Research data can be a bit like leftovers – easy to lose track of what's what and sometimes hard to identify. Without a clear system, finding the right file can feel like digging through a fridge full of mystery containers.Often, talks about research data management are full of overwhelming buzz words and acronyms like FAIR data, RDM, DMPs, etc. This talk is here to help you avoid the data chaos. We'll walk you through simple, practical strategies for organizing your (research) data, covering folder structures, file naming conventions, storage and backup, version control, and documentation.No matter your discipline, good research data organization saves time, reduces frustration, and makes collaboration smoother. For researchers, it ensures that data remains accessible and reusable, and for students, it builds habits that make academic (and later professional) life much easier. Join us for some snackable tips that will keep your data fresh, your workflow smooth, and your future self very, very grateful. Parts of the presentation were created with the help of the following materials: Biernacka, K., Dockhorn, R., Engelhardt, C., Helbig, K., Jacob, J., Kalová, T., Karsten, A., Meier, K., Mühlichen, A., Neumann, J., Petersen, B., Slowig, B., Trautwein-Bruns, U., Wilbrand, J. und Wiljes, C. (2023). Train-the-Trainer-Konzept zum Thema Forschungsdatenmanagement (Version 5). doi: 10.5281/zenodo.1215376 Dörfel, D., Paßmann, S. und Zänkert, S. (2023). Der Datenmanagementplan. Eine praktische Einführung in das Forschungsdatenmanagement deines Projektes. Workshop auf der 48. Jahrestagung Psychologie & Gehirn an der TU Dresden. Vreede, B. (2021). Best practices for Writing Reproducible Code. https://bvreede.github.io/presentations/presentations/2020-09-17_BestPractices-lecture.html#1