As you will see, the space between the points has an effect on how the curve is represented when they are joined. Each sampled point is recorded by your analysis software and contributes to the resulting curve. For example, if your sampling rate is 10 Hz, your software will record a data value 10 times in every second. The regular interval at which the software ‘asks’ the DAQ unit for the voltage of the signal, is known as the sampling rate. The resulting digital signal is sampled (or recorded) at regular intervals by your analysis software which, in turn, will store and display the data on your computer. In this case, you will be using a transducer to collect an analog signal, which is converted to a digital signal by your DAQ unit. We’ll focus on biological signals recorded via a data acquisition unit (DAQ). Here, we’ll discuss the essentials of optimizing your sampling rate, amplification and filtering settings to get the best data quality for waveform signals. And while it can seem tricky to work out the optimal settings for your signal, it is well worth doing right early on - understanding whether your signal needs conditioning as you are recording is much more efficient than trying to manipulate your data retrospectively when some information may be lost. It is the first variable you want to eliminate, leaving you to concentrate on interpreting a result rather than worrying about lack of resolution or introduced artifacts. All scientists know the value of high quality and consistent data.
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