This child page contains a zipped folder which contains all of the items necessary to run load estimation using R-LOADEST to produce results that are published in U.S. Geological Survey Investigations Report 2021-XXXX [Tatge, W.S., Nustad, R.A., and Galloway, J.M., 2021, Evaluation of Salinity and Nutrient Conditions in the Heart River Basin, North Dakota, 1970-2020: U.S. Geological Survey Scientific Investigations Report 2021-XXXX, XX p]. The folder contains an allsiteinfo.table.csv file, a "datain" folder, and a "scripts" folder. The allsiteinfo.table.csv file can be used to cross reference the sites with the main report (Tatge and others, 2021). The "datain" folder contains all the input data necessary to reproduce the load estimation results. The naming convention in the "datain" folder is site_MI_rloadest or site_NUT_rloadest for either the major ion loads or the nutrient loads. The .Rdata files are used in the scripts to run the estimations and the .csv files can be used to look at the data. The "scripts" folder contains the written R scripts to produce the results of the load estimation from the main report. R-LOADEST is a software package for analyzing loads in streams and an accompanying report (Runkel and others, 2004) serves as the formal documentation for R-LOADEST. The package is a collection of functions written in R (R Development Core Team, 2019), an open source language and a general environment for statistical computing and graphics. The following system requirements are necessary for producing results: Windows 10 operating system R (version 3.4 or later; 64-bit recommended) RStudio (version 1.1.456 or later) R-LOADEST program (available at https://github.com/USGS-R/rloadest). Runkel, R.L., Crawford, C.G., and Cohn, T.A., 2004, Load Estimator (LOADEST): A FORTRAN Program for Estimating Constituent Loads in Streams and Rivers: U.S. Geological Survey Techniques and Methods Book 4, Chapter A5, 69 p., [Also available at https://pubs.usgs.gov/tm/2005/tm4A5/pdf/508final.pdf.] R Development Core Team, 2019, R—A language and environment for statistical computing: Vienna, Austria, R Foundation for Statistical Computing, accessed December 7, 2020, at https://www.r-project.org.