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NEW CHAPTER ALERT!
A new sub-chapter on Data Missingness is now live!
Check out the chapter here: https://book.the-turing-way.org/project-design/missing-data
You can read more about the original chapter proposal in this issue:
https://github.com/the-turing-way/the-turing-way/issues/3593
And learn more about the review process in this pull request (now closed!): https://github.com/the-turing-way/the-turing-way/pull/3709
This chapter was concieved and written by Zeena Shawa, a PhD student at University College London and a Turing-Roche Community Scholar.
The Community Scholar Scheme run by the Turing-Roche Partnership supports 10 PhD students to embed themselves within the partnership and develop a community project related to data science in health. Zeena developed this chapter as her project.
The availability & size of datasets are increasing. However, the completeness & quality of these datasets is not always guaranteed. Missing data can happen for many reasons. During her PhD, missing data has been a frequent issue, limiting the statistical power of her analysis.
This is not unique to just Zeena's PhD. She wrote this chapter as an open resource, from a non-expert's perspective, for any experience level. All feedback and contributions to the chapter are appreciated!
This chapter was reviewed by Vicky Hellon, Senior Research Community Manager and Tapabrata (Rohan) Chakraborty, Theme Lead, both for the Turing-Roche Partnership, with plans for others to review later on.
During our 18 September #CollaborationCafe last week, Zeena live-merged her chapter after a few final edits.
