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Judy Cushing, Nalini Nadkarni Mike Finch, Anne Fiala Youngmi Kim, Aaron Crosland and others

The Canopy Database Project Tools for Research & Information Integration http://canopy . evergreen.edu. Judy Cushing, Nalini Nadkarni Mike Finch, Anne Fiala Youngmi Kim, Aaron Crosland and others The Evergreen State College.

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Judy Cushing, Nalini Nadkarni Mike Finch, Anne Fiala Youngmi Kim, Aaron Crosland and others

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  1. The Canopy Database ProjectTools for Research & Information Integrationhttp://canopy.evergreen.edu Judy Cushing, Nalini Nadkarni Mike Finch, Anne Fiala Youngmi Kim, Aaron Crosland and others The Evergreen State College NSF CISE and BIO 04-xxx, 03-xxx, 01-31952, 01-9309 99-75510, 9630316, 93-07771

  2. Canopy DB Vision PI & IM use ofdatabase technology & components can ease metadata provision, data validation and archiving, and data mining for synthesisBUT Researchers aren’t programmers. The technology must be easy to use & increase research productivity.

  3. Stem Model Branch Foliage Model Foliage Start, stop Foliage inner, mid, outer Foliage length and width Upright stepped Upright cone, Upright linear, Upright cylinder, cylinder, Height, DBH Height only Height, DBH Multiple girth measures Branch Length Measurement Branch length Branch length perpendicular along branch to stem The Underlying IdeaDatabase Design with Domain Specific Components • Validate generated databases with rules • e.g., Stem: • depends onstudy area, plot • includes species table Capitalize on core components for tools Visualization, Metadata Provision, Data Acquisition & Validation, research protocol, statistical analysis….

  4. Approach • Pathfinder Projects • Ecologists design & carry out field research at several sites. • Find research, archiving and data mining bottlenecks. • Determine [spatial ] data structures. • Reverse-engineer components. • Database Tools for the Field Ecologist • Design field databases – DataBank. • Visualize data using those databases – CanopyView. • Lab-specific metadata acquisition. • Hand-held (palm pilot) field data acquisition. • Reality-check with LTER Information Managers. • Web Accessible Research Reference -- BCD

  5. Study Design Field Work Data Entry & Verif’n Data Analysis Data Sharing (w/in Group) Journal Pub Data Archive Data Mining Research BottlenecksDatabase Technology for Researcher Productivity Metadata Generation • Archive in Lab(common types) DataVisualization Statistical analysis Data validation (against metadata) Data and metadata capture • Database and Protocol Design • Research Reference Tools Information Synthesis

  6. Recent Work • Finding & maintaining the components • Ecology Theory – spatial categorization of the Canopy • Template Editor • Refine existing software • Template-embedded semantic metadata, carried forward… • DataBank now stand alone • Generate Excel, as well as Access and other RDBMS • New visualizations • Collaborate with other eco-informatics projects • Closer integration with EML, Morpho • LTER IM Collaboration – Kaplan, Melendez-Colom, Ramsey, Vanderbilt, Walsh. • Outreach to computer science community & agencies • NSF/USGS/NASA/EPA/ – JIIS special issue – dg.o

  7. Future Work • Carry out collaborative field studies • Develop and test synthesis hypotheses • Develop theoretical constructs on canopy structure-function • Develop statistical protocols that guide study design • Create and enhance informatics tools • Build theory-based components • Build better UIs, data import & validation, more visualization • Build parameterized queries for standard statistical scripts • Develop better metadata capture and evolution • Develop or adapt warehouse & interface to other tools • Field test tools from the get-go

  8. How DataBank WorksMike Finch

  9. Study Design Field Work Data Entry & Verif’n Data Analysis Data Sharing (w/in Group) Journal Pub Data Archive Data Mining Research BottlenecksDatabase Technology & Research Productivity Gain EML Generation CanopyView • DataBank Database Generator • BCD

  10. Conclusions • Database design is a complex web app • Sociological aspects are important • Proprietary data • Technology adoption • Integrative ecology new • Defining intuitive & adequate set of templates is hard • Spatial is special…. • Visualization is cool….

  11. Database Design • schema element • dependencies • entities • observations • attributes DataBank Workflow Database Components shopping cart DB design Empty DB • convert • SQL • MSSQL • MSAccess

  12. DataBank Software Architecture Internet Browser IE 5+ Netscape 6+ Web Server (Apache) Access Field DB Enhydra (Middleware) • Databank Backend • (Java) Viz Tookkit JDK DB SQL Server

  13. Canopy DataBank • What is it • End-user database design with components (aka templates) • Variable & table level metadata inherent • Study-level metadata available from the BCD • Technology • HTML, Java, Enhydra, SQLServer, Access, JTK • Aim to produce XML/EML for exchange and archive • Status • Some templates (mostly spatial tree structure) • About 5 field studies • Some visualization

  14. DataBank Architecture (workflow) template.xml descr.xml pic.gif bigpc.gif shopping cart DB design Empty DB ‘TEOF’ internal object representation • ‘TDM’ convert • SQL • MSSQL • MSAccess • schema element • dependencies • entities • observation • attributes

  15. Next Steps • XML/EML for data exchange • Outreach to CS community • VLDB Panel on Ecosystem Informatics (August) • NSF BDEI PI’s Meetings & Forum (May, Nov) • Further define & support spatial data structures -- additional collaborator(s)? • Visualization (!!!)

  16. Discussion Are we on the “right track” with visualization? What off the shelf viz. tools are available? Who might consult with us on visualization, How about spatial scaling? How to refine our spatial categorization scheme? What collaborators (data sets) should we seek? How is modeling linked to visualization? Comments about DataBank?

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