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

Integration Tools

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

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

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

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)

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

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

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