Metadata

Metadata is structured information that describes one or more aspects of your research data. In other words, metadata = ‘data about data’. Metadata is machine-readable and helps make your data findable and citable.

Metadata exists at different levels:

Project-Level Metadata

This type of metadata describes higher-order aspects of your dataset: the “who, what, where, when, how and why” … It provides context for understanding why the data were collected and how they were used.

• Name of the project • Dataset title • Project description • Dataset abstract • Principal investigator and collaborators • Contact information • Dataset handle (DOI or URL) • Dataset citation • Data publication date • Geographic description • Time period of data collection • Subject/keywords • Project sponsor • Dataset usage rights

Data-Level Metadata

• Data origin: experimental, observational, raw or derived, physical collections, models, images, etc. • Data type: integer, Boolean, character, floating point, etc. • Instrument(s) used • Data acquisition details: sensor deployment methods, experimental design, sensor calibration methods, etc. • File type: CSV, mat, xlsx, tiff, HDF, NetCDF, etc. • Data processing methods, software used • Data processing scripts or codes • Dataset parameter list, including ⚬ Variable names ⚬ Description of each variable ⚬ Units

This type of metadata is more granular and describes the data (variables) and dataset in detail.