Advancing Strategic Science A Spatial Data Infrastructure Roadmap for the U.S. Geological Survey (2012) / Chapter Skim
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5 A Roadmap for Spatial Data Infrastructure Implementation
5 A Roadmap for Spatial Data Infrastructure Implementation
Pages 71-86
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From page 71... ...
This chapter provides a roadmap of both general guidance and specific actions needed for SDI implementation that is based on the committee's findings and its vision for the USGS SDI. A ROADMAP A roadmap can serve as a starting point for planning, developing, and implementing a comprehensive SDI program at the USGS.
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From page 72... ...
72 ADVAnCIng stRAtEgIC sCIEnCE: A sPAtIAl DAtA InFRAstRUCtURE RoADmAP Phase 1: Prepare & Plan Review Best Practices Leadership Assignments Strategic Planning Phase 2: Design, Develop, & Test Process Identification & Definition of Development Standards Software Development Pilot Training Phase 3: Development Program Program Rollout & Refine Follow-up Metrics Organizational Rollout Process Adjustments Foundation for All Phases Leadership · Communication · Cultural Change · Resources IT management · User Priorities · Partnerships Figure 5.1 Roadmap for implementing the USGS spatial data infrastructure. specific tasks for carrying out its vision for the SDI.
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From page 73... ...
-- for best practices and lessons learned at a higher level of detail than those already outlined in this report; determining and defining SDI system requirements on the basis of the six science directions of the USGS Science Strategy and user needs of other agencies, local governments, academe, and the public; determining the organizational structure for the SDI; and identifying goals, establishing timeframes and milestones, and developing performance metrics. Once the initial planning is complete, it will be important to announce a general outline for implementing the SDI program because communication and outreach will play a decisive role in the success of the program.
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From page 74... ...
The relationship between the considerations above and ongoing efforts at data integration and SDI development at the USGS will need to be determined early in the process. The National Map currently forms a central part of the SDI at the USGS, but the USGS also has many other data infrastructures, such as the National Biological Information Infrastructure, and these multiple SDI's would need to be incorporated into the coherent SDI envisioned in this report.
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From page 75... ...
Furthermore, managers involved in implementing the SDI will need to clearly define current and future job responsibilities for their staff so that written job responsibilities reflect and comply with the corporate information policy. A paradigm shift in management practices will need to occur that embraces integrated team achievement and includes incentives and resources to encourage USGS scientists to share data.
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Finally, SDI implementation will require substantial time and effort. Partnerships Partnerships are vital to a successful SDI and can be used to support the USGS Science Strategy.
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From page 77... ...
For example, managers were periodi cally evaluated on their performance in achieving Six Sigma goals, and train ing resources remained available in case a manager required assistance. As a result of the Six Sigma process at Motorola, there was a gradual but dramatic elimination of defects and an increase in product quality, and Motorola eventually became known for its product quality.
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USGS partnerships will be important for creating or augmenting USGS data assets through a collaborative process. Conversely, USGS scientists provide research support for an extensive array of external agencies while maintaining interfaces with state agencies that create statewide data content.
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The government-wide effort to catalog spatial data on-line through Data.gov1 includes a large number of USGS spatial datasets and this effort provides a basis for a future catalog that is fully integrated into the USGS SDI. Online catalogs of USGS data can foster research collaborations with other research communities and ensure that data are used by as many researchers as possible to answer research questions that are important 1http://www.data.gov.
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From page 80... ...
For instance, ground-based field data that are collected by the USGS, USDA, or NSF can easily be used to validate or calibrate data collected from the air or from space, such as data on forest height or speciation observed with radar, light detection and ranging, or spectral imagers. In another example, crustal deformation data collected by NSF's Plate Boundary Observatory can be fused with data collected by NASA through uninhabited aerial vehicle synthetic aperture radar and paleoseismic and seismic data collected by the USGS to improve earthquake understanding and forecasting.
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From page 81... ...
The USGS will need to implement a comprehensive, long-term knowledgemanagement infrastructure that supports end-to-end spatial data management, including the collection, integration, maintenance, and delivery of multidisciplinary scientific data. To carry that out, the USGS would need to · Identify data assets most critical for supporting the Science Strategy and give high priority to making them discoverable and interoperable.
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From page 82... ...
Cloud hosting can also facilitate long-term data preservation, a task that is challenging for universities and government agencies and is critical for conducting longitudinal experiments. Provisioning key core geospatial datasets on the cloud would increase the value of the data and facilitate exploitation by a large scientific community.
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From page 83... ...
Given the multidisciplinary research activities of the USGS, digital data will need to be made available on various scales, archived so that they are discoverable by USGS researchers, and exist in standard formats so that reformatting is kept to a minimum when they are made accessible to outside users. Application Services to Engage and Support Scientific Efforts An SDI will need to create a foundation for a more open approach to application services that would promote greater transparency, easier integration,
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From page 84... ...
into a combined dataset or database or other derived product, such as a map. They can include linking services to identify data that have overlapping geographic coordinates or similar geographic features or characteristics, alignment services that adjust geometric models to improve spatial matching be tween different spatial datasets or images, and filtering services that select data for inclusion or exclusion on the basis of specified constraints and data characteristics.
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From page 85... ...
The Spatial Data Infrastructure as a Workflow Platform Over the last decade, various scientific programs have begun to incorporate workflow methods into their best practices. Most of the programs highlighted in Chapter 3 use workflows to collect raw observation data and make them available to thousands of researchers worldwide through specialized analytical and visualization tools (such as National Center for Atmospheric Research and National Science Foundation collaboratories)
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The committee recognizes that there are many feasible ways of implementing an SDI program successfully, but all will require that an SDI program be properly defined, led, and supported. The committee believes that such an effort can be best framed by the phrase "discover and share for the long term", and it hopes that this phrase can become the mantra for spatial data handling throughout the USGS.
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