This module focuses on a primary data product case study based on analytical chemstry data from water samples collected in the Upper Clark Fork River in Montana. The following link it to a zip file that contains an initial pipeline for this case study, including an input folder populated with the relavant laboratory data.

Download the initial pipeline for this case study

General service analytical chemistry labs provide an example of another kind of data product that might be included in the scientific workflow. The principles emphasized in this class are being used to help streamline the operations of the Montana State University Environmental Analysis Lab (MSUEAL). For each instrument, lab operations generally incude two Rmarkdown driven pipelines. The first pipeline generates a data product with metadata related to the quality assurance and control (QAQC) for individual instrument runs. 

Download an example of a QAQC product pipeline from the MSUEAL used in this case study

The second pipeline can pull data from one or more QAQC pipelines to assemble an Excel tabular data product for distribution to a lab customer.

Download an example of a customer product pipeline from the MSUEAL used in this case study

Reviewing the details of the strategy and Rmarkdown code for these examples is outside the scope of this module, but these pipelines provide some detailed examples of how principles from this class have been applied to the workflow of a general purpose analytical chemistry lab.

If you have studied the content and worked through the exercises in this module, you hopefully now have some of the following abilities:  

• Be able to...