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Data Management
A reliable, efficient and quality assured database management system (DBMS) is fundamental to a program’s ability to use monitoring information effectively to solve environmental problems. For biomonitoring programs in the initial development stage, information management often begins with simple spreadsheet storage of sampling event data (e.g., Excel, Lotus). Eventually, a relational database offers major advantages in terms of efficiency of multi-user data access and editing, quality control, integration with spatial data, and web-based access to data. A proper system for aggregating data and performing the necessary quality control checks is essential. Furthermore, integration of assessment information from multiple assemblages (fish, macroinvertebrate, algae, etc) can contribute important diagnostic information. For programs that have established numeric biocriteria thresholds, data management includes not only proper stewardship of raw data elements but also proper computation of biological metrics and biocriteria threshold information (i.e., the site biocriteria outcome). State and tribal biomonitoring programs often collect data over years and decades in contrast to research projects that are typically designed to address research questions that can be answered in one to a few years. The value of long-term datasets to states/tribes and other users depends upon well-documented and properly implemented quality assurance protocols that ensure data integrity, and a DBMS that allows efficient and transparent statistical and graphical analysis of the data. A strong geographic information system (GIS) linked to a well-designed relational database moves programs toward a more comprehensive watershed perspective in interpreting monitoring data and improves the ability of biological data to meet the increasing information demands of State and federal programs, responsible parties, and the public.
For additional information on this topics, please visit Perils of Data Management in Developing Biological Indicators: Lessons Learned from Mid-Atlantic Streams.