Reproducibility and open scientific practices are increasingly in demand and needed by scientists and researchers in modern research environments. Our work often require or involve a high level of hands-on collaboration on scientific projects throughout all stages of the research lifecycle. We are faced with obstacles that we have no training for nor knowledge on how to address. Obstacles that can be as simple as not having a shared way of writing code or managing files and that can impede effective collaboration. Or they can be complex, like documenting software dependencies or steps in an analysis pipeline in a way that makes it easy to resolve issues and get the most recent set of results after collaborators have worked on a project.
Aside from the impact on collaboration, these barriers can even affect projects with just one primary researcher. Ultimately, this can have consequences on the reliability and reproducibility of scientific results, especially considering that the measures taken to address these barriers are often not explicitly shared in traditional scientific outputs (like publications).
With this workshop, we aim to begin addressing this gap. By using a highly practical, hands-on approach that revolves around code-along sessions (instructor and learner coding together), reading activities, and hands-on exercises, our overarching learning outcome is that at the end of the workshop, participants will be able to:
Describe what an open, collaboration-friendly, and nearly-automated reproducible data analysis pipeline and workflow looks like, and then create a project that follows these practices using R.
Our specific learning outcomes are to:
And we will not learn:
Because learning and coding is ultimately not just a solo activity, during this course we also aim to provide opportunities to chat with fellow participants, learn about their work and how they do analyses, and to build networks of support and collaboration.
The specific software and technologies we will cover in this workshop are R, RStudio, Quarto, Git, and GitHub, while the specific R packages are styler, targets, and more advanced functionality from the tidyverse packages.
Instructors:
Luke Johnston, Steno Diabetes Center Aarhus
Anders Askeland, Novo Nordisk A/S
Assistant instructors:
Birgitte Dige Semark, Aarhus University
Isabell Victoria Strandby Ernst, University of Southern Denmark
Morten Dall, University of Copenhagen
Participants will be selected based on a motivational statement provided via the registration form. We will select participants with a clear, relevant motivation.
This workshop is designed in a specific way and is ideal for you if:
Considering that this is a natural extension of the introductory and intermediate r-cubed workshops, this workshop builds on the knowledge and skills learned during those workshops, including Git, RStudio R Projects, functions, functional programming, and Quarto / R Markdown. If you do not have familiarity with these tools, you will need to go over the material from the introductory and intermediate workshops beforehand (more details about pre-workshop tasks will be sent out a couple of weeks before the workshop).
While having these assumptions help to focus the content of the workshop, if you have an interest in learning R but don’t fit any of the above assumptions, you are still welcome to attend the workshop! Priority is given to participants employed at Danish institutions and in the Danish life science industry. If the event is overbooked, the DDEA reserves its right to select participants based on the defined requirements and country of employment.
Pre-course tasks
Participants will have to reserve time in their calendar to do pre-course tasks, which are available online here.
Considering that this is a natural extension of the introductory and intermediate r-cubed courses, this course builds on the knowledge and skills learned during those courses, including Git, RStudio R Projects, functions, functional programming, and Quarto. If you do not have familiarity with these tools, you will need to go over the material from the introductory and intermediate courses beforehand (more details about pre-workshop tasks will be sent out a couple of weeks before the course).
Deadline to complete pre-workshop tasks: 26 November 2025.
Bring your own laptop
Make sure to bring your own laptop, since the course includes hands-on learning.
Accommodation
DDEA offers accommodation in Copenhagen to all participants living outside the Copenhagen area with check in 2 December and check out 4 December.
Please state if you need accommodation when you register.
Networking Dinner
DDEA organises networking dinners on 2 and 3 December. Please state if you would like to join these when you register. And if you have any special diet requirements.
Certificate of Attendance
A course certificate of attendance and participation can be issued upon request at the end of the course. Full participation in the course is required to receive the certificate. Apply to your PhD school for ECTS credits with the certificate and course programme
No-show fee
Please note that it is free of charge to participate in the course however DDEA will charge a no-show fee of 1000 DKK if you do not show up and have not unregistered from the event by 3 December, except in the case of emergencies or illness.
Ninna Matthews
Coordinator
ninna.matthews@rsyd.dk
+45 21 59 69 82
EAN: 5798 0022 30642
Reference: 1025 0006
CVR: 29 19 09 09
