This workshop will equip participants with tools (Git/GitHub, R/Rstudio and `renv`- an R dependency management tool) and best practices for implementing data analysis workflows that promote collaboration and reproducibility. These tools and workflows are integral to creating reproducible and transparent research - research where the same result can be reached given the same input, computational methods, and conditions, as well as one that has a history which records how and why decisions were made that shaped the analysis. These tools are essential for modern computational research, or for any analysis pipelines such as are common in environmental and ecological science. 

Harness the power of cloud-based, Google Earth Engine (GEE), for answering environmental science questions in this workshop. The workshop will be a hands-on introduction to GEE. Attendees will learn how to access, analyze, and process a catalog of datasets such as satellite imagery and climate data. Some applications will include characterization of areas of interest, change detection, visualizing trends and exporting time series data at local and regional scales. All analyses will be conducted on GEE’s free and simple browser interface (on code editor using JavaScript). By the end of the workshop, attendees will be familiar with different datasets available in the GEE catalog, data analysis techniques, and applications for environmental and climate research. We encourage all attendees to sign up for a GEE account (free for academic and research use) prior to the workshop (https://code.earthengine.google.com/register) as all code will be shared via a GEE code repository and bring a portable computing device.