1. Hawaii Geospatial Data Repository
Donna M. Delparte, PhD
University of Hawaii at Hilo, Geography and Env. Studies
HIGICC Hawaii Pacific GIS Conference 2012 "Geospatial - It's Everywhere"
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2. Where does your digital data go?
0 10 15 20+
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3. Consequences?
• Data is lost or too costly to retrieve
• Data re-discovery
• Data re-collection
• Data time series incomplete
• Data duplication
• Data lacks metadata preventing creation of derived
products
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4. So what?
How do you implement advanced cyberinfrastructure
that enables GIScience for researchers?
How do you get them to use it?
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5. Hawaii Geospatial Data Repository Goal:
Centralized integrative capability to store and manage
access to (terabytes) research datasets
University of Hawaii Broad statewide
Users: research teams research community
Objectives:
Collect, store and manage
access to data Discovery, manipulation, fusion and
visualization
Utilize user portals
Utilize and link to High Performance
Computing
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6. Geospatial Information and Mass Storage
High Performance
Computing 6

7. Survey
Main Types of User Data
• Flat files with x, y coordinates
– Spreadsheets, csv, xls
– Sensor data , csv
• GIS Data Layers
– Geodatabases, shapefiles
• Other
– LiDAR
– Imagery
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8. User Sophistication
• General User Requests: (Consumer)
– Data Storage, Discovery and Mining:
• Store, query, upload and download and sharing
• Visualize data overlays on maps and graphing /charting options
• Metadata
• QA/QC
• Advanced User Requests: (Producer)
– All of the above plus
• Webservices, HPC, WPS
• Customized applications
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9. Dialog/Discussion/One-to-One Interaction
Must-haves for Users:
• Full control of their data
– Easy to use interface for uploading/downloading data
• Web-accessible interface
• Select persons can upload data
• Anyone can download data (caveat: select persons for sensitive
information)
• Access to other collaborators data (who is collecting what
data and where?)
– Displaying their data as overlapped with other datasets in the
same location
Stratified User Accounts:
• Automated QA/QC -Data Manager
-Data Uploader
• Extension and Outreach -Public Viewer
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10. Scientific Data Management –
spreadsheet upload/download
ESRI Web Mapping Services and
customized apps
Outreach through virtual tours
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11. Scientific Data
Management
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13. User Requirements
Select persons can upload data Anyone can download data (caveat: select
Easy to use by non-technical people persons for sensitive information)
CSV format can be uploaded  Data retrieval can be restricted if
Data is stored in a secure location necessary
Data is controlled for quality (QC)  Data can be downloaded in any format
Erroneous data is flagged to be requested
corrected  Downloaded data will include metadata
Data can be corrected at time of input
 Downloaded data will be of best available
Metadata can be created-on-the-fly
quality (QA)
 Data is selectable such that a subset may
be downloaded
 Data will be downloadable from multiple
EPSCoR projects at the same time
 Data will be downloadable from multiple
projects at the same time – EPSCoR and
outside research stations (NOAA buoy)
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14. ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED
APPLICATIONS FOR OUTREACH - Web Mapping Services
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15. ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED
APPLICATIONS FOR OUTREACH - Integration of Virtual Tours
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16. Engaging User Participation
through Cross-Cutting Projects
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17. Summary - Engaging Researcher
Participation – What’s Working?
• Integrating their requests into the system
• Working directly with researchers to enable
their role as data managers / custodians
through the web interface
• Opportunities of collaboration
• Attractive outreach and extension tools
• NSF data management plans
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18. Small Scale Repository Challenges
• Small staff to customize applications for many
users – training and enabling component
• Which software utilities?
• Metadata entry and crawling
• Implementing data standards and models
• Are we re-inventing the wheel? Many EPSCoR
institutions are struggling with the same issues –
– coming up with different solutions.
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19. Small Scale Repository Challenges
• Spreadsheet data collection methods
• Researchers lack of knowledge of data
management standards and databases in their
fields (or too many choices)
• Metadata – varied
• Standards – difficult to match datasets
(regional bias)
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20. Next Steps for the Hawaii Geospatial
Data Repository
• Building user participation and interaction
• Increasing collaborations with other Statewide
and National Initiatives
• Accessing geoprocessing (HPC) capabilities
• Metadata search tools
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21. Acknowledgments:
Hawaii EPSCoR Staff, Grad Students, Researchers and
Collaborators:
• Kohei Miyagi • John Burns
• Lisa Canale • Jo-Ann Leong
• Michael Best • Jim Beets
• Chris Nishioka • Gwen Jacobs
• Nick Turner • David Lassner
• Marie VanZandt • Misaki Takabayashi
• Joanna Wu • Redlands Institute
• Michael Nullet
• Tom Giambelluca
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22. off-the-shelf technologies?
• No pre-developed commercial product
• Agency/research exploration included (incomplete list):
 DataONE
 NEON
 Comparative Analysis of Marine Ecosystem Organization (CAMEO)
 DNA barcoding project at UHH
 Geographic Information Network of Alaska (GINA)
 Hierarchical Data Format (HDF 5)
 Intelesense - Inteleview platform
 Long-Term Ecological Research Network Office (LTER-LNO)
 National Centers for Coastal Ocean Science (NOAA NCCOS)
 Pacific Basin Information Node (PBIN) - gone
 Scientific Data Management Center - Lawrence Berkeley National Lab
(SDMC-LBNL)
 Virtual Observatory and Ecological Informatics System (VOEIS)
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