giulange / egu23_orrshortcourse

EGU Short course

Communicating data quality through open reproducible research

This repository contains the materials for the short course given at EGU 2023. The idea is to demonstrate the use of reproducibility tools (R Markdown, GitHub, Binder, The Whole Tale) in the context of the MINKE research project.

Installation guide

Local: Clone repository, run install.R, run rmd_idw.Rmd. Cloud: Follow links below for running the code on Binder or The Whole Tale.

Branches

• main contains a computational workflow to generate a map showing an interpolation and quality parameters to filter the underlying dataset. The structure of the repository is based on this binder template.
• map1 and map2 contain a short workflow to show the importance of the versions of the used library (The Turing Way Community 2019).
• calc contains the same code as map1/2 but with a different Python version. It is used to show that the result of a simple calculation 1/5 is different in Python 2 and Python 3.

Links

• Binder: https://mybinder.org/
• The Whole Tale: https://wholetale.org/

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• Binder for map1: https://mybinder.org/v2/gh/MarkusKonk/egu_shortcourse.git/map1
• Binder for map2: https://mybinder.org/v2/gh/MarkusKonk/egu_shortcourse.git/map2
• Binder for calc: https://mybinder.org/v2/gh/MarkusKonk/egu_shortcourse.git/calc
• Whole Tale for map1: tba
• Whole Tale for map2: tba
• Whole Tale for calc: tba
• Whole Tale for main: tba

References

The Turing Way Community, Becky Arnold, Louise Bowler, Sarah Gibson, Patricia Herterich, Rosie Higman, … Kirstie Whitaker. (2019, March 25). The Turing Way: A Handbook for Reproducible Data Science (Version v0.0.4). Zenodo. http://doi.org/10.5281/zenodo.3233986

Manuel Gimond (2023). Intro to GIS and Spatial Analysis. https://mgimond.github.io/Spatial/index.html