Metadata to the rescue - enabling understanding of data and fitness for use through quality descriptions that also trace its history

In the era of overwhelming amounts of data and information being readily available over the web and other media sources it is vitally important to adopt machine-to-machine readable techniques that enable quick, reliable and repeatable resource discovery and then based on rules and definitions, facilitate determination as to whether the data and information are relevant and fit for purpose. Quality metadata can provide such a tool as:

It allows the creation of multiple discipline specific metadata profiles based on international generic standards (e.g. ISO 19115-1, DCAT2) thus improving data management and interoperability of data

When expressed as an XML, Turtle or RDFXML it provides a machine readable format which is easy to manipulate and automate

Through cross-walks to other community defined standards, it can be easily translated and used by multiple communities, (e.g. from the ISO 19115-1 to DCAT2 and schema.org)

It enables the user to understand the data, its purpose, suitability and usability by capturing the history of acquisition and subsequent transformations, the description and evaluations of data quality, and the data dictionaries used

Through the application of consistent vocabulary tags and persistent identifiers it helps improve data discoverability on the web and also trace its usage and incorporation in derivative products

It records and explains how to access and use data by related services, APIs and other tools

Australian and New Zealand Metadata Working Group (ANZ MDWG) has been working on development a consistent methods of implementing such tool across disciplines, communities and sectors to facilitate a conversation, support a wider understanding and consistent application. Numerous communication and educational materials have been developed to support it. The current focus of the group is on development of improving interoperability and consistency of data management and description through developing discipline specific profiles and ontologies.

This presentation will examine challengers, achievements and current plans of the ANZ MDWG.