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

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.

**Course is open to current University of Minnesota faculty, staff and students**

• HPC for Ag: Upskilling Agri-Food Researchers to Utilize HPC Resources 

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. Through this 3-week introductory course, you will 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. In each 2 hour lecture, you will have the opportunity to immediately practice your new skills via hands-on exercises focused on agri-food applications.

Have your research, studies or work required you to examine the geographic distribution of various species? This could be for crop protection, forestry, environmental protection, environmental impact assessment, urban and natural landscape design and development, or simply an interest in any given species, whether insects, pathogens or weeds. Then you are at the right place, species distribution models allow us to understand the potential and realized distribution of various species across our landscapes at different scales. Understanding of species distribution at different scales provides different insights, for example global maps support decision makers to make high level biosecurity and conservation related decisions. Meso-scale or regional maps can be used for planning of cross-border projects, informing biosecurity related cross-border operations and various environmental projects. Small scale or local maps allow researchers to design custom pest prevention, monitoring and control strategies. Allow conservationists to use evidence-based information in their plans etc.

Data is everywhere in agriculture, but knowing what to do with it isn't always easy or straightforward. This module will give you the basic tools for analyzing a decision-making context, evaluating the data needs, collecting or integrating data, and then performing basic analysis and visualization in Excel, R, Python, and QGIS. We will also cover some of the common pitfalls of using data to drive decision-making to be sure you and your teams can avoid them.