A5073190153- Scientific Computing and Data Management
- Distributed and Parallel Computing Systems
- Research Data Management Practices
- Advanced Text Analysis Techniques
- Educational Games and Gamification
- Machine Learning and Data Classification
- Educational Assessment and Pedagogy
- Software Engineering Research
- Data Mining Algorithms and Applications
- Online Learning and Analytics
- Data Visualization and Analytics
- Statistics Education and Methodologies
University of Zurich
2021-2023
Data science is an exploratory and iterative process that often leads to complex unstructured code. This code usually poorly documented and, consequently, hard understand by a third party. In this paper, we first collect empirical evidence for the non-linearity of data from real-world Jupyter notebooks, confirming need new approaches aid in interaction comprehension. Second, propose visualisation method elucidates implicit workflow information assists scientists navigating so-called garden...
Despite the ubiquity of data science, we are far from rigorously understanding how coding in science is performed. Even though scientific literature has hinted at iterative and explorative nature coding, need further empirical evidence to understand this practice its workflows detail. Such critical recognise needs scientists and, for instance, inform tooling support. To obtain a deeper analysed 470 Jupyter notebooks publicly available GitHub repositories. We focused on extent which...