Contact us at datamanagement @ duke (dot) edu or attend one of our data management workshops.
It is important to document your work so that others can understand what you did (and so that you can remember later!). Be sure to maintain records of: the context of your research data (its purpose and methods of collection), information about variables, processes involved in data cleanup and analysis, file naming schemas and directory structures, and the roles and responsibilities of project personnel.
Depending on your preferences and the needs of your research project, you can document this information using README.txt files, data dictionaries or codebooks, lab notebooks, a data narrative, or some combination of the aforementioned.
For more information about documentation practices, check out these helpful guides from Cornell, ICPSR, the University of California San Diego, and the UK Data Archive.
Metadata, or “data about data” is documentation that helps other researchers discover and cite your dataset. The term, “metadata” is also sometimes used to refer to data documentation more generally.
Ideal metadata uses standardized language to enable machine readability and interoperability with various search systems. Metadata standards vary by discipline, data type and in their level of complexity, and repositories often have specific metadata requirements to be submitted with your dataset.
Become familiar with disciplinary metadata standards in your field, so that you choose the correct terminology and level of detail to describe your research.
