Introductory workshop on data visualization
Learn the basics of visualization in research. Organized by the NMBU's arena for sustainable food systems in collaboration with Carpentry UiO.
A key part of the research process is communicating findings, and data visualization is one of the most effective ways to do so.
In research inherently interdisciplinary, clear and trustworthy visuals are essential to share research across disciplines, support decision-making, and communicate to both expert and public audiences.
In this workshop, you will be introduced to the fundamentals of data visualization and the principles that make research visuals clear, accurate, and engaging. The session will combine short lectures, discussions, and hands-on exercises, giving you practical tips, tricks, and an overview of important open-access tools.
The workshop is open to master’s students, PhD candidates, and experienced researchers who want to improve how they present their data in publications or presentations.
Participation is limited to 30 people. Once the limit is reached, additional registrants will be placed on a waiting list and notified if a spot becomes available.
When: October 14th, from 12.00-14.00
Where: Sørhellinga, room S124.
Tentative program:
12.00: Arrival and coffee
12.15: Workshop starts
- What is data visualization?
We begin by exploring what data visualization entails and why focus on visualization is important. - Where to start?
Identifying the context, audience, data type, and single key message. - Hands on exercise
- Choosing and Designing Visuals
Selection of the right chart and application of design principles, to draw attention and strengthen impact.
13.00-13.15: Break with coffee and pastries
- Tools
Participants will explore accessible open-source coding libraries (ggplot2, matplotlib, seaborn, plotly) and non-coding tools (PowerPoint, Canva, Inkscape) through a short exercise. - Hands on exercise
- Dashboards and AI Visualization
We conclude with demonstrations of dashboards, interactive charts in R Shiny and Plotly, and AI-assisted visualization tools, highlighting their possibilities and limitations.
For more information reach out to Caroline Karlsson (NMBU), or Elisa Pierfederici (UiO).