Hawaii Geospatial Data Repository                Donna M. Delparte, PhDUniversity of Hawaii at Hilo, Geography and Env. St...
Where does your digital data go?0            10       15               20+                                             2
Consequences?•   Data is lost or too costly to retrieve•   Data re-discovery•   Data re-collection•   Data time series inc...
So what?How do you implement advanced cyberinfrastructure     that enables GIScience for researchers?         How do you g...
Hawaii Geospatial Data Repository Goal:            Centralized integrative capability to store and manage                 ...
Geospatial Information and Mass Storage                            High Performance                               Computin...
Survey          Main Types of User Data• Flat files with x, y coordinates   – Spreadsheets, csv, xls   – Sensor data , csv...
User Sophistication• General User Requests: (Consumer)  – Data Storage, Discovery and Mining:     •   Store, query, upload...
Dialog/Discussion/One-to-One Interaction          Must-haves for Users: • Full control of their data    – Easy to use inte...
Scientific Data Management –spreadsheet upload/downloadESRI Web Mapping Services andcustomized appsOutreach through virtua...
Scientific DataManagement                  11
12
User RequirementsSelect persons can upload data                 Anyone can download data (caveat: select     Easy to use ...
ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Web Mapping Services                      ...
ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Integration of Virtual Tours              ...
Engaging User Participationthrough Cross-Cutting Projects                            16
Summary - Engaging Researcher    Participation – What’s Working?• Integrating their requests into the system• Working dire...
Small Scale Repository Challenges• Small staff to customize applications for many  users – training and enabling component...
Small Scale Repository Challenges• Spreadsheet data collection methods• Researchers lack of knowledge of data  management ...
Next Steps for the Hawaii Geospatial           Data Repository• Building user participation and interaction• Increasing co...
Acknowledgments:Hawaii EPSCoR Staff, Grad Students, Researchers and                  Collaborators:•   Kohei Miyagi       ...
off-the-shelf technologies?• No pre-developed commercial product• Agency/research exploration included (incomplete list): ...
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Hawaii Pacific GIS Conference 2012: GIS in Education: K-12 and University - Hawaii Geospatial Data Repository

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Hawaii Pacific GIS Conference 2012: GIS in Education: K-12 and University - Hawaii Geospatial Data Repository

  1. 1. Hawaii Geospatial Data Repository Donna M. Delparte, PhDUniversity of Hawaii at Hilo, Geography and Env. StudiesHIGICC Hawaii Pacific GIS Conference 2012 "Geospatial - Its Everywhere" 1
  2. 2. Where does your digital data go?0 10 15 20+ 2
  3. 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 3
  4. 4. So what?How do you implement advanced cyberinfrastructure that enables GIScience for researchers? How do you get them to use it? 4
  5. 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 communityObjectives: Collect, store and manage access to data Discovery, manipulation, fusion and visualization Utilize user portals Utilize and link to High Performance Computing 5
  6. 6. Geospatial Information and Mass Storage High Performance Computing 6
  7. 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 7
  8. 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 8
  9. 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 9
  10. 10. Scientific Data Management –spreadsheet upload/downloadESRI Web Mapping Services andcustomized appsOutreach through virtual tours 10
  11. 11. Scientific DataManagement 11
  12. 12. 12
  13. 13. User RequirementsSelect 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) 13
  14. 14. ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Web Mapping Services 14
  15. 15. ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Integration of Virtual Tours 15
  16. 16. Engaging User Participationthrough Cross-Cutting Projects 16
  17. 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 17
  18. 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. 18
  19. 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) 19
  20. 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 20
  21. 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 21
  22. 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) 22

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