hawaii pacific gis conference 2012: gis in education: k-12 and university - hawaii geospatial data...

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" 1

Where does your digital data go?

0 10 15 20+

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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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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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Centralized integrative capability to store and manage access to (terabytes) research datasets

Hawaii Geospatial Data Repository Goal:

Users: University of Hawaii

research teams Broad statewide

research community

Objectives:

Collect, store and manage access to data

Utilize user portals

Utilize and link to High Performance Computing

Discovery, manipulation, fusion and visualization

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Geospatial Information and Mass Storage

High Performance Computing

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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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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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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

• Automated QA/QC • Extension and Outreach

Stratified User Accounts: -Data Manager -Data Uploader -Public Viewer

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Scientific Data Management – spreadsheet upload/download

ESRI Web Mapping Services and customized apps

Outreach through virtual tours

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Scientific Data Management

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User Requirements Anyone can download data (caveat: select

persons for sensitive information)

Data retrieval can be restricted if necessary

Data can be downloaded in any format requested

Downloaded data will include metadata

Downloaded data will be of best available 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)

Select persons can upload data Easy to use by non-technical people CSV format can be uploaded Data is stored in a secure location Data is controlled for quality (QC) Erroneous data is flagged to be corrected Data can be corrected at time of input Metadata can be created-on-the-fly

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ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED APPLICATIONS FOR OUTREACH - Web Mapping Services

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ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED APPLICATIONS FOR OUTREACH - Integration of Virtual Tours

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Engaging User Participation through Cross-Cutting Projects

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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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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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• 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)

Small Scale Repository Challenges

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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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Acknowledgments: Hawaii EPSCoR Staff, Grad Students, Researchers and

Collaborators: • Kohei Miyagi

• Lisa Canale • Michael Best • Chris Nishioka • Nick Turner • Marie VanZandt • Joanna Wu • Michael Nullet • Tom Giambelluca

• John Burns • Jo-Ann Leong • Jim Beets • Gwen Jacobs • David Lassner • Misaki Takabayashi • Redlands Institute

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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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