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

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

    • 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
    • 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 incomplete• Data duplication• Data lacks metadata preventing creation of derived products 3
    • So what?How do you implement advanced cyberinfrastructure that enables GIScience for researchers? How do you get them to use it? 4
    • 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
    • Geospatial Information and Mass Storage High Performance Computing 6
    • 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
    • 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
    • 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
    • Scientific Data Management –spreadsheet upload/downloadESRI Web Mapping Services andcustomized appsOutreach through virtual tours 10
    • Scientific DataManagement 11
    • 12
    • 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
    • ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Web Mapping Services 14
    • ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZEDAPPLICATIONS FOR OUTREACH - Integration of Virtual Tours 15
    • Engaging User Participationthrough Cross-Cutting Projects 16
    • 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
    • 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
    • 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
    • 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
    • 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
    • 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