While the data processing often consumes the majority of the time spent doing coding, there is little to no training and support provided for it. This has led to minimal attention, scrutiny, and rigour in describing, detailing, and reviewing these procedures in studies, and contributes to the systemic lack of code sharing among researchers. Taken together, this aspect of research is often completely hidden and may likely be the source of many unintentional irreproducible results. With this course, we aim to begin addressing this gap in training.

The learning objectives of the course will be to:

1. Learn and demonstrate what an open and reproducible data analysis workflow looks like.
2. Learn and apply some fundamental concepts, techniques, and skills needed for processing and managing data in a reproducible and well-documented way.
3. Learn where to go to get help and to continue learning modern data analysis skills.

During the course, we will:

• learn how to use R, specifically aimed at the mid-beginner to early-intermediate level
• focus only on the data processing and cleaning stage of a data analysis project
• teach from a reproducible research and open scientific perspective (e.g. by making use of Git)
• be using practical, applied, and hands-on lessons and exercises

And we will not learn:

• the basics of using R and RStudio
• statistics (these are already covered by most university curriculum)

Considering that this is a natural extension of the introductory r-cubed course, this course incorporates tools learned during that course, including basic Git usage as well as use of RStudio R projects. If you do not have familiarity with these tools, you will need to go over the material from the introduction course beforehand (more details about pre-course tasks will be sent out a couple of weeks before the course).

Instructors:

Luke Johnston, Steno Diabetes Center Aarhus (DK)

Anders Askeland, Novo Nordisk A/S (DK)

Assistant Instructors: 

Kaja Madsen, University of Southern Denmark (DK)

Isabell Victoria Strandby Ernst, University of Southern Denmark (DK)

Participants will be selected based on a motivational statement provided via the registration form. We will select participants with a clear, relevant motivation.

This course is designed a specific way and is ideal for you if:

• You are a researcher, preferably working in the biomedical field (ranging from experimental to epidemiological). Specifically, this course targets those working in diabetes, metabolism and classical endocrinology.
• You currently or will soon do some quantitative data analysis.
• You either:
  • have taken the introduction to Reproducible Research in R course, since this course is a natural extension to that one;
  • know a little to a moderate amount of R (or computing in general);
  • know how to use R and have some familiarity with the tidyverse and RStudio.

While having these assumptions help to focus the content of the course, if you have an interest in learning R but don’t fit any of the above assumptions, you are still welcome to attend the course! We welcome everyone, that is until the course capacity is reached.

Please note that you are not guaranteed a seat at the event if you do not meet the target group requirements. Priority may be given to participants employed at Danish research and health institutions or life science industry. Early-career researchers from abroad are welcome to apply. If the event is overbooked, DDEA reserves the right to select participants based on the defined requirements

Pre-course tasks
Participants will have to reserve time in their calendar to do pre-course tasks. Course material is available online at https://r-cubed-intermediate.rostools.org/.

Deadline for completing pre-course tasks: May 1

Considering that this is a natural extension of the introductory r-cubed course, this course incorporates tools learned during that course, including basic Git usage as well as use of RStudio R projects. If you do not have familiarity with these tools, you will need to go over the material from the introduction course beforehand.

Bring your own laptop
Make sure to bring your own laptop, since the course includes hands-on learning. Make sure to check that your laptop is compatible with the programmes needed in good time.

Dinner registration
DDEA organises a networking dinner on 6 and 7 May. Participation in the dinner is free of charge. Please sign up for the dinner upon registration, and indicate whether you have any dietary requirements.

Accommodation
DDEA offers accommodation for participants from outside the Odense area from 6 May (check in) to 8 May (check out).

Please sign up for accommodation when you register for the event.

You will be informed about your overnight accommodation by the DDEA after the registration deadline.

Certification
A course certificate will be sent to all participants upon request at the end of the course. Full participation is required to attain 1,8 ECTS points.

Latest cancellation date & no-show fee
Please note that it is free of charge to participate in the event however the DDEA will charge a no-show fee of 1000 DKK if you do not show up and have not unregistered from the event by 1 May 2025.