Thursday, 10 September 2020
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FAIR (findable, accessible, interoperable, reusable) is a well-established concept in data science, and the benefits of FAIR data should be quite obvious to any data scientist: not only can you find the data that you need, you can even access it, import it into your computing environment and (re)use it! FAIR principles can also be applied to programming: you can learn from others, you can share innovation, and you can make sure that others can make use of your inventions. However, fairness is also a more fundamental component of our everyday work life, forming the basis for collaboration and exchange with our colleagues and stakeholders.
At this year’s Basel SDE, we want to explore how we can achieve a greater degree of FAIR/fairness in our work. How can we make data FAIR? How can we collaborate on code effectively? How do we facilitate fair partnerships in our professional life? These are some of the questions that we hope to delve into.
Here are some suggestions for topics and ideas you might want to share:
- How to share data internally and/or externally
- Data sharing processes and platforms
- How to make data accessible
- Data standards and their implementation
- Code sharing solutions and platforms
- Good programming practices
- Good documentation practices
- How to make code more readable
- Programming techniques (e.g. pair programming, peer reviewing)
- Collaborative business communication
- Stakeholder management
- How to manage expectations
F. Hoffmann-La Roche
4070 Basel, Switzerland
Holger Langkabel, Roche
Elisabeth Deutschmann, Roche
If you have any questions, or require any information, please contact the SDE Chairs.
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