Working with Open Code mainly requires knowledge of reproducible research, as data and code are usually considered together. This includes:

• Knowledge of an open programming language such as R, Python or Julia
• Knowledge of software development and version control (e.g. Git)
• Knowledge of Open Source and licences
• Knowledge of how to publish software in a citable way and how to cite software

Training

• The Turing Way: “Guide for Reproducible Research”
• QuantEcon offers, among other things, introductions to quantitative economic research with Python.
• Code Publishing Tutorial by the LMU Open Science Center
• Workshops by Software Carpentry
• CodeRefinery offers training in research software development, including lessons for self-study, and points to training programmes from third parties.
• ZBW online seminar “Good Scientific Practice and Reproducible Research” (in German)
• NASA TOPS OS101, Module 4: Open Code

Also check which courses your university or research institution offers, for example in programming or statistical data analysis.

Help with specific questions

• The Turing Way offers, among other things, community calls and a “Turing Way Practitioners Hub”.
• Stack Overflow is a popular online community where programmers ask and answer questions about technical problems in software development. Researchers from the data sciences are also active there.
• On GitHub, many Open Source projects have discussion areas where you can ask questions. You can also report problems in the “Issues” section of the respective repository.
• Many programming languages and tools have their own forums. RStudio, for example, has its own community, and a directory maintained by Jumping Rivers lists local R user groups.

If you have any questions or suggestions about the Open Economics Guide, please contact us.