Metadata is data describing your data: thus, information (data) describing the features of a dataset (or also of individual data, such as a picture). Metadata is crucial for finding datasets amongst many datasets, enhancing findability. Metadata also allows to understand the context of research data, thus fostering its interoperability and reusability: for example, a table of numbers has no meaning without a descriptive set of information explaining what the numbers relate to and/or explain.
Importantly, when uploading your data on a repository you are required to compile metadata fields, thus do not underestimate this: the more you describe your data (and the more you do it properly) the easier it will be to find, consult and replicate your data. Indeed, filling in metadata when you upload research data in a repository allows other researchers to understand what your research data is about and possibly use it for their research. Similarly, other researchers' metadata allows you as a researcher to better understand their research data.
Metadata are available in standard schemas, such as the Dublin Core metadata standard set which is an example of useful metadata fields for many disciplines. Indeed, there are many distinct types of metadata, amongst which the main three are the ones reported below:
