Investing time in creating data documentation will help to ensure that your research data can be found, understood, and used by others.
Ideally, you should begin to document your research data at the outset of your project, and continue to create and update the documentation throughout the course of your project. This will decrease the risk that your documentation will be incomplete, or that you will forget important details about your data.
Research data documentation falls in to two categories - project documentation and dataset documentation.
Project documentation includes:
Dataset documentation includes:
An essential piece of research data documentation, the readme file provides basic information about a data file or dataset to help ensure that the data can be correctly interpreted, both by you at a later date or by others when sharing or publishing data. For readme file best practices and recommended content, see Guide to writing "readme" style metadata from Cornell University Research Data Management Service Group.
Metadata means, simply, data about data. In the context of research data management, metadata refers to both data documentation, and to structured information that conforms to a metadata standard.
Metadata structures are often referred to as schemas. A schema is a logical plan which shows the relationships between metadata elements. The completed metadata are often reported in a machine-readable language such as XML.
Most data repositories require that your project metadata follows a specific standard. A widely-used, general purpose metadata standard is the Dublin Core Metadata Element Set. This standard defines fifteen properties used to describe data: