Metadata Plenary Session

Metadata is omnipresent in RDA activities.  Over the last year the metadata groups have coordinated their activities and now present our achievements, activities and plans so that all interested RDA colleagues can minimally be informed and hopefully be involved in metadata activities.  In particular we appeal to RDA colleagues to document standards in the directory and to provide use cases so that we can ensure our work towards defining appropriate recommended metadata for RDA can be as inclusive and relevant as possible.
Introduction to Metadata & Principles of Metadata Groups  
The four ‘core’ metadata groups of RDA, Metadata Standards Directory Working Group, Data In Context Interest Group, Research Data Provenance Interest Group, and Metadata Interest Group (MIG), have worked together and are coordinated by MIG.  One aspect of that work is to bring forward a set of principles for metadata that the groups believe RDA should adopt and promote. This presentation will present the five principles, illustrations and examples as well as the implications of these principles.
Keith Jeffery, Keith G Jeffery Consultants
Session Chair 

Keith Jeffery is an independent consultant and past Director IT at STFC Rutherford Appleton Laboratory with 360,000 users, 1100 servers and 140 staff. Keith holds 3 honorary visiting professorships is a Fellow of the Geological Society of London and the British Computer Society, is a Chartered Engineer and Chartered IT Professional and an Honorary Fellow of the Irish Computer Society.  Keith is past-President of ERCIM and past President of euroCRIS, and serves on international expert groups, conference boards and assessment panels.  He had advised government on security and green computing.  He chaired the EC Expert Groups on GRIDs and on CLOUD Computing.

Use Case Template 
A use case template for metadata was jointly developed by the Data in Context Interest Group and the Metadata Interest Group.  The template was presented to the Metadata Standards Directory WG, Metadata IG, and Data in Context IG at the RDA Plenary 4 in Dublin.  The template was modified based on feedback from these groups. We continue to request and receive use cases using this template (along with example) from researchers representing diverse domains. The status of this activity and future plans will be presented.
Rebecca Koskela, Executive Director, DataONE, University of New Mexico

Rebecca Koskela is the Executive Director of DataONE at the University of New Mexico. Prior to this position, Rebecca was the Life Sciences Informatics Manager for Alaska INBRE and the Biostatistics and Epidemiology Core Manager for the Center for Alaska Native Health Research at the University of Alaska Fairbanks. In addition to her bioinformatics experience, Rebecca has over 25 years experience in high performance computing including positions at Sandia National Laboratories, Los Alamos National Laboratory, Cray Research and Intel.
Standards Directory
The RDA Metadata Standards Directory Working Group (MSDWG) includes individuals involved in the development, implementation, and use of metadata for scientific data. The continued proliferation and abundance of metadata standards for scientific data present significant challenges for those seeking guidance in the selection of appropriate metadata standards. The UK Digital Curation Centre (DCC) launched a Disciplinary Metadata Standards Catalogue near the time the Working Group (WG) started its activity.  The catalogue was adopted by the WG, who enriched its entries and expanded its coverage. The WG also developed a functional prototype directory based on the GitHub infrastructure. It places the information from the DCC directory into an environment where the community, with full version control, can maintain it transparently.
Alex Ball, Digital Curation Centre 

As one of the staff attached to the Digital Curation Centre (DCC), Alex provides support to UK Higher Education Institutions on research data management matters, and looks after the DCC's Tools Catalogue and Metadata Standards Catalogue. He is the Production Editor for the IJDC (International Journal of Digital Curation) and responsible for monitoring its impact. Previously, Alex worked on the SCARP Project Case Study for Engineering research, as well as reporting on the state of the art in Web archiving and in preservation and curation for institutional repositories. More recently, Alex has written guidance on various aspects of research data management including data licensing and data citation. He is Co-Moderator of the Dublin Core Science and Metadata Community, and Metadata Coordinator for the Jisc Research Data Discovery Service.
Data provenance enables search and discovery, reuse, reproducibility and is essential for establishing the quality and trustworthiness of data. This presentation will present the status of the provenance use cases that the Research Data Provenance IG is collecting as well as the interactions with the Metadata Standards WG, the Data in Context IG, and Metadata IG.
Dave Dubin, University of Illinois

David Dubin is a Research Associate Professor at the UIUC Graduate School of Library and Information Science in Champaign, IL. He teaches and conducts research on the foundations of information encoding and expression.
Conclusion & Discussion
A major goal of RDA is sharing of research datasets.  For this to scale beyond one researcher sending a dataset to another, interoperability is required using computer systems to discover, contextualise, select, access, transmit or process datasets.  Interoperability means essentially that a user accessing the world through a local / institutional / national portal sees not only local datasets and software but also all datasets and software known to RDA organisations and members as if they were local.  This is achieved through the use of metadata characterising the objects (datasets, software, users, computing resources) and techniques to match and map those descriptions leading to generation of convertors for the underlying data instances.  Interoperation among many metadata models preserves the richness of the original schemes but uses techniques to establish relationships between attributes in the different schemes (matching and mapping).  



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