In today’s scientific landscape, terms like data management plans, FAIR data, metadata, and the data life cycle are thrown around like confetti New Year's Eve. But if you’ve ever thought, “My code runs, I don’t need documentation!” - spoiler alert - you do. These concepts are vital, yet it’s often unclear why they matter or how to implement them effectively.
This beginner-friendly workshop aims to answer questions like “What does FAIR even mean? Is it an acronym, or just wishful thinking?” Whether you’re a first-year student, a PhD candidate, or an experienced researcher still muttering “I’ll organize my data tomorrow”: this session is for you.
You’ll gain understanding of key concepts, practical steps, and where to find help when things get chaotic. Together, we will discuss why done is better than perfect (even if your file names still include “final_version_REALLYFINAL2”). This workshop is interdisciplinary, but examples will focus on STEM fields because, let’s face it, we’re the ones most likely to store critical data in a file named data_dump.csv.