Page 306 - Invited Paper Session (IPS) - Volume 1

IPS155 Stefan B. et al.
discipline-specific practical experience and recognised rules of a community.
Meanwhile, there is a large array of metadata standards focusing on a
particular subject domain, content type, function or application, such as:
• Data Documentation Initiative (DDI)
• Dublin Core Metadata Initiative (DCMI)
• DataCite Metadata Schema
• da|ra Metadata Schema
• Metadata Encoding and Transmission Standard (METS)
• Preservation Metadata Maintenance Activity (PREMIS)
• Statistical Data and Metadata Exchange (SDMX)

The significance of a metadata standard lies in the uniform language, which
makes it possible to record information about data in an understandable and
structured manner. In the case of domain-specific standards, metadata often
constitutes very granular – semantically rich – statements about an object. In
order to describe data uniformly and unambiguously, standardised
terminologies (eg controlled vocabulary such as thesaurus or keywords) are
used for certain metadata elements to describe particular metadata with
uniform information. This should be distinguished from semi-structured or
unstructured metadata, which describe important contextual information on
the creation of the data.
Since its foundation in January 2017, the members of the International
Network for Exchanging Experiences on Statistical Handling of Granular Data
(INEXDA) have, amongst other things, collaborated to harmonise metadata
structures and complete an extensive stock-take of available datasets in
member institutions. This stock-taking exercise has three aims.
• First, it is designed to give member institutions an overview of the
available and possible comparable granular datasets.
• Second, it makes it easier for data users to discover and use datasets
appropriate for research and analysis by using harmonised metadata.
• Third, it provides a framework to facilitate a possible harmonisation of
datasets in the (near) future.
Because the description of the data should be comparable, all members
agreed on a metadata schema for the granular data. This paper presents the
metadata schema underlying the stock-taking exercise by INEXDA
members. We also explain practical considerations when implementing a
metadata schema for microdata, which can easily be adopted by other
institutions. Naturally, the INEXDA metadata schema was designed with
certain goals in mind. Therefore, some choices may not translate to other
situations. For example, the INEXDA schema allows for information about
the relation of different datasets to each other, which is essential for the
possible harmonising of datasets.

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