Panel 1 Data Life Cycle Management

Panel Questions:

• Which phases of the data life cycle are managed by your repository?
• How do data management requirements differ across the data life cycle?
• What systems do you use to support the data life cycle?
• Can you generalize the mechanisms used to migrate data between different phases of the data life cycle?

Mark McFarland, Texas Digital Repository {PowerPoint}

• TDL has 19 member institutions and use DSpace/Manakin.
• currently moving contents from inside the TDL to a distributed datastore using iRODS.
• still to build a federated discovery layer
• are only just starting to deal with datasets
• collaboration with Mexican research infrastructure

Erin O'Meara, electronic records archivist, Carolina Digital Repository [PowerPoint]

• mission to ensure UNC digital scholarly output is safe and accessible far into the future
• use Fedora as middleware and iRODS for data grid
• content model uses PREMIS/METS/MODS
• custodial shift after objects are deposited
• they apply an OAIS model throughout the architecture
• noted that the data management effort increases during SIP preparation
• their challenge is to develop an approach to active at-risk data - potential approach is precustodial intervention
• CDR Demo

Stacy Kowalczyk, Indiana University, Scholarly Data Services [PowerPoint]

• have a new "Data Publishing service" combining their IUScholarWorks institutional repository and existing Scholarly Data System (was called Massive Data Storage System - MDSS)
• provide data management support and data access
• data will have a persistent URL so it can be linked to publications
• Preservation – bit level
• Use Dspace and for small datasets DSpace bitstreams - for large datasets DSpace records link to stable URLs in MDSS
• facilitate the process for submitters via the DSpace Configurable Submission system
• facilitate the data collection manager’s process via steps in the DSpace workflow system
• engage researchers during projects not at the end
• use immediate benefits to drive automatic capture and 'volunteering’ of metadata
• reduce costs by re-engineering curation processes to leverage this rich metadata and volunteered effort
• Diagram of Data Curation Lifecycle Elements

Cynthia Ghering, Michigan State University [Paper]

• very decentralised environment
• there is NO institutional repository
• Primary Investigator is responsible for curation of their data (chair and dean have oversight)
• they have a digital curation planning project to support investigators

Michael Wright, National Science Digital Library [Paper]

• michael wright was unable to present
• NSDL since 2008 is now a network of repositories for science education materials and not research materials and they have actually de-accessioned much of the research material