Metadata as Data Intelligence

Metadata, as a type of data, describes content, provides context, documents transactions, and situates data. Interest in metadata has steadily grown over the last several decades, motivated initially by the increase in digital information, open access, early data sharing policies, and interoperability goals. This foundation has accelerated in more recent times, due to the increase in research data management policies and advances in AI. Specific to research data management, one of the larger factors has been the global adoption of the FAIR (findable, accessible, interoperable, and reusable) data principles [1, 2], which are highly metadatadriven. Additionally, researchers across nearly every domain are interested in leveraging metadata for machine learning and other AI applications. The accelerated interest in metadata expands across other communities as well. For example, industry seeks metadata to meet company goals; and users of information systems and social computing applications wish to know how their metadata is being used and demand greater control of who has access to their data and metadata. All of these developments underscore the fact that metadata is intelligent data, or what Riley has called value added data [3]. Overall, this intense and growing interest in metadata helps to frame the contributions included in this special issue of Data Intelligence.