Dr. Arnim Bleier, Dr. Haiko Lietz, & Dr. N. Gizem Bacaksizlar Turbic

Open Science comprises the principle to make data and methods openly available in order to increase the reproducibility of research. In Computational Social Science (CSS), as in all computational sciences, reproducibility means that a piece of research can be executed repeatedly by the producer of that research or by other researchers with identical results. Full reproducibility makes it necessary to document all data and code. One reason why reproducibility is necessary is that workflows in CSS are often quite complicated. Documentation helps researchers keep an overview of all research steps and, ultimately, ensure research quality. Another reason is that documentation easily allows researchers to collaborate or build upon existing data, methods, or both, all of which is in the general knowledge-production interest of science. The workshop will consist of two parts. In the first part, we will introduce Open Science software tools that allow researchers to collaborate, document their work, and demonstrate it to the outside world. In particular, we will introduce the programing coordination system Git, the interactive computing product Jupyter Notebook, and the code execution service Binder. Participants will learn how to share their computer code in GitHub, develop and document it using Jupyter Notebooks, and execute those in the cloud without having to install a programming language. In the second part of the workshop, we will introduce a set of teaching materials that add up to a coherent “Introduction to Computational Social Science methods with Python”. These materials developed in the Social ComQuant project include sessions about data collection, preprocessing, and analysis, make use of the Open Science tools introduced in the first part, and are fully self-explanatory to enable self-training.

Slides are available here.

For the exercise solution see here.