Discovery science (also known as discovery-based science) is a scientific methodology which emphasizes analysis of large volumes of experimental data with the goal of finding new patterns or correlations, leading to hypothesis formation and other scientific methodologies.
Discovery-based methodologies are often viewed in contrast to traditional scientific practice, where hypotheses are formed before close examination of experimental data. However, from a philosophical perspective where all or most of the observable "low hanging fruit" has already been plucked, examining the phenomenological world more closely than the senses alone (even augmented senses, e.g. via microscopes, telescopes, bifocals etc.) opens a new source of knowledge for hypothesis formation.
Data mining is the most common tool used in discovery science, and is applied to data from diverse fields of study such as DNA analysis, climate modeling, nuclear reaction modeling, and others.
The use of data mining in discovery science follows a general trend of increasing use of computers and computational theory in all fields of science. Further following this trend, the cutting edge of data mining employs specialized machine learning algorithms for automated hypothesis forming and automated theorem proving.
Famous quotes containing the words discovery and/or science:
“The gain is not the having of children; it is the discovery of love and how to be loving.”
—Polly Berrien Berends (20th century)
“It is clear that everybody interested in science must be interested in world 3 objects. A physical scientist, to start with, may be interested mainly in world 1 objects—say crystals and X-rays. But very soon he must realize how much depends on our interpretation of the facts, that is, on our theories, and so on world 3 objects. Similarly, a historian of science, or a philosopher interested in science must be largely a student of world 3 objects.”
—Karl Popper (1902–1994)