Recognised & Endorsed

IG

Federated Identity Management

Status: 
Recognised & Endorsed
TAB Liaison: 
Andrew Treloar

A strategic element to maintaining a competitive advantage in research is by focussing energies in areas of innovation, which is facilitated by sharing of resources and support of inter-disciplinary collaborations at the national and international levels. The primary impediment to this resource sharing and collaboration is the lack of an effective FIM (Federated Identity Management) ecosystem.

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IG

Sharing Rewards and Credit (SHARC) IG

Status: 
Recognised & Endorsed
Secretariat Liaison: 
Lynn Yarmey
TAB Liaison: 
Paul Uhlir

Short presentation of Sharing Rewards and Credit (SHARC) IG

SHARC is now a recognised and endorsed interest group within RDA (Research Data Alliance).  It is seeking to unpack and improve crediting and rewarding mechanisms in the data/resources sharing process (see link to the previous Research Data Alliance BoF meeting here:

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WG

DMP Common Standards WG

Status: 
Recognised & Endorsed
Secretariat Liaison: 
Lynn Yarmey
TAB Liaison: 
Wenbo Chu

The specific focus of this working group is on developing common data model and specifying access mechanisms that make Data Management Plans (DMPs) machine-actionable.

To achieve this vision we will develop a common data model with a core set of elements. Its modular design will allow customisations and extensions using existing standards and vocabularies to follow best practices developed in various research communities. We will provide reference implementations of the data model using popular formats, such as JSON, XML, RDF, etc.  This will enable tools and systems involved in processing research data to read and write information to/from DMPs.

 

The outputs of this working group will help in making systems interoperable and will allow for automatic exchange, integration, and validation of information provided in DMPs, for example, by checking whether a provided PID links to an existing dataset, if hashes of files match to their provenance traces, or whether a license was specified. The common information models are NOT intended to be prescriptive templates or questionnaires, but to provide re-usable ways of representing machine-actionable information on themes covered by DMPs

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