Data collection: 8 best practices to avoid costly surprises

Data collection: 8 best practices to avoid costly surprises

Every researcher’s goal is to obtain usable field data for the entire duration of a study. A good data set is one a scientist can use to draw conclusions or learn something about the behavior of environmental factors in a particular application. However, as many researchers have painfully discovered, getting good data is not as simple as installing sensors, leaving them in the field, and returning to find an accurate record. Those who don’t plan ahead, check the data often, and troubleshoot regularly often come back to find unpleasant surprises such as unplugged data logger cables, sensor cables damaged by rodents, or worse: that they don’t have enough data to interpret their results. Fortunately, most data collection mishaps are avoidable with quality equipment, some careful forethought, and a small amount of preparation.  In this article, learn:

  • The most common mistakes people make when designing a study
  • Pre-installation steps that save time and money
  • What your preparation plans should include
  • Why site selection can make or break a study
  • Why you should record more metadata, and what metadata is useful
  • The key to higher accuracy
  • Sensor protection
  • Data logging best practices
  • Insider data interpretation tips

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