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Signals, sensors, and data

As scientists, we study the physical environment and a wide range of phenomena that appear within it. Our job is to understand the nature of causes and effects that govern the dynamics of such appearances. As we are arguably living within an "information age," it may be important to understand some basic concepts about where we get our information from.

The notion of a signal was formalized by electrical and communications engineers and has been used by cognitive scientists, biologists, and others. Like so many important concepts, this one has several closely related but distinct definitions. The basic concept of a signal may be expressed as an environmental event or state, the measurement of that state by a device, the transmission or communication of that state, and/or the recording of such measurements or transmissions. While these definitions refer to different processes that may flow in a sequence, they share a common idea, which is fidelity, sameness, or common dependence on some original cause without significant corruption or distortion.

A sensor is a physical device that is causally sensitive to some external phenomenon, but also selective for that phenomenon and not others.  Isolating certain phenomena for measurement is not a trivial thing, because physical force carriers radiate evidence of a wide variety of phenomena rather indiscriminately. Sensors require properties like tuning and filtering to deliberately become insensitive to the wide variety of available signals, so that they might focus on certain specific signals. A good sensor ignores most of the universe and only conveys evidence from a particular event or of a particular type.

Data formally means a recording of a measurement and has a sometimes subtle distinction from a signal. The most pure notion of a signal is something apprehended by a sensor more or less immediately in time, whereas data may be recorded using physical processes with the property of "memory\ for what may be a long time.

For more information on signals, check out the follow up post: Signal detection theory: Signals and noise

The author's affiliation with The MITRE Corporation is provided for identification purposes only, and is not intended to convey or imply MITRE's concurrence with, or support for, the positions, opinions, or viewpoints expressed by the author.

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