Last modified: 31 May 2022 13:05
The way we do science is changing. Scientific results that can be independently verified increase trust in science and accelerate future work.
This course will give students the tools they need to do open and reproducible health data science. The skills they will develop are becoming a requirement for funding agencies and scientific publishers, and are important for data-intensive careers in academia, NHS or industry.
| Study Type | Postgraduate | Level | 5 |
|---|---|---|---|
| Term | First Term | Credit Points | 15 credits (7.5 ECTS credits) |
| Campus | Aberdeen | Sustained Study | No |
| Co-ordinators |
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The aim of the course is to enable students to carry out open and reproducible health data science. The course will cover
principles of open science; advantages and barriers to reproducibility; health data management; reproducibility initiatives such as registered reports and preprints; version control; collaboration using GitHub; code development using R (no coding experience is required).
Information on contact teaching time is available from the course guide.
| Assessment Type | Summative | Weighting | 70 | |
|---|---|---|---|---|
| Assessment Weeks | 12 | Feedback Weeks | 15 | |
| Feedback |
A homework assignment covering all aspects of a reproducible data science workflow |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Analyse | Discuss the advantages of Open and Reproducible Health Data Science and the barriers to its adoption |
| Conceptual | Understand | Explain how openness can be embedded in the scientific process |
| Procedural | Apply | Use the R programming language to import, analyse and visualise health data |
| Assessment Type | Summative | Weighting | 30 | |
|---|---|---|---|---|
| Assessment Weeks | 11 | Feedback Weeks | 14 | |
| Feedback |
Critique of published study focusing on openness and reproducibility, with suggestions for improvement |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Procedural | Analyse | Embed reproducibility principles into the life cycle of health data |
| Procedural | Create | Design a reproducible health data science workflow |
There are no assessments for this course.
| Assessment Type | Summative | Weighting | 100 | |
|---|---|---|---|---|
| Assessment Weeks | 40 | Feedback Weeks | 43 | |
| Feedback |
A report describing a reproducible data science workflow from initial dataset to visualisation of results, including code (in R |
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
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| Knowledge Level | Thinking Skill | Outcome |
|---|---|---|
| Conceptual | Understand | Explain how openness can be embedded in the scientific process |
| Procedural | Apply | Use the R programming language to import, analyse and visualise health data |
| Conceptual | Analyse | Discuss the advantages of Open and Reproducible Health Data Science and the barriers to its adoption |
| Procedural | Analyse | Embed reproducibility principles into the life cycle of health data |
| Procedural | Create | Design a reproducible health data science workflow |
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