Data curation refers to the stewardship of data (and in the case of UWC, research data) throughout its lifecycle.
Data does not exist in perpetuity once it is created. In fact, digital assets are far more fragile than most physical artefacts, and are even more vulnerable to deterioration. What is known as the data lifecycle is the sequence of stages that a particular unit of data goes through from its initial generation to its eventual archiving and/or deletion at the end of its useful life. The Digital Curation Centre (DCC) offers a useful ‘Data Curation Lifecycle Model’ which illustrates the cyclical nature of data.
Metadata at its most simple is ‘data about data’. It is structured information (that is machine-readable), which makes it possible to describe, locate, or retrieve a digital resource. Metadata enriches a digital resource, and helps make it searchable and findable. Without metadata, a digital resource or a dataset is almost meaningless. Moreover, repositories require rich metadata to accompany all data sets, because if research data exists in a repository without metadata, chaos would ensue.
Part of data management is having sufficient and clear data describing not only the data but also what has happened to it. In the digital and networked world, metadata is the currency of exchange that enables data to link with other data and researchers.
Broadly speaking, three are three types of metadata:
Descriptive Metadata includes authorship, title, description.
Structural Metadata documents the relationships within and among objects. Structural metadata often links to other components.
Administrative Metadata includes more technical information, including versioning and licensing, for the purpose of research data management.
Different disciplines require different metadata structures, containing different elements and with differing rules and semantics. This is known as a metadata schema. There are many different metadata schemas, some of them well known, and some far more obscure. Generally, there is a metadata schema that will suit a researcher’s specific needs. Metadata schemas that are maintained and standardized are known as metadata standards. Dublin Core is perhaps the most well-known metadata standard.
A comprehensive directory of metadata standards can be found at the Research Data Alliance Metadata Directory.
Importantly, metadata is not the same as documentation, although they both provide context and essentially tell the story of the data for users and aid reuse of that data. The main difference, however, is that metadata is structured information, which aids discovery.