program. To ensure that people are properly informed and trained, an institution-wide training program will need to be in place before the SDI is unveiled, and retraining will need to be offered periodically for users to understand the system as it develops. The SDI program will need to implement follow-up metrics to determine how well it is being executed to meet its strategic goals. On the basis of findings gathered with those metrics, there will need to be a process for making adjustments to serve users and fulfill USGS priorities.

A series of organizational and technical considerations are necessary for following the roadmap. It is important that SDI implementation have high priority for USGS leadership. A designated senior SDI staff officer will need support from all levels of leadership—from senior managers to the USGS director—and would need to be given commensurate authority to develop and deploy standards. Implementation of an SDI is a major program for establishing a geospatial base for USGS professional staff and outside users, and it would need to be viewed as such by the Survey. The incentive structure for scientists may need to be modified to reward sharing of spatial data. The USGS will need to consider expanding partnerships of five kinds: strategic partnerships with agencies such as the National Aeronautics and Space Administration, the National Oceanic and Atmospheric Administration, and the National Science Foundation; data partnerships with agencies such as the Census Bureau; standards partnerships; academic partnerships; and technology partnerships with the commercial sector.

Among the technical considerations, supporting the diverse science workflows will require the Survey to evaluate its current information-technology infrastructure to ensure that it is aligned with the USGS Science Strategy. In light of that assessment, the USGS can implement robust enterprise data management, begin the transition to using the Web as a computing platform, and ultimately implement a comprehensive, long-term knowledge-management infrastructure that supports end-to-end spatial data management, including the collection, integration, maintenance, and delivery of multidisciplinary scientific data.

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