Computational Competencies

As big data and modelling are becoming more and more common tools in science, computational competencies are increasingly important. We therefore believe that computational competencies are key for the education of students and early-career scientists and that these skills are best deepened by embedding their application in subject-specific disciplinary courses. In a first step, we developed a task library in Jupyter Notebooks and R for research-related exercises and datasets, funded through the Rector’s Impulse Fund.

As the next step, computational competence exercises are now an integral part of our courses. Learning goals build on each other, i.e., they are taught like stepping stones from the 3^rd to the 6^th semester during the Bachelor's and be followed up in the Master's programme. Stepping stones are:

• use data visualisation as first step to explorative data analysis (with R in Jupyter Notebooks),
• discover concepts of data quality and uncertainty estimation (e.g., outliers, standard variation),
• apply data visualisation and statistical analyses to detect processes as functional relationships and patterns (such as linear and curvilinear relationships, correlation and regression analyses),
• synthesize disciplinary knowledge on environmental drivers on biogeochemical and physiological processes by means of statistics (such as interaction terms, covariate analysis),
• identify own misconceptions by means of quantitative data analysis,
• transfer own understanding from one system or textbook knowledge to another system or to own data by formulating and test hypotheses,
• discover existing databases, learn to assess their quality,
• develop own research question and carry out an own research project based on existing big data, and
• assess quality of research proposals.

The following courses taught by our group currently contain computational competence related tasks:

• Ecophysiology (3^rd semester): handling of measurement data using Jupyter Notebooks to test knowledge and implement understanding of ecophysiological processes

• Grassland Systems (6^th semester ): assessing Download existing datasets from databases (DOCX, 23 KB), planing a Download research project (DOCX, 21 KB), performing a Download pre-study (DOCX, 20 KB) using existing data

• Biogeochemistry and Sustainable Management (Master): programming a data logger to meteorological variables, analyzing large datasets to evaluate effects of weather events and management practices on the ecosystem greenhouse gas exchange