Scrape wikipedia-science: 6877 new, 3293 updated, 10464 total (kb-cron)
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data/en.wikipedia.org/wiki/Archaeological_Recording_Kit-0.md
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data/en.wikipedia.org/wiki/Archaeological_Recording_Kit-0.md
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title: "Archaeological Recording Kit"
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source: "https://en.wikipedia.org/wiki/Archaeological_Recording_Kit"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:39.716591+00:00"
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---
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Archaeological Recording Kit (ARK) is a web-based, open source software package for recording and disseminating archaeological data. ARK is primarily designed for recording excavations, but can also be used for archaeological surveys, palaeoenvironmental research and collections management.
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ARK is based on the LAMP stack and MapServer, and is free software released under the GNU GPL. It was developed by L-P Archaeology, a British commercial archaeology practice.
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The Fasti Online project was built using an ARK back-end, and demonstrates its usage beyond normal archaeological recording.
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== References ==
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== External links ==
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ARK
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data/en.wikipedia.org/wiki/Armadillo_(C++_library)-0.md
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data/en.wikipedia.org/wiki/Armadillo_(C++_library)-0.md
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---
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title: "Armadillo (C++ library)"
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source: "https://en.wikipedia.org/wiki/Armadillo_(C++_library)"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:40.886820+00:00"
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instance: "kb-cron"
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---
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Armadillo is a linear algebra software library for the C++ programming language. It aims to provide efficient and streamlined base calculations, while at the same time having a straightforward and easy-to-use interface. Its intended target users are scientists and engineers.
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It supports integer, floating point (single and double precision), complex numbers, and a subset of trigonometric and statistics functions. Dense and sparse matrices are supported. Various matrix decompositions are provided through optional integration with Linear Algebra PACKage (LAPACK), Automatically Tuned Linear Algebra Software (ATLAS), and ARPACK. High-performance BLAS/LAPACK replacement libraries such as OpenBLAS and Intel MKL can also be used.
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The library employs a delayed-evaluation approach (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. Where applicable, the order of operations is optimised. Delayed evaluation and optimisation are achieved through template metaprogramming.
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Armadillo is related to the Boost Basic Linear Algebra Subprograms (uBLAS) library, which also uses template metaprogramming. However, Armadillo builds upon ATLAS and LAPACK libraries, thereby providing machine-dependent optimisations and functions not present in uBLAS.
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It is open-source software distributed under the permissive Apache License, making it applicable for the development of both open source and proprietary software. The project is supported by the NICTA research centre in Australia.
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An interface to the Python language is available through the PyArmadillo package,
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which facilitates prototyping of algorithms in Python followed by relatively straightforward conversion to C++.
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Armadillo is a core dependency of the mlpack machine learning library and the ensmallen C++ library for numerical optimization.
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== Example ==
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Here is a trivial example demonstrating Armadillo functionality:
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A different example, in C++98, is:
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== See also ==
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mlpack
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List of numerical analysis software
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List of numerical libraries
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List of open-source mathematical libraries
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Numerical linear algebra
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Scientific computing
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== References ==
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== External links ==
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Official website
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source: "https://en.wikipedia.org/wiki/Artificial_Intelligence_System"
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category: "reference"
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tags: "science, encyclopedia"
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data/en.wikipedia.org/wiki/Ascalaph_Designer-0.md
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data/en.wikipedia.org/wiki/Ascalaph_Designer-0.md
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title: "Ascalaph Designer"
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source: "https://en.wikipedia.org/wiki/Ascalaph_Designer"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:43.266788+00:00"
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instance: "kb-cron"
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---
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Ascalaph Designer is a computer program for general purpose molecular modelling for molecular design and simulations. It provides a graphical environment for the common programs of quantum and classical molecular modelling ORCA, NWChem, Firefly, CP2K and MDynaMix
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. The molecular mechanics calculations cover model building, energy optimizations and molecular dynamics. Firefly (formerly named PC GAMESS) covers a wide range of quantum chemistry methods. Ascalaph Designer is free and open-source software, released under the GNU General Public License, version 2 (GPLv2).
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== Key features ==
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== Uses ==
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== See also ==
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List of software for molecular mechanics modeling
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Molecular design software
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Molecule editor
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Abalone
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== References ==
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== External links ==
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Official website
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SourceForge
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Twitter
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source: "https://en.wikipedia.org/wiki/Berkeley_Open_Infrastructure_for_Network_Computing"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T06:49:51.612005+00:00"
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instance: "kb-cron"
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---
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data/en.wikipedia.org/wiki/BigDL-0.md
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data/en.wikipedia.org/wiki/BigDL-0.md
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title: "BigDL"
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source: "https://en.wikipedia.org/wiki/BigDL"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:45.702369+00:00"
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---
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BigDL is a distributed deep learning framework for Apache Spark, created by Jason Dai at Intel. BigDL has its source code hosted on GitHub.
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== Features ==
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== Applications ==
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== See also ==
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Comparison of deep learning software
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== References ==
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data/en.wikipedia.org/wiki/Biskit-0.md
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data/en.wikipedia.org/wiki/Biskit-0.md
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title: "Biskit"
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source: "https://en.wikipedia.org/wiki/Biskit"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:46.850468+00:00"
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---
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Biskit is an open source software package that facilitates research in structural bioinformatics and molecular modelling. Written in Python, it consists of:
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An object-oriented programming library for manipulating and analyzing macromolecular structures, protein complexes and molecular dynamics trajectories
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A set of programs for solving specific tasks, such as automatic prediction of protein structures by homology modeling, and possible prediction of protein complex structures through flexible protein-protein docking
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The library delegates many calculations to more specialized third-party software. It currently utilizes 15 external applications, including X-PLOR, Hex, T-Coffee, DSSP and MODELLER.
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The latest Biskit version, 2.4.0, was released on 4 Mar 2012. It was originally developed at the Pasteur Institute. The name "Biskit" refers to the research group's name, Unité de BioInformatique Structurale.
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== References ==
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== External links ==
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Official website
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data/en.wikipedia.org/wiki/Brian_(software)-0.md
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data/en.wikipedia.org/wiki/Brian_(software)-0.md
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title: "Brian (software)"
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source: "https://en.wikipedia.org/wiki/Brian_(software)"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:48.484045+00:00"
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---
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Brian is an open source Python package for developing simulations of networks of spiking neurons.
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== Details ==
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Brian is aimed at researchers developing models based on networks of spiking neurons. The general design is aimed at maximising flexibility, simplicity and users' development speed. Users specify neuron models by giving their differential equations in standard mathematical form as strings, create groups of neurons and connect them via synapses. This is in contrast to the approach taken by many neural simulators in which users select from a predefined set of neuron models.
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Brian is written in Python. Computationally, it is based around the concept of code generation: users specify the model in Python but behind the scenes Brian generates, compiles and runs code in one of several languages (including Python, Cython and C++). In addition there is a "standalone" mode in which Brian generates an entire C++ source code tree with no dependency on Brian, allowing models to be run on platforms where Python is not available.
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== Example ==
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The following code defines, runs and plots a randomly connected network of leaky integrate and fire neurons with exponential inhibitory and excitatory currents.
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== Comparison to other simulators ==
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Brian is primarily, although not solely, aimed at single compartment neuron models. Simulators focused on multi-compartmental models include Neuron, GENESIS, and its derivatives.
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The focus of Brian is on flexibility and ease of use, and only supports simulations running on a single machine. The NEST simulator includes facilities for distributing simulations across a cluster.
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== Awards ==
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2023 Open Science Award for Open Source Research Software in the category "Documentation"
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== Footnotes ==
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== References ==
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Goodman, D.; Brette, R. (2008). "Brian: a simulator for spiking neural networks in Python". Front. Neuroinform. 2: 5. doi:10.3389/neuro.11.005.2008. PMC 2605403. PMID 19115011.
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Goodman, D.F.M.; Brette, R. (2009). "The Brian simulator". Front. Neurosci. 3: 192–197. doi:10.3389/neuro.01.026.2009. hdl:10044/1/40622. PMC 2751620.
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== External links ==
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Official website
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data/en.wikipedia.org/wiki/CGAL-0.md
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data/en.wikipedia.org/wiki/CGAL-0.md
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---
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title: "CGAL"
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chunk: 1/1
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source: "https://en.wikipedia.org/wiki/CGAL"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:50.777246+00:00"
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instance: "kb-cron"
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---
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The Computational Geometry Algorithms Library (CGAL) is an open source software library of computational geometry algorithms. While primarily written in C++, Scilab bindings and bindings generated with SWIG (supporting Python and Java for now) are also available.
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The software is available under dual licensing scheme. When used for other open source software, it is available under open source licenses (LGPL or GPL depending on the component). In other cases commercial license may be purchased, under different options for academic/research and industrial customers.
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== History ==
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The CGAL project was founded in 1996, as a consortium of eight research institutions in Europe and Israel:
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Utrecht University, ETH Zurich, Free University of Berlin, INRIA Sophia Antipolis, Martin-Luther-University Halle-Wittenberg, Max Planck Institute for Informatics Saarbrücken, Johannes Kepler University Linz, and Tel-Aviv University. The original funding for the project came from the ESPRIT project of the European Union. Originally, its licensing terms allowed its software to be used freely for academic purposes, with commercial licenses available for other uses. CGAL Releases 3.x were distributed under the QPL license. Starting with CGAL 4.0, released in 2012, CGAL is distributed under the GPL version 3. As of 2013 it is managed by a thirteen-member editorial board, with an additional 30 developers and reviewers.
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The project started in 1996 as the pooling of the previous efforts of several project participants:PlaGeo and SpaGeo from Utrecht University, LEDA of the Max-Planck-Institute for Informatics and C++GAL of INRIA Sophia Antipolis. The LEDA library encompasses a broader range of algorithms. A comparison of the two libraries is provided by Kettner and Näher. Three CGAL User workshops held in 2002, 2004, and 2008 highlighted research results related to CGAL, and many additional papers related to CGAL have appeared in other conferences, workshops, and journals.
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In 2023 the project won the SoCG Test of Time Award
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== Scope ==
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The library covers the following topics:
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== Platforms ==
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The library is supported on a number of platforms:
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Microsoft Windows (GNU G++, Microsoft Visual C++, Intel C++ Compiler)
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GNU g++ (Solaris, Linux, Mac OS)
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Clang
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The CGAL library depends on the Boost libraries, and several CGAL packages on the Eigen C++ library.
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== See also ==
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OPEN CASCADE
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OpenSCAD (uses CGAL)
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GDAL (similar lib for Geo)
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PostGIS (uses CGAL and GDAL)
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== References ==
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== External links ==
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CGAL Homepage
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data/en.wikipedia.org/wiki/COMBINE-0.md
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data/en.wikipedia.org/wiki/COMBINE-0.md
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---
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title: "COMBINE"
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chunk: 1/1
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source: "https://en.wikipedia.org/wiki/COMBINE"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:56.875009+00:00"
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instance: "kb-cron"
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---
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COMBINE, the COmputational Modeling in BIology NEtwork, is an initiative to coordinate the development of the various community standards and formats for computational models, initially in systems biology and related fields.
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== History ==
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The COMBINE initiative was started in 2010 in an attempt to start a broader series of scientific meetings in order to replace several smaller and more focused meetings and hackathons, notably the Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML) meetings. The first COMBINE meeting was organised by Igor Goryanin and held at the University of Edinburgh School of Informatics in October 2010. The final session of the meeting was followed by an event marking the 10th anniversary of SBML. COMBINE meetings have been held annually since; COMBINE 2014 was organised by the University of Southern California and COMBINE 2015 will be organised by the group of Chris Myers at the University of Utah.
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== Representation formats ==
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The COMBINE initiative aims to coordinate the development of community standards and formats for computational modelling, particularly in systems biology. In doing so, it is expected a set of complementary but non-overlapping standards will be developed, covering all aspects of computational modelling in all areas of biology. The major representation formats covered by COMBINE activity are the BioPAX standards language, SBGN, SBML and the SED-ML and CellML markup languages. The associated standardisation efforts are MIRIAM, SBO, KiSAO and the BioModels.net model repository.
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== References ==
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== External links ==
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COMBINE Home
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data/en.wikipedia.org/wiki/CP2K-0.md
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data/en.wikipedia.org/wiki/CP2K-0.md
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---
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title: "CP2K"
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source: "https://en.wikipedia.org/wiki/CP2K"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:11:00.486339+00:00"
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instance: "kb-cron"
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---
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CP2K is a freely available (GPL) quantum chemistry and solid state physics program package, written in Fortran 2008, to perform atomistic simulations of solid state, liquid, molecular, periodic, material, crystal, and biological systems. It provides a general framework for different methods: density functional theory (DFT) using a mixed Gaussian and plane waves approach (GPW) via LDA, GGA, MP2, or RPA levels of theory, classical pair and many-body potentials, semi-empirical (AM1, PM3, MNDO, MNDOd, PM6) and tight-binding Hamiltonians, as well as Quantum Mechanics/Molecular Mechanics (QM/MM) hybrid schemes relying on the Gaussian Expansion of the Electrostatic Potential (GEEP). The Gaussian and Augmented Plane Waves method (GAPW) as an extension of the GPW method allows for all-electron calculations. CP2K can do simulations of molecular dynamics, metadynamics, Monte Carlo, Ehrenfest dynamics, vibrational analysis, core level spectroscopy, energy minimization, and transition state optimization using NEB or dimer method.
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CP2K provides editor plugins for Vim and Emacs syntax highlighting, along with other tools for input generation and output processing.
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== See also ==
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Car–Parrinello molecular dynamics
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Computational chemistry
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Molecular dynamics
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Monte Carlo algorithm
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Energy minimization
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Quantum chemistry
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Quantum chemistry computer programs
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Ab initio quantum chemistry methods
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Møller–Plesset perturbation theory
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Hartree–Fock method
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Random phase approximation
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Density functional theory
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Harris functional
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Tight binding
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Semi-empirical quantum chemistry method
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== Key Papers ==
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Kühne, Thomas; Iannuzzi, Marcella; et al. (2020). "CP2K: An electronic structure and molecular dynamics software package - Quickstep: Efficient and accurate electronic structure calculations". Journal of Chemical Physics. 152 (19): 194103. arXiv:2003.03868. Bibcode:2020JChPh.152s4103K. doi:10.1063/5.0007045. PMID 33687235.
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Lippert, Gerald; Hutter, Jürg; Parrinello, Michele (1997). "A hybrid Gaussian and plane wave density functional scheme". Molecular Physics. 92 (3): 477–487. Bibcode:1997MolPh..92..477L. doi:10.1080/002689797170220.
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Lippert, Gerald; Hutter, Jürg; Parrinello, Michele (1999). "The Gaussian and augmented-plane-wave density functional method for ab initio molecular dynamics simulations". Theoretical Chemistry Accounts: Theory, Computation, and Modeling. 103 (2): 124–140. doi:10.1007/s002140050523. S2CID 124305820.
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Kühne, Thomas D.; Krack, Matthias; Mohamed, Fawzi; Parrinello, Michele (2007). "Efficient and Accurate Car-Parrinello-like Approach to Born-Oppenheimer Molecular Dynamics". Physical Review Letters. 98 (6) 066401. arXiv:cond-mat/0610552. Bibcode:2007PhRvL..98f6401K. doi:10.1103/PhysRevLett.98.066401. PMID 17358962. S2CID 8088072.
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Krack, Matthias; Parrinello, Michele (2000). "All-electron ab-initio molecular dynamics". Physical Chemistry Chemical Physics. 2 (10): 2105–2112. Bibcode:2000PCCP....2.2105K. doi:10.1039/B001167N. S2CID 97061785.
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Kühne, Thomas D. (2014). "Second generation Car–Parrinello molecular dynamics". WIREs Computational Molecular Science. 4 (4): 391–406. arXiv:1201.5945. doi:10.1002/wcms.1176. S2CID 15360296.
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Laino, Teodoro; Mohamed, Fawzi; Laio, Alessandro; Parrinello, Michele (2005). "An Efficient Real Space Multigrid QM/MM Electrostatic Coupling". Journal of Chemical Theory and Computation. 1 (6): 1176–1184. doi:10.1021/ct050123f. PMID 26631661.
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Laino, Teodoro; Mohamed, Fawzi; Laio, Alessandro; Parrinello, Michele (2006). "An Efficient Linear-Scaling Electrostatic Coupling for Treating Periodic Boundary Conditions in QM/MM Simulations". Journal of Chemical Theory and Computation. 2 (5): 1370–1378. doi:10.1021/ct6001169. PMID 26626844.
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== References ==
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== External links ==
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Official website
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Users' Forum
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1st CP2K Tutorial: Enabling the power of imagination in MD Simulations
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2nd CP2K Tutorial: Enabling the power of imagination in MD Simulations
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CP2K User Tutorial: "Computational Spectroscopy"
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Ascalaph, a 3rd party graphical shell for CP2K and other quantum chemistry software
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data/en.wikipedia.org/wiki/Caffe_(software)-0.md
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data/en.wikipedia.org/wiki/Caffe_(software)-0.md
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---
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title: "Caffe (software)"
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chunk: 1/1
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source: "https://en.wikipedia.org/wiki/Caffe_(software)"
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category: "reference"
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tags: "science, encyclopedia"
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date_saved: "2026-05-05T10:10:49.666525+00:00"
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instance: "kb-cron"
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---
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Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface.
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== History ==
|
||||
Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub.
|
||||
|
||||
|
||||
== Features ==
|
||||
Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL.
|
||||
|
||||
|
||||
== Applications ==
|
||||
Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework.
|
||||
|
||||
|
||||
== Caffe2 ==
|
||||
In April 2017, Facebook announced Caffe2, which included new features such as recurrent neural network (RNN).
|
||||
At the end of March 2018, Caffe2 was merged into PyTorch.
|
||||
|
||||
|
||||
== See also ==
|
||||
Comparison of deep learning software
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
30
data/en.wikipedia.org/wiki/Citcom-0.md
Normal file
30
data/en.wikipedia.org/wiki/Citcom-0.md
Normal file
@ -0,0 +1,30 @@
|
||||
---
|
||||
title: "Citcom"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Citcom"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:10:51.934088+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
CitCom (for California Institute of Technology Convection in the Mantle) is a finite element code designed to solve thermal convection problems relevant to Earth's mantle released under the GNU General Public License. Written in C, the code's latest version, CitComS, runs on a variety of parallel processing computers, including shared and distributed memory platforms.
|
||||
|
||||
|
||||
== History ==
|
||||
CitCom was originally written in the early 1990s by Louis Moresi (Monash U.). Although the code for three-dimensional problems was incorporated from its inception, early versions of the software only solved for time-dependent convection problems within two-dimensional Cartesian domains. Moresi's original code turned out to be incredibly modular and easily extensible. Consequently, the fundamental finite element infrastructure which Louis wrote is still in place and forms the basis for much of the code contained in the present release.
|
||||
In the mid-1990s Moresi wrote versions of the code that solved the equations within three-dimensional Cartesian domains. Then Shijie Zhong (U. of Colorado, Boulder) successfully parallelized CitCom using message passing routines on a limited release Intel supercomputer. Zhong then created a spherical version of the code which he named CitComS. Lijie Han (Planetary Science Institute) then created a regional version of CitComS as well as an alternate version of message passing for an arbitrarily large number of processors. Clint Conrad (Johns Hopkins) created the first Beowulf implementations of the code, then Conrad and Eh Tan (Computational Infrastructure for Geodynamics) re-coded the message passing of the fully spherical version so that problems run on arbitrarily large numbers of processors could also be solved. A plethora of different versions of CitCom exist both on computers at the California Institute of Technology and around the world.
|
||||
Consequently, by 2002, there were so many different versions of the code that some rationalization was in order. The software was migrated into a version control system and Eh Tan and Eun-seo Choi (Caltech) created a version of CitComS that generates either a fully spherical or regional model, CitcomSFull and CitcomSRegional respectively. CitComS was released to the community through the former GeoFramework project as version 1.0 and 1.1.
|
||||
By 2004, in order to increase the functionality of CitComS, the developers began to reengineer the code into an object-oriented environment specifically so it could work with a Python-based modeling framework called Pyre. This release of the software, now named CitComS.py, is essentially the product of those reengineering efforts. Eh Tan was the principal developer of CitComS.py, with considerable help from Eun-seo Choi and Michael Aivazis (Caltech).
|
||||
CitComS is one component of a larger collection of software encompassed by the former GeoFramework project, a collaboration between the Center for Advanced Computing Research (CACR) and the Seismological Laboratory, both at Caltech, and the Victorian Partnership for Advanced Computing in Australia. The GeoFramework project developed a suite of tools to model multi-scale deformation for Earth science problems. This effort was motivated by the need to understand interactions between the long-term evolution of plate tectonics and shorter term processes such as the evolution of faults during and between earthquakes. During 2005 and 2006 much of the remaining software developed by GeoFramework was released under a GPL license and made available from Computational Infrastructure for Geodynamics (CIG).
|
||||
The second major release of CitComS (2.0) incorporated the software framework Pyre, free surface modeling methods, and stress boundary conditions on the top and bottom surfaces. In the summer of 2005, as part of the 2.0.1 release, CIG replaced the old build procedure with the GNU Build System. A subsequent release, version 2.0.2, could compile and run on 64-bit systems.
|
||||
The third major release of CitComS (2.1) incorporated new features and functionality, the most important being the use of HDF5 (a parallel version of the Hierarchical Data Format). The HDF5 format allows you to deal with the massive data output created for production runs. This version accepted .cfg files on input, which are easier to create and read.
|
||||
Other improvements included the incorporation of geoid calculations that had been left out of earlier releases, as well as new scripts to allow results to be visualized with MayaVi2 in addition to Generic Mapping Tools (GMT) and OpenDX. Instructions were provided on using this version as a preinstalled package on some of the NSF TeraGrid sites.
|
||||
The latest release of CitComS (2.2, 3/27/07) incorporates the ability of tracing particles in the flow. The tracer code was developed by Allen McNamara and Shijie Zhong in 2004 and donated to CIG in early 2007. The tracer code has a wide range of applications in the mantle convection. It can be used in tracing the trajectory of passive particles, in delineating the top boundary of subducted slabs to define the low viscosity wedges, or in tracking the evolution of the chemical composition field.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
CitComS User Manual
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Clean_Energy_Project"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:49:57.744230+00:00"
|
||||
date_saved: "2026-05-05T10:10:53.152563+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
26
data/en.wikipedia.org/wiki/Cn3D-0.md
Normal file
26
data/en.wikipedia.org/wiki/Cn3D-0.md
Normal file
@ -0,0 +1,26 @@
|
||||
---
|
||||
title: "Cn3D"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Cn3D"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:10:54.478349+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Cn3D is a Windows, Macintosh and Unix-based software from the United States National Library of Medicine that acts as a helper application for web browsers to view three-dimensional structures from The National Center for Biotechnology Information's Entrez retrieval service. It "simultaneously displays structure, sequence, and alignment, and now has powerful annotation and alignment editing features", according to its official site. Cn3D is in public domain with source code available.
|
||||
The latest version of the software 4.3.1 was released 06 Dec 2013. This version has the ability to view superpositions of 3D structures with similar biological units and an enhanced version of the Vector Alignment Search Tool (VAST).
|
||||
|
||||
|
||||
== See also ==
|
||||
List of molecular graphics systems
|
||||
Molecular graphics
|
||||
List of software for molecular mechanics modeling
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Cn3D Home Page
|
||||
source code tarball of NCBI C++ toolkit which includes Cn3D
|
||||
119
data/en.wikipedia.org/wiki/Code_Saturne-0.md
Normal file
119
data/en.wikipedia.org/wiki/Code_Saturne-0.md
Normal file
@ -0,0 +1,119 @@
|
||||
---
|
||||
title: "Code Saturne"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Code_Saturne"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:10:55.676043+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
code_saturne is a free and open-source computational fluid dynamics (CFD) solver developed by the research and development division of Électricité de France (EDF). Released under the GNU General Public License since 2007, it is based on a co-located finite volume method and can simulate incompressible or low-Mach number dilatable flows, with or without turbulence and heat transfer.
|
||||
The software is integrated into the SALOME platform through the salome_cfd distribution and can be coupled with the solid thermal code SYRTHES and the structural mechanics code code_aster, both also developed by EDF under free software licences.
|
||||
Its two-phase extension, Neptune_CFD, co-developed by EDF and the CEA, handles two-phase flows such as water-steam and water-air mixtures.
|
||||
|
||||
|
||||
== History ==
|
||||
Development of code_saturne began in 1997 within EDF's R&D division as an internal tool for nuclear safety studies and thermohydraulics of nuclear power plants.
|
||||
In March 2007, EDF released the code under the GNU General Public License, adopting an open development model. This release encouraged adoption by industrial and academic partners in France and internationally.
|
||||
The software has since been selected as a benchmark application within the European PRACE (Partnership for Advanced Computing in Europe) project for evaluating supercomputer performance.
|
||||
|
||||
|
||||
== Features ==
|
||||
|
||||
|
||||
=== Numerical method ===
|
||||
code_saturne solves the Navier–Stokes equations using a co-located finite volume method. It accepts meshes of any type — structured, unstructured, hybrid, conforming or non-conforming — and a variety of cell shapes: tetrahedra, hexahedra, prisms, pyramids, or arbitrary polyhedra.
|
||||
Simulated flows can be steady or unsteady, laminar or turbulent, isothermal or non-isothermal, and incompressible or compressible.
|
||||
|
||||
|
||||
=== Turbulence models ===
|
||||
The software provides a wide range of turbulence models covering the following approaches:
|
||||
|
||||
RANS (Reynolds-Averaged Navier–Stokes);
|
||||
LES (Large Eddy Simulation);
|
||||
hybrid RANS/LES methods (DES, SAS).
|
||||
|
||||
|
||||
=== Specific physical modules ===
|
||||
In addition to the general-purpose solver, code_saturne includes dedicated modules for specific physics:
|
||||
|
||||
Combustion: gas flames (diffusion, premixed), pulverised coal combustion with Lagrangian particle tracking, and fire simulation;
|
||||
Thermal radiation: radiative transfer in semi-transparent media;
|
||||
Atmospheric flows: atmospheric boundary layer, thermal stratification, pollutant dispersion;
|
||||
Lagrangian particle tracking: transport of particles, droplets or bubbles in an Eulerian field with two-way coupling;
|
||||
Magnetohydrodynamics: coupling of the Navier–Stokes and Maxwell equations;
|
||||
Turbomachinery: rotating meshes with sliding mesh interfaces.
|
||||
|
||||
|
||||
=== Parallelisation and coupling ===
|
||||
The code is parallelised using the Message Passing Interface (MPI) library, enabling it to run on high-performance computing (HPC) architectures. It can be coupled with the solid thermal code SYRTHES and the structural mechanics code code_aster, notably through the SALOME platform.
|
||||
|
||||
|
||||
=== Interoperability ===
|
||||
code_saturne supports various mesh types, including arbitrary polyhedral and non-structured elements, with non-conforming mesh joining. It does not include a built-in mesher or visualisation module, but is compatible with many standard tools and formats.
|
||||
Supported mesh import formats:
|
||||
|
||||
Post-processing output formats:
|
||||
|
||||
MED
|
||||
CGNS
|
||||
EnSight Gold
|
||||
|
||||
|
||||
== Applications and users ==
|
||||
code_saturne is used in both industrial and academic settings in France and internationally. Approximately 500 engineers and researchers use it within EDF.
|
||||
|
||||
|
||||
=== Energy and nuclear safety ===
|
||||
Originally designed for nuclear power plant safety studies, the software is used to analyse flows in primary circuits, cooling systems, coupled fluid-structure heat transfer, and steam generators. It is also employed for modelling wind farms, including turbine wake interactions and layout optimisation.
|
||||
|
||||
|
||||
=== Environment and atmosphere ===
|
||||
The software is used for modelling atmospheric flows, pollutant dispersion, air quality assessment, and wind-structure interactions.
|
||||
|
||||
|
||||
=== Hydrodynamics and industry ===
|
||||
code_saturne has been used by industrial partners for hydrodynamics applications, notably in the field of naval architecture.
|
||||
|
||||
|
||||
=== Research and education ===
|
||||
The software is used in universities and research organisations for developing and validating numerical models, as well as for teaching computational fluid dynamics.
|
||||
|
||||
|
||||
== Development and community ==
|
||||
Development is led by the R&D division of EDF. The source code is hosted on GitHub. Releases follow regular cycles.
|
||||
A community of users, including engineers and researchers from industry and academia, contributes to the project through the development of physical models, associated tools, and validation cases. An official forum and technical documentation (user guides, reference manuals, tutorial cases) are available on the project website.
|
||||
|
||||
|
||||
== Key publications ==
|
||||
The numerical foundations and validation of code_saturne are described in several peer-reviewed publications:
|
||||
|
||||
Archambeau, F.; Méchitoua, N.; Sakiz, M. (2004). "Code_saturne: A finite volume code for the computation of turbulent incompressible flows – Industrial applications". International Journal on Finite Volumes. 1 (1).
|
||||
Fournier, Y.; Bonelle, J.; Moulinec, C.; Shang, Z.; Sunderland, A.G.; Uribe, J.C. (2011). "Optimizing Code_Saturne computations on Petascale systems". Computers & Fluids. 45 (1): 103–108. doi:10.1016/j.compfluid.2011.01.028.
|
||||
|
||||
|
||||
== Availability ==
|
||||
code_saturne runs on Linux and Unix. It is available as pre-compiled packages for distributions such as Debian and Ubuntu, through container images (Docker, Apptainer), or by compiling from source available on the official website.
|
||||
Pre-compiled binaries and Singularity (.sif) and Docker container images are also provided by the Open Simulation Center platform, facilitating deployment on workstations or high-performance computing environments.
|
||||
On Windows, the software can be used through the Windows Subsystem for Linux.
|
||||
|
||||
|
||||
== Comparable software ==
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== See also ==
|
||||
code_aster
|
||||
SALOME
|
||||
Computational fluid dynamics
|
||||
OpenFOAM
|
||||
Finite volume method
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
Code_Saturne at EDF
|
||||
code_saturne on GitHub
|
||||
94
data/en.wikipedia.org/wiki/Coot_(software)-0.md
Normal file
94
data/en.wikipedia.org/wiki/Coot_(software)-0.md
Normal file
@ -0,0 +1,94 @@
|
||||
---
|
||||
title: "Coot (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Coot_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:10:58.073831+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The program Coot (Crystallographic Object-Oriented Toolkit) is used to display and manipulate atomic models of macromolecules, typically of proteins or nucleic acids, using 3D computer graphics. It is primarily focused on building and validation of atomic models into three-dimensional electron density maps obtained by X-ray crystallography methods, although it has also been applied to data from electron microscopy.
|
||||
|
||||
|
||||
== Overview ==
|
||||
Coot displays electron density maps and atomic models and allows model manipulations such as idealization, real space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers, and Ramachandran idealization. The software is designed to be easy-to-learn for novice users, achieved by ensuring that tools for common tasks are 'discoverable' through familiar user interface elements (menus and toolbars), or by intuitive behaviour (mouse controls). Recent developments have enhanced the usability of the software for expert users, with customisable key bindings, extensions, and an extensive scripting interface.
|
||||
Coot is free software, distributed under the GNU GPL. It is available from the Coot web site originally at the University of York, and now at the MRC Laboratory of Molecular Biology. Pre-compiled binaries are also available for Linux and Windows from the web page and CCP4, and for Mac OS X through Fink and CCP4. Additional support is available through the Coot wiki and an active COOT mailing list.
|
||||
The primary author is Paul Emsley (MRC-LMB at Cambridge). Other contributors include Kevin Cowtan, Bernhard Lohkamp and Stuart McNicholas (University of York), William Scott (University of California at Santa Cruz), and Eugene Krissinel (Daresbury Laboratory).
|
||||
|
||||
|
||||
== Features ==
|
||||
Coot can be used to read files containing 3D atomic coordinate models of macromolecular structures in a number of formats, including pdb, mmcif, and Shelx files. The model may then be rotated in 3D and viewed from any viewpoint. The atomic model is represented by default using a stick-model, with vectors representing chemical bonds. The two halves of each bond are coloured according to the element of the atom at that end of the bond, allowing chemical structure and identity to be visualised in a manner familiar to most chemists.
|
||||
Coot can also display electron density, which is the result of structure determination experiments such as X-ray crystallography and EM reconstruction. The density is contoured using a 3D-mesh. The contour level controlled using the mouse wheel for easy manipulation - this provides a simple way for the user to get an idea of the 3D electron density profile without the visual clutter of multiple contour levels. Electron density may be read into the program from ccp4 or cns map formats, though it is more common to calculate an electron density map directly from the X-ray diffraction data, read from an mtz, hkl, fcf or mmcif file.
|
||||
Coot provides extensive features for model building and refinement (i.e. adjusting the model to better fit the electron density), and for validation (i.e. checking that the atomic model agrees with the experimentally derived electron density and makes chemical sense). The most important of these tools is the real space refinement engine, which will optimize the fit of a section of atomic model to the electron density in real time, with graphical feedback. The user may also intervene in this process, dragging the atoms into the right places if the initial model is too far away from the corresponding electron density.
|
||||
|
||||
|
||||
=== Model building tools ===
|
||||
|
||||
Tools for general model building:
|
||||
|
||||
C-alpha baton mode - trace the main chain of a protein by placing correctly spaced alpha-carbon atoms.
|
||||
Ca Zone -> Mainchain - convert an initial trace of the alpha-carbon atoms to a full main-chain trace.
|
||||
Place helix here - fit a sequence of amino acids in alpha helix conformation into density.
|
||||
Place strand here - fit a sequence of amino acids in beta strand conformation into density.
|
||||
Ideal DNA/RNA - build an ideal DNA or RNA fragment.
|
||||
Find ligands - find and fit a model to any small molecule which may be bound to the macromolecule.
|
||||
Tools for moving existing atoms:
|
||||
|
||||
Real space refine zone - optimize the fit of the model to the electron density, while preserving stereochemistry.
|
||||
Regularize zone - optimize stereochemistry.
|
||||
Rigid body fit zone - optimize the fit of a rigid body to the electron density.
|
||||
Rotate/translate zone - manually position a rigid body.
|
||||
Rotamer tools (auto fit rotamer, manual rotamer, mutate and autofit, simple mutate)
|
||||
Torsion editing (edit chi angles, edit main chain torsions, general torsions)
|
||||
Other protein tools (flip peptide, flip sidechain, cis <-> trans)
|
||||
Tools for adding atoms to the model:
|
||||
|
||||
Find waters - add ordered solvent molecules to the model
|
||||
Add terminal residue - extend a protein or nucleotide chain
|
||||
Add alternate conformation
|
||||
Place atom at pointer
|
||||
|
||||
|
||||
=== Validation tools ===
|
||||
|
||||
In macromolecular crystallography, the observed data is often weak and the observation-to-parameter ratio near 1. As a result, it is possible to build an incorrect atomic model into the electron density in some cases. To avoid this, careful validation is required. Coot provides a range of validation tools, listed below. Having built an initial model, it is usual to check all of these and reconsider any parts of the model which are highlighted as problematic before deposition of the atomic coordinates with a public database.
|
||||
|
||||
Ramachandran plot - validate the torsion angles of a protein chain.
|
||||
Kleywegt plot - examine differences between the torsions of NCS-related chains.
|
||||
Incorrect chiral volumes - check for chiral centres with the wrong handedness.
|
||||
Unmodelled blobs - check for electron density not accounted for by existing atoms.
|
||||
Difference map peaks - check for large differences between observed and calculated density.
|
||||
Check/Delete waters - check for water molecules which do not fit the density.
|
||||
Check waters by difference map variance
|
||||
Geometry analysis - check for improbable bond lengths, angles, etc.
|
||||
Peptide omega analysis - check for non-planar peptide bonds.
|
||||
Temperature factor variance analysis -
|
||||
GLN and ASN B-factor outliers -
|
||||
Rotamer analysis - check for unusual protein side-chain conformations.
|
||||
Density fit analysis - identify parts of the model which don't fit the density.
|
||||
Probe clashes - check for Hydrogen atoms with inappropriate environments (using Molprobity).
|
||||
NCS differences - check for general differences between NCS related chains.
|
||||
Pukka puckers - check for unusual DNA/RNA conformations.
|
||||
|
||||
|
||||
== Program architecture ==
|
||||
|
||||
Coot is built upon a number of libraries. Crystallographic tools include the Clipper library for manipulating electron density and providing crystallographic algorithms, and the MMDB for the manipulation of atomic models. Other dependencies include FFTW, and the GNU Scientific Library.
|
||||
Much of the program's functionality is available through a scripting interface, which provides access from both the Python and Guile scripting languages.
|
||||
|
||||
|
||||
== Relation to CCP4mg ==
|
||||
The CCP4mg molecular graphics software from Collaborative Computational Project Number 4 is a related project with which Coot shares some code. The projects are focused on slightly different problems, with CCP4mg dealing with presentation graphics and movies, whereas Coot deals with model building and validation.
|
||||
|
||||
|
||||
== Impact in the crystallographic computing community ==
|
||||
The software has gained considerable popularity, overtaking widely used packages such as 'O', XtalView, and Turbo Frodo. As of April 2026, the primary publication has been cited in over 35,000 independent scientific papers since 2004.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Coot website
|
||||
Coot on Flathub
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Cosmology@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:00.165404+00:00"
|
||||
date_saved: "2026-05-05T10:10:59.313086+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
108
data/en.wikipedia.org/wiki/CuPy-0.md
Normal file
108
data/en.wikipedia.org/wiki/CuPy-0.md
Normal file
@ -0,0 +1,108 @@
|
||||
---
|
||||
title: "CuPy"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/CuPy"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:01.714180+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them.
|
||||
CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0.
|
||||
CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017.
|
||||
CuPy is a part of the NumPy ecosystem array libraries and is widely adopted to utilize GPU with Python, especially in high-performance computing environments such as Summit, Perlmutter, EULER, and ABCI.
|
||||
CuPy is a NumFOCUS sponsored project.
|
||||
|
||||
|
||||
== Features ==
|
||||
CuPy implements NumPy/SciPy-compatible APIs, as well as features to write user-defined GPU kernels or access low-level APIs.
|
||||
|
||||
|
||||
=== NumPy-compatible APIs ===
|
||||
The same set of APIs defined in the NumPy package (numpy.*) are available under cupy.* package.
|
||||
|
||||
Multi-dimensional array (cupy.ndarray) for boolean, integer, float, and complex data types
|
||||
Module-level functions
|
||||
Linear algebra functions
|
||||
Fast Fourier transform
|
||||
Random number generator
|
||||
|
||||
|
||||
=== SciPy-compatible APIs ===
|
||||
The same set of APIs defined in the SciPy package (scipy.*) are available under cupyx.scipy.* package.
|
||||
|
||||
Sparse matrices (cupyx.scipy.sparse.*_matrix) of CSR, COO, CSC, and DIA format
|
||||
Discrete Fourier transform
|
||||
Advanced linear algebra
|
||||
Multidimensional image processing
|
||||
Sparse linear algebra
|
||||
Special functions
|
||||
Signal processing
|
||||
Statistical functions
|
||||
|
||||
|
||||
=== User-defined GPU kernels ===
|
||||
Kernel templates for element-wise and reduction operations
|
||||
Raw kernel (CUDA C/C++)
|
||||
Just-in-time transpiler (JIT)
|
||||
Kernel fusion
|
||||
|
||||
|
||||
=== Distributed computing ===
|
||||
Distributed communication package (cupyx.distributed), providing collective and peer-to-peer primitives
|
||||
|
||||
|
||||
=== Low-level CUDA features ===
|
||||
Stream and event
|
||||
Memory pool
|
||||
Profiler
|
||||
Host API binding
|
||||
CUDA Python support
|
||||
|
||||
|
||||
=== Interoperability ===
|
||||
DLPack
|
||||
CUDA Array Interface
|
||||
NEP 13 (__array_ufunc__)
|
||||
NEP 18 (__array_function__)
|
||||
Array API Standard
|
||||
|
||||
|
||||
== Examples ==
|
||||
|
||||
|
||||
=== Array creation ===
|
||||
|
||||
|
||||
=== Basic operations ===
|
||||
|
||||
|
||||
=== Raw CUDA C/C++ kernel ===
|
||||
|
||||
|
||||
== Applications ==
|
||||
spaCy
|
||||
XGBoost
|
||||
turboSETI (Berkeley SETI)
|
||||
NVIDIA RAPIDS
|
||||
einops
|
||||
scikit-learn
|
||||
MONAI
|
||||
Chainer
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Array programming
|
||||
List of numerical-analysis software
|
||||
List of open-source mathematical libraries
|
||||
Dask
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
cupy on GitHub
|
||||
28
data/en.wikipedia.org/wiki/DEAP_(software)-0.md
Normal file
28
data/en.wikipedia.org/wiki/DEAP_(software)-0.md
Normal file
@ -0,0 +1,28 @@
|
||||
---
|
||||
title: "DEAP (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/DEAP_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:02.887423+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic flow and estimation of distribution algorithm. It is developed at Université Laval since 2009.
|
||||
|
||||
|
||||
== Example ==
|
||||
The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP.
|
||||
|
||||
|
||||
== See also ==
|
||||
Python SCOOP (software)
|
||||
Free software portal
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
deap on GitHub
|
||||
68
data/en.wikipedia.org/wiki/Deeplearning4j-0.md
Normal file
68
data/en.wikipedia.org/wiki/Deeplearning4j-0.md
Normal file
@ -0,0 +1,68 @@
|
||||
---
|
||||
title: "Deeplearning4j"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Deeplearning4j"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:04.064418+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark.
|
||||
Deeplearning4j is open-source software released under Apache License 2.0, developed mainly by a machine learning group headquartered in San Francisco. It is supported commercially by the startup Skymind, which bundles DL4J, TensorFlow, Keras and other deep learning libraries in an enterprise distribution called the Skymind Intelligence Layer. Deeplearning4j was contributed to the Eclipse Foundation in October 2017.
|
||||
|
||||
|
||||
== Introduction ==
|
||||
Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs).
|
||||
Deeplearning4j has been used in several commercial and academic applications. The code is hosted on GitHub. A support forum is maintained on Gitter.
|
||||
The framework is composable, meaning shallow neural nets such as restricted Boltzmann machines, convolutional nets, autoencoders, and recurrent nets can be added to one another to create deep nets of varying types. It also has extensive visualization tools, and a computation graph.
|
||||
|
||||
|
||||
== Distributed ==
|
||||
Training with Deeplearning4j occurs in a cluster. Neural nets are trained in parallel via iterative reduce, which works on Hadoop-YARN and on Spark. Deeplearning4j also integrates with CUDA kernels to conduct pure GPU operations, and works with distributed GPUs.
|
||||
|
||||
|
||||
== Scientific computing for the JVM ==
|
||||
Deeplearning4j includes an n-dimensional array class using ND4J that allows scientific computing in Java and Scala, similar to the functions that NumPy provides to Python. It's effectively based on a library for linear algebra and matrix manipulation in a production environment.
|
||||
|
||||
|
||||
== DataVec vectorization library for machine-learning ==
|
||||
DataVec vectorizes various file formats and data types using an input/output format system similar to Hadoop's use of MapReduce; that is, it turns various data types into columns of scalars termed vectors. DataVec is designed to vectorize CSVs, images, sound, text, video, and time series.
|
||||
|
||||
|
||||
== Text and NLP ==
|
||||
Deeplearning4j includes a vector space modeling and topic modeling toolkit, implemented in Java and integrating with parallel GPUs for performance. It is designed to handle large text sets.
|
||||
Deeplearning4j includes implementations of term frequency–inverse document frequency (tf–idf), deep learning, and Mikolov's word2vec algorithm, doc2vec, and GloVe, reimplemented and optimized in Java. It relies on t-distributed stochastic neighbor embedding (t-SNE) for word-cloud visualizations.
|
||||
|
||||
|
||||
== Real-world use cases and integrations ==
|
||||
Real-world use cases for Deeplearning4j include network intrusion detection and cybersecurity, fraud detection for the financial sector, anomaly detection in industries such as manufacturing, recommender systems in e-commerce and advertising, and image recognition. Deeplearning4j has integrated with other machine-learning platforms such as RapidMiner, Prediction.io, and Weka.
|
||||
|
||||
|
||||
== Machine Learning Model Server ==
|
||||
Deeplearning4j serves machine-learning models for inference in production using the free developer edition of SKIL, the Skymind Intelligence Layer. A model server serves the parametric machine-learning models that makes decisions about data. It is used for the inference stage of a machine-learning workflow, after data pipelines and model training. A model server is the tool that allows data science research to be deployed in a real-world production environment.
|
||||
What a Web server is to the Internet, a model server is to AI. Where a Web server receives an HTTP request and returns data about a Web site, a model server receives data, and returns a decision or prediction about that data: e.g. sent an image, a model server might return a label for that image, identifying faces or animals in photographs.
|
||||
The SKIL model server is able to import models from Python frameworks such as Tensorflow, Keras, Theano and CNTK, overcoming a major barrier in deploying deep learning models.
|
||||
|
||||
|
||||
== Benchmarks ==
|
||||
Deeplearning4j is as fast as Caffe for non-trivial image recognition tasks using multiple GPUs. For programmers unfamiliar with HPC on the JVM, there are several parameters that must be adjusted to optimize neural network training time. These include setting the heap space, the garbage collection algorithm, employing off-heap memory and pre-saving data (pickling) for faster ETL. Together, these optimizations can lead to a 10x acceleration in performance with Deeplearning4j.
|
||||
|
||||
|
||||
== API Languages: Java, Scala, Python, Clojure & Kotlin ==
|
||||
Deeplearning4j can be used via multiple API languages including Java, Scala, Python, Clojure and Kotlin. Its Scala API is called ScalNet. Keras serves as its Python API. And its Clojure wrapper is known as DL4CLJ. The core languages performing the large-scale mathematical operations necessary for deep learning are C, C++ and CUDA C.
|
||||
|
||||
|
||||
== Tensorflow, Keras & Deeplearning4j ==
|
||||
Tensorflow, Keras and Deeplearning4j work together. Deeplearning4j can import models from Tensorflow and other Python frameworks if they have been created with Keras.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Comparison of deep learning software
|
||||
Artificial intelligence
|
||||
Machine learning
|
||||
Deep learning
|
||||
|
||||
|
||||
== References ==
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Docking@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:02.723654+00:00"
|
||||
date_saved: "2026-05-05T10:11:05.279096+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
85
data/en.wikipedia.org/wiki/EAS3-0.md
Normal file
85
data/en.wikipedia.org/wiki/EAS3-0.md
Normal file
@ -0,0 +1,85 @@
|
||||
---
|
||||
title: "EAS3"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EAS3"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:07.608607+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
EAS3 (EAS = Ein-Ausgabe-System) is a software toolkit for reading and writing structured binary data with geometry information and for postprocessing of these data. It is meant to exchange floating-point data according to IEEE standard between different computers, to modify them or to convert them into other file formats. It can be used for all kinds of structured data sets. It is mainly used in the field of direct numerical simulations.
|
||||
|
||||
|
||||
== EAS3 package ==
|
||||
The complete package consists of libraries intended for usage in own codes and a separate command-line tool. It is written in Fortran and C and runs on all POSIX operating systems. The libraries include different numerical algorithms and subroutines for reading and writing files in the binary EAS3 file format. The read/write routines are provided in Fortran and C. Implemented numerical methods include, for example, Fast Fourier transform, Thomas algorithm and interpolation routines. The libraries are also suitable for vector computers.
|
||||
|
||||
|
||||
== History ==
|
||||
EAS3 has been developed at the Institut für Aerodynamik und Gasdynamik (IAG) of the University of Stuttgart. The previous versions (EAS, EAS2) range back to the end of the 1980s, when computer power allowed the first spatial DNS computations. The upcoming amount of data required efficient handling and postprocessing. Typically, simulations were, and are still today, performed on a high-performance computer and afterwards postprocessed on other machines of opposite endianness. This required an endianness-independent file format for data handling.
|
||||
Since the publication of EAS3 in the 1999, the software has been developed continuously by members of the involved institutes. Since 2007, EAS3 is also available via the heise software directory. EAS3 is used by applications within the European PRACE project. The current version number is 1.6.7 from April, 2009.
|
||||
|
||||
|
||||
== File Format ==
|
||||
|
||||
The EAS3 file format is used to store floating point data in IEEE format and to exchange the files between different computer architectures (little/big endian). The data is organized as parameters with one parameter being a one-, two- or three-dimensional floating point array. Several of these parameters may be combined to one time step. This allows to store five-dimensional arrays. Data can be written in single-precision (32 Bit), double-precision (64 Bit) or quadruple-precision (128 Bit). Geometry information for the different directions are saved in the header of the file. It is also possible to store additional information in user defined arrays there. With the file size being limited only by the computer itself (e.g. file system), EAS3 files are suitable for large simulations and thus for high-performance computing.
|
||||
|
||||
|
||||
== Functionality ==
|
||||
The actual EAS3 executable is a command-line interface for alteration of EAS3 files. The implemented commands range from basic operations, e.g. simple computations, file operations, to rather complex operations like Fourier transformation or the computation of derivatives. Specific commands for DNS data are also available, e.g. the lambda2 vortex criterion. As the commands are read from standard input, EAS3 may be used in shell scripts for automated calls.
|
||||
|
||||
Outline of important functions
|
||||
|
||||
file management: rearrangement, attaching two files, cutting
|
||||
conversion to other file formats (ASCII, Covise, Tecplot)
|
||||
mathematical operationes: basic operations, logarithm, etc.
|
||||
derivatives and integration
|
||||
interpolation
|
||||
data reduction: mean values, RMS-values, etc.
|
||||
Fourier transformation: single/double, real/complex
|
||||
DNS specific: vortex criterion
|
||||
|
||||
|
||||
== Installation ==
|
||||
The sources can be obtained directly from the CVS repository or one may download a zipped tar file. Makefiles for different machine types are included, providing an easy compilation. As linking of object files, created with different Fortran compilers can cause problems, binary packages (RPM, .deb) are not offered up to now.
|
||||
|
||||
|
||||
== Advantages and disadvantages ==
|
||||
|
||||
|
||||
=== Advantages ===
|
||||
The main profit for the programmer is the easy implementation of reading/writing large (>2GB) binary data sets. The library provides that the data is always written big endian. The resulting platform independence allows data exchange between different hardware architectures, e.g. supercomputers. The users benefits from the different methods provided for postprocessing, which can be automated using shell scripts.
|
||||
|
||||
|
||||
=== Disadvantages ===
|
||||
Being specialized on structured grids may be a problem for some users. Up to now, only cartesian grids or a representation of the data in spectral space are implemented. Data in other types of data alignment, e.g. cylindrical coordinates, can be stored in EAS3 files but the existing postprocessing commands may not be used. As the usually used visualization programs do not support the EAS3 file format directly, it is often necessary to convert the data to the corresponding file format. Commands in the EAS3 program are given by a text interface, a graphical user interface does not exist. Completion of the commands in the EAS3 command line provides support for interactive usage but for an extensive help, the descriptions on the webpage are necessary.
|
||||
|
||||
|
||||
== License ==
|
||||
EAS3 is published under the MIT License. The MIT License is a free software license originating at the Massachusetts Institute of Technology (MIT). Specifically, it is a GPL-compatible permissive license, meaning that it permits reuse within proprietary software on the condition that the license is distributed with that software.
|
||||
|
||||
|
||||
== Usage ==
|
||||
Transition group at the Institute of Aerodynamics and Gasdynamics (IAG) of the University of Stuttgart: http://www.iag.uni-stuttgart.de
|
||||
Computational Fluid Dynamics Laboratory of the University of Arizona: https://web.archive.org/web/19971222125309/http://cfd.ame.arizona.edu/
|
||||
Institute of Fluid Dynamics, ETH Zurich: http://www.ifdmavt.ethz.ch
|
||||
Lehrstuhl für Aerodynamik at the Technical University of Munich (high-speed aerodynamics group): http://www.aer.mw.tum.de
|
||||
|
||||
|
||||
== Related file formats ==
|
||||
Common Data Format (CDF)
|
||||
CGNS (CFD General Notation System)
|
||||
FITS (Flexible Image Transport System)
|
||||
GRIB (GRIdded Binary)
|
||||
Hierarchical Data Format (HDF)
|
||||
NetCDF (Network Common Data Form)
|
||||
Tecplot binary files
|
||||
XMDF (eXtensible Model Data Format)
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
|
||||
EAS3 project web page
|
||||
38
data/en.wikipedia.org/wiki/EGS_(program)-0.md
Normal file
38
data/en.wikipedia.org/wiki/EGS_(program)-0.md
Normal file
@ -0,0 +1,38 @@
|
||||
---
|
||||
title: "EGS (program)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EGS_(program)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:09.909054+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The EGS (Electron Gamma Shower) computer code system is a general purpose package for the Monte Carlo simulation of the coupled transport of electrons and photons in an arbitrary geometry for particles with energies from a few keV up to several hundreds of GeV. It originated at SLAC but National Research Council of Canada and KEK have been involved in its development since the early 80s.
|
||||
Development of the original EGS code ended with version EGS4. Since then two groups have re-written the code with new physics:
|
||||
|
||||
EGSnrc, maintained by the Ionizing Radiation Standards Group, Measurement Science and Standards, National Research Council of Canada
|
||||
EGS5, maintained by KEK, the Japanese particle physics research facility.
|
||||
|
||||
|
||||
== EGSnrc ==
|
||||
|
||||
EGSnrc is a general-purpose software toolkit that can be applied to build Monte Carlo simulations of coupled electron-photon transport, for particle energies ranging from 1 keV to 10 GeV. It is widely used internationally in a variety of radiation-related fields. The EGSnrc implementation improves the accuracy and precision of the charged particle transport mechanics and the atomic scattering cross-section data. The charged particle multiple scattering algorithm allows for large step sizes without sacrificing accuracy - a key feature of the toolkit that leads to fast simulation speeds. EGSnrc also includes a C++ class library called egs++ that can be used to model elaborate geometries and particle sources.
|
||||
EGSnrc is open source and distributed on GitHub under the GNU Affero General Public License. The documentation for EGSnrc is also available online.
|
||||
EGSnrc is distributed with a wide range of applications that utilize the radiation transport physics to calculate specific quantities. These codes have been developed by numerous authors over the lifetime of EGSnrc to support the large user community. It is possible to calculate quantities such as absorbed dose, kerma, particle fluence, and much more, with complex geometrical conditions. One of the most well-known EGSnrc applications is BEAMnrc, which was developed as part of the OMEGA project. This was a collaboration between the National Research Council of Canada and a research group at the University of Wisconsin–Madison. All types of medical linear accelerators can be modelled using the BEAMnrc's component module system.
|
||||
|
||||
|
||||
== See also ==
|
||||
GEANT (program)
|
||||
Geant4
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
NRC-CNRC page for EGSnrc
|
||||
KEK page for EGS5
|
||||
EGSnrc Github page
|
||||
EGSnrc online documentation
|
||||
EGSnrc subreddit
|
||||
40
data/en.wikipedia.org/wiki/ELKI-0.md
Normal file
40
data/en.wikipedia.org/wiki/ELKI-0.md
Normal file
@ -0,0 +1,40 @@
|
||||
---
|
||||
title: "ELKI"
|
||||
chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/ELKI"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:18.253498+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is a data mining (KDD, knowledge discovery in databases) software framework developed for use in research and teaching. It was originally created by the database systems research unit at LMU Munich, Germany, led by Professor Hans-Peter Kriegel. The project has continued at the Technical University of Dortmund, Germany. It aims at allowing the development and evaluation of advanced data mining algorithms and their interaction with database index structures.
|
||||
|
||||
== Description ==
|
||||
The ELKI framework is written in Java and built around a modular architecture. Most currently included algorithms perform clustering, outlier detection, and database indexes. The object-oriented architecture allows the combination of arbitrary algorithms, data types, distance functions, indexes, and evaluation measures. The Java just-in-time compiler optimizes all combinations to a similar extent, making benchmarking results more comparable if they share large parts of the code. When developing new algorithms or index structures, the existing components can be easily reused, and the type safety of Java detects many programming errors at compile time.
|
||||
ELKI is a free tool for analyzing data, mainly focusing on finding patterns and unusual data points without needing labels. It's written in Java and aims to be fast and able to handle big datasets by using special structures. It's made for researchers and students to add their own methods and compare different algorithms easily.
|
||||
ELKI has been used in data science to cluster sperm whale codas, for phoneme clustering, for anomaly detection in spaceflight operations, for bike sharing redistribution, and traffic prediction.
|
||||
|
||||
== Objectives ==
|
||||
The university project is developed for use in teaching and research. The source code is written with extensibility and reusability in mind, but is also optimized for performance. The experimental evaluation of algorithms depends on many environmental factors and implementation details can have a large impact on the runtime. ELKI aims at providing a shared codebase with comparable implementations of many algorithms.
|
||||
As research project, it currently does not offer integration with business intelligence applications or an interface to common database management systems via SQL. The copyleft (AGPL) license may also be a hindrance to an integration in commercial products; nevertheless it can be used to evaluate algorithms prior to developing an own implementation for a commercial product. Furthermore, the application of the algorithms requires knowledge about their usage, parameters, and study of original literature. The audience is students, researchers, data scientists, and software engineers.
|
||||
|
||||
== Architecture ==
|
||||
ELKI is modeled around a database-inspired core, which uses a vertical data layout that stores data in column groups (similar to column families in NoSQL databases). This database core provides nearest neighbor search, range/radius search, and distance query functionality with index acceleration for a wide range of dissimilarity measures. Algorithms based on such queries (e.g. k-nearest-neighbor algorithm, local outlier factor and DBSCAN) can be implemented easily and benefit from the index acceleration.
|
||||
The database core also provides fast and memory efficient collections for object collections and associative structures such as nearest neighbor lists.
|
||||
ELKI makes extensive use of Java interfaces, so that it can be extended easily in many places. For example, custom data types, distance functions, index structures, algorithms, input parsers, and output modules can be added and combined without modifying the existing code. This includes the possibility of defining a custom distance function and using existing indexes for acceleration.
|
||||
ELKI uses a service loader architecture to allow publishing extensions as separate jar files.
|
||||
ELKI uses optimized collections for performance rather than the standard Java API. For loops for example are written similar to C++ iterators:
|
||||
|
||||
In contrast to typical Java iterators (which can only iterate over objects), this conserves memory, because the iterator can internally use primitive values for data storage. The reduced garbage collection improves the runtime. Optimized collections libraries such as GNU Trove3, Koloboke, and fastutil employ similar optimizations. ELKI includes data structures such as object collections and heaps (for, e.g., nearest neighbor search) using such optimizations.
|
||||
|
||||
== Visualization ==
|
||||
The visualization module uses SVG for scalable graphics output, and Apache Batik for rendering of the user interface as well as lossless export into PostScript and PDF for easy inclusion in scientific publications in LaTeX.
|
||||
Exported files can be edited with SVG editors such as Inkscape. Since cascading style sheets are used, the graphics design can be restyled easily.
|
||||
Unfortunately, Batik is rather slow and memory intensive, so the visualizations are not very scalable to large data sets (for larger data sets, only a subsample of the data is visualized by default).
|
||||
|
||||
== Awards ==
|
||||
Version 0.4, presented at the "Symposium on Spatial and Temporal Databases" 2011, which included various methods for spatial outlier detection, won the conference's "best demonstration paper award".
|
||||
|
||||
== Included algorithms ==
|
||||
Select included algorithms:
|
||||
102
data/en.wikipedia.org/wiki/ELKI-1.md
Normal file
102
data/en.wikipedia.org/wiki/ELKI-1.md
Normal file
@ -0,0 +1,102 @@
|
||||
---
|
||||
title: "ELKI"
|
||||
chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/ELKI"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:18.253498+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Cluster analysis:
|
||||
K-means clustering (including fast algorithms such as Elkan, Hamerly, Annulus, and Exponion k-Means, and robust variants such as k-means--)
|
||||
K-medians clustering
|
||||
K-medoids clustering (PAM) (including FastPAM and approximations such as CLARA, CLARANS)
|
||||
Expectation-maximization algorithm for Gaussian mixture modeling
|
||||
Hierarchical clustering (including the fast SLINK, CLINK, NNChain and Anderberg algorithms)
|
||||
Single-linkage clustering
|
||||
Leader clustering
|
||||
DBSCAN (Density-Based Spatial Clustering of Applications with Noise, with full index acceleration for arbitrary distance functions)
|
||||
OPTICS (Ordering Points To Identify the Clustering Structure), including the extensions OPTICS-OF, DeLi-Clu, HiSC, HiCO and DiSH
|
||||
HDBSCAN
|
||||
Mean-shift clustering
|
||||
BIRCH clustering
|
||||
SUBCLU (Density-Connected Subspace Clustering for High-Dimensional Data)
|
||||
CLIQUE clustering
|
||||
ORCLUS and PROCLUS clustering
|
||||
COPAC, ERiC and 4C clustering
|
||||
CASH clustering
|
||||
DOC and FastDOC subspace clustering
|
||||
P3C clustering
|
||||
Canopy clustering algorithm
|
||||
Anomaly detection:
|
||||
k-Nearest-Neighbor outlier detection
|
||||
LOF (Local outlier factor)
|
||||
LoOP (Local Outlier Probabilities)
|
||||
OPTICS-OF
|
||||
DB-Outlier (Distance-Based Outliers)
|
||||
LOCI (Local Correlation Integral)
|
||||
LDOF (Local Distance-Based Outlier Factor)
|
||||
EM-Outlier
|
||||
SOD (Subspace Outlier Degree)
|
||||
COP (Correlation Outlier Probabilities)
|
||||
Frequent Itemset Mining and association rule learning
|
||||
Apriori algorithm
|
||||
Eclat
|
||||
FP-growth
|
||||
Dimensionality reduction
|
||||
Principal component analysis
|
||||
Multidimensional scaling
|
||||
T-distributed stochastic neighbor embedding (t-SNE)
|
||||
Spatial index structures and other search indexes:
|
||||
R-tree
|
||||
R*-tree
|
||||
M-tree
|
||||
k-d tree
|
||||
X-tree
|
||||
Cover tree
|
||||
iDistance
|
||||
NN descent
|
||||
Locality sensitive hashing (LSH)
|
||||
Evaluation:
|
||||
Precision and recall, F1 score, Average Precision
|
||||
Receiver operating characteristic (ROC curve)
|
||||
Discounted cumulative gain (including NDCG)
|
||||
Silhouette index
|
||||
Davies–Bouldin index
|
||||
Dunn index
|
||||
Density-based cluster validation (DBCV)
|
||||
Visualization
|
||||
Scatter plots
|
||||
Histograms
|
||||
Parallel coordinates (also in 3D, using OpenGL)
|
||||
Other:
|
||||
Statistical distributions and many parameter estimators, including robust MAD based and L-moment based estimators
|
||||
Dynamic time warping
|
||||
Change point detection in time series
|
||||
Intrinsic dimensionality estimators
|
||||
|
||||
== Version history ==
|
||||
Version 0.1 (July 2008) contained several Algorithms from cluster analysis and anomaly detection, as well as some index structures such as the R*-tree. The focus of the first release was on subspace clustering and correlation clustering algorithms.
|
||||
Version 0.2 (July 2009) added functionality for time series analysis, in particular distance functions for time series.
|
||||
Version 0.3 (March 2010) extended the choice of anomaly detection algorithms and visualization modules.
|
||||
Version 0.4 (September 2011) added algorithms for geo data mining and support for multi-relational database and index structures.
|
||||
Version 0.5 (April 2012) focuses on the evaluation of cluster analysis results, adding new visualizations and some new algorithms.
|
||||
Version 0.6 (June 2013) introduces a new 3D adaption of parallel coordinates for data visualization, apart from the usual additions of algorithms and index structures.
|
||||
Version 0.7 (August 2015) adds support for uncertain data types, and algorithms for the analysis of uncertain data.
|
||||
Version 0.7.5 (February 2019) adds additional clustering algorithms, anomaly detection algorithms, evaluation measures, and indexing structures.
|
||||
Version 0.8 (October 2022) adds automatic index creation, garbage collection, and incremental priority search, as well as many more algorithms such as BIRCH.
|
||||
|
||||
== Similar applications ==
|
||||
scikit-learn: machine learning library in Python
|
||||
Weka: A similar project by the University of Waikato, with a focus on classification algorithms
|
||||
RapidMiner: An application available commercially (a restricted version is available as open source)
|
||||
KNIME: An open source platform which integrates various components for machine learning and data mining
|
||||
|
||||
== See also ==
|
||||
Comparison of statistical packages
|
||||
|
||||
== References ==
|
||||
|
||||
== External links ==
|
||||
Official website of ELKI with download and documentation.
|
||||
31
data/en.wikipedia.org/wiki/EMBOSS-0.md
Normal file
31
data/en.wikipedia.org/wiki/EMBOSS-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "EMBOSS"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EMBOSS"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:14.772731+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
EMBOSS is a free c software analysis package developed for the needs of the molecular biology and bioinformatics user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole.
|
||||
EMBOSS is an acronym for European Molecular Biology Open Software Suite. The European part of the name hints at the wider scope. The core EMBOSS groups are collaborating with many other groups to develop the new applications that the users need. This was done from the beginning with EMBnet, the European Molecular Biology Network. EMBnet has many nodes worldwide most of which are national bioinformatics services. EMBnet has the programming expertise.
|
||||
In September 1998, the first workshop was held, when 30 people from EMBnet went to Hinxton to learn about EMBOSS and to discuss the way forward.
|
||||
The EMBOSS package contains a variety of applications for sequence alignment, rapid database searching with sequence patterns, protein motif identification (including domain analysis), and much more.
|
||||
The AJAX and NUCLEUS libraries are released under the GNU Library General Public Licence. EMBOSS applications are released under the GNU General Public Licence.
|
||||
|
||||
|
||||
== EMBOSS application groups ==
|
||||
|
||||
|
||||
== See also ==
|
||||
Open Bioinformatics Foundation
|
||||
Soaplab - A SOAP web service interface including EMBOSS
|
||||
Genostar - Integration of some of EMBOSS tools in a graphical application
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EOn"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:05.162861+00:00"
|
||||
date_saved: "2026-05-05T10:11:19.475402+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
23
data/en.wikipedia.org/wiki/EXMARaLDA-0.md
Normal file
23
data/en.wikipedia.org/wiki/EXMARaLDA-0.md
Normal file
@ -0,0 +1,23 @@
|
||||
---
|
||||
title: "EXMARaLDA"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EXMARaLDA"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:21.843918+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
EXMARaLDA (Extensible Markup Language for Discourse Annotation) is a set of free software tools for creating, managing and analyzing spoken language corpora. It consists of a transcription tool (comparable to tools like Praat or Transcriber), a tool for administering corpus meta data and a tool for doing queries (KWIC searches) on spoken language corpora. EXMARaLDA is used for doing conversation and discourse analysis, dialectology, phonology and research into first and second language acquisition in children and adults. EXMARaLDA is based on the open standards XML and Unicode and programmed in Java.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
Schmidt, Thomas and Wörner, Kai (2014). "EXMARaLDA" In: Handbook on Corpus Phonology. Oxford University Press, 402–419.
|
||||
Schmidt, Thomas and Wörner, Kai (2009). "EXMARaLDA – Creating, analysing and sharing spoken language corpora for pragmatic research." In: Pragmatics 19.
|
||||
Schmidt, Thomas and Bennöhr, Jasmine (2008). "Rescuing Legacy Data." In: Language Documentation and Conservation 2, 109–129.
|
||||
|
||||
|
||||
== External links ==
|
||||
exmaralda.org - Official project website
|
||||
std.metu.edu.tr - Website of the METU Spoken Turkish Corpus, a corpus constructed with EXMARaLDA
|
||||
@ -0,0 +1,57 @@
|
||||
---
|
||||
title: "Earth System Modeling Framework"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Earth_System_Modeling_Framework"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:06.463575+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Earth System Modeling Framework (ESMF) is open-source software for building climate, numerical weather prediction, data assimilation, and other Earth science software applications. These applications are computationally demanding and usually run on supercomputers. The ESMF is considered a technical layer, integrated into a sophisticated common modeling infrastructure for interoperability. Other aspects of interoperability and shared infrastructure include: common experimental protocols, common analytic methods, common documentation standards for data and data provenance, shared workflow, and shared model components.
|
||||
The ESMF project is distinguished by its strong emphasis on community governance and distributed development, and by a diverse customer base that includes modeling groups from universities, major U.S. research centers, the National Weather Service, the Department of Defense, and NASA. The ESMF development team was centered at NCAR until 2009, after which it moved to the NOAA Earth System Research Laboratories.
|
||||
Editing Earth System Modeling Framework is free software released under the University of Illinois/NCSA Open Source License.
|
||||
|
||||
|
||||
== Purpose ==
|
||||
ESMF increases the interoperability of Earth-science modeling software developed at different sites and promotes code reuse. The idea is to transform distributed, specialized knowledge and resources into a collaborative, integrated modeling community that operates more efficiently, can address a wider variety of problems more effectively, and is more responsive to societal needs.
|
||||
|
||||
|
||||
== Software architecture ==
|
||||
ESMF is based on principles of component-based software engineering. The components within an ESMF software application usually represent large-scale physical domains such as the atmosphere, ocean, cryosphere, or land surface. Some models also represent specific processes (e.g. ocean biogeochemistry, the impact of solar radiation on the atmosphere) as components. In ESMF, components can create and drive other components so that an ocean biogeochemistry component can be part of a larger ocean component.
|
||||
The software that connects physical domains is called a coupler in the Earth system modeling community. Couplers follow the mediator pattern and take the outputs from one component and transform them into the inputs that are needed to run another component. Transformations may include unit conversions, grid interpolation or remapping, mergers (i.e., combining land and ocean surfaces to form a completely covered global surface) or other specialized transformations. In ESMF, couplers are also software components.
|
||||
|
||||
|
||||
== Capabilities ==
|
||||
ESMF represents user data in the form of data objects such as grids, fields, and arrays. The user data within a component may be copied or referenced into these ESMF objects. Once user data is part of an ESMF data object, framework methods can be used to transform and transfer the data as required to other components in the system. This generally happens within a coupler component.
|
||||
Grid interpolation and remapping are core utilities of ESMF. Interpolation weights can be generated in ESMF using bilinear interpolation, finite element patch recovery, and conservative remapping methods.
|
||||
ESMF can associate metadata with data objects. The metadata, in the form of name and value pairs, is grouped into packages, which can be written out in XML and other standard formats. ESMF metadata packages are based on community conventions including the Climate and Forecast Metadata Conventions and the METAFOR Common Information Model.
|
||||
|
||||
|
||||
== History ==
|
||||
The ESMF collaboration had its roots in the Common Modeling Infrastructure Working Group (CMIWG), an unfunded, grass-roots effort to explore ways of enhancing collaborative Earth system model development. The CMIWG attracted broad participation from major weather and climate modeling groups at research and operational centers. In a series of meetings held from 1998 to 2000, CMIWG members established general requirements and a preliminary design for a common software framework.
|
||||
In September 2000, the NASA Earth Science Technology Office (ESTO) released a solicitation that called for the creation of an ESMF. A critical mass of CMIWG participants agreed to develop a coordinated response, based on their strawman framework design, and submitted three linked proposals. The first focused on development of the core ESMF software, the second on deployment of Earth science modeling applications, and the third on deployment of ESMF data assimilation applications. All three proposals were funded, at a collective level of $9.8 million over a three-year period. As the ESMF project gained momentum, it replaced the CMIWG as the focal point for developing community modeling infrastructure.
|
||||
During the period of NASA funding, the ESMF team developed a prototype of the framework and used it in a number of experiments that demonstrated coupling of modeling components from different institutions. ESMF was also used as the basis for the construction of a new model, the Goddard Earth Observing System (GEOS) atmospheric general circulation model at NASA Goddard.
|
||||
As the end of the first funding cycle for ESMF neared, its collaborators wrote a project plan that described how ESMF could transition to an organization with multi-agency sponsorship for its next funding cycle. Major new five-year grants came from NASA, through the Modeling Analysis and Prediction (MAP) program for climate change and variability, and from the Department of Defense Battlespace Environments Institute. The National Science Foundation (NSF) continued funding part of the development team through NCAR core funds. Many smaller ESMF-based application adoption projects were funded in domains as diverse as space weather and sediment transport.
|
||||
Also at the end of the first funding cycle, the ESMF collaborators wrote a white paper on future directions for the ESMF. This paper formed the basis for a proposal to NSF to combine ESMF (and other software frameworks) with data services to create a computational environment that supports an end-to-end modeling workflow.
|
||||
In 2008, a project manager was appointed for the National Unified Operational Prediction Capability (NUOPC), a joint project for weather prediction of the United States Navy, the National Weather Service, and the United States Air Force.
|
||||
|
||||
|
||||
== See also ==
|
||||
Coupled Model Intercomparison Project (CMIP)
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== Further reading ==
|
||||
N. Collins; G. Theurich; C. DeLuca; M. Suarez; A. Trayanov; V. Balaji; P. Li; W. Yang; C. Hill; A. da Silva (2005). "Design and Implementation of Components in the Earth System Modeling Framework". International Journal of High Performance Computing Applications. 19 (3): 341. Bibcode:2005IJHPC..19..341C. doi:10.1177/1094342005056120. S2CID 41367442.
|
||||
Hill, C.; Deluca, C.; Balaji; Suarez, M.; Da Silva, A. (2004). "The architecture of the earth system modeling framework". Computing in Science & Engineering. 6 (1): 18–28. Bibcode:2004CSE.....6a..18H. doi:10.1109/MCISE.2004.1255817. S2CID 9311752.
|
||||
Dunlap, Rocky; Mark, Leo; Rugaber, Spencer; Balaji, V.; Chastang, Julien; Cinquini, Luca; Deluca, Cecelia; Middleton, Don; Murphy, Sylvia (2008). "Earth system curator: Metadata infrastructure for climate modeling". Earth Science Informatics. 1 (3–4): 131–149. doi:10.1007/s12145-008-0016-1.
|
||||
|
||||
|
||||
== External links ==
|
||||
Earth System Modeling Framework (ESMF)
|
||||
Earth System Prediction Capability (ESPC)
|
||||
Modeling Analysis and Prediction Program (NASA)
|
||||
Earth System Grid Federation (ESGF)
|
||||
42
data/en.wikipedia.org/wiki/EgoNet-0.md
Normal file
42
data/en.wikipedia.org/wiki/EgoNet-0.md
Normal file
@ -0,0 +1,42 @@
|
||||
---
|
||||
title: "EgoNet"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/EgoNet"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:08.766768+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
EgoNet (Egocentric Network Study Software) is a program for the collection and analysis of egocentric social network data. It helps the user to collect and analyse all the egocentric network data (all social network data of a website on the Internet), and provide general global network measures and data matrixes that can be used for further analysis by other software. The egonet is the result of the links that it gives and receives certain address on the Internet, and EgoNet is dedicated to collecting information about them and present it in a way useful to the users.
|
||||
Egonet is written in Java, so that the computer where it is going to be used must have the JRE installed. EgoNet is open source software, licensed under GPL.
|
||||
Egonet was created by Christopher McCarty, a professor at the University of Florida, United States.
|
||||
|
||||
|
||||
== Features ==
|
||||
The program allows to create questionnaires, collect data and provide comprehensive measures and arrays of data that can be used for subsequent analysis by other software.
|
||||
Its main benefits are the generation of questionnaires for relational data, the calculation of relevant General measurements for the analysis of social networks and production graphs.
|
||||
|
||||
|
||||
== Components ==
|
||||
Egonet is composed of the following modules:
|
||||
|
||||
EgoNetW, that allows to create formats of questionnaires for the pursuit of studies;
|
||||
EgoNetClientW: used for data load - once defined the relevant questions and the structure of the questionnaires.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Graphviz
|
||||
GraphStream
|
||||
graph-tool
|
||||
JUNG
|
||||
NetworkX
|
||||
Tulip
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
@ -4,7 +4,7 @@ chunk: 1/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 3/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 4/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 5/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 6/6
|
||||
source: "https://en.wikipedia.org/wiki/Einstein@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:03.971656+00:00"
|
||||
date_saved: "2026-05-05T10:11:11.134747+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
48
data/en.wikipedia.org/wiki/Elastix_(image_registration)-0.md
Normal file
48
data/en.wikipedia.org/wiki/Elastix_(image_registration)-0.md
Normal file
@ -0,0 +1,48 @@
|
||||
---
|
||||
title: "Elastix (image registration)"
|
||||
chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Elastix_(image_registration)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:12.309035+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for case-specific applications. It was first developed by Stefan Klein and Marius Staring under the supervision of Josien P.W. Pluim at Image Sciences Institute (ISI). Its first version was command-line based, allowing the final user to employ scripts to automatically process big data-sets and deploy multiple registration pipelines with few lines of code. Nowadays, to further widen its audience, a version called SimpleElastix is also available, developed by Kasper Marstal, which allows the integration of elastix with high level languages, such as Python, Java, and R.
|
||||
|
||||
== Image registration fundamentals ==
|
||||
|
||||
Image registration is a well-known technique in digital image processing that searches for the geometric transformation that, applied to a moving image, obtains a one-to-one map with a target image. Generally, the images acquired from different sensors (multimodal), time instants (multitemporal), and points of view (multiview) should be correctly aligned to proceed with further processing and feature extraction. Even though there are a plethora of different approaches to image registration, the majority is composed of the same macro building blocks, namely the transformation, the interpolator, the metric, and the optimizer. Registering two or more images can be framed as an optimization problem that requires multiple iterations to converge to the best solution. Starting from an initial transformation computed from the image moments the optimization process searches for the best transformation parameters based on the value of the selected similarity metric. The figure on the right shows the high-level representation of the registration of two images, where the reference remains constant during the entire process, while the moving one will be transformed according to the transformation parameters. In other words, the registration ends when the similarity metric, which is a mathematical function with a certain number of parameters to be optimized, reaches the optimal value which is highly dependent on the specific application.
|
||||
|
||||
== Main building blocks ==
|
||||
Following the structure of the image registration workflow, the elastix toolbox proposes a modular solution that implements for each of the building blocks different algorithms, highly employed in medical image registration, and helps the final users to build their specific pipeline by selecting the most suitable algorithm for each of the main building blocks. Each block is easily configurable both by selecting pre-defined initialization values or by trying multiple sets of parameters and then choosing the most performing one. The registration is performed on images, and the elastix toolbox supports all the data formats supported by ITK, ranging from JPEG and PNG to medical standard formats such as DICOM and NIFTI. It also stores physical pixel spacing, the origin and the relative position to an external world reference system, when provided in the metadata, to facilitate the registration process, especially in medical field applications.
|
||||
|
||||
=== Transformation ===
|
||||
The transformation is an essential building block, since it defines the allowable transformations. In image registration, the main distinction can be done between parallel-to-parallel and parallel-to-non parallel (deformable) line mapping transformations. In the elastix toolbox, the final users can select one transformation or compose more transformations either through addition or via composition. Below are reported the different transformation models in order of increasing flexibility, along with the corresponding elastix class names between brackets.
|
||||
|
||||
Translation (TranslationTransform) allows only translations
|
||||
Rigid (EulerTransform) expands the translation adding rotations and the object is seen as a rigid body
|
||||
Similarity (SimilarityTransform) expands the rigid transformation by introducing isotropic scaling
|
||||
Affine (AffineTransform) expands the rigid transformation allowing both scaling and shear
|
||||
B-splines (BSplineTransform) is a deformable transformation usually preceded by a rigid or affine one
|
||||
Thin-plate splines (SplineKernelTransform) is a deformable transformation belonging to the class of kernel-based transformations that is a composition of and affine and a non-rigid part
|
||||
|
||||
=== Metric ===
|
||||
The similarity metric is the mathematical function whose parameters should be optimized to reach the desired registration, and, during the process, it is computed multiple times. Below are reported the available metrics computed employing the reference and the transformed images and the corresponding elastix class names between brackets.
|
||||
|
||||
Mean squared difference (AdvancedMeanSquares) to be used for mono-modal applications
|
||||
Normalized correlation coefficient (AdvancedNormalizedCorrelation) to be used for images that have an intensity linear relationship
|
||||
Mutual information (AdvancedMattesMutualInformation) to be used for both mono- and multi-modal applications and optimized to reach better performance compared to the normalized version
|
||||
Normalized mutual information (NormalizedMutualInformation) for both mono- and multi-modal applications
|
||||
Kappa statistic (AdvancedKappaStatistic) to be used only for binary images
|
||||
|
||||
=== Sampler ===
|
||||
For the computation of the similarity metrics, it is not always necessary to consider all the voxels and, sometimes, it can be useful to use only a fraction of the voxels of the images, i.e. to reduce the execution time for big input images. Below are reported the available criteria for selecting a fraction of the voxels for the similarity metric computation and the corresponding elastix class names between brackets.
|
||||
|
||||
Full (Full) to employ all the voxels
|
||||
Grid (Grid) to employ a regular grid defined by the user to downsample the image
|
||||
Random (Random) to randomly select a percentage of voxels defined by the users (all voxels have equal probability to be selected)
|
||||
Random coordinate (RandomCoordinate) like the random criterion, but in this case also off-grid positions can be selected to simplify the optimization process
|
||||
|
||||
=== Interpolator ===
|
||||
After the application of the transformation, it may occur that the voxels used for the similarity metric computation are at non-voxel positions, so intensity interpolation should be performed to ensure the correctness of the computed values. Below are reported the implemented interpolators and the corresponding elastix class names between brackets.
|
||||
34
data/en.wikipedia.org/wiki/Elastix_(image_registration)-1.md
Normal file
34
data/en.wikipedia.org/wiki/Elastix_(image_registration)-1.md
Normal file
@ -0,0 +1,34 @@
|
||||
---
|
||||
title: "Elastix (image registration)"
|
||||
chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Elastix_(image_registration)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:12.309035+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Nearest neighbor (NearestNeighborInterpolator) exploits little resources, but gives low quality results
|
||||
Linear (LinearInterpolator) is sufficient in general applications
|
||||
N-th order B-spline (BSplineInterpolator) can be used to increase the order N, increasing quality and computation time. N=0 and N=1 indicate the nearest neighbor and linear cases respectively.
|
||||
|
||||
=== Optimizer ===
|
||||
The optimizer defines the strategy employed for searching the best transformation parameter to reach the correct registration, and it is commonly an iterative strategy. Below are reported some of the implemented optimization strategies.
|
||||
|
||||
Gradient descent
|
||||
Robbins-Monro, similar to the gradient descent, but employing an approximation of the cost function derivatives
|
||||
A wider range of optimizers is also available, such as Quasi-Newton or evolutionary strategies.
|
||||
|
||||
=== Other features ===
|
||||
The elastix software also offers other features that can be employed to speed up the registration procedure and to provide more advanced algorithms to the end-users. Some examples are the introduction of blur and Gaussian pyramid to reduce data complexity, and multi-image and multi-metric framework to deal with more complex applications.
|
||||
|
||||
== Applications ==
|
||||
Elastix has applications mainly in the medical field, where image registration is fundamental to get comprehensive information regarding the analysed anatomical region. It is widely employed in image-guided surgery, tumour monitoring, and treatment assessment.
|
||||
For example, in radiotherapy planning, image registration allows to correctly deliver the treatment and evaluate the obtained results. Thanks to the wide range of implemented algorithms, the use of the elastix software allows physicians and researchers to test different registration pipelines from the simplest to more complex ones, and to save the best one as a configuration file. This file and the fact that the software is completely open-source makes it easy to reproduce the work, that can help supporting the open science paradigm, and allows fast reuse on different patients data.
|
||||
In image-guided surgery, registration time and accuracy are critical points, considering that, during the registration, the patient is on the operating table, and the images to be registered have lower resolution compared to the target ones. In this field, the possibility to exploit elastix with high-level languages, such as OpenCL, opens to research in the usage of GPUs and other hardware accelerators.
|
||||
|
||||
== References ==
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
GitHub repository
|
||||
33
data/en.wikipedia.org/wiki/Elmer_FEM_solver-0.md
Normal file
33
data/en.wikipedia.org/wiki/Elmer_FEM_solver-0.md
Normal file
@ -0,0 +1,33 @@
|
||||
---
|
||||
title: "Elmer FEM solver"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Elmer_FEM_solver"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:13.574379+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Elmer is a computational tool for multi-physics problems. It has been developed by CSC in collaboration with Finnish universities, research laboratories and industry. Elmer FEM solver is free and open-source software, subject to the requirements of the GNU General Public License (GPL), version 2 or any later.
|
||||
Elmer includes physical models of fluid dynamics, structural mechanics, electromagnetics, heat transfer and acoustics, for example. These are described by partial differential equations which Elmer solves by the Finite Element Method (FEM).
|
||||
Elmer comprises several different parts:
|
||||
|
||||
ElmerGrid – A mesh conversion tool, which can be used to convert differing mesh formats into Elmer-suitable meshes.
|
||||
ElmerGUI – A graphical interface which can be used on an existing mesh to assign physical models, this generates a "case file" which describes the problem to be solved. Does not show the whole ElmerSolver functionality in GUI.
|
||||
ElmerSolver – The numerical solver which performs the finite element calculations, using the mesh and case files.
|
||||
ElmerPost – A post-processing/visualisation module. (Development stopped in favour of other post-processing tools such as ParaView, VisIt, etc.)
|
||||
The different parts of Elmer software may be used independently. Whilst the main module is the ElmerSolver tool, which includes many sophisticated features for physical model solving, the additional components are required to create a full workflow. For pre- and post-processing other tools, such as Paraview can be used to visualise the output.
|
||||
The software runs on Unix and Windows platforms and can be compiled on a large variety of compilers, using the CMake building tool. The solver can also be used in a multi-host parallel mode on platforms that support MPI. Elmer's parallelisation capability is one of the strongest sides of this solver.
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Finite Element Method
|
||||
List of finite element packages
|
||||
|
||||
|
||||
== References ==
|
||||
34
data/en.wikipedia.org/wiki/Emergent_(software)-0.md
Normal file
34
data/en.wikipedia.org/wiki/Emergent_(software)-0.md
Normal file
@ -0,0 +1,34 @@
|
||||
---
|
||||
title: "Emergent (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Emergent_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:15.914377+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Emergent (formerly PDP++) is a biologically-based neural simulation software that is primarily intended for creating models of the brain and cognitive processes. Development initially began in 1995 at Carnegie Mellon University, and as of 2014, continues at the University of Colorado at Boulder. The 3.x release of the software, which was known as PDP++, is featured in the textbook Computational Explorations in Cognitive Neuroscience.
|
||||
|
||||
|
||||
== Features ==
|
||||
Emergent features a modular design, based on the principles of object-oriented programming. It runs on Microsoft Windows, Darwin / macOS and Linux. C-Super-Script (variously, CSS and C^C), a built-in C++-like interpreted scripting language, allows access to virtually all simulator objects and can initiate all the same actions as the GUI, and more. Version 4 and upward features a full 3D environment for visualizations, based on Qt and Open Inventor. Robotics simulations are made possible by integration with the Open Dynamics Engine. A plugin system allows for expanding the software in many ways. Version 5 introduced parallel threading support, numerous speed improvements, a help browser featuring an interface to the project's Wiki and auto-generated documentation, undo and redo using diffs and a definable undo depth. In addition, 5.0.2 introduced a built-in plugin source code editor, and plugins can now be compiled from the main interface, enabling full development of plugins within Emergent.
|
||||
Emergent also provides an implementation of Leabra which was developed by Randall C. O'Reilly in his PhD thesis.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
David Rumelhart
|
||||
Randall C. O'Reilly
|
||||
James McClelland (psychologist)
|
||||
Biologically inspired computing
|
||||
Computational neuroscience
|
||||
Leabra
|
||||
|
||||
|
||||
== Bibliography ==
|
||||
Aisa B, Mingus B, O'Reilly RC (October 2008). "The Emergent neural modeling system" (PDF). Neural Networks. 21 (8): 1146–1152. doi:10.1016/j.neunet.2008.06.016. ISSN 0893-6080. PMID 18684591.
|
||||
O'Reilly, Randall; Munakata, Yuko (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. The MIT Press. ISBN 978-0-262-65054-0.
|
||||
|
||||
|
||||
== References ==
|
||||
61
data/en.wikipedia.org/wiki/Encog-0.md
Normal file
61
data/en.wikipedia.org/wiki/Encog-0.md
Normal file
@ -0,0 +1,61 @@
|
||||
---
|
||||
title: "Encog"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Encog"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:17.070373+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Encog is a machine learning framework available for Java and .Net.
|
||||
Encog supports different learning algorithms such as Bayesian Networks, Hidden Markov Models and Support Vector Machines.
|
||||
However, its main strength lies in its neural network algorithms. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using many different techniques. Multithreading is used to allow optimal training performance on multicore machines.
|
||||
Encog can be used for many tasks, including medical and financial research. A GUI based workbench is also provided to help model and train neural networks. Encog has been in active development since 2008.
|
||||
|
||||
|
||||
== Neural Network Architectures ==
|
||||
ADALINE Neural Network
|
||||
Adaptive Resonance Theory 1 (ART1)
|
||||
Bidirectional Associative Memory (BAM)
|
||||
Boltzmann Machine
|
||||
Counterpropagation Neural Network (CPN)
|
||||
Elman Recurrent Neural Network
|
||||
Neuroevolution of augmenting topologies (NEAT)
|
||||
Feedforward Neural Network (Perceptron)
|
||||
Hopfield Neural Network
|
||||
Jordan Recurrent Neural Network
|
||||
Radial Basis Function Network
|
||||
Recurrent Self Organizing Map (RSOM)
|
||||
Self Organizing Map (Kohonen)
|
||||
|
||||
|
||||
== Training techniques ==
|
||||
Backpropagation
|
||||
Resilient Propagation (RProp)
|
||||
Scaled Conjugate Gradient (SCG)
|
||||
Levenberg–Marquardt algorithm
|
||||
Manhattan Update Rule Propagation
|
||||
Competitive learning
|
||||
Hopfield Learning
|
||||
Genetic algorithm training
|
||||
Instar Training
|
||||
Outstar Training
|
||||
ADALINE Training
|
||||
|
||||
|
||||
== See also ==
|
||||
JOONE: another neural network programmed in Java
|
||||
FANN, a neural network written in C with bindings to most other languages.
|
||||
Deeplearning4j: An open-source deep learning library written for Java/C++ w/LSTMs and convolutional networks. Parallelization with Apache Spark and Aeron on CPUs and GPUs.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Encog Homepage
|
||||
Encog Project (GitHub)
|
||||
Basic Market Forecasting with Encog Neural Networks (DevX Article)
|
||||
An Introduction to Encog Neural Networks for Java (Code Project)
|
||||
Benchmarking and Comparing Encog, Neuroph and JOONE Neural Networks
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Evolution@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:06.339767+00:00"
|
||||
date_saved: "2026-05-05T10:11:20.684010+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
42
data/en.wikipedia.org/wiki/Fityk-0.md
Normal file
42
data/en.wikipedia.org/wiki/Fityk-0.md
Normal file
@ -0,0 +1,42 @@
|
||||
---
|
||||
title: "Fityk"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Fityk"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:23.018116+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Fityk is curve fitting and data analysis application, predominantly used to fit analytical,
|
||||
bell-shaped functions to experimental data. It is positioned to fill the gap between general plotting software and programs specific for one field, e.g. crystallography or XPS.
|
||||
Originally, Fityk was developed to analyse powder diffraction data. It is also used in other fields that require peak analysis and peak-fitting, like chromatography or various kinds of spectroscopy.
|
||||
Fityk is free and open source, distributed under the terms of GNU General Public License, with binaries/installers available free of charge on the project's website. It runs on Linux, macOS, Microsoft Windows, FreeBSD and other platforms. It operates either as a command line program or with a graphical user interface.
|
||||
It is written in C++, using wxWidgets, and providing bindings for Python and other scripting languages.
|
||||
|
||||
|
||||
== Features ==
|
||||
three weighted least squares methods:
|
||||
Levenberg-Marquardt algorithm,
|
||||
Nelder-Mead method
|
||||
Genetic algorithm
|
||||
about 20 built-in functions and support for user-defined functions
|
||||
equality constraints
|
||||
data manipulations,
|
||||
handling series of datasets,
|
||||
automation of common tasks with scripts.
|
||||
|
||||
|
||||
== Alternatives ==
|
||||
The programs LabPlot, MagicPlot and peak-o-mat have similar scope.
|
||||
More generic data analysis programs with spread-sheet capabilities include the proprietary Origin and its clones QtiPlot (paid, closed source) and SciDAVis (non-paid, open source).
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
|
||||
|
||||
== References ==
|
||||
42
data/en.wikipedia.org/wiki/Fluentd-0.md
Normal file
42
data/en.wikipedia.org/wiki/Fluentd-0.md
Normal file
@ -0,0 +1,42 @@
|
||||
---
|
||||
title: "Fluentd"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Fluentd"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:24.155322+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Fluentd is a cross-platform open-source data collection software project originally developed at Treasure Data. It is written primarily in the C programming language with a thin-Ruby wrapper that gives users flexibility.
|
||||
|
||||
|
||||
== Overview ==
|
||||
Fluentd was positioned for "big data," semi- or un-structured data sets. It analyzes event logs, application logs, and clickstreams. According to Suonsyrjä and Mikkonen, the "core idea of Fluentd is to be the unifying layer between different types of log inputs and outputs.", Fluentd is available on Linux, macOS, and Windows.
|
||||
|
||||
|
||||
== History ==
|
||||
|
||||
Fluentd was created by Sadayuki Furuhashi as a project of the Mountain View-based firm Treasure Data. Written primarily in Ruby, its source code was released as open-source software in October 2011. The company announced $5 million of funding in 2013.
|
||||
Treasure Data was then sold to Arm Ltd. in 2018.
|
||||
|
||||
|
||||
== Users ==
|
||||
Fluentd was one of the data collection tools recommended by Amazon Web Services in 2013, when it was said to be similar to Apache Flume or Scribe. Google Cloud Platform's BigQuery recommends Fluentd as the default real-time data-ingestion tool, and uses Google's customized version of Fluentd, called google-fluent, as a default logging agent.
|
||||
|
||||
|
||||
== Fluent Bit ==
|
||||
Fluent Bit is a log processor and log forwarder that is being developed as a CNCF sub-project under the umbrella of Fluent project. Fluentd is written in C and Ruby and consumes at least 60 megabytes of memory. Fluent Bit is written only in C, with no dependencies, and consumes approximately one megabyte of memory, making it easier to run under embedded Linux and in containers.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== Further reading ==
|
||||
Goasguen, Sébastien (2014). 60 Recipes for Apache CloudStack: Using the CloudStack Ecosystem, "Chapter 6: Advanced Recipes". O'Reilly Media. ISBN 1491910127
|
||||
Wilkins, Phil (2022). Logging in Action, With Fluentd, Kubernetes and more. Manning. ISBN 9781617298356
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
fluentd on GitHub
|
||||
37
data/en.wikipedia.org/wiki/FreeFem++-0.md
Normal file
37
data/en.wikipedia.org/wiki/FreeFem++-0.md
Normal file
@ -0,0 +1,37 @@
|
||||
---
|
||||
title: "FreeFem++"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/FreeFem++"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:25.301770+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
FreeFem++ is a programming language and a software focused on solving partial differential equations using the finite element method. FreeFem++ is written in C++ and developed and maintained by Université Pierre et Marie Curie and Laboratoire Jacques-Louis Lions. It runs on Linux, Solaris, macOS and Microsoft Windows systems. FreeFem++ is free software (LGPL).
|
||||
FreeFem++ language is inspired by C++. There is an IDE called FreeFem++-cs.
|
||||
|
||||
|
||||
== History ==
|
||||
The first version was created in 1987 by Olivier Pironneau and was named MacFem (it only worked on Macintosh); PCFem appeared some time later. Both were written in Pascal.
|
||||
In 1992 it was re-written in C++ and named FreeFem. Later versions, FreeFem+ (1996) and FreeFem++ (1998), used that programming language too.
|
||||
|
||||
|
||||
== Other versions ==
|
||||
FreeFem++ includes versions for console mode and MPI
|
||||
FreeFem3D
|
||||
Deprecated versions:
|
||||
|
||||
FreeFem+
|
||||
FreeFem
|
||||
|
||||
|
||||
== See also ==
|
||||
List of finite element software packages
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/FreeHAL"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:10.015392+00:00"
|
||||
date_saved: "2026-05-05T10:11:26.504428+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
18
data/en.wikipedia.org/wiki/GENtle-0.md
Normal file
18
data/en.wikipedia.org/wiki/GENtle-0.md
Normal file
@ -0,0 +1,18 @@
|
||||
---
|
||||
title: "GENtle"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GENtle"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:33.591770+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
GENtle is a free software under GPL license.
|
||||
|
||||
|
||||
== Features ==
|
||||
GENtle is an equivalent to the proprietary Vector NTI, a tool for molecular biologists to analyze and edit DNA sequence files. Invitrogens' removal of the free-of-cost academic licence for Vector NTI v11 has had a severe impact on many molecular biology labs that have come to rely on that tool, which led to vendor lock-in effects, which angered many molecular biologists. The GENtle code is developed and maintained by Magnus Manske. By design, GENtle is coded to be cross-platform utilizing wxGTK.
|
||||
|
||||
|
||||
== References ==
|
||||
56
data/en.wikipedia.org/wiki/GIMIAS-0.md
Normal file
56
data/en.wikipedia.org/wiki/GIMIAS-0.md
Normal file
@ -0,0 +1,56 @@
|
||||
---
|
||||
title: "GIMIAS"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GIMIAS"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:35.966266+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
GIMIAS is a workflow-oriented environment focused on biomedical image computing and simulation. The open-source framework is extensible through plug-ins and is focused on building research and clinical software prototypes. Gimias has been used to develop clinical prototypes in the fields of cardiac imaging and simulation, angiography imaging and simulation, and neurology
|
||||
GIMIAS is being funded by several national and international projects like cvREMOD, euHeart or VPH NoE.
|
||||
|
||||
|
||||
== About GIMIAS ==
|
||||
GIMIAS stands for Graphical Interface for Medical Image Analysis and Simulation. GIMIAS provides a graphical user interface with all main data IO, visualization and interaction functions for images, meshes and signals. GIMIAS features include:
|
||||
|
||||
DICOM browser and PACS connection
|
||||
Support for different imaging modalities
|
||||
Biomedical data visualization in 2D and 3D: multiplanar reformation, ortho slice view, multi slice view, volume rendering, X-ray rendering, maximum intensity projection
|
||||
Several input and output formats: DICOM, vtk, stl, Nifty, Analyze.
|
||||
Movie control: play, pause, speed control
|
||||
Multiple data objects: 2D DICOM images, 3D images, surface meshes, volumetric meshes, signals or annotations
|
||||
Image and surface mesh annotations: landmarks, measurements and regions of interest
|
||||
Clinical workflow navigation that can help the user to navigate from patient data to useful information for patient treatment.
|
||||
Other additional tools for image segmentation, mesh manipulation and signal navigation.
|
||||
GIMIAS is a development framework that allows developers to create their own medical applications using different plug-ins that can be dynamically loaded and combined. The prototypes developed on GIMIAS can be verified by end users in real scenarios and with real data at early development stages.
|
||||
Is developed using C++ language, has a plug-in architecture, and is cross-platform by means of the standard CMake tool. Is possible to integrate new libraries using CSnake tool and is based on common open source libraries like VTK, ITK, MITK, BOOST and wxWidgets. A plug-in can extend the framework adding new processing components, GUI components like toolbars or windows, new data processing types or new rendering libraries.
|
||||
GIMIAS supports several types of plug-ins, starting from a simple DLL, a 3D Slicer compatible command line plug-in or a more complex GIMIAS plug-in with customized graphical interface. Automated GUI generation and extensible data object model allow to share plug-ins with other frameworks and empower interoperability.
|
||||
The software is available on Windows and Linux, 64-bit and 32-bit.
|
||||
|
||||
|
||||
== History ==
|
||||
Initial versions of the open source framework was released by the end of 2009 (GIMIAS 0.6.15 was released in October 2009).
|
||||
In 2010, more effort was done to empower the open source framework itself, providing more functionality like workflow manager, 3D Slicer plug-in compatibility, signal viewer and customizable views. GIMIAS version 0.8.1, 1.0.0, 1.1.0 and 1.2.0 were released during this year.
|
||||
GIMIAS Team have collaborated with:
|
||||
|
||||
cmgui team: to trial the use of the interim cmgui API from the GIMIAS software platform
|
||||
CTK group
|
||||
B3C group (MAF)
|
||||
GIMIAS is one of the tools used in the Virtual Physiological Human.
|
||||
|
||||
|
||||
== Clinical Prototypes ==
|
||||
AngioLab is a software tool developed within the GIMIAS framework and is part of a more ambitious pipeline for the integrated management of cerebral aneurysms. AngioLab currently includes four plug-ins: angio segmentation, angio morphology virtual stenting and virtual angiography. In December 2009, 23 clinicians completed an evaluation questionnaire about AngioLab. This activity was part of a teaching course held during the 2nd European Society for Minimally Invasive Neurovascular Treatment (ESMINT) Teaching Course held at the Universitat Pompeu Fabra, Barcelona, Spain. The Automated Morphological Analysis (angio morphology plug-in) and the Endovascular Treatment Planning (stenting plug-in) were evaluated. In general, the results provided by these tools were considered as relevant and as an emerging need in their clinical field.
|
||||
CardioLab: The CardioLab suite for GIMIAS allows performing an entire workflow from medical images to characterization and quantification of myocardial diseases and Cardiac Resynchronization Therapy (CRT) planning.
|
||||
FocusDET: Accurate localization of epileptogenic foci in intractable partial epilepsy is essential for assessing the possibility of surgery as a treatment. A specific software package was developed to locate the epileptogenic focus using Ictal and Inter-ictal SPECT images and MRI employing the SISCOM methodology. FocusDET was developed using GIMIAS facilities.
|
||||
QuantiDopa is a software that allows performing a semiautomatic quantification of the striatal uptake in neurotransmission SPECT studies of the dopaminergic system.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
SourceForge.net (linux debian)
|
||||
MITK
|
||||
32
data/en.wikipedia.org/wiki/GNU_Data_Language-0.md
Normal file
32
data/en.wikipedia.org/wiki/GNU_Data_Language-0.md
Normal file
@ -0,0 +1,32 @@
|
||||
---
|
||||
title: "GNU Data Language"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GNU_Data_Language"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:38.334973+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The GNU Data Language (GDL) is a free alternative to IDL (Interactive Data Language), achieving full compatibility with IDL 7 and partial compatibility with IDL 8. Together with its library routines, GDL is developed to serve as a tool for data analysis and visualization in such disciplines as astronomy, geosciences, and medical imaging.
|
||||
GDL is licensed under the GPL. Other open-source numerical data analysis tools similar to GDL include Julia, Jupyter Notebook, GNU Octave, NCAR Command Language (NCL), Perl Data Language (PDL), R, Scilab, SciPy, and Yorick.
|
||||
GDL as a language is dynamically-typed, vectorized, and has object-oriented programming capabilities. GDL library routines handle numerical calculations (e.g. FFT), data visualisation, signal/image processing, interaction with host OS, and data input/output. GDL supports several data formats, such as NetCDF, HDF (v4 & v5), GRIB, PNG, TIFF, and DICOM. Graphical output is handled by X11, PostScript, SVG, or z-buffer terminals, the last one allowing output graphics (plots) to be saved in raster graphics formats. GDL features integrated debugging facilities, such as breakpoints. GDL has a Python bridge (Python code can be called from GDL; GDL can be compiled as a Python module). GDL uses the Eigen numerical library (similar to Intel MKL) to offer high computing performance on multi-core processors.
|
||||
Packaged versions of GDL are available for several Linux and BSD flavours as well as macOS. The source code compiles on Microsoft Windows and other UNIX systems, including Solaris.
|
||||
GDL is not an official GNU package.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Interpreter (computing)
|
||||
IDL (programming language)
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
Running the GNU Data Language on coLinux
|
||||
GNU Data Language at Open Hub
|
||||
Linux packages: ArchLinux, Debian, Fedora, Gentoo, Ubuntu,
|
||||
BSD/OSX ports: Fink, FreeBSD, Macports
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GPUGRID.net"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:11.346003+00:00"
|
||||
date_saved: "2026-05-05T10:11:39.562907+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
61
data/en.wikipedia.org/wiki/Geant4-0.md
Normal file
61
data/en.wikipedia.org/wiki/Geant4-0.md
Normal file
@ -0,0 +1,61 @@
|
||||
---
|
||||
title: "Geant4"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Geant4"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:27.672969+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Geant4 (for GEometry ANd Tracking) is a platform for "the simulation of the passage of particles through matter" using Monte Carlo methods. It is the successor of the GEANT series of software toolkits developed by The Geant4 Collaboration, and the first to use object oriented programming (in C++). Its development, maintenance and user support are taken care by the international Geant4 Collaboration. Application areas include high energy physics and nuclear experiments, accelerator and space physics studies. The software is used by a number of research projects around the world.
|
||||
The Geant4 software and source code is freely available from the project web site; until version 8.1 (released June 28, 2006), no specific software license for its use existed; Geant4 is now provided under the Geant4 Software License.
|
||||
|
||||
|
||||
== Features ==
|
||||
Geant4 includes facilities for handling geometry, tracking, detector response, run management, visualization and user interface. For many physics simulations, this means less time needs to be spent on the low level details, and researchers can start immediately on the more important aspects of the simulation.
|
||||
Following is a summary of each of the facilities listed above:
|
||||
|
||||
Geometry is an analysis of the physical layout of the experiment, including detectors, absorbers, etc., and considering how this layout will affect the path of particles in the experiment.
|
||||
Tracking is simulating the passage of a particle through matter. This involves considering possible interactions and decay processes.
|
||||
Detector response is recording when a particle passes through the volume of a detector, and approximating how a real detector would respond.
|
||||
Run management is recording the details of each run (a set of events), as well as setting up the experiment in different configurations between runs.
|
||||
Geant4 offers a number of options for visualization, including OpenGL, Open Inventor, VRML or VTK and a familiar user interface, based on tcsh or Qt.
|
||||
Geant4 can also perform basic histogramming; it requires external analysis tools for exploiting advanced histogramming features.
|
||||
Since release 10.0, Geant4 implements multithreading, making use of thread-local storage to allow for efficient generation of simulated events in parallel. Geant4 can be installed under a Unix-based operating system such as MacOS, Linux or under Microsoft Windows.
|
||||
|
||||
|
||||
== Some high energy physics experiments using Geant4 ==
|
||||
BES III at BEPCII
|
||||
BaBar and GLAST at SLAC
|
||||
ATLAS, CMS and LHCb at LHC, CERN
|
||||
COMPASS at SPS, CERN
|
||||
Borexino at Gran Sasso Laboratory
|
||||
DUNE, MINOS, Muon g-2, MicroBooNE/MiniBooNE, and Mu2e at Fermilab
|
||||
Enriched Xenon Observatory (EXO)
|
||||
SNO+
|
||||
IceCube
|
||||
T2K
|
||||
CUORE
|
||||
Dark Matter Detectors: SuperCDMS, LUX, LZ, XENON
|
||||
|
||||
|
||||
== Applications outside high energy physics ==
|
||||
|
||||
Because of its general purpose nature, Geant4 is well suited for development of computational tools for analysing interactions of particle with matter in many areas. These include:
|
||||
|
||||
Space applications where it is used to study interactions between the natural space radiation environment and space hardware or astronauts;
|
||||
Radiation effects in microelectronics where ionizing effects on semiconductor devices are modeled.
|
||||
Nuclear physics
|
||||
|
||||
|
||||
== See also ==
|
||||
CLHEP and FreeHEP, libraries for high energy physics.
|
||||
Accelerator physics codes for the modeling of charged particles in the rest of an accelerator.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
63
data/en.wikipedia.org/wiki/GenMAPP-0.md
Normal file
63
data/en.wikipedia.org/wiki/GenMAPP-0.md
Normal file
@ -0,0 +1,63 @@
|
||||
---
|
||||
title: "GenMAPP"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GenMAPP"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:31.209983+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
GenMAPP (Gene Map Annotator and Pathway Profiler) is a free, open-source bioinformatics software tool designed to visualize and analyze genomic data in the context of pathways (metabolic, signaling), connecting gene-level datasets to biological processes and disease. First created in 2000, GenMAPP is developed by an open-source team based in an academic research laboratory. GenMAPP maintains databases of gene identifiers and collections of pathway maps in addition to visualization and analysis tools. Together with other public resources, GenMAPP aims to provide the research community with tools to gain insight into biology through the integration of data types ranging from genes to proteins to pathways to disease.
|
||||
|
||||
|
||||
== History ==
|
||||
GenMAPP was first created in 2000 as a prototype software tool in the laboratory of Bruce Conklin at the J. David Gladstone Institutes in San Francisco and continues to be developed in the same non-profit, academic research environment. The first release version of GenMAPP 1.0 was available in 2002, supporting analysis of DNA microarray data from human, mouse, rat and yeast. In 2004, GenMAPP 2.0 was released, combining the previously accessory programs MAPPFinder and MAPPBuilder, and expanding support to additional species. GenMAPP 2.1 was released in 2006 with new visualization features and support for a total of eleven species.
|
||||
|
||||
|
||||
== Usage ==
|
||||
GenMAPP was developed by biologists and is focused on pathway visualization for bench biologists. Unlike many other computational systems biology tools, GenMAPP is not designed for cell/systems modeling; it focuses on the immediate needs of bench biologists by enabling them to rapidly interpret genomic data with an intuitive, easy-to-use interface.
|
||||
GenMAPP is implemented in Visual Basic 6.0 and is available as a stand-alone application for Microsoft Windows operating systems, including Boot Camp or Parallels Workstation on a Mac.
|
||||
|
||||
|
||||
== Content and Features ==
|
||||
GenMAPP builds and maintains gene databases for a variety of key model organisms:
|
||||
|
||||
human - Homo sapiens
|
||||
mouse - Mus musculus
|
||||
rat - Rattus norvegicus
|
||||
yeast - Saccharomyces cerevisiae
|
||||
zebrafish - Danio rerio
|
||||
worm - Caenorhabditis elegans
|
||||
fruit fly - Drosophila melanogaster
|
||||
dog - Canis familiaris
|
||||
cow - Bos taurus
|
||||
mosquito - Anopheles gambiae
|
||||
E.coli - Escherichia coli
|
||||
|
||||
GenMAPP provides tools to create, edit and annotate biological pathway maps.
|
||||
|
||||
GenMAPP allows users to visualize and analyze their data in the context of pathway collections and the Gene Ontology.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
WikiPathways
|
||||
Cytoscape
|
||||
Ensembl
|
||||
KEGG
|
||||
Netpath
|
||||
Reactome
|
||||
Gene Ontology
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
GenMAPP – Data Links Archived 2006-10-10 at the Wayback Machine
|
||||
Cytoscape
|
||||
PathVisio Archived 2016-03-06 at the Wayback Machine
|
||||
WikiPathways
|
||||
GenMAPP on SourceForge
|
||||
58
data/en.wikipedia.org/wiki/GenePattern-0.md
Normal file
58
data/en.wikipedia.org/wiki/GenePattern-0.md
Normal file
@ -0,0 +1,58 @@
|
||||
---
|
||||
title: "GenePattern"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GenePattern"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:28.836661+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
GenePattern is a freely available computational biology open-source software package originally created and developed at the Broad Institute for the analysis of genomic data. Designed to enable researchers to develop, capture, and reproduce genomic analysis methodologies, GenePattern was first released in 2004. GenePattern is currently developed at the University of California, San Diego.
|
||||
|
||||
|
||||
== Functionality ==
|
||||
GenePattern is a powerful scientific workflow system that provides access to hundreds of genomic analysis tools. Use these analysis tools as building blocks to design sophisticated analysis pipelines that capture the methods, parameters, and data used to produce analysis results. Pipelines can be used to create, edit and share reproducible in silico results.
|
||||
|
||||
|
||||
== Project Objectives ==
|
||||
Accessibility: Run over 200 regularly updated analysis and visualization tools (that support data preprocessing, gene expression analysis, proteomics, Single nucleotide polymorphism (SNP) analysis, flow cytometry, and next-generation sequencing) and create analytic workflows without any programming through a point and click user interface.
|
||||
Reproducibility: Automated history and provenance tracking with versioning so that any user can share, repeat and understand a complete computational analysis
|
||||
Extensibility: Computational users can import their methods and code for sharing using tools that support easy creation and integration
|
||||
Multiple interfaces: Web browser, application, and programmatic interfaces make analysis modules and pipelines available to a broad range of users; public hosted server
|
||||
|
||||
|
||||
== Features ==
|
||||
A regularly updated repository of hundreds of computational analysis modules that support data preprocessing, gene expression analysis, proteomics, single nucleotide polymorphism (SNP) analysis, flow cytometry, and short-read sequencing.
|
||||
A programmatic interface that makes analysis modules available to computational biologists and developers from Python, Java, MATLAB, and R.
|
||||
The GenePattern Notebook Environment: Built on the Jupyter Notebook environment, GenePattern Notebook allows researchers to run GenePattern analyses within notebooks that interleave text, graphics, and executable code, creating a single "research narrative."
|
||||
GParc: Repository and community for GenePattern users to share and discuss their own GenePattern modules
|
||||
|
||||
|
||||
== Availability ==
|
||||
GenePattern is available:
|
||||
|
||||
As a free public web application, hosted on Amazon Web Services. Users can create accounts, perform analyses, and create pipelines on the server.
|
||||
As open-source software that can be downloaded and installed locally.
|
||||
Public web servers hosted by other organizations.
|
||||
|
||||
|
||||
== Notes ==
|
||||
|
||||
|
||||
== References ==
|
||||
Reich, M; Tabor, T; Liefeld, T; Thorvaldsdóttir, H; Hill, B; Tamayo, P; Mesirov, JP (2017). "The GenePattern Notebook Environment". Cell Syst. 5 (2): 149–151.e1. doi:10.1016/j.cels.2017.07.003. PMC 5572818. PMID 28822753.
|
||||
Qu, K; Garamszegi, S; Wu, F; Thorvaldsdottir, H; Liefeld, T; Ocana, M; Borges-Rivera, D; Pochet, N; Robinson, JT; Demchak, B; Hull, T; Ben-Artzi, G; Blankenberg, D; Barber, GP; Lee, BT; Kuhn, RM; Nekrutenko, A; Segal, E; Ideker, T; Reich, M; Regev, A; Chang, HY; Mesirov, JP (2016). "Integrative genomic analysis by interoperation of bioinformatics tools in GenomeSpace". Nat Methods. 13 (3): 245–247. doi:10.1038/nmeth.3732. PMC 4767623. PMID 26780094.
|
||||
Kuehn, H.; Liberzon, A.; Reich, M.; Mesirov, J. P. (2008). "Using GenePattern for Gene Expression Analysis". Current Protocols in Bioinformatics. 22: 7.12.1–7.12.39. doi:10.1002/0471250953.bi0712s22. PMC 3893799. PMID 18551415.
|
||||
Reich, Michael; Liefeld, Ted; Gould, Joshua; Lerner, Jim; Tamayo, Pablo; Mesirov, Jill P. (2006). "GenePattern 2.0". Nature Genetics. 38 (5): 500–501. doi:10.1038/ng0506-500. PMID 16642009.
|
||||
|
||||
|
||||
== External links ==
|
||||
Official GenePattern website
|
||||
Official GenePattern Notebook website
|
||||
GParc Archived 2017-10-10 at the Wayback Machine
|
||||
GenePattern on GitHub
|
||||
Related software:
|
||||
|
||||
GenomeSpace
|
||||
Integrative Genomics Viewer
|
||||
@ -0,0 +1,44 @@
|
||||
---
|
||||
title: "General Architecture for Text Engineering"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/General_Architecture_for_Text_Engineering"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:30.046327+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
General Architecture for Text Engineering (GATE) is a Java suite of natural language processing (NLP) tools for man tasks, including information extraction in many languages. It is now used worldwide by a wide community of scientists, companies, teachers and students. It was originally developed at the University of Sheffield beginning in 1995.
|
||||
As of May 28, 2011, 881 people are on the gate-users mailing list at SourceForge.net, and 111,932 downloads from SourceForge are recorded since the project moved to SourceForge in 2005. The paper "GATE: A framework and graphical development environment for robust NLP tools and applications" has received over 2000 citations since publication (according to Google Scholar). Books covering the use of GATE, in addition to the GATE User Guide, include "Building Search Applications: Lucene, LingPipe, and Gate", by Manu Konchady, and "Introduction to Linguistic Annotation and Text Analytics", by Graham Wilcock.
|
||||
GATE community and research has been involved in several European research projects including: Transitioning Applications to Ontologies, SEKT, NeOn, Media-Campaign, Musing, Service-Finder, LIRICS and KnowledgeWeb.
|
||||
|
||||
|
||||
== Features ==
|
||||
GATE includes an information extraction system called ANNIE (A Nearly-New Information Extraction System) which is a set of modules comprising a tokenizer, a gazetteer, a sentence splitter, a part of speech tagger, a named entities transducer and a coreference tagger. ANNIE can be used as-is to provide basic information extraction functionality, or provide a starting point for more specific tasks.
|
||||
Languages currently handled in GATE include English, Chinese, Arabic, Bulgarian, French, German, Hindi, Italian, Cebuano, Romanian, Russian, Danish.
|
||||
Plugins are included for machine learning with Weka, RASP, MAXENT, SVM Light, as well as a LIBSVM integration and an in-house perceptron implementation, for managing ontologies like WordNet, for querying search engines like Google or Yahoo, for part of speech tagging with Brill or TreeTagger, and many more. Many external plugins are also available, for handling e.g. tweets.
|
||||
GATE accepts input in various formats, such as TXT, HTML, XML, Doc, PDF documents, and Java Serial, PostgreSQL, Lucene, Oracle Databases with help of RDBMS storage over JDBC.
|
||||
JAPE transducers are used within GATE to manipulate annotations on text. Documentation is provided in the GATE User Guide. A tutorial has also been written by Press Association Images.
|
||||
|
||||
|
||||
== GATE Developer ==
|
||||
|
||||
The screenshot shows the document viewer used to display a document and its annotations. In pink are <a> hyperlink annotations from an HTML file. The right list is the annotation sets list, and the bottom table is the annotation list. In the center is the annotation editor window.
|
||||
|
||||
|
||||
== GATE Mímir ==
|
||||
GATE generates vast quantities of information including; natural language text, semantic annotations, and ontological information. Sometimes the data itself is the end product of an application but often the information would be more useful if it could be efficiently searched. GATE Mimir provides support for indexing and searching the linguistic and semantic information generated by such applications and allows for querying the information using arbitrary combinations of text, structural information, and SPARQL.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Unstructured Information Management Architecture (UIMA)
|
||||
OpenNLP
|
||||
Pheme, a major EU project managed by the GATE group on early detection of false information in social media
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
37
data/en.wikipedia.org/wiki/Gensim-0.md
Normal file
37
data/en.wikipedia.org/wiki/Gensim-0.md
Normal file
@ -0,0 +1,37 @@
|
||||
---
|
||||
title: "Gensim"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Gensim"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:32.349307+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning.
|
||||
Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing.
|
||||
|
||||
|
||||
== Main features ==
|
||||
Gensim includes streamed parallelized implementations of fastText, word2vec and doc2vec algorithms, as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections.
|
||||
Some of the novel online algorithms in Gensim were also published in the 2011 PhD dissertation Scalability of Semantic Analysis in Natural Language Processing of Radim Řehůřek, the creator of Gensim.
|
||||
|
||||
|
||||
== Uses of Gensim ==
|
||||
Gensim library has been used and cited in over 1400 commercial and academic applications as of 2018, in a diverse array of disciplines from medicine to insurance claim analysis to patent search. The software has been covered in several new articles, podcasts and interviews.
|
||||
|
||||
|
||||
== Free and commercial support ==
|
||||
The open source code is developed and hosted on GitHub and a public support forum is maintained on Google Groups and Gitter.
|
||||
Gensim is commercially supported by the company rare-technologies.com, who also provide student mentorships and academic thesis projects for Gensim via their Student Incubator programme.
|
||||
|
||||
|
||||
== See also ==
|
||||
Comparison of machine learning software
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
24
data/en.wikipedia.org/wiki/Gnaural-0.md
Normal file
24
data/en.wikipedia.org/wiki/Gnaural-0.md
Normal file
@ -0,0 +1,24 @@
|
||||
---
|
||||
title: "Gnaural"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Gnaural"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:37.113700+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Gnaural is brainwave entrainment software for Microsoft Windows, Mac OS X, and Linux licensed under GPL-2.0-or-later. Gnaural is free software for creating binaural beats intended to be used as personal brainwave synchronization software, for scientific research, or by professionals.
|
||||
Gnaural allows for the creation of binaural beat tracks specifying different frequencies and exporting tracks into different audio formats. Gnaural runnings can also be linked over the internet, allowing synchronous sessions between many users.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Brainwave Entrainment
|
||||
Binaural beats
|
||||
Linux audio software
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
https://www.youtube.com/watch?v=Nck9SX52qpU - A Gnaural Video Tutorial
|
||||
31
data/en.wikipedia.org/wiki/GrADS-0.md
Normal file
31
data/en.wikipedia.org/wiki/GrADS-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "GrADS"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GrADS"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:40.734924+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access, manipulation, and visualization of earth science data. The format of the data may be either binary, GRIB, NetCDF, or HDF-SDS (Scientific Data Sets). GrADS has been implemented worldwide on a variety of commonly used operating systems and is freely distributed over the Internet.
|
||||
GrADS uses a 4-Dimensional data environment: longitude, latitude, vertical level, and time. Data sets are placed within the 4-D space by use of a data descriptor file. GrADS interprets station data as well as gridded data, and the grids may be regular, non-linearly spaced, Gaussian, or of variable resolution. Data from different data sets may be graphically overlaid, with correct spatial and time registration. It uses the ctl mechanism to join differing time group data sets. Operations are executed interactively by entering FORTRAN-like expressions at the command line. A rich set of built-in functions are provided, but users may also add their own functions as external routines written in any programming language.
|
||||
Data may be displayed using a variety of graphical techniques: line and bar graphs, scatter plots, smoothed contours, shaded contours, streamlines, wind vectors, grid boxes, shaded grid boxes, and station model plots. Graphics may be output in PostScript or image formats. GrADS provides geophysically intuitive defaults, but the user has the option to control all aspects of graphics output.
|
||||
GrADS has a programmable interface (scripting language) that allows for sophisticated analysis and display applications. Scripts can display buttons and drop menus as well as graphics, and then take action based on user point-and-clicks. GrADS can be run in batch mode, and the scripting language facilitates using GrADS to do long overnight batch jobs. As of version 2.2.0, graphics display and printing are now handled as independent plug-ins. A C-language Python extension for GrADS called GradsPy was introduced in version 2.2.1.
|
||||
|
||||
|
||||
== See also ==
|
||||
Climate Data Analysis Tool
|
||||
Giovanni (meteorology)
|
||||
IDL (programming language) and GNU Data Language
|
||||
MATLAB and GNU Octave
|
||||
NCAR Command Language
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
GrADS official site Archived 2016-02-13 at the Wayback Machine
|
||||
OpenGrADS official site
|
||||
HiGrads - A GrADS script function library for managing subplots and charts
|
||||
48
data/en.wikipedia.org/wiki/Gravit-0.md
Normal file
48
data/en.wikipedia.org/wiki/Gravit-0.md
Normal file
@ -0,0 +1,48 @@
|
||||
---
|
||||
title: "Gravit"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Gravit"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:41.911950+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Gravit
|
||||
is a free and open-source gravity simulator distributed under the GNU General Public License. The program is available for all major operating systems, including Linux and other Unix-like systems, Microsoft Windows and Mac OS X.
|
||||
Gravit uses the Barnes–Hut algorithm to simulate the n-body problem.
|
||||
|
||||
|
||||
== Description ==
|
||||
Gravit is a gravity simulator which runs under Linux, Windows and Mac OS X. It is released under the GNU General Public License which makes it free. It uses Newtonian physics using the Barnes-Hut N-body algorithm. Although the main goal of Gravit is to be as accurate as possible, it also creates beautiful looking gravity patterns. It records the history of each particle so it can animate and display a path of its travels. At any stage you can rotate your view in 3D and zoom in and out. Gravit uses OpenGL with Lua, SDL, SDL_ttf and SDL_image.
|
||||
Features
|
||||
|
||||
View the simulation in 3D, optionally using stereoscopic imaging
|
||||
Can be installed as a screen saver in Windows
|
||||
Record a simulation, then play back at any speed
|
||||
Load / Save a recorded simulation
|
||||
Mouse controllable rotation
|
||||
Console with script execution
|
||||
See an octtree being created in real-time
|
||||
Colours can be based on mass, velocity, acceleration, momentum or kinetic energy
|
||||
Initial particle locations are scriptable (Lua)
|
||||
|
||||
|
||||
== Status ==
|
||||
As of some time in 2017, the website is dead, though the GitHub repository remains alive.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Galaxy- A similar open source stellar simulator
|
||||
Gravitation
|
||||
Newtonian mechanics
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official Gravit Website.
|
||||
Gravit Source code repository on github
|
||||
Project Page on Ohloh Archived 2008-05-29 at the Wayback Machine
|
||||
36
data/en.wikipedia.org/wiki/GumTree-0.md
Normal file
36
data/en.wikipedia.org/wiki/GumTree-0.md
Normal file
@ -0,0 +1,36 @@
|
||||
---
|
||||
title: "GumTree"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/GumTree"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:43.084707+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
GumTree is an open-source scientific workbench for performing scientific experiments under a distributed network environment. It provides a multi-platform graphical user interface for instrument data acquisition, online or offline data visualisation and analysis. GumTree is designed to provide a highly Integrated Scientific Experiment Environment (ISEE), allowing interaction between different components within the workbench. Several instrument control server systems including TANGO, EPICS and SICS have been adapted to GumTree. Current developments include acquisition, control and analysis on neutron and synchrotron beamlines. In the future it will be extended telescope control and other scientific instruments with distributed hardware.
|
||||
|
||||
|
||||
== History ==
|
||||
GumTree was first started as a small graphical user interface project to fulfill the IT requirement for the Neutron Beam Instrument Project (NBIP) at ANSTO. Later in the year, the GumTree project has been approved to go open source for international collaboration.
|
||||
|
||||
02/2004 GumTree project started
|
||||
08/2004 GumTree was approved to go open source
|
||||
09/2005 GumTree 1.0 milestone 7 released
|
||||
03/2006 GumTree has received the Best Open Source RCP Application from the Eclipse Foundation
|
||||
01/2007 Codehaus has accepted to host the GumTree Project on their website
|
||||
09/2008 GumTree 1.0 released
|
||||
|
||||
|
||||
== Architecture ==
|
||||
GumTree is based on the Eclipse Rich Client Platform (RCP). In order to support scientific operations, GumTree extends RCP with data handling framework and visualisation toolkit as part of the GumTree platform API.
|
||||
|
||||
|
||||
== GumTree Extension ==
|
||||
Adapting GumTree on a particular instrument requires special customisation to fit the scientific workbench to its instrument ecosystem. Customisation of GumTree can be achieved by adding new plug-ins to the existing GumTree application. In a broader sense, the common base of GumTree is a generic platform which provides all the necessary infrastructure to realise the ISEE concept for the scientific instrument. This platform, known as the GumTree Platform, is built and modelled upon an award-winning Java based universal platform called Eclipse. The GumTree Platform consists of an Eclipse Rich Client Platform (RCP) application, and an application framework for handling data exchange, experiment life cycle, device control (via distributed control system e.g. TANGO), application accessibility, data visualisation, and data analysis. All services from the platform can be extended and modified to suit any particular scientific instrument. A developer adds a GumTree workbench (or RCP based GumTree application) which integrates all services provided by the GumTree Platform. The GumTree Platform encourages developers to encapsulate the knowledge of an experiment method or procedure in the workbench.
|
||||
|
||||
|
||||
== External links ==
|
||||
Official GumTree website (at web.archive.org) - Project Information, News and Issue Tracking
|
||||
SourceForge project page - GumTree project at SF.net.
|
||||
GumTree M7 – New and Noteworthy - Available features from the latest GumTree release.
|
||||
53
data/en.wikipedia.org/wiki/Gwyddion_(software)-0.md
Normal file
53
data/en.wikipedia.org/wiki/Gwyddion_(software)-0.md
Normal file
@ -0,0 +1,53 @@
|
||||
---
|
||||
title: "Gwyddion (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Gwyddion_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:44.258523+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Gwyddion is a multiplatform modular free software for visualization and analysis of data from scanning probe microscopy (SPM) techniques (like AFM, MFM, STM, SNOM/NSOM). The project is led by its main developers David Nečas (Yeti) and Petr Klapetek who work together with various developers across the world. The software is made available as free software under the terms of the GNU General Public License. The name “Gwyddion” is that of a prominent god in Welsh Mythology (see Gwydion).
|
||||
It is created for the analysis of height fields and other 2D (image) data. While it is primarily intended for data originating from scanning probe microscopy techniques (like AFM, MFM, STM, SNOM/NSOM), it may also be used for the analysis of profilometry data, for instance.
|
||||
Data is calculated and stored in the native file format (.gwy) in double precision.
|
||||
Gwyddion supports many different file types and performs many image-based functions. Among others it can open and save the following graphics file formats:
|
||||
|
||||
Windows bitmap (.bmp)
|
||||
Joint Photographic Experts Group (.jpeg, .jpg, or .jpe)
|
||||
Portable Network Graphics (.png)
|
||||
TARGA (.tga)
|
||||
Tagged image file format (.tiff, .tif)
|
||||
Portable aNy Map (.pnm)
|
||||
|
||||
|
||||
== Technical features ==
|
||||
Quote from homepage of Gwyddion: 'Gwyddion uses a fairly general physical unit system, there are no principal limitations on the types of physical quantities data (and lateral dimensions) can represent. Units of slopes, areas, volumes, and other derived quantities are correctly calculated. SI unit system is used whenever possible.
|
||||
Tools and other dialogs remember their parameters, not only between tool invocations during one session, but also between sessions. Gwyddion native file format (.gwy) supports saving all data specific settings: false color palette, masks, presentations, selections, associated 3D view parameters, graphs associated with that data and their settings, etc.'
|
||||
Gwyddion is mainly developed on Linux platform using GNU set of compilers and utilities. Its graphical user interface is based on the popular interface toolkit GTK+.
|
||||
|
||||
|
||||
== Availability and versions ==
|
||||
It is available for Linux platforms and has been ported to other unix flavors that has support the GNU Autotools or its equivalent. The Windows version is slightly incomplete due to limitations of the platform, but supports nearly all major features. The Mac OS version can be built using Xcode, and some pre-built binaries are available.
|
||||
Apple Darwin, or OpenDarwin is the only major platform Gwyddion has not been thoroughly tested on. Gwyddion program could also be ported to other branches of the BSD operating system.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Atomic force microscopy
|
||||
ImageJ
|
||||
MountainsMap
|
||||
Scanning probe microscopy
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
|
||||
Official website
|
||||
Gwyddion on SourceForge
|
||||
https://github.com/Drilack7/Python-Scripts-for-Gwyddion
|
||||
Benedykt R. Jany. (2016-05-13). Workshop on Digital Image Processing of SPM(AFM,SEM) data using ImageJ and Gwyddion. Zenodo. https://doi.org/10.5281/zenodo.51400 Presentation and example Gwyddion files.
|
||||
Gwyddion — Tutorials
|
||||
Pygwy Console step-by-step installation guide video (on Windows) (3:44)
|
||||
48
data/en.wikipedia.org/wiki/HMMER-0.md
Normal file
48
data/en.wikipedia.org/wiki/HMMER-0.md
Normal file
@ -0,0 +1,48 @@
|
||||
---
|
||||
title: "HMMER"
|
||||
chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/HMMER"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:50.267033+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM (a Hidden Markov model constructed explicitly for a particular search) to either a single sequence or a database of sequences. Sequences that score significantly better to the profile-HMM compared to a null model are considered to be homologous to the sequences that were used to construct the profile-HMM. Profile-HMMs are constructed from a multiple sequence alignment in the HMMER package using the hmmbuild program. The profile-HMM implementation used in the HMMER software was based on the work of Krogh and colleagues. HMMER is a console utility ported to every major operating system, including different versions of Linux, Windows, and macOS.
|
||||
HMMER is the core utility that protein family databases such as Pfam and InterPro are based upon. Some other bioinformatics tools such as UGENE also use HMMER.
|
||||
HMMER3 also makes extensive use of vector instructions to increase computational speed. This work is based upon an earlier publication showing a significant acceleration of the Smith-Waterman algorithm for aligning two sequences.
|
||||
|
||||
== Profile HMMs ==
|
||||
A profile HMM is a variant of an HMM relating specifically to biological sequences. Profile HMMs turn a multiple sequence alignment into a position-specific scoring system, which can be used to align sequences and search databases for remotely homologous sequences. They capitalise on the fact that certain positions in a sequence alignment tend to have biases in which residues are most likely to occur, and are likely to differ in their probability of containing an insertion or a deletion. Capturing this information gives them a better ability to detect true homologs than traditional BLAST-based approaches, which penalise substitutions, insertions and deletions equally, regardless of where in an alignment they occur.
|
||||
|
||||
Profile HMMs center around a linear set of match (M) states, with one state corresponding to each consensus column in a sequence alignment. Each M state emits a single residue (amino acid or nucleotide). The probability of emitting a particular residue is determined largely by the frequency at which that residue has been observed in that column of the alignment, but also incorporates prior information on patterns of residues that tend to co-occur in the same columns of sequence alignments. This string of match states emitting amino acids at particular frequencies is analogous to position specific score matrices or weight matrices.
|
||||
A profile HMM takes this modelling of sequence alignments further by modelling insertions and deletions, using I and D states, respectively. D states do not emit a residue, while I states do. Multiple I states can occur consecutively, corresponding to multiple residues between consensus columns in an alignment. M, I and D states are connected by state transition probabilities, which also vary by position in the sequence alignment, to reflect the different frequencies of insertions and deletions across sequence alignments.
|
||||
The HMMER2 and HMMER3 releases used an architecture for building profile HMMs called the Plan 7 architecture, named after the seven states captured by the model. In addition to the three major states (M, I and D), six additional states capture non-homologous flanking sequence in the alignment. These 6 states collectively are important for controlling how sequences are aligned to the model, e.g. whether a sequence can have multiple consecutive hits to the same model (in the case of sequences with multiple instances of the same domain).
|
||||
|
||||
== Programs in the HMMER package ==
|
||||
The HMMER package consists of a collection of programs for performing functions using profile hidden Markov models. The programs include:
|
||||
|
||||
=== Profile HMM building ===
|
||||
hmmbuild – construct profile HMMs from multiple sequence alignments
|
||||
|
||||
=== Homology searching ===
|
||||
hmmscan – search protein sequences against a profile HMM database
|
||||
hmmsearch – search profile HMMs against a sequence database
|
||||
jackhmmer – iteratively search sequences against a protein database
|
||||
nhmmer – search DNA/RNA queries against a DNA/RNA sequence database
|
||||
nhmmscan – search nucleotide sequences against a nucleotide profile
|
||||
phmmer – search protein sequences against a protein database
|
||||
|
||||
=== Other functions ===
|
||||
hmmalign – align sequences to a profile HMM
|
||||
hmmemit – produce sample sequences from a profile HMM
|
||||
hmmlogo – produce data for an HMM logo from an HMM file
|
||||
The package contains numerous other specialized functions.
|
||||
|
||||
== The HMMER web server ==
|
||||
In addition to the software package, the HMMER search function is available in the form of a web server. The service facilitates searches across a range of databases, including sequence databases such as UniProt, SwissProt, and the Protein Data Bank, and HMM databases such as Pfam, TIGRFAMs and SUPERFAMILY. The four search types phmmer, hmmsearch, hmmscan and jackhmmer are supported (see Programs). The search function accepts single sequences as well as sequence alignments or profile HMMs.
|
||||
The search results are accompanied by a report on the taxonomic breakdown, and the domain organization of the hits. Search results can then be filtered according to either parameter.
|
||||
The web service is currently run out of the European Bioinformatics Institute (EBI) in the United Kingdom, while development of the algorithm is still performed by Sean Eddy's team in the United States. Major reasons for relocating the web service were to leverage the computing infrastructure at the EBI, and to cross-link HMMER searches with relevant databases that are also maintained by the EBI.
|
||||
|
||||
== The HMMER3 release ==
|
||||
The latest stable release of HMMER is version 3.0. HMMER3 is complete rewrite of the earlier HMMER2 package, with the aim of improving the speed of profile-HMM searches. Major changes are outlined below:
|
||||
48
data/en.wikipedia.org/wiki/HMMER-1.md
Normal file
48
data/en.wikipedia.org/wiki/HMMER-1.md
Normal file
@ -0,0 +1,48 @@
|
||||
---
|
||||
title: "HMMER"
|
||||
chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/HMMER"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:50.267033+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Improvements in speed ===
|
||||
A major aim of the HMMER3 project, started in 2004 was to improve the speed of HMMER searches. While profile HMM-based homology searches were more accurate than BLAST-based approaches, their slower speed limited their applicability. The main performance gain is due to a heuristic filter that finds high-scoring un-gapped matches within database sequences to a query profile. This heuristic results in a computation time comparable to BLAST with little impact on accuracy. Further gains in performance are due to a log-likelihood model that requires no calibration for estimating E-values, and allows the more accurate forward scores to be used for computing the significance of a homologous sequence.
|
||||
HMMER still lags behind BLAST in speed of DNA-based searches; however, DNA-based searches can be tuned such that an improvement in speed comes at the expense of accuracy.
|
||||
|
||||
=== Improvements in remote homology searching ===
|
||||
The major advance in speed was made possible by the development of an approach for calculating the significance of results integrated over a range of possible alignments. In discovering remote homologs, alignments between query and hit proteins are often very uncertain. While most sequence alignment tools calculate match scores using only the best scoring alignment, HMMER3 calculates match scores by integrating across all possible alignments, to account for uncertainty in which alignment is best. HMMER sequence alignments are accompanied by posterior probability annotations, indicating which portions of the alignment have been assigned high confidence and which are more uncertain.
|
||||
|
||||
=== DNA sequence comparison ===
|
||||
A major improvement in HMMER3 was the inclusion of DNA/DNA comparison tools. HMMER2 only had functionality to compare protein sequences.
|
||||
|
||||
=== Restriction to local alignments ===
|
||||
While HMMER2 could perform local alignment (align a complete model to a subsequence of the target) and global alignment (align a complete model to a complete target sequence), HMMER3 only performs local alignment. This restriction is due to the difficulty in calculating the significance of hits when performing local/global alignments using the new algorithm.
|
||||
|
||||
== See also ==
|
||||
Hidden Markov model
|
||||
Sequence alignment software
|
||||
Pfam
|
||||
UGENE
|
||||
Several implementations of profile HMM methods and related position-specific scoring matrix methods are available. Some are listed below:
|
||||
|
||||
HH-suite
|
||||
SAM
|
||||
PSI-BLAST
|
||||
MMseqs2
|
||||
PFTOOLS
|
||||
GENEWISE
|
||||
PROBE
|
||||
META-MEME Archived 2009-08-31 at the Wayback Machine
|
||||
BLOCKS
|
||||
GPU-HMMER
|
||||
DeCypherHMM
|
||||
|
||||
== References ==
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
HMMER3 announcement
|
||||
A blog posting on HMMER policy on trademark, copyright, patents, and licensing Archived 2016-03-06 at the Wayback Machine
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/HashClash"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:14.988479+00:00"
|
||||
date_saved: "2026-05-05T10:11:45.500905+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Help_Conquer_Cancer"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:16.307885+00:00"
|
||||
date_saved: "2026-05-05T10:11:46.729963+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Help_Cure_Muscular_Dystrophy"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:17.545763+00:00"
|
||||
date_saved: "2026-05-05T10:11:47.949794+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
32
data/en.wikipedia.org/wiki/HippoDraw-0.md
Normal file
32
data/en.wikipedia.org/wiki/HippoDraw-0.md
Normal file
@ -0,0 +1,32 @@
|
||||
---
|
||||
title: "HippoDraw"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/HippoDraw"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:49.085963+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
HippoDraw is a object-oriented statistical data analysis package written in C++, with user interaction via a Qt-based GUI and a Python-scriptable interface. It was developed by Paul Kunz at SLAC, primarily for the analysis and presentation of particle physics and astrophysics data, but can be equally well used in other fields where data handling is important.
|
||||
|
||||
|
||||
== About ==
|
||||
HippoDraw can read and write files in an XML-based format, astrophysics FITS files, data objects produced by ROOT (optional), and through the Python bindings, anything that can be read/written by Python (HDF5, for instance, with PyTables).
|
||||
|
||||
HippoDraw can be used as a Python extension module, allowing users to use HippoDraw data objects with the full power of the Python language. This includes other scientific Python extension modules such Numeric and numarray, whose use with HippoDraw can lead to a large increase in processing speed, even for ROOT objects.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Java Analysis Studio (JAS)
|
||||
ROOT
|
||||
AIDA
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
License
|
||||
65
data/en.wikipedia.org/wiki/HyperSpy-0.md
Normal file
65
data/en.wikipedia.org/wiki/HyperSpy-0.md
Normal file
@ -0,0 +1,65 @@
|
||||
---
|
||||
title: "HyperSpy"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/HyperSpy"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:51.438108+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
HyperSpy is an open source Python package for multidimensional data analysis. Over time, it has grown into an ecosystem that includes a number of extension packages dedicated to specific experimental methods.
|
||||
|
||||
|
||||
== Details ==
|
||||
HyperSpy is a community-driven, open-source library providing a framework to facilitate interactive exploration, analysis and visualization of multidimensional datasets – in particular spectrum images – in an easy and reproducible fashion. It facilitates the application of analytical procedures operating on individual spectra/images to a multi-dimensional dataset and gives easy access to tools that exploit the multi-dimensionality of the dataset. Born out of the electron microscopy scientific community and building on the extensive scientific Python environment, HyperSpy provides tools to efficiently handle complex datasets of arbitrary dimensionality, including those exceeding the size of the system memory.
|
||||
|
||||
|
||||
== Features ==
|
||||
Functionalities provided by HyperSpy include the following:
|
||||
|
||||
Tools for loading/saving various scientific data file formats through its extension RosettaSciIO
|
||||
Background subtraction, artefact removal, etc.
|
||||
Data visualization: evaluate datasets during the analysis, provide interactive operation for certain functions, and publication-ready plotting of data
|
||||
Efficient handling of big datasets ("lazy" and parallel processing)
|
||||
Extracting subsets of data from multidimensional datasets through regions of interest and a powerful numpy-style indexing mechanism
|
||||
Handling of non-uniform data axes
|
||||
User-friendly and powerful framework for multidimensional model fitting that provides many standard functions, but also is easily extended to custom ones
|
||||
Machine learning algorithms useful for e.g. denoising data or decomposition of complex datasets
|
||||
|
||||
|
||||
== Extension packages ==
|
||||
The following packages extend the functionalities of HyperSpy, e.g. dedicated to certain scientific measurement techniques:
|
||||
|
||||
RosettaSciIO: for reading and writing scientific data formats
|
||||
eXSpy: for Energy Dispersive X-ray Spectroscopy (EDS) and Electron energy loss spectroscopy (EELS)
|
||||
pyxem: for 4D scanning transmission electron microscopy (4D-STEM) (electron diffraction data)
|
||||
kikuchipy: for Electron backscatter diffraction (EBSD)
|
||||
lumiSpy: for luminescence spectroscopy data (e.g. cathodoluminescence, photoluminescence, Raman spectroscopy, etc.)
|
||||
Atomap: for atomic resolution scanning transmission electron microscopy images
|
||||
holoSpy: for Off-axis electron holography
|
||||
ParticleSpy for segmentation and analysis of nanoparticles from electron microscopy data
|
||||
ETSpy: for processing, alignment, and reconstruction of electron tomography data
|
||||
HyperSpyUI: Graphical user interface to HyperSpy
|
||||
|
||||
|
||||
== History ==
|
||||
The package was originally developed as EELSlab starting in 2007 for electron energy loss spectroscopy data analysis. It was renamed to HyperSpy in 2010 and open-sourced on GitHub in 2011 when it was realized that it could be readily generalized to other mapping techniques in electron microscopy and beyond.
|
||||
Migration from Python 2 to Python 3 was implemented in 2015. The last version supporting Python 2 was 0.8.3. Subsequently, the first major release, version 1.0.0, was released in 2016.
|
||||
HyperSpy was extended with a mechanism to register extension packages in 2019 with version 1.5. First domain-specific packages were developed in the following years. In 2023, with the second major release, version 2.0.0, all domain-specific code as well as the input/output capabilities were moved to the dedicated packages.
|
||||
Despite the original development of HyperSpy originating from the data analysis needs of the electron microscopy community, it has in the meantime proven to be useful in many other scientific fields, e.g. luminescence spectroscopy.
|
||||
|
||||
|
||||
== Awards ==
|
||||
2025 Open Science Award for Open Source Research Software in the category "Jury's Favourite" (Coup de cœur du jury)
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
Documentation: User Guide
|
||||
hyperspy on GitHub
|
||||
Python Package Index (PyPI) project page
|
||||
Conda-forge project page
|
||||
36
data/en.wikipedia.org/wiki/IT++-0.md
Normal file
36
data/en.wikipedia.org/wiki/IT++-0.md
Normal file
@ -0,0 +1,36 @@
|
||||
---
|
||||
title: "IT++"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/IT++"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:57.326315+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
IT++ is a C++ library of classes and functions for linear algebra, numerical optimization, signal processing, communications, and statistics. It is being developed by researchers in these areas and is widely used by researchers, both in the communications industry and universities. The IT++ library originates from the former Department of Information Theory at the Chalmers University of Technology, Gothenburg, Sweden.
|
||||
The kernel of the IT++ library is templated vector and matrix classes, and a set of accompanying functions. Such a kernel makes IT++ library similar to Matlab/Octave. For increased functionality, speed and accuracy, IT++ can make extensive use of existing free and open source libraries, especially BLAS, LAPACK and FFTW libraries. Instead of BLAS and LAPACK, some optimized platform-specific libraries can be used as well, i.e.:
|
||||
|
||||
ATLAS (Automatically Tuned Linear Algebra Software) - includes optimised BLAS, CBLAS and a limited set of LAPACK routines;
|
||||
MKL (Intel Math Kernel Library) - includes all required BLAS, CBLAS, LAPACK and FFT routines (FFTW not required);
|
||||
ACML (AMD Core Math Library) - includes BLAS, LAPACK and FFT routines (FFTW not required).
|
||||
It is possible to compile and use IT++ without any of the above-listed libraries, but the functionality will be reduced. IT++ works on Linux, Solaris, Windows (with Cygwin, MinGW/MSYS, or Microsoft Visual C++) and OS X operating systems.
|
||||
|
||||
|
||||
== Example ==
|
||||
Here is a trivial example demonstrating the IT++ functionality similar to Matlab/Octave,
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
List of numerical analysis software
|
||||
List of numerical libraries
|
||||
Numerical linear algebra
|
||||
Scientific computing
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
@ -4,7 +4,7 @@ chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Ibercivis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:21.490058+00:00"
|
||||
date_saved: "2026-05-05T10:11:52.674714+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Ibercivis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:21.490058+00:00"
|
||||
date_saved: "2026-05-05T10:11:52.674714+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
46
data/en.wikipedia.org/wiki/ImageJ-0.md
Normal file
46
data/en.wikipedia.org/wiki/ImageJ-0.md
Normal file
@ -0,0 +1,46 @@
|
||||
---
|
||||
title: "ImageJ"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/ImageJ"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:53.859164+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
ImageJ is a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). Its first version, ImageJ 1.x, is developed in the public domain, while ImageJ2 and the related projects SciJava, ImgLib2, and SCIFIO are licensed with a permissive BSD-2 license. ImageJ was designed with an open architecture that provides extensibility via Java plugins and recordable macros. Custom acquisition, analysis and processing plugins can be developed using ImageJ's built-in editor and a Java compiler. User-written plugins make it possible to solve many image processing and analysis problems, from three-dimensional live-cell imaging to radiological image processing, multiple imaging system data comparisons to automated hematology systems. ImageJ's plugin architecture and built-in development environment has made it a popular platform for teaching image processing.
|
||||
ImageJ can be run as an online applet, a downloadable application, or on any computer with a Java 5 or later virtual machine. Downloadable distributions are available for Microsoft Windows, the classic Mac OS, macOS, Linux, and the Sharp Zaurus PDA. The source code for ImageJ is freely available.
|
||||
The project developer, Wayne Rasband, retired from the Research Services Branch of the NIH's National Institute of Mental Health in 2010, but continues to develop the software.
|
||||
|
||||
|
||||
== Features ==
|
||||
|
||||
ImageJ can display, edit, analyze, process, save, and print 8-bit color and grayscale, 16-bit integer, and 32-bit floating point images. It can read many image file formats, including TIFF, PNG, GIF, JPEG, BMP, DICOM, and FITS, as well as raw formats. ImageJ supports image stacks, a series of images that share a single window, and it is multithreaded, so time-consuming operations can be performed in parallel on multi-CPU hardware. ImageJ can calculate area and pixel value statistics of user-defined selections and intensity-thresholded objects. It can measure distances and angles. It can create density histograms and line profile plots. It supports standard image processing functions such as logical and arithmetical operations between images, contrast manipulation, convolution, Fourier analysis, sharpening, smoothing, edge detection, and median filtering. It does geometric transformations such as scaling, rotation, and flips. The program supports any number of images simultaneously, limited only by available memory.
|
||||
|
||||
|
||||
== History ==
|
||||
Before the release of ImageJ in 1997, a similar freeware image analysis program known as NIH Image had been developed in Object Pascal for Macintosh computers running pre-OS X operating systems. Further development of this code continues in the form of Image SXM, a variant tailored for physical research of scanning microscope images. A Windows version – ported by Scion Corporation (now defunct), so-called Scion Image for Windows – was also developed. Both versions are still available but – in contrast to NIH Image – closed-source.
|
||||
On January 25, 2017, AstroImageJ (AIJ) was published by Karen Collins, John Kielkopf, Keivan Stassun, and Frederic Hessman. AIJ built upon the ImageJ processing pipeline, streamlining the software to excel at time-series differential photometry, light curve detrending and fitting, and ultra-precise light curve plotting. AIJ reads and write FITS files, generates master frames, and handles dark, bias, and flat-field subtraction all within the software. AIJ is used for multi-aperture differential photometry and exoplanet transit modeling, with built-in support for modifying aperture and annulus radii, choosing target and comparison objects simply by clicking, adjusting transit priors, and exporting data as a FITS and/or XML file.
|
||||
|
||||
|
||||
== See also ==
|
||||
Eclipse ImageJ Plugin - An plugin which integrates ImageJ in a flexible tabbed view interface and also offers a powerful macro editor with a debugging interface.
|
||||
Bitplane - producers of image processing software with ImageJ compatibility
|
||||
CellProfiler, a software package for high-throughput image analysis by interactive construction of workflow. The workflow could include ImageJ macro
|
||||
CVIPtools A complete open-source GUI-based Computer Vision and Image Processing software, with C functions libraries COM based dll along with two utilities program for algorithm development and batch processing.
|
||||
Fiji (software), an image processing package based on ImageJ
|
||||
Gwyddion (software)
|
||||
KNIME - an open-source data mining environment supporting image analysis developed in close collaboration with the next generation of ImageJ
|
||||
List of free and open-source software packages
|
||||
Microscope image processing
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
|
||||
Official website ImageJ project
|
||||
Official website ImageJ 1.x at NIH
|
||||
Official website ImageJ2
|
||||
NIH Image Official
|
||||
35
data/en.wikipedia.org/wiki/InVesalius-0.md
Normal file
35
data/en.wikipedia.org/wiki/InVesalius-0.md
Normal file
@ -0,0 +1,35 @@
|
||||
---
|
||||
title: "InVesalius"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/InVesalius"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:56.171938+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
InVesalius is a free medical software used to generate virtual reconstructions of structures in the human body. Based on two-dimensional images, acquired using computed tomography or magnetic resonance imaging equipment, the software generates virtual three-dimensional models correspondent to anatomical parts of the human body. After constructing three-dimensional DICOM images, the software allows the generation of STL (stereolithography) files. These files can be used for rapid prototyping.
|
||||
InVesalius was developed at CTI (Renato Archer Information Technology Center), a research institute of the Brazilian Science and Technology Center and is available at no cost at the homepage of Public Software Portal homepage. The software license is CC-GPL 2. It is available in English, Japanese, Czech, Portuguese (Brazil), Russian, Spanish, Italian, German, Portuguese, Turkish (Turkey), Romanian, French, Korean, Catalan, Chinese (Taiwan) and Greek.
|
||||
InVesalius was developed using Python and works under Linux, Windows and Mac OS X. It also uses graphic libraries VTK, wxPython, Numpy, Scipy and GDCM.
|
||||
The software's name is a tribute to Belgian physician Andreas Vesalius (1514–1564), considered the "father of modern anatomy".
|
||||
Developed since 2001 for attending Brazilian Public Hospitals demands, InVesalius development was directed for promoting social inclusion of individuals with severe facial deformities. Since then, however, it has been employed in various research areas of dentistry, medicine, veterinary medicine, paleontology and anthropology. It has been used not only in public hospitals, but also in private clinics and hospitals.
|
||||
Until 2017, the software had already been used for generating more than 5000 rapid prototyping models of anatomical structures at Promed project.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official InVesalius website
|
||||
Alternative InVesalius website
|
||||
InVesalius source code
|
||||
InVesalius Translation page at Transifex
|
||||
InVesalius at Ohloh Archived 2010-02-02 at the Wayback Machine
|
||||
InVesalius at Twitter
|
||||
Public Software Portal (Portuguese)
|
||||
Rapid Prototyping for Medicine (Portuguese)
|
||||
|
||||
|
||||
== Related works ==
|
||||
Confex.com (in English)
|
||||
Studierfenster
|
||||
@ -0,0 +1,68 @@
|
||||
---
|
||||
title: "Insight Segmentation and Registration Toolkit"
|
||||
chunk: 1/2
|
||||
source: "https://en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:55.021887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
ITK is a cross-platform, open-source application development framework widely used for the development of image segmentation and image registration programs. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with an MRI scan in order to combine the information contained in both.
|
||||
ITK was developed with funding from the National Library of Medicine (U.S.) as an open resource of algorithms for analyzing the images of the Visible Human Project. ITK stands for The Insight Segmentation and Registration Toolkit. The toolkit provides leading-edge segmentation and registration algorithms in two, three, and more dimensions. ITK uses the CMake build environment to manage the configuration process. The software is implemented in C++ and it is wrapped for Python. An offshoot of the ITK project providing a simplified interface to ITK in eight programming languages, SimpleITK, is also under active development.
|
||||
|
||||
== Introduction ==
|
||||
|
||||
=== Origins ===
|
||||
In 1999 the US National Library of Medicine of the National Institutes of Health awarded a three-year contract to develop an open-source registration and segmentation toolkit, which eventually came to be known as the Insight Toolkit (ITK). ITK's NLM Project Manager was Dr. Terry Yoo, who coordinated the six prime contractors who made up the Insight Software Consortium. These consortium members included the three commercial partners GE Corporate R&D, Kitware, Inc., and MathSoft (the company name is now Insightful); and the three academic partners University of North Carolina (UNC), University of Tennessee (UT), and University of Pennsylvania (UPenn). The Principal Investigators for these partners were, respectively, Bill Lorensen at GE CRD, Will Schroeder at Kitware, Vikram Chalana at Insightful, Stephen Aylward with Luis Ibáñez at UNC (both of whom subsequently moved to Kitware), Ross Whitaker with Josh Cates at UT (both now at Utah), and Dimitris Metaxas at UPenn (Dimitris Metaxas is now at Rutgers University). In addition, several subcontractors rounded out the consortium including Peter Ratiu at Brigham & Women's Hospital, Celina Imielinska and Pat Molholt at Columbia University, Jim Gee at UPenn's Grasp Lab, and George Stetten at University of Pittsburgh.
|
||||
|
||||
=== Technical details ===
|
||||
ITK is an open-source software toolkit for performing registration and segmentation. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with an MRI scan in order to combine the information contained in both.
|
||||
ITK is implemented in C++. ITK is cross-platform, using the CMake build environment to manage the compilation process. In addition, an automated wrapping process generates interfaces between C++ and other programming languages such as Java and Python. This enables developers to create software using a variety of programming languages. ITK's implementation employs the technique of generic programming through the use of C++ templates.
|
||||
Because ITK is an open-source project, developers from around the world can use, debug, maintain, and extend the software. ITK uses a model of software development referred to as extreme programming. Extreme programming collapses the usual software creation methodology into a simultaneous and iterative process of design-implement-test-release. The key features of extreme programming are communication and testing. Communication among the members of the ITK community is what helps manage the rapid evolution of the software. Testing is what keeps the software stable. In ITK, an extensive testing process (using CDash) is in place that measures the quality on a daily basis. The ITK Testing Dashboard is posted continuously, reflecting the quality of the software.
|
||||
|
||||
=== Developers and contributors ===
|
||||
The Insight Toolkit was initially developed by six principal organizations
|
||||
|
||||
Kitware
|
||||
GE Corporate R&D
|
||||
Insightful
|
||||
University of North Carolina at Chapel Hill
|
||||
University of Utah
|
||||
University of Pennsylvania
|
||||
and three subcontractors
|
||||
|
||||
Harvard Brigham & Women's Hospital
|
||||
University of Pittsburgh
|
||||
Columbia University
|
||||
After its inception the software continued growing with contributions from other institutions including
|
||||
|
||||
University of Iowa
|
||||
Georgetown University
|
||||
Stanford University
|
||||
King's College London
|
||||
Creatis INSA
|
||||
|
||||
=== Funding ===
|
||||
The funding for the project is from the National Library of Medicine at the National Institutes of Health. NLM in turn was supported by member institutions of NIH (see sponsors).
|
||||
The goals for the project include the following:
|
||||
|
||||
Support the Visible Human Project.
|
||||
Establish a foundation for future research.
|
||||
Create a repository of fundamental algorithms.
|
||||
Develop a platform for advanced product development.
|
||||
Support commercial application of the technology.
|
||||
Create conventions for future work.
|
||||
Grow a self-sustaining community of software users and developers.
|
||||
The source code of the Insight Toolkit is distributed under an Apache 2.0 License (as approved by the Open Source Initiative)
|
||||
The philosophy of Open Source of the Insight Toolkit was extended to support open science, in particular by providing open access to publications in the domain of Medical Image Processing. These publications are made freely available through the Insight Journal
|
||||
|
||||
=== Community participation ===
|
||||
Because ITK is an open-source system, anybody can make contributions to the project. A person interested in contributing to ITK can take the following actions
|
||||
|
||||
Read the ITK Software Guide. (This book can be purchased from Kitware's store.)
|
||||
Read the instructions on how to contribute classes and algorithms to the Toolkit via submissions to the Insight Journal
|
||||
Obtain access to GitHub.
|
||||
Follow the Git contribution instructions.
|
||||
Join the ITK Discourse discussion. The community is open to everyone.
|
||||
Anyone can submit a patch, and write access to the repository is not necessary to get a patch merged or retain authorship credit. For more information, see the ITK Bar Camp documentation on how to submit a patch.
|
||||
@ -0,0 +1,101 @@
|
||||
---
|
||||
title: "Insight Segmentation and Registration Toolkit"
|
||||
chunk: 2/2
|
||||
source: "https://en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:55.021887+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
=== Copyright and license ===
|
||||
ITK is copyrighted by the Insight Software Consortium, a non-profit alliance of organizations and individuals interested in supporting ITK. Starting with ITK version 3.6, the software is distributed under a BSD open-source license. It allows use for any purpose, with the possible exception of code found in the patented directory, and with proper recognition. The full terms of the copyright and the license are available at www.itk.org/ITK/project/license.html. Version 4.0 uses Apache 2.0 License.
|
||||
The licensed was changed to Apache 2.0 with version 4.0 to adopt a modern license with patent protection provisions. From version 3.6 to 3.20, a simplified BSD license was used. Versions of ITK previous to ITK 3.6 were distributed under a modified BSD License. The main motivation for adopting a BSD license starting with ITK 3.6, was to have an OSI-approved license.
|
||||
|
||||
== Technical Summary ==
|
||||
The following sections summarize the technical features of the NLM's Insight ITK toolkit.
|
||||
Design Philosophy
|
||||
The following are key features of the toolkit design philosophy.
|
||||
|
||||
The toolkit provides data representation and algorithms for performing segmentation and registration. The focus is on medical applications; although the toolkit is capable of processing other data types.
|
||||
The toolkit provides data representations in general form for images (arbitrary dimension) and (unstructured) meshes.
|
||||
The toolkit does not address visualization or graphical user interface. These are left to other toolkits (such as VTK, VISPACK, 3DViewnix, MetaImage, etc.)
|
||||
The toolkit provides minimal tools for file interface. Again, this is left to other toolkits/libraries to provide.
|
||||
Multi-threaded (shared memory) parallel processing is supported.
|
||||
The development of the toolkit is based on principles of extreme programming. That is, design, implementation, and testing is performed in a rapid, iterative process. Testing forms the core of this process. In Insight, testing is performed continuously as files are checked in, and every night across multiple platforms and compilers. The ITK testing dashboard, where testing results are posted, is central to this process.
|
||||
|
||||
=== Architecture ===
|
||||
The following are key features of the toolkit architecture.
|
||||
|
||||
The toolkit is organized around a data-flow architecture. That is, data is represented using data objects which are in turn processed by process objects (filters). Data objects and process objects are connected together into pipelines. Pipelines are capable of processing the data in pieces according to a user-specified memory limit set on the pipeline.
|
||||
Object factories are used to instantiate objects. Factories allow run-time extension of the system.
|
||||
A command/observer design pattern is used for event processing.
|
||||
|
||||
=== Implementation philosophy ===
|
||||
The following are key features of the toolkit implementation philosophy.
|
||||
|
||||
The toolkit is implemented using generic programming principles. Such heavily templated C++ code challenges many compilers; hence development was carried out with the latest versions of the MSVC, Sun, gcc, Intel, and SGI compilers.
|
||||
The toolkit is cross-platform (Unix, Windows and Mac OS X).
|
||||
The toolkit supports multiple language bindings, including such languages as Tcl, Python, and Java. These bindings are generated automatically using an auto-wrap process.
|
||||
The memory model depends on "smart pointers" that maintain a reference count to objects. Smart pointers can be allocated on the stack, and when scope is exited, the smart pointers disappear and decrement their reference count to the object that they refer to.
|
||||
|
||||
=== Build environment ===
|
||||
ITK uses the CMake (cross-platform make) build environment. CMake is an operating system and compiler independent build process that produces native build files appropriate to the OS and compiler that it is run with. On Unix CMake produces makefiles and on Windows CMake generates projects and workspaces.
|
||||
|
||||
=== Testing environment ===
|
||||
ITK supports an extensive testing environment. The code is tested daily (and even continuously) on many hardware/operating system/compiler combinations and the results are posted daily on the ITK testing dashboard. We use Dart to manage the testing process, and to post the results to the dashboard.
|
||||
|
||||
=== Background references: C++ patterns and generics ===
|
||||
ITK uses many advanced design patterns and generic programming. You may find these references useful in understanding the design and syntax of Insight.
|
||||
|
||||
Design Patterns. by Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides, Grady Booch
|
||||
Generic Programming and the Stl : Using and Extending the C++ Standard Template Library (Addison-Wesley Professional Computing Series) by Matthew H. Austern
|
||||
Advanced C++ Programming Styles and Idioms by James O. Coplien
|
||||
C/C++ Users Journal
|
||||
C++ Report
|
||||
|
||||
== Examples ==
|
||||
|
||||
=== Gaussian-smoothed image gradient ===
|
||||
|
||||
=== Region growing segmentation ===
|
||||
|
||||
== Additional information ==
|
||||
|
||||
=== Resources ===
|
||||
A number of resources are available to learn more about ITK.
|
||||
|
||||
The ITK web pages are located at www.itk.org.
|
||||
Users and developers alike should read the ITK Software Guide
|
||||
Many compilable examples are available on the ITK Examples Wiki
|
||||
Tutorials are available at www.itk.org/ITK/help/tutorials.html
|
||||
The software can be downloaded from www.itk.org/ITK/resources/software.html.
|
||||
Developers, or users interested in contributing code, should look in the document Insight/Documentation/InsightDeveloperStart.pdf or InsightDeveloperStart.doc found in the source code distribution.
|
||||
Developers should also look at the ITK style guide Insight/Documentation/Style.pdf found in the source distribution.
|
||||
|
||||
=== Applications ===
|
||||
A great way to learn about ITK is to see how it is used. There are four places to find applications of ITK.
|
||||
|
||||
The Insight/Examples/ source code examples distributed with ITK. The source code is available. In addition, it is heavily commented and works in combination with the ITK Software Guide.
|
||||
The separate InsightApplications checkout.
|
||||
The Applications web pages. These are extensive descriptions, with images and references, of the examples found in #1 above.
|
||||
The testing directories distributed with ITK are simple, mainly undocumented examples of how to use the code.
|
||||
In 2004 ITK-SNAP (website) was developed from SNAP and became a popular free segmentation software using ITK and having a nice and simple user interface.
|
||||
|
||||
=== Data ===
|
||||
Data is available in ITK data.kitware.com Girder Community.
|
||||
See also the ITK Data web page.
|
||||
|
||||
== See also ==
|
||||
|
||||
=== Related tools ===
|
||||
CMake
|
||||
VTK
|
||||
|
||||
=== Contacts ===
|
||||
Visit the ITK discussion forum for contacts and help from the community.
|
||||
|
||||
== References ==
|
||||
|
||||
== External links ==
|
||||
ITK
|
||||
116
data/en.wikipedia.org/wiki/JASP-0.md
Normal file
116
data/en.wikipedia.org/wiki/JASP-0.md
Normal file
@ -0,0 +1,116 @@
|
||||
---
|
||||
title: "JASP"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/JASP"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:58.479946+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
JASP is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form. JASP generally produces APA style results tables and plots to ease publication. It promotes open science via integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by sponsors, several universities, and research funds.
|
||||
|
||||
|
||||
== Overview ==
|
||||
In recognition of Bayesian pioneer Sir Harold Jeffreys, JASP stands for Jeffreys’s Amazing Statistics Program.
|
||||
|
||||
|
||||
== Analyses ==
|
||||
JASP offers frequentist inference and Bayesian inference on the same statistical models. Frequentist inference uses p-values and confidence intervals to control error rates in the limit of infinite perfect replications. Bayesian inference uses credible intervals and Bayes factors to estimate credible parameter values and model evidence given the available data and prior knowledge.The following analyses are available in JASP in comparison to SPSS:
|
||||
|
||||
|
||||
=== Other features ===
|
||||
Accessibility features (full and partial app zooming, key-board shortcut support / ALT-Navigation, VPAT)
|
||||
integrated help files and annotated data library examples for many analyses.
|
||||
R syntax editing and highlighting.
|
||||
Extensive plot and formula (LaTeX) editing capabilities.
|
||||
Exports results as PDF or HTML; tables can also be copy pasted in LaTeX format.; plots as PNG, PPTX (Powerpoint) etc.; data can be exported as CSV.
|
||||
Imports R, Excel, SAS and SPSS files etc. (.Rdata, .rds, .xls, xlsx, .csv, .txt, .tsv, .ods, .dta, .sav, .zsav, .por, .sas7bdat, .sas7bcat, .xpt, .jasp).
|
||||
Connects and syncs to SQL data bases, the Cochrane data base and the Open Science Framework.
|
||||
Data filtering: Use either R code or a drag-and-drop GUI to select cases of interest.
|
||||
Recode data with only one click.
|
||||
Full data editing with one-click recoding; full undo / redo functionality.
|
||||
Compute columns with R code (e.g. row-wise functions like rowMean, rowMeanNaRm, rowSum, rowSD ...) or a drag-and-drop GUI to create new variables or compute them from existing ones or with simulated data.
|
||||
Empty values settings per variable, per data set or globally.
|
||||
Assumption checks via export and then plotting of residuals and/or per analyses via tests and plots (Levene's, Brown-Forsythe, Shapiro–Wilk, Q–Q, Raincloud, Mardia's test and many more).
|
||||
|
||||
|
||||
== Modules ==
|
||||
JASP features seven common modules that are enabled by default:
|
||||
|
||||
Descriptives: Explore the data with tables and plots.
|
||||
T-Tests: Evaluate the difference between two means.
|
||||
ANOVA: Evaluate the difference between multiple means.
|
||||
Mixed Models: Evaluate the difference between multiple means with random effects.
|
||||
Regression: Evaluate the association between variables.
|
||||
Frequencies: Analyses for count data.
|
||||
Factor: Explore hidden structure in the data.
|
||||
JASP also features multiple additional modules that can be activated via the module menu:
|
||||
|
||||
Acceptance Sampling: Methods for acceptance sampling and a quality control setting.
|
||||
Audit: Statistical methods for auditing. The audit module offers planning, selection and evaluation of statistical audit samples, methods for data auditing (e.g., Benford’s law) and algorithm auditing (e.g., model fairness).
|
||||
Bain: Bayesian informative hypotheses evaluation for t-tests, ANOVA, ANCOVA, linear regression and structural equation modeling.
|
||||
Bayes Factor Functions (for Z-Tests, T-Tests, Regression, Frequencies)
|
||||
BFpack (for T-Tests, ANOVA, Regression, Variances)
|
||||
BSTS: Bayesian take on linear Gaussian state space models suitable for time series analysis.
|
||||
Circular Statistics: Basic methods for directional data.
|
||||
Cochrane Meta-Analyses: Analyse Cochrane medical datasets.
|
||||
Distributions: Visualise probability distributions and fit them to data.
|
||||
Equivalence T-Tests: Test the difference between two means with an interval-null hypothesis.
|
||||
JAGS: Implement Bayesian models with the JAGS program for Markov chain Monte Carlo.
|
||||
Learn Bayes: Learn Bayesian statistics with simple examples and supporting text (with Binary Classification, Counts, The Problem of Points, Buffon’s Needle)
|
||||
Learn Stats: Learn classical statistics with simple examples and supporting text (with Normal Distribution, Binomial Distribution, Central Limit Theorem, Standard Error, Descriptive Statistics, Sample Variability, P Values, Confidence Intervals, Effect Sizes, Statistical Test Decision Tree).
|
||||
Machine Learning: Explore the relation between variables using data-driven methods for supervised learning and unsupervised learning. The module contains 19 analyses for regression, classification and clustering:
|
||||
Regression
|
||||
Boosting Regression
|
||||
Decision Tree Regression
|
||||
K-Nearest Neighbors Regression
|
||||
Neural Network Regression
|
||||
Random Forest Regression
|
||||
Regularized Linear Regression
|
||||
Support Vector Machine Regression
|
||||
Classification
|
||||
Boosting Classification
|
||||
Decision Tree Classification
|
||||
K-Nearest Neighbors Classification
|
||||
Neural Network Classification
|
||||
Linear Discriminant Classification
|
||||
Random Forest Classification
|
||||
Support Vector Machine Classification
|
||||
Clustering
|
||||
Density-Based Clustering
|
||||
Fuzzy C-Means Clustering
|
||||
Hierarchical Clustering
|
||||
Model-based clustering
|
||||
Neighborhood-based Clustering (i.e., K-Means Clustering, K-Medians clustering, K-Medoids clustering)
|
||||
Random Forest Clustering
|
||||
Prediction
|
||||
Meta Analysis: Synthesise evidence across multiple studies. Includes techniques for fixed and random effects analysis, fixed and mixed effects meta-regression, forest and funnel plots, tests for funnel plot asymmetry, trim-and-fill and fail-safe N analysis.
|
||||
Network: Explore the connections between variables organised as a network. Network Analysis allows the user to analyze the network structure.
|
||||
Power: Conduct power analyses and sample size planning.
|
||||
Predictive Analytics: This module offers predictive analytics.
|
||||
Process: Implementation of Hayes' popular SPSS PROCESS module for JASP
|
||||
Prophet: A simple model for time series prediction.
|
||||
Quality Control: Investigate if a manufactured product adheres to a defined set of quality criteria (with Measurement Systems Analysis, Control Charts, Capibility Study, Design of Experiments).
|
||||
Reliability: Quantify the reliability of test scores.
|
||||
Robust T-Tests: Robustly evaluate the difference between two means.
|
||||
SEM (Structural equation modeling): Evaluate latent data structures with Yves Rosseel's lavaan program (with Structural Equation Modeling, Partial Least Squares SEM, Mediation Analysis, MMIC Model, Latent Growth).
|
||||
Summary statistics: Apply common Bayesian tests from frequentist summary statistics for t-test, regression, and binomial tests.
|
||||
Survival Analyses: non-parametric, semi-parametric, parametric
|
||||
Time Series: Time series analysis with Descriptives, Stationarity, ARIMA, Spectral Analysis.
|
||||
Visual Modeling: Graphically explore the dependencies between variables.
|
||||
R Console: Execute R code in a console.
|
||||
|
||||
|
||||
== See also ==
|
||||
Comparison of statistical packages
|
||||
Statistics
|
||||
Bayesian statistics
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
jasp-desktop on GitHub
|
||||
59
data/en.wikipedia.org/wiki/JOELib-0.md
Normal file
59
data/en.wikipedia.org/wiki/JOELib-0.md
Normal file
@ -0,0 +1,59 @@
|
||||
---
|
||||
title: "JOELib"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/JOELib"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:11:59.648047+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
JOELib is computer software, a chemical expert system used mainly to interconvert chemical file formats. Because of its strong relationship to informatics, this program belongs more to the category cheminformatics than to molecular modelling. It is available for Windows, Unix and other operating systems supporting the programming language Java. It is free and open-source software distributed under the GNU General Public License (GPL) 2.0.
|
||||
|
||||
|
||||
== History ==
|
||||
JOELib and OpenBabel were derived from the OELib Cheminformatics library.
|
||||
|
||||
|
||||
== Logo ==
|
||||
The project logo is just the word JOELib in the Tengwar script of J. R. R. Tolkien. The letters are grouped as JO-E-Li-b. Vowels are usually grouped together with a consonant, but two following vowels must be separated by a helper construct.
|
||||
|
||||
|
||||
== Major features ==
|
||||
Chemical expert system
|
||||
Query and substructure search (based on Simplified molecular-input line-entry system (SMARTS), a SMILES extension
|
||||
Clique detection
|
||||
QSAR
|
||||
Data mining
|
||||
Molecule mining, special case of Structured Data Mining
|
||||
Feature–descriptor calculation
|
||||
Partition coefficient, log P
|
||||
Rule-of-five
|
||||
Partial charges
|
||||
Fingerprint calculation
|
||||
etc.
|
||||
Chemical file formats
|
||||
Chemical table file: MDL Molfile, SD format
|
||||
SMILES
|
||||
Gaussian
|
||||
Chemical Markup Language
|
||||
MOPAC
|
||||
|
||||
|
||||
== See also ==
|
||||
OpenBabel - C++ version of JOELib-OELib
|
||||
Jmol
|
||||
Chemistry Development Kit (CDK)
|
||||
Comparison of software for molecular mechanics modeling
|
||||
Blue Obelisk
|
||||
Molecule editor
|
||||
List of free and open-source software packages
|
||||
|
||||
|
||||
== References ==
|
||||
The Blue Obelisk-Interoperability in Chemical Informatics, Rajarshi Guha, Michael T. Howard, Geoffrey R. Hutchison, Peter Murray-Rust, Henry Rzepa, Christoph Steinbeck, Jörg K. Wegner, and Egon L. Willighagen, J. Chem. Inf. Model.; 2006; doi:10.1021/ci050400b
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website at SourceForge
|
||||
Algorithm dictionary
|
||||
33
data/en.wikipedia.org/wiki/KAlgebra-0.md
Normal file
33
data/en.wikipedia.org/wiki/KAlgebra-0.md
Normal file
@ -0,0 +1,33 @@
|
||||
---
|
||||
title: "KAlgebra"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/KAlgebra"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:00.853503+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
KAlgebra is a mathematical graph calculator included in the KDE education package. While it is based on the MathML content markup language, knowledge of MathML is not required for use. The calculator includes numerical, logical, symbolic, and analytical functions, and can plot the results onto a 2D or 3D graph. KAlgebra is free and open source software, licensed under the GPL-2.0-or-later license.
|
||||
KAlgebra has been mentioned by various media sources as free / open source educational programs.
|
||||
|
||||
|
||||
== User interface and syntax ==
|
||||
|
||||
KAlgebra uses an intuitive algebraic syntax, similar to those used on modern graphing calculators. User-entered expressions are converted to MathML in the background, or they can be entered directly. The program is divided into four views, Console, 2D Graph, 3D Graph, and Dictionary. A series of calculations can be performed with user-defined scripts, which are macros that can be reused and shared.
|
||||
The dictionary includes a comprehensive list of all built-in functions in KAlgebra. Functions can be looked up with parameters, examples, formulas and sample plots. Over 100 functions and operations are currently supported.
|
||||
|
||||
|
||||
== Graphing and dictionary ==
|
||||
|
||||
In the 2D and 3D graph views, functions can evaluated and plotted. Currently KAlgebra only supports 3D graphs explicitly dependent only on the x and y. Both views support defining the viewpoint. The user can hover their cursor over a line and find the exact X and Y values for 2D graphs, as well as create a live tangent line.
|
||||
In the 3D view, the user can control the viewpoint position with the keyboard's arrow keys, and zooming in and out is done with the W and S keys respectively. The user can also draw lines and make dots on the 3D graph and export the graph in various formats.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
KAlgebra page on kde.org
|
||||
KAlgebra page on Kdeapps Archived 2016-03-04 at the Wayback Machine
|
||||
The KAlgebra Handbook (manual/documentation)
|
||||
44
data/en.wikipedia.org/wiki/KLettres-0.md
Normal file
44
data/en.wikipedia.org/wiki/KLettres-0.md
Normal file
@ -0,0 +1,44 @@
|
||||
---
|
||||
title: "KLettres"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/KLettres"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:04.308594+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
KLettres is an educational program that helps the users learn the alphabet as well as pronunciation. It is free and open source software, licensed under the terms of the GPL. The software is part of the KDE Education Project, and is meant to teach very young children aged 2 to 6 years the alphabet. There are currently 4 levels in the game and supports 25 different languages.
|
||||
|
||||
|
||||
== Levels ==
|
||||
KLettres features four levels, with settings for adult ("grown up") and children ("kid").
|
||||
|
||||
In level 1, the letter is displayed and the user hears it.
|
||||
In level 2, the letter is not displayed and the user only hears it.
|
||||
In level 3, the syllable is displayed and the user hears it.
|
||||
In level 4, the syllable is not displayed and the user only hears it.
|
||||
|
||||
|
||||
== Languages supported ==
|
||||
Arabic, Czech, Brazilian Portuguese, Danish, Dutch, British English, English, English Phonix, French, German, Hebrew, Hungarian, Italian, Kannada, Hebrew, Hindi Romanized, Low Saxon, Luganda, Malayalam, Norwegian Bokmål, Punjabi, Spanish, Slovak, Ukrainian and Telugu.
|
||||
|
||||
|
||||
== Release history ==
|
||||
May 13, 2004: v1.3, added Italian and special characters.
|
||||
March 8, 2005: Code refactoring, open usability review.
|
||||
March 14, 2005: 3 themes included (classroom, arctic and desert).
|
||||
March 15, 2005: Added Spanish and Romanized Hindi sounds.
|
||||
April 15, 2005: Support for Lunganda.
|
||||
July 15, 2006: German sounds added.
|
||||
September 23, 2006: Hebrew added.
|
||||
February 9, 2007: Low Saxon is added.
|
||||
November 2, 2007: 3 languages - Telugu, Kannada and Brazilian Portuguese is added with sound from a 9 year old.
|
||||
April 13, 2011: Milestone of 25 languages supported in KLettres.
|
||||
|
||||
|
||||
== External links ==
|
||||
KLettres Project Page
|
||||
|
||||
|
||||
== References ==
|
||||
45
data/en.wikipedia.org/wiki/Kalypso_(software)-0.md
Normal file
45
data/en.wikipedia.org/wiki/Kalypso_(software)-0.md
Normal file
@ -0,0 +1,45 @@
|
||||
---
|
||||
title: "Kalypso (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Kalypso_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:01.997932+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Kalypso is an open source modelling program. It focuses on numerical simulations in water management and ecology such as the generation of inundation and flood risk maps by hydrologic and hydrodynamic models and GIS functionality.
|
||||
The Kalypso software system has been collaboratively developed in a joint project by the company Björnsen Consulting Engineers (BCE) and the department for river and coastal engineering at Hamburg University of Technology, Germany. It is available for download at SourceForge.net under the GNU LGPL.
|
||||
|
||||
|
||||
== Kalypso Modules ==
|
||||
Kalypso is currently (November 2010) divided into six modules: three numerical simulation modules, two tools for complex flood and risk mapping and an evacuation tool.
|
||||
|
||||
Kalypso Hydrology (rainfall-runoff simulation)
|
||||
Kalypso WSPM (one-dimensional steady hydrodynamic simulation)
|
||||
Kalypso 1D/2D (coupled one- and two-dimensional unsteady hydrodynamic simulation)
|
||||
Kalypso Flood (flood mapping tool)
|
||||
Kalypso Risk (flood risk assessment tool)
|
||||
Kalypso Evacuation (evacuation strategy tool)
|
||||
The modules are based on a common modelling framework called KalypsoBASE and are published as open source software under the LGPL license. KalypsoBASE itself is a collection of Eclipse plug-ins and can be easily extended to provide new and independent modules.
|
||||
|
||||
|
||||
== Projects ==
|
||||
A few software projects are using KalypsoBASE.
|
||||
|
||||
|
||||
=== nofdp IDSS ===
|
||||
nofdp (nature-oriented flood damage prevention) is a decision support system designed by the Hessian Ministry for Environment, Rural Areas and Consumer Protection (Germany) in the context of the EU INTERREG III B program in order to provide means for environmentally-oriented flood protection measures.
|
||||
The nofdp IDSS has been designed as a tool for planners and decision makers in water resources engineering allowing them to have an integrative view on river catchment areas. This is used to improve flood damage protection taking into account aspects of spatial planning, water resources management and ecology.
|
||||
|
||||
|
||||
== References ==
|
||||
"A GIS-based Platform for Environmental and Water Resources Modeling - Kalypso Open Source" Archived 2011-07-18 at the Wayback Machine, Publication in GEO Informatics magazine, March 2009.
|
||||
|
||||
|
||||
== Notes ==
|
||||
This article incorporates text from the Kalypso article at GISWiki, where the content is licensed under the GFDL.
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
31
data/en.wikipedia.org/wiki/KiSAO-0.md
Normal file
31
data/en.wikipedia.org/wiki/KiSAO-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "KiSAO"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/KiSAO"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:03.144516+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. KiSAO is part of the BioModels.net project and of the COMBINE initiative.
|
||||
|
||||
|
||||
== Structure ==
|
||||
KiSAO consists of three main branches:
|
||||
|
||||
simulation algorithm
|
||||
simulation algorithm characteristic
|
||||
simulation algorithm parameter
|
||||
The elements of each algorithm branch are linked to characteristic and parameter branches using has characteristic and has parameter relationships accordingly. The algorithm branch itself is hierarchically structured using relationships which denote that the descendant algorithms were derived from, or specify, more general ancestors.
|
||||
|
||||
|
||||
== See also ==
|
||||
COMBINE
|
||||
SED-ML
|
||||
MIRIAM
|
||||
SBO
|
||||
TEDDY
|
||||
|
||||
|
||||
== References ==
|
||||
62
data/en.wikipedia.org/wiki/LAMMPS-0.md
Normal file
62
data/en.wikipedia.org/wiki/LAMMPS-0.md
Normal file
@ -0,0 +1,62 @@
|
||||
---
|
||||
title: "LAMMPS"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/LAMMPS"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:06.699876+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) is a molecular dynamics program developed by Sandia National Laboratories. It utilizes the Message Passing Interface (MPI) for parallel communication, enabling high-performance simulations. LAMMPS is a free and open-source software, distributed under the terms of the GNU General Public License. It is available on Linux, Windows, and macOS platforms.
|
||||
|
||||
|
||||
== History ==
|
||||
LAMMPS was developed in the mid-1990s under a Cooperative Research and Development Agreement between two laboratories from the United States Department of Energy (Sandia National Laboratories and Lawrence Livermore National Laboratory) and three companies (Cray, DuPont, and Bristol-Myers Squibb). The goal was to create a parallel molecular dynamics code capable of running on large supercomputers for materials and biomolecular modeling. Initially written in Fortran, LAMMPS has since been rewritten in C++ to provide more flexibility and ease in adding new features.
|
||||
|
||||
|
||||
== Features ==
|
||||
LAMMPS is a highly flexible and scalable molecular dynamics simulator that supports both single-processor and parallel execution through MPI and OpenMP. GPU acceleration is also available. LAMMPS can be run from an input script, as well as a graphical interface GUI. Its modular, open-source C++ design is easy to extend or integrate with other codes or languages like Python. Users can define variables, use loops, and run multiple simulations simultaneously from a single script.
|
||||
|
||||
|
||||
=== Particle and model types ===
|
||||
LAMMPS supports a wide variety of particle and model types, ranging from simple atoms to complex systems like molecules, metals, and granular materials. It also handles finite-size shapes, such as spherical and ellipsoidal particles, point dipole particles, and magnetic spins, and offers the possibility of using hybrid combinations of these particle and model types.
|
||||
|
||||
|
||||
=== Interatomic potentials ===
|
||||
LAMMPS supports a vast array of potentials, including pairwise (e.g., Lennard-Jones, Coulombic), many-body (e.g., EAM, REBO, ReaxFF), machine learning (e.g., ACE, GAP), and specialized models (e.g., TIP4P water). It also accommodates hybrid and overlaid potentials, enabling the combination of multiple potential types in a single simulation.
|
||||
|
||||
|
||||
=== Ensembles, constraints, and boundary conditions ===
|
||||
LAMMPS supports both 2D and 3D systems with orthogonal or non-orthogonal (triclinic) simulation domains. It includes multiple thermostat and barostat choices, such as Nose/Hoover, Berendsen, and Parrinello/Rahman. Different rigid body constraints and advanced algorithms like SHAKE and RATTLE can be combines with additional harmonic forces. Additionally, LAMMPS supports some Monte Carlo move, atom and molecule insertion and deletion, non-equilibrium molecular dynamics (NEMD), and a variety of boundary conditions (e.g., periodic, shrink-wrapped) and walls (static and moving).
|
||||
|
||||
|
||||
=== Integrators ===
|
||||
Several integrators can be used with LAMMPS, including the velocity-Verlet integrator, Brownian dynamics, and rigid body integration. It also supports energy minimization techniques like conjugate gradient, steepest descent, and damped dynamics (FIRE, Quickmin), as well as rRESPA hierarchical timestepping and fixed or adaptive time steps. Additionally, the rerun command allows for post-processing of dump files.
|
||||
|
||||
|
||||
=== Outputs ===
|
||||
LAMMPS provides numerous fix and compute commands for monitoring system properties. Thermodynamic data, such as system temperature and energy, is logged, and atom-level information such as positions and velocities can be output via text or binary dump files at chosen intervals. LAMMPS also allows output to be customized for spatial or temporal resolution using chunks, time averaging, or histogramming. The state of the simulation can be saved in text and binary restart files. Additionally, LAMMPS can export atom snapshots in various formats.
|
||||
|
||||
|
||||
=== Various ===
|
||||
For computing efficiency, LAMMPS uses neighbor lists (Verlet lists) to keep track of nearby particles. The lists are optimized for systems with particles that repel at short distances, so that the local density of particles never grows too large.
|
||||
On parallel computers, LAMMPS uses spatial-decomposition techniques to partition the simulation domain into small 3D sub-domains, one of which is assigned to each processor. Processors communicate and store ghost atom information for atoms that border their subdomain. LAMMPS is most efficient (in a parallel computing sense) for systems whose particles fill a 3D rectangular box with approximately uniform density. Lots of accelerators are supported by LAMMPS, including GPU (CUDA, OpenCL, HIP, SYCL), Intel Xeon Phi, and OpenMP, due to its integration with Trilinos.
|
||||
|
||||
|
||||
== Coupling LAMMPS with Other Software ==
|
||||
LAMMPS can be coupled with a variety of external analysis tools and visualization engines, including VMD and OVITO. LAMMPS can be coupled with Python libraries for setting up and analyzing simulations, such as MDAnalysis, MDTraj, and ASE. In addition, LAMMPS supports coupling with free energy calculation tools such as PLUMED and the Colvars module.
|
||||
|
||||
|
||||
== See also ==
|
||||
Parallel computing
|
||||
Comparison of software for molecular mechanics modeling
|
||||
Molecular design software
|
||||
List of free and open-source software packages
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/LHC@home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:25.119778+00:00"
|
||||
date_saved: "2026-05-05T10:12:10.388593+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
31
data/en.wikipedia.org/wiki/LabPlot-0.md
Normal file
31
data/en.wikipedia.org/wiki/LabPlot-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "LabPlot"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/LabPlot"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:05.482566+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
LabPlot is a free and open-source, cross-platform computer program for interactive scientific plotting, curve fitting, nonlinear regression, data processing and data analysis. LabPlot is available, under the GPL-2.0-or-later license, for Windows, macOS, Linux, FreeBSD and Haiku operating systems.
|
||||
It has a graphical user interface, a command-line interface, and an interactive and animated notebook interface. It is similar to Origin and able to import Origin's data files. Features include the Hilbert transform function, statistics, color maps, conditional formatting, plot digitization and multi-axes.
|
||||
|
||||
|
||||
== History ==
|
||||
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.
|
||||
Starting April 2024, LabPlot received funding from NLnet's NGI0 Core grant to add scripting capabilities (via Python and a public interface), more data analysis functions, and statistical analysis features.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
List of statistical software
|
||||
List of information graphics software
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
NLnet Foundation grant
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Leiden_Classical"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:23.883152+00:00"
|
||||
date_saved: "2026-05-05T10:12:09.200501+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
78
data/en.wikipedia.org/wiki/LibSBML-0.md
Normal file
78
data/en.wikipedia.org/wiki/LibSBML-0.md
Normal file
@ -0,0 +1,78 @@
|
||||
---
|
||||
title: "LibSBML"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/LibSBML"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:11.547428+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
LibSBML is an open-source software library that provides an application programming interface (API) for the SBML (Systems Biology Markup Language ) format. The libSBML library can be embedded in a software application or used in a web servlet (such as one that might be served by Apache Tomcat) as part of the application or servlet's implementation of support for reading, writing, and manipulating SBML documents and data streams. The core of libSBML is written in ISO standard C++; the library provides API for many programming languages via interfaces generated with the help of SWIG.
|
||||
The libSBML library is free software released under the terms of the GNU Lesser General Public License (LGPL) as published by the Free Software Foundation; either version 2.1 of the License, or any later version. LibSBML was developed thanks to funding from many agencies, particularly the National Institute of General Medical Sciences (NIGMS, USA) as well as the Defense Advanced Research Projects Agency (DARPA, USA) under the Bio-SPICE program.
|
||||
|
||||
|
||||
== Description ==
|
||||
The Systems Biology Markup Language (SBML) is an XML-based format for encoding computational models of a sort common in systems biology. Although SBML is based upon XML, and thus software developers could support SBML using off-the-shelf XML parser libraries, libSBML offers numerous advantages that make it easier for developers to implement support for SBML in their software. The premise behind the development of libSBML is that it is more convenient and efficient for developers to start with a higher-level API tailored specifically to SBML and its distinctive features than it is to start with a plain XML parser library.
|
||||
|
||||
|
||||
=== Significant features of libSBML ===
|
||||
The following is a partial list of libSBML's features:
|
||||
|
||||
Supports all Levels and Versions of SBML with common API classes and methods, thus smoothing the differences between different flavors of SBML from the perspective of the application software.
|
||||
Provides facilities for manipulating mathematical formulas in both text-string format and MathML 2.0 format, as well as the ability to interconvert mathematical expressions between these forms. Internally, libSBML uses familiar Abstract Syntax Trees (ASTs) to represent formulas, and provides AST-oriented methods for calling applications.
|
||||
Performs validation of XML and SBML at the time of parsing files and data streams. This helps verify the correctness of models in a way that goes beyond simple syntactic validation.
|
||||
Offers support for dimensional analysis and unit checking. LibSBML implements a thorough system for dimensional analysis and checking units of quantities in a model.
|
||||
Provides facilities for the creation and manipulation of SBML annotations and notes. These have a specific format dictated by the SBML specifications. The formats and standards supported by libSBML include MIRIAM (Minimal Information Requested in the Annotation of a Model) and SBO (the Systems Biology Ontology).
|
||||
Supports transparently reading and writing compressed files in the ZIP, GZIP and BZIP formats.
|
||||
Provides interfaces for the C, C++, C#, Java, Python, Perl, MATLAB, Octave, and Ruby programming languages. The C and C++ interfaces are implemented natively; the C#, Java, Perl, Python, and Ruby interfaces are implemented using SWIG, the Simplified Wrapper Interface Generator; and the MATLAB and Octave interfaces are implemented through custom hand-written code.
|
||||
Provides many convenience methods, such as for obtaining a count of the number of boundary condition species, determining the modifier species of a reaction (assuming the reaction provides kinetics), constructing the stoichiometric matrix for all reactions in a model, and more.
|
||||
|
||||
|
||||
=== Manipulation of mathematical formulas ===
|
||||
Some further explanations may be warranted concerning libSBML's support for working with mathematical formulas. In SBML Level 1, mathematical formulas are represented as text strings using a C-like syntax. This representation was chosen because of its simplicity, widespread familiarity and use in applications such as GEPASI and Jarnac, whose authors contributed to the initial design of SBML. In SBML Levels 2 and 3, there was a need to expand the mathematical vocabulary of Level 1 to include additional functions (both built-in and user-defined), mathematical constants, logical operators, relational operators and a special symbol to represent time. Rather than growing the simple C-like syntax into something more complicated and esoteric in order to support these features, and consequently having to manage two standards in two different formats (XML and text string formulas), SBML Levels 2 and 3 leverage an existing standard for expressing mathematical formulas, namely the content portion of MathML.
|
||||
As mentioned above, LibSBML provides an abstraction for working with mathematical expressions in both text-string and MathML form: Abstract Syntax Trees (ASTs). Abstract Syntax Trees are well known in the computer science community; they are simple recursive data structures useful for representing the syntactic structure of sentences in certain kinds of languages (mathematical or otherwise). Much as libSBML allows programmers to manipulate SBML at the level of domain-specific objects, regardless of SBML Level or version, it also allows programmers to work with mathematical formula at the level of ASTs regardless of whether the original format was C-like infix or MathML. LibSBML goes one step further by allowing programmers to work exclusively with infix formula strings and instantly convert them to the appropriate MathML whenever needed.
|
||||
|
||||
|
||||
=== Dependencies ===
|
||||
LibSBML requires a separate library to do low-level read/write operations on XML. It can use any one of three XML parser libraries: Xerces, expat or libxml2. Users can specify which library they wish to use at libSBML compilation time. LibSBML hides the differences between these parser libraries behind an abstraction layer; it seamlessly uses whichever library against which a given instance of libSBML has been compiled. (However, released binary distributions of libSBML all make use of the libxml2 library.)
|
||||
|
||||
|
||||
== Usage ==
|
||||
LibSBML uses software objects (i.e., instances of classes) that correspond to SBML components, with member variables representing the attributes of the corresponding SBML objects. The libSBML API is constructed to provide an intuitive way of relating SBML and the code needed to create or manipulate it with a class hierarchy that mimics the SBML structure. More information about the libSBML objects is available in the libSBML API documentation.
|
||||
|
||||
|
||||
=== Reading and writing SBML ===
|
||||
LibSBML enables reading from and writing to either files or strings. Once an SBML document is read, libSBML stores the SBML content in an SBMLDocument object. This object can be written out again later. The following is an example written in Python:
|
||||
|
||||
|
||||
=== Creating and manipulating SBML ===
|
||||
The libSBML API allows easy creation of objects and subobjects representing SBML elements and the subelements contained within them. The following is an example written in C++:
|
||||
|
||||
|
||||
=== Accessing attributes ===
|
||||
Each component in SBML has a number of attributes associated with it. These are stored as member variables of a given class, and libSBML provides functions to retrieve and query these values. The syntax of these functions is consistent throughout libSBML. The following is an example written in Python:
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
JSBML
|
||||
libxml2
|
||||
Xerces
|
||||
Expat
|
||||
XML validation
|
||||
XML
|
||||
BioModels Database
|
||||
BioPAX
|
||||
CellML
|
||||
MIASE
|
||||
MIRIAM
|
||||
Systems Biology Ontology (SBO)
|
||||
MathML
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
libSBML Home Page
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/List_of_free_geology_software"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T08:09:07.478300+00:00"
|
||||
date_saved: "2026-05-05T10:11:34.805450+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
31
data/en.wikipedia.org/wiki/Lumi_(software)-0.md
Normal file
31
data/en.wikipedia.org/wiki/Lumi_(software)-0.md
Normal file
@ -0,0 +1,31 @@
|
||||
---
|
||||
title: "Lumi (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Lumi_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:12.707973+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
lumi is a free, open source and open development software project for the analysis and comprehension of Illumina expression and methylation microarray data. The project was started in the summer of 2006 and set out to provide algorithms and data management tools of Illumina in the framework of Bioconductor. It is based on the statistical R programming language.
|
||||
|
||||
|
||||
== Features ==
|
||||
The lumi package provides an analysis pipeline for probe-level Illumina expression and methylation microarray data, including probe-identifier management (nuID), updated probe-to-gene mapping and annotation using the latest release of RefSeq (nuIDblast), probe-intensity transformation (VST) and normalization (RSN), quality control (QA/QC) and preprocessing methods specific for Illumina methylation data. By extending the ExprSet object with Illumina-specific features, lumi is designed to work with other Bioconductor packages, such as Limma and GOstats to detect differential genes and conduct Gene Ontology analysis.
|
||||
|
||||
|
||||
== History ==
|
||||
The lumi project was started in the summer of 2006 at the Bioinformatics Core Facility of the Robert H. Lurie Comprehensive Cancer Center, Northwestern University. Originally lumi was designed for the analysis of Illumina Expression BeadArray data. Starting from 2010 (version > 2.0), functions of analyzing Illumina methylation microarray data was added. The project team consists of Drs. Pan Du, Simon M. Lin, and Warren A. Kibbe. The project was started upon a request for collaboration from Dr. Serdar E. Bulun to analyze a set of new Illumina microarray data acquired at his lab on the study of the effect of retinoic acids on cancers. Dr. Pan Du led the software development of the project. lumi was the first software package to utilize the unique design of redundancy of beadArrays for the data transformation and normalization processes. The first release of lumi was on January 3, 2007 through the Bioconductor website. Before its formal release, it was beta-tested at Norwegian Radiumhospital, Leiden University Medical Center, Universiteit van Amsterdam, Università degli Studi di Brescia, UC Davis, Wayne State University, NIH, M.D. Anderson Cancer Center, Case Western Reserve University, Harvard University, Washington University, and Walter and Eliza Hall Institute of Medical Research.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Bioconductor, integrated software for the statistical analysis of wet lab data in molecular biology
|
||||
Illumina Inc. and its beadArray technology
|
||||
|
||||
|
||||
== External links ==
|
||||
lumi software release website
|
||||
Old lumi Website
|
||||
Official Bioconductor Website
|
||||
43
data/en.wikipedia.org/wiki/MDynaMix-0.md
Normal file
43
data/en.wikipedia.org/wiki/MDynaMix-0.md
Normal file
@ -0,0 +1,43 @@
|
||||
---
|
||||
title: "MDynaMix"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/MDynaMix"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:18.988770+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Molecular Dynamics of Mixtures (MDynaMix) is a computer software package for general purpose molecular dynamics to simulate mixtures of molecules, interacting by AMBER- and CHARMM-like force fields in periodic boundary conditions.
|
||||
Algorithms are included for NVE, NVT, NPT, anisotropic NPT ensembles, and Ewald summation to treat electrostatic interactions.
|
||||
The code was written in a mix of Fortran 77 and 90 (with Message Passing Interface (MPI) for parallel execution). The package runs on Unix and Unix-like (Linux) workstations, clusters of workstations, and on Windows in sequential mode.
|
||||
MDynaMix is developed at the Division of Physical Chemistry, Department of Materials and Environmental Chemistry, Stockholm University, Sweden. It is released as open-source software under a GNU General Public License (GPL).
|
||||
|
||||
|
||||
== Programs ==
|
||||
md is the main MDynaMix block
|
||||
makemol is a utility which provides help to create files describing molecular structure and the force field
|
||||
tranal is a suite of utilities to analyze trajectories
|
||||
mdee is a version of the program which implements expanded ensemble method to compute free energy and chemical potential (is not parallelized)
|
||||
mge provides a graphical user interface to construct molecular models and monitor dynamics process
|
||||
|
||||
|
||||
== Field of application ==
|
||||
Thermodynamic properties of liquids
|
||||
Nucleic acid - ions interaction
|
||||
Modeling of lipid bilayers
|
||||
Polyelectrolytes
|
||||
Ionic liquids
|
||||
X-ray spectra of liquid water
|
||||
Force Field development
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
Ascalaph, graphical shell for MDynaMix (GNU GPL)
|
||||
69
data/en.wikipedia.org/wiki/Madagascar_(software)-0.md
Normal file
69
data/en.wikipedia.org/wiki/Madagascar_(software)-0.md
Normal file
@ -0,0 +1,69 @@
|
||||
---
|
||||
title: "Madagascar (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Madagascar_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:14.291215+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Madagascar is a software package for multidimensional data analysis and reproducible computational experiments.
|
||||
Technology developed using the Madagascar project management system is transferred in the form of recorded processing histories, which become "computational recipes" to be verified, exchanged, and modified by users of the system.
|
||||
|
||||
|
||||
== Features ==
|
||||
The Madagascar environment consists of:
|
||||
|
||||
Standalone programs for out-of-core data analysis;
|
||||
Standalone programs for geophysical data processing and imaging;
|
||||
A development kit for C, C++, Java, Fortran-77, Fortran-90, Python, Matlab, and Octave;
|
||||
A framework for reproducible numerical experiments, based on SCons;
|
||||
A framework for scientific publications, based on SCons and LaTeX;
|
||||
A collection of reproducible scientific articles also used as usage examples and regression tests for the standalone programs;
|
||||
A collection of datasets used as input to reproducible numerical experiments.
|
||||
|
||||
|
||||
== Example script ==
|
||||
An example SConstruct file is shown below
|
||||
|
||||
Note that SConstruct by itself does not do any job other than setting rules for building different targets. The targets get built when one executes scons on the command line. Running scons produces
|
||||
|
||||
bash$ scons
|
||||
scons: Reading SConscript files ...
|
||||
scons: done reading SConscript files.
|
||||
scons: Building targets ...
|
||||
retrieve(["wz.35.H"], [])
|
||||
< wz.35.H /RSF/bin/sfdd form=native | /RSF/bin/sfwindow n1=400 j1=2 | /RSF/bin/sfsmooth rect1=3 > wind.rsf
|
||||
< wind.rsf /RSF/bin/sfpow pow1=2 | /RSF/bin/sfgrey > wind.vpl
|
||||
< wind.rsf /RSF/bin/sfmutter v0=0.31 half=n > mute.rsf
|
||||
< mute.rsf /RSF/bin/sfpow pow1=2 | /RSF/bin/sfgrey > mute.vpl
|
||||
/RSF/bin/vppen yscale=2 vpstyle=n gridnum=2,1 wind.vpl mute.vpl > Fig/denmark.vpl
|
||||
scons: done building targets.
|
||||
|
||||
|
||||
== License ==
|
||||
Madagascar is free software and is licensed under the GPL.
|
||||
|
||||
|
||||
== History ==
|
||||
Madagascar was first publicly presented at the EAGE Workshop in Vienna in June 2006. The work on the package (previously named RSF) was started by Sergey Fomel in 2003. Since then, many people have contributed to it.
|
||||
While being written mostly from scratch, Madagascar borrows ideas from the design of SEPlib, an open-source package maintained by Bob Clapp at the Stanford Exploration Project (SEP). Generations of SEP students and researchers contributed to SEPlib. Most important contributions came from Rob Clayton, Jon Claerbout, Dave Hale, Stew Levin, Rick Ottolini, Joe Dellinger, Steve Cole, Dave Nichols, Martin Karrenbach, Biondo Biondi, and Bob Clapp.
|
||||
Madagascar also borrows ideas from Seismic Unix (SU), a package maintained by John Stockwell at the Center for Wave Phenomenon (CWP) at the Colorado School of Mines (Stockwell, 1997; Stockwell, 1999). Main contributors to SU included Einar Kjartansson, Shuki Ronen, Jack Cohen, Chris Liner, Dave Hale, and John Stockwell. SU adopted an open-source BSD-style license starting with release 40 (April 10, 2007).
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Reproducibility
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Scientific conference presentations about Madagascar
|
||||
For reproducible research, go to Madagascar
|
||||
Sergey Fomel and Jon Claerbout, Guest Editors' Introduction: Reproducible Research: Computing in Science and Engineering, vol. 11, no. 1, pp. 5–7, Jan./Feb. 2009, doi:10.1109/MCSE.2009.14
|
||||
Sergey Fomel, Paul Sava, Ioan Vlad, Yang Liu, and Vladimir Bashkardin, 2013, Madagascar: open-source software project for multidimensional data analysis and reproducible computational experiments: Journal of Open Research Software, 1(1):e8, doi:10.5334/jors.ag
|
||||
Sergey Fomel, Reproducible Research as a Community Effort: Lessons from the Madagascar Project: Computing in Science and Engineering, vol. 17, no. 1, pp. 20–26, Jan./Feb. 2015, doi:10.1109/MCSE.2014.94
|
||||
John Holden, The genesis of Madagascar: The Leading Edge, vol. 34, no. 11, Nov. 2015, doi:10.1190/tle34111386.1
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Malaria_Control_Project"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:26.328036+00:00"
|
||||
date_saved: "2026-05-05T10:12:15.513424+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
29
data/en.wikipedia.org/wiki/Marble_(software)-0.md
Normal file
29
data/en.wikipedia.org/wiki/Marble_(software)-0.md
Normal file
@ -0,0 +1,29 @@
|
||||
---
|
||||
title: "Marble (software)"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Marble_(software)"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:16.672710+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Marble is a virtual globe application which allows the user to choose among the Earth, the Moon, Venus, Mars and other planets to display as a 3-D model. It is free software under the terms of the GNU LGPL, developed by KDE for use on personal computers and smart phones. It is written in C++ and uses Qt.
|
||||
Marble is intended to be very flexible; beyond its cross-platform design, the core components can easily be integrated into other programs. It is designed to run without the need for hardware acceleration, but it can be extended to use OpenGL. An important user-experience objective being that the application start fairly quickly, it ships with a minimal but useful off-line dataset (5–10MB).
|
||||
Contributors have added support for on-line mapping sources such as OpenStreetMap and the ability to interpret KML files. Marble also provides route planning capabilities. A navigation mode called MarbleToGo was developed as part of Google Summer of Code 2010. It was later partially rewritten and renamed to Marble Touch.
|
||||
Geothek is a fork of Marble adding a statistics module, pixel maps, and a 3D view. It is developed and used by Austrian publisher Ed. Hölzel as atlas software for classrooms.
|
||||
|
||||
|
||||
== See also ==
|
||||
Deep-Sky Planner
|
||||
List of software for astronomy research and education
|
||||
Shadows (software)
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
|
||||
Official website
|
||||
Geothek website
|
||||
83
data/en.wikipedia.org/wiki/Massive_Online_Analysis-0.md
Normal file
83
data/en.wikipedia.org/wiki/Massive_Online_Analysis-0.md
Normal file
@ -0,0 +1,83 @@
|
||||
---
|
||||
title: "Massive Online Analysis"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Massive_Online_Analysis"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:17.835224+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand.
|
||||
|
||||
|
||||
== Description ==
|
||||
MOA is an open-source framework software that allows to build and run experiments
|
||||
of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API.
|
||||
MOA contains several collections of machine learning algorithms:
|
||||
|
||||
Classification
|
||||
Bayesian classifiers
|
||||
Naive Bayes
|
||||
Naive Bayes Multinomial
|
||||
Decision trees classifiers
|
||||
Decision Stump
|
||||
Hoeffding Tree
|
||||
Hoeffding Option Tree
|
||||
Hoeffding Adaptive Tree
|
||||
Meta classifiers
|
||||
Bagging
|
||||
Boosting
|
||||
Bagging using ADWIN
|
||||
Bagging using Adaptive-Size Hoeffding Trees.
|
||||
Perceptron Stacking of Restricted Hoeffding Trees
|
||||
Leveraging Bagging
|
||||
Online Accuracy Updated Ensemble
|
||||
Function classifiers
|
||||
Perceptron
|
||||
Stochastic gradient descent (SGD)
|
||||
Pegasos
|
||||
Drift classifiers
|
||||
Self-Adjusting Memory
|
||||
Probabilistic Adaptive Windowing
|
||||
Multi-label classifiers
|
||||
Active learning classifiers
|
||||
Regression
|
||||
FIMTDD
|
||||
AMRules
|
||||
Clustering
|
||||
StreamKM++
|
||||
CluStream
|
||||
ClusTree
|
||||
D-Stream
|
||||
CobWeb.
|
||||
Outlier detection
|
||||
STORM
|
||||
Abstract-C
|
||||
COD
|
||||
MCOD
|
||||
AnyOut
|
||||
Recommender systems
|
||||
BRISMFPredictor
|
||||
Frequent pattern mining
|
||||
Itemsets
|
||||
Graphs
|
||||
Change detection algorithms
|
||||
These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams in real time.
|
||||
MOA supports bi-directional interaction with Weka. MOA is free software released under the GNU GPL.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
ADAMS Workflow: Workflow engine for MOA and Weka
|
||||
Streams: Flexible module environment for the design and execution of data stream experiments
|
||||
Vowpal Wabbit
|
||||
List of numerical analysis software
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
MOA Project home page at University of Waikato in New Zealand
|
||||
SAMOA Project home page at Yahoo Labs
|
||||
@ -4,7 +4,7 @@ chunk: 1/3
|
||||
source: "https://en.wikipedia.org/wiki/MilkyWay@home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:27.628511+00:00"
|
||||
date_saved: "2026-05-05T10:12:20.204648+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 2/3
|
||||
source: "https://en.wikipedia.org/wiki/MilkyWay@home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:27.628511+00:00"
|
||||
date_saved: "2026-05-05T10:12:20.204648+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
@ -4,7 +4,7 @@ chunk: 3/3
|
||||
source: "https://en.wikipedia.org/wiki/MilkyWay@home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:27.628511+00:00"
|
||||
date_saved: "2026-05-05T10:12:20.204648+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
33
data/en.wikipedia.org/wiki/Mimee-0.md
Normal file
33
data/en.wikipedia.org/wiki/Mimee-0.md
Normal file
@ -0,0 +1,33 @@
|
||||
---
|
||||
title: "Mimee"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Mimee"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:21.376962+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
Mimee is a program which can convert geographical coordinates between various datums and formats.
|
||||
|
||||
|
||||
== Features ==
|
||||
Supported coordinates formats are :
|
||||
|
||||
Latitude and longitude in decimal degrees, degrees and decimal minutes, degrees-minutes-seconds, or grads
|
||||
Geocentric (or Cartesian) coordinates (XYZ)
|
||||
UTM
|
||||
Transverse Mercator, Oblique Mercator, and Conic.
|
||||
232 datums and 36 grids are provided in Mimee.
|
||||
|
||||
|
||||
== Compatibility ==
|
||||
The online version is cross-platform. It can be used in any navigator in Linux, Mac, Windows, and in mobile phones. The stand-alone versions runs on Linux and Palm OS.
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
(in French) Official website
|
||||
(in English) Online version
|
||||
@ -4,7 +4,7 @@ chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/MindModeling@Home"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T06:50:28.862433+00:00"
|
||||
date_saved: "2026-05-05T10:12:22.618731+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
|
||||
156
data/en.wikipedia.org/wiki/Mlpack-0.md
Normal file
156
data/en.wikipedia.org/wiki/Mlpack-0.md
Normal file
@ -0,0 +1,156 @@
|
||||
---
|
||||
title: "Mlpack"
|
||||
chunk: 1/1
|
||||
source: "https://en.wikipedia.org/wiki/Mlpack"
|
||||
category: "reference"
|
||||
tags: "science, encyclopedia"
|
||||
date_saved: "2026-05-05T10:12:23.835170+00:00"
|
||||
instance: "kb-cron"
|
||||
---
|
||||
|
||||
mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers.
|
||||
It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world.
|
||||
|
||||
|
||||
== Features ==
|
||||
|
||||
|
||||
=== Classical machine learning algorithms ===
|
||||
mlpack contains a wide range of algorithms that are used to solve problems from classification and regression in the Supervised learning paradigm to clustering and dimension reduction algorithms. In the following, a non exhaustive list of algorithms and models that mlpack supports:
|
||||
|
||||
Collaborative Filtering
|
||||
Decision stumps (one-level decision trees)
|
||||
Density Estimation Trees
|
||||
Euclidean minimum spanning trees
|
||||
Gaussian Mixture Models (GMMs)
|
||||
Hidden Markov Models (HMMs)
|
||||
Kernel density estimation (KDE)
|
||||
Kernel Principal Component Analysis (KPCA)
|
||||
K-Means Clustering
|
||||
Least-Angle Regression (LARS/LASSO)
|
||||
Linear Regression
|
||||
Bayesian Linear Regression
|
||||
Local Coordinate Coding
|
||||
Locality-Sensitive Hashing (LSH)
|
||||
Logistic regression
|
||||
Max-Kernel Search
|
||||
Naive Bayes Classifier
|
||||
Nearest neighbor search with dual-tree algorithms
|
||||
Neighbourhood Components Analysis (NCA)
|
||||
Non-negative Matrix Factorization (NMF)
|
||||
Principal Components Analysis (PCA)
|
||||
Independent component analysis (ICA)
|
||||
Rank-Approximate Nearest Neighbor (RANN)
|
||||
Simple Least-Squares Linear Regression (and Ridge Regression)
|
||||
Sparse Coding, Sparse dictionary learning
|
||||
Tree-based Neighbor Search (all-k-nearest-neighbors, all-k-furthest-neighbors), using either kd-trees or cover trees
|
||||
Tree-based Range Search
|
||||
Class templates for GRU, LSTM structures are available, thus the library also supports Recurrent Neural Networks.
|
||||
|
||||
|
||||
=== Bindings ===
|
||||
There are bindings to R, Go, Julia, Python, and also to Command Line Interface (CLI) using terminal. Its binding system is extensible to other languages.
|
||||
|
||||
|
||||
=== Reinforcement learning ===
|
||||
mlpack contains several Reinforcement Learning (RL) algorithms implemented in C++ with a set of examples as well, these algorithms can be tuned per examples and combined with external simulators. Currently mlpack supports the following:
|
||||
|
||||
Q-learning
|
||||
Deep Deterministic Policy Gradient
|
||||
Soft Actor-Critic
|
||||
Twin Delayed DDPG (TD3)
|
||||
|
||||
|
||||
== Design features ==
|
||||
mlpack includes a range of design features that make it particularly well-suited for specialized applications, especially in the Edge AI and IoT domains. Its C++ codebase allows for seamless integration with sensors, facilitating direct data extraction and on-device preprocessing at the Edge. Below, we outline a specific set of design features that highlight mlpack's capabilities in these environments:
|
||||
|
||||
|
||||
=== Low number of dependencies ===
|
||||
mlpack is low dependencies library which makes it perfect for easy deployment of software. mlpack binaries can be linked statically and deployed to any system with minimal effort. The usage of Docker container is not necessary and even discouraged. This makes it suitable for low resource devices, as it requires only the ensmallen and Armadillo or Bandicoot depending on the type of hardware we are planning to deploy to. mlpack uses Cereal library for serialization of the models. Other dependencies are also header-only and part of the library itself.
|
||||
|
||||
|
||||
=== Low binary footprint ===
|
||||
In terms of binary size, mlpack methods have a significantly smaller footprint compared to other popular libraries. Below, we present a comparison of deployable binary sizes between mlpack, PyTorch, and scikit-learn. To ensure consistency, the same application, along with all its dependencies, was packaged within a single Docker container for this comparison.
|
||||
|
||||
Other libraries exist such as Tensorflow Lite, However, these libraries are usually specific for one method such as neural network inference or training.
|
||||
|
||||
|
||||
== Example ==
|
||||
The following shows a simple example how to train a decision tree model using mlpack, and to use it for the classification. Of course you can ingest your own dataset using the Load function, but for now we are showing the API:
|
||||
|
||||
The above example demonstrate the simplicity behind the API design, which makes it similar to popular Python based machine learning kit (scikit-learn). Our objective is to simplify for the user the API and the main machine learning functions such as Classify and Predict. More complex examples are located in the examples repository, including documentations for the methods
|
||||
|
||||
|
||||
== Backend ==
|
||||
|
||||
|
||||
=== Armadillo ===
|
||||
Armadillo is the default linear algebra library that is used by mlpack, it provide matrix manipulation and operation necessary for machine learning algorithms. Armadillo is known for its efficiency and simplicity. it can also be used in header-only mode, and the only library we need to link against are either OpenBLAS, IntelMKL or LAPACK.
|
||||
|
||||
|
||||
=== Bandicoot ===
|
||||
Bandicoot is a C++ Linear Algebra library designed for scientific computing, it has the an identical API to Armadillo with objective to execute the computation on Graphics Processing Unit (GPU), the purpose of this library is to facilitate the transition between CPU and GPU by making a minor changes to the source code, (e.g. changing the namespace, and the linking library). mlpack currently supports partially Bandicoot with objective to provide neural network training on the GPU. The following examples shows two code blocks executing an identical operation. The first one is Armadillo code and it is running on the CPU, while the second one can runs on OpenCL supported GPU or NVIDIA GPU (with CUDA backend)
|
||||
|
||||
|
||||
=== ensmallen ===
|
||||
ensmallen is a high quality C++ library for non linear numerical optimizer, it uses Armadillo or bandicoot for linear algebra and it is used by mlpack to provide optimizer for training machine learning algorithms. Similar to mlpack, ensmallen is a header-only library and supports custom behavior using callbacks functions allowing the users to extend the functionalities for any optimizer. In addition ensmallen is published under the BSD license.
|
||||
ensmallen contains a diverse range of optimizer classified based on the function type (differentiable, partially differentiable, categorical, constrained, etc). In the following we list a small set of optimizer that available in ensmallen. For the full list please check this documentation website.
|
||||
|
||||
Limited memory Broyden–Fletcher–Goldfarb–Shanno (L-BFGS)
|
||||
GradientDescent
|
||||
FrankWolfe
|
||||
Covariance matrix adaptation evolution strategy (CMA-ES)
|
||||
AdaBelief
|
||||
AdaBound
|
||||
AdaDelta
|
||||
AdaGrad
|
||||
AdaSqrt
|
||||
Adam
|
||||
AdaMax
|
||||
AMSBound
|
||||
AMSGrad
|
||||
Big Batch SGD
|
||||
Eve
|
||||
FTML
|
||||
IQN
|
||||
Katyusha
|
||||
Lookahead
|
||||
Momentum SGD
|
||||
Nadam
|
||||
NadaMax
|
||||
NesterovMomentumSGD
|
||||
OptimisticAdam
|
||||
QHAdam
|
||||
QHSGD
|
||||
RMSProp
|
||||
SARAH/SARAH+
|
||||
Stochastic Gradient Descent SGD
|
||||
Stochastic Gradient Descent with Restarts (SGDR)
|
||||
Snapshot SGDR
|
||||
SMORMS3
|
||||
SPALeRA
|
||||
SWATS
|
||||
SVRG
|
||||
WNGrad
|
||||
|
||||
|
||||
== Support ==
|
||||
mlpack is fiscally sponsored and supported by NumFOCUS, Consider making a tax-deductible donation to help the developers of the project. In addition mlpack team participates each year Google Summer of Code program and mentors several students.
|
||||
|
||||
|
||||
== See also ==
|
||||
|
||||
Armadillo (C++ library)
|
||||
Comparison of machine learning software
|
||||
List of numerical analysis software
|
||||
List of numerical libraries
|
||||
Numerical linear algebra
|
||||
Scientific computing
|
||||
|
||||
|
||||
== References ==
|
||||
|
||||
|
||||
== External links ==
|
||||
Official website
|
||||
mlpack on GitHub
|
||||
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