Real and Psuedosynthetic timeseries used in "Characterizing High Rate GNSS Velocity Noise for Synthesizing a GNSS Strong Motion Learning Catalog"

<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?) <strong>Datasets included:</strong> Pseudosynthetic timeseries, ambient timeseries and training featuresets generated for Dittmann, et al (202?) Real GNSS 5Hz validation featuresets from Dittmann, et al. (2022) Timeseries and Featuresets are stored in Apache Parquet format. <strong>Getting Started:</strong><br> Notebook demos for reading using conda+ jupyterlab (easiest).<br> In a terminal: Unzip untar (mac/linux tar -xvf psuedo_synth_gnssvel.tar.gz) <pre><code>conda env create -f environment.yml conda activate pgv23_zenodo jupyter lab</code></pre> Open the notebook “reading_data.ipynb” <strong>Data References:</strong> NGA-West 2 (NGAW2) Ground Motion Database SNIVEL GNSS Velocity Processing