It helps to add documentation and metadata (data about data) to the documents and datasets you create to ensure that you understand your own data and so that others can find, use, and properly cite it. Here are some tips pertaining to documentation and metadata that we have compiled from the University of Cambridge (Teperek, 2015), MIT (Documentation & Metadata, n.d.), and the Consortium of European Social Science Data Archives (CESSDA Training, 2017-2020b).
What Are ‘Documentation’ and ‘Metadata’?
The term ‘documentation’ refers to all of the data and information required to interpret, comprehend, and use a dataset or set of documents. Examples of documentation include title, description, creator, funder, keywords, and affiliation.
We use the terms ‘documentation’ and ‘metadata’ (data about data – usually embedded in data files/documents) interchangeably in this resource.
When and How Do I Include Documentation/Metadata?
Beginning to document your data at the start of your research project and continuing to add information as the project progresses is a good practice. Documentation procedures should be included in your data planning.
There Are a Variety of Ways You Can Add Documentation to Your Data:
The data or document itself can contain information about a file or dataset. This means that the documentation for digital datasets can be stored in separate files (for example, text files) or integrated into the data file(s), either as a header or at specific locations in the file. The following are some examples of embedded documentation:
Supporting documentation is data that is accompanied by information in separate files that provides context, explanation, or instructions on confidentiality and data use or reuse. The following are some examples of supporting documentation: