Down the rabbit hole. A 101 on reproducible workflows with Python

<div><br></div><div>There has been a massive interest in reproducible research / data</div><div>analysis pipelines over the last few years.</div><div>But... how can I ensure that what I produce as a Python</div><div>user is reproducible?</div><div>In this tutorial we'll be taking you on a journey down the rabbit hole</div><div>of reproducibility.</div><div>We'll be taking a step by step approach to reproducible scientific development</div><div>in Python.</div><div>This means you get a crash course on version control, execution environments, testing,</div><div>and continuous integration. And a guide on how to integrate all of these in your</div><div>software projects.</div><div>By the end of the course we hope you will have the necessary tools to make your</div><div>Python workflows reproducible no matter if you're starting a brand new project</div><div>or if this is ready to be shared with the world.</div><div><b><br></b></div><div><b>Audience</b></div><div><br></div><div>This tutorial should be appropriate for anyone using Python for research / data analysis.</div><div>It would be better suited for beginners/ intermediate level users aiming to enhance their</div><div>code craftmanship and learn more about software development best practices.</div><div>By the end of the course the attendees will have learnt about best practices for reproducible</div><div>scientific code development and should be able to implement these techniques to their</div><div>day to day workflows.</div><div><br></div><div><b>Outline</b><br></div><div><br></div><div>- Introduction to reproducibility and its importance (20 mins): presentation</div><div>- How reproducible are my workflows?: team discussion (attendees will make teams and discuss)</div><div>if they have reproducible practices or how to ensure reproducibility (20 mins)</div><div>- Hands on:</div><div> - **Starting a project:** early considerations for reproducibility</div><div> - Introduction to licensing, data and</div><div> software curation strategies that have to be considered in the early strategies of the project. Students will</div><div> start setting up a 'mock project' (30 mins)</div><div> - How do I make sure my results are correct?</div><div> - Testing in Python</div><div> - How to develop software and test it at the same time.</div><div> Students will acquire hands on experience on writing test cases. (30 mins)</div><div> - How can I identify bugs easier?</div><div> - Students will learn about test automation and Continuous integration (30 mins)</div><div> - My test is ready.. how can I share it? Measuring test quality, getting cited for your software, documentation, containers? execution environments? (40 mins)</div><div><br></div><div>closing up (10 mins) </div><div><br>