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. Scientific method refers to bodies of Techniques for investigating phenomena A hypothesis (from Greek) consists either of a suggested explanation for a phenomenon (an event that is observable or of a reasoned proposal suggesting a possible

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. Data mining is the process of Sorting through large amounts of data and picking out relevant information For terminology see glossary below A DNA microarray is a High-throughput technology used in Molecular biology and in This article is about the theories and mathematics of climate modeling Nuclear engineering is the application of the breakdown of atomic nuclei and/or other sub-atomic physics based on the principles of Nuclear physics.

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. The theory of computation is the branch of Computer science that deals with whether and how efficiently problems can be solved on a Model of computation, using an Further following this trend, the cutting edge of data mining employs specialized machine learning algorithms for automated hypothesis forming and automated theorem proving. Machine learning is a subfield of Artificial intelligence that is concerned with the design and development of Algorithms and techniques that allow computers to "learn" Automated theorem proving ( ATP) or automated deduction, currently the most well-developed subfield of Automated reasoning (AR is the

References

Discovery-Based Science Education: Functional Genomic Dissection in Drosophila by Undergraduate Researchers, PLoS Biology, Volume 3, Issue 2, February 2005