Finding the Forest in the Trees The Challenge of Combining Diverse Environmental Data (1995) / Chapter Skim
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8 Interfacing Diverse Environmental Data-Issues and Recommendations
8 Interfacing Diverse Environmental Data-Issues and Recommendations
Pages 81-118
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From page 81... ...
Real-world illustrations of problems and solutions relevant to data interfacing are used as examples throughout this chapter. Some of these are drawn from circumstances or applications that do not directly involve interfacing geophysical and ecological data.
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to la a data collection raw data processing analysis & summarization FIGURE 8.1 Generali~ |
From page 83... ...
Effective solutions must address and accommodate all of these relevant contexts. Interfacing efforts can be confounded by a variety of obstacles (see Mathews, 1983; Henderson-Sellers, 1990~.
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From page 84... ...
For example, in the FIFE study, data on canopy-leaf-area index, green-leaf weight, dead-leaf weight, and litter weight had to be collected by hand from relatively small study plots. This could not be avoided, even though the study was designed from the outset to integrate ecological and geophysical data over larger areas.
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From page 85... ...
Long-term time series of ecological data are relatively rare. This may make it difficult or impossible to create integrated data sets that focus on long-term changes in coupled ecological-geophysical systems.
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For example, environmentally induced changes in ecological systems often occur with time lags of varying lengths (e.g., Cole, 1985; Lewin, 1985; Pennington, 1986; Davis, 1989; Loehle et al., 1990; Steele, 1991~. This can make it difficult to determine which ecological and geophysical data should most appropriately be interfaced.
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. This means that scientists engaged in data interfacing efforts must exercise extreme care when attempting to integrate data across different scales.
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The methods used to accommodate or match inherent scales in different data types in any attempts to facilitate modeling and analysis should be carefully evaluated for their potential to produce artificial patterns and correlations. Preliminary Data Processing and Statistical Uncertainty A wide range of models and data processing algorithms typically are used in the development of ecological and geophysical data sets.
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From page 89... ...
Further, the metadata also should describe and quantify to the extent feasible the statistical uncertainty resulting from each processing step. Planning for studies that involve interfacing should explicitly consider the effects of preliminary processing on the utility of the resultant integrated data setts)
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This can make it impossible or impractical to continue using traditional data management and data interfacing methods. For example, the staff at the Carbon Dioxide Information Analysis Center (CDIAC)
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From page 91... ...
Other scientists use this term to refer to extensive or large areas. The FIFE and NAPAP studies provide a rich variety of examples of how seemingly innocuous data characteristics can bedevil data interfacing efforts.
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From page 92... ...
Creating synoptic ecological data sets that match the broader coverage of geophysical data is time-consuming, costly, and technically demanding. Gathering ecological data over large areas and times is often labor-intensive and difficult.
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In addition, the committee recommends that agencies that perform or support environmental research and assessment generally, and global change research particularly, identifier and define key ecological data sets that do not exist but are important to their mission. A careful review should be made of options for finding, rescuing, or creating these crucial data, and funding should be set aside to implement the most feasible optionts)
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From page 94... ...
As a result of experiences such as these, many recent reports on data management related to global change research have emphasized the central importance of thorough and readily accessible metadata (e.g., NRC, 1991; OSTP, 1991; CEES, 1992~. Providing such metadata can have implications for the design of hardware and software systems to support interfacing (see the subsection "Complex Metadata" in the section "Addressing Barriers Deriving from Information System Considerations," below)
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The data, analytical methods, models, motivating questions, and related hardware and software all are changing and evolving rapidly. The committee heard unequivocal statements from participants in every case study that a critically important condition for successful data management and data integration is the direct and
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The committee urges project scientists and data managers to adopt the view that one of their primary responsibilities is the creation of long-lasting data and information resources for the broad research community. Data management systems and practices, particularly the development of metadata, should be designed to balance the needs of this larger user community with those of project scientists.
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The committee heard in every case study that such organizational interactions were central to the success or failure of data management and data interfacing efforts. As a result, the committee concludes that organizational and technical considerations interact strongly and should be given equal weight in the design and development of data interfacing systems.
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From page 98... ...
By listing data sources as primary authors of data packages once they are published and encouraging users of the data to cite data sources in their publications, the Center tries to use the academic reward system as a motivation for data sources to participate actively ire the preparation of the data packages. Whatever the means used, successful data interfacing depends on surmounting the ingrained handsets arid priorities fostered In research scientists by the existing organizational reward system.
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From page 99... ...
Despite the geophysical science community's greater familiarity with sophisticated hardware and software, however, both communities are relatively unschooled in the information management concepts and system design skills required for successful data interfacing. As a result, the most successful data interfacing efforts in the case studies were those where information management professionals were an integral part of the research team (Kanciruk and Farrell, 1989~.
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Successful broad-scale interfacing efforts require strategies that break down such ingrained attitudes and equalize the perceived status between the two groups. In the NAPAP study, conflicts developed between the data managers and the scientific team.
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The committee recommends that in order to encourage interdisciplinary research and to make data available as quickly as possible to all researchers, specific guidelines be established for when and under what conditions data will be made available to users other than those who collected them. Such guidelines are particularly important when data collectors, data managers, and other users are in different organizations.
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In addition, management must foster productive teamwork between geophysical and ecological scientists, as well as between scientists and information management specialists. The committee recommends that in the planning of any interdisciplinary research program, as much consideration be given to organizational and institutional issues as to technical issues.
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In addition, it would be wise to look closely at the potential synergism between any new ecosystem data and information analysis center and all other existing environmental data centers. ADDRESSING BARRIERS DERIVING FROM INFORMATION SYSTEM CONSIDERATIONS Actual interfacing activities will be carried out primarily by means of computerized hardware and software systems, and interfacing capabilities will depend in large part on their characteristics.
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From page 104... ...
For example, initial data management and interfacing efforts in the FIFE program were based on a traditional engineering approach in which system designers who lacked the requisite scientific expertise gathered input from users, established a set of users' requirements, and then attempted to develop and deliver a completed system without further interaction with the intended users. Not surprisingly, this design approach failed and had to be replaced with one based more directly on active and ongoing interaction between the information management staff and the research scientists.
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From page 105... ...
In the collaborative process, users and information management specialists work In parallel to contribute their knowledge and insight to the design as it develops (adapted from Strebel et al., 1990~. Thus, data integration went smoothly based on watershed models, and well-documented databases were made available to a wide user population in a timely fashion.
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Second are the semantic differences between data from disparate databases. These result from the data themselves, as discussed earlier in this chapter, and include, for example, incompatible scientific naming conventions and fundamental differences in spatial and temporal scale.
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This approach has proved satisfactory for relatively small data sets. However, the committee found widespread concern among data management specialists that the proliferating and ever-larger data sets used in global change research would make this approach unworkable, especially when interfacing different data types.
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FINDING THE FOREST IN THE TREES are interfacing large data sets cannot necessarily depend on embed codes and flags to automatically subdivide, transform, or otherwise operate on the data. Thus, while researchers cannot necessarily depend on automation to solve this data management problem, neither can they be expected to exhaustively examine these detailed metadata manually to evaluate their relevance.
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From page 109... ...
The traditional approach to metadata, In which they are considered as information separate from the data themselves, will not meet the challenges just described. The committee found agreement among a large segment of the data management specialists it consulted that a new conceptual model of metadata is required in which metadata are somehow integral to the data themselves.
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However, when a large user community is simultaneously using, updating, and modifying a considerable number of data sets (as ~ the FIFE study) , stand-alone documentation is not adequate.
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Where stand-alone documentation is not adequate (for large and complex data sets or where multiple users are simultaneously updating and modifying data) , data managers should investigate the feasibility of incorporating an audit trail into the data themselves.
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, each of which incorporates both technical and cultural aspects. Keys 1 and 2 deal with the appropriate use of available information management technology.
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From page 113... ...
On the other hand, failing to use appropriate, up-todate information management technology can impede or even prevent data interfacing efforts. For example, the committee heard of numerous instances of the problems created by researchers' use of spreadsheets, rather than actual database software, for data management functions.
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Both research scientists and information management specialists uniformly stress the importance of collaboration as the best way of dealing with the human element in data interfacing applications. This is particu
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Interactions among data managers and researchers at various sites help exploit historical data in new ways and encourage new proposals focused on intersite analyses. These activities are intended to lead to additional publications and funding of research-positive incentives for data sharing that NSF promotes with supplemental research funding.
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Such tangible demonstrations of ~nterfacing's benefits are vitally important. Successful interfacing efforts require participants to change fundamental aspects of their attitudes and behavior.
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1989. The Casefor Issue-Oriented Information Analysis Centers in Support of the U.S.
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U.S. Global Change Research Program, National Science Foundation, Washington, D.C.
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