Managing Sustainability Solutions
Initiative (SSI) data
Kate Beard, Steve Cousins
University of Maine
NERACOOS/NECOSP Data Management
Workshop, Sept. 26, 2012
SSI data management project
objectives and research approach
• Identify data needs and types of data used within the
portfolio of SSI projects
• Develop framework for data search, access, archiving
• Provide access to computational resources
• Support data documentation and curation
• Provide a foundation for linking knowledge and data
through ontologies
• Enhance and complement the research portfolio
Team: Beard, Chawathe, Colgan, Jain, McGill, and Segee (University of
Maine, University of Southern Maine)
Activities: SSI Data Inventory
• Catalog the breadth and depth of SSI data holdings
– Thematic variables: urbanization, forestry, climate change
– Spatial and temporal scales
– Data type
• Identify data management tools and protocols
– Metadata development
– Data access and sharing within teams and among
stakeholders
– Archiving and storage of data
– Restrictions on data access and sharing (IRB)
Data Survey Findings
• Data types collected include
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Geospatial- GIS layers and imagery
Surveys and survey data (social surveys)
Model runs
Time series - geochemical, hydrological, land use observations
• Data management methods and needs varied broadly across
teams, but some common themes were:
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Metadata standards for non-GIS datasets are largely unknown
Limited time and expertise for data documentation
Graduate students as keepers of data-poses challenges to continuity
Use of cloud computing and access to a common server is not well
understood
Project Data Server
• Housed at UM
• Accessible statewide
• Redmine Front End
• Dspace: Data Storage
and Metadata
management for
Communities
• GeoServer-Post-GIS For
Handling Spatial Data
Data Management Successes
• DSpace has worked as a general repository
although limited in search capability.
• Data (e.g. tax parcel information) is shared
and made generally accessible to all project
teams through DSpace.
Challenges and Opportunities
• Share data and economize in data collection
and reuse
• Contextualize stakeholder surveys
• Link stakeholder views within and across
project findings
• Link findings and develop insights across
projects
Summary points
• So far we have not been able to identify any
repository framework that can handle the
level of data diversity represented in SSI.
• Metadata resolves to least common
denominator: Dublin Core
NSF- DIBBS: Data infrastructure Building
Blocks
Challenges of this scientific generation: how to develop, implement and support
the new methods, management structures and technologies to store and manage
the diversity, size, and complexity of current and future data sets and data
streams.
Planning awards aimed at further developing disciplinary and interdisciplinary
communities' understanding of their data storage and management
requirements with the goal of developing an initial prototype. Any activity that
brings the community together to address common problems, further refine
requirements and avoid unnecessary and wasteful duplication of resources and
efforts will be eligible for funding.