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

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

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

Unsere Klausurtagung brachte wichtige Zukunftsimpulse für eine mögliche Fortsetzung von SODa. ✨
Neben der Evaluierung unserer bisherigen Aktivitäten haben wir uns intensiv mit der Frage beschäftigt, wie sich SODa in den kommenden Jahren weiterentwickeln kann.💡

Entstanden sind Ideen zu Austauschformaten, Workshops, zur Weiterentwicklung des Semantic Coworking Space und der Arbeit mit LLMs. Danke an alle, die SODa als offenen Lernraum mitgestalten. 💛
#SODaZentrum #DataLiteracy #Sammlungen

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.

👉 https://dkz2r.de/activities_offers/trainings/

#Python #Carpentries #DataScience #Coding #DataLiteracy

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

Sometimes you just need to talk through a data problem with someone who gets it.

That's the idea behind our Data Cafés — informal drop-in sessions where researchers discuss data challenges over coffee. No registration, no slides, just conversation.

Have a look at our past events we had, for example at @rwthaachenuniversity

👉 https://blog.rwth-aachen.de/forschungsdaten/en/2025/07/31/data-cafe-aachen/

#DataCafe #DataLiteracy #Research #Networking

/bos

Data literacy isn't about becoming a data scientist. It's about being able to ask the right questions about data, understand what methods are appropriate, critically evaluate results, and communicate findings clearly.

Every researcher needs these skills, regardless of discipline.
We are here to help.

👉 https://dkz2r.de/about/

#DataLiteracy #Research #DataCompetence #Science

/bos

"thesis_final.docx"
"thesis_final_v2.docx"
"thesis_FINAL_really_final.docx"

There's a better way. Git tracks every change, lets you go back to any version, and makes collaboration painless. It's not just for developers.

👉 https://swcarpentry.github.io/git-novice/

#Git #VersionControl #Research #DataLiteracy

/bos