I've turned one of my teaching modules in #Paleontology into an #OER. It's only one single lab, but many more are waiting to be OER-ised... therefore I really need your input to avoid future mistakes.
https://github.com/EmiliaJarochowska/Yankovich_cave/

Thanks to the feedback (shoutout to @leouieda and @JMMaok) I knew this:
✅ Must be editable
✅ Must include rubric
✅ Must include an example: I included a possible solution but without an essay, just the code. Now in #Rlanguage but #Python and #JuliaLang will follow.

The most watershed decision was to use #Quarto for everything to make it editable and renderable using Open Source tools and to make it version control-compatible. For people not familiar with #Quarto this may be a barrier?
Not sure what a good alternative would be. #OpenPedagogy

GitHub - EmiliaJarochowska/Yankovich_cave: Reconstruction of Pleistocene climate based on assemblages of mammal microfossils from a cave in Hungary

Reconstruction of Pleistocene climate based on assemblages of mammal microfossils from a cave in Hungary - GitHub - EmiliaJarochowska/Yankovich_cave: Reconstruction of Pleistocene climate based on ...

^GitHub

Best practice question on Open Educational Materials #OER

I created a course website/booklet.

The website is under: https://berd-nfdi.github.io/BERD-reproducible-research-course/

The source is on GitHub: https://github.com/BERD-NFDI/BERD-reproducible-research-course

Now I'd like to generate a DOI for this via OSF. What would you do?

👉 Connect the GitHub rep to OSF and create DOI for that?
👉 Put the html on OSF and create a DOI for that?
👉 Both of the above?
👉 Other?

I could also generate a PDF with the same material, for example.

I am confused.

🙏 Please help!

GitHub - BERD-NFDI/BERD-reproducible-research-course

Contribute to BERD-NFDI/BERD-reproducible-research-course development by creating an account on GitHub.

^GitHub

We have a new version of the data visualization tutorials written in #Python using #Pandas and #Altair:

https://infovis.fh-potsdam.de/tutorials/

The first tutorials lay the foundation, after which common data types are treated in 3 steps:

🛒 Prepare → 🥒 Process → 🥗 Present

If you have any ideas or suggestions for improvement, or if you're using these tutorials in your own teaching (or learning!), I'd love to hear about it. Many thanks already to @ikyriazi for lots of feedback on this revision!

#DataVis #OER

Data Visualization · FH Potsdam · Summer 2023

Hands-on tutorials for basic visualization techniques and the necessary data processing

^{infovis.fh-potsdam.de}