# kb

id: modflow-20260505-3
file: data/en.wikipedia.org/wiki/MODFLOW-3.md
category: reference
title: MODFLOW
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MODFLOW
date_saved: 2026-05-05T10:12:39.169012+00:00
instance: kb-cron
parent: modflow-20260505-0
summary:
=== Non-commercial interfaces === Non-commercial MODFLOW versions are free, however, their licensing usually limit the use to non-profit educational or research purposes. ModelMuse is a grid-independent graphical user interface from the USGS for MODFLOW 6, MODPATH, SUTRA, and PHAST version 1.51. There are no license restrictions. The source code is included. FloPy is a Python package for creating, running, and post-processing MODFLOW-based models. MODFLOW-GUI – Made by the USGS: it is updated often to match the current USGS MODFLOW development. It supports MODFLOW-96, MODFLOW-2000, MODFLOW-2005, MODPATH, ZONEBUDGET, GWT, MT3DMS, SEAWAT, and GWM. Source code for MODFLOW-GUI is included. It depends on Argus ONE: a commercial interface for constructing generic models. There are no license restrictions beyond those of Argus ONE. PMWIN – "Processing MODFLOW" (for Windows) – powerful freeware for MODFLOW processing and visualization, provided alongside an instructional book; also available in Traditional Chinese. The license for this version is limited to non-commercial use. mflab - mflab is a MATLAB interface to MODFLOW. The user builds and analyzes models by writing a set of MATLAB scripts. This results in flexible and efficient workflows, allowing a great deal of automation. iMOD - Free and open source interface developed by Deltares. iMOD


id: moea-framework-20260505-0
file: data/en.wikipedia.org/wiki/MOEA_Framework-0.md
category: reference
title: MOEA Framework
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MOEA_Framework
date_saved: 2026-05-05T10:12:40.349809+00:00
instance: kb-cron
parent: moea-framework-20260505-0
summary:
The MOEA Framework is an open-source evolutionary computation library for Java that specializes in multi-objective optimization. It supports a variety of multiobjective evolutionary algorithms (MOEAs), including genetic algorithms, genetic programming, grammatical evolution, differential evolution, and particle swarm optimization. As a result, it has been used to conduct numerous comparative studies to assess the efficiency, reliability, and controllability of state-of-the-art MOEAs. == Features == The MOEA Framework is an extensible framework for rapidly designing, developing, executing, and statistically testing multiobjective evolutionary algorithms (MOEAs). It features 25 different MOEAs and over 80 test problems. New problems are defined in the MOEA Framework using one or more decision variables of a varying type. This includes common representations such as binary strings, real-valued numbers, and permutations. It additionally supports evolving grammars in Backus–Naur form and programs using an internal Turing complete programming language. Once the problem is defined, the user can optimize the problem using any of supported MOEAs. == Sensitivity analysis == The MOEA Framework is the only known framework for evolutionary computation that provides support for sensitivity analysis. Sensitivity analysis in this context studies how an MOEA's parameters impact its output (i.e., the quality of the results). Alternatively, sensitivity analysis measures the


id: mofem-joseph-20260505-0
file: data/en.wikipedia.org/wiki/MoFEM_JosePH-0.md
category: reference
title: MoFEM JosePH
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MoFEM_JosePH
date_saved: 2026-05-05T10:12:41.526922+00:00
instance: kb-cron
parent: mofem-joseph-20260505-0
summary:
MoFEM (Mesh Orientated Finite Element Method) is an open source finite element analysis code developed and maintained at the University of Glasgow. MoFEM is tailored for the solution of multi-physics problems with arbitrary levels of approximation, different levels of mesh refinement and optimised for high-performance computing. MoFEM is the blend of the Boost (C++ libraries) MultiIndex containers, MOAB (Mesh Oriented Database) and PETSc (Portable, Extensible Toolkit for Scientific Computation). MoFEM is developed in C++ and it is open-source software under the GNU Lesser General Public License (GPL). == Motivation == Building a scientific simulation environment for finite element methods is a complicated task. The longest part of finite element code development is in dealing with technical problems related to software implementation, rather than resolving the underlying physics that the code is intended to tackle. The demand for accurate solutions of increasingly complicated real-world problems means that the underlying data structures also become increasingly complicated. This is particularly evident with multi-physics, hp-adaptivity, and/or evolving geometries (e.g. crack propagation). Established commercial software is often limited in this respect, or can be relatively slow to adopt new innovations. Working around these problems has been the main motivation for the development of MoFEM, recognising


id: montage-image-software-20260505-0
file: data/en.wikipedia.org/wiki/Montage_(image_software)-0.md
category: reference
title: Montage (image software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Montage_(image_software)
date_saved: 2026-05-05T10:12:42.716723+00:00
instance: kb-cron
parent: montage-image-software-20260505-0
summary:
Montage is a software toolkit used in astrophotography to assemble astronomical images in Flexible Image Transport System (FITS) format into composite images, called mosaics, that preserve the calibration and positional fidelity of the original input images. It won a NASA Space Act Award in 2006. Montage was developed to support scientific research. It enables astronomers to create images of regions of the sky that are too large to be produced by astronomical cameras. It also creates composite images of a region of the sky that has been measured with different wavelengths and with different instruments; the composite appears as if the area was measured with the same instrument on the same telescope. There is also associated software developed by the Montage user community including Bash and C shell scripts to make mosaics, and the montage-wrapper Python application programming interface, a part of the Astropy project. Montage uses a BSD 3-Clause License, which permits unlimited redistribution of the Montage code for any purpose as long as its copyright notices and the license's disclaimers of warranty are included. == See also == Afocal photography Outline of photography Image editing == References == == External links == Official website


id: mtex-20260505-0
file: data/en.wikipedia.org/wiki/MTEX-0.md
category: reference
title: MTEX
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MTEX
date_saved: 2026-05-05T10:12:43.890528+00:00
instance: kb-cron
parent: mtex-20260505-0
summary:
MTEX is an open-source MATLAB package specifically designed for the analysis of electron backscatter diffraction (EBSD) data, which are widely used to analyse the crystallographic orientation of materials at the microscale. == History == The development of MTEX began in 2008, spearheaded by Ralf Hielscher, who aimed to create a user-friendly platform that could facilitate the analysis of large datasets generated by EBSD. The toolbox has since evolved, incorporating various features that allow for the manipulation and visualisation of crystallographic data. EBSD allows for the mapping of crystallographic orientations in materials, providing insights into their microstructural properties. The integration of EBSD with MATLAB through MTEX has enabled researchers to perform advanced analyses, such as orientation distribution function (ODF) calculations, pole figure plotting, calculation of anisotropic physical properties from texture data, and grain boundary and grain reconstruction, which are crucial for understanding the mechanical properties of materials, as the crystallographic texture can significantly influence their behaviour under stress. Moreover, the open-source nature of MTEX has fostered a collaborative environment among researchers, allowing for continuous improvements and updates to the toolbox. This community-driven approach has led to the incorporation of new features and functionalities. MTEX's versatility is further demonstrated by its application


id: natural-language-toolkit-20260505-0
file: data/en.wikipedia.org/wiki/Natural_Language_Toolkit-0.md
category: reference
title: Natural Language Toolkit
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Natural_Language_Toolkit
date_saved: 2026-05-05T10:12:45.056399+00:00
instance: kb-cron
parent: natural-language-toolkit-20260505-0
summary:
The Natural Language Toolkit, or more commonly NLTK, is a suite of libraries and programs for symbolic and statistical natural language processing (NLP) for English written in the Python programming language. It supports classification, tokenization, stemming, tagging, parsing, and semantic reasoning functionalities. It was developed by Steven Bird and Edward Loper in the Department of Computer and Information Science at the University of Pennsylvania. NLTK includes graphical demonstrations and sample data. It is accompanied by a book that explains the underlying concepts behind the language processing tasks supported by the toolkit, plus a cookbook. NLTK is intended to support research and teaching in NLP or closely related areas, including empirical linguistics, cognitive science, artificial intelligence, information retrieval, and machine learning. NLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems. == Library highlights == Discourse representation Lexical analysis: Word and text tokenizer n-gram and collocations Part-of-speech tagger Tree model and Text chunker for capturing Named-entity recognition == See also == SpaCy == References == == External links == Official website


id: nek5000-20260505-0
file: data/en.wikipedia.org/wiki/Nek5000-0.md
category: reference
title: Nek5000
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nek5000
date_saved: 2026-05-05T10:12:46.233456+00:00
instance: kb-cron
parent: nek5000-20260505-0
summary:
Nek5000 is a highly scalable spectral element computational fluid dynamics code for solving the incompressible Navier-Stokes equations on 2D quadrilateral and 3D hexahedral meshes. Nek5000 was awarded the 1999 Gordon Bell Prize and a 2016 R&D 100 Award. == History == == Related and derived codes == === Gslib === === Nekbone === === Neko === === NekCEM === === NekLBM === === NekROM === === NekRS === === ParRSB === == See also == List of computational fluid dynamics software == References ==


id: nektar-20260505-0
file: data/en.wikipedia.org/wiki/Nektar++-0.md
category: reference
title: Nektar++
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Nektar++
date_saved: 2026-05-05T10:12:47.396780+00:00
instance: kb-cron
parent: nektar-20260505-0
summary:
Nektar++ is a spectral/hp element framework designed to support the construction of efficient high-performance scalable solvers for a wide range of partial differential equations (PDE). The code is released as open-source under the MIT license. Although primarily driven by application-based research, it has been designed as a platform to support the development of novel numerical techniques in the area of high-order finite element methods. Nektar++ is modern object-oriented code written in C++ and is being actively developed by members of the SherwinLab at Imperial College London (UK) and Kirby's group at the University of Utah (US). == Capabilities == Nektar++ includes the following capabilities: One-, two- and three-dimensional problems; Multiple and mixed element types, i.e. triangles, quadrilaterals, tetrahedra, prisms and hexahedra; Both hierarchical and nodal expansion bases with variable and heterogeneous polynomial order between elements; Continuous Galerkin, discontinuous Galerkin, hybridizable discontinuous Galerkin and flux reconstruction operators; Multiple implementations of finite element operators for efficient execution on a wide range of CPU architectures; Comprehensive range of explicit, implicit and implicit-explicit (IMEX) time-integration schemes; Preconditioners tailored to high-order finite element methods; Numerical stabilization techniques such as dealiasing and spectral vanishing viscosity; Parallel execution and scalable to thousands of processor cores; Pre-processing tools


id: nest-software-20260505-0
file: data/en.wikipedia.org/wiki/NEST_(software)-0.md
category: reference
title: NEST (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/NEST_(software)
date_saved: 2026-05-05T10:12:48.597476+00:00
instance: kb-cron
parent: nest-software-20260505-0
summary:
NEST is a simulation software for spiking neural network models, including large-scale neuronal networks. NEST was initially developed by Markus Diesmann and Marc-Oliver Gewaltig and is now developed and maintained by the NEST Initiative. == Modeling philosophy == A NEST simulation tries to follow the logic of an electrophysiological experiment that takes place inside a computer with the difference, that the neural system to be investigated must be defined by the experimenter. The neural system is defined by a possibly large number of neurons and their connections. In a NEST network, different neuron and synapse models can coexist. Any two neurons can have multiple connections with different properties. Thus, the connectivity can in general not be described by a weight or connectivity matrix but rather as an adjacency list. To manipulate or observe the network dynamics, the experimenter can define so-called devices which represent the various instruments (for measuring and stimulation) found in an experiment. These devices write their data either to memory or to file. NEST is extensible and new models for neurons, synapses, and devices can be added. == Example == The following example simulates spiking activity in a sparse random network with recurrent excitation and inhibition The


id: numpy-20260505-0
file: data/en.wikipedia.org/wiki/NumPy-0.md
category: reference
title: NumPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/NumPy
date_saved: 2026-05-05T10:12:49.753519+00:00
instance: kb-cron
parent: numpy-20260505-0
summary:
NumPy (pronounced NUM-py) is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. The predecessor of NumPy, Numeric, was originally created by Jim Hugunin with contributions from several other developers. In 2005, Travis Oliphant created NumPy by incorporating features of the competing Numarray into Numeric, with extensive modifications. NumPy is open-source software and has many contributors. NumPy is fiscally sponsored by NumFOCUS. == History == === matrix-sig === The Python programming language was not originally designed for numerical computing, but attracted the attention of the scientific and engineering community early on. In 1995 the special interest group (SIG) matrix-sig was founded with the aim of defining an array computing package; among its members was Python designer and maintainer Guido van Rossum, who extended Python's syntax (in particular the indexing syntax) to make array computing easier. === Numeric === An implementation of a matrix package was completed by Jim Fulton, then expanded to support multi-dimensional arrays by Jim Hugunin and called Numeric (also variously known as the "Numerical Python extensions" or "NumPy"), with influences from the APL family of languages, Basis,


id: numpy-20260505-1
file: data/en.wikipedia.org/wiki/NumPy-1.md
category: reference
title: NumPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/NumPy
date_saved: 2026-05-05T10:12:49.753519+00:00
instance: kb-cron
parent: numpy-20260505-0
summary:
=== Limitations === Inserting or appending entries to an array is not as trivially possible as it is with Python's lists. The np.pad(...) routine to extend arrays actually creates new arrays of the desired shape and padding values, copies the given array into the new one and returns it. NumPy's np.concatenate([a1,a2]) operation does not actually link the two arrays but returns a new one, filled with the entries from both given arrays in sequence. Reshaping the dimensionality of an array with np.reshape(...) is only possible as long as the number of elements in the array does not change. These circumstances originate from the fact that NumPy's arrays must be views on contiguous memory buffers. Algorithms that are not expressible as a vectorized operation will typically run slowly because they must be implemented in "pure Python", while vectorization may increase memory complexity of some operations from constant to linear, because temporary arrays must be created that are as large as the inputs. Runtime compilation of numerical code has been implemented by several groups to avoid these problems; open source solutions that interoperate with NumPy include numexpr and Numba. Cython and Pythran are static-compiling alternatives to these. Many modern large-scale scientific computing


id: octadist-20260505-0
file: data/en.wikipedia.org/wiki/OctaDist-0.md
category: reference
title: OctaDist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OctaDist
date_saved: 2026-05-05T10:12:50.961916+00:00
instance: kb-cron
parent: octadist-20260505-0
summary:
OctaDist is computer software for crystallography and inorganic chemistry program. It is mainly used for computing distortion parameters of coordination complex such as spin crossover complex (SCO), magnetic metal complex and metal–organic framework (MOF). The program is developed and maintained in an international collaboration between the members of the Computational Chemistry Research Unit at Thammasat University, the Functional Materials & Nanotechnology CoE at Walailak University and the Switchable Molecules and Materials group at University of Bordeaux. OctaDist is written entirely in Python binding to Tkinter graphical user interface toolkit. It is available for Windows, macOS, and Linux. It is free and open-source software distributed under a GNU General Public License (GPL) 3.0. == Standard abilities == The following are the main features of the latest version of OctaDist: Structural distortion analysis Determination of regular and irregular distorted octahedral molecular geometry Octahedral distortion parameters Volume of the octahedron Tilting distortion parameter for perovskite complex Molecular graphics 3D modelling of complex Display of the eight faces of octahedron Atomic orthogonal projection and projection plane Twisting triangular faces Molecular superposition (Overlay) Other utilities Scripting language Surface area of the faces of octahedron Jahn–Teller distortion parameters Root-mean-square deviation of atomic positions == Capabilities ==


id: octopus-software-20260505-0
file: data/en.wikipedia.org/wiki/Octopus_(software)-0.md
category: reference
title: Octopus (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Octopus_(software)
date_saved: 2026-05-05T10:12:52.169762+00:00
instance: kb-cron
parent: octopus-software-20260505-0
summary:
Octopus is an open-source software package for performing Kohn‍–‍Sham density functional theory (DFT) and time-dependent density functional theory (TDDFT) calculations. Octopus employs pseudopotentials and real-space numerical grids to propagate the Kohn‍–‍Sham orbitals in real time under the influence of time-varying electromagnetic fields. Specific functionality is provided for simulating one-, two-, and three-dimensional systems. Octopus can calculate static and dynamic polarizabilities and first hyperpolarizabilities, static magnetic susceptibilities, absorption spectra, and perform molecular dynamics simulations with Ehrenfest and Car–Parrinello methods. The code is written predominantly in Fortran and is released under the GPL. It supported multiple levels of parallelism using OpenMPI and MPI on the CPU as well as CUDA and ROCm (via HIP) on the GPU. The latest version 16.3 was released on October 21, 2025. As of version 16, Octopus introduces enhanced GPU parallelization and support for hybrid functionals. == Target problems == Linear optical (i.e. electronic) response of molecules or clusters, also second-order nonlinear response. Non-linear response to classical high-intensity electromagnetic fields, taking into account both the ionic and electronic degrees of freedom. Ground-state and excited state electronic properties of systems with lower dimensionality, such as quantum dots. Photo-induced reactions of molecules (e.g., photo-dissociation, photo-isomerization, etc.). Ongoing development includes


id: oelib-20260505-0
file: data/en.wikipedia.org/wiki/OELib-0.md
category: reference
title: OELib
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OELib
date_saved: 2026-05-05T10:12:53.370288+00:00
instance: kb-cron
parent: oelib-20260505-0
summary:
OELib was an open source Cheminformatics library written by Matt Stahl and based on the ideas of OBabel. Its actual GPLed C++ and Java based successors are OpenBabel and JOELib, with Its commercial successor being called OEChem. == See also == JOELib OpenBabel == External links == Archived copy of OELib in 2008 on Internet Archive. Design flaws in OELib == References ==


id: olex2-20260505-0
file: data/en.wikipedia.org/wiki/Olex2-0.md
category: reference
title: Olex2
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Olex2
date_saved: 2026-05-05T10:12:54.531355+00:00
instance: kb-cron
parent: olex2-20260505-0
summary:
Olex and Olex2 are versatile software for crystallographic research. Olex used to be a research project developed during PhD to implement topological (as connectivity) analysis of polymeric chemical structures and still is widely used around the world. Olex2 is an open source project with the C++ code portable to Windows, Mac and Linux. Although the projects share the common name they are not related at the source code level. == Olex == The Olex program is designed for the analysis of extended structural networks. It only runs on Windows systems and source code is available only on request. It allows packing the structure, constructing the topological network and the evaluation of the networks Schläfli and vertex symbols and to produce raster pictures of the model visible on screen. This kind of the topological network analysis is normally done to find relevance of considered structures and possibly to predict physical properties of the investigated material. Olex2 is a relatively mature open source software with a BSD licence which provides tools for crystallographic structure solution, refinement, and final report preparation. It is still in the stage of active development. Olex2 provides numerous tools for structure analysis and publication, including Fourier maps, void


id: open-source-physics-20260505-0
file: data/en.wikipedia.org/wiki/Open_Source_Physics-0.md
category: reference
title: Open Source Physics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Source_Physics
date_saved: 2026-05-05T10:12:55.634947+00:00
instance: kb-cron
parent: open-source-physics-20260505-0
summary:
Open Source Physics, or OSP, is a project sponsored by the National Science Foundation and Davidson College, whose mission is to spread the use of open source code libraries that take care of a lot of the heavy lifting for physics: drawing and plotting, differential equation solvers, exporting to animated GIFs and movies, etc., tools, and compiled simulations for physics and other numerical simulations . The OSP collection provides curriculum resources that engage students in physics, computation, and computer modeling. The core library is in the Java programming language and licensed with GNU General Public License (GNU GPL or simply GPL) licenses. The site now serves over 10,000 visitors per month. The Open Source Physics Project is an extension of the Physlet Project. == Sub-projects == They have four projects with this purpose. OSP libraries: Java code libraries for numerical simulations. The OSP code library was created to meet the need by the broader science education community for a synthesis of curriculum development, computational physics, computer science, and physics education that will be useful for scientists and students wishing to write their own simulations and develop their own curricular material. OSP code library is described in the OSP User's Guide


id: openfoam-20260505-0
file: data/en.wikipedia.org/wiki/OpenFOAM-0.md
category: reference
title: OpenFOAM
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OpenFOAM
date_saved: 2026-05-05T10:12:56.783384+00:00
instance: kb-cron
parent: openfoam-20260505-0
summary:
OpenFOAM (Open Field Operation And Manipulation) is a C++ toolbox for the development of customized numerical solvers, and pre-/post-processing utilities for the solution of continuum mechanics problems, most prominently including computational fluid dynamics (CFD). The OpenFOAM software is used in research organisations, academic institutes and across many types of industries, for example, automotive, manufacturing, process engineering, environmental engineering and marine energy. OpenFOAM is open-source software which is freely available and licensed under the GNU General Public License Version 3, with the following variants: OpenFOAM, released by OpenCFD Ltd. (with the name trademarked since 2007) first released as open-source in 2004. (Note: since 2012, OpenCFD Ltd is wholly-owned subsidiary of ESI Group) FOAM-Extend, released by Wikki Ltd. (since 2009) OpenFOAM, released by OpenFOAM Foundation. (since 2011) == History == The name FOAM has been claimed to appear for the first time as a post-processing tool written by Charlie Hill, in the early 90s in Prof. David Gosman's group in Imperial College London. As a counter argument , it has been claimed that Henry Weller created the FOAM library for field operation and manipulation which interfaced to the GUISE (Graphical User Interface Software Environment) which was created by Charlie Hill for interfacing


id: openlb-20260505-0
file: data/en.wikipedia.org/wiki/OpenLB-0.md
category: reference
title: OpenLB
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OpenLB
date_saved: 2026-05-05T10:12:57.958029+00:00
instance: kb-cron
parent: openlb-20260505-0
summary:
OpenLB is an object-oriented implementation of the lattice Boltzmann methods (LBM). It is the first implementation of a generic platform for LBM programming, which is shared with the open source community (GPLv2). The code is written in C++ and is used by application programmers as well as developers, with the ability to implement custom models OpenLB supports complex data structures that allow simulations in complex geometries and parallel execution using MPI, OpenMP and CUDA on high-performance computers. The source code uses the concepts of interfaces and templates, so that efficient, direct and intuitive implementations of the LBM become possible. The efficiency and scalability has been checked and proved by code reviews. A user manual and a source code documentation by DoxyGen are available on the project page. == Functions == OpenLB is being constantly developed. By now the following features are implemented: Computational fluid dynamics in complex geometry Automatic generation of a grid Turbulent flow Multi-component flow Thermal flow Light radiation Topology optimizing Particle flow (Euler–Euler and Euler–Lagrange method) == Automated grid generation == Automated grid generation is one of the great advantages of OpenLB over other CFD software packages. The main advantages are listed below: Use of geometries in


id: openms-20260505-0
file: data/en.wikipedia.org/wiki/OpenMS-0.md
category: reference
title: OpenMS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OpenMS
date_saved: 2026-05-05T10:12:59.650069+00:00
instance: kb-cron
parent: openms-20260505-0
summary:
OpenMS is an open-source project for data analysis and processing in mass spectrometry and is released under the 3-clause BSD licence. It supports most common operating systems including Microsoft Windows, MacOS and Linux. OpenMS has tools for analysis of proteomics data, providing algorithms for signal processing, feature finding (including de-isotoping), visualization in 1D (spectra or chromatogram level), 2D and 3D, map mapping and peptide identification. It supports label-free and isotopic-label based quantification (such as iTRAQ and TMT and SILAC). OpenMS also supports metabolomics workflows and targeted analysis of DIA/SWATH data. Furthermore, OpenMS provides tools for the analysis of cross linking data, including protein-protein, protein-RNA and protein-DNA cross linking. Lastly, OpenMS provides tools for analysis of RNA mass spectrometry data. == History == OpenMS was originally released in 2007 in version 1.0 and was described in two articles published in Bioinformatics in 2007 and 2008 and has since seen continuous releases. In 2009, the visualization tool TOPPView was published and in 2012, the workflow manager and editor TOPPAS was described. In 2013, a complete high-throughput label-free analysis pipeline using OpenMS 1.8 was described and compared with similar, proprietary software (such as MaxQuant and Progenesis QI). The authors conclude that "[...] all


id: the-openms-proteomics-pipeline-20260505-0
file: data/en.wikipedia.org/wiki/The_OpenMS_Proteomics_Pipeline-0.md
category: reference
title: The OpenMS Proteomics Pipeline
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_OpenMS_Proteomics_Pipeline
date_saved: 2026-05-05T10:13:00.785237+00:00
instance: kb-cron
parent: the-openms-proteomics-pipeline-20260505-0
summary:
The OpenMS Proteomics Pipeline (TOPP) is a set of computational tools that can be chained together to tailor problem-specific analysis pipelines for HPLC-MS data. It transforms most of the OpenMS functionality into small command line tools that are the building blocks for more complex analysis pipelines. The functionality of the tools ranges from data preprocessing (file format conversion, baseline reduction, noise reduction, peak picking, map alignment,...) over quantitation (isotope-labeled and label-free) to identification (wrapper tools for Mascot, Sequest, InsPecT and OMSSA). TOPP is developed in the groups of Prof. Knut Reinert [1] at the Free University of Berlin and in the group of Prof. Kohlbacher [2] at the University of Tübingen. For more detailed information about the TOPP tools, see the TOPP documentation of the latest release and the TOPP publication in the references. The OpenMS Proteomics Pipeline is free software released under the 3-clause BSD license. == References == Sturm, Marc; Bertsch, Andreas; Gröpl, Clemens; Hildebrandt, Andreas; Hussong, Rene; Lange, Eva; Pfeifer, Nico; Schulz-Trieglaff, Ole; Zerck, Alexandra; Reinert, Knut; Kohlbacher, Oliver (December 2008). "OpenMS – An open-source software framework for mass spectrometry". BMC Bioinformatics. 9 (1): 163. doi:10.1186/1471-2105-9-163. PMC 2311306. PMID 18366760. Kohlbacher, O.; Reinert, K.; Gropl, C.; Lange,


id: oproject-home-20260505-0
file: data/en.wikipedia.org/wiki/OProject@Home-0.md
category: reference
title: OProject@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OProject@Home
date_saved: 2026-05-05T10:13:02.034767+00:00
instance: kb-cron
parent: oproject-home-20260505-0
summary:
OProject@Home was a volunteer computing project running on the Berkeley Open Infrastructure for Network Computing (BOINC) and was based on a dedicated library OLib. The project was directed by Lukasz Swierczewski, an IT student at the College of Computer Science and Business Administration in Łomża, Computer Science and Automation Institute. As of 2016 it seems to have been abandoned. == Subprojects == Shor's Algorithm Shor's Algorithm DP GSCE-SV ALX Weird Engine Shor's Algorithm and Shor's Algorithm DP were the main subprojects of OProject@Home. The objective was to test quantum algorithms (e.g. Shor's algorithm) of quantum computing. GSCE-SV verifies the correctness of Goldbach's conjecture, while ALX is a Non-CPU-intensive (nci) subproject capable of running on ARM-based CPUs running Android or Linux. It is used to research and develop artificial intelligence and computer networks. The project supports the PlayStation 3. The Weird Engine subproject calculates the weird numbers (sequence A006037 in the OEIS). Numbers are available in the project database. According to the OEIS it is the largest publicly available database of such numbers. These ongoing work on the application analyzing status of water on Earth. OProject@Home uses data from NASA and NOAA satellites. Analyzed data are taken from devices AVHRR and


id: orange-software-20260505-0
file: data/en.wikipedia.org/wiki/Orange_(software)-0.md
category: reference
title: Orange (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orange_(software)
date_saved: 2026-05-05T10:13:03.222839+00:00
instance: kb-cron
parent: orange-software-20260505-0
summary:
Orange is an open-source data visualization, machine learning and data mining toolkit. It features a visual programming front-end for exploratory qualitative data analysis and interactive data visualization. == Description == Orange is a component-based visual programming software package for data visualization, machine learning, data mining, and data analysis. Orange components are called widgets. They range from simple data visualization, subset selection, and preprocessing to empirical evaluation of learning algorithms and predictive modeling. Visual programming is implemented through an interface in which workflows are created by linking predefined or user-designed widgets, while advanced users can use Orange as a Python library for data manipulation and widget alteration. == Software == Orange is an open-source software package released under GPL and hosted on GitHub. Versions up to 3.0 include core components in C++ with wrappers in Python. From version 3.0 onwards, Orange uses common Python open-source libraries for scientific computing, such as numpy, scipy and scikit-learn, while its graphical user interface operates within the cross-platform Qt framework. The default installation includes a number of machine learning, preprocessing and data visualization algorithms in 6 widget sets (data, transform, visualize, model, evaluate and unsupervised). Additional functionalities are available as add-ons (text-mining, image analytics, bioinformatics,


id: orange-software-20260505-1
file: data/en.wikipedia.org/wiki/Orange_(software)-1.md
category: reference
title: Orange (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orange_(software)
date_saved: 2026-05-05T10:13:03.222839+00:00
instance: kb-cron
parent: orange-software-20260505-0
summary:
In 2002, first prototypes to create a flexible graphical user interface were designed using Pmw Python megawidgets. In 2003, the graphical user interface was redesigned and re-developed for Qt framework using PyQt Python bindings. The visual programming framework was defined, and the development of widgets (graphical components of the data analysis pipeline) began. In 2005, extensions for data analysis in bioinformatics was created. In 2008, Mac OS X DMG and Fink-based installation packages were developed. In 2009, over 100 widgets were created and maintained. In 2009, Orange 2.0 beta was released, offering installation packages on the website based on the daily compiling cycle. In 2012, a new object hierarchy was imposed, replacing the old module-based structure. In 2013, a significant redesign of the graphical user interface included a new toolbox and depiction of workflows. In 2015, Orange 3.0 was released. Orange stores the data in NumPy arrays; machine learning algorithms mostly use scikit-learn. In 2015, a text analysis add-on for Orange3 was released. In 2016, Orange released version 3.3. Development scheduled a monthly cycle for stable releases. In 2016, Orange began development and release of an Image Analytics add-on, with server-side deep neural networks for image embedding In 2017, a


id: orbit-home-20260505-0
file: data/en.wikipedia.org/wiki/Orbit@home-0.md
category: reference
title: Orbit@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Orbit@home
date_saved: 2026-05-05T10:13:04.412283+00:00
instance: kb-cron
parent: orbit-home-20260505-0
summary:
orbit@home was a BOINC-based volunteer computing project of the Planetary Science Institute. It uses the "Orbit Reconstruction, Simulation and Analysis" framework to optimize the search strategies that are used to find near-Earth objects. On March 4, 2008, orbit@home completed the installation of its new server and officially opened to new members. On April 11, orbit@home launched a Windows version of their client. On February 16, 2013, the project was halted due to lack of grant funding. However, on July 23, 2013, the Orbit@home project was selected for funding by NASA's Near Earth Object Observation program. It was announced that orbit@home is to resume operations sometime in 2014 or 2015. As of July 13, 2018, orbit@home is offline according to its website, and the upgrade announcement has been removed. == See also == List of volunteer computing projects == References == == External links == Official website


id: owl-scientific-computing-20260505-0
file: data/en.wikipedia.org/wiki/Owl_Scientific_Computing-0.md
category: reference
title: Owl Scientific Computing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Owl_Scientific_Computing
date_saved: 2026-05-05T10:13:05.560226+00:00
instance: kb-cron
parent: owl-scientific-computing-20260505-0
summary:
Owl Scientific Computing is a software system for scientific and engineering computing developed in the Department of Computer Science and Technology, University of Cambridge. The System Research Group (SRG) in the department recognises Owl as one of the representative systems developed in SRG in the 2010s. The source code is licensed under the MIT License and can be accessed from the GitHub repository. The library is mostly designed and developed in the functional programming language OCaml. As a unique functional programming language, OCaml offers runtime efficiency, flexible module system, static type checking, intelligent garbage collector, and powerful type inference. Owl inherits these features directly from OCaml. With Owl, users can write succinct type-safe numerical applications in a concise functional language without sacrificing performance. It speeds up the development life-cycle, and reduces the cost from prototype to production use. The system serves as the de facto tool for computation intensive tasks in OCaml. == History == Owl was developed when Dr. Liang Wang was working as a Post-Doc in the OCaml Labs. Owl originated from a research project which studied the design of synchronous parallel machines for large-scale distributed computing in July 2016. Back then the libraries for numerical computing in


id: perl-data-language-20260505-0
file: data/en.wikipedia.org/wiki/Perl_Data_Language-0.md
category: reference
title: Perl Data Language
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Perl_Data_Language
date_saved: 2026-05-05T10:13:06.697937+00:00
instance: kb-cron
parent: perl-data-language-20260505-0
summary:
Perl Data Language (abbreviated PDL) is a set of free software array programming extensions to the Perl programming language. PDL extends the data structures built into Perl, to include large multidimensional arrays, and adds functionality to manipulate those arrays as vector objects. It also provides tools for image processing, machine learning, computer modeling of physical systems, and graphical plotting and presentation. Simple operations are automatically vectorized across complete arrays, and higher-dimensional operations (such as matrix multiplication) are supported. == Language design == PDL is a vectorized array programming language: the expression syntax is a variation on standard mathematical vector notation, so that the user can combine and operate on large arrays with simple expressions. In this respect, PDL follows in the footsteps of the APL programming language, and it has been compared to commercial languages such as MATLAB and Interactive Data Language, and to other free languages such as NumPy and Octave. Unlike MATLAB and IDL, PDL allows great flexibility in indexing and vectorization: for example, if a subroutine normally operates on a 2-D matrix array, passing it a 3-D data cube will generally cause the same operation to happen to each 2-D layer of the cube. PDL borrows from


id: physics-analysis-workstation-20260505-0
file: data/en.wikipedia.org/wiki/Physics_Analysis_Workstation-0.md
category: reference
title: Physics Analysis Workstation
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Physics_Analysis_Workstation
date_saved: 2026-05-05T10:13:07.854449+00:00
instance: kb-cron
parent: physics-analysis-workstation-20260505-0
summary:
The Physics Analysis Workstation (PAW) is an interactive, scriptable computer software tool for data analysis and graphical presentation in high-energy physics. The development of this software tool started at CERN in 1986, it was optimized for the processing of very large amounts of data. It was based on and intended for inter-operation with components of CERNLIB, an extensive collection of Fortran libraries. PAW had been a standard tool in high energy physics for decades, yet was essentially unmaintained. Despite continuing popularity as of 2008, it has been losing ground to the C++-based ROOT package. Conversion tutorials exist. In 2014, development and support were stopped. == Sample script == PAW uses its own scripting language. Here is sample code (with its actual output), which can be used to plot data gathered in files. * read data vector/read X,Y input_file.dat * eps plot fort/file 55 gg_ggg_dsig_dphid_179181.eps meta 55 -113 opt linx | linear scale opt logy | logarithmic scale * here goes plot set plci 1 | line color set lwid 2 | line width set dmod 1 | line type (solid, dotted, etc.) graph 32 X Y AL | 32 stands for input data lines in input file * plot title


id: plotly-20260505-0
file: data/en.wikipedia.org/wiki/Plotly-0.md
category: reference
title: Plotly
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Plotly
date_saved: 2026-05-05T10:13:09.001016+00:00
instance: kb-cron
parent: plotly-20260505-0
summary:
Plotly is a technical computing company headquartered in Montreal, Quebec, that develops online data analytics and visualization tools. Plotly provides online graphing, analytics, and statistics tools for individuals and collaboration, as well as scientific graphing libraries for Python, R, MATLAB, Perl, Julia, Arduino, JavaScript and REST. == History == Plotly was founded by Alex Johnson, Jack Parmer, Chris Parmer, and Matthew Sundquist. The founders' backgrounds are in science, energy, and data analysis and visualization. Early employees include Christophe Viau, a Canadian software engineer and Ben Postlethwaite, a Canadian geophysicist. Plotly was named one of the Top 20 Hottest Innovative Companies in Canada by the Canadian Innovation Exchange. Plotly was featured in "startup row" at PyCon 2013, and sponsored the SciPy 2018 conference. Plotly raised $5.5 million during its Series A funding, led by MHS Capital, Siemens Venture Capital, Rho Ventures, Real Ventures, and Silicon Valley Bank. The Boston Globe and Washington Post newsrooms have produced data journalism using Plotly. In 2020, Plotly was named a Best Place to Work by the Canadian SME National Business Awards, and nominated as Business of the Year. == Products == Plotly offers open-source and enterprise products. Dash is an open-source Python, R, and Julia


id: poem-home-20260505-0
file: data/en.wikipedia.org/wiki/POEM@Home-0.md
category: reference
title: POEM@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/POEM@Home
date_saved: 2026-05-05T10:13:10.235604+00:00
instance: kb-cron
parent: poem-home-20260505-0
summary:
POEM@Home was a volunteer computing project hosted by the Karlsruhe Institute of Technology and running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform. It modeled protein folding using Anfinsen's dogma. POEM@Home was started in 2007 and, due to advances using GPUs that rendered the BOINC program redundant, concluded in October 2016. The POEM@home applications were proprietary. == Scientific objectives == The project studied how protein structure determined protein function, predict a protein's structure from its amino acid sequence, investigated how proteins interact with each other, and understand how malfunctioning proteins can cause functional disorders. The resulting knowledge could then be used in the development of medical treatments. == See also == List of volunteer computing projects == References == == External links == POEM@Home website Berkeley Open Infrastructure for Network Computing (BOINC)


id: predictor-home-20260505-0
file: data/en.wikipedia.org/wiki/Predictor@home-0.md
category: reference
title: Predictor@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Predictor@home
date_saved: 2026-05-05T10:13:11.432786+00:00
instance: kb-cron
parent: predictor-home-20260505-0
summary:
Predictor@home was a volunteer computing project that used BOINC software to predict protein structure from protein sequence in the context of the 6th biannual CASP, or Critical Assessment of Techniques for Protein Structure Prediction. A major goal of the project was the testing and evaluating of new algorithms to predict both known and unknown protein structures. Predictor@home was complementary to Folding@home. Whereas the latter aims to study the dynamics of protein folding, Predictor@home aimed to specify what the final tertiary structure will be. Also, the two projects differ significantly in the infrastructure they use. The project used BOINC software, whereas Folding@home maintains its own software completely outside of BOINC. However, for a time, Predictor@home competed with other BOINC protein structure prediction projects, such as Rosetta@home. Each uses different methods of rapidly and reliably predicting the final tertiary structure. Predictor@home is currently inactive. == History == Predictor@home holds the distinction of being the first independent BOINC project to be launched. The project was set up and run by Michela Taufer at The Scripps Research Institute. On September 6, 2006, Predictor@home was temporarily taken off line, with no new work units being sent out. In May, 2008, the project reverted to Alpha


id: primegrid-20260505-0
file: data/en.wikipedia.org/wiki/PrimeGrid-0.md
category: reference
title: PrimeGrid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PrimeGrid
date_saved: 2026-05-05T10:13:12.640012+00:00
instance: kb-cron
parent: primegrid-20260505-0
summary:
PrimeGrid is a volunteer computing project that searches for very large (up to world-record size) prime numbers whilst also aiming to solve long-standing mathematical conjectures. It uses the Berkeley Open Infrastructure for Network Computing (BOINC) platform. PrimeGrid offers a number of subprojects for prime-number sieving and discovery. Some of these are available through the BOINC client, others through the PRPNet client. Some of the work is manual, i.e. it requires manually starting work units and uploading results. Different subprojects may run on different operating systems, and may have executables for CPUs, GPUs, or both; while running the Lucas–Lehmer–Riesel test, CPUs with Advanced Vector Extensions and Fused Multiply-Add instruction sets will yield the fastest results for non-GPU accelerated workloads. PrimeGrid awards badges to users in recognition of achieving certain defined levels of credit for work done. The badges have no intrinsic value but are valued by many as a sign of achievement. The issuing of badges should also benefit PrimeGrid by evening out the participation in the less popular sub projects. The easiest of the badges can often be obtained in less than a day by a single computer, whereas the most challenging badges will require far more time and computing


id: primegrid-20260505-1
file: data/en.wikipedia.org/wiki/PrimeGrid-1.md
category: reference
title: PrimeGrid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PrimeGrid
date_saved: 2026-05-05T10:13:12.640012+00:00
instance: kb-cron
parent: primegrid-20260505-0
summary:
=== Riesel Problem === As of 13 December 2022, PrimeGrid has eliminated 18 values of k from the Riesel problem and is continuing the search to eliminate the 43 remaining numbers. 3 values of k are found by independent searchers. === Twin prime search === Primegrid worked with the Twin Prime Search to search for a record-sized twin prime at approximately 58,700 digits. The new world's largest known twin prime 2003663613 × 2195000 ± 1 was eventually discovered on January 15, 2007 (sieved by Twin Prime Search and tested by PrimeGrid). The search continued for another record twin prime at just above 100,000 digits. It was completed in August 2009 when PrimeGrid found 65516468355 × 2333333 ± 1. Continued testing for twin primes in conjunction with the search for a Sophie Germain prime yielded a new record twin prime in September 2016 upon finding the number 2996863034895 × 21290000 ± 1 composed of 388,342 digits. === Woodall prime search === As of 22 April 2018, the project has discovered the four largest Woodall primes known to date. The largest of these is 17016602 × 217016602 − 1 and was found on 21 March 2018. The search continues for an even


id: proteins-home-20260505-0
file: data/en.wikipedia.org/wiki/Proteins@home-0.md
category: reference
title: Proteins@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Proteins@home
date_saved: 2026-05-05T10:13:14.003094+00:00
instance: kb-cron
parent: proteins-home-20260505-0
summary:
proteins@home was a volunteer computing project that used the BOINC architecture. The project was run by the Department of Biology at École Polytechnique. The project began on December 28, 2006, and ended in June 2008. == Purpose == proteins@home was a large-scale non-profit protein structure prediction project utilizing volunteer computing to perform intensive computations in a small amount of time. From their website: The amino acid sequence of a protein determines its three-dimensional structure, or 'fold'. Conversely, the three-dimensional structure is compatible with a large, but limited set of amino acid sequences. Enumerating the allowed sequences for a given fold is known as the 'inverse protein folding problem'. We are working to solve this problem for a large number of known protein folds (a representative subset: about 1500 folds). The most expensive step is to build a database of energy functions that describe all these structures. For each structure, we consider all possible sequences of amino acids. Surprisingly, this is computationally tractable, because our energy functions are sums over pairs of interactions. Once this is done, we can explore the space of amino acid sequences in a fast and efficient way, and retain the most favorable sequences. This large-scale mapping


id: proteowizard-20260505-0
file: data/en.wikipedia.org/wiki/ProteoWizard-0.md
category: reference
title: ProteoWizard
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ProteoWizard
date_saved: 2026-05-05T10:13:15.181942+00:00
instance: kb-cron
parent: proteowizard-20260505-0
summary:
ProteoWizard is a set of open-source, cross-platform tools and libraries for proteomics data analyses. It provides a framework for unified mass spectrometry data file access and performs standard chemistry and LCMS dataset computations. Specifically, it is able to read many of the vendor-specific, proprietary formats and converting the data into an open data format. On the application level, the software provides executables for data conversion (msConvert, msConvertGUI and idConvert), data visualization (msPicture and seeMS), data access (msAccess, msCat, idCat and msPicture), data analysis (peekaboo and msPrefix14) and basic proteomics utilities (chainsaw). In addition, the project also hosts the Skyline software which helps to create, acquire and analyze targeted proteomics experiments such as SRM experiments. The main contributors to the project are the Tabb, MacCoss and Mallick research labs as well as Insilicos. == See also == OpenMS Trans-Proteomic Pipeline Mass spectrometry software Skyline (software) == References == == External links == Project Homepage


id: psychtoolbox-for-matlab-20260505-0
file: data/en.wikipedia.org/wiki/Psychtoolbox_for_MATLAB-0.md
category: reference
title: Psychtoolbox for MATLAB
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychtoolbox_for_MATLAB
date_saved: 2026-05-05T10:13:16.339780+00:00
instance: kb-cron
parent: psychtoolbox-for-matlab-20260505-0
summary:
Psychophysics Toolbox Version 3 (PTB-3) is a collection of free functions for MATLAB or GNU Octave intended for use by neuroscience and vision researchers. It synthesizes and shows accurately controlled visual and auditory stimuli and interacts with the observer. It has at least twelve thousand active users, an active forum, and many citations. PTB-3 is based on the Psychophysics Toolbox Version 2 (PTB-2) but its MATLAB extensions (in C) were rewritten to be more modular and use OpenGL. Psychtoolbox is offered alongside many alternative toolboxes for programming Psychophysics and Psychology experiments, such as PsychoPy for Python or jsPsych for JavaScript. Psychophysics Toolbox Version 3 is free software. Most of it is released under the MIT License, but parts of it are licensed under other open source and free software licenses. == References == == External links == Official website Psychtoolbox Forum


id: ptolemy-project-20260505-0
file: data/en.wikipedia.org/wiki/Ptolemy_Project-0.md
category: reference
title: Ptolemy Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ptolemy_Project
date_saved: 2026-05-05T10:13:17.482780+00:00
instance: kb-cron
parent: ptolemy-project-20260505-0
summary:
The Ptolemy Project is an ongoing project aimed at modeling, simulating, and designing concurrent, real-time, embedded systems. The focus of the Ptolemy Project is on assembling concurrent components. The principal product of the project is the Ptolemy II model based design and simulation tool. The Ptolemy Project is conducted in the Industrial Cyber-Physical Systems Center (iCyPhy) in the Department of Electrical Engineering and Computer Sciences of the University of California, Berkeley, and is directed by Prof. Edward A. Lee. The key underlying principle in the project is the use of well-defined models of computation that govern the interaction between components. A major problem area being addressed is the use of heterogeneous mixtures of models of computation. The project is named after Claudius Ptolemaeus, the 2nd century Greek astronomer, mathematician, and geographer. The Kepler Project, a community-driven collaboration among researchers at three other University of California campuses has created the Kepler scientific workflow system which is based on Ptolemy II. == References == == External links == Official website The Kepler Project


id: pwpaw-20260505-0
file: data/en.wikipedia.org/wiki/Pwpaw-0.md
category: reference
title: Pwpaw
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pwpaw
date_saved: 2026-05-05T10:13:18.648838+00:00
instance: kb-cron
parent: pwpaw-20260505-0
summary:
PWPAW A Projector Augmented Wave (PAW) code for electronic structure calculation. It is a free software package, distributed under the copyleft GNU General Public License. It is a plane wave implementation of the projector augmented wave (PAW) method developed by Peter E. Blöchl for electronic structure calculations within the framework of density functional theory. In addition to the self-consistent calculation of the electronic structure of a periodic solid, the program has a number of other capabilities, including structural geometry optimization and molecular dynamics simulations within the Born–Oppenheimer approximation. == See also == Atompaw Software package for electron configuration calculations EXCITING Bloch's theorem == References ==


id: pytorch-20260505-0
file: data/en.wikipedia.org/wiki/PyTorch-0.md
category: reference
title: PyTorch
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/PyTorch
date_saved: 2026-05-05T10:13:19.861916+00:00
instance: kb-cron
parent: pytorch-20260505-0
summary:
PyTorch is an open-source deep learning library, originally developed by Meta Platforms and currently developed with support from the Linux Foundation. The successor to Torch, PyTorch provides a high-level API that builds upon optimised, low-level implementations of deep learning algorithms and architectures, such as the Transformer, or SGD. Notably, this API simplifies model training and inference to a few lines of code. PyTorch allows for automatic parallelization of training and, internally, implements CUDA bindings that speed training further by leveraging GPU resources. PyTorch utilises the tensor as a fundamental data type, similarly to NumPy. Training is facilitated by a reversed automatic differentiation system, Autograd, that constructs a directed acyclic graph of the operations (and their arguments) executed by a model during its forward pass. With a loss, backpropagation is then undertaken. As of 2025, PyTorch remains one of the most popular deep learning libraries, alongside others such as TensorFlow and Keras. It can be installed using Anaconda package managers. A number of commercial deep learning architectures are built on top of PyTorch, including ChatGPT, Tesla Autopilot, Uber's Pyro, and Hugging Face's Transformers. == History == In 2001, Torch was written and released under a GPL. It was a machine-learning library


id: qmc-home-20260505-0
file: data/en.wikipedia.org/wiki/QMC@Home-0.md
category: reference
title: QMC@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/QMC@Home
date_saved: 2026-05-05T10:13:21.032688+00:00
instance: kb-cron
parent: qmc-home-20260505-0
summary:
QMC@Home was a volunteer computing project for the BOINC client aimed at further developing and testing Quantum Monte Carlo (QMC) for use in quantum chemistry. It is hosted by the University of Münster with participation by the Cavendish Laboratory. QMC@Home allows volunteers from around the world to donate idle computer cycles to help calculate molecular geometry using Diffusion Monte Carlo. The project is developing a new application using density functional theory. The project began its Beta testing on 23 May 2006. As of February 2010, QMC@Home has about 7,500 active participants from 102 countries, contributing about 5 teraFLOPS of computation power. == Workunits == In order to get results from home computers the work is split into "workunits". The time it takes to complete a workunit depends on the size of the calculated system and the speed of the user's computer. The target time is between 4 and 48 hours on a 2.4 GHz system. This is a list of molecules recently tested: 1a Ammonia; 1 Ammonia dimer; 2a Water; 2 Water dimer; 3a Formic acid; 3 Formic acid dimer; 4a Formamide; 4 Formamide dimer; 5a Uracil; 5 Uracil dimer; 6a 2-pyridoxine; 6b 2-aminopyridine; 6 2-pyridoxine/2-aminopyridine; 7a Adenine; 7b Thymine;


id: quake-catcher-network-20260505-0
file: data/en.wikipedia.org/wiki/Quake-Catcher_Network-0.md
category: reference
title: Quake-Catcher Network
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quake-Catcher_Network
date_saved: 2026-05-05T10:13:22.263846+00:00
instance: kb-cron
parent: quake-catcher-network-20260505-0
summary:
The Quake-Catcher Network was an initiative run by the University of Southern California that aimed to use computer-based accelerometers to detect earthquakes. It used the BOINC volunteer computing platform (a form of distributed computing, similar to SETI@home). It supported mobile devices (smartphones and some tablets/laptops) that have a built-in accelerometer. It also supported three external USB devices - the codemercs.com JoyWarrior 24F8, the ONavi sensor, and the MotionNode Accel. In 2011, project scientist Elizabeth Cochran was awarded a Presidential Early Career Award from US President Barack Obama in large part due to her founding of the Quake-Catcher Network project. The Quake Catcher Network project started at Stanford University in 2008, then moved to Caltech, and joined the Southern California Earthquake Center (SCEC) and the Incorporated Research Institutions for Seismology (IRIS) in 2016. The Quake-Catcher Network was discontinued on June 1st 2023 == References == == External links == Interactive world map, showing recent earthquakes (day/week/month) Archived 2018-01-07 at the Wayback Machine – result of QCN


id: quantum-espresso-20260505-0
file: data/en.wikipedia.org/wiki/Quantum_ESPRESSO-0.md
category: reference
title: Quantum ESPRESSO
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quantum_ESPRESSO
date_saved: 2026-05-05T10:13:23.445932+00:00
instance: kb-cron
parent: quantum-espresso-20260505-0
summary:
Quantum ESPRESSO (Quantum Open-Source Package for Research in Electronic Structure, Simulation, and Optimization; QE) is a suite for first-principles electronic-structure calculations and materials modeling, distributed for free and as free software under the GNU General Public License. It is based on density functional theory (DFT), plane wave basis sets, and pseudopotentials (both norm-conserving and ultrasoft). The core plane wave DFT functions of QE are provided by the PWscf component (PWscf previously existed as an independent project). PWscf (Plane-Wave Self-Consistent Field) is a set of programs for electronic structure calculations within DFT and density functional perturbation theory, using plane wave basis sets and pseudopotentials. The software is released under the GNU General Public License. The latest stable version QE-7.4.1 was released on 14 March 2025. == Quantum ESPRESSO Project == Quantum ESPRESSO is an open initiative of the CNR-IOM DEMOCRITOS National Simulation Center in Trieste (Italy) and its partners, in collaboration with different centers worldwide such as MIT, Princeton University, the University of Minnesota and the École Polytechnique Fédérale de Lausanne. The project is coordinated by the QUANTUM ESPRESSO foundation, which was formed by many research centers and groups all over the world. The first version, called pw.1.0.0, was released on


id: quarto-software-20260505-0
file: data/en.wikipedia.org/wiki/Quarto_(software)-0.md
category: reference
title: Quarto (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Quarto_(software)
date_saved: 2026-05-05T10:13:24.596354+00:00
instance: kb-cron
parent: quarto-software-20260505-0
summary:
Quarto is a free and open-source scientific and technical publishing system developed by Posit PBC (formerly RStudio). It is a command-line tool built on Pandoc that converts plain text documents mixed with executable code including Python, R, Julia, and Observable JavaScript into static formats including PDF, HTML, MS Word, PowerPoint, ePub, RevealJS, and MediaWiki. Quarto is considered the successor to R Markdown, extending support beyond the R ecosystem to support a broader range of data science languages and environments. == History == Quarto was officially announced in July 2022 at the `rstudio::conf(2022)` conference. The release coincided with the corporate rebranding of the developer from RStudio to Posit. This rebranding and the launch of Quarto reflected a strategic shift to support language-agnostic data science workflows, specifically acknowledging the growing adoption of Python alongside R in scientific research. == Design and implementation == Quarto is implemented as a Command Line Interface (CLI), which allows it to function independently of any specific Integrated Development Environment (IDE). However, extensions are available for IDEs such as RStudio, Visual Studio Code, and Jupyter. === File formats === The native source format for Quarto uses the `.qmd` extension. These files are plain text files that combine Markdown


id: qutemol-20260505-0
file: data/en.wikipedia.org/wiki/QuteMol-0.md
category: reference
title: QuteMol
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/QuteMol
date_saved: 2026-05-05T10:13:25.744050+00:00
instance: kb-cron
parent: qutemol-20260505-0
summary:
QuteMol is an open-source, interactive, molecular visualization system that was based on advanced rendering techniques that make possible to have for the first time a set of innovative visualization modalities in an interactive real-time application. QuteMol was the first system to use the capabilities of GPUs through OpenGL shaders to offer an array of innovative visual effects. QuteMol visualization techniques are aimed at improving clarity and an easier understanding of the 3D shape and structure of large molecules or complex proteins. In 2021 the IEEE Visualization Conference awarded the paper introducing QuteMol with the Test of Time Award to testify that "contents are still vibrant and useful today and have had a major impact and influence within and beyond the visualization community." Features available in QuteMol include: Real Time ambient occlusion Depth Aware Silhouette Enhancement Ball and Sticks, space-filling and Liquorice visualization modes High resolution antialiased snapshots for creating publication quality renderings Interactive rendering of large molecules and protein (100k atoms) Standard Protein Data Bank input. Notably after many years since the last release QuteMol is still used in the molecular visualization community as can be testified by searching on google scholar for papers citing explicitly the tool or the


id: rasmol-20260505-0
file: data/en.wikipedia.org/wiki/RasMol-0.md
category: reference
title: RasMol
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/RasMol
date_saved: 2026-05-05T10:13:26.941484+00:00
instance: kb-cron
parent: rasmol-20260505-0
summary:
RasMol is a computer program written for molecular graphics visualization intended and used mainly to depict and explore biological macromolecule structures, such as those found in the Protein Data Bank (PDB). == History == It was originally developed by Roger Sayle in the early 1990s. Historically, it was an important tool for molecular biologists since the extremely optimized program allowed the software to run on (then) modestly powerful personal computers. Before RasMol, visualization software ran on graphics workstations that, due to their cost, were less accessible to scholars. RasMol continues to be important for research in structural biology, and has become important in education. RasMol has a complex licensing version history. Starting with the version 2.7 series, RasMol source code is dual-licensed under a GNU General Public License (GPL), or custom license RASLIC. Starting with version 2.7.5, a GPL is the only license valid for binary distributions. RasMol includes a scripting language, to perform many functions such as selecting certain protein chains, changing colors, etc. Jmol and Sirius software have incorporated this language into their commands. Protein Data Bank (PDB) files can be downloaded for visualization from members of the Worldwide Protein Data Bank (wwPDB). These have been uploaded by


id: rmetrics-20260505-0
file: data/en.wikipedia.org/wiki/Rmetrics-0.md
category: reference
title: Rmetrics
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rmetrics
date_saved: 2026-05-05T10:13:28.611150+00:00
instance: kb-cron
parent: rmetrics-20260505-0
summary:
Rmetrics is a free and open-source software project designed for teaching computational finance. Rmetrics is based primarily on the statistical R programming language, but does contain contributions in other programming languages, such as Fortran, C, and C++. The project was started in 2001 by Diethelm Wuertz, based at the Swiss Federal Institute of Technology in Zurich. == Rmetrics packages == Most Rmetrics components are distributed as R packages, which are add-on modules for R. == Goals == The broad goals of the projects are: To provide widespread access to a broad range of powerful statistical and graphical methods for the analysis of market data and risk management in finance. To provide a common software platform that enables the rapid development and deployment of extensible, scalable, and interoperable software. To strengthen scientific understanding by producing high-quality documentation and reproducible research. To train researchers on computational and statistical methods for the analysis of financial data and for financial risk management. == R/Rmetrics project == Rmetrics provides a high-level interpreted language, as well as data and model visualization capabilities. It allows for research in parallel statistical computing with an object oriented framework for computational finance, and financial engineering. == Open source commitment ==


id: rnn-software-20260505-0
file: data/en.wikipedia.org/wiki/Rnn_(software)-0.md
category: reference
title: Rnn (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rnn_(software)
date_saved: 2026-05-05T10:13:30.648216+00:00
instance: kb-cron
parent: rnn-software-20260505-0
summary:
rnn is an open-source machine learning framework that implements recurrent neural network architectures, such as LSTM and GRU, natively in the R programming language, that has been downloaded over 100,000 times (from the RStudio servers alone). The rnn package is distributed through the Comprehensive R Archive Network under the open-source GPL v3 license. == Workflow == The below example from the rnn documentation show how to train a recurrent neural network to solve the problem of bit-by-bit binary addition. == sigmoid == The sigmoid functions and derivatives used in the package were originally included in the package, from version 0.8.0 onwards, these were released in a separate R package sigmoid, with the intention to enable more general use. The sigmoid package is a dependency of the rnn package and therefore automatically installed with it. == Reception == With the release of version 0.3.0 in April 2016 the use in production and research environments became more widespread. The package was reviewed several months later on the R blog The Beginner Programmer as "R provides a simple and very user friendly package named rnn for working with recurrent neural networks.", which further increased usage. The book Neural Networks in R by Balaji


id: root-20260505-0
file: data/en.wikipedia.org/wiki/ROOT-0.md
category: reference
title: ROOT
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ROOT
date_saved: 2026-05-05T10:13:31.807619+00:00
instance: kb-cron
parent: root-20260505-0
summary:
ROOT is an object-oriented computer program and library developed by CERN. It was originally designed for particle physics data analysis and contains several features specific to the field, but it is also used in other applications such as astronomy and data mining. The latest minor release is 6.34, as of 2025-04-08. == Description == CERN maintained the CERN Program Library written in FORTRAN for many years. Its development and maintenance were discontinued in 2003 in favour of ROOT, which is written in the C++ programming language. ROOT development was initiated by René Brun and Fons Rademakers in 1994. Some parts are published under the GNU Lesser General Public License (LGPL) and others are based on GNU General Public License (GPL) software, and are thus also published under the terms of the GPL. It provides platform independent access to a computer's graphics subsystem and operating system using abstract layers. Parts of the abstract platform are: a graphical user interface and a GUI builder, container classes, reflection, a C++ script and command line interpreter (CINT in version 5, cling in version 6), object serialization and persistence. The packages provided by ROOT include those for Histogramming and graphing to view and analyze distributions


id: rosetta-home-20260505-0
file: data/en.wikipedia.org/wiki/Rosetta@home-0.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
Rosetta@home is a volunteer computing project researching protein structure prediction on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, run by the Baker lab. Rosetta@home aims to predict protein–protein docking and design new proteins with the help of about fifty-five thousand active volunteered computers processing at over 487,946 gigaFLOPS on average as of September 19, 2020. Foldit, a Rosetta@home videogame, aims to reach these goals with a crowdsourcing approach. Though much of the project is oriented toward basic research to improve the accuracy and robustness of proteomics methods, Rosetta@home also does applied research on malaria, Alzheimer's disease, and other pathologies. Like all BOINC projects, Rosetta@home uses idle computer processing resources from volunteers' computers to perform calculations on individual workunits. Completed results are sent to a central project server where they are validated and assimilated into project databases. The project is cross-platform, and runs on a wide variety of hardware configurations. Users can view the progress of their individual protein structure prediction on the Rosetta@home screensaver. In addition to disease-related research, the Rosetta@home network serves as a testing framework for new methods in structural bioinformatics. Such methods are then used in other Rosetta-based applications, like RosettaDock or the Human Proteome


id: rosetta-home-20260505-1
file: data/en.wikipedia.org/wiki/Rosetta@home-1.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
A primary feature of the Rosetta@home graphical user interface (GUI) is a screensaver which shows a current workunit's progress during the simulated protein folding process. In the upper-left of the current screensaver, the target protein is shown adopting different shapes (conformations) in its search for the lowest energy structure. Depicted immediately to the right is the structure of the most recently accepted. On the upper right the lowest energy conformation of the current decoy is shown; below that is the true, or native, structure of the protein if it has already been determined. Three graphs are included in the screensaver. Near the middle, a graph for the accepted model's thermodynamic free energy is displayed, which fluctuates as the accepted model changes. A graph of the accepted model's root-mean-square deviation (RMSD), which measures how structurally similar the accepted model is to the native model, is shown far right. On the right of the accepted energy graph and below the RMSD graph, the results from these two functions are used to produce an energy vs. RMSD plot as the model is progressively refined. Like all BOINC projects, Rosetta@home runs in the background of the user's computer, using idle computer power, either at


id: rosetta-home-20260505-2
file: data/en.wikipedia.org/wiki/Rosetta@home-2.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
== Disease-related research == In addition to basic research in predicting protein structure, docking and design, Rosetta@home is also used in immediate disease-related research. Numerous minor research projects are described in David Baker's Rosetta@home journal. As of February 2014, information on recent publications and a short description of the work are being updated on the forum. The forum thread is no longer used since 2016, and news on the research can be found on the general news section of the project. === Alzheimer's disease === A component of the Rosetta software suite, RosettaDesign, was used to accurately predict which regions of amyloidogenic proteins were most likely to make amyloid-like fibrils. By taking hexapeptides (six amino acid-long fragments) of a protein of interest and selecting the lowest energy match to a structure similar to that of a known fibril forming hexapeptide, RosettaDesign was able to identify peptides twice as likely to form fibrils as are random proteins. Rosetta@home was used in the same study to predict structures for amyloid beta, a fibril-forming protein that has been postulated to cause Alzheimer's disease. Preliminary but as yet unpublished results have been produced on Rosetta-designed proteins that may prevent fibrils from forming, although it


id: rosetta-home-20260505-3
file: data/en.wikipedia.org/wiki/Rosetta@home-3.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
== Rosetta software == Rosetta is the software responsible for performing structure prediction in Rosetta@home. Besides a BOINC cluster, Rosetta can run on a single local computer, or on a local supercomputer. Similar to other bioinformatic programs, there are online public servers offering to run Rosetta from a web interface. The software is freely licensed to the academic community and available to pharmaceutical companies for a fee. Originally introduced by the Baker laboratory at the University of Washington in 1998 as an ab initio approach to structure prediction, Rosetta has since branched into several development streams and distinct services, providing features such as macromolecular docking and protein design. Many of the graduate students and other researchers involved in Rosetta's initial development have since moved to other universities and research institutions, and subsequently enhanced different parts of the Rosetta project. The Rosetta platform derives its name from the Rosetta Stone, as it attempts to decipher the structural "meaning" of proteins' amino acid sequences. Development of the Rosetta code is done by Rosetta Commons. Rosetta participates in CASP and CAPRI. Rosetta was rewritten in C++ to allow easier development than that allowed by its original version, which was written in Fortran. This


id: rosetta-home-20260505-4
file: data/en.wikipedia.org/wiki/Rosetta@home-4.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
=== Robetta === The Robetta (Rosetta Beta) server is an automated protein structure prediction service offered by the Baker laboratory for non-commercial ab initio and comparative modeling. It has participated as an automated prediction server in the biannual CASP experiments since CASP5 in 2002, performing among the best in the automated server prediction category. Robetta has since competed in CASP6 and 7, where it did better than average among both automated server and human predictor groups. It also participates in the CAMEO3D continuous evaluation. Robetta tasks run on Baker lab servers, Janelia Research Campus machines, and Rosetta@home participant computers. In modeling protein structure as of CASP6, Robetta first searches for structural homologs using BLAST, PSI-BLAST, and 3D-Jury, then parses the target sequence into its individual domains, or independently folding units of proteins, by matching the sequence to structural families in the Pfam database. Domains with structural homologs then follow a "template-based model" (i.e., homology modeling) protocol. Here, the Baker laboratory's in-house alignment program, K*sync, produces a group of sequence homologs, and each of these is modeled by the Rosetta de novo method to produce a decoy (possible structure). The final structure prediction is selected by taking the lowest energy model


id: rosetta-home-20260505-5
file: data/en.wikipedia.org/wiki/Rosetta@home-5.md
category: reference
title: Rosetta@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rosetta@home
date_saved: 2026-05-05T10:13:33.048042+00:00
instance: kb-cron
parent: rosetta-home-20260505-0
summary:
== Volunteer contributions == Rosetta@home depends on computing power donated by individual project members for its research. As of March 28, 2020, about 53,000 users from 150 countries were active members of Rosetta@home, together contributing idle processor time from about 54,800 computers for a combined average performance of over 1.7 PetaFLOPS. Users are granted BOINC credits as a measure of their contribution. The credit granted for each workunit is the number of decoys produced for that workunit multiplied by the average claimed credit for the decoys submitted by all computer hosts for that workunit. This custom system was designed to address significant differences between credit granted to users with the standard BOINC client and an optimized BOINC client, and credit differences between users running Rosetta@home on Windows and Linux operating systems. The amount of credit granted per second of CPU work is lower for Rosetta@home than most other BOINC projects. Rosetta@home is thirteenth out of over 40 BOINC projects in terms of total credit. Rosetta@home users who predict protein structures submitted for the CASP experiment are acknowledged in scientific publications regarding their results. Users who predict the lowest energy structure for a given workunit are featured on the Rosetta@home homepage


id: rule-based-modeling-20260505-0
file: data/en.wikipedia.org/wiki/Rule-based_modeling-0.md
category: reference
title: Rule-based modeling
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Rule-based_modeling
date_saved: 2026-05-05T10:13:34.226929+00:00
instance: kb-cron
parent: rule-based-modeling-20260505-0
summary:
Rule-based modeling is a modeling approach that uses a set of rules that indirectly specifies a mathematical model. The rule-set can either be translated into a model such as Markov chains or differential equations, or be treated using tools that directly work on the rule-set in place of a translated model, as the latter is typically much bigger. Rule-based modeling is especially effective in cases where the rule-set is significantly simpler than the model it implies, meaning that the model is a repeated manifestation of a limited number of patterns. An important domain where this is often the case is biochemical models of living organisms. Groups of mutually corresponding substances are subject to mutually corresponding interactions. BioNetGen is a suite of software tools used to generate mathematical models consisting of ordinary differential equations without generating the equations directly. For example below is an example rule in the BioNetGen format: A ( a , a ) + B ( b ) − > A ( a ! 1 ) . B ( b ! 1 ) {\displaystyle A(a,a)+B(b)->A(a!1).B(b!1)} Where: A(a,a): Represents a model species A with two free binding sites a B(b): Represents a model species B with one free binding


id: scipy-20260505-0
file: data/en.wikipedia.org/wiki/SciPy-0.md
category: reference
title: SciPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SciPy
date_saved: 2026-05-05T10:13:35.400057+00:00
instance: kb-cron
parent: scipy-20260505-0
summary:
SciPy (pronounced "sigh pie") is a free and open-source Python library used for scientific computing and technical computing. SciPy contains modules for optimization, linear algebra, integration, interpolation, special functions, fast Fourier transform, signal and image processing, ordinary differential equation solvers and other tasks common in science and engineering. SciPy is also a family of conferences for users and developers of these tools: SciPy (in the United States), EuroSciPy (in Europe) and SciPy.in (in India). Enthought originated the SciPy conference in the United States and continues to sponsor many of the international conferences as well as host the SciPy website. The SciPy library is currently distributed under the BSD license, and its development is sponsored and supported by an open community of developers. It is also supported by NumFOCUS, a community foundation for supporting reproducible and accessible science. == Components == The SciPy package is at the core of Python's scientific computing capabilities. Available sub-packages include: cluster: hierarchical clustering, vector quantization, K-means constants: physical constants and conversion factors datasets: various example datasets for demonstrating image and data processing differentiate: numerical differentiation for first and second derivatives fft: Discrete Fourier Transform algorithms fftpack: Legacy interface for Discrete Fourier Transforms integrate: numerical integration


id: seasonal-attribution-project-20260505-0
file: data/en.wikipedia.org/wiki/Seasonal_Attribution_Project-0.md
category: reference
title: Seasonal Attribution Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Seasonal_Attribution_Project
date_saved: 2026-05-05T10:13:36.622040+00:00
instance: kb-cron
parent: seasonal-attribution-project-20260505-0
summary:
The Seasonal Attribution Project is a climate'prediction.net sub-project, with support from the WWF. It runs a high resolution model in order to try to determine the extent to which extreme weather events are attributable to human-induced global warming. The project did cease giving out more work, however there has been a project extension to try a fourth sea surface temperature pattern. Current work will still be accepted and used for collaborations and possibly revisions of papers during the review process. A further extension will start soon. == The experiments == United Kingdom floods of Autumn 2000 – Current project. Mountain snowpack decline in western North America Developed in collaboration with the Climate Impacts Group at the University of Washington. Heatwave occurrence in South Africa and India The latter two will use the same models. Information has been uploaded but analysis of information generated has not yet started. == See also == Effects of global warming == References == == External links == Seasonal Attribution Website Archived 2011-02-20 at the Wayback Machine


id: seismic-unix-20260505-0
file: data/en.wikipedia.org/wiki/Seismic_Unix-0.md
category: reference
title: Seismic Unix
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Seismic_Unix
date_saved: 2026-05-05T10:13:37.762608+00:00
instance: kb-cron
parent: seismic-unix-20260505-0
summary:
Seismic Unix is an open source seismic utilities package which was supported by the Center for Wave Phenomena (CWP) at the Colorado School of Mines (CSM). Currently it is supported by John Stockwell. == History == Einar Kjartansson began writing what is now called SU (the SY package) in the late 1970s while still a graduate student at Jon Claerbout's Stanford Exploration Project (SEP). He continued to expand the package while he was a professor at the University of Utah in the early eighties. In 1984, during an extended visit to SEP Kjartansson introduced SY to Shuki Ronen, then a graduate student at Stanford. Ronen further developed SY from 1984 to 1986. Other students at SEP started to use it and contributed code and ideas. SY was inspired by much other software developed at SEP and benefited from the foundations laid by Claerbout and many of his students; Rob Clayton, Stew Levin, Dave Hale, Jeff Thorson, Chuck Sword, and others who pioneered seismic processing on Unix in the seventies and early eighties. In 1986, Shuki Ronen brought this work to the CWP at Colorado School of Mines during his one-year postdoctoral appointment there, Ronen aided Cohen in turning SU into


id: seti-home-20260505-0
file: data/en.wikipedia.org/wiki/SETI@home-0.md
category: reference
title: SETI@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SETI@home
date_saved: 2026-05-05T10:13:38.996090+00:00
instance: kb-cron
parent: seti-home-20260505-0
summary:
SETI@home ("SETI at home") is a project of the Berkeley SETI Research Center to analyze radio signals with the aim of searching for signs of extraterrestrial intelligence. Until March 2020, it was run as an Internet-based public volunteer computing project that employed the BOINC software platform. It is hosted by the Space Sciences Laboratory at the University of California, Berkeley, and is one of many activities undertaken as part of the worldwide SETI effort. SETI@home software was released to the public on May 17, 1999, making it the third large-scale use of volunteer computing over the Internet for research purposes, after Great Internet Mersenne Prime Search (GIMPS) was launched in 1996 and distributed.net in 1997. Along with MilkyWay@home and Einstein@home, it has the investigation of phenomena in interstellar space as its primary purpose. In March 2020, the project stopped sending out new work to SETI@home users, bringing the crowdsourced computing aspect of the project to a stop. At the time, the team intended to shift focus onto the analysis and interpretation of the 20 years' worth of accumulated data. However, the team left open the possibility of eventually resuming volunteer computing using data from other radio telescopes, such as MeerKAT


id: seti-home-20260505-1
file: data/en.wikipedia.org/wiki/SETI@home-1.md
category: reference
title: SETI@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SETI@home
date_saved: 2026-05-05T10:13:38.996090+00:00
instance: kb-cron
parent: seti-home-20260505-0
summary:
== Technology == Anybody with an at least intermittently Internet-connected computer was able to participate in SETI@home by running a free program that downloaded and analyzed radio telescope data. Observational data were recorded on 2-terabyte SATA hard disk drives fed from the Arecibo Telescope in Puerto Rico, each holding about 2.5 days of observations, which were then sent to Berkeley. Arecibo does not have a broadband Internet connection, so data must go by postal mail to Berkeley. Once there, it is divided in both time and frequency domains work units of 107 seconds of data, or approximately 0.35 megabytes (350 kilobytes or 350,000 bytes), which overlap in time but not in frequency. These work units are then sent from the SETI@home server over the Internet to personal computers around the world to analyze. Data was merged into a database using SETI@home computers in Berkeley. Interference was rejected, and various pattern-detection algorithms were applied to search for the most interesting signals. The project used CUDA for GPU processing starting in 2015. In 2016 SETI@home began processing data from the Breakthrough Listen project. === Software === The SETI@home volunteer computing software ran either as a screensaver or continuously while a user worked,


id: seti-home-20260505-2
file: data/en.wikipedia.org/wiki/SETI@home-2.md
category: reference
title: SETI@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SETI@home
date_saved: 2026-05-05T10:13:38.996090+00:00
instance: kb-cron
parent: seti-home-20260505-0
summary:
== Competitive aspect == SETI@home users quickly started to compete with one another to process the maximum number of work units. Teams were formed to combine the efforts of individual users. The competition continued and grew larger with the introduction of BOINC. As with any competition, attempts have been made to "cheat" the system and claim credit for work that has not been performed. To combat cheats, the SETI@home system sends every work unit to multiple computers, a value known as "initial replication" (currently 2). Credit is only granted for each returned work unit once a minimum number of results have been returned and the results agree, a value known as "minimum quorum" (currently 2). If, due to computation errors or cheating by submitting false data, not enough results agree, more identical work units are sent out until the minimum quorum can be reached. The final credit granted to all machines which returned the correct result is the same and is the lowest of the values claimed by each machine. Some users have installed and run SETI@home on computers at their workplaces; an act known as "Borging", after the assimilation-driven Borg of Star Trek. In some cases, SETI@home users have


id: seti-home-20260505-3
file: data/en.wikipedia.org/wiki/SETI@home-3.md
category: reference
title: SETI@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SETI@home
date_saved: 2026-05-05T10:13:38.996090+00:00
instance: kb-cron
parent: seti-home-20260505-0
summary:
SETI@home is a test bed for further development not only of BOINC but of other hardware and software (database) technology. Under SETI@home processing loads, these experimental technologies can be more challenging than expected, as SETI databases do not have typical accounting and business data or relational structures. The non-traditional database uses often do incur greater processing overheads and risk of database corruption and outright database failure. Hardware, software and database failures can (and do) cause dips in project participation. The project has had to shut down several times to change over to new databases capable of handling more massive datasets. Hardware failure has proven to be a substantial source of project shutdowns, as hardware failure is often coupled with database corruption. == See also == == References == == Further reading == Carrigan, Richard A. Jr. (2003). "The Ultimate Hacker: SETI Signals May Need to Be Decontaminated". Bioastronomy 2002: Life Among the Stars. 213. Astronomical Society of the Pacific: 519. Bibcode:2004IAUS..213..519C. Sample, Ian (November 25, 2005). "Scientists be on guard..." The Guardian. London. Retrieved November 25, 2005. Scoles, Sarah (May 23, 2017). "A Brief History of SETI@Home". The Atlantic. == External links == Official website SETI@home screensaver video on YouTube


id: seti-home-beta-20260505-0
file: data/en.wikipedia.org/wiki/SETI@home_beta-0.md
category: reference
title: SETI@home beta
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SETI@home_beta
date_saved: 2026-05-05T10:13:40.213482+00:00
instance: kb-cron
parent: seti-home-beta-20260505-0
summary:
SETI@home beta, is a hibernating volunteer computing project using the Berkeley Open Infrastructure for Network Computing (BOINC) platform, as a test environment for future SETI@home projects: AstroPulse is a volunteer computing project searching for primordial black holes, pulsars, and ETI. AstroPulse clients have been tested by this project for nearly 6 years. It is already running on SETI@home, testing new GPU/CPU optimized apps and performing other tasks. SETI Southern Hemisphere Search, which is another SETI@home project that was due to join BOINC. It was expected that this project would use a slightly modified version of the SETI enhanced client, as the Parkes Observatory has a feedhorn with more beams than the Arecibo Observatory. == Applications Testing == 11 Dec 2008, CUDA applications test 3 Jun 2013, SETI@home v7 test 01 Dec 2015 SETI@home v8 test == External links == Official website SETI@home beta screensaver video on YouTube == References ==


id: shelxle-20260505-0
file: data/en.wikipedia.org/wiki/ShelXle-0.md
category: reference
title: ShelXle
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ShelXle
date_saved: 2026-05-05T10:13:41.339494+00:00
instance: kb-cron
parent: shelxle-20260505-0
summary:
The program ShelXle is a graphical user interface for the structure refinement program SHELXL. ShelXle combines an editor with syntax highlighting for the SHELXL-associated .ins (input) and .res (output) files with an interactive graphical display for visualization of a three-dimensional structure including the electron density (Fo) and difference density (Fo-Fc) maps. == Overview == ShelXle can display electron density maps like the macromolecular program Coot but is more intended for smaller molecules. A number of excellent graphical user interfaces (GUIs) exist for small molecule crystal structure refinement with SHELX (e.g., WINGX, Olex2, XSEED, PLATON and SYSTEM-S, and the Bruker programs XP and XSHELL) ShelXle is free software, distributed under the GNU LGPL. It is available from the ShelXle website or from SourceForge. Binaries are available for Windows, macOS and the Linux distributions SuSE, Debian and Ubuntu as well as Arch via the AUR. The Windows binary is distributed with the NSIS Installer. == Features == Editor featuring syntax highlighting and code completion for the SHELX instructions. Clicking on an atom in the structure view sets the text cursor to the line that contains this atom. Locating atoms in structure view from the editor. Rename mode with support of residues and


id: shogun-toolbox-20260505-0
file: data/en.wikipedia.org/wiki/Shogun_(toolbox)-0.md
category: reference
title: Shogun (toolbox)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Shogun_(toolbox)
date_saved: 2026-05-05T10:13:42.507319+00:00
instance: kb-cron
parent: shogun-toolbox-20260505-0
summary:
Shogun is a free, open-source machine learning software library written in C++. It offers numerous algorithms and data structures for machine learning problems. It offers interfaces for Octave, Python, R, Java, Lua, Ruby and C# using SWIG. It is licensed under the terms of the GNU General Public License version 3 or later. == Description == The focus of Shogun is on kernel machines such as support vector machines for regression and classification problems. Shogun also offers a full implementation of Hidden Markov models. The core of Shogun is written in C++ and offers interfaces for MATLAB, Octave, Python, R, Java, Lua, Ruby and C#. Shogun has been under active development since 1999. Today there is a rich user community across the world using Shogun as a base for research and education, and contributing to the core package. == Supported algorithms == Currently Shogun supports the following algorithms: Support vector machines Dimensionality reduction algorithms, such as PCA, Kernel PCA, Locally Linear Embedding, Hessian Locally Linear Embedding, Local Tangent Space Alignment, Linear Local Tangent Space Alignment, Kernel Locally Linear Embedding, Kernel Local Tangent Space Alignment, Multidimensional Scaling, Isomap, Diffusion Maps, Laplacian Eigenmaps Online learning algorithms such as SGD-QN, Vowpal Wabbit Clustering


id: siconos-20260505-0
file: data/en.wikipedia.org/wiki/Siconos-0.md
category: reference
title: Siconos
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Siconos
date_saved: 2026-05-05T10:13:43.796765+00:00
instance: kb-cron
parent: siconos-20260505-0
summary:
SICONOS is an open source scientific software primarily targeted at modeling and simulating non-smooth dynamical systems (NSDS): Mechanical systems (Rigid body or solid) with Unilateral contact and Coulomb friction as we find in Non-smooth mechanics, Contact dynamics or Granular material. Switched Electrical Circuit such as Power converter, Rectifier, Phase-locked loop (PLL) or Analog-to-digital converter Sliding mode control systems Other applications are found in Systems and Control (hybrid systems, differential inclusions, optimal control with state constraints), Optimization (Complementarity problem and Variational inequality) Biology Gene regulatory network, Fluid Mechanics and Computer graphics, etc. == Components == The software is based on 3 main components Siconos/Numerics (C API). Collection of low-level algorithms for solving basic Algebra and optimization problems arising in the simulation of nonsmooth dynamical systems Linear complementarity problem (LCP) Mixed linear complementarity problem (MLCP) Nonlinear complementarity problem (NCP) Quadratic programming problems (QP) Friction-contact problems (2D or 3D) (Second-order cone programming (SOCP)) Primal or Dual Relay problems Siconos/Kernel. API C++ that allows one to model and simulate the nonsmooth dynamical systems. It contains Dynamical systems classes : first order one, Lagrangian systems, Newton-Euler systems Nonsmooth laws : complementarity, Relay, Friction, Contact, impact Siconos/Front-end (API Python) Mainly an auto-generated SWIG interface of the


id: sim-js-20260505-0
file: data/en.wikipedia.org/wiki/SIM.JS-0.md
category: reference
title: SIM.JS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SIM.JS
date_saved: 2026-05-05T10:13:45.220630+00:00
instance: kb-cron
parent: sim-js-20260505-0
summary:
SIM.JS is an event-based discrete-event simulation library based on standard JavaScript. The library has been written in order to enable simulation within standard browsers by utilizing web technology. SIM.JS supports entities, resources (Facility, Buffers and Stores), communication (via Timers, Events and Messages) and statistics (with Data Series, Time Series and Population statistics). The SIM.JS distribution contains tutorials, in-depth documentation, and a large number of examples. SIM.JS is released as open source software under the LGPL license. The first version was released in January 2011. == Example == There are several examples bundled with the library download. Trafficlight simulation is a standard simulation problem, which may be simulated as within this example: == External links == Analysis of the potential role of open source discrete event simulation software in the manufacturing sector issued by Proceedings of the Operational Research Society Simulation Workshop 2012 comparing SIM.JS among other open source simulation solutions Bachelor thesis on Web-Based Single-Player Project Simulation Game reviewing SIM.JS Interactive Model-Centric Systems Engineering report by Systems Engineering Research Center managed by Stevens Institute of Technology mentioning SIM.JS "Just-In-Time parallel simulation" compares simulation programmed in JIT languages versus AOT languages referencing SIM.JS Modeling and Simulation of Tape Libraries for Hierarchical


id: similarity-matrix-of-proteins-20260505-0
file: data/en.wikipedia.org/wiki/Similarity_Matrix_of_Proteins-0.md
category: reference
title: Similarity Matrix of Proteins
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Similarity_Matrix_of_Proteins
date_saved: 2026-05-05T10:13:46.428893+00:00
instance: kb-cron
parent: similarity-matrix-of-proteins-20260505-0
summary:
Similarity Matrix of Proteins (SIMAP) is a database of protein similarities created using volunteer computing. It is freely accessible for scientific purposes. SIMAP uses the FASTA algorithm to precalculate protein similarity, while another application uses hidden Markov models to search for protein domains. SIMAP is a joint project of the Technical University of Munich, the Helmholtz Zentrum München, and the University of Vienna. == Project == The project usually got new work units at the beginning of each month. More recently, (2010), inclusion of environmental sequences into the database has required longer periods of activity, several months of continuous work for example. Typically, these updates occurred twice each year. In the fourth quarter of 2010, the project relocated to the University of Vienna due to the failing electrical infrastructure at the Technical University of Munich. Part of this exercise involved the creation of a project specific URL requiring existing volunteers and users to detach/reattach to the project. On May 30, 2014, it was announced by project administrators that after a 10-year history, SIMAP would be leaving BOINC by the end of 2014. SIMAP research, however, will go forward with the use of local hardware consisting of "ordinary multi-core CPUs (some


id: simpy-20260505-0
file: data/en.wikipedia.org/wiki/SimPy-0.md
category: reference
title: SimPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SimPy
date_saved: 2026-05-05T10:13:47.593139+00:00
instance: kb-cron
parent: simpy-20260505-0
summary:
SimPy stands for “Simulation in Python”, is a process-based discrete-event simulation framework based on standard Python. It enables users to model active components such as customers, vehicles, or agents as simple Python generator functions. SimPy is released as open source software under the MIT License. The first version was released in December 2002. == Overview == Its event dispatcher is based on Python's generators and can be used for asynchronous networking or to implement multi-agent systems (with both, simulated and real communication). Simulations can be performed “as fast as possible”, in real time (wall clock time) or by manually stepping through the events. Though it is theoretically possible to do continuous simulations with SimPy, it lacks features to support them. However, for simulations with a fixed step size where processes don't interact with each other or with shared resources, a simple while loop is sufficient. Additionally, SimPy provides different types of shared resources to simulate congestion points that have limited capacity, such as servers, checkout counters, and tunnels. In version 3.1 and above, SimPy offers monitoring capabilities to assist in collecting statistics about processes and resources. SimPy 3.0 requires Python 3., while SimPy 4.0 requires Python 3.6+. SimPy distribution contains


id: simthyr-20260505-0
file: data/en.wikipedia.org/wiki/SimThyr-0.md
category: reference
title: SimThyr
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SimThyr
date_saved: 2026-05-05T10:13:48.776319+00:00
instance: kb-cron
parent: simthyr-20260505-0
summary:
SimThyr is a free continuous dynamic simulation program for the pituitary-thyroid feedback control system. The open-source program is based on a nonlinear model of thyroid homeostasis. In addition to simulations in the time domain the software supports various methods of sensitivity analysis. Its simulation engine is multi-threaded and supports multiple processor cores. SimThyr provides a GUI, which allows for visualising time series, modifying constant structure parameters of the feedback loop (e.g. for simulation of certain diseases), storing parameter sets as XML files (referred to as "scenarios" in the software) and exporting results of simulations in various formats that are suitable for statistical software. SimThyr is intended for both educational purposes and in-silico research. == Mathematical model == The underlying model of thyroid homeostasis is based on fundamental biochemical, physiological and pharmacological principles, e.g. Michaelis-Menten kinetics, non-competitive inhibition and empirically justified kinetic parameters. The model has been validated in healthy controls and in cohorts of patients with hypothyroidism and thyrotoxicosis. == Scientific uses == Multiple studies have employed SimThyr for in silico research on the control of thyroid function. The original version was developed to check hypotheses about the generation of pulsatile TSH release. Later and expanded versions of the software


id: skyline-software-20260505-0
file: data/en.wikipedia.org/wiki/Skyline_(software)-0.md
category: reference
title: Skyline (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Skyline_(software)
date_saved: 2026-05-05T10:13:49.893321+00:00
instance: kb-cron
parent: skyline-software-20260505-0
summary:
Skyline is an open source software for targeted proteomics and metabolomics data analysis. It runs on Microsoft Windows and supports the raw data formats from multiple mass spectrometric vendors. It contains a graphical user interface to display chromatographic data for individual peptide or small molecule analytes. Skyline supports multiple workflows including selected reaction monitoring (SRM) / multiple reaction monitoring (MRM), parallel reaction monitoring (PRM), data-independent acquisition (DIA/SWATH) and targeted data-dependent acquisition. == See also == ProteoWizard OpenMS Trans-Proteomic Pipeline Mass spectrometry software == References == == External links == Skyline Homepage


id: slinca-home-20260505-0
file: data/en.wikipedia.org/wiki/SLinCA@Home-0.md
category: reference
title: SLinCA@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SLinCA@Home
date_saved: 2026-05-05T10:13:51.082867+00:00
instance: kb-cron
parent: slinca-home-20260505-0
summary:
SLinCA@Home (Scaling Laws in Cluster Aggregation) was a research project that uses Internet-connected computers to do research in fields such as physics and materials science. == Introduction == SLinCA@Home was based at the G. V. Kurdyumov Institute for Metal Physics (IMP) of the National Academy of Sciences of Ukraine (NASU) in Kyiv, Ukraine's capital city. It ran on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform, the SZTAKI Desktop Grid platform, and the Distributed Computing API (DC-API) by SZTAKI. SLinCA@Home hosts several scientific applications dedicated to research into scale-invariant dependencies in experimental data in physics and materials science. Statistics at the BOINCstats site (as of 16 March 2011), show over 2,000 volunteers in 39 countries have participated in the project; it is the second most popular BOINC project in Ukraine (after the Magnetism@Home project, which is now inactive). About 700 active users contribute about 0.5–1.5 teraFLOPS of computational power, which would rank SLinCA@Home among the top 20 on the TOP500 list of supercomputers in June 2005. == History == The SLinCA@Home project was previously launched in January 2009 as part of the EGEE project in the European Union's Seventh Framework Programme (FP7) for the funding of research and


id: spacy-20260505-0
file: data/en.wikipedia.org/wiki/SpaCy-0.md
category: reference
title: SpaCy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SpaCy
date_saved: 2026-05-05T10:13:52.274523+00:00
instance: kb-cron
parent: spacy-20260505-0
summary:
spaCy ( spay-SEE) is an open-source software library for advanced natural language processing, written in the programming languages Python and Cython. The library is published under the MIT license and its main developers are Matthew Honnibal and Ines Montani, the founders of the software company Explosion. Unlike NLTK, which is widely used for teaching and research, spaCy focuses on providing software for production usage. spaCy also supports deep learning workflows that allow connecting statistical models trained by popular machine learning libraries like TensorFlow, PyTorch or MXNet through its own machine learning library Thinc. Using Thinc as its backend, spaCy features convolutional neural network models for part-of-speech tagging, dependency parsing, text categorization and named entity recognition (NER). Prebuilt statistical neural network models to perform these tasks are available for 23 languages, including English, Portuguese, Spanish, Russian and Chinese, and there is also a multi-language NER model. Additional support for tokenization for more than 65 languages allows users to train custom models on their own datasets as well. == History == Version 1.0 was released on October 19, 2016, and included preliminary support for deep learning workflows by supporting custom processing pipelines. It further included a rule matcher that supported entity annotations,


id: spatiotemporal-epidemiological-modeler-20260505-0
file: data/en.wikipedia.org/wiki/Spatiotemporal_Epidemiological_Modeler-0.md
category: reference
title: Spatiotemporal Epidemiological Modeler
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spatiotemporal_Epidemiological_Modeler
date_saved: 2026-05-05T10:13:53.464871+00:00
instance: kb-cron
parent: spatiotemporal-epidemiological-modeler-20260505-0
summary:
The Spatiotemporal Epidemiological Modeler (STEM) is free software available through the Eclipse Foundation. Originally developed by IBM Research, STEM is a framework and development tool designed to help scientists create and use spatial and temporal models of infectious disease. STEM uses a component software architecture based on the OSGi standard. The Eclipse Equinox platform is a reference implementation of that standard. By using a component software architecture, all of the components or elements required for a disease model, including the code and the data are available as software building blocks that can be independently exchanged, extended, reused, or replaced. These building blocks or plug-ins are called eclipse "plug-ins" or "extensions". STEM plug-ins contain denominator data for administrative regions of interest. The regions are indexed by standard (ISO3166) codes. STEM currently includes a large number of plug-ins for the 244 countries and dependent areas defined by the Geographic Coding Standard maintained by the International Organization for Standardization. These plug-ins contain global data including geographic data, population data, demographics, and basic models of disease. The disease models distributed with STEM include epidemiological compartment models. Other plug-ins describe relationships between regions including nearest-neighbor or adjacency relationships as well as information about transportation, such


id: spinhenge-home-20260505-0
file: data/en.wikipedia.org/wiki/Spinhenge@Home-0.md
category: reference
title: Spinhenge@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spinhenge@Home
date_saved: 2026-05-05T10:13:54.660926+00:00
instance: kb-cron
parent: spinhenge-home-20260505-0
summary:
Spinhenge@home was a volunteer computing project on the BOINC platform, which performs extensive numerical simulations concerning the physical characteristics of magnetic molecules. It is a project of the Bielefeld University of Applied Sciences, Department of Electrical Engineering and Computer Science, in cooperation with the University of Osnabrück and Ames Laboratory. The project began beta testing on September 1, 2006, and used the Metropolis Monte Carlo algorithm to calculate and simulate spin dynamics in nanoscale molecular magnets. On September 28, 2011, a hiatus was announced while the project team reviewed results and upgraded hardware. As of July 10, 2022 the hiatus continues and it is likely that the project has been closed down permanently. == See also == Spintronics BOINC List of volunteer computing projects == References == == External links == Project Website Spinhenge@home screensaver video on YouTube More Information about Spinhenge@Home Project Statistics at BOINCStats


id: spyder-software-20260505-0
file: data/en.wikipedia.org/wiki/Spyder_(software)-0.md
category: reference
title: Spyder (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Spyder_(software)
date_saved: 2026-05-05T10:13:55.867629+00:00
instance: kb-cron
parent: spyder-software-20260505-0
summary:
Spyder is an open-source cross-platform integrated development environment (IDE) for scientific programming in the Python language. Spyder integrates with a number of prominent packages in the scientific Python stack, as well as other open-source software. Created by Pierre Raybaut and released in 2009 under the MIT license, since 2012 Spyder has been maintained and continuously improved by Python developers and the community. Spyder is extensible with first-party and third-party plugins, and includes support for interactive tools for data inspection and embeds Python-specific code quality assurance and introspection instruments, such as Pyflakes, Pylint and Rope. Spyder uses Qt for its GUI and is designed to use either of the PyQt or PySide Python bindings. QtPy, a thin abstraction layer developed by the Spyder project and later adopted by multiple other packages, provides the flexibility to use either backend. == History == Initially created and developed by Pierre Raybaut, it was published on October 18, 2009 under the MIT license. Since 2012 Spyder has been maintained and continuously improved by a team of scientific Python developers and the community. As of 2024, the Spyder website lists the Chan Zuckerberg Initiative and NumFOCUS as their two major sponsors, also noting donations received from


id: su2-code-20260505-0
file: data/en.wikipedia.org/wiki/SU2_code-0.md
category: reference
title: SU2 code
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/SU2_code
date_saved: 2026-05-05T10:13:57.043776+00:00
instance: kb-cron
parent: su2-code-20260505-0
summary:
SU2 (formerly Stanford University Unstructured) is a suite of open-source software tools written in C++ and Python for the numerical solution of partial differential equations (PDE) and performing PDE-constrained optimization. While initially developed for aerodynamics and compressible flow, it has evolved into a general-purpose multiphysics framework capable of simulating incompressible and compressible flows across all Mach regimes, species transport, conjugate heat transfer and combustion. The framework is specialized for gradient-based design optimization using integrated continuous and discrete adjoint solvers. A distinguishing feature for researchers is its use of algorithmic differentiation (AD) to provide exact discrete adjoint sensitivities for complex multiphysics chains, including fluid-structure interaction (FSI) and conjugate heat transfer. It supports unstructured meshes and offers extensibility through User Defined Functions (UDFs) and high-level Python wrappers. To stimulate development and use of the software, the SU2 Foundation was established as a non-profit organization to coordinate the global community of users and developers. SU2 is released under the GNU Lesser General Public License (LGPL) version 2.1. == Developers == SU2 is being developed by individuals and organized teams around the world. The original SU2 Lead Developers are: Dr. Francisco Palacios and Dr. Thomas D. Economon. The most active groups developing SU2 are:


id: tanagra-machine-learning-20260505-0
file: data/en.wikipedia.org/wiki/Tanagra_(machine_learning)-0.md
category: reference
title: Tanagra (machine learning)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Tanagra_(machine_learning)
date_saved: 2026-05-05T10:13:58.200299+00:00
instance: kb-cron
parent: tanagra-machine-learning-20260505-0
summary:
Tanagra is a free suite of machine learning software for research and academic purposes developed by Ricco Rakotomalala at the Lumière University Lyon 2, France. Tanagra supports several standard data mining tasks such as: Visualization, Descriptive statistics, Instance selection, feature selection, feature construction, regression, factor analysis, clustering, classification and association rule learning. Tanagra is an academic project. It is widely used in French-speaking universities. Tanagra is frequently used in real studies and in software comparison papers. == History == The development of Tanagra was started in June 2003. The first version was distributed in December 2003. Tanagra is the successor of Sipina, another free data mining tool which is intended only for supervised learning tasks (classification), especially the interactive and visual construction of decision trees. Sipina is still available online and is maintained. Tanagra is an "open source project" as every researcher can access the source code and add their own algorithms, as long as they agree and conform to the software distribution license. The main purpose of the Tanagra project is to give researchers and students a user-friendly data mining software, conforming to the present norms of the software development in this domain (especially in the design of its


id: theano-software-20260505-0
file: data/en.wikipedia.org/wiki/Theano_(software)-0.md
category: reference
title: Theano (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Theano_(software)
date_saved: 2026-05-05T10:14:00.110578+00:00
instance: kb-cron
parent: theano-software-20260505-0
summary:
Theano is a Python library and optimizing compiler for manipulating and evaluating mathematical expressions, especially matrix-valued ones. In Theano, computations are expressed using a NumPy-esque syntax and compiled to run efficiently on either CPU or GPU architectures. == History == Theano is an open source project primarily developed by the Montreal Institute for Learning Algorithms (MILA) at the Université de Montréal. The name of the software references the ancient philosopher Theano, long associated with the development of the golden mean. On 28 September 2017, Pascal Lamblin posted a message from Yoshua Bengio, Head of MILA: major development would cease after the 1.0 release due to competing offerings by strong industrial players. Theano 1.0.0 was then released on 15 November 2017. On 17 May 2018, Chris Fonnesbeck wrote on behalf of the PyMC development team that the PyMC developers will officially assume control of Theano maintenance once the MILA development team steps down. On 29 January 2021, they started using the name Aesara for their fork of Theano. On 29 Nov 2022, the PyMC development team announced that the PyMC developers will fork the Aesara project under the name PyTensor. == Sample code == The following code is the original Theano's


id: theskynet-20260505-0
file: data/en.wikipedia.org/wiki/TheSkyNet-0.md
category: reference
title: TheSkyNet
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/TheSkyNet
date_saved: 2026-05-05T10:14:01.318409+00:00
instance: kb-cron
parent: theskynet-20260505-0
summary:
theSkyNet was a research project that used volunteer Internet-connected computers to carry out research in astronomy. It was an initiative of the International Centre for Radio Astronomy Research (ICRAR), a joint venture of Curtin University and the University of Western Australia. theSkyNet had two projects, Sourcefinder and POGS. Both projects have been completed. theSkyNet Sourcefinder aimed to test and refine automatic radio sourcefinding algorithms in preparation for radio galaxy surveys using the Australian Square Kilometre Array Pathfinder and the Square Kilometre Array. theSkyNet POGS used Spectral Energy Distribution fitting to calculate characteristics of many galaxies using images taken by the Pan-STARRS PS1 optical telescope in Hawaii. == History == theSkyNet Sourcefinder project was introduced publicly on 13 September 2011, operating on a Java-based user platform, processing data using new distributed computing software called Nereus. One year later, theSkyNet celebrated its first birthday and at the same time theSkyNet POGS project became the first public Australian based project to participate in the well established volunteer computing platform BOINC. The acronym POGS is a reference to a game played with discs that originated on Maui, Hawaii, in the 1920s, and the fact that the Pan-STARRS PS1 telescope, is situated on Mount Haleakala,


id: tomopy-20260505-0
file: data/en.wikipedia.org/wiki/TomoPy-0.md
category: reference
title: TomoPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/TomoPy
date_saved: 2026-05-05T10:14:02.467149+00:00
instance: kb-cron
parent: tomopy-20260505-0
summary:
TomoPy is an open-sourced Python toolbox to perform tomographic data processing and image reconstruction. == Overview == Tomographic reconstruction creates three-dimensional views of an object by combining two-dimensional images taken from multiple directions, for example in how a computer-aided tomography scanner allows 3D views of the heart or brain. Data collection can be rapid, but the required computations are massive. Further, many common experimental perturbations can degrade the quality of tomographs, unless corrections are applied. Unless automated tools make these corrections, beamline staff can be overwhelmed by data that can be collected far faster than corrections and reconstruction can be performed. To address the needs for image correction and tomographic reconstruction in an instrument independent manner, the TomoPy code was developed, which is a parallelizable high performance tomography library. == References ==


id: trans-proteomic-pipeline-20260505-0
file: data/en.wikipedia.org/wiki/Trans-Proteomic_Pipeline-0.md
category: reference
title: Trans-Proteomic Pipeline
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Trans-Proteomic_Pipeline
date_saved: 2026-05-05T10:14:04.020283+00:00
instance: kb-cron
parent: trans-proteomic-pipeline-20260505-0
summary:
The Trans-Proteomic Pipeline (TPP) is an open-source data analysis software for proteomics developed at the Institute for Systems Biology (ISB) by the Ruedi Aebersold group under the Seattle Proteome Center. The TPP includes PeptideProphet, ProteinProphet, ASAPRatio, XPRESS and Libra. == Software Components == === Probability Assignment and Validation === PeptideProphet performs statistical validation of peptide-spectra-matches (PSM) using the results of search engines by estimating a false discovery rate (FDR) on PSM level. The initial PeptideProphet used a fit of a Gaussian distribution for the correct identifications and a fit of a gamma distribution for the incorrect identification. A later modification of the program allowed the usage of a target-decoy approach, using either a variable component mixture model or a semi-parametric mixture model. In the PeptideProphet, specifying a decoy tag will use the variable component mixture model while selecting a non-parametric model will use the semi-parametric mixture model. ProteinProphet identifies proteins based on the results of PeptideProphet. Mayu performs statistical validation of protein identification by estimating a false discovery rate (FDR) on protein level. === Spectral library handling === The SpectraST tool is able to generate spectral libraries and search datasets using these libraries. == See also == OpenMS ProteoWizard Mass


id: ugene-20260505-0
file: data/en.wikipedia.org/wiki/UGENE-0.md
category: reference
title: UGENE
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/UGENE
date_saved: 2026-05-05T10:14:05.861173+00:00
instance: kb-cron
parent: ugene-20260505-0
summary:
UGENE is computer software for bioinformatics. It helps biologists to analyze various biological genetics data, such as sequences, annotations, multiple alignments, phylogenetic trees, NGS assemblies, and others. UGENE integrates dozens of well-known biological tools, algorithms, and original tools in the context of genomics, evolutionary biology, virology, and other branches of life science. UGENE works on personal computer operating systems such as Windows, macOS, or Linux. It is released as free and open-source software, under a GNU General Public License (GPL) version 2. The data can be stored both locally and on shared/networked storage. The graphical user interface (GUI) provides access to pre-built tools so users with no computer programming experience can access those tools easily. UGENE also has a command-line interface to execute Workflows. Using UGENE Workflow Designer, it is possible to streamline a multi-step analysis. The workflow consists of blocks such as data readers, blocks executing embedded tools and algorithms, and data writers. Blocks can be created with command line tools or a script. A set of sample workflows is available in the Workflow Designer, to annotate sequences, convert data formats, analyze NGS data, etc. To improve performance, UGENE uses multi-core processors (CPUs) and graphics processing units (GPUs) to


id: vcell-20260505-0
file: data/en.wikipedia.org/wiki/VCell-0.md
category: reference
title: VCell
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/VCell
date_saved: 2026-05-05T10:14:07.768630+00:00
instance: kb-cron
parent: vcell-20260505-0
summary:
VCell (Virtual Cell) is an open-source software platform for modeling and simulation of living organisms, primarily cells. It has been designed to be a tool for a wide range of scientists, from experimental cell biologists to theoretical biophysicists. == Concept == VCell is an advanced software platform for modeling and simulating reaction kinetics, membrane transport and diffusion in the complex geometries of cells and multicellular tissues. VCell models have a hierarchical tree structure. The trunk level is the "Physiology" consisting of compartments, species and chemical reactions, and reaction rates that are functions of concentrations. Given initial concentrations of species, VCell can calculate how these concentrations change over time. How these numerical simulations are performed, is determined through a number of "Applications", which specify whether simulations will be deterministic or stochastic, and spatial or compartmental; multiple "Applications" can also specify initial concentrations, diffusion coefficients, flow rates and a variety of modeling assumptions. Thus "Applications" can be viewed as computational experiments to test ideas about the physiological system. Each "Application" corresponds to a mathematical description, which is automatically translated into the VCell Math Description Language. Multiple "Simulations", including parameter scans and changes in solver specifications, can be run within each "Application". Models


id: vtk-20260505-0
file: data/en.wikipedia.org/wiki/VTK-0.md
category: reference
title: VTK
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/VTK
date_saved: 2026-05-05T10:14:08.973454+00:00
instance: kb-cron
parent: vtk-20260505-0
summary:
The Visualization Toolkit (VTK) is a free software system for 3D computer graphics, image processing and scientific visualization. VTK is distributed under the GNU-approved and FSF-approved BSD 3-clause License. == Functionality == VTK consists of a C++ class library and several interpreted interface layers including Tcl/Tk, Java, and Python. The toolkit is created and supported by the Kitware team. VTK supports various visualization algorithms including: scalar, vector, tensor, texture, and volumetric methods; and advanced modeling techniques such as: implicit modeling, polygon reduction, mesh smoothing, cutting, contouring, and Delaunay triangulation. VTK has an information visualization framework, has a suite of 3D interaction widgets, supports parallel processing, and integrates with various databases and GUI toolkits such as Qt and Tk. VTK is cross-platform and runs on Linux, Windows, Mac and Unix platforms. The core of VTK is implemented as a C++ toolkit, requiring users to build applications by combining various objects into an application. The system also supports automated wrapping of the C++ core into Python, Java and Tcl, so that VTK applications may also be written using these programming languages. == History == VTK was initially created in 1993 as companion software to the book The Visualization Toolkit: An Object-Oriented Approach


id: waffles-machine-learning-20260505-0
file: data/en.wikipedia.org/wiki/Waffles_(machine_learning)-0.md
category: reference
title: Waffles (machine learning)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Waffles_(machine_learning)
date_saved: 2026-05-05T10:14:10.119311+00:00
instance: kb-cron
parent: waffles-machine-learning-20260505-0
summary:
Waffles is a collection of command-line tools for performing machine learning operations developed at Brigham Young University. These tools are written in C++, and are available under the GNU Lesser General Public License. == Description == The Waffles machine learning toolkit contains command-line tools for performing various operations related to machine learning, data mining, and predictive modeling. The primary focus of Waffles is to provide tools that are simple to use in scripted experiments or processes. For example, the supervised learning algorithms included in Waffles are all designed to support multi-dimensional labels, classification and regression, automatically impute missing values, and automatically apply necessary filters to transform the data to a type that the algorithm can support, such that arbitrary learning algorithms can be used with arbitrary data sets. Many other machine learning toolkits provide similar functionality, but require the user to explicitly configure data filters and transformations to make it compatible with a particular learning algorithm. The algorithms provided in Waffles also have the ability to automatically tune their own parameters (with the cost of additional computational overhead). Because Waffles is designed for script-ability, it deliberately avoids presenting its tools in a graphical environment. It does, however, include a graphical


id: weka-software-20260505-0
file: data/en.wikipedia.org/wiki/Weka_(software)-0.md
category: reference
title: Weka (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Weka_(software)
date_saved: 2026-05-05T10:14:11.287524+00:00
instance: kb-cron
parent: weka-software-20260505-0
summary:
Waikato Environment for Knowledge Analysis (Weka) is a collection of machine learning and data analysis free software licensed under the GNU General Public License. It was developed at the University of Waikato, New Zealand and is the companion software to the book "Data Mining: Practical Machine Learning Tools and Techniques". == Description == Weka contains a collection of visualization tools and algorithms for data analysis and predictive modeling, together with graphical user interfaces for easy access to these functions. The original non-Java version of Weka was a Tcl/Tk front-end to (mostly third-party) modeling algorithms implemented in other programming languages, plus data preprocessing utilities in C, and a makefile-based system for running machine learning experiments. This original version was primarily designed as a tool for analyzing data from agricultural domains, but the more recent fully Java-based version (Weka 3), for which development started in 1997, is now used in many different application areas, in particular for educational purposes and research. Advantages of Weka include: Free availability under the GNU General Public License. Portability, since it is fully implemented in the Java programming language and thus runs on almost any modern computing platform. A comprehensive collection of data preprocessing and modeling techniques.


id: word2vec-20260505-0
file: data/en.wikipedia.org/wiki/Word2vec-0.md
category: reference
title: Word2vec
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Word2vec
date_saved: 2026-05-05T10:14:12.458984+00:00
instance: kb-cron
parent: word2vec-20260505-0
summary:
Word2vec is a technique in natural language processing for obtaining vector representations of words. These vectors capture information about the meaning of the word based on the surrounding words. The word2vec algorithm estimates these representations by modeling text in a large corpus. Once trained, such a model can detect synonymous words or suggest additional words for a partial sentence. Word2vec was developed by Tomáš Mikolov, Kai Chen, Greg Corrado, Ilya Sutskever and Jeff Dean at Google, and published in 2013. Word2vec represents a word as a high-dimension vector of numbers which capture relationships between words. In particular, words which appear in similar contexts are mapped to vectors which are nearby as measured by cosine similarity. This indicates the level of semantic similarity between the words, so for example the vectors for walk and ran are nearby, as are those for "but" and "however", and "Berlin" and "Germany". == Approach == Word2vec is a group of related models that are used to produce word embeddings. These models are shallow, two-layer neural networks that are trained to reconstruct linguistic contexts of words. Word2vec takes as its input a large corpus of text and produces a mapping of the set of words to


id: word2vec-20260505-1
file: data/en.wikipedia.org/wiki/Word2vec-1.md
category: reference
title: Word2vec
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Word2vec
date_saved: 2026-05-05T10:14:12.458984+00:00
instance: kb-cron
parent: word2vec-20260505-0
summary:
The idea of CBOW is to represent each word with a vector, such that it is possible to predict a word using the sum of the vectors of its neighbors. Specifically, for each word w i {\displaystyle w_{i}} in the corpus, the one-hot encoding of the word is used as the input to the neural network. The output of the neural network is a probability distribution over the dictionary, representing a prediction of individual words in the neighborhood of w i {\displaystyle w_{i}} . The objective of training is to maximize ∑ i ln ⁡ Pr ( w i ∣ w i + j : j ∈ N ) {\displaystyle \sum _{i}\ln \Pr(w_{i}\mid w_{i+j}\colon j\in N)} where N {\displaystyle N} is a set of (non-zero) indices representing the relative locations of nearby words considered to be in w i {\displaystyle w_{i}} 's neighborhood. For example, if we want each word in the corpus to be predicted by every other word in a small span of 4 words. The set of relative indexes of neighbor words will be: N = { − 2 , − 1 , + 1 , + 2 } {\displaystyle N=\{-2,-1,+1,+2\}} , and the objective is to


id: word2vec-20260505-2
file: data/en.wikipedia.org/wiki/Word2vec-2.md
category: reference
title: Word2vec
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Word2vec
date_saved: 2026-05-05T10:14:12.458984+00:00
instance: kb-cron
parent: word2vec-20260505-0
summary:
== History == During the 1980s, there were some early attempts at using neural networks to represent words and concepts as vectors. In 2010, Tomáš Mikolov (then at Brno University of Technology) with co-authors applied a simple recurrent neural network with a single hidden layer to language modelling. Word2vec was created, patented, and published in 2013 by a team of researchers led by Mikolov at Google over two papers. The original paper was rejected by reviewers for ICLR conference 2013. It also took months for the code to be approved for open-sourcing. Other researchers helped analyse and explain the algorithm. Embedding vectors created using the Word2vec algorithm have some advantages compared to earlier algorithms such as those using n-grams and latent semantic analysis. GloVe was developed by a team at Stanford specifically as a competitor, and the original paper noted multiple improvements of GloVe over word2vec. Mikolov argued that the comparison was unfair as GloVe was trained on more data, and that the fastText project showed that word2vec is superior when trained on the same data. As of 2022, the straight Word2vec approach was described as "dated". Transformer-based models, such as ELMo and BERT, which add multiple neural-network attention layers


id: word2vec-20260505-3
file: data/en.wikipedia.org/wiki/Word2vec-3.md
category: reference
title: Word2vec
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Word2vec
date_saved: 2026-05-05T10:14:12.458984+00:00
instance: kb-cron
parent: word2vec-20260505-0
summary:
=== Radiology and intelligent word embeddings (IWE) === An extension of word vectors for creating a dense vector representation of unstructured radiology reports has been proposed by Banerjee et al. One of the biggest challenges with Word2vec is how to handle unknown or out-of-vocabulary (OOV) words and morphologically similar words. If the Word2vec model has not encountered a particular word before, it will be forced to use a random vector, which is generally far from its ideal representation. This can particularly be an issue in domains like medicine where synonyms and related words can be used depending on the preferred style of radiologist, and words may have been used infrequently in a large corpus. IWE combines Word2vec with a semantic dictionary mapping technique to tackle the major challenges of information extraction from clinical texts, which include ambiguity of free text narrative style, lexical variations, use of ungrammatical and telegraphic phases, arbitrary ordering of words, and frequent appearance of abbreviations and acronyms. Of particular interest, the IWE model (trained on the one institutional dataset) successfully translated to a different institutional dataset which demonstrates good generalizability of the approach across institutions. == Analysis == The reasons for successful word embedding learning in


id: world-community-grid-20260505-0
file: data/en.wikipedia.org/wiki/World_Community_Grid-0.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
World Community Grid (WCG) is an effort to create the world's largest volunteer computing platform to perform scientific research that benefits humanity. Launched on November 16, 2004, with proprietary Grid MP client from United Devices and adding support for Berkeley Open Infrastructure for Network Computing (BOINC) in 2005, World Community Grid eventually discontinued the Grid MP client and consolidated on the BOINC platform in 2008. In September 2021, it was announced that IBM transferred ownership to the Krembil Research Institute of University Health Network in Toronto, Ontario. World Community Grid uses unused processing power of consumer devices (PCs, Laptops, Android Smartphones, etc.) to analyse data created by the research groups that participate in the grid. WCG projects have analysed data related to the human genome, the human microbiome, HIV, dengue, muscular dystrophy, cancer, influenza, Ebola, Zika virus, virtual screening, rice crop yields, clean energy, water purification and COVID-19, among other research areas. There are currently four active projects and 28 completed projects. Several of these projects have published peer-reviewed papers based on the analysis of the data generated by WCG. These include an OpenZika project paper on the discovery of a compound (FAM 3) that inhibits the NS3 Helicase protein


id: world-community-grid-20260505-1
file: data/en.wikipedia.org/wiki/World_Community_Grid-1.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
== Statistics and competition == The contributions of each user are recorded and user contribution statistics are publicly available. Due to the fact that the processing time of each workunit varies from computer to computer, depending on the difficulty of the workunit, the speed of the computer, and the amount of idle resources available, contributions are usually measured in terms of points. Points are awarded for each workunit depending on the effort required to process it. Upon completing a workunit, the BOINC client will request the number of points it thinks it deserves based on software benchmarks (see BOINC Credit System#Cobblestones). Since multiple computers process the same workunit to ensure accuracy, the World Community Grid servers can look at the points claimed by each of those computers. The WCG servers disregard statistical outliers, average the remaining values and award the resulting number of points to each computer. Within the grid, users may join teams that have been created by organizations, groups, or individuals. Teams allow for a heightened sense of community identity and can also inspire competition. As teams compete against each other, more work is done for the grid overall. == Outreach == World Community Grid recognizes companies and


id: world-community-grid-20260505-2
file: data/en.wikipedia.org/wiki/World_Community_Grid-2.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
In February 2014, the Help Fight Childhood Cancer project scientists announced the discovery of 7 compounds that destroy neuroblastoma cancer cells without any apparent side effects. This discovery, made with the support of the WCG volunteers, is a positive step towards a new treatment. The project has announced that it is seeking a collaboration with a pharmaceutical company in order to develop the compounds into treatments. Given the success of the project, the scientists have stated that they are already planning a follow-up project which will focus on other pediatric cancers, possibly in collaboration with a newly formed Pan-Asian oncology group, of which they are a founding member. As of July 2012, the Human Proteome Folding Project has published several papers using data from WCG. These include a paper on validation methods and a new database of protein structure and function predictions; a paper on the identification of proteins that regulate human processes; a paper on the analysis of the genomes from five plant families and their proteomes, for which WCG was used in the creation of over 29,000 protein structures; a paper on the proteome of Saccharomyces cerevisiae. The GO Fight Against Malaria project reported the discovery of several


id: world-community-grid-20260505-3
file: data/en.wikipedia.org/wiki/World_Community_Grid-3.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
=== OpenPandemics - COVID-19 === On April 1, 2020, IBM announced OpenPandemics - COVID-19. The project aims to identify possible treatments for the Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) which is responsible for the COVID-19 pandemic. WCG will partner with Scripps Research, with whom it has partnered in the past, notably in FightAIDS@Home projects. The project runs on CPUs and GPUs and will also serve to create a "fast-response, open source tool that will help all scientists quickly search for treatments for future pandemics." The project launched on May 14, 2020. === Mapping Cancer Markers === Mapping Cancer Markers (launched November 8, 2013). The project aims to identify the markers associated with various types of cancer, and is analyzing millions of data points collected from thousands of healthy and cancerous patient tissue samples. These include tissues with lung, ovarian, prostate, pancreatic and breast cancers. By comparing these different data points, researchers aim to identify patterns of markers for different cancers and correlate them with different outcomes, including responsiveness to various treatment options. The project is focusing on 4 types of cancer, with the first focus being on lung cancer, and will move on to ovarian cancer, prostate cancer and


id: world-community-grid-20260505-4
file: data/en.wikipedia.org/wiki/World_Community_Grid-4.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
=== AfricanClimate@Home === The mission of AfricanClimate@Home was to develop more accurate climate models of specific regions in Africa. It was intended to serve as a basis for understanding how the climate will change in the future so that measures designed to alleviate the adverse effects of climate change could be implemented. World Community Grid's tremendous computing power was used to understand and reduce the uncertainty with which climate processes were simulated over Africa. Phase 1 of African Climate@Home launched on September 3, 2007, and ended in July 2008. === Help Conquer Cancer === Help Conquer Cancer project (launched November 1, 2007) is sponsored by the Ontario Cancer Institute (OCI), Princess Margaret Hospital and University Health Network of Toronto, Canada. The project involves X-ray crystallography. The mission of Help Conquer Cancer is to improve the results of protein X-ray crystallography, which helps researchers not only annotate unknown parts of the human proteome, but importantly improves their understanding of cancer initiation, progression and treatment. The HCC project was the first WCG project benefiting from graphics processing units (GPU)s which helped finish it a lot earlier than initially projected due to the massive power of GPUs. In the April 2013 status report


id: world-community-grid-20260505-5
file: data/en.wikipedia.org/wiki/World_Community_Grid-5.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
World Community Grid and researchers supported by Decrypthon, a partnership between AFM (French Muscular Dystrophy Association), CNRS (French National Center for Scientific Research), Universite Pierre et Marie Curie, and IBM were investigating protein–protein interactions for more than 2,200 proteins whose structures are known, with particular focus on those proteins that play a role in neuromuscular diseases. Phase 2 was launched on May 12, 2009, and completed on September 26, 2012. The database of information produced will help researchers design molecules to inhibit or enhance binding of particular macromolecules, hopefully leading to better treatments for muscular dystrophy and other neuromuscular diseases. Phase 2 of the Help Cure Muscular Dystrophy project began once the results from the first phase had been analyzed. Phase 2 ran on the BOINC platform. === Discovering Dengue Drugs – Together – Phase 2 === Discovering Dengue Drugs – Together – Phase 2 (launched February 17, 2010) is sponsored by The University of Texas Medical Branch (UTMB) in Galveston, Texas, United States and the University of Chicago in Illinois, USA. The mission is to identify promising drug candidates to combat the Dengue, Hepatitis C, West Nile, Yellow Fever, and other related viruses. The extensive computing power of World


id: world-community-grid-20260505-6
file: data/en.wikipedia.org/wiki/World_Community_Grid-6.md
category: reference
title: World Community Grid
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/World_Community_Grid
date_saved: 2026-05-05T10:14:13.754375+00:00
instance: kb-cron
parent: world-community-grid-20260505-0
summary:
FightAIDS@Home (launched November 19, 2005) was World Community Grid's second project and its first to target a single disease. Each individual computer processes one potential drug molecule and tests how well it would dock with HIV protease, acting as a protease inhibitor. Scripps Research Institute published its first peer-reviewed scientific paper about the results of FightAIDS@Home on April 21, 2007. This paper explains that the results up to that point will primarily be used to improve the efficiency of future FightAIDS@Home calculations. === FightAIDS@Home Phase 2 === FightAIDS@Home Phase 2 (launched September 30, 2015) is looking more closely at the results of Phase 1. The project has two goals in the early experiments; the simulation architecture is functioning correctly and giving reliable results, and using BEDAM and AutoDock together provides better results than using just BEDAM or AutoDock. === Microbiome Immunity Project === Microbiome Immunity Project (launched August 2017) is a study of proteins in bacteria located in and on the human body; the human microbiome, which comprises around 3 million separate bacterial genes. By studying bacterial genes, researchers can determine their individual shapes, which in turn dictate the function of the bacteria. Collaborative institutions includes the University of California


