Managing Data from Multiple Disciplines, Scales, and Sites To Support Synthesis and Modeling

Olson, Richard J.; Briggs, John M.; Porter, John H.; Mah, G. R.; Stafford, Susan G.

doi:10.1016/s0034-4257(99)00060-7

Cited by 12 publications

(5 citation statements)

References 4 publications

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“…The congenial feedback loop between PIs, data specialists, and surveyors was essential to maintaining a high quality, consistent, and complete data collection. Managers of previous large-scale field experiments have (Newcomer et al, 2001), flexible information systems, and scientific involvement in data management (Strebel, et al, 1990;Olson et al, 1999). Experience from CLPX suggests that the direct field involvement of professional data specialists, in close collaboration with scientific investigators, has resulted in a higher quality, more complete data set than would otherwise have been possible.…”

Section: Data Management In the Fieldmentioning

confidence: 99%

“…Investigators and data managers are beginning to realize that they should consider long-term preservation needs early in the data acquisition process, and that it is inappropriate to manage archival and operational data independently (National Research Council, 1995Olson et al, 1999;Newcomer et al, 2001). Nevertheless, audiences and applications for a given data set change over time, and this dictates an evolutionary approach to managing a data set.…”

Section: Some Guiding Data Management Principlesmentioning

confidence: 99%

“…Data sets should outlive any particular field study or analysis, but data preservation is not simple, and as the overall data volumes and complexities increase, so do the challenges to data management (Duerr et al, 2004). Consequently, there is increasing national and international guidance on scientific data management in general (CCSDS, 2002;CCSP, 2003;National Research Council, 2003 and specifically in support of large field experiments (Strebel et al, 1990(Strebel et al, , 1998National Research Council, 1995;Olson et al, 1999;Newcomer et al, 2001). The guidance tends to be in the form of guidelines, techniques, frameworks, and general principles, which we consciously applied during CLPX.…”

Section: Introductionmentioning

confidence: 99%

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Data management for the Cold Land Processes Experiment: improving hydrological science

Parsons

Brodzik

Rutter

2004

Hydrological Processes

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Abstract:The Cold Land Processes Field Experiment (CLPX) produced 37 data sets totalling about one terabyte in volume. This presented a considerable challenge for data managers. Two unique aspects of the CLPX data management process include participation of data specialists in field data collection and identification of one person, the 'data wrangler', to coordinate the acquisition of the entire collection of the experiment data. These unique aspects increased the quality and completeness of the overall CLPX collection. Data managers were guided by basic data management principles from the literature, including consideration of potential future users and applications and an imperative to keep data as simple and as flexible as possible. These principles guided data management decisions about data formats, data integration, data presentation, and documentation. This principle-based approach improved the usability and long-term viability of CLPX data. It is recommended that data managers be involved early in the planning of future large-scale field experiments and that data managers develop appropriate principle-based rubrics for guidance through persistent data-management challenges.

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Section: Data Management In the Fieldmentioning

confidence: 99%

Section: Some Guiding Data Management Principlesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Data management for the Cold Land Processes Experiment: improving hydrological science

Parsons

Brodzik

Rutter

2004

Hydrological Processes

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“…This means that data stored only in this manner may also change over time or vanish. Scientific research requires a data source that is stable over time and can be referenced so that others can repeat the work [Poumay, 1998]. The interdisciplinary nature and short time period of many field studies makes it advantageous to store the data from different disciplines in an integrated and cohesive form.…”

Section: Publishing the Boreas Datamentioning

confidence: 99%

“…The weekly meetings exposed investigators to other measurements being made, provoked discussions that often planted seeds for future collaboration, and served to maintain focus on larger, projectwide issues. These meetings also helped the project staff gain a better understanding of the sometimes unique perspective of the various science disciplines, an important aspect of data management in multidisciplinary science [Olson et al, 1999;National Research Council, 1995].…”

Section: Boreas Study Executionmentioning

confidence: 99%

Managing and supporting large integrated and interdisciplinary field studies: The BOREAS example

Newcomer¹,

Huemmrich²,

Landis³

et al. 2001

J. Geophys. Res.

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Abstract. Large integrated and interdisciplinary field studies, such as the Boreal Ecosystem-Atmosphere Study (BOREAS), are conducted to refine our understanding of the interactions between the land surface and the atmosphere. Viewed as a case study, the BOREAS research objectives and final data set exemplify the complex nature and requirements of earth systems science research. The management and data system activities required to execute the study also echo this complexity. Rather than several research teams providing the needed management and data support, BOREAS management used a dedicated project staff to handle these functions. As the study progressed, the project staff transitioned from support of logistics and study management to information system operation and data publication, drawing upon the background knowledge gained from the earlier stages of the project. Data publication involves the creation and distribution of quality-checked and documented data with all ancillary information required to make it useful to someone unfamiliar with the study. We assert that the success of large integrated and interdisciplinary field studies depends upon having a dedicated staff. This staff focuses on the overall goals of the study throughout all phases of the effort: contributing to project planning, logistics, management, and data collection efforts; distributing, quality checking, and integrating the diverse data sets; working with the science teams to develop standardized data set documentation; integrating the diverse data and documentation for archiving; and publishing the data for long-term use by the larger scientific community. In this paper, the different phases of BOREAS are discussed, and the contributions that the dedicated staff made are examined. The value of spending resources on a centralized staff for project support and data publication activities is also examined. IntroductionEarth systems science (ESS) attempts to understand the complex interactions of Earth's climate using data from a wide variety of science disciplines. Field studies are an important element of ESS: They help to develop an understanding of important processes and provide a baseline data set for future climate studies of specific regions. Managing a large field study requires a great deal of planning and organization, and the large amount of collected data must also be handled and archived for ongoing and future analyses.The , 1995, 1997a] is a useful case study that illustrates the problems and challenges of a large interdisciplinary field study. In order to create a complete data set that characterizes the ecosystem-atmosphere interactions studied in BOREAS and to preserve this data set for future interdisciplinary modeling and analysis work, resources needed to be allocated for the central storage and integration of the data. The BOREAS Information System (BORIS) integrated and documented the BOREAS data, completing its task with the release of 266 fully documented data sets to an on-line archive and a large subset of th...

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The EOS Prototype Validation Exercise (PROVE) at Jornada

Privette

Asner

Conel

et al. 2000

Remote Sensing of Environment

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Managing Data from Multiple Disciplines, Scales, and Sites To Support Synthesis and Modeling

Cited by 12 publications

References 4 publications

Data management for the Cold Land Processes Experiment: improving hydrological science

Data management for the Cold Land Processes Experiment: improving hydrological science

Managing and supporting large integrated and interdisciplinary field studies: The BOREAS example

The EOS Prototype Validation Exercise (PROVE) at Jornada

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