View
110
Download
3
Tags:
Embed Size (px)
DESCRIPTION
Citation preview
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+
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
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
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
• Automated QA/QC • Extension and Outreach
Stratified User Accounts: -Data Manager -Data Uploader -Public Viewer
9
Scientific Data Management – spreadsheet upload/download
ESRI Web Mapping Services and customized apps
Outreach through virtual tours
10
Scientific Data Management
11
12
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
13
ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED APPLICATIONS FOR OUTREACH - Web Mapping Services
14
ENGAGING RESEARCHER PARTICIPATION THROUGH CUSTOMIZED APPLICATIONS FOR OUTREACH - Integration of Virtual Tours
15
Engaging User Participation through 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
• 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
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
• 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
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