Information in computer science is composed of three main types: Mathematical logic, data, and cross-disciplinary data.
Mathematical logic is at the core of programming: all programs are, at the most basic level, composed of mathematical expressions. In most programs the level of math involved is rudimentary, for example finding the average of a set of values or incrementing a number. This is not the case in computer science research, where the math involved is consistently higher level calculus.
This example shows the theorem behind a math-based computer science research paper. It also illustrates well the level of technical jargon involved in the computer science discourse community.
Data is collected through the scientific method, using repeatable, logical tests. Experiments in pure computer science can focus on fields like machine learning, new hardware, and information retrieval. Nearly all of the data obtained for these fields comes from mathematical logic applied.
This is data obtained from the mathematical theorem above. Often before even creating the mathematical proof and collecting data, a programmer will have a specific purpose for obtaining data. With the above example, the purpose of this research was to find an effective way to recover data from a corrupted file, for something like images. Having this data, the theory can now transition to knowledge for use.
Cross-disciplinary data cannot be discounted in computer science. Programming is used in every major scientific discipline, for uses ranging from data storage and analysis to creating new digital measurement tools. While this crossover application usually doesn’t go from other disciplines to computer science, there are notable exceptions: Machine learning and evolutionary computing are subfields that draw from other disciplines for the use of programming: machine learning incorporates theories of probability to create effective decision-making, and evolutionary computing incorporates the ideas of evolution to create adaptive decision-making. In some cases, data obtained in other fields can become information in computer science, if a use can be found for it.
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