Taxonomic rankIn biology, taxonomic rank is the relative level of a group of organisms (a taxon) in an ancestral or hereditary hierarchy. A common system of biological classification (taxonomy) consists of species, genus, family, order, class, phylum, kingdom, and domain. While older approaches to taxonomic classification were phenomenological, forming groups on the basis of similarities in appearance, organic structure and behaviour, methods based on genetic analysis have opened the road to cladistics.
Phylogenetic treeA phylogenetic tree (also phylogeny or evolutionary tree) is a branching diagram or a tree showing the evolutionary relationships among various biological species or other entities based upon similarities and differences in their physical or genetic characteristics. All life on Earth is part of a single phylogenetic tree, indicating common ancestry. In a rooted phylogenetic tree, each node with descendants represents the inferred most recent common ancestor of those descendants, and the edge lengths in some trees may be interpreted as time estimates.
Linnaean taxonomyLinnaean taxonomy can mean either of two related concepts: The particular form of biological classification (taxonomy) set up by Carl Linnaeus, as set forth in his Systema Naturae (1735) and subsequent works. In the taxonomy of Linnaeus there are three kingdoms, divided into classes, and they, in turn, into lower ranks in a hierarchical order. A term for rank-based classification of organisms, in general. That is, taxonomy in the traditional sense of the word: rank-based scientific classification.
Computational phylogeneticsComputational phylogenetics is the application of computational algorithms, methods, and programs to phylogenetic analyses. The goal is to assemble a phylogenetic tree representing a hypothesis about the evolutionary ancestry of a set of genes, species, or other taxa. For example, these techniques have been used to explore the family tree of hominid species and the relationships between specific genes shared by many types of organisms.
Observational errorObservational error (or measurement error) is the difference between a measured value of a quantity and its true value. In statistics, an error is not necessarily a "mistake". Variability is an inherent part of the results of measurements and of the measurement process. Measurement errors can be divided into two components: random and systematic. Random errors are errors in measurement that lead to measurable values being inconsistent when repeated measurements of a constant attribute or quantity are taken.
CladogramA cladogram (from Greek clados "branch" and gramma "character") is a diagram used in cladistics to show relations among organisms. A cladogram is not, however, an evolutionary tree because it does not show how ancestors are related to descendants, nor does it show how much they have changed, so many differing evolutionary trees can be consistent with the same cladogram. A cladogram uses lines that branch off in different directions ending at a clade, a group of organisms with a last common ancestor.
Family (biology)Family (familia, : familiae) is one of the eight major hierarchical taxonomic ranks in Linnaean taxonomy. It is classified between order and genus. A family may be divided into subfamilies, which are intermediate ranks between the ranks of family and genus. The official family names are Latin in origin; however, popular names are often used: for example, walnut trees and hickory trees belong to the family Juglandaceae, but that family is commonly referred to as the "walnut family".
Data structureIn computer science, a data structure is a data organization, management, and storage format that is usually chosen for efficient access to data. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data, i.e., it is an algebraic structure about data. Data structures serve as the basis for abstract data types (ADT). The ADT defines the logical form of the data type. The data structure implements the physical form of the data type.
Data (computer science)In computer science, data (treated as singular, plural, or as a mass noun) is any sequence of one or more symbols; datum is a single symbol of data. Data requires interpretation to become information. Digital data is data that is represented using the binary number system of ones (1) and zeros (0), instead of analog representation. In modern (post-1960) computer systems, all data is digital. Data exists in three states: data at rest, data in transit and data in use. Data within a computer, in most cases, moves as parallel data.
Phylogenetic nomenclaturePhylogenetic nomenclature is a method of nomenclature for taxa in biology that uses phylogenetic definitions for taxon names as explained below. This contrasts with the traditional approach, in which taxon names are defined by a type, which can be a specimen or a taxon of lower rank, and a description in words. Phylogenetic nomenclature is currently regulated by the International Code of Phylogenetic Nomenclature (PhyloCode). Phylogenetic nomenclature ties names to clades, groups consisting of an ancestor and all its descendants.
Soft errorIn electronics and computing, a soft error is a type of error where a signal or datum is wrong. Errors may be caused by a defect, usually understood either to be a mistake in design or construction, or a broken component. A soft error is also a signal or datum which is wrong, but is not assumed to imply such a mistake or breakage. After observing a soft error, there is no implication that the system is any less reliable than before. One cause of soft errors is single event upsets from cosmic rays.
Data typeIn computer science and computer programming, a data type (or simply type) is a collection or grouping of data values, usually specified by a set of possible values, a set of allowed operations on these values, and/or a representation of these values as machine types. A data type specification in a program constrains the possible values that an expression, such as a variable or a function call, might take. On literal data, it tells the compiler or interpreter how the programmer intends to use the data.
TaxonomyTaxonomy is the practice and science of categorization or classification. A taxonomy (or taxonomical classification) is a scheme of classification, especially a hierarchical classification, in which things are organized into groups or types. Among other things, a taxonomy can be used to organize and index knowledge (stored as documents, articles, videos, etc.), such as in the form of a library classification system, or a search engine taxonomy, so that users can more easily find the information they are searching for.
Order (biology)Order (ordo) is one of the eight major hierarchical taxonomic ranks in Linnaean taxonomy. It is classified between family and class. In biological classification, the order is a taxonomic rank used in the classification of organisms and recognized by the nomenclature codes. An immediately higher rank, superorder, is sometimes added directly above order, with suborder directly beneath order. An order can also be defined as a group of related families.
Error detection and correctionIn information theory and coding theory with applications in computer science and telecommunication, error detection and correction (EDAC) or error control are techniques that enable reliable delivery of digital data over unreliable communication channels. Many communication channels are subject to channel noise, and thus errors may be introduced during transmission from the source to a receiver. Error detection techniques allow detecting such errors, while error correction enables reconstruction of the original data in many cases.
ExperimentAn experiment is a procedure carried out to support or refute a hypothesis, or determine the efficacy or likelihood of something previously untried. Experiments provide insight into cause-and-effect by demonstrating what outcome occurs when a particular factor is manipulated. Experiments vary greatly in goal and scale but always rely on repeatable procedure and logical analysis of the results. There also exist natural experimental studies.
Multiple comparisons problemIn statistics, the multiple comparisons, multiplicity or multiple testing problem occurs when one considers a set of statistical inferences simultaneously or infers a subset of parameters selected based on the observed values. The more inferences are made, the more likely erroneous inferences become. Several statistical techniques have been developed to address that problem, typically by requiring a stricter significance threshold for individual comparisons, so as to compensate for the number of inferences being made.
Race (biology)In biological taxonomy, race is an informal rank in the taxonomic hierarchy for which various definitions exist. Sometimes it is used to denote a level below that of subspecies, while at other times it is used as a synonym for subspecies. It has been used as a higher rank than strain, with several strains making up one race. Races may be genetically distinct populations of individuals within the same species, or they may be defined in other ways, e.g. geographically, or physiologically.
Family-wise error rateIn statistics, family-wise error rate (FWER) is the probability of making one or more false discoveries, or type I errors when performing multiple hypotheses tests. John Tukey developed in 1953 the concept of a familywise error rate as the probability of making a Type I error among a specified group, or "family," of tests. Ryan (1959) proposed the related concept of an experimentwise error rate, which is the probability of making a Type I error in a given experiment.
Persistent data structureIn computing, a persistent data structure or not ephemeral data structure is a data structure that always preserves the previous version of itself when it is modified. Such data structures are effectively immutable, as their operations do not (visibly) update the structure in-place, but instead always yield a new updated structure. The term was introduced in Driscoll, Sarnak, Sleator, and Tarjans' 1986 article. A data structure is partially persistent if all versions can be accessed but only the newest version can be modified.
