Data visualization in the context of Cyberinfrastructure

Data (/ˈdeɪtə/ DAY-tə, US also /ˈdætə/ DAT-ə) are a collection of discrete or continuous values that convey information, describing the quantity, quality, fact, statistics, other basic units of meaning, or simply sequences of symbols that may be further interpreted formally. A datum is an individual value in a collection of data. Data are usually organized into structures such as tables that provide additional context and meaning, and may themselves be used as data in larger structures. Data may be used as variables in a computational process. Data may represent abstract ideas or concrete measurements.Data are commonly used in scientific research, economics, and virtually every other form of human organizational activity. Examples of data sets include price indices (such as the consumer price index), unemployment rates, literacy rates, and census data. In this context, data represent the raw facts and figures from which useful information can be extracted.

Data are collected using techniques such as measurement, observation, query, or analysis, and are typically represented as numbers or characters that may be further processed. Field data are data that are collected in an uncontrolled, in-situ environment. Experimental data are data that are generated in the course of a controlled scientific experiment. Data are analyzed using techniques such as calculation, reasoning, discussion, presentation, visualization, or other forms of post-analysis. Prior to analysis, raw data (or unprocessed data) is typically cleaned: Outliers are removed, and obvious instrument or data entry errors are corrected.

A chart (sometimes known as a graph) is a graphical representation for data visualization, in which "the data is represented by symbols, such as bars in a bar chart, lines in a line chart, or slices in a pie chart". A chart can represent tabular numeric data, functions or some kinds of quality structure and provides different info.

The term "chart" as a graphical representation of data has multiple meanings:

Computing is any goal-oriented activity requiring, benefiting from, or creating computing machinery. It includes the study and experimentation of algorithmic processes, and the development of both hardware and software. Computing has scientific, engineering, mathematical, technological, and social aspects. Major computing disciplines include computer engineering, computer science, cybersecurity, data science, information systems, information technology, and software engineering.

The term computing is also synonymous with counting and calculating. In earlier times, it was used in reference to the action performed by mechanical computing machines, and before that, to human computers.

A visual variable, in cartographic design, graphic design, and data visualization, is an aspect of a graphical object that can visually differentiate it from other objects, and can be controlled during the design process. The concept was first systematized by Jacques Bertin, a French cartographer and graphic designer, and published in his 1967 book, Sémiologie Graphique. Bertin identified a basic set of these variables and provided guidance for their usage; the concept and the set of variables has since been expanded, especially in cartography, where it has become a core principle of education and practice.

Vision science is the scientific study of visual perception. Researchers in vision science can be called vision scientists, especially if their research spans some of the science's many disciplines.

Vision science encompasses all studies of vision, such as how human and non-human organisms process visual information, how conscious visual perception works in humans, how to exploit visual perception for effective communication, and how artificial systems can do the same tasks. Vision science overlaps with or encompasses disciplines such as ophthalmology and optometry, neuroscience(s), psychology (particularly sensation and perception psychology, cognitive psychology, linguistics, biopsychology, psychophysics, and neuropsychology), physics (particularly optics), ethology, and computer science (particularly computer vision, artificial intelligence, and computer graphics), as well as other engineering related areas such as data visualization, user interface design, and human factors and ergonomics. Below is a list of pertinent journals and international conferences.

In statistics, exploratory data analysis (EDA) or exploratory analytics is an approach of analyzing data sets to summarize their main characteristics, often using statistical graphics and other data visualization methods. A statistical model can be used or not, but primarily EDA is for seeing what the data can tell beyond the formal modeling and thereby contrasts with traditional hypothesis testing, in which a model is supposed to be selected before the data is seen. Exploratory data analysis has been promoted by John Tukey since 1970 to encourage statisticians to explore the data, and possibly formulate hypotheses that could lead to new data collection and experiments. EDA is different from initial data analysis (IDA), which focuses more narrowly on checking assumptions required for model fitting and hypothesis testing, and handling missing values and making transformations of variables as needed. EDA encompasses IDA.

Economic forecasting is the process of making predictions about the economy. Forecasts can be made at a high level of aggregation—for example, for GDP, inflation, unemployment, or the fiscal deficit. They can also be made at a more disaggregated level, targeting specific economic sectors or even individual firms. This practice is a fundamental part of economic analysis, providing a measure of a potential investment's future prospects and helping shape policy decisions.Many institutions engage in economic forecasting: national governments, banks and central banks, consultants and private sector entities such as think-tanks, and companies or international organizations such as the International Monetary Fund, World Bank and the OECD. A broad range of forecasts are collected and compiled by "Consensus Economics". Some forecasts are produced annually, but many are updated more frequently.

The economist typically considers risks (i.e., events or conditions that can cause the result to vary from their initial estimates). These risks help illustrate the reasoning process used in arriving at the final forecast numbers. Economists typically use commentary along with data visualization tools such as tables and charts to communicate their forecast. In preparing economic forecasts a variety of information has been used in an attempt to increase the accuracy.

In computing, a voxel is a representation of a value on a three-dimensional regular grid, akin to the two-dimensional pixel. Voxels are frequently used in the visualization and analysis of medical and scientific data (e.g. geographic information systems (GIS)). Voxels also have technical and artistic applications in video games, largely originating with surface rendering in Outcast (1999). Minecraft (2011) makes use of an entirely voxelated world to allow for a fully destructible and constructable environment. Voxel art, of the sort used in Minecraft and elsewhere, is a style and format of 3D art analogous to pixel art.

As with pixels in a 2D bitmap, voxels themselves do not typically have their position (i.e. coordinates) explicitly encoded with their values. Instead, rendering systems infer the position of a voxel based upon its position relative to other voxels (i.e., its position in the data structure that makes up a single volumetric image). Some volumetric displays use voxels to describe their resolution. For example, a cubic volumetric display might be able to show 512×512×512 (or about 134 million) voxels.

A heat map (or heatmap) is a 2-dimensional data visualization technique that represents the magnitude of individual values within a dataset as a color. The variation in color may be by hue or intensity.

In some applications such as crime analytics or website click-tracking, color is used to represent the density of data points rather than a value associated with each point.

Edward Rolf Tufte (/ˈtʌfti/ ; born March 14, 1942), sometimes known as "ET", is an American statistician and professor emeritus of political science, statistics, and computer science at Yale University. He is noted for his writings on information design and as a pioneer in the field of data visualization.

Infographics (a clipped compound of "information" and "graphics") are graphic visual representations of information, data, or knowledge intended to present information quickly and clearly. They can improve cognition by using graphics to enhance the human visual system's ability to see patterns and trends. Similar pursuits are information visualization, data visualization, statistical graphics, information design, or information architecture. Infographics have evolved in recent years to be for mass communication, and thus are designed with fewer assumptions about the readers' knowledge base than other types of visualizations. Isotypes are an early example of infographics conveying information quickly and easily to the masses.

Dimensionality reduction, or dimension reduction, is the transformation of data from a high-dimensional space into a low-dimensional space so that the low-dimensional representation retains some meaningful properties of the original data, ideally close to its intrinsic dimension. Working in high-dimensional spaces can be undesirable for many reasons; raw data are often sparse as a consequence of the curse of dimensionality, and analyzing the data is usually computationally intractable. Dimensionality reduction is common in fields that deal with large numbers of observations and/or large numbers of variables, such as signal processing, speech recognition, neuroinformatics, and bioinformatics.

Methods are commonly divided into linear and nonlinear approaches. Linear approaches can be further divided into feature selection and feature extraction. Dimensionality reduction can be used for noise reduction, data visualization, cluster analysis, or as an intermediate step to facilitate other analyses.

Big data primarily refers to data sets that are too large or complex to be dealt with by traditional data-processing software. Data with many entries (rows) offer greater statistical power, while data with higher complexity (more attributes or columns) may lead to a higher false discovery rate.

Big data analysis challenges include capturing data, data storage, data analysis, search, sharing, transfer, visualization, querying, updating, information privacy, and data source. Big data was originally associated with three key concepts: volume, variety, and velocity. The analysis of big data that have only volume velocity and variety can pose challenges in sampling. A fourth concept, veracity, that refers to the level of relaibility of data was thus added. Without sufficient investment in expertise for big data veracity, the volume and variety of data can produce costs and risks that exceed an organization's capacity to create and capture value from big data.

Statistical graphics, also known as statistical graphical techniques, are graphics used in the field of statistics for data visualization.