This repository contains the data and code for the following Master’s thesis:
Schneider, A. (2021).
iSEGMound – a Reproducible Workflow for Mound Detection in LiDAR-derived DTMs. Master’s Thesis https://doi.org/xxx/xxx
Please cite this compendium as:
Authors, (2021). Compendium of R code and data for iSEGMound. Accessed 30 Sep 2021. Online at https://doi.org/xxx/xxx
The analysis directory contains:
- 📁 thesis: R Markdown source
document for manuscript. Includes code to reproduce the figures and
tables generated by the analysis. It also has a rendered version,
paper.docx, suitable for reading (the code is replaced by figures and tables in this file) - 📁 data: Data used in the analysis.
- 📁 figures: Plots and other illustrations
- 📁supplementary-materials: Supplementary materials including tables created during the analysis, which were to big or complicated to input in Rmarkdown directly.
This research compendium has been developed using the statistical programming language R. To work with the compendium, you will need installed on your computer the R software itself and optionally RStudio Desktop.
You can download the compendium as a zip from from this URL:
master.zip. After unzipping: - open the .Rproj
file in RStudio - run devtools::install() to ensure you have the
packages this analysis depends on (also listed in the
DESCRIPTION file). - finally, open
analysis/paper/Master_Thesis.Rmd and knit to produce the paper.docx,
or run rmarkdown::render("analysis/paper/Master_Thesis.Rmd") in the R
console
Text and figures : CC-BY-4.0
Code : See the DESCRIPTION file
Data : CC-0 attribution requested in reuse
We welcome contributions from everyone. Before you get started, please see our contributor guidelines. Please note that this project is released with a Contributor Code of Conduct. By participating in this project you agree to abide by its terms.