Research data have a lifecycle — and every stage matters.
🔄 Plan → Collect → Process → Analyze → Preserve → Share → Reuse
Generally one thinks first about the "Analyze" part. But how you plan, document, and store your data determines whether anyone (including future you) can ever use it again.
👉 https://forschungsdaten.info/themen/informieren-und-planen/was-ist-forschungsdatenmanagement/
The 3-2-1 backup rule for research data:
3️⃣ Keep 3 copies of your data
2️⃣ Store them on 2 different media types
1️⃣ Keep 1 copy off-site
Hard drives fail. Laptops get stolen. Cloud services go down. Your data is worth more than your equipment. Take care of it and spend some thoughts how to manage it.
👉 https://forschungsdaten.info/themen/organisieren-und-aufbereiten/speichern-und-sichern/
Can your analysis be reproduced by someone else? By you, in six months?
Reproducibility isn't just about publishing code — it's about documenting your environment, your data, and every decision along the way.
Small steps: use virtual environments, pin your dependencies, write a README.
👉 https://the-turing-way.netlify.app/reproducible-research/
#Reproducibility #OpenScience #ResearchData #DataLiteracy
— bos
Interested in learning how to use LaTeX but don't know where to start?
Want to see how to move away from MS Word and still make professional looking papers?
Join us in person in Cologne on April 9th and 10th for a FREE LaTeX workshop presented by DKZ.2R!
Additional Info and Registration here 👉 https://www.dkz2r.de/events/2026-04-09-latex-academic-publishing/
Stuck on a data problem in your research project? Not sure which tool or method fits?
Our Rent-an-Expert program matches you with a data specialist who works alongside you — tailored support for your specific challenge, not a generic tutorial.
Free for researchers in the Rhine-Ruhr region.
👉 https://dkz2r.de/activities_offers/consulting/
#DataScience #ResearchSupport #Consulting #DataLiteracy
— bos
Want to share research data with collaborators but don't trust random cloud services?
We wrote a survival guide to data sharing services in the Rhine-Ruhr region — covering institutional repositories, discipline-specific archives, and when to use what.
Research data is only as valuable as its accessibility. The FAIR principles (Findable, Accessible, Interoperable, Reusable) aren't just buzzwords — they're the foundation of reproducible science.
Not sure where to start? We offer free consulting to help you make your data FAIR from day one.
👉 https://dkz2r.de/activities_offers/consulting/
#ResearchDataManagement #FAIRData #OpenScience #DataLiteracy
— bos
Python is the Swiss army knife of research computing: data cleaning, visualization, statistics, machine learning, automation — all in one language.
Our Carpentries workshops start from zero and get you productive fast. No prior programming experience needed.
Confused by Data Warehouse vs. Data Lake vs. Data Mesh?
Think of it this way:
- 📦 Warehouse = organized storage room
- 🌊 Lake = throw everything in, sort later
- 🕸️ Mesh = each team owns and serves its own data - but there is still a common hub.
The key insight: Mesh isn't a storage technology. You can run a Data Mesh on top of a Warehouse or Lake. It's about ownership, not infrastructure.
👉 https://www.kdnuggets.com/data-lake-vs-data-warehouse-vs-lakehouse-vs-data-mesh-whats-the-difference
#DataMesh #DataLake #DataWarehouse #DataLiteracy
— bos | 🖼️ ai-generated
Dorothea Strecker