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Origin is a proprietary computer program for interactive scientific graphing and data analysis.It is produced by OriginLab Corporation, and runs on Microsoft Windows.It has inspired several platform-independent open-source clones and alternatives like LabPlot and SciDAVis.
OriginLab 1991 2019b 24 April 2019: $1095 (std.)/$1800 (Pro) $550 (std., academic) $850 (Pro, academic) $69/yr. (Pro, student) Proprietary: Integrated data analysis graphing software for science and engineering. Flexible multi-layer graphing framework. 2D, 3D and statistical graph types. Built-in digitizing tool.
Origin (data analysis software), scientific graphing and data analysis software developed by OriginLab Corp; Original equipment manufacturer (OEM), any company which manufactures products for another company's brand name; SGI Origin 200, a series of entry-level MIPS-based server computers made by Silicon Graphics
In 2008, developers of LabPlot and SciDAVis (another Origin clone, forked from QtiPlot) "found their project goals to be very similar" and decided to merge their code into a common backend while maintaining two frontends: LabPlot, integrated with the KDE desktop environment (DE); and SciDAVis, written in DE-independent Qt with fewer dependencies for easier cross-platform use.
Laboratory Virtual Instrument Engineering Workbench (LabVIEW) [1]: 3 is a graphical system design and development platform produced and distributed by National Instruments, based on a programming environment that uses a visual programming language.
Visual Studio Code was first announced on April 29, 2015 by Microsoft at the 2015 Build conference. A preview build was released shortly thereafter. [13]On November 18, 2015, the project "Visual Studio Code — Open Source" (also known as "Code — OSS"), on which Visual Studio Code is based, was released under the open-source MIT License and made available on GitHub.
NetCDF (Network Common Data Form) is a set of software libraries and self-describing, machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data.
Kernel density estimation of 100 normally distributed random numbers using different smoothing bandwidths.. In statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate the probability density function of a random variable based on kernels as weights.