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Dataset results
18 results for “Channel network”
Supplementary Data for "Interplay of river and tidal forcings promotes loops in coastal channel networks"
<p>This dataset contains supplemental data required to reproduce the results of the paper <em>Interplay of river and tidal forcings promotes loops in coastal channel networks</em> (in review at Geophysical Research Letters). We provide raw and extracted channel network data for 19 river deltas/coastal marsh sites. For each site, the following files are provided:</p> <p><strong>XXX_base.tif</strong> : the raw binary mask of the river channel network<br> <strong>XXX_clipper.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : polygon(s) used to clip the raw mask<br> <strong>XXX_clipped.tif</strong> : the binary mask of the river channel network after being clipped by XXX_clipper.shp<br> <strong>XXX_filled.tif</strong> : the binary mask after filling islands via the method specified in the paper<br> <strong>XXX_inlet_nodes.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : locations of the inlet nodes; used by RivGraph<br> <strong>XXX_shoreline.shp</strong> (and associated .dbf, .prj, .qpj, and .shx files) : location of the shoreline; used by RivGraph<br> <strong>XXX_links.json</strong> : GeoJSON file containing the geometries, connectivities, and widths of each link in the network<br> <strong>XXX_nodes.json</strong> : GeoJSON file containing the locations of each node of the network<br> <strong>process_XXX.py</strong> : the python script used to generate the above files</p> <p>All files listed below (except .py files) are georeferenced (i.e. can be opened with QGIS, ArcGIS or another GIS). Exceptions to the provided files include:</p> <p><strong>Barnstable</strong>: no "base.tif" is provided. Use "filled.tif".<br> <strong>GBM</strong>: some hand-cleaning was performed on "filled.tif".<br> <strong>Mackenize</strong>: "clipper.shp" is not provided, but "clipped.tif" is.<br> <strong>Mississippi</strong>: "clipper.shp" is not provided as the mask was made from a shapefile.</p> <p>In order to run process_XXX.py, the RivGraph package will need to be installed. Instructions<br> can be found at https://github.com/jonschwenk/RivGraph.</p>
Influence of social networks as a distribution channel in volatile markets of non-fungible tokens..
<p>Dataset with tweets, prices (ETH & USD), volume (USD) and sentiment from BAYC, WOW and CoolCats NFTs. </p> <p>Obtained from Twitter and from the Ethereum blockchain using Dune Analytics (Dune.xyz)</p>
A 5 m vertical distance to channel network index (VDCNI) across France
<p>The vertical distance to channel network index (VDCNI) expresses the vertical height (in meter) between the elevation of a pixel and the nearest channel. It was derived from the national airborne DTM (RGE ALTI ®) at 5 m spatial resolution, which is available from the website of the French National Geographic Institute (IGN) (<a href="https://geoservices.ign.fr/">https://geoservices.ign.fr/</a>), and the GIS layer of the channel network in the national hydrological database <a href="https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274">https://www.sandre.eaufrance.fr/atlas/srv/fre/catalog.search#/metadata/3b3d3c56-d9b6-4625-a57e-ba054e798274</a>.</p> <p>Dataset includes:</p> <ul> <li>280 GeoTIFF raster files (VDCNI_000.tif) projected in the French Lambert-93 system (EPSG code 2154), each file corresponding to a 50 x 50 km tile. The number indicates the tile of interest ;</li> <li>1 vector tile index at Google Earth format (tile_index.kmz) showing the location of each tile. This file has been created to facilitate download layer only on area of interest.</li> </ul> <p>To reduce storage space and download time, each raster file has been packed at 7-Zip freeware format.</p> <p>A complete description of the dataset can be found in Panhelleux, L., Rapinel, S., Lemercier, B., Gayet, G., Hubert-Moy, L., 2023. A 5 m dataset of digital terrain model derivatives across mainland France. Data in Brief 109369. https://doi.org/10.1016/j.dib.2023.109369</p>
Exploring Kv1.2 channel inactivation through MD simulations and network analysis
<p>MD equilibration trajectories of Kv1.2 WT and mutants in dcd format can be visualized using visualization tools such as VMD or PyMol after uploading the topology file (.prmtop).</p>
Channel State Information (CSI) analysis for predictive maintenance using Convolutional Neural Network (CNN)
<p>Dataset manual:</p> <p>This dataset contains CSI amplitude values for rotating motors in an office environment. Details of the experiments may be found in the corresponding paper published in the DATA'19 workshop, SenSys (<a href="https://doi.org/10.1145/3359427.3361917">https://doi.org/10.1145/3359427.3361917</a>). </p> <p>Folder structure:<br> The folders for servo motor and stepper motor contains separate folders for network reconnection conditions (w_recc: with reconnections, wo_recc: without recconnections) and load conditions (w_load: with load and wo_load: without load). The data is stores as Matlab files with .mat extentions. </p> <p>File structure:<br> In each file name, the digits after the '_' at the end of the file name correspond to the speed of the motor. In case of stepper motor these numbers could be directly interpreted as rpm. Ex: table_inj_with_load_5_0.mat corresponds to stationary motor (0 rpm) and table_inj_with_load_5_250.mat corresponds to motor rotating with 250 rpm speed. In the case of servo motor these numbers should be mapped with the following table in order to get the speeds.</p> <p>0: 0 rpm<br> 50: 14.45 rpm<br> 100: 8.02 rpm<br> 150: 5.38 rpm<br> 200: 4.05 rpm<br> 250: 3.26 rpm<br> 300: 2.67 rpm<br> Ex: table_inj_with_load_50.mat corresponds to motor running with 14.45 rpm.</p> <p>Each file has 3 columns, each corresponding to CSI value, labels (speed/last digits in the file name) and the data sample number (not in sequence as a result of packer loss) respectively. CSI values are typically a matrix of size 3000*180 (3000 CSI samples for 3sec data @1kHz sampling rate and 180 channels for 6 antenna pairs @ 30 subcarrier data per antenna).</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
<p>Dynamic changes in the active portion of stream networks represent a phenomenon common to diverse climates and geologic settings. However, the ecological implications of river network expansions/retractions remain poorly understood owing to operational difficulties in mechanistically describing these processes at the relevant spatio-temporal scales. Here we present a novel Bayesian framework for the simulation of event-based channel network dynamics capitalizing on the concept of "hierarchical structuring of temporary streams" - a general principle to identify the activation/deactivation order of network nodes. The framework incorporates a dynamic version of a stochastic occupancy metapopulation model, and is used to analyze the impact of pulsing river networks on species persistence in different scenarios. Climate strongly controls temporal variations of the active length, influencing the preferential configuration of the active channels and the speed of network retraction during drying. We also identify a climate-dependent detrimental effect of network dynamics on species spread and persistence. This effect is enhanced by dry climates, where flashy expansions and retractions of the flowing channels induce metapopulation extinction. Survival probabilities are particularly reduced in settings where the spatial heterogeneity of network connectivity is pronounced. The proposed framework provides novel insight on the multi-faced ecological legacies of channel network dynamics.</p>
Coupled stochastic modelling of hierarchical channel network dynamics and metapopulation persistency - Dataset
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Broadcasting Competitively against Adaptive Adversary in Multi-channel Radio Networks
Full video presentation of the paper: Broadcasting Competitively against Adaptive Adversary in Multi-channel Radio Networks.<br><br>Appears in Session 1 of the 24th International Conference on Principles of Distributed Systems OPODIS 2020<br><a href="https://opodis2020.unistra.fr">https://opodis2020.unistra.fr</a>
Morph_CNeT: A new GIS tool to extract morphometric attributes characterising channel network topology of Indian catchments
<p><span>Morph_CNeT” (Morphometric Channel Network Extraction Tool) can facilitate extraction of the topology based new morphometric attributes by processing DEM datasets within GIS framework. Morph_CNeT tool is used to create a repository named as Morph_CNeT-India of topological catchment attributes for 1749 gauging stations maintained by the Central Water Commission (CWC) across 22 River basin Systems of India.</span></p>
Demodulation Combining 1D-CNN and Bi-LSTM Network over Strong Solar Wind Turbulence Channel
<p>The data in this dataset is derived MATLAB simulation dataset by us to illustrate the GMSK signal demodualtion over strong solar wind turbulence using deep learing.<br> Disclaimer<br> The data provided in the files is provided as is. Despite our best efforts at filtering out potential issues, some information could be erroneous.<br> Description of the dataset<br> One file per is provided as a csv file with the following features:<br> train_dataset: Including data_kb2 and data_awgn. data_kb2 is the GMSK modulated fading signal data and lable data under the influence of strong solar wind turbulence, data_awgn is the GMSK modulated signal data and lable data under the influence of Gaussian white noise only.In which per mod_data and lable_data have 21000 csv file, respectively. And per csv file has 800 data. Among them ,the lable is the original binary number. The train_dataset is used to train neural network demodulator model.<br> test_dataset:Including test_data and test_lable.The test_data and test_lable have 6 file respectively. And they are used to test the trained neural network demodulator model.<br> test_data:<br> kb2_Tb0d5_m1d4: GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> Awgn_Tb0d5:GMSK at data the BTb=0.5 under the influence of Gaussian white noise only.<br> Awgn_Tb0d5:GMSK data at the BTb=0.3 under the influence of Gaussian white noise only.<br> test_lable:<br> kb2_Tb0d5_m1d4: GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d5_m1d2:GMSK lable data at the BTb=0.5 , solar wind turbulence scintillation index m=1.2.<br> kb2_Tb0d3_m1d4:GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.4.<br> kb2_Tb0d3_m1d4: GMSK labe data at the BTb=0.3 , solar wind turbulence scintillation index m=1.2.<br> awgn_Tb0d5:GMSK lable data at the BTb=0.5 under the influence of Gaussian white noise only.<br> awgn_Tb0d5:GMSK labe data at the BTb=0.3 under the influence of Gaussian white noise only.<br> </p>
AggMapNet: Enhanced and Explainable Low-Sample Omics Deep Learning with Feature-Aggregated Multi-Channel Networks
<p>This data contains the datasets used in the paper "AggMapNet: Enhanced and Explainable Low-Sample Omics Deep Learning with Feature-Aggregated Multi-Channel Networks", each folder is named by the dataset name in the paper</p>
Data from: Fine-scale population structure and riverscape genetics of brook trout (Salvelinus fontinalis) distributed continuously along headwater channel networks
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Applying High-Speed Video Images to Inverse Channel Base Current Based on NARX Neural Network
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Data from: Common internal allosteric network links anesthetic binding sites in a pentameric ligand-gated ion channel
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Certificate of the Minimal Size of 11-Channel Sorting Networks
<p>A machine checkable certificate proving the absence of 11-channel sorting networks with fewer than 35 comparators. See <a href="http://github.com/jix/sortnetopt">github.com/jix/sortnetopt</a> (<a href="https://doi.org/10.5281/zenodo.4139151">10.5281/zenodo.4139151</a>) for software that can check this certificate and for further information.</p>
Dataset (Aggegation, Variance and the Mean in Braided Channel Networks)
<p>This is the dataset of braided channels' images (in .bmp format ) extracted from Landsat satellite images ,available through the United States Geological Survey Earth Explorer Tool. Presented in .txt format there are the parameters Nwc (Number of braided channels) and Wwc (Width of braided channels) at maximum resolution.</p>
The Quantitative Study of the Habenula Based on Multi-channel Cascaded Neural Network and the Establishment of the Prediction Model of the Curative Effect in Patients With Depression
ClinicalTrials.gov study NCT05872607. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Identification datasets of OPM- MEG signal artifacts based on channel attention convolution network
<p>In order to realize the automatic identification and removal of OPM-MEG artifacts, we created this data set based on OPM magnetometer reference signal and OPM-MEG signal itself. If you need to use this data set, you need to contact the author for permission. Contact email: by2017335@buaa.edu.cn.</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.