Creating reproducible research is crucial for data scientists as it ensures transparency, understanding, and accuracy in the research process. Not only does it help others understand your work, but it also allows for the reproduction and verification of your results in the future.
Heidi Seibold, an expert in reproducible research, suggests three steps for achieving reproducibility:
- Document everything
- Develop reusable code, and
- Share results with others.
By following these steps, you can ensure that your research is reproducible and accessible to anyone who needs it. Share this resource with your colleagues who want to enhance the quality of their research!
Dr. Heidi Seibold
She helps research teams with open innovation and reproducible data science by providing interactive workshops and consulting. As a well known expert on open science and research software I give keynote talks and serve as an ambassador for good scientific practice in the digital era. She used to work as a biostatistics and machine learning researcher at the University of Zurich, LMU Munich, University of Bielefeld and Helmholtz AI.