Want to ensure your research data doesn’t contain errors and can be understood by other users and your future self? 🗂️ This tutorial by the LMU Open Science Center can show you how to document and validate your research data in R, making your research data more reusable.
The Tutorial of the Day: Data Documentation and Validation using R 📑 https://lmu-osc.github.io/data-documentation-validation-R/
Data documentation such as data dictionaries provides the contextual data needed to understand, navigate, and reuse research data whereas data validation involves verifying the collected data before it’s used. By documenting and validating your data, you can:
✔️ Create clear and comprehensive data dictionaries
✔️ Use summary statistics to spot data quality issues
✔️ Validate your data to catch errors before analysis
✔️ Produce professional reports that integrate documentation and analysis
Check out this tutorial to start making your research data more understandable and shareable! 💡
This tutorial has a CC BY license, so you can freely reuse and adapt it for your own courses or workshops by giving attribution.
