emiliajarochowska / yankovich_cave

Reconstruction of Pleistocene climate based on assemblages of mammal microfossils from a cave in Hungary

Open Educational Resource (OER)

Reconstruction of Pleistocene climate based on assemblages of mammal microfossis from a cave in Hungary

Target audience: University students (BSc, MSc) with basic skills (input/output, plotting, data manipulation) in a reactive scripting language such as R or Python. The module was originally developed for a course in Paleontology in the BSc in Geosciences programme at Utrecht University. Solutions included here cover R and Python, details of these solutions may differ. The level of difficulty can be lowered by providing part of the codes for the solution to students or by reducing the scope of the exercise by eliminating some tasks.

Disciplines: Earth sciences, geoscience, environmental science

Contents

This repository contains source files (see below under file formats) and has a Zenodo equivalent where html files are included.

• Instructions.md - Instructions file which you can preview directly on GitHub
• Instructions.qmd - Markup file used to generate Instructions.md, can be rendered to other formats (see below)
• Rubric.qmd and Rubric.md - example grading rubric (source and output, respectively)
• data - Files needed to complete the exercise, extraced from figures in the article by Kretzoi (1957)
  • Table2.csv - proportion of fossil voles per bed and absolute age of each bed
  • Table2_abs.csv - absolute counts of fossil voles per bed and absolute age of each bed
  • Table4.csv - proportions of non-vole fossil vertebrates per bed and absolute age of each bed
  • Table4_abs.csv counts of non-vole fossil vertebrates per bed and absolute age of each bed
  • Absolute_counts.qmd and Absolute_counts.qmd Documentation how the absolute counts were generated
• solutions - Example solutions and data needed to generate them
  • solutions/R_solution.md - Example solution in R
  • solutions/R_solution.qmd - Code for the example solution, prepared using R 4.3.1
  • solutions/Python_solution.ipynb - Python solution
  • solutions/data - Data needed to generate the solutions, otherwise would have been generated by the students during the exercise.

The exercise can be completed in any reactive language that allows data manipulation and plotting: R, Python, Julia, etc.

File formats and editing

Text and presentation files are provided as Quarto files. This is an open source publishing format based on markdown. Quarto files can be edited using text editors and IDEs such as R Studio and rendered to any format: docx, pdf, html. You can preview the contents of the OER directly on GitHub in files with .md extension and .html files are provided in the Zenodo repository.

If you would like to edit the OER and export it e.g. to Word or PDF, you can do it in three ways.

1. Edit .html files provided on Zenodo

2. Edit and render Quarto files

   First, clone the repository.

   • Open the file in R Studio, set the following line to your preferred format, e.g.:

   format: pdf
   

   And click Render.

   • Change the line as above using any text editor and use Quarto CLI to render it:

   quarto render
   

How to contribute

If you would like to contribute to this OER, please see CONTRIBUTING.md.

License

Please see LICENSE.md

References

The instructions and solution use data from the following papers:

• Beyer, R.M., Krapp, M. & Manica, A. (2020) High-resolution terrestrial climate, bioclimate and vegetation for the last 120,000 years. Sci Data 7, 236 https://doi.org/10.1038/s41597-020-0552-1

• Kretzoi M. (1957) Neuere Forschungen aus der Jankovich-Höhle. Folia Archaeologica, 9, pp. 16 - 21

and datasets and code from:

• Beyer, R. (2020) Late Quaternary climate, bioclimate and vegetation. figshare. Dataset. https://doi.org/10.6084/m9.figshare.12293345.v4, distributed under the CC BY 4.0 license.

Dependencies

• De Bruin, J. Datahugger - One downloader for many scientific repositories [Computer software]. https://github.com/J535D165/datahugger

• Pierce D (2023). ncdf4: Interface to Unidata netCDF (Version 4 or Earlier) Format Data Files. R package version 1.21, https://CRAN.R-project.org/package=ncdf4.

• Sarkar D (2008). Lattice: Multivariate Data Visualization with R. Springer, New York. ISBN 978-0-387-75968-5, http://lmdvr.r-forge.r-project.org.

• Ushey K, Allaire J, Tang Y (2023). reticulate: Interface to 'Python'. R package version 1.32.0, https://CRAN.R-project.org/package=reticulate.

• Wickham H. (2007). Reshaping Data with the reshape Package. Journal of Statistical Software, 21(12), 1-20. URL http://www.jstatsoft.org/v21/i12/.

• Wickham H. ggplot2: Elegant Graphics for Data Analysis. Springer-Verlag New York, 2016.

Authors

Emilia Jarochowska Utrecht University email: e.b.jarochowska [at] uu.nl Web page: www.uu.nl/staff/EBJarochowska ORCID: 0000-0001-8937-9405 Instructions, comparison with the climate model, R solution and parts of the Python solution

Wilma Wessels Utrecht University email: w.wessels [at] uu.nl Web page: https://www.uu.nl/staff/WWessels ORCID: 0000-0001-9027-9698 Original exercise in Excel

Alraune Zech Utrecht University email: a.zech [at] uu.nl Web page: https://www.uu.nl/staff/AZech Python solution and parts of instruction

Copyright

Copyright 2023-2024 Utrecht University

yankovich_cave's Issues

Update R solution

Based on extensions made in December

Add more tasks for students

• Line/bar plot of species richness/genus richness and add sorting by frequency (using argsort)
• Species rank distribution
• Extract more data from the climate model
• Come up with a function that students need to write