Science Communication – Creating Scientific Illustrations @ University of Bergen
Jan 28 @ 9:00 am – Jan 31 @ 4:30 pm

Lecturer: Pina Kingman, MScBMC
ECTS: 1 (If you complete the course, you get a diploma stating your participation, the content of the course and the work effort the course has required. You can apply to your home institution for getting the course accepted as ECTS in your degree. They decide if you will get the ECTS for the participation.)
Maximum no. of participants: 20 in total
Registration: please apply before 15 December 2019 with this online form. (registration closed)

Do you want to use illustration as an effective communication tool? Learn the essentials of graphic design and visual communication theory, drawing by hand and drawing digitally during this 4-day course.

Course description:

This course will introduce the theory and method of how to visually represent your scientific research. Being able to translate complex research into information that can be understood by a wide range of audiences is an important skill that will help you throughout your career.

Communicating your work using different methods helps you to think about your work from different perspectives. Not only will this help you understand your own work better, but it will also give you the tools to be able to explain your work to others.

The skills you will learn in this course are highly transferable to any design project you may do in the future.

Through lectures and workshops, we will cover the following:

  • Principles of design and visual communication
  • How to apply these principles to illustration and graphic design, which in turn will inform all visual material you might want to create, including; graphical abstracts, presentation slides, poster presentations, journal articles, graphs, data visualisation, project logos, animations and outreach material.
  • Best practices for poster and slide presentation design
  • Step by step method on how to draw your own research
  • Introduction to sketching by hand
  • Crash course in digital illustration with mandatory pre-course digital tutorials

By the end of the course, you will have practiced the theory and methods discussed in class by creating an illustration of your own research. Taking your ideas from conceptualisation to final digital artwork.

Completing the digital illustration tutorials before the course begins is mandatory. It is important that you come prepared because we are covering a lot of new skills in a short time and it will be beneficial for you if you already have a foundation to work from.

Course Schedule:

Course dates are 28-31 January, from 9:00 to 16:30 each day.

Day 1: 6.5 hrs lectures & workshops, 1 hr lunch
Day 2: 3 hrs lectures & workshops, 1 hr lunch, 3.5 hrs digital illustration
Day 3: 1 hr lecture, 5.5 hrs digital illustration, 1 hr lunch
Day 4: 1.5 hrs digital illustration, 1 hr lunch, 5 hrs student presentation & group feedback

Software used in the course:

  • Adobe Illustrator, for those who have access
  • Gravit, free vector illustration software
    Note: If any students are already familiar with another digital illustration software, then feel free to use this program. But for the sake of time, I will only provide technical support for those using Gravit Designer or Adobe Illustrator.

Student’s will need to bring to the course: Laptop

Before the course starts, students will need to:

  • Download Gravit Designer or Illustrator onto your laptop
  • Do mandatory digital illustration tutorials (to be provided)

Final assessment:

Students will need to present their illustration on the last day of the course and describe one design principle they used in order to solve a visual problem. It will be okay to show “work in progress.”


Pina Kingman in a biomedical illustrator and animator whose work focuses on telling scientific stories in order to disseminate complex research and promote public awareness of science and medicine. She holds a BSc in Cell Biology and Genetics from the University of British Columbia and a MSc in Biomedical Communication from the University of Toronto.


This course is offered as a joint effort of 4 Norwegian research schools: CHESS, DEEP, ForBio and IBA.

Crash Course on Data Assimilation – Theoretical foundations and advanced applications with focus on ensemble methods @ Nansen Center (NERSC), Bergen
May 5 – May 8 all-day

The 4-days course aims at PhD-level students and early-stage scientists intending to apply data assimilation as part of their research. The course should also be useful for students with beginner or little notions of data assimilation. The crash course will cover the basic concepts of data assimilation, focusing on ensemble methods, illustrated with real-scale / operational applications and with practical exercises. It will also include notions of Machine Learning of relevance for data assimilation.

The event is organised by NERSC and NORCE in the framework of the projects DIGIRES and REDDA from the Norwegian Research Council, and supported by the climate research school CHESS (travel costs of CHESS members will be covered by CHESS).

For more details and application, please see their webpage:

Deadline for application: 15 February

Fundamentals of Ocean/Atmosphere Data Analysis @ Oslo Research Park
May 11 – May 15 all-day

Responsible: Joe LaCasce / UiO
International lecturer: Jonathan Lilly / Theiss Research
Max. no. of participants: 12 CHESS students (total participants: 20)
Credit points: 2 ECTS
Registration form here. Deadline: 13 February
Submitted applicant list

Course description:  This course introduces students to essential statistical and conceptual tools for analyzing any type of dataset from oceanography, atmospheric science, or climate.

In this course, the students will learn how to use our creativity together with simple statistical tools to delve into datasets, uncovering whatever information they may contain, and how to shape that information into stories.  In particular, a powerful method called “distributional data analysis” allows us to deconstruct potentially large, multivariate datasets by examining their statistics in two-dimensional slices.  Careful attention is given to the variance ellipse, the fundamental second-order statistical quantity for bivariate data such as velocity.  Data organization and manipulation techniques, visualization strategies, and healthy coding habits are all also addressed.  Finally, the course provides innovative training in the essential mental factors of curiosity, imagination, and objectivity.

Students apply techniques to datasets of their own choosing using the Matlab programming language, and learn further through homework problems and group exercises.

This will be the fourth time a version of this course is offered in Oslo, and the second time focusing on a greatly expanded version of the “low-tech” methods that form the foundation of the data analyst’s toolbox.

This course is strongly recommended for all students wishing to participate in a more advanced time series analysis course with the same instructor, to be offered over two weeks in Fall 2020 at the Alfred Wegener Institute in Bremerhaven, Germany.

Learning modules/structure:  There will be two hours of lectures in the morning sessions and a two-to-three hour lab session in the afternoons. Lectures will be given in the mornings and lab sessions in the afternoons, allowing the students to apply the methods directly to data. The students will also complete a final project on data of their choice. The students employ the statistical and time series analysis toolbox jLab developed by the instructor (  Course notes are available online at (specifically chapters 1-8).

Learning outcomes: At the end of the course, students will be well-prepared to begin efficiently analyzing any dataset they might encounter, while avoiding common pitfalls.  Students gain practical experience through hands-on demonstrations and exercises in Matlab.

Prerequisites:  Students must have a fully functioning version of Matlab with jLab already installed at the start of the course.  Students are expected to bring a dataset of any type that they would like to analyze for a course project.  Multivariate datasets are encouraged.  Model output is also acceptable.