Transforming data to add value  

To maximise the value of data holdings in a university they need to be transformed in a number of different ways. Through its support for data management processes, ANDS hopes to assist institutions with these transformations as follows. 

Unmanaged to managed 

Driver: Institutions can manage all of their research data outputs

Consequence: Seeding the Commons project activity should encourage whole‐of‐institution data management, coupled with descriptions that cover as many as possible of the available data collections, and supported by consistent and robust records management processes

Invisible to findable 

Driver: Research data outputs should be as findable as possible through RDA (Research Data Australia) and web search engines

Consequence: the Seeding the Commons activity should lead to collections descriptions that are of as high a quality as possible and are exposed to external discovery (including but not restricted to RDA, and allowing discovery by discipline repositories)

Disconnected to connected 

Driver: Collections descriptions should be embedded in rich context about associated parties (people), activities (projects), services and publications

Consequence: Metadata stores need to be connected into other institutional systems (research management, finance, HR), with up‐to‐date contextual information about collections made available from authoritative systems

Single use to reusable 

Driver: ANDS cares about ultimate data access and re‐use; this means that as much of this data as possible should have associated information for re‐use, which will often need to be held at object level

Consequence: It would be desirable to see more systematic capturing of re‐use metadata at the collection level, including software and equipment descriptions, methodology, and data interpretation