Data quality

Data quality can be maintained in a number of ways that can be grouped into three general categories:

1. Quality checks at data capture/ generation or collection built into instruments (e.g. calibration) or data systems (e.g. range limits, codesets, logic)
2. Subsequent verification checks (e.g. scripts or cleaning processes to deal with missing data, unexpected distributions, associations, contradictions)
3. Verification against third party data (e.g. through data linkage to other trusted sources of data)

Maintaining a record of what quality/ verification activities are to be undertaken and a log of what data has been cleaned or processed, will be an important part of maintaining a record of your research.  Some specific data systems (e.g. Electronic Notebooks, REDCap) provide extensive logging of these changes.  It is important you understand what is logged, how you can access it and how you will retain it when your research ceases.  Documenting it in your DMP+ is a useful way of keeping a record.