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342 results for “gnss”
GNSS data and Python code for data selection
<p>The Python code in the folder describes a simple algorithm for selection of GNSS data by spatial resolution indexing. An example shown in the code is set up for 2 degrees latitude by 2 degrees longitude. </p>
Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'
<p>The *.pre.*.neu files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.neu files are the raw position time series after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epoches of breaks due to equipment changes or that are indentified visually.</p>
Raw time series of GNSS sites used in the manuscript 'South China plate motion modified by 2008 Mw7.9 great Wenchuan earthquake'
<p>The *.pre.*.neu files are raw position time series before the Wenchuan earthquake (from July 2001 to December 2004) and the *.aft.*.neu files are the raw position time series after the Wenchuan earthquake (from July 2014 to December 2017). The file 'breaks' contains the epochs of breaks due to equipment changes or that are identified visually.</p>
RInex files from HK GNSS network in observing simultaneous and consecutive occurrence of thunderstorm and geomagnetic storm
<p>These are the RInex files from HK GNSS network in observing simultaneous and consecutive occurrence of thunderstorm and geomagnetic storm</p>
Data set of ""Reconciling high-resolution strain rate of continental China from GNSS data with the spherical spline interpolation""
<p>Date data of ”Reconciling high-resolution strain rate of continental China from GNSS data with the spherical spline interpolation“</p> <p>"readme.txt" is the description of those zips. And Each zip also contains a "readme.txt", which is a description of the respective zip.</p>
Shallow Slow Slip Events Identified Offshore the Osa Peninsula in Southern Costa Rica from GNSS Time Series
<p>Using new continuous GNSS time series from the region, we identify 5 distinct shallow SSEs that occurred immediately offshore and beneath the Osa peninsula: an event detected by one site in 2013, and two events occurring in close succession in both 2018 and 2022 detected by multiple stations, indicating a preliminary recurrence interval of ~4-5 years. While SSEs have been observed to the north at Nicoya, this is their first documentation in southern Costa Rica. Modeled slip distributions of the 2018 and 2022 events indicate that they ruptured the same or overlapping patches of the plate interface, nearly at the trench, updip of historic earthquakes including the 1983 Mw 7.4 Osa event. Immediately offshore, the estimated cumulative slip from the 2018 and 2022 events is sufficient to close the slip deficit from tectonic loading over the recurrence interval, likely influencing the magnitude and spatial slip distribution of future large ruptures.</p>
Data acquired at Forest with RTK GNSS
<p>These bag files were recorded to test the GNSS RTK using two different technologies. The first bag "rosbag2_2023_04_11-16_40_36" used a RENEP public RTCM base while the second bag used the a fixed based installed in our terrain. </p> <p> </p> <p>We have also recorded:</p> <p> Topic: /base/fix | Type: sensor_msgs/msg/NavSatFix | Count: 754 | Serialization Format: cdr<br> Topic: /base/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9 | Count: 754 | Serialization Format: cdr<br> Topic: /odom | Type: nav_msgs/msg/Odometry | Count: 1510 | Serialization Format: cdr<br> Topic: /rover/fix | Type: sensor_msgs/msg/NavSatFix | Count: 394 | Serialization Format: cdr<br> Topic: /rover/navrelposned | Type: ublox_msgs/msg/NavRELPOSNED9 | Count: 394 | Serialization Format: cdr<br> Topic: /rslidar_points | Type: sensor_msgs/msg/PointCloud2 | Count: 755 | Serialization Format: cdr<br> Topic: /tf | Type: tf2_msgs/msg/TFMessage | Count: 3021 | Serialization Format: cdr<br> Topic: /tf_static | Type: tf2_msgs/msg/TFMessage | Count: 1 | Serialization Format: cdr<br> <br> </p> <p> </p> <p> </p>
Positioning GNSS data of "Optimizing simultaneous water level and wave measurements from multi-GNSS interferometric reflectometry over one year at an exposed coastal site"
<p>These files show the position of the GNSS antenna used in the experiment. Files are obtained for DOY 53 and 242 in 2020.</p> <p> </p>
Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"
<p><strong>5Hz GNSS Velocity Data for the submitted work: </strong>"Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog" Dittmann et al (202?)</p> <p><strong>Datasets included:</strong></p> <ol> <li>Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) </li> <li>Real GNSS 5Hz validation featuresets from <a href="https://doi.org/10.1029/2022JB024854">Dittmann, et al. (2022) </a></li> </ol> <p>Timeseries and Featuresets are stored in <a href="https://parquet.apache.org/">Apache Parquet</a> format.</p> <p><strong>Getting Started:</strong><br> Notebook demos for reading using <a href="https://docs.conda.io/en/latest/">conda</a>+ jupyterlab (easiest).<br> In a terminal:</p> <ol> <li>Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz)</li> <li> <pre><code>conda env create -f environment.yml conda activate pgv23_zenodo jupyter lab</code></pre> <p> </p> </li> <li>Open the notebook “reading_data.ipynb”</li> </ol> <p> </p> <p><strong>Data References:</strong></p> <p><a href="https://ngawest2.berkeley.edu/">NGA-West 2 (NGAW2) Ground Motion Database </a></p> <p><a href="https://github.com/crowellbw/SNIVEL">SNIVEL</a> GNSS Velocity Processing</p> <p> </p>
GNSS time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)
<p>GNSS raw time series from "Subdaily slow fault slip dynamics captured by low-frequency earthquakes" (Mouchon et al., 2023)</p> <p>The raw GNSS time series are available for each three GNSS stations we focus on in this study (COYU, IGUA and MECZ), as well as the three distant stations use to correct from common mode (INEG, TAMP and UXAL), at both 24-hour and 6-hour sampling rates.<br> Each file contains the date, East, North and Vertical displacement and associated errors in meters (format: Decimal year, E (m), N (m), U (m), dE (m), dN (m), dU (m)).</p>
Shallow Slow Slip Events Identified Offshore the Osa Peninsula in Southern Costa Rica from GNSS Time Series
<p>Using new continuous GNSS time series from the region, we identify 5 distinct shallow SSEs that occurred immediately offshore and beneath the Osa peninsula: an event detected by one site in 2013, and two events occurring in close succession in both 2018 and 2022 detected by multiple stations, indicating a preliminary recurrence interval of ~4-5 years. While SSEs have been observed to the north at Nicoya, this is their first documentation in southern Costa Rica. Modeled slip distributions of the 2018 and 2022 events indicate that they ruptured the same or overlapping patches of the plate interface, nearly at the trench, updip of historic earthquakes including the 1983 Mw 7.4 Osa event. Immediately offshore, the estimated cumulative slip from the 2018 and 2022 events is sufficient to close the slip deficit from tectonic loading over the recurrence interval, likely influencing the magnitude and spatial slip distribution of future large ruptures.</p>
GNSS PWV data for WRF-enhanced PPP experiment
<p>The GNSS PWV data uploaded are used in WRF-enhanced PPP experiment</p>
Three-dimensional Topside Ionospheric Model and Onboard GNSS Observations from LEO Satellites
<p>Here are three-dimensional topside ionospheric models based on machine learning methods and data from LEO satellites. </p>
Data from: How a GNSS receiver is held may affect static horizontal position accuracy
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GNSS uplift time series and ice surface elevation changes
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CroCoord v1.0 – an Android application for nearly real-time 3D PPP multi-frequency multi-GNSS static surveying using smartphones
<p>Two hour of GNSS data collected on 31st December 2019 on one point with Android dual-frequency smartphone Xiaomi Mi Mix-3 and permanent station in the surrounding of the measured point.</p>
Measurements for "Evaluation of Ionospheric Scintillation in GNSS Radio Occultation Measurements and Simulations"
<p>This dataset contains measurements addressed in the manuscript "Evaluation of Ionospheric Scintillation in GNSS Radio Occultation Measurements and Simulations".</p> <p>These files and further measurements from different radio occultation missions are available in: https://cdaac-www.cosmic.ucar.edu (registration required)</p>
Deep Learning-Enhanced GNSS Signal Integrity for Critical Supply Chain Logistics (Data availability statement)
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GNSS signals acquired by a Software Defined Radio (SDR) system subject to ionospheric scintillations
<p>Global Navigation Satellite System (GNSS) signals crossing small-scale electron density irregularities in the ionosphere may be subject to rapid fluctuations of their amplitude and phase known as ionospheric scintillations. Ionospheric scintillations may cause cycle slips and loss of lock of the GNSS signals, thus hindering the accuracy and integrity of precise positioning applications. GNSS scintillation can be studied and monitored by processing the GNSS signals through dedicated ground-based monitoring equipment such as the Ionospheric Scintillation Monitoring Receiver (ISMR). Moreover, Software Defined Radio (SDR) equipment has been demonstrated as a powerful tool to support the analysis of GNSS signals subject to this kind of phenomena. </p> <p>This dataset contains 3 hours of GNSS signal acquired by an SDR system located in Lampedusa, Italy (lat:35.52, lon: 12.63). The dataset was acquired on the 23th March 2023, from 20 to 22 UTC. Within this timespan, a co-located ISMR receiver detected ionospheric scintillations on a few of the GNSS satellites in view. </p> <p>Besides the binary data from the SDR system, a json file containing the Scintillation Indices at 1 min resolution acquired by the ISMR receiver is also provided.</p>
Datasets for '3D Reconstruction of Equatorial Plasma Bubbles Using EOF and GNSS Radio Occultation Data From MSS-1 and COSMIC-2'
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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)
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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.