustrate the extent of its ties to German and international infrastructures, particularly in the area of computing. The survey - Infrastructure Networks within NFDI - ran based on a Google Form. The cleaned and anonymized dataset lists these infrastructure ties for 113 unique institutions and has a total of 163 entries as some institutions are partners to multiple consortia. The dataset does not cover all 200+ member institutions of the NFDI Association.</p> <p> </p>
Cheating interactions favor modularity in mutualistic networks
<p>A fundamental fact about mutualisms is that these mutually beneficial interactions often harbor cheaters that benefit from the use of resources and services without providing any positive feedback to the other players. The role of cheaters on the evolutionary dynamics of mutualisms has long been recognized, yet their broader consequences to the community level, and beyond species they interact with, is still poorly understood. Because mutualisms form networks that often involve dozens to hundreds of species, indirect effects generated by cheaters may cascade through the whole community, reshaping trait evolution. Here, we study how harboring cheating interactions can influence coevolution in mutualistic networks. We combine a coevolutionary model, empirical data on animal-plant mutualistic networks, and numerical simulations and show that a higher frequency of cheating interactions in a network can lead to the formation of groups of species phenotypically similar to each other and distinct from other groups, generating higher trait disparity. The resulting clustered trait patterns, in turn, change the patterns of interaction in simulated networks, fostering the formation of modules of interacting species. Our results indicate that cheaters contribute to generate phenotypic clusters in mutualistic networks, counteracting selection for convergence imposed by mutualists, and favoring the emergence of modules of interacting species. Based on these results, we suggest that cheaters might be a fundamental element for our understanding of the evolution of mutualistic networks.</p>
Data of Symbolic Quantitative Verification of Quantized Neural Networks
<p>Experimental Results for the Symbolic Quantitative Verification of Quantized Neural Networks Paper</p>
research data supporting "Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"
<p>This repository contains all the data generated and analyzed including the starting structures, the input files, the trajectory files, the output data from cpptraj and network analyses, and in-house scripts used to prepare the network and module files shown in the paper <strong>"Revealing the organization of catalytic sequence-defined oligomers via combined molecular dynamics simulations and network analysis"</strong> published in <strong>Journal of Chemical Information and Modeling</strong> (DOI: 10.1021/acs.jcim.2c00101). </p>
The Cytoscape session file for the network of siderophore BGCs predicted from Streptomyces genomes
<p>The Cytoscape session file for the network of siderophore BGCs predicted from Streptomyces genomes</p>
The Cytoscape session file for the network of indole BGCs predicted from Streptomyces genomes
<p>The Cytoscape session file for the network of indole BGCs predicted from Streptomyces genomes</p>
Data from: Computing the local field potential (LFP) from integrate-and-fire network models
Leaky integrate-and-fire (LIF) network models are commonly used to study how the spiking dynamics of neural networks changes with stimuli, tasks or dynamic network states. However, neurophysiological studies in vivo often rather measure the mass activity of neuronal microcircuits with the local field potential (LFP). Given that LFPs are generated by spatially separated currents across the neuronal membrane, they cannot be computed directly from quantities defined in models of point-like LIF neurons. Here, we explore the best approximation for predicting the LFP based on standard output from point-neuron LIF networks. To search for this best "LFP proxy", we compared LFP predictions from candidate proxies based on LIF network output (e.g, firing rates, membrane potentials, synaptic currents) with "ground-truth" LFP obtained when the LIF network synaptic input currents were injected into an analogous three-dimensional (3D) network model of multi-compartmental neurons with realistic morphology, spatial distributions of somata and synapses. We found that a specific fixed linear combination of the LIF synaptic currents provided an accurate LFP proxy, accounting for most of the variance of the LFP time course observed in the 3D network for all recording locations. This proxy performed well over a broad set of conditions, including substantial variations of the neuronal morphologies. Our results provide a simple formula for estimating the time course of the LFP from LIF network simulations in cases where a single pyramidal population dominates the LFP generation, and thereby facilitate quantitative comparison between computational models and experimental LFP recordings in vivo.
Supplementary for Cross-linker design determines microtubule network organization by opposing motors
<p>This file contains Cytosim version, simulation configuration files and analysis scripts used in Cross-linker design determines microtubule network organization by opposing motors (10.1073/pnas.2206398119). </p>
Data for Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents
<p>Results data for the manuscript "Herb-paths, a network and statistical model to explore health-beneficial effects of herbs and herbal constituents".</p>
data of 5GC network fault
<p>include alarm information,network element information and performance metrics</p> <ul> <li>01.performance metrics:Performance index data such as time and index value when the digital twin network element is running;</li> <li>02.alarm information:Alarm information occured by the digital twin network element, such as the occurrence time and the alarm name;</li> <li>03.network element information:Basic information of digital twin network elements such as name, generator room;</li> </ul>
Backdoored Face-Networks (BFN) dataset
<p><strong>Description</strong></p> <p>This database is a database of backdoored neural networks intended for face recognition. The networks are of the FaceNet architecture and are trained on Casia-WebFace, with and without additional samples (which are the source of the backdoor). More information regarding backdoors and the project within which this fits can be found in the public release of the source code : <a href="https://gitlab.idiap.ch/bob/bob.paper.backdoored_facenets.biosig2022">https://gitlab.idiap.ch/bob/bob.paper.backdoored_facenets.biosig2022</a>.</p> <p>There are two sets of backdoored networks. A first one with backdoors with varying triggers (the triggers dataset) and a second one with backdoors with varying trigger placement strategies (the locations dataset). A third set of networks is also provided, just regular networks without any backdoor, referred to as the clean dataset. Configuration yaml files are provided to replicate the backdoored networks using the repository content linked above, in addition to pickle files containing validation scores on all validation datasets.</p> <p> </p> <p><strong>Reference</strong></p> <p>If you use this dataset, please cite the following publication:</p> <p><em>An anomaly detection approach for backdoored neural networks: face recognition as a case study», by Alexander Unnervik and Sébastien Marcel, published at 21st International Conference of the Biometrics Special Interest Group (BIOSIG 2022)</em></p>
Biodiversity information retrieval across networked data sets
<p>Recording of presentation. An approach to integrate data through wrapping of various datasets stored in relational databases located on networked platforms. It is designed to overcome copyright problems in data sharing.</p>
Water networks in complexes between proteins and FDA-approved drugs
<p>Dataset to accompany manuscript - prepared protein-ligand complexes</p>
Research Artifact for paper "Decompiling x86 Deep Neural Network Executables"
<p>Research Artifact for USENIX Security 2023 paper "Decompiling x86 Deep Neural Network Executables"</p>
Robust and fast post-processing of single-shot spin qubit detection events with a neural network
<p>Dataset to the paper 'Robust and fast post-processing of single-shot spin qubit detection events with a neural network'</p>
ScienceDex guides
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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.