Use Case Description
Activity – The aggregator harvests, or otherwise obtains, contributions in a variety of formats and adhering to various metadata schema. Merge data into a single aggregation mapping common fields and merging duplicate records.
Actors – Data owners (e.g. galleries, libraries, archives, museums); Aggregation service
Data involved – Metadata describing a wide variety of resources typically found in GLAMs. Could also include data describing entities such as places or people which may be referred to (e.g. authors, place of publication, person/place represented etc.)
Workflows – Data owners provide data to aggregation service (either publishing in such a way it can be harvested by aggregator, or by providing directly to aggregator by some mechanism)
Aggregator analyses all data received, and works with data owners to ensure the data is clearly understood
Aggregator work to map common fields, and merge (or otherwise co-identify) resources/records where appropriate (e.g. two records representing the same book). This work could include creating mappings between identifiers for resources/entities
Aggregator publishes aggregated data for use/reuse
Current Examples – DigitalNZ (they take data in any format) http://www.digitalnz.org/; COPAC (who take MARC records from the largest contributors, but also a variety of formats from other libraries) http://copac.ac.uk
Benefits – What is the business case?
Data owner – Low barrier for entry (cost mainly with aggregator) Aggregation may enrich resources (bringing together multiple records and include additional contextual metadata)
End Users – Aggregation provides possibility of searching cross-institutionally and cross-sectorally
Aggregators – Low barrier to data owners increases likelihood of obtaining data from many contributors
