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.
Make no mistake, it will cost you
Below are some common mistakes people make when designing a study that cost them time and money and may prevent their data from being usable.
- Site characterization: Not enough is known about the site, its variability, or other influential environmental factors that guide data interpretation
- Sensor location: Sensors are installed in a location that doesn’t address the goals of the study (i.e., in soils, both the geographic location of the sensors and the location in the soil profile must be applicable to the research question)
- Sensor installation: Sensors are not installed correctly, causing inaccurate readings
- Data collection: Sensors and logger are not protected, and data are not checked regularly to maintain a continuous and accurate data record
- Data dissemination: Data cannot be understood or replicated by other scientists
When designing a study, use the following best practices to simplify data collection and avoid oversights that keep data from being usable and ultimately, publishable.