Solving the Global Change Puzzle A U.S. Strategy for Managing Data and Information. (1991) / Chapter Skim
Currently Skimming:
A Vision of a National Information System for Global Change
A Vision of a National Information System for Global Change
Pages 38-48
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From page 38... ...
for global change built on existing systems, so that it can take full advantage of the strengths of existing centers, datasets, software, and expertise. Our approach does not require extensive reformatting or refiling of data nor the replacement of existing systems; rather we describe enhancements, methods for increasing the scientific content of the datasets and the overview of the system's operation, development of coordination and data exchange procedures, and improved user services.
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2. Quality control and analysis level.
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An example of a multifunction center is the Carbon Dioxide Information Analysis Center (CDIAC) , which includes data gathering, quality control, documentation, preservation, and distribution of data.
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• Supply data and metadata to the quality control level. • Respond to queries and requests for more information or more detailed data either from the higher nodes or from users.
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• Aid in the development of user interfaces and browse capabilities. • Establish feedback loops with scientists to ensure quality of the datasets.
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By incorporating existing components directly, the system gains immediate access to a tremendous amount of data and expertise. This in turn builds stronger ties between researchers and existing data centers, develops a national center for coordination, and strengthens mechanisms for acquiring and assembling the required datasets.
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An NIS user should not have to know about the different individual databases, data centers, agencies, and computers. Software and hardware developments can concentrate on defining protocols and standards (being careful not to make them limiting)
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2. Scientific Involvement: • Establish procedures, funding, and incentives for researchers to work with data centers in exercising datasets, generating new products, updating quality assessments, and influencing the data center.
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The suggested structure requires a national center for coordinating die centers, which would allow data to flow from one center to another and then to the user. Such a coordinating center must be able to work effectively across national agencies, a tricky issue that we bring up but leave open.
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3. Establish staff scientist positions at the data centers.
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Here again, joint projects involving data center personnel and scientific users (rather than a simple service from the data center to the user) can ensure interested, motivated, and knowledgeable support personnel.
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