Data must be fully described to be used properly by you, your colleagues, and other researchers in the future. Data documentation and metadata enable one to understand your data in detail and will enable other researchers to find, use, and properly cite your data.Reproducibility is at the core of the scientific process. If results are not reproducible, they lose credibility. Good documentation of the data and the analysis are essential!
It is critical to begin to document your data at the very beginning of your research project, even before data collection begins; doing so will make data documentation easier and reduce the likelihood that you will forget aspects of your data later in the research project. Data documentation includes supplemental documents such as software manuals, survey designs, code books, and user guides.
At minimum, include supplement documents and store additional details in a readme.txt file, together with the data. If you are depositing your data into a repository, the repository may have a preferred metadata standard.
Researchers can choose among various metadata standards, often tailored to a particular file format or discipline. One such standard is DDI (the Data Documentation Initiative), designed to document numeric data files.
Metadata Standards provide specific data fields or elements to be used in describing data for a particular use. Some research fields have predefined metadata standards, such as those listed below.
For more discipline-specific metadata standards check out the RDA Metadata Directory.