Data documentation allows others to understand your data. It is key to make your data reusable and your research reproducible. Documentation in the form of structured metadata supports the findability and interoperability of your data.
Documentation is all information that is required to understand your research data sets and should be done continuously throughout the research project.
Good documentation is necessary to:
Documentation is needed on (at least) two levels. At the project level general questions need to be answered:
On a data level individual objects or files in your dataset might need further explanation, for example the meaning of column names in an Excel sheet or some context for an individual interview in an Oral History project.
When publishing your data set(s), documentation should be included in one or more separate text-files, accompanying your data.
The key to producing good documentation is to switch your perspective. Put yourself in the shoes of a potential data-re-user: What information would you need to understand the data and to be able to re-use it? Start documentation at the beginning of your research process and continuously work on it throughout the project.
Documentation that is structured in a standardised way is called metadata. Metadata standards provide defined elements into which information can be mapped to become machine readable (and actionable).
When you publish your data, some descriptive metadata (like title and description) is generated to enable your data set to be found.
Structuring information on your research data according to discipline specific metadata standards greatly enhances the interoperability of your data with data generated by others. The following resources list existing metadata standards: