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dc.contributorEnvironmental and Water Quality Operational Studies (U.S.)-
dc.contributor.authorFarrell, Michael P.-
dc.contributor.authorMagoun, A. Dale.-
dc.contributor.authorDaniels, Karen.-
dc.contributor.authorPennington, C. H.-
dc.contributor.authorStrand, R. H.-
dc.descriptionMiscellaneous Paper-
dc.descriptionAbstract: To expedite the research data management system (RDMS) required for complex and large-scale ecological field studies being done at the U.S. Army Engineer Waterways Experiment Station (WES), a graphical display system was developed. The Statistical Analysis System (SAS) provides the framework for making open-ended RDMS practical and cost-effective. PROC VIVIPLOT is the first of a series of SAS procedures that will produce copy-ready figures with some independence as to choice of plotter. The system has been developed by the McDonnell Douglas Automation Company (MCAUTO) at the request of personnel at WES. The use of PROC VIVIPLOT in the MCAUTO environment, including plotting characteristics and cost, are discussed. The SAS procedure VIVIPLOT produces an Intermediate Plot File (IPF)-(MCAUTO) from IPF system routines that generate a data file which is postprocessed to produce a file to drive Calcomp (748, 663, 960), Xynetics and Houston pen and ink plots, Gould 4800 and Varian electrostatic plots, and displays Computek and Tektronix storage tubes. Since the capabilities and syntax of PROC VIVIPLOT are nearly identical to PROC PLOT, line plots from PLOT can be easily converted to copy-ready figures using VIVIPLOT. Several enhancements to the capabilities of PROC PLOT have been made in PROC VIVIPLOT and include multiple vertical and horizontal axis labeling, reserved area position and labeling, and connected lines from data vectors in SAS data sets. In studies such as the Environmental and Water Quality Operational Studies (EWQOS) that utilize multiple data bases composed of hierarchical file structures, there is a high probability that errors may be perpetuated into summary reports unless some form of quality assurance is integrated into the research data base management program. In studies that substitute numeric codes for character variable values, this problem of error propagation is even more acute. This report addresses the problem of error propagation in those studies employing a coding scheme to represent longer alphanumeric values. Several approaches are available that minimize errors in coding variables. Numeric codes, "smart codes," with embedded information allocated to positions within the value codes are widely used but unacceptable for variables with many values and/or many levels of classification. "Nonsense'' codes, or codes without embedded information, however, efficiently circumvent the problems associated with smart codes. Using nonsense codes, alphanumeric variable values are assigned a sequential numeric code as new values are encountered in the data base, irrespective of the position of the value in the classification scheme for that variable. With the use of nonsense codes, the management approach is open-ended and does not require a knowledge of the number of potential classification levels for the variables. In addition, experience with several large environmental data bases indicates that coding errors appear to be less frequent using nonsense codes than in those studies in which a smart code approach was used. This is Report 7 of the series "Aquatic Habitat Studies on the Lower Mississippi River, River Mile 480 to 530."-
dc.publisherEnvironmental Laboratory (U.S.)-
dc.publisherEngineer Research and Development Center (U.S.)-
dc.rightsApproved for public release; distribution is unlimited.-
dc.sourceThis Digital Resource was created from scans of the Print Resource-
dc.subjectAquatic ecology-
dc.subjectAquatic biology-
dc.subjectMississippi River-
dc.subjectComputer programming management-
dc.subjectData management-
dc.subjectData processing-
dc.subjectWater quality-
dc.subjectEnvironmental engineering-
dc.titleAquatic habitat studies on the Lower Mississippi River, river mile 480 to 530. Report 7, Management of ecological data in large river ecosystems-
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