A 3h hands-on workshop that introduces a DataLad-approach to computational reproducibility, where participants end up fully reproducing results and manuscript of a peer-reviewed paper, published in 2020.
- HTML 98.3%
- CSS 1.3%
- Makefile 0.4%
| .datalad | ||
| common@3d6c8935fd | ||
| img | ||
| .gitattributes | ||
| .gitignore | ||
| .gitmodules | ||
| index.css | ||
| index.html | ||
| Makefile | ||
| README.md | ||
Computational reproducibility: How could it be done in practice?
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Given at
- Workshop "Reproducible Quantitative Data Science", Copenhagen, October 2023
- Workshop "Reproducible Quantitative Data Science", Copenhagen, October 2024
- Workshop "Reproducible Quantitative Data Science", Copenhagen, August 2025
How to obtain the slides
This is a DataLad dataset. Clone the source repository:
datalad clone <url>
Enter the newly created directory
cd <directory>
And obtain all content:
datalad get . -r
Now open index.html in a browser compatible with
reveal.js.