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Andrews LTER Information Management

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Presentation on theme: "Andrews LTER Information Management"— Presentation transcript:

1 Andrews LTER Information Management http://www.fsl.orst.edu/lter/

2 AND – Andrews LTER ARC – Arctic Tundra LTER BES – Baltimore Ecosystem Study BNZ – Bonanza Creek LTER CAP – Central Arizona-Phoenix LTER CDR – Cedar Creek LTER CWT – Coweeta LTER FCE – Florida Coastal Eeverglades GCE – Georgia Coastal Ecosystem HBR – Hubbard Brook LTER JRN – Jornada Basin KBS – Kellogg Biological Station KNZ – Konza LTER LUQ – Luquillo LTER MCM – McMurdo Dry Valleys NET – LTER Network Office NWT – Niwot Ridge LTER NTL – North Temperate Lakes PAL – Palmer Station PIE – Plum Island Ecosystem SBC – Santa Barbara Coastal SEV – Sevilleta LTER SGS – Shortgrass Steppe VCR – Virginia Coast Reserve LTER - Long Term Ecological Research Network

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5 Biodiversity/Arthropods 2005 1980s 1970s 1990s1960s1950s 1948 US Forest Service Information Management Decomposition/C dynamics Soils/nutrient dynamics Vegetation successionForest-stream interactionsDisturbance/landscape Hydrology/Small watershed/Climate IBP LTER forestry researchecosystem research Time Line

6 H. J. Andrews Experimental Forest Long-Term Ecological Research (LTER) Site Information Management Personnel  Don Henshaw U.S.D.A. Forest Service, Pacific Northwest Research Station  Suzanne Remillard Oregon State University, Forest Science Department  Theresa Valentine U.S.D.A. Forest Service, Pacific Northwest Research Station  Fred Bierlmaier Oregon State University, Forest Science Department

7 Research Study Databases Downloads Downloads ComponentOnline 2005 1999-2001 2002-2004 Climate 5 68 607 Vegetation 23 42450 Hydrology 19 150370 Carbon & Nutrients 23 58338 Biodiversity 19 46275 Soils 18 13227 Disturbance 17 15117 Stream-Forest 5 8 76 Ecophysiology 1 0 5 Total 130 400 2465 Forest Science Data Bank (FSDB) Summary of LTER Online Databases and Usage (July 2005):

8 Information System Development PeriodPlatformMetadata storage Data storage Primary tool 1980’sMainframePaper formsASCIIFortran / SAS Transition period 1988-1992 1990’sLocal Area Network File Server Desktop RDBMS ASCII/ RDBMS SAS / Foxpro Transition period 2000-2004 2000’sDatabase server/ Web Server (UNIX) RDBMS Server/ XML RDBMSSQLServer/ Visual Foxpro

9 Online LTER Component of FSDB  140 databases (metadata and data) Over 500 data tables Includes 25 spatial databases –50 more in preparation (300 coverages / Shape files) Over 100 tables updated annually 10 new databases per year  Additional 40 legacy databases not online

10 Andrews LTER Data Access Policy Data Release  Type I status Data is freely available within 2 years of collection Users must agree to terms of use agreement  Type II status Legal issues –Sensitive resources, personal privacy, copyright laws Data quality assurance issues –Quality too low, new techniques, legacy data Publication issues –Vulnerable PI (grad student or post-doc), long sampling interval

11 User Registration summary

12 Why researchers don’t share data (Vogel, EOS 1998)  No professional credit  Loss of proprietary rights to data  Fear of others publishing data  Costs of data documentation  Lack of proper quality assurance

13 Data Use Agreement  Assures data provider of ethical use of data  Provides citation for data source  Gives protection through disclaimer  Requires notification of data usage  Requests copies of derivative publications  Encourages good scientific citizenship

14 Systematic Approach to Ecological Research Develop Research Objectives Define Statistical Design/Sampling Protocols Design Database, Begin Documentation Collect Data / Do Field Work Capture or Enter Data, Assure Data Quality Analyze and Interpret Data Synthesize Research Results Archive Data and Publish Research Findings on the Web

15 Ecoinformatics Challenges  Ecologists use heterogeneous data from many sources for synthesis  Data volumes increasing exponentially  Requirements for metadata quality/quantity increasing  Expectations for data accessibility increasing

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21 H. J. Andrews Experimental Forest Telemetry System RF Base Station Repeater/Monitoring Repeater Only Key Monitoring Only Cable Connection Radio Link CR10 Data Logger Upper Lookout Met Station Vanilla Leaf Met Station CR23X CR10 Central Met Station CR10 Roswell Ridge Repeater WS 1, 2, 7 Gauging Stations Mack Cr., WS 8 Gauging Stations RF Base Station Primary Met Station T-1 Connection CR10 Andrews HQ Site OSU Network /WWW http://hja.fsl.orst.edu/UPLOPREC.HTM http://hja.fsl.orst.edu/MACK.HTM http://hja.fsl.orst.edu/WS01.HTM http://hja.fsl.orst.edu/precip.htm WS 3, 6, 9, 10 Gauging Stations Blue Ridge Repeater

22 … Two radio repeater Blue River Ridge Radio-equipped data loggers Watershed 1 Radio Links Spread Spectrum Telemetry: Internet Connected Data Loggers Airshed Project on Watershed 1 900 mhz spread spectrum radio Andrews Headquarters Control computer: data logger communication Tower anemometers, sap flow, soil moisture, and other sensors Serial server LAN WWW

23 Increasing Demand for Information Resources  Re-use of long-term study data and other information resources  Synthesis of cross-site data in large-scale collaborative efforts  Real-time data distribution from a network of environmental observatories  Demands for easy discovery and access via the web  Dynamic compilation of resource metadata

24 Data Management to Information Management  More information products to manage: Non-spatial databases Spatial data (GIS coverages and remote sensing images) Publications Analytical Tools: models, software Photographs, Images, etc. Taxonomic Collections Documents (study plans, methods manuals, web documents) Data request/download histories, web logs

25 Publications Analytical Tools Data Sets GIS Coverages Images Information Products Main Catalog Related Objects Search Object Tables People Keywords Species Locations Data Object Tables Remote Sensing Outline of Metadata Database

26 Simplified view of the normalized metadata structure DatabasesPublication Catalog Image Cross-referenceRequest_history Personnel Grants Organization Project Attribute Enumerated domain Methodology Related File Distribution Entity Taxonomic keyword Theme keyword Place keyword Analytical Tool

27 Databases List of all Databases Entity List of all entities (tables) Enum_domain Database-specific and shared codes Taxonomic_classif Shared species codes Place_keyword Shared location codes Attribute Global list of all attributes (database-specific) Study Data: Entities and Attributes Domain_type Domain classification Methodology Shared list of all methods

28 Web Search

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31 Data Catalog on the web

32 Metadata on the web

33 Metadata Standards  LTER (Non-geospatial metadata) Origins in IBP (1970’s) Kellogg workshops (NSF 1984, 1990) Minimum standards (Kirchner et al. 1994) ESA/FLED (Michener et al. 1997)  FGDC (Spatial metadata) Dominant spatial standard  NBII (Non-geospatial and spatial metadata) Biological Data Profile  EML (Ecological Metadata Language) Implemented as modular XML documents http://ecoinformatics.org/

34 Structured Metadata - Webpage creation Distribution - Request tracking - Security Quality Assurance - Data validation - QA Reports - Metadata validation Data Production - Data entry system - Data import/export - Metadata export Metadata is more than just documentation: Generic Tools

35 EML Generation from a Relational Database Management System

36 Data Distribution Entity_Attribute field_order key_field Attribute Entity data_restriction_rule

37 Data Distribution Entity_Attribute field_order key_field Attribute Entity data_restriction_rule

38 Data Distribution Entity_Attribute field_order key_field Attribute Entity data_restriction_rule

39 Spatial Management Strategy  Provide access to spatial information Andrews Forest and surrounding areas (regional context)  Support researcher and student GIS efforts Provide GIS analysis Training on GIS / metadata tools  Provide linkages to scientific data Integrated data catalog  Investigate new technology Internet map server (searches for study sites and data sets) Visualization

40 Study Site Inventory Control area Small watersheds Study locations

41 Internet Mapping Applications

42 LTER Network–Level Data Activities  Network Information System (NIS) All-site personnel directory All-site bibliography Distributed Table of Contents (DTOC) Prototype Site Description Database (SiteDB) Climate/Hydrology Data Projects (ClimDB/HydroDB) Ecological Metadata Language (EML) harvester

43 Review Criteria for LTER Information Management Systems Key points:  Consistent standards across network  Focus on information management system  Focus on functionality  Set the bar high  Compliance levels: shall, should, encouraged  References to policy & best practices documents April 2005

44 DiscoveryAccessUsability Tier 1  Unstructured, online site catalog with minimal metadata  Data discovery through manual searches  Establish data access policy  Data and metadata access requires human intervention  Unstructured, machine- readable metadata and data  Data use requires human intervention Tier 2  Online, enhanced metadata with consistent internal structure  Data discovery through machine search  Automated access to data  Access to site data and metadata does not require human intervention  Structured, comprehensive metadata and data  Data use does not require human intervention Tier 3  Discovery-enabling metadata structured in EML  Data discovery integrated across network  Access-enabling metadata structured in EML  Data access is integrated across network  Complete validated EML  Data analysis is integrated across the network Future Outcome Semantic-based discovery through machine-based searches Data access through a knowledge-based query process Semi-automated knowledge extraction Tiered Trajectory Metadata completeness Metadata structure

45 Lessons and Summary  Increasing informational needs necessitate information management policies and systems  Metadata serves an important role in integrating and locating information products  Research community involvement is critical to success Offer incentives: i.e., data quality assurance, data archiving, data and metadata entry tools, web publishing

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