LTER Information Management

Scientific data is without value if it is unreliable or erroneous. Quality assurance (QA) procedures are applied after data has already been collected to provide assessments of data quality, indicating whether the data are of high quality, or contain elements of questionable reliability. Quality control (QC) procedures focus on the data collection and processing steps and minimizing the errors at each level. A cautionary note is that although QA techniques allow the identification of unusual data (outliers), which may result from a variety of errors, not all unusual data are erroneous. Indeed, many major scientific discoveries were predicated upon "unusual" data that initially were considered to be erroneous, but that upon further investigation turned out to be caused by novel or unexpected phenomena. Minimal quality assurance checking typically consists of checking the magnitude of observed variables against expected ranges. For categorical variables, recorded codes can be checked against allowed codes. However, much more sophisticated checks are possible. An excellent guide to QA/QC is available from the Data and Information Management for Ecological Research workshop. One of the many fine presentations on QA/QC made by Kristin Vanderbilt is available HERE.