Learn how to make your research methods more reproducible and automated at workshops hosted by UW–Madison’s Data Science Hub on Jan 22 & 23.
Open to all campus members, Software Carpentry & Reproducible Research: Practical Tools & Applications will take place at the Discovery Building and in the new American Family Insurance Data Science Institute space in the McArdle Building from 8am-4:30pm on both days.
Software Carpentry aims to help researchers get their work done in less time–and with less pain–by teaching them basic research computing skills. The hands-on workshop will cover basic concepts and tools, including:
- Program design
- Version control
- Data management
- Task automation
The workshops are designed to “meet researchers where they are,” giving attendees a starting point to help them develop their research methods. No prior knowledge of specific software or coding processes is necessary. Participants will be encouraged to apply what they learn to their own research problems and bring their learnings back to their labs and research groups.
For UW–Madison faculty, staff, students and affiliates, the cost to attend the two-day workshop is $10 plus a small processing fee. On each day, participants may select one of two options:
Wed, Jan 22
- Reproducible Research: Practical Tools & Applications
- Automation with the Unix Shell & Version Control with Git
Thu, Jan 23
- Introduction to Programming & Plotting in R
- Introduction to Programming & Plotting with Python
Reproducible Research: Practical Tools & Applications
An overview of reproducible practices to incorporate into your research (e.g., using citation managers, finding datasets, electronic lab notebooks, publishing your dataset/scripts/workflow). This session will include lectures, active learning activities, and demos of popular tools. No prior scripting/programming experience is required.
Automation with the Unix Shell & Version Control with Git
The first half of this hands-on session will teach learners how to use the Unix Shell to navigate their file system and how to automate repetitive tasks. The second half of this session will teach learners how to use the popular version control software Git to track changes to their scripts and files, and how to use Git in conjunction with GitHub to back up and share their scripts.
Introduction to Programming & Plotting with Python
This lesson is an introduction to programming in Python for people with little or no previous programming experience. Python is a popular language for scientific computing, and great for general-purpose programming. This lesson uses plotting as its motivating example and teaches best practices for scientific computing including:
- Breaking down analyses into modular units
- Task automation
- Encapsulation
Introduction to Programming & Plotting in R
For reproducible scientific analysis: The goal of this lesson is to teach novice programmers to write modular code and best practices for using R for data analysis. R is commonly used in many scientific disciplines for statistical analysis and its array of third-party packages. The emphasis is to give attendees a strong foundation in the fundamentals of R, and to teach best practices for scientific computing.
Registration
Learn more about the research workshops & register (use your wisc.edu email address for UW–Madison affiliate pricing).