Aim:
You will learn to visually communicate your complex research ideas and results so your messages are effortlessly understood by any specific audience (scientists or non-scientists). We will not focus on aesthetics but on how understanding human visual perception can inform your design decision for better comprehension of your scientific images, posters, and slides. You will also design a graphical abstract of your research, discuss it with peer scientists in a group exercise, and get actionable advice and feedback on your own materials. It is an immersive workshop, comprehensive, structured, memorable, easy to follow, useful and fun. More at https://www.seyens.com

Contents & Method:
The training is offered as blended learning that combines a self-study module and a live online workshop. All participants get 12 month access to all materials.

1. Self-study via an online platform (6-8 hours of engaging video content & a useful assignment):
1.1. Communicating with scientific vs non-scientific audiences
1.2. Visual perception and what humans find intuitive
1.3. Layout: simplifying comprehension through structured layout
1.4. Eye-flow: effortlessly guide the audience through the design
1.5. Colors: how to amplify, not ‘fancify’
1.6. Typography for legibility, structure and aesthetics
1.7. Digital images in science: the optimal use of vector and raster images
1.8. Slides that amplify messages and don't distract when presenting
1.9. Posters: strategy and process for creating posters that attract and explain
1.10. Homework: participants submit images and slides to the trainer to receive feedback
2. Live Online Workshop (April 10, 2025, 9 am – 1 pm via Zoom, interactive and hands-on)
2.1. Recap of fundamentals and Q&A: trainer facilitates an effective recap of lessons learned in self-study module and answers all further questions.
2.2. Exercises & group work: participants draw a graphical abstract of their research and share their posters and we form groups so everyone gives and receives informed feedback.

Discussion on pre-submitted materials: participants receive actionable suggestions on how to improve their own images and slides from the trainer and on posters from fellow researchers.

In the open science movement, research data is expected to be as open as possible and as closed as necessary. But what does this mean in practice, especially when dealing with patents, personal data or other sensitive information? This lecture will present the principles of open and FAIR data, research data management and the legal, ethical and institutional frameworks that govern data sharing. Participants will gain an overview of researchers’ rights and obligations and funders’ requirements, as well as guidance on data repositories.

In this Basic Statistics Course you will learn the basics of statistical data analysis for your research projects. The course is application based and will empower you to improve your study planning and get more confident with your statistical analysis.

It contains the following topics:
Design of experiments / study design
Data collection and entry
Structure of statistical analysis
Significance tests
Choosing the appropriate statistical method
Paired and unpaired data
Level of measurement / variable type
Checking of assumptions and normality
Effect size measures
Sample size calculation and power analysis

The course is organized with self-learning material (short videos, handout and exercises) and two live and online Q&A sessions. After a short kick-off meeting you watch the videos and do the exercises at your own pace. During this time, you can pose your questions in a chat. Within an interval of one week we meet for two Q&A sessions online, where you can ask questions concerning the topics or your own projects.

The course runs for just over two weeks. Plan approx. 2.5 hours per week for independent work with the materials plus the time for the meetings (30 minutes kick-off and 2x 45 minutes Q&A session).