Course info

The development of eScience and Data Science across research fields means many
researchers have to spend a significant amount of time at their computers. As
a consequence, we need to ensure that our skill set and toolbox is up to date
and that we can accurately, effectively and in a research-wise justifiable manner
conduct our research with a computer.

Who is this course for?
If you in your daily work do any of these:

1. Process data on a computer
2. Adapt code and scripts from colleagues or peers
3. Write code/scripts used by you, your colleagues or peers then this course is for you.

Objectives
In this course you will learn the practical skills and craftsmanship to increase
your day-to-day research productivity and be able to produce scientific software
with a high degree of compliance to modern research standards. After the course
you should be able to

1. Apply the widely used commandline interface/shell bash in your daily work.
2. Apply the widely used version control system Git in your daily work.
3. Understand concepts related to computational reproducibility and data management.

Format
Hands-on interactive three-day event with participatory live-coding, demos and
presentations. The participants are encouraged to follow and run the same
examples as shown during the course. The workshop will contain several smaller
practical 5-10 minutes exercises and breaks.

Course structure
1. Day:

• Introduction: why are we here?
• Get efficient with the command line interface (shells: MacOS(zsh), Linux(bash) - Windows users will use a Linux environment)
• (If time permits) Be smart: using automatic testing (with examples in Matlab, R og Python)

2. Day:

• Version Control with Git
• What you need for your everyday work.
• Advanced topics (continuous integration, pull request)

3. Day:

• Get more out of your code: Computational Reproducibility
• Show off your examples with Jupyter notebook
• Get more out of your data: FAIR (findability, accessibility, interoperability, and reusability).
• Work in practice: what IT resources are available to me?

We will not teach a specific programming language and will try to keep the
presented material as language-independent as possible.

Prerequisite

• You will need to bring a laptop with Windows / OS X / Linux.

• You know the basics of a least one programming language.

• You can navigate your computer, locate files etc.

• Read Wilson et. al. “Good enough practices in scientific computing” and start thinking about the presented ideas and to what extent it can be adapted in your work.

We expect that:

• You actively participate and work on the examples and exercises.

• You talk to your neighbors and help each other.

• Ask for help if both you and your neighbors are stuck.

Course project
The course project will contain several elements from the course. Participants
are presented with a default project, or can take on a project based on their
existing work if they find this option suitable. The project will require additional
work following the three course days.

ECTS: 2
Participants attending at least 80% of the course and submitting an acceptable
course project receive credits.

Lecturers
Special consultants Thomas Arildsen, Vang Le-Quy, and Tobias Lindstrøm
Jensen, and a data management specialist.

Dates: 1-3 September 2021

Deadline: 11 August 2021

Important information concerning PhD courses: We have over some time experienced problems with no-show for both project and general courses. It has now reached a point where we are forced to take action. Therefore, the Doctoral School has decided to introduce a no-show fee of DKK 3.000 for each course where the student does not show up. Cancellations are accepted no later than 2 weeks before start of the course. Registered illness is of course an acceptable reason for not showing up on those days. Furthermore, all courses open for registration approximately four months before start. This can hopefully also provide new students a chance to register for courses during the year. We look forward to your registrations.