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About ANDS One of the chief goals of ANDS is to build the Australian Research Data Commons, a cohesive collection of research data outputs from all Australian.

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Presentation on theme: "About ANDS One of the chief goals of ANDS is to build the Australian Research Data Commons, a cohesive collection of research data outputs from all Australian."— Presentation transcript:

1 About ANDS One of the chief goals of ANDS is to build the Australian Research Data Commons, a cohesive collection of research data outputs from all Australian research institutions. Funded by the Australian Commonwealth Government's Department of Industry, Innovation, Science Research and Tertiary Education (DIISRTE), ANDS is engaged with Australian universities to ensure that research data is better described, more connected, more integrated and organised, more accessible and more easily used for new purposes. VIVO as an Institutional Data Registry Philippa Broadley, Gawri Edussuriya, Lance De Vine, Stephanie Bradbury Queensland University of Technology. Brisbane, Australia Contact: VIVO as an Institutional Data Registry Philippa Broadley, Gawri Edussuriya, Lance De Vine, Stephanie Bradbury Queensland University of Technology. Brisbane, Australia Contact: This project is supported by the Australian National Data Service (ANDS) ANDS is supported by the Australian Government through the National Collaborative Research Infrastructure Strategy Program and the Education Investment Fund (EIF) Super Science Initiative. The Queensland University of Technology (QUT) in Brisbane, Australia, is involved in a number of projects funded by the Australian National Data Service (ANDS). Currently, QUT is working on a project (Metadata Stores Project) that uses open source VIVO software to aid in the storage and management of metadata relating to data sets created/managed by the QUT research community. The registry (called QUT Research Data Finder) will support the sharing and reuse of research datasets, within and external to QUT. QUT uses VIVO for both the display and the editing of research metadata. Several customisations to VIVO are under development to better facilitate it for its role as a dataset registry. A general architecture with workflow consisting of a public facing VIVO as well as a restricted access VIVO for data integration is in the final stages of development (see Figure 1). Key Outcomes of Metadata Stores Project  A public research profile portal (QUT Research Data Finder, beta version until December  Workflow for registering new research data collections in the university  Alignment of metadata records about research activities with our institutional research management system (ResearchMaster)  Alignment of metadata records about parties with an institutional name authority e.g. QUT’s Staff Profiles system -  Strategic reporting on contents and coverage of the metadata store (for internal use) As well as displaying records in QUT Research Data Finder, we will contribute collection, party, activity and service records to Research Data Australia (ANDS’ discovery portal - (see Figures 2 and 4). Figure 1. Architecture of the metadata capture and publishing system. Figure 6. Basic overview of metadata transformation and integration using the Java Object Model (CRMM). Several novel approaches have been used in the development with the anticipated outcome that the process of importing and exporting data to and from VIVO will be more efficient. For example, we have created DOI minting web application. Currently, the DOI minting process in integrated with VIVO. This application will aid researchers in tracking who is reusing their data sets and how they are being reused. Transformation and Integration QUT has developed and will use a Java object model (CRMM) as an intermediate data representation for data integration and transformation tasks (see Figure 6 below). Metadata mappings have been developed for various formats and mappings are presently being constructed for moving data between CRMM and VIVO. The rationale behind this is that the Java objects are relatively easy to manipulate, especially via high level scripting languages that run on the JVM. Groovy technology was used for data manipulation. VIVO at QUT Figure 2. Party record in Research data Australia (left). Figure 3. Party record in QUT Research Data Finder (right). Figure 4. Collection record in Research Data Australia (left). Figure 5. Collection record in QUT Research Data Finder (right). CRMM (placeholder name) Java model YAML (YAML Ain’t Markup Language) Mediaflux RIF-CS/XML RIF-CS/XML encapsulated within OAI- PMH VIVO Jena Model Java Object DB or RDB Mapping Transformation Scripts This work is licensed under a Creative Commons Attribution 3.0 Australia License.


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