Metadata is documentation that describes data. It is data about your data. It describes the who, what, when, where, why and how of your research.
Humans forget things and others will need to understand the context of your data. Properly describing and documenting your research data allows others to understand your data. Metadata also facilitates search and retrieval of the data when deposited in a data repository.
opendatasoft. (2016). What is metadata and why is it as important as the data itself?
Standards-based metadata is generally preferable, if one works for your research or exists for your discipline. Dublin Core is a commonly used domain agnostic metadata standard. Some examples of domain-specific metadata standards include EML (Ecological Metadata Language) and FITS (Flexible Image Transport System)
Use these resources to explore metadata standards:
Please review Dr. Stasser's Research Data Management: A Primer, Documenting Research Data (p. 7) for metadata guidelines and information
If you need help constructing your metadata, you can contact your subject librarian.
A readme file provides information about a data file and is intended to help ensure that the data can be correctly interpreted, by yourself at a later date or by others when sharing or publishing data. Standards-based metadata (from Cornell University) is generally preferable, but where no appropriate standard exists, for internal use, writing “readme” style metadata is an appropriate strategy.
See more at Cornell University's Guide to writing "readme" style metadata.
Attribution to the Research Data Management Librarian Academy (RDMLA).