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138 results for “Broadband”
Dataset for Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters
<p>This dataset contains the raw data for the figures (Fig. 5, Fig. 6 and Fig. 7) in the publication entitled "Broadband three-mode converter and multiplexer based on cascaded symmetric Y-junctions and subwavelength engineered MMI and phase shifters" published by Optics and Laser Technology (DOI: 10.1016/j.optlastec.2023.109513). Datafiles are in .txt format.</p> <p>All relevant information regarding the dataset, how it was obtained and its context is contained in the manuscript. </p>
Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s' Molecular Dynamics
<p><strong>Related publication:</strong><br> Soccio, M.; Martínez-Tong, D.E.; Guidotti, G.; Robles-Hernández, B.; Munari, A.; Lotti, N.; Alegria, A. Broadband Dielectric Spectroscopy Study of Biobased Poly(alkylene 2,5-furanoate)s’ Molecular Dynamics. <em>Polymers</em> 2020, <em>12</em>, 1355.<br> <a href="https://doi.org/10.3390/polym12061355">10.3390/polym12061355</a></p> <p><strong>EUSMI proposal codes:</strong><br> E171100040, E171100043</p>
Real Dataset From Broadband Customers of a Brazilian Telecom Operator
<p>This dataset includes information from broadband users of a telecom operator located in Brazil.</p> <p>It includes modem parameters extracted from the operator's Network Management System (NMS) and customer complaint information extracted from the operators' CRM. The modem is an Optical Network Terminal (ONT), located in customer premises, responsible for providing Wi-Fi for the customer, and is the terminal for the optical fiber in a Gigabit Passive Optical Network (GPON)<br>Collection Date: January/23 to June/23</p> <p>The modem data was collected for six (6) months for a set of specific customers. At the end of the period, data from this set of customers were collected using the Customer Relationship Management (CRM) system. If the customer complained during that period, we filtered the modem parameters on the day of the last complaint. If the customer has not complained within the period, we use the parameters from the last parameter collection from its modem.</p> <p>The file contains raw data in Comma-Separated Values (CSV) format using UTF-8 encoding and has a set of different parameters.</p> <p>"Customer ID" - A unique ID for the customer in the dataset. The customer's personal information was anonymized.<br>"Latency" - Value of the latency the user is experiencing. Latency is the time it takes for data to get from one network point to another.<br>"Jitter" - Value of the jitter (variation in latency) the user is experiencing.<br>"Packet Loss" - The percentage (%) of packets lost during the transmission<br>"Channel2_quality" - The Quality of the customer's 2.4GHz channel, rated on a scale of 1 to 5 by the modem<br>"Channel5_quality" - The Quality of the customer's 5GHz channel, rated on a scale of 1 to 5 by the modem<br>"N distant devices" - The Number of devices far from the modem (more than 10 m)<br>"CRM_Complaint?" - Indicates whether the user complains (value = 1) about its broadband experience or not (value = 0).</p>
Database of GeoNet Broadband and Short Period Stations operating between 2001 and 2021.
<p>A station xml file containing all GeoNet stations from 2001 to 2021. In this version not all channels are included for every station but all channels should be listed. Later updates will expand to include all channels. This includes unlisted stations in the GeoNet station search tool. Data obtained from GeoNet New Zealand Seismograph Network (<a href="https://doi.org/10.21420/G19Y-9D40">https://doi.org/10.21420/G19Y-9D40</a>).</p>
SCEC Broadband Platform Release 22.4.0 Validation Data
<p>This package includes the set of validation plots generated with the Broadband Platform Release 22.4.</p> <p>Fabio Silva, Kevin Milner, & Philip Maechling. (2022). SCECcode/bbp: Broadband Platform Release v22.4.0 (v22.4.0). Zenodo. https://doi.org/10.5281/zenodo.7062972</p> <p>The following folders include:</p> <p>2022-08-30-bbp-part-a - Broadband validation runs using ground motions from 17 historical events.</p> <p>2022-08-30-bbp-part-a-all - Broadband validation runs using ground motions from 17 historical events (same as above, but includes additional GoF plots such as maps and distance)</p> <p>2022-08-30-bbp-part-b - Broadband verification runs against NGA-West 2 GMPEs.</p> <p>2022-08-30-bbp-converge - Convergence plots for each method and event</p> <p>2022-08-30-bbp-tables - Summary tables for each method, along with aggregate results from all methods/events per distance and period range. Also includes Dreger figure 3 plots (see references below for more information)</p>
Data for "Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes" by Shao et al.
<p>Data set for manuscript of “Three-Dimensional Broadband Interferometric Mapping and Polarization (BIMAP-3D) Observations of Lightning Discharge Processes” by Shao et al. submitted to Journal of Geophysical Research-atmosphere</p>
Nonhuman Primate Reaching with Multichannel Sensorimotor Cortex Electrophysiology: broadband for indy_20170131_02
<p>This dataset supplements <a href="https://doi.org/10.5281/zenodo.583331">https://doi.org/10.5281/zenodo.583331</a> .<br> <br> <strong>General description.</strong> These data consist of extracellular neural recordings ("broadband") from primate subject "Indy", session identifier "indy_20170131_02".</p> <p><strong>Filtering. </strong>The data are sampled at 24414.0625 Hz and are unfiltered, except for an anti-aliasing filter built-in to the recording amplifier: a 4th order low-pass with a roll-off of 24 dB per octave at 7.5 kHz, operating at the sampling rate.</p> <p><strong>File format.</strong> The data are contained in an HDF5 formatted file, organized according to the <a href="https://github.com/NeurodataWithoutBorders/specification">Neurodata Without Borders (NWB) version 1.0.6</a> specification.</p> <p><strong>Datasets. </strong>A few of the relevant dataset paths are listed here for convenience. In the below, <em>n</em> refers to the number of recording channels and <em>k</em> refers to the number of samples.</p> <ul> <li>"/acquisition/timeseries/broadband/data" - k x n <ul> <li>The broadband neural recordings.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/data/conversion" (scalar attribute) <ul> <li>When multiplied by each sample converts the data into units of volts.</li> </ul> </li> <li>"/acquisition/timeseries/broadband/timestamps" - k x 1 <ul> <li>Timestamps for each sample, seconds.</li> </ul> </li> <li>"/general/extracellular_ephys/electrode_map" - n x 3 <ul> <li>The relative coordinates of each electrode contact (x, y, z), meters.</li> </ul> </li> </ul> <p>Please refer to the <a href="https://doi.org/10.5281/zenodo.583331">master dataset</a> for further information.</p> <p><strong>History</strong></p> <ul> <li>Version 2 - corrects a error with the electrode mapping.</li> <li>Version 1 - initial release.</li> </ul>
Dataset for "Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data"
<p>This dataset contains the MESWA (Middle East and Southwest Asia) seismic model and auxiliary data used in the creation of the model (Rodgers, 2023). MESWA is a three-dimensional model of the seismic properties of crust and upper mantle of the Middle East and Southwest Asia. The MESWA model is provided in NetCDF format (readable by for example, <em>xarray</em>, Hoyer & Hamman, <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0057">2017</a>) and HDF5 format for viewing with <em>ParaView</em> (Ahrens et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0002">2005</a>) and interaction with <em>Salvus</em> (Afanasiev et al., <a href="https://agupubs.onlinelibrary.wiley.com/doi/10.1029/2021JB022930#jgrb55516-bib-0001">2019</a>). </p> <p> </p> <p>Also included are the earthquake source parameters for all 327 Global Centroid Moment Tensor events considered in this study in ASCII text format. Also included are lists of the selected 192 inversion events and 66 validation events in ASCII text format. Lastly, we include a list of all receivers used in the creation and validation of MESWA. This is a simple ASCII file with the event name and receiver name (composed of the network_code and station_code).</p> <p> </p> <p>The following table provides a listing of the files in the dataset:</p> <table> <tbody> <tr> <td> <p><strong>File</strong></p> </td> <td> <p><strong>Description</strong></p> </td> </tr> <tr> <td> <p>MESWA.nc</p> </td> <td> <p>MESWA model in NetCDF format</p> </td> </tr> <tr> <td> <p>MESWA.h5</p> </td> <td> <p>MESWA model in HDF5 format, used by Salvus</p> </td> </tr> <tr> <td> <p>MESWA.xmdf</p> </td> <td> <p>Auxiliary file for MESWA.h5, used to import model into Paraview</p> </td> </tr> <tr> <td> <p>events_project.csv</p> </td> <td> <p>Table of event source parameters for all 327 events considered in the project</p> </td> </tr> <tr> <td> <p>inversion_events_192.csv</p> </td> <td> <p>Table of 192 inversion events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>validation_events_66.csv</p> </td> <td> <p>Table of 66 validation events </p> <p>(ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_inversion.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the inversion (ASCII comma separated value)</p> </td> </tr> <tr> <td> <p>events_receivers_validation.csv</p> </td> <td> <p>Table of waveform (event-receiver-channel) data used in the validation (ASCII comma separated value)</p> </td> </tr> </tbody> </table> <p> </p> <p> </p> <p><strong>References</strong></p> <p>Afanasiev, M, C Boehm, M van Driel, L Krischer, M Rietmann, DA May, MG Knepley, and A Fichtner (2019). Modular and flexible spectral-element waveform modelling in two and three dimensions, <em>Geophys. J. Int.</em>, 216(3), 1675–1692, doi: 10.1093/gji/ggy469</p> <p> </p> <p>Ahrens, J., Geveci, B., & Law, C. (2005). Paraview: An end-user tool for large data visualization. <em>The Visualization Handbook</em>, 717(8). <a href="https://doi.org/10.1016/b978-012387582-2/50038-1">https://doi.org/10.1016/b978-012387582-2/50038-1</a></p> <p> </p> <p>Hoyer, S., & Hamman, J. (2017). Xarray: N-D labeled arrays and datasets in Python. <em>Journal of Open Research Software</em>, 5(1). <a href="https://doi.org/10.5334/jors.148">https://doi.org/10.5334/jors.148</a></p> <p> </p> <p>Rodgers, A. (2023). Adjoint Waveform Tomography for Crustal and Upper Mantle Structure the Middle East and Southwest Asia for Improved Waveform Simulations Using Openly Available Broadband Data, technical report, LLNL-TR- 851939.</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>This project was support by Lawrence Livermore National Laboratory’s Laboratory Directed Research and Development project 20-ERD-008 and the National Nuclear Security Administration. This work was performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344. LLNL-MI-852402</p> <p> </p>
Data and Supplementary Material for "Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations" by Jensen et al.
<p>Data and Supplementary Material for the manuscript “Insights into Lightning K-Leader Initiation and Development from Three Dimensional Broadband Interferometric Observations” by Jensen et al., submitted to Journal of Geophysical Research: Atmospheres. Further description in included PDF (01file_description.pdf)</p>
Chirped-Pulse Broadband Spectra of Benzene Discharges
<p>Broadband chirped-pulse spectra of electrical discharges mixtures of Benzene with O<sub>2</sub> and N<sub>2</sub>.</p> <p>Spectra were recorded at the Center for Astrophysics | Harvard & Smithsonian on separate occasions; integration times are around ~12 hours (run overnight).</p> <p>X000 and X001.csv files correspond to assignments made in each experiment; the leading number corresponds to the mixture, while the last digit corresponds to mid-band (0—6–19 GHz) and high-band (1—18–27 GHz) measurements.</p> <p>4000/4001 - Benzene + Ne buffer gas</p> <p>5000/5001 - Benzene + O<sub>2</sub> + Ne buffer gas</p> <p>6000/6001 - Benzene + N<sub>2</sub> + Ne buffer gas</p> <p> </p> <p>fit_SI.pdf corresponds to a compiled PDF of all of the assignments made across the experiments, including a `.fit` and `.lin` printout, where applicable. Entries with the fit outputs contain the parameter encodings used in the SPFIT program. This PDF was generated using `rosetta_stone_final.csv`, which provides a comprehensive mapping/naming used to refer to molecules studied in these mixtures.</p> <p> </p> <p>The spectra used for analysis are provided as `.txt` files—prefixed by the mixture, with the corresponding frequency range for the acquisition.</p>
MONROE_Profiling_Mobile_Broadband_Coverage
<p>Dataset for TMA'16 paper Profiling Mobile Broadband Coverage. </p> <p>The dataset consists of grid blocks traversed by the train routes me measure in Norway, more specifically Oslo-Stavanger, Oslo-Voss, Oslo-Trondheim, Trondheim- Bodø.</p> <p>For each of the grids and for each run on a route, we measure the Radio Access Technology an end-user could access while in the train for two different Mobile Broadband providers, namely Telenor and Netcom (Telia) in Norway. The dataset csv files we upload here are organized per operator and per route. </p> <p>Each row in one file consists of:</p> <p>grid_id = unique ID of the grid block that delimits a portion of the route</p> <p>lat1 = latitude of the grid </p> <p>lon1 = longitude of the grid </p> <p>avg_speed = average speed of the train when traversing the grid block </p> <p>start = timestamp of when the train enters the grid</p> <p>end = timestamp when the train exits the grid </p> <p>ccu_desig = unique ID of the NSB passenger train </p> <p>total = total number of datapoints within the grid block </p> <p>4G = number of points within the grid where the RAT is 4G </p> <p>3G = number of points within the grid where the RAT is 3G </p> <p>2G = number of points within the grid where the RAT is 2G </p> <p>nos = number of points within the grid where the RAT is No Service </p> <p>4gd = 4G distribution in the grid block </p> <p>3gd = 3G distribution in the grid block </p> <p>2gd = 2G distribution in the grid block</p> <p>nosd = No Service distribution in the grid block </p> <p>run_id = the ID of the run</p> <p>static = 1 if the train stops at any point in the grid, 0 is the train doesn't stop in the grid</p> <p>mobile = 1 if the train is mobile, 0 is it is not</p> <p>full_mobile = 1 if the train is moving at all times, 0 if it is not </p> <p>tunnel = 1 if the train traverses a tunnel within the grid block, 0 if there are no tunnels</p> <p>full_tunnel = 1 if the train is in train the whole time it is in the respective grid block </p> <p>way = route direction </p> <p>route = train route </p> <p> </p>
Mobile broadband speedtest traces
<p>The goal of this research is to collect a wide range speedtest traces for the mobile broadband (MBB) networks, as seen from actual users while moving around the city using public or private vehicles. For collecting this dataset we ask students to participate and run Mobile BroadBand speedtest. You can find the instruction of our test here. Traces were mostly collected in the city of Torino in Italy, and refer to three technologies (WiFi, 3G, and 4G), and multiple Mobile Network Operators (MNO). The networks were in normal operating conditions (and unaware of our tests). Our terminals (both Android and iOS smartphones) accessed the mobile networks to upload data to a server on campus, using both TCP and UDP at the transport layer.<br> <br> We used a hybrid method in the trace collection process: we run repetitive active measurements from mobile terminals using iperf2, and we collect passive traces on server side using tcpdump. In each experiment, the mobile terminal runs iperf2 in the upload direction for 600 seconds while tcpdump captures packets at the server.<br> We collected traces for different MNOs in Italy (Tim, Wind, and Vodafone). For WiFi, we considered the open WiFi community WoW-Fi offered automatically by Fastweb customers that share their DLS or FTTH home network via the access gateway. Mobile phones automatically authenticate using IEEE 802.1x with no action from the user. Traces shorter than 300 seconds are iperf2 experiments run from the stationary MONROE nodes or failed experiments.<br> </p>
Global scale leaf broadband optical properties derived from CliMA Land and associated CESM simulations
<p>Leaf level broadband reflectance and transmittance computed from leaf traits.</p> <ul> <li>clm_refl_tran_1m_weighted.nc: monthly data (144*96 pixels)</li> <li>surfdata_CMIP6_fluspect_v3.nc: surface data to run CESM (144*96 pixels)</li> </ul> <p>Global scale simulation results</p> <ul> <li>research_data_coupled_future_v2.nc: CESM coupled future simulations</li> <li>research_data_coupled_history_v2.nc: CESM coupled historical simulations</li> <li>research_data_uncoupled_history_v2.nc: CESM uncoupled future simulations</li> <li>research_data_uncoupled_ssp_v2.nc: CESM uncoupled SSP245 and SSP585 simulations</li> </ul> <p>Code changes</p> <ul> <li>SurfaceAlbedoMod.F90: modified CLM module</li> <li>Julia-and-Python-Code.tar.gz: code used for processing the data and plot the figures</li> </ul>
Data for "Broadband thulium fiber amplifier for spectral region located beyond the L-band"
<p>Includes data for absorption and emission measurements, profiles of refractive index, and data for spectral dependence of amplifier output, as well as typical output spectra presented in the graphs.</p> <p> </p>
A temporary broadband seismic array in the largest desert of China: TASTE
<p>This includes the dataset of our manuscript submitted to Seismological Research Letters entitled with <em>A temporary broadband seismic array in the largest desert of China: TASTE</em></p>
Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip
<p>Dataset accompanying "Broadband microwave detection using electron spins in a hybrid diamond-magnet sensor chip". </p>
Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements
<p>All data presented in the figures of "Vibrational coherences in half-broadband 2D electronic spectroscopy: spectral filtering to identify excited state displacements".</p>
Dataset: Liberty Broadband Corporation (LBRDP) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Liberty Broadband Corporation (LBRDK) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Dataset: Liberty Broadband Corporation (LBRDA) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
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.