Introduction

The best-practices page is a combination of introductory information and guidelines geared towards data scientists looking to improve their development practices. Following the recommendations given here will result in research and development that is easier to reproduce, transfer to other usecases and collaborate on. The recommendations here can be seen as a first step to improving your process, and best practices to follow in absence of better alternatives. These best practices are developed by the Institute of Data Science at Maastricht University.

The Guidelines provided here are clustered into four categories that help improve research output:

• Project Management Practices to improve project transparancy for both internal and external researchers looking to collaborate on code.
• Research Development General coding practices that will improve productivity for both individual use as well as collaboration on and reuse of code within the organization.
• IDS Resources Reusing and collaborating on existing IDS resources will ensure improved quality of the existing software landscape as well as prevent unnecessarily reinventing the wheel.

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Contribute

Contributions to this documentation are encouraged!

📝 See contribute for more details.