Documenting your data collection practices and provenance is an important part of research data management. Why is it important?
- Allows for the easy sharing and re-use of data by others
- Allows for the replication of the research by grant funders and others
- Provides a reference for yourself or research team to check data findings, compare research with others.
Documenting your data can take many forms and some examples include:
- Data dictionary
- Readme files: Cornell University readme guide or Dryad readme guidance
- Machine readable Metadata formats or standards such as Data Documentation Initiative (DDI) or Dublin Core
Metadata is used to describe data so that other researchers can find it and use it appropriately. Since the types of research data can be so diverse, there are many different metadata standards available. Your chosen data repository should be able to assist with determining what metadata to record.
Some examples of of the more popular metadata standards include: