PILOT: Data Management Best Practice
Please note, this is a PILOT workshop and may not run as smoothly as a standard workshop
Research increasingly depends on digital data. Whether you are collecting measurements in the field, analysing simulation outputs, recording interviews, working with images, or managing spreadsheets, the data you create must be stored, organised, documented, and maintained throughout its lifecycle.
Good data management makes research more efficient, more reproducible, and easier to share with collaborators. Poor data management can result in wasted time, duplicated effort, lost information, and difficulty reproducing results.
In this lesson we will work through a realistic scenario that many researchers encounter: inheriting a project from somebody else. Over the course of the lesson we will begin with a chaotic collection of files and gradually improve it using practical data management techniques.
- Organising folder structures
- Creating meaningful file names
- Documenting data and workflows
- Choosing appropriate storage locations
- Managing storage and file transfers
- Deciding what data to keep and what to remove
- Improving tabular data structure
- Choosing appropriate data formats
The focus of this lesson is not programming, data science, or statistics. Instead, we will concentrate on practical skills that help people find, understand, share, and preserve research data.
2026-11-05-NCL
https://newcastlerse.github.io/data-management-carpentry
Henry Daysh Building, PGR Learning Lab R6.19 https://what3words.com/bars.hips.hired
Newcastle University
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