Reproducibility and open scientific practices are increasingly demanded of scientists and researchers. Training on how to apply these practices in data analysis is still limited and has not kept up with demand. This course is aimed at early career researchers conducting quantitative analyses (ranging from lab-based research to epidemiology). By the end of the course, students will have:
Students will develop proficiency in using the R statistical computing language, as well as improving their data and code literacy. Throughout this course we will focus on a general quantitative analytical workflow, using the R statistical software and other modern tools. The course will place particular emphasis on research in diabetes and metabolism; it will be taught by instructors working in this field and it will use relevant examples where possible. This course will not teach statistical techniques, as these topics are already covered in university curriculums.
No experience in data analysis or programming assumed or required. However, before attending the workshop, there are a few prerequisites to complete.
The workshop is structured as a series of participatory live-coding sessions (instructor and learner coding together) interspersed with hands-on exercises, using either a practice dataset or the participants’ own datasets. Some lectures will be given, mainly at the start and end of the workshop.
Date and time | Session topic | Type | Instructor |
---|---|---|---|
March 4 | |||
9:30-10:00 | Arrival; coffee and snacks | ||
10:00-10:30 | Introduction to the course, to reproducibility, and to open science | Lecture | Luke |
10:30-12:30 | Project management and best practices | Code-along | Luke |
12:30-13:15 | Lunch | ||
13:15-15:00 | Data management, wrangling, and best practices | Code-along | Anna |
15:00-15:45 | Science in the era of (ir)reproducibility | Lecture | Daniel |
15:45-16:00 | Coffee break | ||
16:00-16:45 | Collaboration and teamwork in research | Lecture | Daniel |
16:45-17:00 | Describe assignment | Luke | |
17:00-17:40 | Form groups and exercises | Group work | |
17:40-18:30 | Free time | ||
18:30-20:00 | Dinner | ||
March 5 | |||
7:00-7:30 | Run / swim (optional) | ||
7:30-8:30 | Breakfast | ||
8:30-9:00 | Review of last day’s topics | Lecture | |
9:00-9:45 | Finding and obtaining open datasets | Lecture | Daniel |
9:45-11:45 | Version control and collaborative practices | Code-along | Luke |
11:45-12:15 | Group hands-on practical work | Group work | |
12:15-13:00 | Lunch | ||
13:00-15:15 | Data visualization and best practices | Code-along | Luke |
15:15-15:30 | Wrap up | ||
March 18 | |||
9:30-10:00 | Arrival; coffee and snacks | ||
10:00-10:30 | Review of last session’s topics | Lecture | |
10:30-12:30 | Creating reproducible documents | Code-along | Santiago |
12:30-13:15 | Lunch | ||
13:15-15:15 | Efficiency in data analysis and best practices | Code-along | Luke |
15:15-15:45 | Coffee break | ||
15:45-17:45 | Group hands-on practical work | Group work | |
17:45-18:30 | Free time | ||
18:30-20:00 | Dinner | ||
March 19 | |||
7:00-7:30 | Run / swim (optional) | ||
7:30-8:30 | Breakfast | ||
8:30-9:30 | Review of last day’s topics | Lecture | |
9:30-10:30 | Data analysis in the era of reproducibility and open science | Lecture | Daniel/Luke |
10:30-12:15 | Hands-on practical and coding exercises | Group work | |
12:15-13:00 | Lunch | ||
13:00-13:30 | Publishing your project’s output (code and paper) | Lecture | Luke |
13:30-14:30 | Presentations of group work | Group work | |
14:30-15:15 | Discussion of assignments | ||
15:15-15:30 | Closing remarks |