GEMS Learning

Are you a field or bench scientist and always wanted to feel more comfortable with your computing skills? These courses are designed for those who have never used the command line, but realize that the responsibilities they have or will soon take on require them to automate tasks. It will teach basic UNIX command-line skills, enable participants to work remotely on more powerful machines, create and run scripts to automate complex workflows, and synchronize your scripts with the larger community with Github.

• Introducing the GEMS platform  + Jupyter Lab
• Demystifying the UNIX command line
• Working Remotely and Scheduling Jobs on MSI’s systems
• Synching your work with the community

Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? This course is designed for those who are interested in explicitly accounting for location in their analyses. Learn how to work with spatial data in Python, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data. Each module includes the opportunity to practice your new skills via hands-on exercises focused on agri-food applications. 

• Introduction to spatial data analysis in Python
• Spatial Regression in Python

Would you like to use R for your data analysis needs? This two hour introductory workshop is designed for those who are new to R and are interested in learning the basics of using R for data analysis. You will learn how to use R on the GEMS platform, starting from installing packages, setting up your workspace, to the basics of R object types. 

• Introduction to Data Analysis with R

Would you like to leverage spatial data to start exploring the relationships of agricultural processes across geographies? Is accounting for spatial dependency in your analyses critical to your work? Or do you need to create a continuous surface of data (i.e., raster) based on a sample point date taken at selected locations?   Learn how to work with spatial data in R, starting from importing different spatial datasets and creating simple maps, to conducting basic geocomputation on vector and raster data.
Each module includes the opportunity to practice your new skills via hands-on exercises focused on agri-food applications.  

• Introduction to spatial data analysis in R

If you are a researcher that works in the Agri-food domain (e.g., breeder, molecular biologist, food scientist, socioeconomist), you know a little bit of programming (e.g., in R and/or Python), but you feel a little limited (e.g., some of your calculations run for days on your laptop), then you could benefit from this course. We wish to show you how to step up to the next level, improve your coding efficiency, and make use of High Performance Computing (HPC) and Cloud resources readily available to you.

Familiarity at a beginner level with a programming language (e.g. Python, SQL, R, JavaScript, or Scala) is required. Given the nature of course material, some familiarity with the Python language is recommended.

Note: The Fall 2024 section of the GEMSx009 course is only for UMN affiliates with a standard UMN ID (x.500)

• HPC for Ag: Breaking the Compute Barrier, Upskilling Agri-Food Researchers to Utilize HPC Resources