IMCI is sponsoring the University of Idaho as a member organization in The Carpentries training program to improve data literacy and reproducible science.

The Carpentries teaches foundational computational and data science skills to researchers worldwide. They train and certify volunteer instructors and provide curriculum in a variety of topics designed to be presented as workshops.

At the University of Idaho, graduate students and postdocs will run three 2-day workshops for anyone interested in learning practical data analysis skills. These workshops are open to upper-level undergraduate stdents, new graduate students, and anyone else interested in good-practices in data management and analysis.

Students wishing to take the workshops for credit need to register via the UI course schedule for any combination of BCB 503 01, BCB 503 02, and/or BCB 503 03. Each workshop is 1 credit each. All participants must also register via The Carpentries. Check the git-hub pages listed with each workshop for additional class information and links.


Software Carpentry: Unix, Git, and Python for Novices

Workshop dates: January 29-30

Instructors: Amanda Stahlke, Breanna Sipley, Salvador (Chava) Castaneda Barba, and Clint Elg

Description: Software Carpentry  aims to help researchers get their work done in less time and with less pain by teaching them basic research computing skills. This hands-on workshop will cover basic concepts and tools, including program design, version control, data management, and task automation in Unix, GitHub, and Python. Participants will be encouraged to help one another and to apply what they have learned to their own research problems. The course is aimed at graduate students and other researchers. You don’t need to have any previous knowledge of the tools that will be presented at the workshop.  Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. More details can be found at https://astahlke.github.io/2020-01-30-uidaho/.


Introduction to R for Reproducible Science

Workshop dates: February 27-28

Instructors: Lihong  Zhao and Amanda Culley

Description: This introductory course will showcase reproducible research through simple analysis examples. The goal is to teach novice programmers to write modular code and best practices for using R for data analysis. This 2-day hands-on short course will give participants a strong foundation in the fundamentals of R, and to teach best practices for scientific computing: breaking down analyses into modular units, task automation, and encapsulation. Note that this workshop will focus on teaching basic programming in R, and will not teach statistical analysis. No prior knowledge of R or RStudio is needed. More details can be found at https://dearmint.github.io/2020-02-27-uidaho/.

Requirements:Participants must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.). Please ensure you have the latest version of R and RStudio installed on your machine.


Data Carpentry – Geospatial Analysis

Workshop dates: March 26-27

Instructors: Erich Seamon and Travis Seaborn

Description: This hands-on workshop will focus on managing and understanding spatial data formats, understanding coordinate reference systems, and working with raster and vector data in R for analysis and visualization. An introductory knowledge to R is helpful, but not required. Participants will be encouraged to help one another and to apply what they have learned to their own research problems, and will be aimed towards graduate students and other researchers.

Attendees must bring a laptop with a Mac, Linux, or Windows operating system (not a tablet, Chromebook, etc.) that they have administrative privileges on. More details can be found at https://erichseamon.github.io/2020-03-26-uidaho-geospatial