This resource contains source code and select data products behind the following Master's Thesis: Platt, L. (2024). Basins modulate signatures of river salinization (Master's thesis). University of Wisconsin-Madison, Freshwater and Marine Sciences. The source code represents an R-based data processing and modeling pipeline written using the R package "targets". Some of the folders in the source code zipfile are intentionally left empty (except for a hidden file ".placeholder") in order for the code repository to be setup with the required folder structure. To execute this code, download the zip folder, unzip, and open the salt-modeling-data.Rproj file. Then, reference the instructions in the README.md file for installing packages, building the pipeline, and examining the results. Newer versions of this repository may be updated in GitHub at github.com/lindsayplatt/salt-modeling-data. In addition to the source code, this resource contains three data files containing intermediate products of the pipeline. The first two represent data prepared for the random forest modeling. Data download and processing were completed in pipeline phases 1 - 5, and the random forest modeling was completed in phase 6 (see source code). site_attributes.csv which contains the USGS gage site numbers and their associated basin attributes site_classifications.csv which contains the classification of a site for both episodic signatures ("Episodic" or "Not episodic") and baseflow salinization signatures ("positive", "none", "negative", or NA). Note that an NA in the baseflow classification column means that the site did not meet minimum data requirements for calculating a trend and was not used in the random forest model for baseflow salinization. site_attribute_details.csv contains a table of each attribute shorthand used as column names in site_attributes.csv and their names, units, description, and data source.