Along with describing your method of data gathering, you will need to state the types of data the project is likely to produce.
Occasionally, grant funding agencies provide guidelines on types of data to document. If you are applying for an NSF grant, then check to see if the directorate overseeing the grant program offers any guidance on data types to be documented.
Research data comes in many flavors and may be classified or categorized as the following (from the U. Edinburgh, “Defining research data” (pp. 5-6), in Edinburgh University Data Library Research Data Management Handbook):
- sample or specimen data
- observational (e.g., sensor data, data from surveys)
- experimental (e.g., gene sequencing data)
- simulation (e.g., climate modeling data)
- derived or compiled (e.g., text mining, 3D models)
- reference or canonical (e.g., static, peer-reviewed data sets, likely published or curated, such as gene sequence databanks or chemical structures)
While certain kinds of data can withstand benign neglect, most digital data requires a more active approach for preservation as described by Ben Goldman, Digital Archivist at the Penn State University Libraries.