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Dataset results
104 results for “Network Measurement”
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 3
<p>LENS dataset 2024-07 Part 3</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 2
<p>LENS dataset 2024-07 Part 2</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 1
<p>LENS dataset 2024-07 Part 1</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202406 - Part 1
<p>LENS dataset 2024-06 Part 1</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
LENS: A LEO Satellite Network Measurement Dataset - 202407 - Part 4
<p>LENS dataset 2024-07 Part 4</p> <p>Please see https://github.com/clarkzjw/LENS for the complete description of the dataset.</p>
Surfaces/regoliths used in the training and testing of the deep neural network for surface reconstruction from simulated exospheric measurements
<p>This dataset contains the surfaces/regoliths in terms of elemental surface composition used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".</p>
Inputs and outputs for exospheric simulations used in the deep neural network for surface reconstruction from simulated exospheric measurements
<p>This dataset contains the inputs and outputs of the exospheric simulations performed for v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".</p>
Inputs and outputs for the training and testing of a deep neural network for surface reconstruction from simulated exospheric measurements
<p>This dataset contains inputs (datasets) and outputs (trainings and tests) used in v2.0 - v2.5 of the paper collection: "Conceptual framework for the application of deep neural networks to surface composition reconstruction from Mercury’s exosphere".</p>
Displacement measurement via self mixing interferometry and neural network training set
<p>Self mixing interferometry is a simple and robust sensing method which can be used (among other things) to measure the displacement of a target along the light propagation axis. While conceptually simple, the actual use of this method is less straightforward than originally envisioned because reconstructing the target displacement from the interferometric signal is often tricky. A small neural network can do this task very well after proper training, as described in [10.1364/OE.419844]. This data set was used to train the network in that work (after data augmentation). It consists of a python dictionary with two keys: `truth` and `signal`. The `truth` part is a 195011-elements long numpy array corresponding to the displacement of the target in units of wavelength per 1.024 ms. The `signal` part is the interferometric signal corresponding to the displacement. It is arranged in a (195011,256,1) numpy array. Each segment of length 256 corresponds to the interferometric signal acquired during a 1.024 ms time window. For instance, the displacement value in `truth[618]` corresponds to the interferometric signal segment `signal[618,:,0]`.</p>
Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot
<p><span>To evaluate the synchronization of bivariate time series has been a hot topic and a number of measures have been proposed. In this work, by introducing the ordinal pattern transition network (OPTN) into the crossplot,</span> <span>a new method for measuring the synchronisation of bivariate time series is proposed.</span> <span>After the crossplot been partitioned and coded, the coded partitions are defined as network nodes and a directed weighted network is constructed based on the temporal adjacency of the nodes. The crossplot transition entropy (CPTE) of the network is proposed as an indicator of the synchronization between two time series. To test the characteristics and performance of the method, it is used to analyse the unidirectional coupled Lorentz model</span> <span>and compared it with existing methods. The results showed the new method had the advantages of easy parameter setting, efficiency, robustness, good consistency and suitable for short time series. Finally, EEG data from auditory evoked potential EEG-Biometric dataset are investigated, and some useful and interesting results are obtained.</span></p>
Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields
<p>Supplemental networks of cowords of the paper Measuring the impact of Big Data in the scientific research in Agriculture and allied fields.</p>
Understanding the impact of host networking elements on traffic bursts: Raw measurement data
<p>This record contains the raw trace files gathered by the Valinor network traffic burst measurement framework in Redis dump (rdb) format. Please refer to the artifact repository for instructions on how to parse and use the datasets:</p> <p><a href="https://github.com/hopnets/valinor-rawdata">hopnets/valinor-rawdata: Raw Redis datasets containing the measurement results of Valinor NSDI '23 paper (github.com)</a></p>
Extracted Information for the Systematic Review of Survey Scales for Measuring Information Privacy Concerns on Social Network Sites
<p>The data set is part of a systematic literature review of survey scales for measuring information privacy concerns (IPCs) used in research on social network sites (SNSs).</p> <p>The results of this systematic literature review are available in Bartol, J., Vehovar, V., & Petrovčič, A. (2023). Systematic review of survey scales measuring information privacy concerns on social network sites. <em>Telematics and Informatics</em>, 102063. https://doi.org/10.1016/j.tele.2023.102063</p> <p>The article also includes a detailed description of the methods used in generating this data set.</p>
AIVT: Inference of turbulent thermal convection from measured 3D velocity data by physics-informed Kolmogorov-Arnold Networks
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Application of inverse theory for high spatial resolution reconstructions of thermospheric vector wind fields from Doppler shifts measured by a ground-based network of all-sky Fabry-Perot interferometers
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Ericsson 5G NSA network RF and throughput measurements on AERPAW network
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Data for: Capturing synchronization with complexity measure of ordinal pattern transition network constructed by Crossplot
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Data from: Node-based measures of connectivity in genetic networks
At-site environmental conditions can have strong influences on genetic connectivity, and in particular on the immigration and settlement phases of dispersal. However, at-site processes are rarely explored in landscape genetic analyses. Networks can facilitate the study of at-site processes, where network nodes are used to model site-level effects. We used simulated genetic networks to compare and contrast the performance of 7 node-based (as opposed to edge-based) genetic connectivity metrics. We simulated increasing node connectivity by varying migration in two ways: we increased the number of migrants moving between a focal node and a set number of recipient nodes, and we increased the number of recipient nodes receiving a set number of migrants. We found that two metrics in particular, the average edge weight and the average inverse edge weight, varied linearly with simulated connectivity. Conversely, node degree was not a good measure of connectivity. We demonstrated the use of average inverse edge weight to describe the influence of at-site habitat characteristics on genetic connectivity of 653 American martens (Martes americana) in Ontario, Canada. We found that highly connected nodes had high habitat quality for marten (deep snow and high proportions of coniferous and mature forest) and were farther from the range edge. We recommend the use of node-based genetic connectivity metrics, in particular, average edge weight or average inverse edge weight, to model the influences of at-site habitat conditions on the immigration and settlement phases of dispersal.
Reproducibility and robustness of graph measures of the Associative-Semantic Network
<p>Matfiles and matlab scripts used to study the reproducibility and robustness of graph measures of the Associative-Semantic Network.</p>
Learning topological states from randomized measurements using variational tensor network tomography
<p>Dataset for paper <strong>Learning topological states from randomized measurements using variational tensor network tomography.</strong><br>The numerical code can be found at the repo: https://github.com/teng10/tn-shadow-qst</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.