Data visualization for scientists: preparing data for figures, posters, slides

Academia Raetica

Every scientist has to visually communicate scientific data. As figures in scientific manuscripts, slides in conference talks, posters and increasingly also as graphical abstracts, interactive visualizations, and in dashboards. Yet, the fundamentals for designing data visualizations for audiences are not part of the core undergraduate and graduate curriculum. In this workshop, participants learn to understand and prepare efficient scientific data visualizations.

We will discuss for example:

• What data needs a figure

• How to choose chart types

• How not to lie: ethics and truthfulness in data presentation

• How to increase the legibility by considering graphic design principles

Importantly, we will discuss in detail good and bad examples from scientific literature and from the participants. The course includes lectures, exercises in small groups, and discussion of individual data visualization challenges from the members of the group.The course is a general training in principles of data visualization.

The course does not include: advanced statistics, specific programming languages, or scientific illustrations.

From sketch to publication – the process of creating scientific data visualizations

Important: discussion of individual data visualization challenges requires participants to share a work-in-progress with the members of the group.

Day 1 - Morning

From raw data and numbers to figures

• Exploratory versus explanatory data visualization

• Which display type to choose for what kind of data, univariate data

• Display types for multivariate data

• Exercise: Too little data or too much data – what makes a good figure?

Graphs and Charts

• Categorical, relationship, time-course data, comparisons of datasets

• Statistical data

• Exercise: Choosing a chart type

• Ethics of data presentation

Day 1 - Afternoon

Text & Typography

• Text arrangements, typography

• Table design, heat maps, databases

• Exercise: Figure legends, titles and labels.

Image data

• Choosing an image, picture detail and magnification

• Image data labels

• Exercise: Image legibility, guidelines for image reproducibility

• Exercise: Image ethics, guidelines for image integrity

• Homework: Find a good and a bad example figure, upload

Day 2 - Morning

Group work: Discuss homework

Color in figures

• The do’s and don’ts of color

• Color for qualitative, quantitative, and diverging data

• Exercise: Choosing a color scheme, applying color concisely

Layout of figures and figure sets

• Gestalt principles

• Exercise: Use your space!

Day 2 - Afternoon

Group work Work in peer groups, apply learned principles to your work

Focus the attention & Decluttering

• De-cluttering of figures

• Rapid techniques to increase readability of figures

After the course

• Exercise: improve your work, send in before/after to course instructor for feedback.

About Dr. Helena Jambor

Dr. Helena Jambor is a graduated molecular biologist with a passion for design and associate professor for data visualization at the FH Graubünden/Switzerland. After completing a PhD at the EMBL, Helena Jambor worked with multi-dimensional datasets, images and genome-scale data at the Max-Planck Dresden and with medical and oncological data visualization at the University Hospital Dresden at the TU Dresden.

Helena Jambor is a trainer, teacher and consultant for visualization with a focus on life science data. She has given workshops for EMBO, Helmholtz, Max-Planck Society, National Institute for Science communication, FEBS. She lectured biologists and medical students at TU Dresden, as adjunct professor at Berlin Hochschule für Technik. Helena Jambor is regularly invited to conferences, most recently the Meeting of the American Society for Cell Biology and the Keynote lecture at the Physics of Life Bioimaging Symposium.

Helena Jambor regularly writes academic articles and blogs on a range of data visualization topics.

• Most recently: “Ten simple rules for designing graphical abstracts“ (PLOS Comp Bio 2024).

• Blog about data visualization https://helenajamborwrites.netlify.app/Scientific publications https://orcid.org/0000-0003-3397-1842

• Institute for Data Analysis, Artificial Intelligence, Visualization and Simulation (DAViS), FH Graubünden/Switzerland https://fhgr.ch/davis

General information and fee

• Dates and time: September 11-12, 2025 (Thursday and Friday), 09:00 AM to 05:00 PM

• Location: Davos, TBA

• Lecturer: Prof. Dr. Helena Jambor / FHGR

• Language: English

• Target audience: PhD students and postdocs, all interested scientists

• Course fee for participants from Academia Raetica member or partner institutions: CHF 150. The course fee includes coffee breaks.

• External participants (not working at an Academia Raetica member or partner institution): CHF 300

• Registration deadline: 25. August, 2025

• Max. number participants: 12 (min. number participants 8)

• Cancellation policy: We ex­pect you to be pre­sent for the full du­ra­ti­on of the cour­se. Please see our Terms of Participation for details.

• Course Coordinator: Daniela Heinen, daniela.heinen@academiaraetica.ch, phone 081 410 60 84

• WORKLOAD: 30 hours (1 ECTS) if you complete the additional assignment.

  • Workshop: 2 x 8 hours interactive workshop

  • Optional: additional assignment (14 h) to receive 1 ECTS credit

Plea­se check beforehand with your su­per­vi­sor at your uni­ver­si­ty, if the cour­se counts towards ECTS. Course certificate: You will receive a certificate of attendance, if you fully attend both workshop days.

Registration