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Understanding and Using NEON IS Data Quality Flags

Challenge

Instrumented Systems that collect data (e.g., temperature sensors, anemometers, water quality sondes, etc.) are powerful tools for data collection. However, the data collected by these instruments are not always science-worthy, as there are many factors that can contribute to a corrupted dataset (e.g., wasps nesting in a temperature sensor’s solar shield, ice buildup on an anemometer, or algae growth on an aquatic instrument). While some compromised data may be obvious, other issues are much more subtle, and it can be impossible to distinguish which data are suitable for use in scientific research.

Photo: Battelle NEON Instrumented Systems Data Quality FlagsSolution

NEON Science Team staff go to great lengths to detect, identify, and ‘flag’ issues with instrumented systems data. These ‘quality flags’ indicate a known issue with reported data and, when possible, identify the source of the issue. In order to better understand and use these quality flags, NEON Science Team staff have developed a tutorial (https://www.neonscience.org/resources/learning-hub/tutorials/explore-neon-ais-data) to walk data users through the process of how to find quality flag data, how to interpret the results of the many quality tests that NEON data are subjected to, how to determine if the quality flagged data are suitable for their research objectives, and how to filter out unwanted data from their research. This particular tutorial focuses on water quality data, however, the lessons contained herein are broadly applicable to the suite of NEON instrumented systems data products.

Impact

Scientists and researchers want to know the data they are working with is of the highest quality and worthy of their time and attention. By clearly and thoroughly documenting known quality issues with our instrumented systems data products, NEON assures our data users that our instrumented systems data products are ready to be used for research and other scientific applications. Moreover, by providing a broadly generalizable tutorial to walk our data users through the process of finding and understanding our many quality flags, we support scientific inquiry across a wide range of disciplines in the environmental sciences.

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