An important but sometimes neglected step in generating research data is writing documentation to accompany it. First and foremost this documentation will be useful to you when you come to write up your results, especially if this will be some time later, and should you wish to revisit the data in a future project. The documentation will also be vital for anyone else coming to validate your findings, evaluate your data, or build on your work.
When documenting your data, the aim is to provide enough information so that a fellow researcher who is familiar with your field, but not necessarily your work within it, should be able to understand the data, interpret them correctly, and use them in new research. You may find it helpful to consider what you would need to know in order to use someone else's data in your research. Typically this will include the method used to collect the data and how they have been recorded, structured, processed or manipulated. You may also need to provide some broader context to explain the motivation for the design decisions you have taken and the significance of what you found.
More specifically, you may need to include some of the following elements:
You may be recording some of this information in a lab notebook or research journal. If so, you may find it convenient to record the corresponding page numbers alongside the data files until you have an opportunity to transfer the information into a documentation file.
Depending on the context there are several places where the documentation can be placed:
A readme file is a plain text file that is named 'readme' to encourage users it to read it before looking at the remainder of the content. It can contain documentation directly or instruct the reader where to look to find more information. Even though it is free text, the file should be structured into sections as an aid to the reader. The following are suggestions for what to include:
Metadata is the information someone would need concerning some data in order to do something with them, such as discover them or preserve them. Metadata are most useful when they have been structured, that is, arranged as properties and values.
As a researcher, the main three types of metadata you will be asked to provide are contextual metadata, discovery metadata, and metadata for reuse.
The metadata you provide for reuse will depend on the field of your research:
If you decide or are required to offer your data to a subject-specific data centre, you should contact them in the early stages of your project to discuss their metadata requirements. This can save a lot of additional work later on as some metadata can only be collected accurately at the point of data creation.
For more information about the data and metadata standards available for your subject area, see the following directories:
As an aid to clarity, some subject areas have agreed on a common set of terminology to use when describing data. If metadata standards list the properties that need to be known, vocabularies help with providing useful values.
