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Published byMarianna Jennings Modified over 9 years ago
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U.S. Department of the Interior U.S. Geological Survey IDMN Toolshed in Concept and Practice National Workshop on Large Landscape Conservation Dell Long, USGS; Brendan Ward, CBI; Ed Laurent, Griffin Groups October 2014
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Integration Tools
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IDMN Toolshed Objectives Provide guidance for data integration across platform and analysis tools Catalog existing tools and platforms Identify best practices to support tool development and integration Demonstrate lessons learned: ScienceBase and Data Basin integration
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Deliverables – Available Today Toolshed/Catalog Hosted on Griffin Groups Catalogs efforts by LCC developers and partners Tools consist of Web Uis, ArcGIS toolbars, models, and data sharing efforts ScienceBase-Data Basin integration LCC and landscape projects can use ScienceBase repository in Data Basin visualizations/analysis Documented best practices and lessons learned
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Lesson Learned – Integrating Systems Web services makes this possible, not easy Have to find a way to bring geoprocessing to data, not data to app Significant work to retool ScienceBase to support Data Basin needs
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Lesson Learned – Standards Common data & metadata standards and structures not widely adopted Different data representations complicate info transfer Some standards permit variability (WMS) Some popular servers do not properly expose metadata (ArcGIS Server)
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Lesson Learned – Audience Communication is difficult: Data representation Capabilities Lack of standard visual icons to represent concepts Providing workflows for broad range of users and disciplines is challenging Simplicity for non-specialists, advanced features for power users
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Success Stories ScienceBase-Data Basin integration North Central Climate Science Center phenology modeling tools Arctic LCC ISO Metadata workflow Geo Data Portal GAP Viewers Western Landscapes Explorer GPC&O watershed modeling portal
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Continued Efforts Continue cataloging tools Continue improving existing and investigating new tool integrations Determine goals and objectives for an open source toolshed Evaluate potential contributions according to how they help collaborative dataset building, data discovery, secure and public access, and data understanding
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