Datenkompetenzzentrum Rhein-Ruhr | www.dkz2r.de
Funded by the German Federal Ministry for Research, Technology, and Space
Data Literacy I inter- and multidisciplinary l FDM l HPC l AI
Contact: [email protected]
Authors of posts:
/bos – @lukascbossert
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
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
Was haben kalter Kaffee, Daten und eine Bibliothek gemeinsam? Am 24. Juni 2025 führten sie zu frischem Austausch über FDM und Data Literacy.
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.
#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
@fdm_nrw @dkz2r
🎉 Just published: the instructor guide for my "Resilient Technologies" workshop!
A 3-hour hands-on session on reproducible #RDM using decades-old tools that refuse to go away — curl, sed, grep, diff, make, awk, tar — all tied together with #Emacs and #orgmode in a single literate workflow.
Everything runs on one real dataset: the #NFDI Consortia Collaboration 2025.
Get a sneak preview of the guide
📄 DOI: https://doi.org/10.5281/zenodo.19131602
Still fighting with Word to format your thesis? LaTeX handles citations, cross-references, figures, and formatting automatically — so you can focus on writing, not fixing page breaks.
Our workshop covers everything from setup to submission-ready documents.
Have a look at the self-learn content we created in the style of @thecarpentries
👉 https://dkz2r.github.io/latex-novice-academic-publishing/01-introduction.html
#TeXLaTeX #AcademicWriting #Research #Workshop
/bos
You spent months collecting your data. You documented... nothing.
Sound familiar? Metadata is the difference between data that's reusable in 5 years and a mystery folder called "final_v3_FINAL_real."
Good metadata doesn't have to be complicated. A few consistent habits go a long way.
👉 https://www.go-fair.org/fair-principles/
#ResearchData #Metadata #DataManagement #AcademicLife
/bos
In 2016, the ‘FAIR Guiding Principles for scientific data management and stewardship’ were published in Scientific Data. The authors intended to provide guidelines to improve the Findability, Accessibility, Interoperability, and Reuse of digital assets. The principles emphasise machine-actionability (i.e., the capacity of… Continue reading →
Machine learning isn't just for computer scientists. Linguists use it for text analysis, biologists for image classification, historians for pattern detection in archives.
Our hands-on workshop is designed for researchers from all disciplines — no CS degree required.
👉 https://dkz2r.de/events/2026-04-01_carpentries_machine_learning_intro/
Lukas C. Bossert