Tools for open geospatial science

Open science uses open source to deal with reproducibility challenges in data and computational sciences. However, just using open source software or making the code public does not make the research reproducible. Moreover, the scientists face the challenge of learning new unfamiliar tools and workflows. In this contribution, we will look at a graduate-level course syllabus covering several software tools which make validation and reuse by a wider professional community possible. For the novices in the open science arena, we will look at how scripting languages such as Python and Bash help us reproduce research (starting with our own work). Jupyter Notebook will be introduced as a code editor, data exploration tool, and a lab notebook. We will see how Git helps us not to get lost in revisions and how Docker is used to wrap all the parts together using a single text file so that figures for a scientific paper or a technical report can be generated with a single command. We will look at examples of software and publications in the geospatial domain which use these tools and principles. Scientific contributions to GRASS GIS, a powerful open source desktop GIS and geoprocessing backend, will serve as an example of why and how to publish new algorithms and tools as part of a bigger open source project.