# kb

id: cupy-20260505-0
file: data/en.wikipedia.org/wiki/CuPy-0.md
category: reference
title: CuPy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/CuPy
date_saved: 2026-05-05T10:11:01.714180+00:00
instance: kb-cron
parent: cupy-20260505-0
summary:
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. CuPy shares the same API set as NumPy and SciPy, allowing it to be a drop-in replacement to run NumPy/SciPy code on GPU. CuPy supports Nvidia CUDA GPU platform, and AMD ROCm GPU platform starting in v9.0. CuPy has been initially developed as a backend of Chainer deep learning framework, and later established as an independent project in 2017. CuPy is a part of the NumPy ecosystem array libraries and is widely adopted to utilize GPU with Python, especially in high-performance computing environments such as Summit, Perlmutter, EULER, and ABCI. CuPy is a NumFOCUS sponsored project. == Features == CuPy implements NumPy/SciPy-compatible APIs, as well as features to write user-defined GPU kernels or access low-level APIs. === NumPy-compatible APIs === The same set of APIs defined in the NumPy package (numpy.*) are available under cupy.* package. Multi-dimensional array (cupy.ndarray) for boolean, integer, float, and complex data types Module-level functions Linear algebra functions Fast Fourier transform Random number generator === SciPy-compatible APIs === The same set of APIs defined in


id: deap-software-20260505-0
file: data/en.wikipedia.org/wiki/DEAP_(software)-0.md
category: reference
title: DEAP (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/DEAP_(software)
date_saved: 2026-05-05T10:11:02.887423+00:00
instance: kb-cron
parent: deap-software-20260505-0
summary:
Distributed Evolutionary Algorithms in Python (DEAP) is an evolutionary computation framework for rapid prototyping and testing of ideas. It incorporates the data structures and tools required to implement most common evolutionary computation techniques such as genetic algorithm, genetic programming, evolution strategies, particle swarm optimization, differential evolution, traffic flow and estimation of distribution algorithm. It is developed at Université Laval since 2009. == Example == The following code gives a quick overview how the Onemax problem optimization with genetic algorithm can be implemented with DEAP. == See also == Python SCOOP (software) Free software portal == References == == External links == Official website deap on GitHub


id: deeplearning4j-20260505-0
file: data/en.wikipedia.org/wiki/Deeplearning4j-0.md
category: reference
title: Deeplearning4j
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Deeplearning4j
date_saved: 2026-05-05T10:11:04.064418+00:00
instance: kb-cron
parent: deeplearning4j-20260505-0
summary:
Eclipse Deeplearning4j is a programming library written in Java for the Java virtual machine (JVM). It is a framework with wide support for deep learning algorithms. Deeplearning4j includes implementations of the restricted Boltzmann machine, deep belief net, deep autoencoder, stacked denoising autoencoder and recursive neural tensor network, word2vec, doc2vec, and GloVe. These algorithms all include distributed parallel versions that integrate with Apache Hadoop and Spark. Deeplearning4j is open-source software released under Apache License 2.0, developed mainly by a machine learning group headquartered in San Francisco. It is supported commercially by the startup Skymind, which bundles DL4J, TensorFlow, Keras and other deep learning libraries in an enterprise distribution called the Skymind Intelligence Layer. Deeplearning4j was contributed to the Eclipse Foundation in October 2017. == Introduction == Deeplearning4j relies on the widely used programming language Java, though it is compatible with Clojure and includes a Scala application programming interface (API). It is powered by its own open-source numerical computing library, ND4J, and works with both central processing units (CPUs) and graphics processing units (GPUs). Deeplearning4j has been used in several commercial and academic applications. The code is hosted on GitHub. A support forum is maintained on Gitter. The framework is composable, meaning


id: docking-home-20260505-0
file: data/en.wikipedia.org/wiki/Docking@Home-0.md
category: reference
title: Docking@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Docking@Home
date_saved: 2026-05-05T10:11:05.279096+00:00
instance: kb-cron
parent: docking-home-20260505-0
summary:
Docking@Home was a volunteer computing project hosted by the University of Delaware and running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform. It models protein-ligand docking using the CHARMM program. Volunteer computing allows an extensive search of protein-ligand docking conformations and selection of near-native ligand conformations are achieved by using ligand based hierarchical clustering. The ultimate aim was the development of new pharmaceutical drugs. The project was retired on May 23, 2014. == See also == List of volunteer computing projects == References == == Further reading == "Computer Idle? Now You Can Donate Its Time to Find a Cure for Major Diseases". Newswise. June 16, 2009. Retrieved 2009-07-27. == External links == Official website Docking@Home screensaver video on YouTube


id: earth-system-modeling-framework-20260505-0
file: data/en.wikipedia.org/wiki/Earth_System_Modeling_Framework-0.md
category: reference
title: Earth System Modeling Framework
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Earth_System_Modeling_Framework
date_saved: 2026-05-05T10:11:06.463575+00:00
instance: kb-cron
parent: earth-system-modeling-framework-20260505-0
summary:
The Earth System Modeling Framework (ESMF) is open-source software for building climate, numerical weather prediction, data assimilation, and other Earth science software applications. These applications are computationally demanding and usually run on supercomputers. The ESMF is considered a technical layer, integrated into a sophisticated common modeling infrastructure for interoperability. Other aspects of interoperability and shared infrastructure include: common experimental protocols, common analytic methods, common documentation standards for data and data provenance, shared workflow, and shared model components. The ESMF project is distinguished by its strong emphasis on community governance and distributed development, and by a diverse customer base that includes modeling groups from universities, major U.S. research centers, the National Weather Service, the Department of Defense, and NASA. The ESMF development team was centered at NCAR until 2009, after which it moved to the NOAA Earth System Research Laboratories. Editing Earth System Modeling Framework is free software released under the University of Illinois/NCSA Open Source License. == Purpose == ESMF increases the interoperability of Earth-science modeling software developed at different sites and promotes code reuse. The idea is to transform distributed, specialized knowledge and resources into a collaborative, integrated modeling community that operates more efficiently, can address a wider variety


id: eas3-20260505-0
file: data/en.wikipedia.org/wiki/EAS3-0.md
category: reference
title: EAS3
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EAS3
date_saved: 2026-05-05T10:11:07.608607+00:00
instance: kb-cron
parent: eas3-20260505-0
summary:
EAS3 (EAS = Ein-Ausgabe-System) is a software toolkit for reading and writing structured binary data with geometry information and for postprocessing of these data. It is meant to exchange floating-point data according to IEEE standard between different computers, to modify them or to convert them into other file formats. It can be used for all kinds of structured data sets. It is mainly used in the field of direct numerical simulations. == EAS3 package == The complete package consists of libraries intended for usage in own codes and a separate command-line tool. It is written in Fortran and C and runs on all POSIX operating systems. The libraries include different numerical algorithms and subroutines for reading and writing files in the binary EAS3 file format. The read/write routines are provided in Fortran and C. Implemented numerical methods include, for example, Fast Fourier transform, Thomas algorithm and interpolation routines. The libraries are also suitable for vector computers. == History == EAS3 has been developed at the Institut für Aerodynamik und Gasdynamik (IAG) of the University of Stuttgart. The previous versions (EAS, EAS2) range back to the end of the 1980s, when computer power allowed the first spatial DNS computations. The upcoming


id: egonet-20260505-0
file: data/en.wikipedia.org/wiki/EgoNet-0.md
category: reference
title: EgoNet
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EgoNet
date_saved: 2026-05-05T10:11:08.766768+00:00
instance: kb-cron
parent: egonet-20260505-0
summary:
EgoNet (Egocentric Network Study Software) is a program for the collection and analysis of egocentric social network data. It helps the user to collect and analyse all the egocentric network data (all social network data of a website on the Internet), and provide general global network measures and data matrixes that can be used for further analysis by other software. The egonet is the result of the links that it gives and receives certain address on the Internet, and EgoNet is dedicated to collecting information about them and present it in a way useful to the users. Egonet is written in Java, so that the computer where it is going to be used must have the JRE installed. EgoNet is open source software, licensed under GPL. Egonet was created by Christopher McCarty, a professor at the University of Florida, United States. == Features == The program allows to create questionnaires, collect data and provide comprehensive measures and arrays of data that can be used for subsequent analysis by other software. Its main benefits are the generation of questionnaires for relational data, the calculation of relevant General measurements for the analysis of social networks and production graphs. == Components == Egonet


id: egs-program-20260505-0
file: data/en.wikipedia.org/wiki/EGS_(program)-0.md
category: reference
title: EGS (program)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EGS_(program)
date_saved: 2026-05-05T10:11:09.909054+00:00
instance: kb-cron
parent: egs-program-20260505-0
summary:
The EGS (Electron Gamma Shower) computer code system is a general purpose package for the Monte Carlo simulation of the coupled transport of electrons and photons in an arbitrary geometry for particles with energies from a few keV up to several hundreds of GeV. It originated at SLAC but National Research Council of Canada and KEK have been involved in its development since the early 80s. Development of the original EGS code ended with version EGS4. Since then two groups have re-written the code with new physics: EGSnrc, maintained by the Ionizing Radiation Standards Group, Measurement Science and Standards, National Research Council of Canada EGS5, maintained by KEK, the Japanese particle physics research facility. == EGSnrc == EGSnrc is a general-purpose software toolkit that can be applied to build Monte Carlo simulations of coupled electron-photon transport, for particle energies ranging from 1 keV to 10 GeV. It is widely used internationally in a variety of radiation-related fields. The EGSnrc implementation improves the accuracy and precision of the charged particle transport mechanics and the atomic scattering cross-section data. The charged particle multiple scattering algorithm allows for large step sizes without sacrificing accuracy - a key feature of the toolkit that leads


id: einstein-home-20260505-0
file: data/en.wikipedia.org/wiki/Einstein@Home-0.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
Einstein@Home is a volunteer computing project that searches for signals from spinning neutron stars in data from gravitational-wave detectors, from large radio telescopes, and from a gamma-ray telescope. Neutron stars are detected by their pulsed radio and gamma-ray emission as radio and/or gamma-ray pulsars. They also might be observable as continuous gravitational wave sources if they are rapidly spinning and non-axisymmetrically deformed. The project was officially launched on 19 February 2005 as part of the American Physical Society's contribution to the World Year of Physics 2005 event. Einstein@Home searches data from the LIGO gravitational-wave detectors. The project conducts the most sensitive all-sky searches for continuous gravitational waves. While no such signal has yet been detected, the upper limits set by Einstein@Home analyses provide astrophysical constraints on the galactic population of spinning neutron stars in our Milky Way galaxy. Einstein@Home also searches radio telescope data from the Arecibo Observatory, and has in the past analyzed data from Parkes Observatory. On 12 August 2010, the first discovery by Einstein@Home of a previously undetected radio pulsar J2007+2722, found in data from the Arecibo Observatory, was published in Science. This was the first data-based discovery by a volunteer computing project. As of December 2023,


id: einstein-home-20260505-1
file: data/en.wikipedia.org/wiki/Einstein@Home-1.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
Einstein@Home has carried out many analysis runs using data from the LIGO instruments. Since its first search run in 2005, the sensitivity of the LIGO detectors has been improved in a series of steps and upgrades. This is continuing with the current Advanced LIGO detectors. At the same time, Einstein@Home search algorithms have also improved. Together these have increased the search sensitivity by several orders of magnitude. Einstein@Home's first analysis used data from the "third science run" (S3) of LIGO. Processing of the S3 data set was conducted between 22 February 2005 and 2 August 2005. This analysis employed 60 segments from the LIGO Hanford 4-km detector, totaling ten hours of data each. Each 10-hour segment was analyzed for CW signals by the volunteers' computers using a matched-filtering technique. When all matched-filtering results were returned, the results from different segments were then combined in a "post-processing step" on Einstein@Home servers via a coincidence scheme to further enhance search sensitivity. Results were published on the Einstein@Home webpages. Work on the S4 data set (LIGO's fourth science run) was started via interlacing with the S3 calculations and finished in July 2006. This analysis used 10 segments of 30 hours each from the


id: einstein-home-20260505-2
file: data/en.wikipedia.org/wiki/Einstein@Home-2.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
An Einstein@Home study on how to optimally use the limited computing power for directed searches (where prior information on the target object such as the sky position is available) was published on 31 January 2018. It describes the design of searches for continuous gravitational waves over a wide frequency range from three supernova remnants (Vela Jr, Cassiopeia A, and G347.3). The results from the directed Einstein@Home search for continuous gravitational waves from the central objects of the supernova remnants Vela Jr., Cassiopeia A, and G347.3 was published on 29 July 2019. It covered a frequency range from 20 Hertz to 1500 Hertz and used data from LIGO's first observing run O1. No signal was found and the most stringent upper limit at the time of publication were set, improving earlier results by a factor of two for all three targets. A follow-up of the Einstein@Home search for continuous gravitational waves from the central objects of the supernova remnants Vela Jr., Cassiopeia A, and G347.3 was published on 29 June 2020. It investigated the most promising 10,000 candidates from the previous search and followed them up in two stretches of data from LIGO's second observing run (O2). A single candidate associated


id: einstein-home-20260505-3
file: data/en.wikipedia.org/wiki/Einstein@Home-3.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
== Radio data analysis and results == On 24 March 2009, it was announced that the Einstein@Home project was beginning to analyze data received by the PALFA Consortium at the Arecibo Observatory in Puerto Rico. On 26 November 2009, a CUDA-optimized application for the Arecibo Binary Pulsar Search was first detailed on official Einstein@Home webpages. This application uses both a regular CPU and an NVIDIA GPU to perform analyses faster (in some cases up to 50% faster). On 12 August 2010, the Einstein@Home project announced the discovery of a new disrupted binary pulsar, PSR J2007+2722; it may be the fastest-spinning such pulsar discovered to date. The computers of Einstein@Home volunteers Chris and Helen Colvin and Daniel Gebhardt observed PSR 2007+2722 with the highest statistical significance. On 1 March 2011, the Einstein@Home project announced their second discovery: a binary pulsar system PSR J1952+2630. The computers of Einstein@Home volunteers from Russia and the UK observed PSR J1952+2630 with the highest statistical significance. By 15 May 2012 a new application for ATI/AMD graphic cards had been released. Using OpenCL, the new application was ten times faster than running on a typical CPU. On 22 July 2013, an Android application version of the radio


id: einstein-home-20260505-4
file: data/en.wikipedia.org/wiki/Einstein@Home-4.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
== Gamma-ray data analysis and results == On 1 July 2011 the project announced a new application to search for pulsars in data from the Large Area Telescope on board the Fermi Gamma-ray Space Telescope. On 26 November 2013, the discovery of four young gamma-ray pulsars in LAT data by the Einstein@Home project was published. All four pulsars are located in the plane of our Galaxy and have spin frequencies of less than 10 Hertz and characteristic ages between 35,000 and 56,000 years. No radio waves were detected from any of the pulsars. The discovery of the gamma-ray pulsar PSR J1906+0722 was published on 4 August 2015. The discovery confirmed the pulsar nature of the object which had been suspected since 2012 based on the energy distribution of the gamma-ray photons observed by the LAT. The pulsar is young and energetic. In August 2009 it suffered one of the largest glitches observed from a gamma-ray pulsar. No radio pulsations were detected in any follow-up search, making PSR J1906+0722 likely radio-quiet. Advanced methods of timing the arrival times of gamma-ray pulsations were introduced to improve the parameter inference of astrophysical properties. On 16 November 2016 the discovery and timing measurements of


id: einstein-home-20260505-5
file: data/en.wikipedia.org/wiki/Einstein@Home-5.md
category: reference
title: Einstein@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Einstein@Home
date_saved: 2026-05-05T10:11:11.134747+00:00
instance: kb-cron
parent: einstein-home-20260505-0
summary:
== Scientific Publications == Clark, C. J.; et al. (2016). "The Braking Index of a Radio-quiet Gamma-ray Pulsar". The Astrophysical Journal. 832 (1): L15. arXiv:1611.01292. Bibcode:2016ApJ...832L..15C. doi:10.3847/2041-8205/832/1/L15. ISSN 2041-8213. S2CID 54531854. Clark, Colin J.; et al. (2016). "The Einstein@Home Gamma-ray Pulsar Survey I: Search Methods, Sensitivity and Discovery of New Young Gamma-ray Pulsars". The Astrophysical Journal. 834 (2): 106. arXiv:1611.01015. Bibcode:2017ApJ...834..106C. doi:10.3847/1538-4357/834/2/106. S2CID 5750104. Lyne, A. G.; Stappers, B. W.; Bogdanov, S.; Freire, P. C. C.; Kaspi, V. M.; et al. (2016). "Timing of 29 Pulsars Discovered in the PALFA Survey". The Astrophysical Journal. 834 (2): 137. arXiv:1608.09007. Bibcode:2017ApJ...834..137L. doi:10.3847/1538-4357/834/2/137. S2CID 53639204. Papa, M. A.; Eggenstein, H.-B.; Walsh, S.; Di Palma, I.; Allen, B.; et al. (2016). "Hierarchical follow-up of sub-threshold candidates of an all-sky Einstein@Home search for continuous gravitational waves on LIGO sixth science run data". Physical Review D. 94 (12) 122006. arXiv:1608.08928. Bibcode:2016PhRvD..94l2006P. doi:10.1103/PhysRevD.94.122006. S2CID 4595158. Lazarus, P.; et al. (2016). "Einstein@Home discovery of a Double-Neutron Star Binary in the PALFA Survey". The Astrophysical Journal. 831 (2): 150. arXiv:1608.08211. Bibcode:2016ApJ...831..150L. doi:10.3847/0004-637X/831/2/150. ISSN 1538-4357. S2CID 20833657. Zhu, Sylvia J.; et al. (2016). "Einstein@Home search for continuous gravitational waves from Cassiopeia A". Physical Review D. 94 (8) 082008. arXiv:1608.07589. Bibcode:2016PhRvD..94h2008Z.


id: elastix-image-registration-20260505-0
file: data/en.wikipedia.org/wiki/Elastix_(image_registration)-0.md
category: reference
title: Elastix (image registration)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Elastix_(image_registration)
date_saved: 2026-05-05T10:11:12.309035+00:00
instance: kb-cron
parent: elastix-image-registration-20260505-0
summary:
Elastix is an image registration toolbox built upon the Insight Segmentation and Registration Toolkit (ITK). It is entirely open-source and provides a wide range of algorithms employed in image registration problems. Its components are designed to be modular to ease a fast and reliable creation of various registration pipelines tailored for case-specific applications. It was first developed by Stefan Klein and Marius Staring under the supervision of Josien P.W. Pluim at Image Sciences Institute (ISI). Its first version was command-line based, allowing the final user to employ scripts to automatically process big data-sets and deploy multiple registration pipelines with few lines of code. Nowadays, to further widen its audience, a version called SimpleElastix is also available, developed by Kasper Marstal, which allows the integration of elastix with high level languages, such as Python, Java, and R. == Image registration fundamentals == Image registration is a well-known technique in digital image processing that searches for the geometric transformation that, applied to a moving image, obtains a one-to-one map with a target image. Generally, the images acquired from different sensors (multimodal), time instants (multitemporal), and points of view (multiview) should be correctly aligned to proceed with further processing and feature extraction. Even


id: elastix-image-registration-20260505-1
file: data/en.wikipedia.org/wiki/Elastix_(image_registration)-1.md
category: reference
title: Elastix (image registration)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Elastix_(image_registration)
date_saved: 2026-05-05T10:11:12.309035+00:00
instance: kb-cron
parent: elastix-image-registration-20260505-0
summary:
Nearest neighbor (NearestNeighborInterpolator) exploits little resources, but gives low quality results Linear (LinearInterpolator) is sufficient in general applications N-th order B-spline (BSplineInterpolator) can be used to increase the order N, increasing quality and computation time. N=0 and N=1 indicate the nearest neighbor and linear cases respectively. === Optimizer === The optimizer defines the strategy employed for searching the best transformation parameter to reach the correct registration, and it is commonly an iterative strategy. Below are reported some of the implemented optimization strategies. Gradient descent Robbins-Monro, similar to the gradient descent, but employing an approximation of the cost function derivatives A wider range of optimizers is also available, such as Quasi-Newton or evolutionary strategies. === Other features === The elastix software also offers other features that can be employed to speed up the registration procedure and to provide more advanced algorithms to the end-users. Some examples are the introduction of blur and Gaussian pyramid to reduce data complexity, and multi-image and multi-metric framework to deal with more complex applications. == Applications == Elastix has applications mainly in the medical field, where image registration is fundamental to get comprehensive information regarding the analysed anatomical region. It is widely employed in image-guided surgery,


id: elmer-fem-solver-20260505-0
file: data/en.wikipedia.org/wiki/Elmer_FEM_solver-0.md
category: reference
title: Elmer FEM solver
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Elmer_FEM_solver
date_saved: 2026-05-05T10:11:13.574379+00:00
instance: kb-cron
parent: elmer-fem-solver-20260505-0
summary:
Elmer is a computational tool for multi-physics problems. It has been developed by CSC in collaboration with Finnish universities, research laboratories and industry. Elmer FEM solver is free and open-source software, subject to the requirements of the GNU General Public License (GPL), version 2 or any later. Elmer includes physical models of fluid dynamics, structural mechanics, electromagnetics, heat transfer and acoustics, for example. These are described by partial differential equations which Elmer solves by the Finite Element Method (FEM). Elmer comprises several different parts: ElmerGrid – A mesh conversion tool, which can be used to convert differing mesh formats into Elmer-suitable meshes. ElmerGUI – A graphical interface which can be used on an existing mesh to assign physical models, this generates a "case file" which describes the problem to be solved. Does not show the whole ElmerSolver functionality in GUI. ElmerSolver – The numerical solver which performs the finite element calculations, using the mesh and case files. ElmerPost – A post-processing/visualisation module. (Development stopped in favour of other post-processing tools such as ParaView, VisIt, etc.) The different parts of Elmer software may be used independently. Whilst the main module is the ElmerSolver tool, which includes many sophisticated features for physical


id: emboss-20260505-0
file: data/en.wikipedia.org/wiki/EMBOSS-0.md
category: reference
title: EMBOSS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EMBOSS
date_saved: 2026-05-05T10:11:14.772731+00:00
instance: kb-cron
parent: emboss-20260505-0
summary:
EMBOSS is a free c software analysis package developed for the needs of the molecular biology and bioinformatics user community. The software automatically copes with data in a variety of formats and even allows transparent retrieval of sequence data from the web. Also, as extensive libraries are provided with the package, it is a platform to allow other scientists to develop and release software in true open source spirit. EMBOSS also integrates a range of currently available packages and tools for sequence analysis into a seamless whole. EMBOSS is an acronym for European Molecular Biology Open Software Suite. The European part of the name hints at the wider scope. The core EMBOSS groups are collaborating with many other groups to develop the new applications that the users need. This was done from the beginning with EMBnet, the European Molecular Biology Network. EMBnet has many nodes worldwide most of which are national bioinformatics services. EMBnet has the programming expertise. In September 1998, the first workshop was held, when 30 people from EMBnet went to Hinxton to learn about EMBOSS and to discuss the way forward. The EMBOSS package contains a variety of applications for sequence alignment, rapid database searching with sequence


id: emergent-software-20260505-0
file: data/en.wikipedia.org/wiki/Emergent_(software)-0.md
category: reference
title: Emergent (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Emergent_(software)
date_saved: 2026-05-05T10:11:15.914377+00:00
instance: kb-cron
parent: emergent-software-20260505-0
summary:
Emergent (formerly PDP++) is a biologically-based neural simulation software that is primarily intended for creating models of the brain and cognitive processes. Development initially began in 1995 at Carnegie Mellon University, and as of 2014, continues at the University of Colorado at Boulder. The 3.x release of the software, which was known as PDP++, is featured in the textbook Computational Explorations in Cognitive Neuroscience. == Features == Emergent features a modular design, based on the principles of object-oriented programming. It runs on Microsoft Windows, Darwin / macOS and Linux. C-Super-Script (variously, CSS and C^C), a built-in C++-like interpreted scripting language, allows access to virtually all simulator objects and can initiate all the same actions as the GUI, and more. Version 4 and upward features a full 3D environment for visualizations, based on Qt and Open Inventor. Robotics simulations are made possible by integration with the Open Dynamics Engine. A plugin system allows for expanding the software in many ways. Version 5 introduced parallel threading support, numerous speed improvements, a help browser featuring an interface to the project's Wiki and auto-generated documentation, undo and redo using diffs and a definable undo depth. In addition, 5.0.2 introduced a built-in plugin source code


id: encog-20260505-0
file: data/en.wikipedia.org/wiki/Encog-0.md
category: reference
title: Encog
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Encog
date_saved: 2026-05-05T10:11:17.070373+00:00
instance: kb-cron
parent: encog-20260505-0
summary:
Encog is a machine learning framework available for Java and .Net. Encog supports different learning algorithms such as Bayesian Networks, Hidden Markov Models and Support Vector Machines. However, its main strength lies in its neural network algorithms. Encog contains classes to create a wide variety of networks, as well as support classes to normalize and process data for these neural networks. Encog trains using many different techniques. Multithreading is used to allow optimal training performance on multicore machines. Encog can be used for many tasks, including medical and financial research. A GUI based workbench is also provided to help model and train neural networks. Encog has been in active development since 2008. == Neural Network Architectures == ADALINE Neural Network Adaptive Resonance Theory 1 (ART1) Bidirectional Associative Memory (BAM) Boltzmann Machine Counterpropagation Neural Network (CPN) Elman Recurrent Neural Network Neuroevolution of augmenting topologies (NEAT) Feedforward Neural Network (Perceptron) Hopfield Neural Network Jordan Recurrent Neural Network Radial Basis Function Network Recurrent Self Organizing Map (RSOM) Self Organizing Map (Kohonen) == Training techniques == Backpropagation Resilient Propagation (RProp) Scaled Conjugate Gradient (SCG) Levenberg–Marquardt algorithm Manhattan Update Rule Propagation Competitive learning Hopfield Learning Genetic algorithm training Instar Training Outstar Training ADALINE Training ==


id: elki-20260505-0
file: data/en.wikipedia.org/wiki/ELKI-0.md
category: reference
title: ELKI
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ELKI
date_saved: 2026-05-05T10:11:18.253498+00:00
instance: kb-cron
parent: elki-20260505-0
summary:
ELKI (Environment for Developing KDD-Applications Supported by Index-Structures) is a data mining (KDD, knowledge discovery in databases) software framework developed for use in research and teaching. It was originally created by the database systems research unit at LMU Munich, Germany, led by Professor Hans-Peter Kriegel. The project has continued at the Technical University of Dortmund, Germany. It aims at allowing the development and evaluation of advanced data mining algorithms and their interaction with database index structures. == Description == The ELKI framework is written in Java and built around a modular architecture. Most currently included algorithms perform clustering, outlier detection, and database indexes. The object-oriented architecture allows the combination of arbitrary algorithms, data types, distance functions, indexes, and evaluation measures. The Java just-in-time compiler optimizes all combinations to a similar extent, making benchmarking results more comparable if they share large parts of the code. When developing new algorithms or index structures, the existing components can be easily reused, and the type safety of Java detects many programming errors at compile time. ELKI is a free tool for analyzing data, mainly focusing on finding patterns and unusual data points without needing labels. It's written in Java and aims to be fast


id: elki-20260505-1
file: data/en.wikipedia.org/wiki/ELKI-1.md
category: reference
title: ELKI
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ELKI
date_saved: 2026-05-05T10:11:18.253498+00:00
instance: kb-cron
parent: elki-20260505-0
summary:
Cluster analysis: K-means clustering (including fast algorithms such as Elkan, Hamerly, Annulus, and Exponion k-Means, and robust variants such as k-means--) K-medians clustering K-medoids clustering (PAM) (including FastPAM and approximations such as CLARA, CLARANS) Expectation-maximization algorithm for Gaussian mixture modeling Hierarchical clustering (including the fast SLINK, CLINK, NNChain and Anderberg algorithms) Single-linkage clustering Leader clustering DBSCAN (Density-Based Spatial Clustering of Applications with Noise, with full index acceleration for arbitrary distance functions) OPTICS (Ordering Points To Identify the Clustering Structure), including the extensions OPTICS-OF, DeLi-Clu, HiSC, HiCO and DiSH HDBSCAN Mean-shift clustering BIRCH clustering SUBCLU (Density-Connected Subspace Clustering for High-Dimensional Data) CLIQUE clustering ORCLUS and PROCLUS clustering COPAC, ERiC and 4C clustering CASH clustering DOC and FastDOC subspace clustering P3C clustering Canopy clustering algorithm Anomaly detection: k-Nearest-Neighbor outlier detection LOF (Local outlier factor) LoOP (Local Outlier Probabilities) OPTICS-OF DB-Outlier (Distance-Based Outliers) LOCI (Local Correlation Integral) LDOF (Local Distance-Based Outlier Factor) EM-Outlier SOD (Subspace Outlier Degree) COP (Correlation Outlier Probabilities) Frequent Itemset Mining and association rule learning Apriori algorithm Eclat FP-growth Dimensionality reduction Principal component analysis Multidimensional scaling T-distributed stochastic neighbor embedding (t-SNE) Spatial index structures and other search indexes: R-tree R*-tree M-tree k-d tree X-tree Cover tree iDistance NN descent Locality


id: eon-20260505-0
file: data/en.wikipedia.org/wiki/EOn-0.md
category: reference
title: EOn
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EOn
date_saved: 2026-05-05T10:11:19.475402+00:00
instance: kb-cron
parent: eon-20260505-0
summary:
eOn was a volunteer computing project running on the Berkeley Open Infrastructure for Network Computing (BOINC) platform, which uses theoretical chemistry techniques to solve problems in condensed matter physics and materials science. It was a project of the Institute for Computational Engineering and Sciences at the University of Texas. Traditional molecular dynamics can accurately model events that occur within a fraction of a millisecond. In order to model events that take place on much longer timescales, Eon combines transition state theory with kinetic Monte Carlo. The result is a combination of classical mechanics and quantum methods like density functional theory. Since the generation of new work units depended on the results of previous units, the project could only give each host a few units at a time. On May 26, 2014, it was announced that eOn would be retiring from BOINC. == See also == List of volunteer computing projects == References ==


id: evolution-home-20260505-0
file: data/en.wikipedia.org/wiki/Evolution@Home-0.md
category: reference
title: Evolution@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Evolution@Home
date_saved: 2026-05-05T10:11:20.684010+00:00
instance: kb-cron
parent: evolution-home-20260505-0
summary:
evolution@home was a volunteer computing project for evolutionary biology, launched in 2001. The aim of evolution@home is to improve understanding of evolutionary processes. This is achieved by simulating individual-based models. The Simulator005 module of evolution@home was designed to better predict the behaviour of Muller's ratchet. The project was operated semi-automatically; participants had to manually download tasks from the webpage and submit results by email using this method of operation. yoyo@home used a BOINC wrapper to completely automate this project by automatically distributing tasks and collecting their results. Therefore, the BOINC version was a complete volunteer computing project. yoyo@home has declared its involvement in this project finished. == See also == Artificial life Digital organism Evolutionary computation Folding@home List of volunteer computing projects == References ==


id: exmaralda-20260505-0
file: data/en.wikipedia.org/wiki/EXMARaLDA-0.md
category: reference
title: EXMARaLDA
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/EXMARaLDA
date_saved: 2026-05-05T10:11:21.843918+00:00
instance: kb-cron
parent: exmaralda-20260505-0
summary:
EXMARaLDA (Extensible Markup Language for Discourse Annotation) is a set of free software tools for creating, managing and analyzing spoken language corpora. It consists of a transcription tool (comparable to tools like Praat or Transcriber), a tool for administering corpus meta data and a tool for doing queries (KWIC searches) on spoken language corpora. EXMARaLDA is used for doing conversation and discourse analysis, dialectology, phonology and research into first and second language acquisition in children and adults. EXMARaLDA is based on the open standards XML and Unicode and programmed in Java. == References == Schmidt, Thomas and Wörner, Kai (2014). "EXMARaLDA" In: Handbook on Corpus Phonology. Oxford University Press, 402–419. Schmidt, Thomas and Wörner, Kai (2009). "EXMARaLDA – Creating, analysing and sharing spoken language corpora for pragmatic research." In: Pragmatics 19. Schmidt, Thomas and Bennöhr, Jasmine (2008). "Rescuing Legacy Data." In: Language Documentation and Conservation 2, 109–129. == External links == exmaralda.org - Official project website std.metu.edu.tr - Website of the METU Spoken Turkish Corpus, a corpus constructed with EXMARaLDA


id: fityk-20260505-0
file: data/en.wikipedia.org/wiki/Fityk-0.md
category: reference
title: Fityk
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fityk
date_saved: 2026-05-05T10:11:23.018116+00:00
instance: kb-cron
parent: fityk-20260505-0
summary:
Fityk is curve fitting and data analysis application, predominantly used to fit analytical, bell-shaped functions to experimental data. It is positioned to fill the gap between general plotting software and programs specific for one field, e.g. crystallography or XPS. Originally, Fityk was developed to analyse powder diffraction data. It is also used in other fields that require peak analysis and peak-fitting, like chromatography or various kinds of spectroscopy. Fityk is free and open source, distributed under the terms of GNU General Public License, with binaries/installers available free of charge on the project's website. It runs on Linux, macOS, Microsoft Windows, FreeBSD and other platforms. It operates either as a command line program or with a graphical user interface. It is written in C++, using wxWidgets, and providing bindings for Python and other scripting languages. == Features == three weighted least squares methods: Levenberg-Marquardt algorithm, Nelder-Mead method Genetic algorithm about 20 built-in functions and support for user-defined functions equality constraints data manipulations, handling series of datasets, automation of common tasks with scripts. == Alternatives == The programs LabPlot, MagicPlot and peak-o-mat have similar scope. More generic data analysis programs with spread-sheet capabilities include the proprietary Origin and its clones QtiPlot (paid,


id: fluentd-20260505-0
file: data/en.wikipedia.org/wiki/Fluentd-0.md
category: reference
title: Fluentd
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fluentd
date_saved: 2026-05-05T10:11:24.155322+00:00
instance: kb-cron
parent: fluentd-20260505-0
summary:
Fluentd is a cross-platform open-source data collection software project originally developed at Treasure Data. It is written primarily in the C programming language with a thin-Ruby wrapper that gives users flexibility. == Overview == Fluentd was positioned for "big data," semi- or un-structured data sets. It analyzes event logs, application logs, and clickstreams. According to Suonsyrjä and Mikkonen, the "core idea of Fluentd is to be the unifying layer between different types of log inputs and outputs.", Fluentd is available on Linux, macOS, and Windows. == History == Fluentd was created by Sadayuki Furuhashi as a project of the Mountain View-based firm Treasure Data. Written primarily in Ruby, its source code was released as open-source software in October 2011. The company announced $5 million of funding in 2013. Treasure Data was then sold to Arm Ltd. in 2018. == Users == Fluentd was one of the data collection tools recommended by Amazon Web Services in 2013, when it was said to be similar to Apache Flume or Scribe. Google Cloud Platform's BigQuery recommends Fluentd as the default real-time data-ingestion tool, and uses Google's customized version of Fluentd, called google-fluent, as a default logging agent. == Fluent Bit == Fluent Bit


id: freefem-20260505-0
file: data/en.wikipedia.org/wiki/FreeFem++-0.md
category: reference
title: FreeFem++
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FreeFem++
date_saved: 2026-05-05T10:11:25.301770+00:00
instance: kb-cron
parent: freefem-20260505-0
summary:
FreeFem++ is a programming language and a software focused on solving partial differential equations using the finite element method. FreeFem++ is written in C++ and developed and maintained by Université Pierre et Marie Curie and Laboratoire Jacques-Louis Lions. It runs on Linux, Solaris, macOS and Microsoft Windows systems. FreeFem++ is free software (LGPL). FreeFem++ language is inspired by C++. There is an IDE called FreeFem++-cs. == History == The first version was created in 1987 by Olivier Pironneau and was named MacFem (it only worked on Macintosh); PCFem appeared some time later. Both were written in Pascal. In 1992 it was re-written in C++ and named FreeFem. Later versions, FreeFem+ (1996) and FreeFem++ (1998), used that programming language too. == Other versions == FreeFem++ includes versions for console mode and MPI FreeFem3D Deprecated versions: FreeFem+ FreeFem == See also == List of finite element software packages == References == == External links == Official website


id: freehal-20260505-0
file: data/en.wikipedia.org/wiki/FreeHAL-0.md
category: reference
title: FreeHAL
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FreeHAL
date_saved: 2026-05-05T10:11:26.504428+00:00
instance: kb-cron
parent: freehal-20260505-0
summary:
FreeHAL was a volunteer computing project to build a self-learning chatbot. This project is no longer active. Originally, the program was called JEliza referring to the chatbot ELIZA by Joseph Weizenbaum. The J stood for Java because JEliza has first been programmed in Java. In May 2008, the program has been renamed to FreeHAL because the programming language has changed. The name is related to the computer in the film 2001: A Space Odyssey. FreeHAL uses a semantic network and technologies like pattern recognition, stemming, part of speech databases and Hidden Markov Models in order to imitate a human behaviour. FreeHAL learns autonomously. While communicating by keyboard, the program extends its database. Currently, English and German are supported. By using the BOINC platform, new semantic networks for the program are built. FreeHAL@home appears to have terminated operations. == Awards == In 2008, the program won the first prize in the category "Most Popular" at the Chatterbox Challenge, a yearly competition between different similar chatbots. == Publications == There was an article about FreeHAL in the Linux Magazine, Issue 97 from December 2008. In the German magazine com!, the program was on the CD/DVD and in the list of the Top-10-Open-Source


id: geant4-20260505-0
file: data/en.wikipedia.org/wiki/Geant4-0.md
category: reference
title: Geant4
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Geant4
date_saved: 2026-05-05T10:11:27.672969+00:00
instance: kb-cron
parent: geant4-20260505-0
summary:
Geant4 (for GEometry ANd Tracking) is a platform for "the simulation of the passage of particles through matter" using Monte Carlo methods. It is the successor of the GEANT series of software toolkits developed by The Geant4 Collaboration, and the first to use object oriented programming (in C++). Its development, maintenance and user support are taken care by the international Geant4 Collaboration. Application areas include high energy physics and nuclear experiments, accelerator and space physics studies. The software is used by a number of research projects around the world. The Geant4 software and source code is freely available from the project web site; until version 8.1 (released June 28, 2006), no specific software license for its use existed; Geant4 is now provided under the Geant4 Software License. == Features == Geant4 includes facilities for handling geometry, tracking, detector response, run management, visualization and user interface. For many physics simulations, this means less time needs to be spent on the low level details, and researchers can start immediately on the more important aspects of the simulation. Following is a summary of each of the facilities listed above: Geometry is an analysis of the physical layout of the experiment, including detectors, absorbers,


id: genepattern-20260505-0
file: data/en.wikipedia.org/wiki/GenePattern-0.md
category: reference
title: GenePattern
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GenePattern
date_saved: 2026-05-05T10:11:28.836661+00:00
instance: kb-cron
parent: genepattern-20260505-0
summary:
GenePattern is a freely available computational biology open-source software package originally created and developed at the Broad Institute for the analysis of genomic data. Designed to enable researchers to develop, capture, and reproduce genomic analysis methodologies, GenePattern was first released in 2004. GenePattern is currently developed at the University of California, San Diego. == Functionality == GenePattern is a powerful scientific workflow system that provides access to hundreds of genomic analysis tools. Use these analysis tools as building blocks to design sophisticated analysis pipelines that capture the methods, parameters, and data used to produce analysis results. Pipelines can be used to create, edit and share reproducible in silico results. == Project Objectives == Accessibility: Run over 200 regularly updated analysis and visualization tools (that support data preprocessing, gene expression analysis, proteomics, Single nucleotide polymorphism (SNP) analysis, flow cytometry, and next-generation sequencing) and create analytic workflows without any programming through a point and click user interface. Reproducibility: Automated history and provenance tracking with versioning so that any user can share, repeat and understand a complete computational analysis Extensibility: Computational users can import their methods and code for sharing using tools that support easy creation and integration Multiple interfaces: Web browser, application,


id: general-architecture-for-text-engineering-20260505-0
file: data/en.wikipedia.org/wiki/General_Architecture_for_Text_Engineering-0.md
category: reference
title: General Architecture for Text Engineering
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/General_Architecture_for_Text_Engineering
date_saved: 2026-05-05T10:11:30.046327+00:00
instance: kb-cron
parent: general-architecture-for-text-engineering-20260505-0
summary:
General Architecture for Text Engineering (GATE) is a Java suite of natural language processing (NLP) tools for man tasks, including information extraction in many languages. It is now used worldwide by a wide community of scientists, companies, teachers and students. It was originally developed at the University of Sheffield beginning in 1995. As of May 28, 2011, 881 people are on the gate-users mailing list at SourceForge.net, and 111,932 downloads from SourceForge are recorded since the project moved to SourceForge in 2005. The paper "GATE: A framework and graphical development environment for robust NLP tools and applications" has received over 2000 citations since publication (according to Google Scholar). Books covering the use of GATE, in addition to the GATE User Guide, include "Building Search Applications: Lucene, LingPipe, and Gate", by Manu Konchady, and "Introduction to Linguistic Annotation and Text Analytics", by Graham Wilcock. GATE community and research has been involved in several European research projects including: Transitioning Applications to Ontologies, SEKT, NeOn, Media-Campaign, Musing, Service-Finder, LIRICS and KnowledgeWeb. == Features == GATE includes an information extraction system called ANNIE (A Nearly-New Information Extraction System) which is a set of modules comprising a tokenizer, a gazetteer, a sentence splitter, a part


id: genmapp-20260505-0
file: data/en.wikipedia.org/wiki/GenMAPP-0.md
category: reference
title: GenMAPP
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GenMAPP
date_saved: 2026-05-05T10:11:31.209983+00:00
instance: kb-cron
parent: genmapp-20260505-0
summary:
GenMAPP (Gene Map Annotator and Pathway Profiler) is a free, open-source bioinformatics software tool designed to visualize and analyze genomic data in the context of pathways (metabolic, signaling), connecting gene-level datasets to biological processes and disease. First created in 2000, GenMAPP is developed by an open-source team based in an academic research laboratory. GenMAPP maintains databases of gene identifiers and collections of pathway maps in addition to visualization and analysis tools. Together with other public resources, GenMAPP aims to provide the research community with tools to gain insight into biology through the integration of data types ranging from genes to proteins to pathways to disease. == History == GenMAPP was first created in 2000 as a prototype software tool in the laboratory of Bruce Conklin at the J. David Gladstone Institutes in San Francisco and continues to be developed in the same non-profit, academic research environment. The first release version of GenMAPP 1.0 was available in 2002, supporting analysis of DNA microarray data from human, mouse, rat and yeast. In 2004, GenMAPP 2.0 was released, combining the previously accessory programs MAPPFinder and MAPPBuilder, and expanding support to additional species. GenMAPP 2.1 was released in 2006 with new visualization features and


id: gensim-20260505-0
file: data/en.wikipedia.org/wiki/Gensim-0.md
category: reference
title: Gensim
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gensim
date_saved: 2026-05-05T10:11:32.349307+00:00
instance: kb-cron
parent: gensim-20260505-0
summary:
Gensim is an open-source library for unsupervised topic modeling, document indexing, retrieval by similarity, and other natural language processing functionalities, using modern statistical machine learning. Gensim is implemented in Python and Cython for performance. Gensim is designed to handle large text collections using data streaming and incremental online algorithms, which differentiates it from most other machine learning software packages that target only in-memory processing. == Main features == Gensim includes streamed parallelized implementations of fastText, word2vec and doc2vec algorithms, as well as latent semantic analysis (LSA, LSI, SVD), non-negative matrix factorization (NMF), latent Dirichlet allocation (LDA), tf-idf and random projections. Some of the novel online algorithms in Gensim were also published in the 2011 PhD dissertation Scalability of Semantic Analysis in Natural Language Processing of Radim Řehůřek, the creator of Gensim. == Uses of Gensim == Gensim library has been used and cited in over 1400 commercial and academic applications as of 2018, in a diverse array of disciplines from medicine to insurance claim analysis to patent search. The software has been covered in several new articles, podcasts and interviews. == Free and commercial support == The open source code is developed and hosted on GitHub and a public support


id: gentle-20260505-0
file: data/en.wikipedia.org/wiki/GENtle-0.md
category: reference
title: GENtle
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GENtle
date_saved: 2026-05-05T10:11:33.591770+00:00
instance: kb-cron
parent: gentle-20260505-0
summary:
GENtle is a free software under GPL license. == Features == GENtle is an equivalent to the proprietary Vector NTI, a tool for molecular biologists to analyze and edit DNA sequence files. Invitrogens' removal of the free-of-cost academic licence for Vector NTI v11 has had a severe impact on many molecular biology labs that have come to rely on that tool, which led to vendor lock-in effects, which angered many molecular biologists. The GENtle code is developed and maintained by Magnus Manske. By design, GENtle is coded to be cross-platform utilizing wxGTK. == References ==


id: list-of-free-geology-software-20260505-0
file: data/en.wikipedia.org/wiki/List_of_free_geology_software-0.md
category: reference
title: List of free geology software
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/List_of_free_geology_software
date_saved: 2026-05-05T10:11:34.805450+00:00
instance: kb-cron
parent: list-of-free-geology-software-20260505-0
summary:
This is a list of free and open-source software for geological data handling and interpretation. The list is split into broad categories, depending on the intended use of the software and its scope of functionality. Notice that 'free and open-source' requires that the source code is available and users are given a free software license. Simple being 'free of charge' is not sufficient—see gratis versus libre. == Well logging & Borehole visualisation == == Geosciences software platforms == == Geostatistics == == Forward modeling == == Geomodeling == == Visualization, interpretation & analysis packages == == Geographic information systems (GIS) == This important class of tools is already listed in the article List of GIS software. == Not true free and open-source projects == The following projects have unknown licensing, licenses or other conditions which place some restriction on use or redistribution, or which depend on non-open-source software like MATLAB or XVT (and therefore do not meet the Open Source Definition from the Open Source Initiative). == References ==


id: gimias-20260505-0
file: data/en.wikipedia.org/wiki/GIMIAS-0.md
category: reference
title: GIMIAS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GIMIAS
date_saved: 2026-05-05T10:11:35.966266+00:00
instance: kb-cron
parent: gimias-20260505-0
summary:
GIMIAS is a workflow-oriented environment focused on biomedical image computing and simulation. The open-source framework is extensible through plug-ins and is focused on building research and clinical software prototypes. Gimias has been used to develop clinical prototypes in the fields of cardiac imaging and simulation, angiography imaging and simulation, and neurology GIMIAS is being funded by several national and international projects like cvREMOD, euHeart or VPH NoE. == About GIMIAS == GIMIAS stands for Graphical Interface for Medical Image Analysis and Simulation. GIMIAS provides a graphical user interface with all main data IO, visualization and interaction functions for images, meshes and signals. GIMIAS features include: DICOM browser and PACS connection Support for different imaging modalities Biomedical data visualization in 2D and 3D: multiplanar reformation, ortho slice view, multi slice view, volume rendering, X-ray rendering, maximum intensity projection Several input and output formats: DICOM, vtk, stl, Nifty, Analyze. Movie control: play, pause, speed control Multiple data objects: 2D DICOM images, 3D images, surface meshes, volumetric meshes, signals or annotations Image and surface mesh annotations: landmarks, measurements and regions of interest Clinical workflow navigation that can help the user to navigate from patient data to useful information for patient treatment. Other additional


id: gnaural-20260505-0
file: data/en.wikipedia.org/wiki/Gnaural-0.md
category: reference
title: Gnaural
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gnaural
date_saved: 2026-05-05T10:11:37.113700+00:00
instance: kb-cron
parent: gnaural-20260505-0
summary:
Gnaural is brainwave entrainment software for Microsoft Windows, Mac OS X, and Linux licensed under GPL-2.0-or-later. Gnaural is free software for creating binaural beats intended to be used as personal brainwave synchronization software, for scientific research, or by professionals. Gnaural allows for the creation of binaural beat tracks specifying different frequencies and exporting tracks into different audio formats. Gnaural runnings can also be linked over the internet, allowing synchronous sessions between many users. == See also == Brainwave Entrainment Binaural beats Linux audio software == External links == Official website https://www.youtube.com/watch?v=Nck9SX52qpU - A Gnaural Video Tutorial


id: gnu-data-language-20260505-0
file: data/en.wikipedia.org/wiki/GNU_Data_Language-0.md
category: reference
title: GNU Data Language
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GNU_Data_Language
date_saved: 2026-05-05T10:11:38.334973+00:00
instance: kb-cron
parent: gnu-data-language-20260505-0
summary:
The GNU Data Language (GDL) is a free alternative to IDL (Interactive Data Language), achieving full compatibility with IDL 7 and partial compatibility with IDL 8. Together with its library routines, GDL is developed to serve as a tool for data analysis and visualization in such disciplines as astronomy, geosciences, and medical imaging. GDL is licensed under the GPL. Other open-source numerical data analysis tools similar to GDL include Julia, Jupyter Notebook, GNU Octave, NCAR Command Language (NCL), Perl Data Language (PDL), R, Scilab, SciPy, and Yorick. GDL as a language is dynamically-typed, vectorized, and has object-oriented programming capabilities. GDL library routines handle numerical calculations (e.g. FFT), data visualisation, signal/image processing, interaction with host OS, and data input/output. GDL supports several data formats, such as NetCDF, HDF (v4 & v5), GRIB, PNG, TIFF, and DICOM. Graphical output is handled by X11, PostScript, SVG, or z-buffer terminals, the last one allowing output graphics (plots) to be saved in raster graphics formats. GDL features integrated debugging facilities, such as breakpoints. GDL has a Python bridge (Python code can be called from GDL; GDL can be compiled as a Python module). GDL uses the Eigen numerical library (similar to Intel MKL) to offer


id: gpugrid-net-20260505-0
file: data/en.wikipedia.org/wiki/GPUGRID.net-0.md
category: reference
title: GPUGRID.net
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GPUGRID.net
date_saved: 2026-05-05T10:11:39.562907+00:00
instance: kb-cron
parent: gpugrid-net-20260505-0
summary:
GPUGRID is a volunteer computing project hosted by Pompeu Fabra University and running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform. It performs full-atom molecular biology simulations that are designed to run on Nvidia's CUDA-compatible graphics processing units. == Former support for PS3s == Support for the PS3's Cell microprocessor and the subsequent PS3GRID project was dropped in 2009 due to updated firmware preventing the installation of required third-party software. This included Linux distributions that are required to run BOINC. The massive throughput of Nvidia GPUs has also made the PS3 client largely redundant. As of September 2009, a mid-range Nvidia GPU ran GPUGRID applications approximately five times faster than the Cell microprocessor. == See also == List of volunteer computing projects Molecular dynamics GPGPU == References == == Further reading == Research topics in GPUGRID website's science sections GPUGRID's about us Archived 2016-11-04 at the Wayback Machine section == External links == Official website Berkeley Open Infrastructure for Network Computing (BOINC)


id: grads-20260505-0
file: data/en.wikipedia.org/wiki/GrADS-0.md
category: reference
title: GrADS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GrADS
date_saved: 2026-05-05T10:11:40.734924+00:00
instance: kb-cron
parent: grads-20260505-0
summary:
The Grid Analysis and Display System (GrADS) is an interactive desktop tool that is used for easy access, manipulation, and visualization of earth science data. The format of the data may be either binary, GRIB, NetCDF, or HDF-SDS (Scientific Data Sets). GrADS has been implemented worldwide on a variety of commonly used operating systems and is freely distributed over the Internet. GrADS uses a 4-Dimensional data environment: longitude, latitude, vertical level, and time. Data sets are placed within the 4-D space by use of a data descriptor file. GrADS interprets station data as well as gridded data, and the grids may be regular, non-linearly spaced, Gaussian, or of variable resolution. Data from different data sets may be graphically overlaid, with correct spatial and time registration. It uses the ctl mechanism to join differing time group data sets. Operations are executed interactively by entering FORTRAN-like expressions at the command line. A rich set of built-in functions are provided, but users may also add their own functions as external routines written in any programming language. Data may be displayed using a variety of graphical techniques: line and bar graphs, scatter plots, smoothed contours, shaded contours, streamlines, wind vectors, grid boxes, shaded grid


id: gravit-20260505-0
file: data/en.wikipedia.org/wiki/Gravit-0.md
category: reference
title: Gravit
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gravit
date_saved: 2026-05-05T10:11:41.911950+00:00
instance: kb-cron
parent: gravit-20260505-0
summary:
Gravit is a free and open-source gravity simulator distributed under the GNU General Public License. The program is available for all major operating systems, including Linux and other Unix-like systems, Microsoft Windows and Mac OS X. Gravit uses the Barnes–Hut algorithm to simulate the n-body problem. == Description == Gravit is a gravity simulator which runs under Linux, Windows and Mac OS X. It is released under the GNU General Public License which makes it free. It uses Newtonian physics using the Barnes-Hut N-body algorithm. Although the main goal of Gravit is to be as accurate as possible, it also creates beautiful looking gravity patterns. It records the history of each particle so it can animate and display a path of its travels. At any stage you can rotate your view in 3D and zoom in and out. Gravit uses OpenGL with Lua, SDL, SDL_ttf and SDL_image. Features View the simulation in 3D, optionally using stereoscopic imaging Can be installed as a screen saver in Windows Record a simulation, then play back at any speed Load / Save a recorded simulation Mouse controllable rotation Console with script execution See an octtree being created in real-time Colours can be based on


id: gumtree-20260505-0
file: data/en.wikipedia.org/wiki/GumTree-0.md
category: reference
title: GumTree
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GumTree
date_saved: 2026-05-05T10:11:43.084707+00:00
instance: kb-cron
parent: gumtree-20260505-0
summary:
GumTree is an open-source scientific workbench for performing scientific experiments under a distributed network environment. It provides a multi-platform graphical user interface for instrument data acquisition, online or offline data visualisation and analysis. GumTree is designed to provide a highly Integrated Scientific Experiment Environment (ISEE), allowing interaction between different components within the workbench. Several instrument control server systems including TANGO, EPICS and SICS have been adapted to GumTree. Current developments include acquisition, control and analysis on neutron and synchrotron beamlines. In the future it will be extended telescope control and other scientific instruments with distributed hardware. == History == GumTree was first started as a small graphical user interface project to fulfill the IT requirement for the Neutron Beam Instrument Project (NBIP) at ANSTO. Later in the year, the GumTree project has been approved to go open source for international collaboration. 02/2004 GumTree project started 08/2004 GumTree was approved to go open source 09/2005 GumTree 1.0 milestone 7 released 03/2006 GumTree has received the Best Open Source RCP Application from the Eclipse Foundation 01/2007 Codehaus has accepted to host the GumTree Project on their website 09/2008 GumTree 1.0 released == Architecture == GumTree is based on the Eclipse Rich Client


id: gwyddion-software-20260505-0
file: data/en.wikipedia.org/wiki/Gwyddion_(software)-0.md
category: reference
title: Gwyddion (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gwyddion_(software)
date_saved: 2026-05-05T10:11:44.258523+00:00
instance: kb-cron
parent: gwyddion-software-20260505-0
summary:
Gwyddion is a multiplatform modular free software for visualization and analysis of data from scanning probe microscopy (SPM) techniques (like AFM, MFM, STM, SNOM/NSOM). The project is led by its main developers David Nečas (Yeti) and Petr Klapetek who work together with various developers across the world. The software is made available as free software under the terms of the GNU General Public License. The name “Gwyddion” is that of a prominent god in Welsh Mythology (see Gwydion). It is created for the analysis of height fields and other 2D (image) data. While it is primarily intended for data originating from scanning probe microscopy techniques (like AFM, MFM, STM, SNOM/NSOM), it may also be used for the analysis of profilometry data, for instance. Data is calculated and stored in the native file format (.gwy) in double precision. Gwyddion supports many different file types and performs many image-based functions. Among others it can open and save the following graphics file formats: Windows bitmap (.bmp) Joint Photographic Experts Group (.jpeg, .jpg, or .jpe) Portable Network Graphics (.png) TARGA (.tga) Tagged image file format (.tiff, .tif) Portable aNy Map (.pnm) == Technical features == Quote from homepage of Gwyddion: 'Gwyddion uses a fairly


id: hashclash-20260505-0
file: data/en.wikipedia.org/wiki/HashClash-0.md
category: reference
title: HashClash
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HashClash
date_saved: 2026-05-05T10:11:45.500905+00:00
instance: kb-cron
parent: hashclash-20260505-0
summary:
HashClash was a volunteer computing project running on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform to find collisions in the MD5 hash algorithm. It was based at Department of Mathematics and Computer Science at the Eindhoven University of Technology, and Marc Stevens initiated the project as part of his master's degree thesis. The project ended after Stevens defended his M.Sc. thesis in June 2007. However, SHA1 was added later, and the code repository was ported to git in 2017. The project was used to create a rogue certificate authority certificate in 2009. == See also == Berkeley Open Infrastructure for Network Computing (BOINC) List of volunteer computing projects == References == == External links == HashClash HashClash at Stevens' home page Create your own MD5 collisions on AWS, Nat McHugh's blog


id: help-conquer-cancer-20260505-0
file: data/en.wikipedia.org/wiki/Help_Conquer_Cancer-0.md
category: reference
title: Help Conquer Cancer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Help_Conquer_Cancer
date_saved: 2026-05-05T10:11:46.729963+00:00
instance: kb-cron
parent: help-conquer-cancer-20260505-0
summary:
Help Conquer Cancer is a volunteer computing project that runs on the BOINC platform. It is a joint project of the Ontario Cancer Institute and the Hauptman-Woodward Medical Research Institute. It is also the first project under World Community Grid to run with a GPU counterpart. == Project Purpose == The goal is to enhance the efficiency of protein X-ray crystallography, which will enable researchers to determine the structure of many cancer-related proteins faster. This will lead to improving the understanding of the function of these proteins, and accelerate the development of new pharmaceutical drugs. == See also == BOINC List of volunteer computing projects World Community Grid == References == == External links == Help Conquer Cancer Archived 2010-01-17 at the Wayback Machine


id: help-cure-muscular-dystrophy-20260505-0
file: data/en.wikipedia.org/wiki/Help_Cure_Muscular_Dystrophy-0.md
category: reference
title: Help Cure Muscular Dystrophy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Help_Cure_Muscular_Dystrophy
date_saved: 2026-05-05T10:11:47.949794+00:00
instance: kb-cron
parent: help-cure-muscular-dystrophy-20260505-0
summary:
Help Cure Muscular Dystrophy is a volunteer computing project that runs on the BOINC platform. It is a joint effort of the French muscular dystrophy charity, L'Association française contre les myopathies; and L'Institut de biologie moléculaire et cellulaire (Molecular and Cellular Biology Institute). == Project purpose == Help Cure Muscular Dystrophy studies the function of various proteins that are produced by the two hundred genes known to be involved in the production of neuromuscular proteins by modelling the protein-protein interactions of the forty thousand relevant proteins that are listed in the Protein Data Bank. More specifically, it models how a protein would be affected when another protein or a ligand docks with it. == Scientific publications == Decrypting protein surfaces by combining evolution, geometry, and molecular docking. Proteins: Structure, Function, and Bioinformatics (2019). Hidden partners: Using cross-docking calculations to predict binding sites for proteins with multiple interactions. Proteins: Structure, Function, and Bioinformatics (2018). Protein social behavior makes a stronger signal for partner identification than surface geometry. Proteins: Structure, Function, and Bioinformatics (2017). Great interactions: How binding incorrect partners can teach us about protein recognition and function. Proteins: Structure, Function, and Bioinformatics (2016). Protein-Protein Interactions in a Crowded Environment: An Analysis


id: hippodraw-20260505-0
file: data/en.wikipedia.org/wiki/HippoDraw-0.md
category: reference
title: HippoDraw
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HippoDraw
date_saved: 2026-05-05T10:11:49.085963+00:00
instance: kb-cron
parent: hippodraw-20260505-0
summary:
HippoDraw is a object-oriented statistical data analysis package written in C++, with user interaction via a Qt-based GUI and a Python-scriptable interface. It was developed by Paul Kunz at SLAC, primarily for the analysis and presentation of particle physics and astrophysics data, but can be equally well used in other fields where data handling is important. == About == HippoDraw can read and write files in an XML-based format, astrophysics FITS files, data objects produced by ROOT (optional), and through the Python bindings, anything that can be read/written by Python (HDF5, for instance, with PyTables). HippoDraw can be used as a Python extension module, allowing users to use HippoDraw data objects with the full power of the Python language. This includes other scientific Python extension modules such Numeric and numarray, whose use with HippoDraw can lead to a large increase in processing speed, even for ROOT objects. == See also == Java Analysis Studio (JAS) ROOT AIDA == References == == External links == Official website License


id: hmmer-20260505-0
file: data/en.wikipedia.org/wiki/HMMER-0.md
category: reference
title: HMMER
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HMMER
date_saved: 2026-05-05T10:11:50.267033+00:00
instance: kb-cron
parent: hmmer-20260505-0
summary:
HMMER is a free and commonly used software package for sequence analysis written by Sean Eddy. Its general usage is to identify homologous protein or nucleotide sequences, and to perform sequence alignments. It detects homology by comparing a profile-HMM (a Hidden Markov model constructed explicitly for a particular search) to either a single sequence or a database of sequences. Sequences that score significantly better to the profile-HMM compared to a null model are considered to be homologous to the sequences that were used to construct the profile-HMM. Profile-HMMs are constructed from a multiple sequence alignment in the HMMER package using the hmmbuild program. The profile-HMM implementation used in the HMMER software was based on the work of Krogh and colleagues. HMMER is a console utility ported to every major operating system, including different versions of Linux, Windows, and macOS. HMMER is the core utility that protein family databases such as Pfam and InterPro are based upon. Some other bioinformatics tools such as UGENE also use HMMER. HMMER3 also makes extensive use of vector instructions to increase computational speed. This work is based upon an earlier publication showing a significant acceleration of the Smith-Waterman algorithm for aligning two sequences. == Profile


id: hmmer-20260505-1
file: data/en.wikipedia.org/wiki/HMMER-1.md
category: reference
title: HMMER
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HMMER
date_saved: 2026-05-05T10:11:50.267033+00:00
instance: kb-cron
parent: hmmer-20260505-0
summary:
=== Improvements in speed === A major aim of the HMMER3 project, started in 2004 was to improve the speed of HMMER searches. While profile HMM-based homology searches were more accurate than BLAST-based approaches, their slower speed limited their applicability. The main performance gain is due to a heuristic filter that finds high-scoring un-gapped matches within database sequences to a query profile. This heuristic results in a computation time comparable to BLAST with little impact on accuracy. Further gains in performance are due to a log-likelihood model that requires no calibration for estimating E-values, and allows the more accurate forward scores to be used for computing the significance of a homologous sequence. HMMER still lags behind BLAST in speed of DNA-based searches; however, DNA-based searches can be tuned such that an improvement in speed comes at the expense of accuracy. === Improvements in remote homology searching === The major advance in speed was made possible by the development of an approach for calculating the significance of results integrated over a range of possible alignments. In discovering remote homologs, alignments between query and hit proteins are often very uncertain. While most sequence alignment tools calculate match scores using only the best


id: hyperspy-20260505-0
file: data/en.wikipedia.org/wiki/HyperSpy-0.md
category: reference
title: HyperSpy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HyperSpy
date_saved: 2026-05-05T10:11:51.438108+00:00
instance: kb-cron
parent: hyperspy-20260505-0
summary:
HyperSpy is an open source Python package for multidimensional data analysis. Over time, it has grown into an ecosystem that includes a number of extension packages dedicated to specific experimental methods. == Details == HyperSpy is a community-driven, open-source library providing a framework to facilitate interactive exploration, analysis and visualization of multidimensional datasets – in particular spectrum images – in an easy and reproducible fashion. It facilitates the application of analytical procedures operating on individual spectra/images to a multi-dimensional dataset and gives easy access to tools that exploit the multi-dimensionality of the dataset. Born out of the electron microscopy scientific community and building on the extensive scientific Python environment, HyperSpy provides tools to efficiently handle complex datasets of arbitrary dimensionality, including those exceeding the size of the system memory. == Features == Functionalities provided by HyperSpy include the following: Tools for loading/saving various scientific data file formats through its extension RosettaSciIO Background subtraction, artefact removal, etc. Data visualization: evaluate datasets during the analysis, provide interactive operation for certain functions, and publication-ready plotting of data Efficient handling of big datasets ("lazy" and parallel processing) Extracting subsets of data from multidimensional datasets through regions of interest and a powerful numpy-style indexing mechanism


id: ibercivis-20260505-0
file: data/en.wikipedia.org/wiki/Ibercivis-0.md
category: reference
title: Ibercivis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ibercivis
date_saved: 2026-05-05T10:11:52.674714+00:00
instance: kb-cron
parent: ibercivis-20260505-0
summary:
Ibercivis was a volunteer computing platform which allows internet users to participate in scientific research by donating unused computer cycles to run scientific simulations and other tasks. The original project, which became operational in 2008, was a scientific collaboration between the Portuguese and Spanish governments, but it is open to the general public and scientific community, both within and beyond the Iberian Peninsula. The project's name is a portmanteau of Iberia and the Latin word civis, meaning 'citizen'. In April 2020, the volunteer computing platform was restarted by the Ibercivis Foundation and the Spanish National Research Council in order to screen existing drugs for antiviral activity against Severe acute respiratory syndrome coronavirus 2, the causative agent of the COVID-19 pandemic. == History == Ibercivis was developed in Spain with the cooperation of the Institute of Biocomputation and Physics of Complex Systems at the University of Zaragoza, CIEMAT, CETA-CIEMAT, the Spanish National Research Council (CSIC) and RedIris. The project tasks are issued by different scientific and technological centers in Spain with the aim of creating a functional platform for volunteer-based scientific computing. The project is a European counterpart to the successful United States-based SETI@home and Berkeley Open Infrastructure for Network Computing


id: ibercivis-20260505-1
file: data/en.wikipedia.org/wiki/Ibercivis-1.md
category: reference
title: Ibercivis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ibercivis
date_saved: 2026-05-05T10:11:52.674714+00:00
instance: kb-cron
parent: ibercivis-20260505-0
summary:
Fusion: a star on your screen: this application helped scientists at the Research Center for Energy Environment and Technology (CIEMAT) and at the Institute for Biocomputation and Physics of Complex Systems (BIFI) perform simulations of the plasmas that will be produced in the International Thermonuclear Experimental Reactor (ITER). The ITER project, which will begin operation in 2018, seeks to make nuclear fusion power a reality, replicating on Earth conditions typically found inside stars. Docking: looking for anti-cancer drugs: the Docking application assisted the search for new medicines through the simulation of protein docking. The Bioinformatics Unit of the Centro de Biología Molecular Severo Ochoa (CSIC-UAM) developed a platform to allow the automatic simulation of interactions of proteins and small molecules. Its purpose was to find effective drugs to treat serious illnesses, such as cancer. Materials: simulation of magnetic systems: the Materials application aided physicists from the Universidad Complutense de Madrid, Universidad de Extremadura and the Institute for Biocomputation and Physics of Complex Systems in discovering how non-magnetic impurities in magnetic materials modify the properties of their transition from a magnetic state to a non-magnetic one. The knowledge of these transitions is important not only from a theoretical point of view


id: imagej-20260505-0
file: data/en.wikipedia.org/wiki/ImageJ-0.md
category: reference
title: ImageJ
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ImageJ
date_saved: 2026-05-05T10:11:53.859164+00:00
instance: kb-cron
parent: imagej-20260505-0
summary:
ImageJ is a Java-based image processing program developed at the National Institutes of Health and the Laboratory for Optical and Computational Instrumentation (LOCI, University of Wisconsin). Its first version, ImageJ 1.x, is developed in the public domain, while ImageJ2 and the related projects SciJava, ImgLib2, and SCIFIO are licensed with a permissive BSD-2 license. ImageJ was designed with an open architecture that provides extensibility via Java plugins and recordable macros. Custom acquisition, analysis and processing plugins can be developed using ImageJ's built-in editor and a Java compiler. User-written plugins make it possible to solve many image processing and analysis problems, from three-dimensional live-cell imaging to radiological image processing, multiple imaging system data comparisons to automated hematology systems. ImageJ's plugin architecture and built-in development environment has made it a popular platform for teaching image processing. ImageJ can be run as an online applet, a downloadable application, or on any computer with a Java 5 or later virtual machine. Downloadable distributions are available for Microsoft Windows, the classic Mac OS, macOS, Linux, and the Sharp Zaurus PDA. The source code for ImageJ is freely available. The project developer, Wayne Rasband, retired from the Research Services Branch of the NIH's National Institute of


id: insight-segmentation-and-registration-toolkit-20260505-0
file: data/en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit-0.md
category: reference
title: Insight Segmentation and Registration Toolkit
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit
date_saved: 2026-05-05T10:11:55.021887+00:00
instance: kb-cron
parent: insight-segmentation-and-registration-toolkit-20260505-0
summary:
ITK is a cross-platform, open-source application development framework widely used for the development of image segmentation and image registration programs. Segmentation is the process of identifying and classifying data found in a digitally sampled representation. Typically the sampled representation is an image acquired from such medical instrumentation as CT or MRI scanners. Registration is the task of aligning or developing correspondences between data. For example, in the medical environment, a CT scan may be aligned with an MRI scan in order to combine the information contained in both. ITK was developed with funding from the National Library of Medicine (U.S.) as an open resource of algorithms for analyzing the images of the Visible Human Project. ITK stands for The Insight Segmentation and Registration Toolkit. The toolkit provides leading-edge segmentation and registration algorithms in two, three, and more dimensions. ITK uses the CMake build environment to manage the configuration process. The software is implemented in C++ and it is wrapped for Python. An offshoot of the ITK project providing a simplified interface to ITK in eight programming languages, SimpleITK, is also under active development. == Introduction == === Origins === In 1999 the US National Library of Medicine of the National


id: insight-segmentation-and-registration-toolkit-20260505-1
file: data/en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit-1.md
category: reference
title: Insight Segmentation and Registration Toolkit
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Insight_Segmentation_and_Registration_Toolkit
date_saved: 2026-05-05T10:11:55.021887+00:00
instance: kb-cron
parent: insight-segmentation-and-registration-toolkit-20260505-0
summary:
=== Copyright and license === ITK is copyrighted by the Insight Software Consortium, a non-profit alliance of organizations and individuals interested in supporting ITK. Starting with ITK version 3.6, the software is distributed under a BSD open-source license. It allows use for any purpose, with the possible exception of code found in the patented directory, and with proper recognition. The full terms of the copyright and the license are available at www.itk.org/ITK/project/license.html. Version 4.0 uses Apache 2.0 License. The licensed was changed to Apache 2.0 with version 4.0 to adopt a modern license with patent protection provisions. From version 3.6 to 3.20, a simplified BSD license was used. Versions of ITK previous to ITK 3.6 were distributed under a modified BSD License. The main motivation for adopting a BSD license starting with ITK 3.6, was to have an OSI-approved license. == Technical Summary == The following sections summarize the technical features of the NLM's Insight ITK toolkit. Design Philosophy The following are key features of the toolkit design philosophy. The toolkit provides data representation and algorithms for performing segmentation and registration. The focus is on medical applications; although the toolkit is capable of processing other data types. The toolkit provides


id: invesalius-20260505-0
file: data/en.wikipedia.org/wiki/InVesalius-0.md
category: reference
title: InVesalius
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/InVesalius
date_saved: 2026-05-05T10:11:56.171938+00:00
instance: kb-cron
parent: invesalius-20260505-0
summary:
InVesalius is a free medical software used to generate virtual reconstructions of structures in the human body. Based on two-dimensional images, acquired using computed tomography or magnetic resonance imaging equipment, the software generates virtual three-dimensional models correspondent to anatomical parts of the human body. After constructing three-dimensional DICOM images, the software allows the generation of STL (stereolithography) files. These files can be used for rapid prototyping. InVesalius was developed at CTI (Renato Archer Information Technology Center), a research institute of the Brazilian Science and Technology Center and is available at no cost at the homepage of Public Software Portal homepage. The software license is CC-GPL 2. It is available in English, Japanese, Czech, Portuguese (Brazil), Russian, Spanish, Italian, German, Portuguese, Turkish (Turkey), Romanian, French, Korean, Catalan, Chinese (Taiwan) and Greek. InVesalius was developed using Python and works under Linux, Windows and Mac OS X. It also uses graphic libraries VTK, wxPython, Numpy, Scipy and GDCM. The software's name is a tribute to Belgian physician Andreas Vesalius (1514–1564), considered the "father of modern anatomy". Developed since 2001 for attending Brazilian Public Hospitals demands, InVesalius development was directed for promoting social inclusion of individuals with severe facial deformities. Since then, however, it


id: it-20260505-0
file: data/en.wikipedia.org/wiki/IT++-0.md
category: reference
title: IT++
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/IT++
date_saved: 2026-05-05T10:11:57.326315+00:00
instance: kb-cron
parent: it-20260505-0
summary:
IT++ is a C++ library of classes and functions for linear algebra, numerical optimization, signal processing, communications, and statistics. It is being developed by researchers in these areas and is widely used by researchers, both in the communications industry and universities. The IT++ library originates from the former Department of Information Theory at the Chalmers University of Technology, Gothenburg, Sweden. The kernel of the IT++ library is templated vector and matrix classes, and a set of accompanying functions. Such a kernel makes IT++ library similar to Matlab/Octave. For increased functionality, speed and accuracy, IT++ can make extensive use of existing free and open source libraries, especially BLAS, LAPACK and FFTW libraries. Instead of BLAS and LAPACK, some optimized platform-specific libraries can be used as well, i.e.: ATLAS (Automatically Tuned Linear Algebra Software) - includes optimised BLAS, CBLAS and a limited set of LAPACK routines; MKL (Intel Math Kernel Library) - includes all required BLAS, CBLAS, LAPACK and FFT routines (FFTW not required); ACML (AMD Core Math Library) - includes BLAS, LAPACK and FFT routines (FFTW not required). It is possible to compile and use IT++ without any of the above-listed libraries, but the functionality will be reduced. IT++ works on


id: jasp-20260505-0
file: data/en.wikipedia.org/wiki/JASP-0.md
category: reference
title: JASP
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/JASP
date_saved: 2026-05-05T10:11:58.479946+00:00
instance: kb-cron
parent: jasp-20260505-0
summary:
JASP is a free and open-source program for statistical analysis supported by the University of Amsterdam. It is designed to be easy to use, and familiar to users of SPSS. It offers standard analysis procedures in both their classical and Bayesian form. JASP generally produces APA style results tables and plots to ease publication. It promotes open science via integration with the Open Science Framework and reproducibility by integrating the analysis settings into the results. The development of JASP is financially supported by sponsors, several universities, and research funds. == Overview == In recognition of Bayesian pioneer Sir Harold Jeffreys, JASP stands for Jeffreys’s Amazing Statistics Program. == Analyses == JASP offers frequentist inference and Bayesian inference on the same statistical models. Frequentist inference uses p-values and confidence intervals to control error rates in the limit of infinite perfect replications. Bayesian inference uses credible intervals and Bayes factors to estimate credible parameter values and model evidence given the available data and prior knowledge.The following analyses are available in JASP in comparison to SPSS: === Other features === Accessibility features (full and partial app zooming, key-board shortcut support / ALT-Navigation, VPAT) integrated help files and annotated data library examples for many


id: joelib-20260505-0
file: data/en.wikipedia.org/wiki/JOELib-0.md
category: reference
title: JOELib
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/JOELib
date_saved: 2026-05-05T10:11:59.648047+00:00
instance: kb-cron
parent: joelib-20260505-0
summary:
JOELib is computer software, a chemical expert system used mainly to interconvert chemical file formats. Because of its strong relationship to informatics, this program belongs more to the category cheminformatics than to molecular modelling. It is available for Windows, Unix and other operating systems supporting the programming language Java. It is free and open-source software distributed under the GNU General Public License (GPL) 2.0. == History == JOELib and OpenBabel were derived from the OELib Cheminformatics library. == Logo == The project logo is just the word JOELib in the Tengwar script of J. R. R. Tolkien. The letters are grouped as JO-E-Li-b. Vowels are usually grouped together with a consonant, but two following vowels must be separated by a helper construct. == Major features == Chemical expert system Query and substructure search (based on Simplified molecular-input line-entry system (SMARTS), a SMILES extension Clique detection QSAR Data mining Molecule mining, special case of Structured Data Mining Feature–descriptor calculation Partition coefficient, log P Rule-of-five Partial charges Fingerprint calculation etc. Chemical file formats Chemical table file: MDL Molfile, SD format SMILES Gaussian Chemical Markup Language MOPAC == See also == OpenBabel - C++ version of JOELib-OELib Jmol Chemistry Development Kit (CDK) Comparison


id: kalgebra-20260505-0
file: data/en.wikipedia.org/wiki/KAlgebra-0.md
category: reference
title: KAlgebra
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/KAlgebra
date_saved: 2026-05-05T10:12:00.853503+00:00
instance: kb-cron
parent: kalgebra-20260505-0
summary:
KAlgebra is a mathematical graph calculator included in the KDE education package. While it is based on the MathML content markup language, knowledge of MathML is not required for use. The calculator includes numerical, logical, symbolic, and analytical functions, and can plot the results onto a 2D or 3D graph. KAlgebra is free and open source software, licensed under the GPL-2.0-or-later license. KAlgebra has been mentioned by various media sources as free / open source educational programs. == User interface and syntax == KAlgebra uses an intuitive algebraic syntax, similar to those used on modern graphing calculators. User-entered expressions are converted to MathML in the background, or they can be entered directly. The program is divided into four views, Console, 2D Graph, 3D Graph, and Dictionary. A series of calculations can be performed with user-defined scripts, which are macros that can be reused and shared. The dictionary includes a comprehensive list of all built-in functions in KAlgebra. Functions can be looked up with parameters, examples, formulas and sample plots. Over 100 functions and operations are currently supported. == Graphing and dictionary == In the 2D and 3D graph views, functions can evaluated and plotted. Currently KAlgebra only supports 3D graphs


id: kalypso-software-20260505-0
file: data/en.wikipedia.org/wiki/Kalypso_(software)-0.md
category: reference
title: Kalypso (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Kalypso_(software)
date_saved: 2026-05-05T10:12:01.997932+00:00
instance: kb-cron
parent: kalypso-software-20260505-0
summary:
Kalypso is an open source modelling program. It focuses on numerical simulations in water management and ecology such as the generation of inundation and flood risk maps by hydrologic and hydrodynamic models and GIS functionality. The Kalypso software system has been collaboratively developed in a joint project by the company Björnsen Consulting Engineers (BCE) and the department for river and coastal engineering at Hamburg University of Technology, Germany. It is available for download at SourceForge.net under the GNU LGPL. == Kalypso Modules == Kalypso is currently (November 2010) divided into six modules: three numerical simulation modules, two tools for complex flood and risk mapping and an evacuation tool. Kalypso Hydrology (rainfall-runoff simulation) Kalypso WSPM (one-dimensional steady hydrodynamic simulation) Kalypso 1D/2D (coupled one- and two-dimensional unsteady hydrodynamic simulation) Kalypso Flood (flood mapping tool) Kalypso Risk (flood risk assessment tool) Kalypso Evacuation (evacuation strategy tool) The modules are based on a common modelling framework called KalypsoBASE and are published as open source software under the LGPL license. KalypsoBASE itself is a collection of Eclipse plug-ins and can be easily extended to provide new and independent modules. == Projects == A few software projects are using KalypsoBASE. === nofdp IDSS === nofdp (nature-oriented


id: kisao-20260505-0
file: data/en.wikipedia.org/wiki/KiSAO-0.md
category: reference
title: KiSAO
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/KiSAO
date_saved: 2026-05-05T10:12:03.144516+00:00
instance: kb-cron
parent: kisao-20260505-0
summary:
The Kinetic Simulation Algorithm Ontology (KiSAO) supplies information about existing algorithms available for the simulation of systems biology models, their characterization and interrelationships. KiSAO is part of the BioModels.net project and of the COMBINE initiative. == Structure == KiSAO consists of three main branches: simulation algorithm simulation algorithm characteristic simulation algorithm parameter The elements of each algorithm branch are linked to characteristic and parameter branches using has characteristic and has parameter relationships accordingly. The algorithm branch itself is hierarchically structured using relationships which denote that the descendant algorithms were derived from, or specify, more general ancestors. == See also == COMBINE SED-ML MIRIAM SBO TEDDY == References ==


id: klettres-20260505-0
file: data/en.wikipedia.org/wiki/KLettres-0.md
category: reference
title: KLettres
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/KLettres
date_saved: 2026-05-05T10:12:04.308594+00:00
instance: kb-cron
parent: klettres-20260505-0
summary:
KLettres is an educational program that helps the users learn the alphabet as well as pronunciation. It is free and open source software, licensed under the terms of the GPL. The software is part of the KDE Education Project, and is meant to teach very young children aged 2 to 6 years the alphabet. There are currently 4 levels in the game and supports 25 different languages. == Levels == KLettres features four levels, with settings for adult ("grown up") and children ("kid"). In level 1, the letter is displayed and the user hears it. In level 2, the letter is not displayed and the user only hears it. In level 3, the syllable is displayed and the user hears it. In level 4, the syllable is not displayed and the user only hears it. == Languages supported == Arabic, Czech, Brazilian Portuguese, Danish, Dutch, British English, English, English Phonix, French, German, Hebrew, Hungarian, Italian, Kannada, Hebrew, Hindi Romanized, Low Saxon, Luganda, Malayalam, Norwegian Bokmål, Punjabi, Spanish, Slovak, Ukrainian and Telugu. == Release history == May 13, 2004: v1.3, added Italian and special characters. March 8, 2005: Code refactoring, open usability review. March 14, 2005: 3 themes included (classroom, arctic


id: labplot-20260505-0
file: data/en.wikipedia.org/wiki/LabPlot-0.md
category: reference
title: LabPlot
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/LabPlot
date_saved: 2026-05-05T10:12:05.482566+00:00
instance: kb-cron
parent: labplot-20260505-0
summary:
LabPlot is a free and open-source, cross-platform computer program for interactive scientific plotting, curve fitting, nonlinear regression, data processing and data analysis. LabPlot is available, under the GPL-2.0-or-later license, for Windows, macOS, Linux, FreeBSD and Haiku operating systems. It has a graphical user interface, a command-line interface, and an interactive and animated notebook interface. It is similar to Origin and able to import Origin's data files. Features include the Hilbert transform function, statistics, color maps, conditional formatting, plot digitization and multi-axes. == History == In 2008, developers of LabPlot and SciDAVis (another Origin clone, forked from QtiPlot) "found their project goals to be very similar" and decided to merge their code into a common backend while maintaining two frontends: LabPlot, integrated with the KDE desktop environment (DE); and SciDAVis, written in DE-independent Qt with fewer dependencies for easier cross-platform use. Starting April 2024, LabPlot received funding from NLnet's NGI0 Core grant to add scripting capabilities (via Python and a public interface), more data analysis functions, and statistical analysis features. == See also == List of statistical software List of information graphics software == References == == External links == Official website NLnet Foundation grant


id: lammps-20260505-0
file: data/en.wikipedia.org/wiki/LAMMPS-0.md
category: reference
title: LAMMPS
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/LAMMPS
date_saved: 2026-05-05T10:12:06.699876+00:00
instance: kb-cron
parent: lammps-20260505-0
summary:
LAMMPS (Large-scale Atomic/Molecular Massively Parallel Simulator) is a molecular dynamics program developed by Sandia National Laboratories. It utilizes the Message Passing Interface (MPI) for parallel communication, enabling high-performance simulations. LAMMPS is a free and open-source software, distributed under the terms of the GNU General Public License. It is available on Linux, Windows, and macOS platforms. == History == LAMMPS was developed in the mid-1990s under a Cooperative Research and Development Agreement between two laboratories from the United States Department of Energy (Sandia National Laboratories and Lawrence Livermore National Laboratory) and three companies (Cray, DuPont, and Bristol-Myers Squibb). The goal was to create a parallel molecular dynamics code capable of running on large supercomputers for materials and biomolecular modeling. Initially written in Fortran, LAMMPS has since been rewritten in C++ to provide more flexibility and ease in adding new features. == Features == LAMMPS is a highly flexible and scalable molecular dynamics simulator that supports both single-processor and parallel execution through MPI and OpenMP. GPU acceleration is also available. LAMMPS can be run from an input script, as well as a graphical interface GUI. Its modular, open-source C++ design is easy to extend or integrate with other codes or languages like


id: the-lattice-project-20260505-0
file: data/en.wikipedia.org/wiki/The_Lattice_Project-0.md
category: reference
title: The Lattice Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_Lattice_Project
date_saved: 2026-05-05T10:12:07.911267+00:00
instance: kb-cron
parent: the-lattice-project-20260505-0
summary:
The Lattice Project was a volunteer computing project that combined computing resources, Grid middleware, specialized scientific application software and web services into a comprehensive Grid computing system for scientific analysis. It ran the Genetic Algorithm for Rapid Likelihood Inference (GARLI) software to determine the relationships between different genetic samples. A major aspect of the project makes use of the Berkeley Open Infrastructure for Network Computing (BOINC) platform. The Lattice Project maintained a separate BOINC web site, but the site is dead as of this writing, causing this project to shut down because BOINC depends on this website to get setup information to set this project up on its clients. == References == == External links == The Lattice Project website archive


id: leiden-classical-20260505-0
file: data/en.wikipedia.org/wiki/Leiden_Classical-0.md
category: reference
title: Leiden Classical
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Leiden_Classical
date_saved: 2026-05-05T10:12:09.200501+00:00
instance: kb-cron
parent: leiden-classical-20260505-0
summary:
Leiden Classical was a volunteer computing project run by the Theoretical Chemistry Department of the Leiden Institute of Chemistry at Leiden University. Leiden Classical used the BOINC system, and enabled scientists or science students to submit their own test simulations of various molecules and atoms in a classical mechanics environment. ClassicalDynamics is a program (and with it a library) completely written in C++. The library is covered by the LGPL license and the main program is covered by the GPL. The project shut down on June 5, 2018. == Joining the project == Participation was possible via the BOINC manager. Using this software one was once able to create an account in the project. Then someone can make a model of a dynamic system and simulation participating run. There are several models possible, to interactions between molecules or planets. == User Submitted Calculations == To create a personal calculation, a user's model had to have six defined variables: Colors of the molecules Box in which the model is run Number of particles in the simulation Interaction between the particles Gravity Coulomb force Lennard-Jones interaction Morse interaction Rydberg interaction Harmonic spirit Harmonic bending Recurrent torsion interactions Distance conditions Confirmation parameter(s) ==


id: lhc-home-20260505-0
file: data/en.wikipedia.org/wiki/LHC@home-0.md
category: reference
title: LHC@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/LHC@home
date_saved: 2026-05-05T10:12:10.388593+00:00
instance: kb-cron
parent: lhc-home-20260505-0
summary:
LHC@home is a volunteer computing project researching particle physics that uses the Berkeley Open Infrastructure for Network Computing (BOINC) platform. The project's computing power is utilized by physicists at CERN in support of the Large Hadron Collider and other experimental particle accelerators. The project is run with the help of over 1,260 active volunteer users contributing more than 3,000 computers processing at a combined 52 teraFLOPS as of September 2024. The project is cross-platform, and runs on a variety of computer hardware configurations. == Applications == The LHC@home project currently runs four applications—Atlas, CMS, SixTrack, and Test4Theory—which deal with different aspects of research conducted in LHC such as calculating particle beam stability and simulating proton collisions. Atlas, CMS, and Test4Theory use VirtualBox, an x86 virtualization software package. === Atlas === Atlas uses volunteer computing power to run simulations of the ATLAS experiment. It can be run in VirtualBox or natively on Linux. === Beauty === Beauty (LHCb) compared the decay of bottom quarks (b) and bottom antiquarks (b), which also known as beauty quarks. The participation of volunteers in the application was suspended indefinitely on 19 November 2018. === CMS === The CMS application (formerly a standalone project called CMS@Home)


id: libsbml-20260505-0
file: data/en.wikipedia.org/wiki/LibSBML-0.md
category: reference
title: LibSBML
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/LibSBML
date_saved: 2026-05-05T10:12:11.547428+00:00
instance: kb-cron
parent: libsbml-20260505-0
summary:
LibSBML is an open-source software library that provides an application programming interface (API) for the SBML (Systems Biology Markup Language ) format. The libSBML library can be embedded in a software application or used in a web servlet (such as one that might be served by Apache Tomcat) as part of the application or servlet's implementation of support for reading, writing, and manipulating SBML documents and data streams. The core of libSBML is written in ISO standard C++; the library provides API for many programming languages via interfaces generated with the help of SWIG. The libSBML library is free software released under the terms of the GNU Lesser General Public License (LGPL) as published by the Free Software Foundation; either version 2.1 of the License, or any later version. LibSBML was developed thanks to funding from many agencies, particularly the National Institute of General Medical Sciences (NIGMS, USA) as well as the Defense Advanced Research Projects Agency (DARPA, USA) under the Bio-SPICE program. == Description == The Systems Biology Markup Language (SBML) is an XML-based format for encoding computational models of a sort common in systems biology. Although SBML is based upon XML, and thus software developers could support SBML


id: lumi-software-20260505-0
file: data/en.wikipedia.org/wiki/Lumi_(software)-0.md
category: reference
title: Lumi (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Lumi_(software)
date_saved: 2026-05-05T10:12:12.707973+00:00
instance: kb-cron
parent: lumi-software-20260505-0
summary:
lumi is a free, open source and open development software project for the analysis and comprehension of Illumina expression and methylation microarray data. The project was started in the summer of 2006 and set out to provide algorithms and data management tools of Illumina in the framework of Bioconductor. It is based on the statistical R programming language. == Features == The lumi package provides an analysis pipeline for probe-level Illumina expression and methylation microarray data, including probe-identifier management (nuID), updated probe-to-gene mapping and annotation using the latest release of RefSeq (nuIDblast), probe-intensity transformation (VST) and normalization (RSN), quality control (QA/QC) and preprocessing methods specific for Illumina methylation data. By extending the ExprSet object with Illumina-specific features, lumi is designed to work with other Bioconductor packages, such as Limma and GOstats to detect differential genes and conduct Gene Ontology analysis. == History == The lumi project was started in the summer of 2006 at the Bioinformatics Core Facility of the Robert H. Lurie Comprehensive Cancer Center, Northwestern University. Originally lumi was designed for the analysis of Illumina Expression BeadArray data. Starting from 2010 (version > 2.0), functions of analyzing Illumina methylation microarray data was added. The project team consists of


id: madagascar-software-20260505-0
file: data/en.wikipedia.org/wiki/Madagascar_(software)-0.md
category: reference
title: Madagascar (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Madagascar_(software)
date_saved: 2026-05-05T10:12:14.291215+00:00
instance: kb-cron
parent: madagascar-software-20260505-0
summary:
Madagascar is a software package for multidimensional data analysis and reproducible computational experiments. Technology developed using the Madagascar project management system is transferred in the form of recorded processing histories, which become "computational recipes" to be verified, exchanged, and modified by users of the system. == Features == The Madagascar environment consists of: Standalone programs for out-of-core data analysis; Standalone programs for geophysical data processing and imaging; A development kit for C, C++, Java, Fortran-77, Fortran-90, Python, Matlab, and Octave; A framework for reproducible numerical experiments, based on SCons; A framework for scientific publications, based on SCons and LaTeX; A collection of reproducible scientific articles also used as usage examples and regression tests for the standalone programs; A collection of datasets used as input to reproducible numerical experiments. == Example script == An example SConstruct file is shown below Note that SConstruct by itself does not do any job other than setting rules for building different targets. The targets get built when one executes scons on the command line. Running scons produces bash$ scons scons: Reading SConscript files ... scons: done reading SConscript files. scons: Building targets ... retrieve(["wz.35.H"], []) < wz.35.H /RSF/bin/sfdd form=native | /RSF/bin/sfwindow n1=400 j1=2 | /RSF/bin/sfsmooth


id: malaria-control-project-20260505-0
file: data/en.wikipedia.org/wiki/Malaria_Control_Project-0.md
category: reference
title: Malaria Control Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Malaria_Control_Project
date_saved: 2026-05-05T10:12:15.513424+00:00
instance: kb-cron
parent: malaria-control-project-20260505-0
summary:
malariacontrol.net was a volunteer computing project to simulate the transmission dynamics and health effects of malaria. It was part of the Africa@home project. The project was terminated on 21 June 2016. == History == The malariacontrol.net domain name was first registered on 19 May 2005 under Swiss Tropical Institute. This project was under Africa@home where the latter was conceived and developed by European Organization for Nuclear Research (CERN). malariacontrol.net was the first to use volunteer computing to model diseases. The model simulates malaria infection in 50,000 to 100,000 people. Each work unit lasted for an hour in average personal computers and the results were returned to University of Geneva for evaluation by researchers. malariacontrol.net ran all the simulations by using stochastic simulation model. Since 4 November 2010, using the financial support from Bill & Melinda Gates Foundation, Malariacontrol.net developed an open-source software named "Open Malaria" which can be used to simulate outcomes in various types of malaria transmission settings. On 21 June 2016, malariacontrol.net announced that the project has been terminated due to financial constraints in upgrading their servers for further volunteer computing operations. == Impact == Over 10 years, malariacontrol.net has produced 30 peer-reviewed articles. In 2008, among the


id: marble-software-20260505-0
file: data/en.wikipedia.org/wiki/Marble_(software)-0.md
category: reference
title: Marble (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Marble_(software)
date_saved: 2026-05-05T10:12:16.672710+00:00
instance: kb-cron
parent: marble-software-20260505-0
summary:
Marble is a virtual globe application which allows the user to choose among the Earth, the Moon, Venus, Mars and other planets to display as a 3-D model. It is free software under the terms of the GNU LGPL, developed by KDE for use on personal computers and smart phones. It is written in C++ and uses Qt. Marble is intended to be very flexible; beyond its cross-platform design, the core components can easily be integrated into other programs. It is designed to run without the need for hardware acceleration, but it can be extended to use OpenGL. An important user-experience objective being that the application start fairly quickly, it ships with a minimal but useful off-line dataset (5–10MB). Contributors have added support for on-line mapping sources such as OpenStreetMap and the ability to interpret KML files. Marble also provides route planning capabilities. A navigation mode called MarbleToGo was developed as part of Google Summer of Code 2010. It was later partially rewritten and renamed to Marble Touch. Geothek is a fork of Marble adding a statistics module, pixel maps, and a 3D view. It is developed and used by Austrian publisher Ed. Hölzel as atlas software for classrooms. ==


id: massive-online-analysis-20260505-0
file: data/en.wikipedia.org/wiki/Massive_Online_Analysis-0.md
category: reference
title: Massive Online Analysis
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Massive_Online_Analysis
date_saved: 2026-05-05T10:12:17.835224+00:00
instance: kb-cron
parent: massive-online-analysis-20260505-0
summary:
Massive Online Analysis (MOA) is a free open-source software project specific for data stream mining with concept drift. It is written in Java and developed at the University of Waikato, New Zealand. == Description == MOA is an open-source framework software that allows to build and run experiments of machine learning or data mining on evolving data streams. It includes a set of learners and stream generators that can be used from the graphical user interface (GUI), the command-line, and the Java API. MOA contains several collections of machine learning algorithms: Classification Bayesian classifiers Naive Bayes Naive Bayes Multinomial Decision trees classifiers Decision Stump Hoeffding Tree Hoeffding Option Tree Hoeffding Adaptive Tree Meta classifiers Bagging Boosting Bagging using ADWIN Bagging using Adaptive-Size Hoeffding Trees. Perceptron Stacking of Restricted Hoeffding Trees Leveraging Bagging Online Accuracy Updated Ensemble Function classifiers Perceptron Stochastic gradient descent (SGD) Pegasos Drift classifiers Self-Adjusting Memory Probabilistic Adaptive Windowing Multi-label classifiers Active learning classifiers Regression FIMTDD AMRules Clustering StreamKM++ CluStream ClusTree D-Stream CobWeb. Outlier detection STORM Abstract-C COD MCOD AnyOut Recommender systems BRISMFPredictor Frequent pattern mining Itemsets Graphs Change detection algorithms These algorithms are designed for large scale machine learning, dealing with concept drift, and big data streams


id: mdynamix-20260505-0
file: data/en.wikipedia.org/wiki/MDynaMix-0.md
category: reference
title: MDynaMix
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MDynaMix
date_saved: 2026-05-05T10:12:18.988770+00:00
instance: kb-cron
parent: mdynamix-20260505-0
summary:
Molecular Dynamics of Mixtures (MDynaMix) is a computer software package for general purpose molecular dynamics to simulate mixtures of molecules, interacting by AMBER- and CHARMM-like force fields in periodic boundary conditions. Algorithms are included for NVE, NVT, NPT, anisotropic NPT ensembles, and Ewald summation to treat electrostatic interactions. The code was written in a mix of Fortran 77 and 90 (with Message Passing Interface (MPI) for parallel execution). The package runs on Unix and Unix-like (Linux) workstations, clusters of workstations, and on Windows in sequential mode. MDynaMix is developed at the Division of Physical Chemistry, Department of Materials and Environmental Chemistry, Stockholm University, Sweden. It is released as open-source software under a GNU General Public License (GPL). == Programs == md is the main MDynaMix block makemol is a utility which provides help to create files describing molecular structure and the force field tranal is a suite of utilities to analyze trajectories mdee is a version of the program which implements expanded ensemble method to compute free energy and chemical potential (is not parallelized) mge provides a graphical user interface to construct molecular models and monitor dynamics process == Field of application == Thermodynamic properties of liquids Nucleic acid -


id: milkyway-home-20260505-0
file: data/en.wikipedia.org/wiki/MilkyWay@home-0.md
category: reference
title: MilkyWay@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MilkyWay@home
date_saved: 2026-05-05T10:12:20.204648+00:00
instance: kb-cron
parent: milkyway-home-20260505-0
summary:
MilkyWay@home is a volunteer computing project in the astrophysics category, running on the Berkeley Open Infrastructure for Network Computing (BOINC) platform. Using spare computing power from over 38,000 computers run by over 27,000 active volunteers as of November 2011, the MilkyWay@home project aims to generate accurate three-dimensional dynamic models of stellar streams in the immediate vicinity of the Milky Way. With SETI@home and Einstein@home, it is the third computing project of this type that has the investigation of phenomena in interstellar space as its primary purpose. Its secondary objective is to develop and optimize algorithms for volunteer computing. == Purpose and design == MilkyWay@home is a collaboration between the Rensselaer Polytechnic Institute's departments of Computer Science and Physics, Applied Physics and Astronomy and is supported by the U.S. National Science Foundation. It is operated by a team that includes astrophysicist Heidi Jo Newberg and computer scientists Malik Magdon-Ismail, Bolesław Szymański and Carlos A. Varela. By mid-2009 the project's main astrophysical interest is in the Sagittarius Stream, an immense stellar stream emanating from the Sagittarius Dwarf Spheroidal Galaxy that wraps around the Milky Way. Mapping such interstellar streams and their dynamics with high accuracy may provide crucial clues for understanding the


id: milkyway-home-20260505-1
file: data/en.wikipedia.org/wiki/MilkyWay@home-1.md
category: reference
title: MilkyWay@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MilkyWay@home
date_saved: 2026-05-05T10:12:20.204648+00:00
instance: kb-cron
parent: milkyway-home-20260505-0
summary:
Mendelsohn, Eric J.; Newberg, Heidi Jo; Shelton, Siddhartha; Widrow, Lawrence M.; Thompson, Jeffery M.; Grillmair, Carl J. (1 February 2022). "Estimate of the Mass and Radial Profile of the Orphan–Chenab Stream's Dwarf-galaxy Progenitor Using MilkyWay@home". The Astrophysical Journal. 926 (2): 106. arXiv:2201.03637. Bibcode:2022ApJ...926..106M. doi:10.3847/1538-4357/ac498a. S2CID 245853837. Donlon, T.; Newberg, H.; Weiss, J.; Guffey, A.; Thompson, J. (2021-06-01). "A Trifurcated Sagittarius Stream in the South". AAS/Division of Dynamical Astronomy Meeting. 53 (5): 403.03. Bibcode:2021DDA....5240303D. Mendelsohn, E. J.; Newberg, H. J.; Shelton, S.; Widrow, L.; Thompson, J.; Grillmair, C. (2021-06-01). "Estimate of the Mass and Radial Profile of the Orphan Stream's Dwarf Galaxy Progenitor Using MilkyWay@home". AAS/Division of Dynamical Astronomy Meeting. 53 (5): 403.01. Bibcode:2021DDA....5240301M. Mendelsohn, E. J.; Newberg, H. J.; Donlon, T.; Thompson, J. M. (2020-08-01). "N-Body Simulations with MilkyWay@home". AAS/Division of Dynamical Astronomy Meeting. 52 (4): 200.01. Bibcode:2020DDA....5120001M. Donlon, Thomas; Newberg, Heidi Jo; Sanderson, Robyn; Widrow, Lawrence M. (2020-10-01). "The Milky Way's Shell Structure Reveals the Time of a Radial Collision". The Astrophysical Journal. 902 (2): 119. arXiv:2006.08764. Bibcode:2020ApJ...902..119D. doi:10.3847/1538-4357/abb5f6. S2CID 219708644. Shelton, Siddhartha; Newberg, Heidi Jo; Weiss, Jake; Bauer, Jacob S.; Arsenault, Matthew; Widrow, Larry; Rayment, Clayton; Desell, Travis; Judd, Roland; Magdon-Ismail, Malik; Mendelsohn, Eric; Newby, Matthew; Rice, Colin;


id: milkyway-home-20260505-2
file: data/en.wikipedia.org/wiki/MilkyWay@home-2.md
category: reference
title: MilkyWay@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MilkyWay@home
date_saved: 2026-05-05T10:12:20.204648+00:00
instance: kb-cron
parent: milkyway-home-20260505-0
summary:
Desell, Travis; Szymanski, Boleslaw; Varela, Carlos (2008). "An asynchronous hybrid genetic-simplex search for modeling the Milky Way galaxy using volunteer computing". Proceedings of the 10th annual conference on Genetic and evolutionary computation. pp. 921–928. doi:10.1145/1389095.1389273. ISBN 9781605581309. S2CID 10952453. Cole, Nathan; Newberg, Heidi Jo; Magdon-Ismail, Malik; Desell, Travis; Dawsey, Kristopher; Hayashi, Warren; Liu, Xinyang Fred; Purnell, Jonathan; Szymanski, Boleslaw; Varela, Carlos; Willett, Benjamin; Wisniewski, James (20 August 2008). "Maximum Likelihood Fitting of Tidal Streams with Application to the Sagittarius Dwarf Tidal Tails". The Astrophysical Journal. 683 (2): 750–766. arXiv:0805.2121. Bibcode:2008ApJ...683..750C. doi:10.1086/589681. S2CID 1660060. Desell, Travis; Szymanski, Boleslaw; Varela, Carlos (2008). "Asynchronous genetic search for scientific modeling on large-scale heterogeneous environments". 2008 IEEE International Symposium on Parallel and Distributed Processing. pp. 1–12. doi:10.1109/IPDPS.2008.4536169. ISBN 978-1-4244-1693-6. S2CID 1107218. Szymanski, Boleslaw K.; Desell, Travis; Varela, Carlos (2008). "The Effects of Heterogeneity on Asynchronous Panmictic Genetic Search". Parallel Processing and Applied Mathematics. Lecture Notes in Computer Science. Vol. 4967. pp. 457–468. CiteSeerX 10.1.1.78.2043. doi:10.1007/978-3-540-68111-3_48. ISBN 978-3-540-68105-2. Desell, Travis; Cole, Nathan; Magdon-Ismail, Malik; Newberg, Heidi; Szymanski, Boleslaw; Varela, Carlos (2007). "Distributed and Generic Maximum Likelihood Evaluation". Third IEEE International Conference on e-Science and Grid Computing (E-Science 2007). pp. 337–344. CiteSeerX 10.1.1.65.4065. doi:10.1109/E-SCIENCE.2007.30. ISBN 978-0-7695-3064-2. S2CID


id: mimee-20260505-0
file: data/en.wikipedia.org/wiki/Mimee-0.md
category: reference
title: Mimee
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mimee
date_saved: 2026-05-05T10:12:21.376962+00:00
instance: kb-cron
parent: mimee-20260505-0
summary:
Mimee is a program which can convert geographical coordinates between various datums and formats. == Features == Supported coordinates formats are : Latitude and longitude in decimal degrees, degrees and decimal minutes, degrees-minutes-seconds, or grads Geocentric (or Cartesian) coordinates (XYZ) UTM Transverse Mercator, Oblique Mercator, and Conic. 232 datums and 36 grids are provided in Mimee. == Compatibility == The online version is cross-platform. It can be used in any navigator in Linux, Mac, Windows, and in mobile phones. The stand-alone versions runs on Linux and Palm OS. == References == == External links == (in French) Official website (in English) Online version


id: mindmodeling-home-20260505-0
file: data/en.wikipedia.org/wiki/MindModeling@Home-0.md
category: reference
title: MindModeling@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MindModeling@Home
date_saved: 2026-05-05T10:12:22.618731+00:00
instance: kb-cron
parent: mindmodeling-home-20260505-0
summary:
MindModeling@Home is an inactive non-profit, volunteer computing research project for the advancement of cognitive science. MindModeling@Home is hosted by Wright State University and the University of Dayton in Dayton, Ohio. In BOINC, it is in the area of Cognitive Science and category called Cognitive science and artificial intelligence. It can only operate on a 64-bit operating system, preferably on a computer with multiple cores, running a Microsoft Windows, Mac OS X, or Linux operating system. This project is not compatible with mobile devices, unlike other projects on BOINC. == Research focus == N-2 Repetition: understanding how people have a harder time returning to a task from another one Observing how people read through their eye movement for the purpose of helping people reduce eye strain and processing what they read better and faster. Modeling decision-making: resolving around decisions made from visual processing (focus and filtering) Integrated Learning Models (ILM) to create algorithms based on how people learn and make decisions How the brain performs tasks sequentially and simultaneously by measuring its blood flow == Problems == Its status is inactive. However, it is "not down or closed," as its servers are still running. The projects are long; prolonged amounts of


id: mlpack-20260505-0
file: data/en.wikipedia.org/wiki/Mlpack-0.md
category: reference
title: Mlpack
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Mlpack
date_saved: 2026-05-05T10:12:23.835170+00:00
instance: kb-cron
parent: mlpack-20260505-0
summary:
mlpack is a free, open-source and header-only software library for machine learning and artificial intelligence written in C++, built on top of the Armadillo library and the ensmallen numerical optimization library. mlpack has an emphasis on scalability, speed, and ease-of-use. Its aim is to make machine learning possible for novice users by means of a simple, consistent API, while simultaneously exploiting C++ language features to provide maximum performance and maximum flexibility for expert users. mlpack has also a light deployment infrastructure with minimum dependencies, making it perfect for embedded systems and low resource devices. Its intended target users are scientists and engineers. It is open-source software distributed under the BSD license, making it useful for developing both open source and proprietary software. Releases 1.0.11 and before were released under the LGPL license. The project is supported by the Georgia Institute of Technology and contributions from around the world. == Features == === Classical machine learning algorithms === mlpack contains a wide range of algorithms that are used to solve problems from classification and regression in the Supervised learning paradigm to clustering and dimension reduction algorithms. In the following, a non exhaustive list of algorithms and models that mlpack supports: Collaborative


id: modflow-20260505-0
file: data/en.wikipedia.org/wiki/MODFLOW-0.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:
MODFLOW is the U.S. Geological Survey modular finite-difference flow model, which is a computer code that solves the groundwater flow equation. The program is used by hydrogeologists to simulate the flow of groundwater through aquifers. The source code is free public domain software, written primarily in Fortran, and can compile and run on Microsoft Windows or Unix-like operating systems. Since its original development in the early 1980s, the USGS has made six major releases, and is now considered to be the de facto standard code for aquifer simulation. There are several actively developed commercial and non-commercial graphical user interfaces for MODFLOW. MODFLOW was constructed in what was in 1980's called a modular design. This means it has many of the attributes of what came to be called object-oriented programming. For example, capabilities (called "packages") that simulate subsidence or lakes or streams, can easily be turned on and off and the execution time and storage requirements of those packages go away entirely. If a programmer wants to change something in MODFLOW, the clean organization makes it easy. Indeed, this kind of innovation is exactly what was anticipated when MODFLOW was designed. Importantly, the modularity of MODFLOW makes it possible for different


id: modflow-20260505-1
file: data/en.wikipedia.org/wiki/MODFLOW-1.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:
C V i , j , k − 1 2 h i , j , k − 1 m + C C i − 1 2 , j , k h i − 1 , j , k m + C R i , j − 1 2 , k h i , j − 1 , k m + ( − C V i , j , k − 1 2 − C C i − 1 2 , j , k − C R i , j − 1 2 , k − C R i , j + 1 2 , k − C C i + 1 2 , j , k − C V i , j , k + 1 2 + H C O F i , j , k ) h i , j , k m + C R i , j + 1 2 , k h i , j + 1 , k m + C C i + 1 2 , j , k h i + 1 , j , k m + C V i , j , k + 1 2 h i , j


id: modflow-20260505-2
file: data/en.wikipedia.org/wiki/MODFLOW-2.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:
MF2K-GWM or GWM-2000 (version 1.1.4, May 31, 2011, branched from mf2k 1.17.2), with groundwater management capability using optimization MF2K-FMP (version 1.00, May 19, 2006, based on mf2k 1.15.03), with Farm Process MF2K-GWT (version 1.9.8, October 28, 2008, based on MF2K 1.17.02), groundwater flow and solute-transport model SEAWAT (version 4.00.05, October 19, 2012), variable-density flow and transport processes VSF (version 1.01, July 5, 2006), variably saturated flow === MODFLOW-2005 === MODFLOW-2005 differs from MODFLOW-2000 in that the sensitivity analysis, parameter estimation, and uncertainty evaluation capabilities are removed. Thus, the support for these capabilities now falls to "clip on" codes that are supported externally to the MODFLOW support effort. In addition, the code was reorganized to support multiple models within one MODFLOW run, as needed for the LGR (Local Grid Refinement) capability. MODFLOW-2005 is written primarily in Fortran 90 and C, with C being used for one solver. The current version of MODFLOW-2005 is version 1.12.00, released on February 3, 2017. Related or branched codes include: MODFLOW-CFP (version 1.8.00, February 23, 2011), conduit flow process to simulate turbulent or laminar groundwater flow conditions MODFLOW-LGR (version 2.0, September 19, 2013), local grid refinement GWM-2005 (version 1.4.2, March 25, 2013), groundwater management capability using


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


id: xdrawchem-20260505-0
file: data/en.wikipedia.org/wiki/XDrawChem-0.md
category: reference
title: XDrawChem
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/XDrawChem
date_saved: 2026-05-05T10:14:29.287450+00:00
instance: kb-cron
parent: xdrawchem-20260505-0
summary:
XDrawChem is a free software program for drawing chemical structural formulas, available for Unix and macOS. It is distributed under the GNU GPL. In Microsoft Windows this program is called WinDrawChem. == Major features == Fixed length and fixed angle drawing Automatic alignment of figures Detection of structures, text, and arrows, and their automatic placement Can automatically draw rings and other structures - has all standard amino acids and nucleic acids in a built-in library Retrieval of structures from a network database based on CAS number, formula, or name Retrieval of information on a molecule based on a drawing Symbols such as partial charge and radicals Reading MDL Molfiles, CML (Chemical Markup Language), ChemDraw binary format, ChemDraw XML text format Writing MDL Molfiles, CML, ChemDraw XML text format Integration with OpenBabel, allowing XDrawChem to read and write over 20 different chemical file formats. Image export in Portable Network Graphics (PNG), Windows bitmap, Encapsulated PostScript (EPS), and Scalable Vector Graphics (SVG) 3D structure generation with the help of the external program BUILD3D Simple spectra predictions, including 13C-NMR, 1H-NMR (based on additive rules and functional group lookup methods), and IR Simple property estimation, including pKa, octanol-water partition coefficient, and gas-phase enthalpy change.


id: xmd-20260505-0
file: data/en.wikipedia.org/wiki/XMD-0.md
category: reference
title: XMD
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/XMD
date_saved: 2026-05-05T10:14:30.458640+00:00
instance: kb-cron
parent: xmd-20260505-0
summary:
XMD is a classical molecular dynamics software designed to simulate problems related to materials science. The code was developed by Jon Rifkin of University of Connecticut and is being distributed under GNU General Public License. Source code is available in C and can be compiled using POSIX thread functions to take advantage of multi-CPU computers. == See also == Molecular design software == External links == XMD Homepage


id: xplanet-20260505-0
file: data/en.wikipedia.org/wiki/Xplanet-0.md
category: reference
title: Xplanet
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Xplanet
date_saved: 2026-05-05T10:14:31.609791+00:00
instance: kb-cron
parent: xplanet-20260505-0
summary:
Xplanet is a renderer for planetary and Solar System images, capable of producing various types of graphics depicting the Solar System. It is normally used to create computer wallpapers, which may be updated with the latest cloud maps or the regions of Earth which are in sunlight. Xplanet is free software released under the GNU GPL. == Flat maps == Xplanet can be used to produce projected maps of any planet (but typically Earth), for example Mollweide projections which show the whole Earth at once, or Mercator projections with a rectangular appearance suitable for filling the screen. It is possible to overlay clouds or text (such as the location of recent events) onto these maps; a popular option is shading areas currently experiencing night. == Planetary images == Xplanet can also be used to render more general views of objects in the Solar System, such as a view of the Earth from the Moon. In more recent versions, Xplanet depicts eclipses, and some of its images show Jupiter's moons casting an eclipse onto the planet. == Technical == Xplanet runs on Linux, Mac OS X and other Unix operating systems and also on Microsoft Windows, and was derived from an


id: yooreeka-20260505-0
file: data/en.wikipedia.org/wiki/Yooreeka-0.md
category: reference
title: Yooreeka
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Yooreeka
date_saved: 2026-05-05T10:14:32.751714+00:00
instance: kb-cron
parent: yooreeka-20260505-0
summary:
Yooreeka is a library for data mining, machine learning, soft computing, and mathematical analysis. The project started with the code of the book "Algorithms of the Intelligent Web". Although the term "Web" prevailed in the title, in essence, the algorithms are valuable in any software application. It covers all major algorithms and provides many examples. Yooreeka 2.x is licensed under the Apache License rather than the somewhat more restrictive LGPL (which was the license of v1.x). The library is written 100% in the Java language. == Algorithms == The following algorithms are covered: Clustering Hierarchical—Agglomerative (e.g. MST single link; ROCK) and Divisive Partitional (e.g. k-means) Classification Bayesian Decision trees Neural Networks Rule based (via Drools) Recommendations Collaborative filtering Content based Search PageRank DocRank Personalization == References == == External links == Baynoo Website Yooreeka on GitHub Yooreeka on Google Code (old repository)


id: ufluids-home-20260505-0
file: data/en.wikipedia.org/wiki/UFluids@Home-0.md
category: reference
title: UFluids@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/UFluids@Home
date_saved: 2026-05-05T10:14:33.980869+00:00
instance: kb-cron
parent: ufluids-home-20260505-0
summary:
μFluids@Home is a computer simulation of two-phase flow behavior in microgravity and microfluidics problems at Purdue University, using the Surface Evolver program. == About == The project's purpose is to develop better methods for the management of liquid rocket propellants in microgravity, and to investigate two-phase flow in microelectromechanical systems, taking into account factors like surface tension. Systems using electrowetting, channel geometry, and hydrophobic or hydrophilic coatings to allow the smooth passage of fluids can then be designed. Such systems include compact medical devices, biosensors, and fuel cells. == Computing platform == μFluids@Home uses the BOINC volunteer computing platform. Application notes There is no screensaver. Work unit CPU times are generally less than 20 hours. Work units average in size around 500 kB. You must run many work units to get levels of credit comparable to SETI@home or climateprediction.net BOINC projects. == References == == External links == Website archive


id: history-of-open-access-20260505-0
file: data/en.wikipedia.org/wiki/History_of_open_access-0.md
category: reference
title: History of open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_open_access
date_saved: 2026-05-05T10:14:35.524738+00:00
instance: kb-cron
parent: history-of-open-access-20260505-0
summary:
The history of open access can be traced back to at least the 1950's, with an explosion of interest following the advent of the internet. The idea and practise of providing free online access to journal articles began at least a decade before the term "open access" was formally coined. Computer scientists had been self-archiving in anonymous ftp archives since the 1970s and physicists had been self-archiving in arXiv since the 1990s. The Subversive Proposal to generalize the practice was posted in 1994. The term "open access" itself was first formulated in three public statements in the 2000s: the Budapest Open Access Initiative in February 2002, the Bethesda Statement on Open Access Publishing in June 2003, and the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities in October 2003, and the initial concept of open access refers to an unrestricted online access to scholarly research primarily intended for scholarly journal articles. == Efforts before the Internet == One early proponent of the publisher-pays model was the physicist Leó Szilárd. To help stem the flood of low-quality publications, he jokingly suggested in the 1940s that at the beginning of his career each scientist should be issued with


id: history-of-open-access-20260505-1
file: data/en.wikipedia.org/wiki/History_of_open_access-1.md
category: reference
title: History of open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_open_access
date_saved: 2026-05-05T10:14:35.524738+00:00
instance: kb-cron
parent: history-of-open-access-20260505-0
summary:
== Early years of online open access == An explosion of interest and activity in open-access journals has occurred since the 1990s, largely due to the widespread availability of Internet access. It is now possible to publish a scholarly article and also make it instantly accessible anywhere in the world where there are computers and Internet connections. The fixed cost of producing the article is separable from the minimal marginal cost of the online distribution. These new possibilities emerged at a time when the traditional, print-based scholarly journals system was in a crisis. The number of journals and articles produced had been increasing at a steady rate; however the average cost per journal had been rising at a rate far above inflation for decades, and budgets at academic libraries have remained fairly static. The result was decreased access – ironically, just when technology has made almost unlimited access a very real possibility, for the first time. Libraries and librarians have played an important part in the open-access movement, initially by alerting faculty and administrators to the serials crisis. The Association of Research Libraries developed the Scholarly Publishing and Academic Resources Coalition (SPARC), in 1997, an alliance of academic and research


id: history-of-open-access-20260505-2
file: data/en.wikipedia.org/wiki/History_of_open_access-2.md
category: reference
title: History of open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_open_access
date_saved: 2026-05-05T10:14:35.524738+00:00
instance: kb-cron
parent: history-of-open-access-20260505-0
summary:
== 2000s == The number of open-access journals increased by an estimated 500% during the 2000s. Also, the average number of articles that were published per open-access journal per year increased from approximately 20 to 40 during the same period, resulting in that the number of open-access articles increased by 900% during that decade. In 2000 BioMed Central, a for-profit open access publisher with now dozens of open-access journals, was launched by what was then the Current Science Group (the founder of the Current Opinion series, and now known as the Science Navigation Group). In some ways, BioMed Central resembles Harold Varmus' original E-biomed proposal more closely than does PubMed Central. As of October 2013 BioMed Central publishes over 250 journals. In 2001, 34,000 scholars around the world signed "An Open Letter to Scientific Publishers", calling for "the establishment of an online public library that would provide the full contents of the published record of research and scholarly discourse in medicine and the life sciences in a freely accessible, fully searchable, interlinked form". Scientists signing the letter also pledged not to publish in or peer-review for non-open-access journals. This led to the establishment of the Public Library of Science, an


id: history-of-open-access-20260505-3
file: data/en.wikipedia.org/wiki/History_of_open_access-3.md
category: reference
title: History of open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/History_of_open_access
date_saved: 2026-05-05T10:14:35.524738+00:00
instance: kb-cron
parent: history-of-open-access-20260505-0
summary:
== 2010s == In 2013 John Holdren, Barack Obama's director of the Office of Science and Technology Policy, issued a memorandum directing United States' Federal Agencies with more than $100 million in annual R&D expenditures to develop plans within six months to make the published results of federally funded research freely available to the public within one year of publication. As of March 2015, two agencies had made their plans public: the Department of Energy and the National Science Foundation. In 2013 the UK Higher Education Funding Council for England (HEFCE) proposed adopting a mandate that to be eligible for submission to the UK Research Excellence Framework (REF) all peer-reviewed journal articles submitted after 2014 must be deposited in the author's institutional repository immediately upon acceptance for publication, regardless of whether the article is published in a subscription journal or in an open access journal. HEFCE expresses no journal preference, places no restriction on authors' choice and requires the deposit itself to be immediate, irrespective of whether the publisher imposes an embargo (for an allowable embargo period that remains to be decided) on the date at which access to the deposit can be made open. The HEFCE/REF mandate proposal complements


id: timeline-of-the-open-access-movement-20260505-0
file: data/en.wikipedia.org/wiki/Timeline_of_the_open-access_movement-0.md
category: reference
title: Timeline of the open-access movement
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Timeline_of_the_open-access_movement
date_saved: 2026-05-05T10:14:36.706613+00:00
instance: kb-cron
parent: timeline-of-the-open-access-movement-20260505-0
summary:
The following is a timeline of the international movement for open access to scholarly communication. == 1940s-1990s == 1942 American sociologist Robert King Merton declares: "Each researcher must contribute to the 'common pot' and give up intellectual property rights to allow knowledge to move forward." 1971 "World's first online digital library is launched, Project Gutenberg." 1987 Syracuse University in the US issues one of the world's first open access journals, New Horizons in Adult Education (ISSN 1062-3183). 1991 14 August: ArXiv repository of physics research papers established at Los Alamos National Laboratory in the US. 1994 27 June: Stevan Harnad posts a "Subversive Proposal" for authors to archive their articles for free for everyone online. July 1994. Electronic Green Journal (EGJ) was launched by the University of Idaho Library. Since 2009 it is published by the University of California eScholarship. The EGJ is a peer-reviewed publication devoted to information about international sources on environmental protection, conservation, management of natural resources, and sustainability. 1998 Brazil-based SciELO (Scientific Electronic Library Online) launched. Public Knowledge Project founded in Canada. Scholarly Publishing and Academic Resources Coalition founded in North America. 1999 October: Open Archives Initiative on interoperability standards holds its first meeting, in New


id: diamond-open-access-20260505-0
file: data/en.wikipedia.org/wiki/Diamond_open_access-0.md
category: reference
title: Diamond open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Diamond_open_access
date_saved: 2026-05-05T10:14:37.993676+00:00
instance: kb-cron
parent: diamond-open-access-20260505-0
summary:
Diamond open access refers to academic texts (such as monographs, edited collections, and journal articles) published/distributed/preserved with no fees to either reader or author. Alternative labels include platinum open access, non-commercial open access, cooperative open access or, more recently, open access commons. While these terms were first coined in the 2000s and the 2010s, they have been retroactively applied to a variety of structures and forms of publishing, from subsidized university publishers to volunteer-run cooperatives that existed in prior decades. In 2021, it is estimated that between 17,000 and 29,000 scientific journals rely on a diamond open access model. They make up 73% of the journals registered in the Directory of Open Access Journals and 44% of the articles, as their mean output is smaller than commercial journals. The diamond model has been especially successful in Latin America-based journals (95% of OA journals) following the emergence of large publicly supported platforms, such as SciELO and Redalyc. However, Diamond OA journals are under-represented in the major scholarly databases, such as Web of Science and Scopus. It is also noteworthy, that high-income countries "have the highest share of authorship in every domain and type of journal, except for diamond journals in the


id: diamond-open-access-20260505-1
file: data/en.wikipedia.org/wiki/Diamond_open_access-1.md
category: reference
title: Diamond open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Diamond_open_access
date_saved: 2026-05-05T10:14:37.993676+00:00
instance: kb-cron
parent: diamond-open-access-20260505-0
summary:
=== Debates over the identity of the open access commons (2003–2012) === In the early debates over open access, the distinctions between commercial and non-commercial forms of scientific publishing and community-driven or corporate-owned structures seldom appear, possibly due to the lack of viable business model for open access. Open access publications were rather increasingly categorized into two different editorial forms: open access articles made immediately available by the publisher and pre-published articles hosted on an online archive (either as a pre-print or post-print). Starting in 2003, the ROMEO project started to devise a color-code system to better identify the policy of scientific publishers in regard to open sharing of scientific articles, from "yellow" (pre-print only) to "green" (no restriction in place): "the 'greenest' publishers are those that allow self-archiving not only of the author's accepted manuscript, but of the fully formatted and paginated publisher PDF". In 2004, Harnad et al. repurposed this classification scheme into a highly influential binary scale: articles directly made available by the publisher belong to "gold" open access (instead of "yellow") and online archives are defined as "green" open access. With this breakdown of open access into "green" and "gold", there is no distinction between commercial


id: diamond-open-access-20260505-2
file: data/en.wikipedia.org/wiki/Diamond_open_access-2.md
category: reference
title: Diamond open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Diamond_open_access
date_saved: 2026-05-05T10:14:37.993676+00:00
instance: kb-cron
parent: diamond-open-access-20260505-0
summary:
The number of Diamond OA journals is very large (>29,000), but only ~a third are registered in DOAJ, and only ~5% are indexed in either Scopus or Web of Science. Over half of these Diamond OA journals publish 25 or fewer articles per year. Between 2017 and 2019, paid-access journals published ~80% of all articles, paid-OA journals published ~11%, and Diamond OA journals published ~9%. The share of Diamond OA publications among all OA journal articles peaked in 2018 and has been declining since. Only 4.3% of Diamond OA journals are fully compliant with all Plan S criteria. Only 55% of Diamond OA journals provide DOI numbers for their articles. Only 25% of Diamond OA journals provide their content as XML or HTML (in addition to pdf). Only ~ half of Diamond OA journals provide download statistics for their content. 2/3 of Diamond OA journals use double-blind peer review, higher than subscription journals, which prefer single-blind peer review. 25% of Diamond OA journals operated at a loss, and just over 40% reported breaking even. The rest did not know their financial status. Although all Diamond OA journals rely heavily on volunteer work, they have some revenue sources, such as grants,


id: diamond-open-access-20260505-3
file: data/en.wikipedia.org/wiki/Diamond_open_access-3.md
category: reference
title: Diamond open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Diamond_open_access
date_saved: 2026-05-05T10:14:37.993676+00:00
instance: kb-cron
parent: diamond-open-access-20260505-0
summary:
=== Disciplines === While diamond OA journals are available for most disciplines, they are more prevalent in the humanities and social science. The OA Diamond Study finds that, among the journals registered on the DOAJ, humanities and social science publications make up 60% of diamond open access journals and only 23.9% of APC-based journals. This distribution may be due to the differentiated evolution of scientific publishing during the 20th century, as "small HSS journals are often owned by universities and societies who often prefer OA diamond models, while many big science and medicine journals are owned by commercial publishers, more inclined to use APC models." However, the diamond model is still present in many disciplines, with 22.2% of diamond journals in STEM and 17.1% in Medicine. Medical diamond journals are often embedded in local communities, especially in non-western countries: "It becomes apparent that local diamond OA journals are not only important in HSS, but also in medicine." An additional survey led by the OA Diamond Survey of 1,619 diamond OA journals highlights a more complex disciplinary distribution: although the social sciences (27.2%) and humanities (19.2%) are well represented, more than a quarter of respondents did not favor one discipline in


id: diamond-open-access-20260505-4
file: data/en.wikipedia.org/wiki/Diamond_open_access-4.md
category: reference
title: Diamond open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Diamond_open_access
date_saved: 2026-05-05T10:14:37.993676+00:00
instance: kb-cron
parent: diamond-open-access-20260505-0
summary:
Some journals argue that research funders have the responsibility to support or even favour OA diamond journals since they are often excluded from discussions on funding OA. While, the Plan S Principle 5 states that "the Funders support the diversity of business models for Open Access journals and platforms", perceptions will change once funders focus on OA diamond in addition to Gold OA and legacy publishing. This action has a significant potential to cover existing gaps in OA publishing. In 2020 and 2021, the institutional recognition of the diamond model has significantly progressed with unprecedented commitments from national and international organizations. The 2021 UNESCO recommendation for Open Science calls for "supporting not-for-profit, academic and scientific community-driven publishing models as a common good". The second French Plan for Open Science encouraged a "diversification of economic models" that especially highlight the diamond model as it should enable "a transition from subscription towards open access with no publishing fees". In March 2022, an Action Plan for Diamond Open Access was published with the support of the cOAlition S, Science Europe, OPERAS, and the French National Research Agency. This plan aims to "expand a sustainable, community-driven Diamond scholarly communication ecosystem." In 2024 the Toluca-Cape


id: open-access-20260505-0
file: data/en.wikipedia.org/wiki/Open_access-0.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
Open access (OA) is a set of principles and a range of practices through which nominally copyrightable publications are delivered to readers free of access charges or other barriers. With open access strictly defined (according to the 2001 definition), or libre open access, barriers to copying or reuse are also reduced or removed by applying an open license for copyright, which regulates post-publication uses of the work. The main focus of the open access movement has been on peer reviewed research literature, and more specifically on academic journals. This is because: such publications have been a subject of serials crisis, unlike newspapers, magazines and fiction writing. The main difference between these two groups is in demand elasticity: whereas an English literature curriculum can substitute Harry Potter and the Philosopher's Stone with a public domain alternative, such as A Voyage to Lilliput, an emergency room physician treating a patient for a life-threatening urushiol poisoning cannot substitute the most recent, but paywalled review article on this topic with a 90-year-old copyright-expired article that was published before the invention of prednisone in 1954. the authors of research papers are not paid in any way, so they do not suffer any monetary losses, when


id: open-access-20260505-1
file: data/en.wikipedia.org/wiki/Open_access-1.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
Similar to the free content definition, the terms 'gratis' and 'libre' were used in the Budapest Open Access Initiative definition to distinguish between free to read versus free to reuse. Gratis open access () refers to free online access, to read, free of charge, without re-use rights. Libre open access () also refers to free online access, to read, free of charge, plus some additional re-use rights, covering the kinds of open access defined in the Budapest Open Access Initiative, the Bethesda Statement on Open Access Publishing and the Berlin Declaration on Open Access to Knowledge in the Sciences and Humanities. The re-use rights of libre OA are often specified by various specific Creative Commons licenses; all of which require as a minimum attribution of authorship to the original authors. In 2012, the number of works under libre open access was considered to have been rapidly increasing for a few years, though most open-access mandates did not enforce any copyright license and it was difficult to publish libre gold OA in legacy journals. However, there are no costs nor restrictions for green libre OA as preprints can be freely self-deposited with a free license, and most open-access repositories use Creative


id: open-access-20260505-2
file: data/en.wikipedia.org/wiki/Open_access-2.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
Some open access journals (under the gold, and hybrid models) generate revenue by charging publication fees in order to make the work openly available at the time of publication. The money might come from the author but more often comes from the author's research grant or employer. While the payments are typically incurred per article published (e.g. BMC or PLOS journals), some journals apply them per manuscript submitted (e.g. Atmospheric Chemistry and Physics until recently) or per author (e.g. PeerJ). Charges typically range from $1,000–$3,000 ($5,380 for Nature Communications) but can be under $10, close to $5,000 or well over $10,000. APCs vary greatly depending on subject and region and are most common in scientific and medical journals (43% and 47% respectively), and lowest in arts and humanities journals (0% and 4% respectively). APCs can also depend on a journal's impact factor. Some publishers (e.g. eLife and Ubiquity Press) have released estimates of their direct and indirect costs that set their APCs. Hybrid OA generally costs more than gold OA and can offer a lower quality of service. A particularly controversial practice in hybrid open access journals is "double dipping", where both authors and subscribers are charged. By comparison, journal


id: open-access-20260505-3
file: data/en.wikipedia.org/wiki/Open_access-3.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
==== Effect of preprints on later publication ==== A persistent concern surrounding preprints is that work may be at risk of being plagiarised or "scooped" – meaning that the same or similar research will be published by others without proper attribution to the original source – if publicly available but not yet associated with a stamp of approval from peer reviewers and traditional journals. These concerns are often amplified as competition increases for academic jobs and funding, and perceived to be particularly problematic for early-career researchers and other higher-risk demographics within academia. However, preprints, in fact, protect against scooping. Considering the differences between traditional peer-review based publishing models and deposition of an article on a preprint server, "scooping" is less likely for manuscripts first submitted as preprints. In a traditional publishing scenario, the time from manuscript submission to acceptance and to final publication can range from a few weeks to years, and go through several rounds of revision and resubmission before final publication. During this time, the same work will have been extensively discussed with external collaborators, presented at conferences, and been read by editors and reviewers in related areas of research. Yet, there is no official open record of


id: open-access-20260505-4
file: data/en.wikipedia.org/wiki/Open_access-4.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
The intended audience of research articles is usually other researchers. Open access helps researchers as readers by opening up access to articles that their libraries do not subscribe to. All researchers benefit from open access as no library can afford to subscribe to every scientific journal and most can only afford a small fraction of them. This is known as the "serials crisis". Open access extends the reach of research beyond its immediate academic circle, as an open access article can be read by anyone. A 2008 study revealed that mental health professionals are roughly twice as likely to read a relevant article if it is freely available. ==== Research funders ==== Research funding agencies and universities want to ensure that the research they fund and support in various ways has the greatest possible research impact. As a means of achieving this, research funders are beginning to expect open access to the research they support. Many of them (including all UK Research Councils) have already adopted open-access mandates, and others are on the way to do so (see ROARMAP). ==== Universities ==== A growing number of universities are providing institutional repositories in which their researchers can deposit their published articles.


id: open-access-20260505-5
file: data/en.wikipedia.org/wiki/Open_access-5.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
==== Low-income countries ==== In developing nations, open access archiving and publishing acquires a unique importance. Scientists, health care professionals, and institutions in developing nations often do not have the capital necessary to access scholarly literature. Many open access projects involve international collaboration. For example, the SciELO (Scientific Electronic Library Online), is a comprehensive approach to full open access journal publishing, involving a number of Latin American countries. Bioline International, a non-profit organization dedicated to helping publishers in developing countries is a collaboration of people in the UK, Canada, and Brazil; the Bioline International Software is used around the world. Research Papers in Economics (RePEc), is a collaborative effort of over 100 volunteers in 45 countries. The Public Knowledge Project in Canada developed the open-source publishing software Open Journal Systems (OJS), which is now in use around the world, for example by the African Journals Online group, and one of the most active development groups is Portuguese. This international perspective has resulted in advocacy for the development of open-source appropriate technology and the necessary open access to relevant information for sustainable development. == History == === Extent === Various studies have investigated the extent of open access. A study published


id: open-access-20260505-6
file: data/en.wikipedia.org/wiki/Open_access-6.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
==== Readership ==== OA articles are generally viewed online and downloaded more often than paywalled articles and that readership continues for longer. Readership is especially higher in demographics that typically lack access to subscription journals (in addition to the general population, this includes many medical practitioners, patient groups, policymakers, non-profit sector workers, industry researchers, and independent researchers). OA articles are more read on publication management programs such as Mendeley. Open access practices can reduce publication delays, an obstacle which led some research fields such as high-energy physics to adopt widespread preprint access. ==== Citation rate ==== A main reason authors make their articles openly accessible is to maximize their citation impact. Open access articles are typically cited more often than equivalent articles requiring subscriptions. This 'citation advantage' was first reported in 2001. Although two major studies dispute this claim, the consensus of multiple studies support the effect, with measured OA citation advantage varying in magnitude between 1.3-fold to 6-fold depending on discipline. Citation advantage is most pronounced in OA articles in hybrid journals (compared to the non-OA articles in those same journals), and with articles deposited in green OA repositories. Notably, green OA articles show similar benefits to citation counts


id: open-access-20260505-7
file: data/en.wikipedia.org/wiki/Open_access-7.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
=== Open irony === Open irony refers to the situation where a scholarly journal article advocates open access but the article itself is only accessible by paying a fee to the journal publisher to read the article. This has been noted in many fields, with more than 20 examples appearing since around 2010, including in widely-read journals such as The Lancet, Science and Nature. In 2012 Duncan Hull proposed the Open Access Irony award to publicly humiliate journals that publish these kinds of papers. Examples of these have been shared and discussed on social media using the hashtag #openirony. Typically, these discussions are humorous exposures of articles/editorials that are pro-open access, but locked behind paywalls. The main concern that motivates these discussions is that restricted access to public scientific knowledge is slowing scientific progress. The practice has been justified as important for raising awareness of open access. == Infrastructure == === Databases and repositories === Multiple databases exist for open access articles, journals and datasets. These databases overlap, however each has different inclusion criteria, which typically include extensive vetting for journal publication practices, editorial boards and ethics statements. The main databases of open access articles and journals are DOAJ and


id: open-access-20260505-8
file: data/en.wikipedia.org/wiki/Open_access-8.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
Many universities, research institutions and research funders have adopted mandates requiring their researchers to make their research publications open access. For example, Research Councils UK spent nearly £60m on supporting their open access mandate between 2013 and 2016. New mandates are often announced during the Open Access Week, that takes place each year during the last full week of October. The idea of mandating self-archiving was raised at least as early as 1998. Since 2003 efforts have been focused on open access mandating by the funders of research: governments, research funding agencies, and universities. Some publishers and publisher associations have lobbied against introducing mandates. In 2002, the University of Southampton's School of Electronics & Computer Science became one of the first schools to implement a meaningful mandatory open access policy, in which authors had to contribute copies of their articles to the school's repository. More institutions followed suit in the following years. In 2007, Ukraine became the first country to create a national policy on open access, followed by Spain in 2009. Argentina, Brazil, and Poland are currently in the process of developing open access policies. Making master's and doctoral theses open access is an increasingly popular mandate by many


id: open-access-20260505-9
file: data/en.wikipedia.org/wiki/Open_access-9.md
category: reference
title: Open access
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_access
date_saved: 2026-05-05T10:14:39.240543+00:00
instance: kb-cron
parent: open-access-20260505-0
summary:
== Further reading == Darnton, Robert, "The Dream of a Universal Library" (review of Peter Baldwin, Athena Unbound: Why and How Scholarly Knowledge Should Be Free for All, MIT Press, 2023, 405 pp.), The New York Review of Books, vol. LXX, no. 20 (21 December 2023), pp. 73–74. Reviewer Darnton writes: "Baldwin warns: journal publishers are gouging their customers, scholarly monographs reach a tiny audience, libraries are floundering under budget pressures, academics are pursuing careers rather than truth, and readers are not getting all the information they deserve." (p. 74.) Writes Darnton: "Most scientific research is subsidized by the federal government." Under a 2022 White House directive, "As of December 31, 2025, all agencies... must require immediate open access... The G7 leaders took a similar stand on May 14, 2023, as did the European Council on May 23. The tide is turning in favor of unrestricted access, but the countervailing forces are so complex that the future remains cloudy." (p. 73.) Suber, Peter (2012). Open access (The MIT Press Essential Knowledge Series ed.). Cambridge, Mass.: MIT Press. ISBN 978-0-262-51763-8. Retrieved 20 October 2015. Kirsop, Barbara, and Leslie Chan. (2005) Transforming access to research literature for developing countries. Serials Reviews, 31(4):


id: subscribe-to-open-20260505-0
file: data/en.wikipedia.org/wiki/Subscribe_to_Open-0.md
category: reference
title: Subscribe to Open
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Subscribe_to_Open
date_saved: 2026-05-05T10:14:40.450152+00:00
instance: kb-cron
parent: subscribe-to-open-20260505-0
summary:
Subscribe to Open (S2O) is an economic model used by peer-reviewed scholarly journals to provide readers with open access (OA) to the journal’s content, without charging costs to authors. S2O converts journals that have a traditional subscription model to open access. When the academic libraries subscribing to a journal are asked to renew their subscriptions to the journal, they are told that, with the libraries’ support, the journal will be made open access to all readers, while authors are able to publish in it at no cost. If enough libraries renew on these terms, the journal is converted to open access, and if not, access to the journal remains restricted to subscribing libraries. == Background == The term “Open Access” (OA) was first coined in December 2001 in a call for greater access to academic journal articles, which were mainly available through subscriptions. Influential statements such as the Budapest Open Access Initiative (BOAI) in February 14, 2002, have called for improvement of the circulation of research through open access to scholarly journals. Since then, more than twenty financial models have been developed as alternatives to the traditional subscription model, beginning with authors paying article processing charges (APCs) to BioMed Central


id: academic-spring-20260505-0
file: data/en.wikipedia.org/wiki/Academic_Spring-0.md
category: reference
title: Academic Spring
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_Spring
date_saved: 2026-05-05T10:14:41.679545+00:00
instance: kb-cron
parent: academic-spring-20260505-0
summary:
The Academic Spring was the designation, inspired by the Arab Spring, used for a short time in 2012 to indicate movements by academics, researchers, and scholars opposing the restrictive copyright and circulation of traditional academic journals and promoting free access online instead. == History == There have been previous attempts to increase the availability of free and up-to-date scientific research. In 2004, there were reports in British media of a "revolution in academic publishing" which would make research freely available online but many scientists continued to publish their work in the traditional big name journals like Nature. The barriers to free access for recent scientific research became a hot topic in 2012, after a blog post by mathematician Timothy Gowers went viral in January. According to the Financial Times, the movement was named by Dennis Johnson of Melville House Publishing, though scientist Mike Taylor has suggested the name came from The Economist. === Research Works Act === In December 2011 the Research Works Act was proposed in the US Congress to restrict open access to research funded by the US federal government. This followed other similar proposed measures such as the Fair Copyright in Research Works Act. These attempts to


id: academic-studies-press-20260505-0
file: data/en.wikipedia.org/wiki/Academic_Studies_Press-0.md
category: reference
title: Academic Studies Press
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Academic_Studies_Press
date_saved: 2026-05-05T10:14:42.867762+00:00
instance: kb-cron
parent: academic-studies-press-20260505-0
summary:
Academic Studies Press, (ASP) is an independent scholarly publisher of books and journals, based in Boston, Massachusetts. == History == Founded in 2007, ASP emphasizes Jewish studies and Slavic studies, but also publishes titles in religious studies, comparative literature, and history more broadly. Authors include Jacob Neusner, Fania Oz-Salzberger, Ellendea Proffer Teasley, Maxim D. Shrayer, Mark Lipovetsky, David Berger, Menachem Kellner, Viktor Zhivov, Jerold Auerbach, and Geoffrey Alderman, while works in translation include those of Maimonides, Ahad Ha'am, Mordecai Kaplan, Eliezer Schweid, and Yury Tynyanov. The press also specializes in Ukrainian translations, many of which received renewed interest during the 2022 Russian invasion of Ukraine. Its titles have won awards from the Jewish Book Council, the Modern Language Association, AATSEEL, and the Koffler Centre of the Arts, and have also appeared on reading lists published by Mosaic and The Washington Post. In 2017, ASP was the recipient of funding from the National Endowment for the Humanities to aid in the "creation of freely accessible e-books for 42 seminal titles in Russian literary and cultural history." In the same year, ASP collaborated jointly with the NEH and the Ukrainian Research Institute at Harvard University to publish Words for War, an anthology


id: article-processing-charge-20260505-0
file: data/en.wikipedia.org/wiki/Article_processing_charge-0.md
category: reference
title: Article processing charge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Article_processing_charge
date_saved: 2026-05-05T10:14:46.180705+00:00
instance: kb-cron
parent: article-processing-charge-20260505-0
summary:
An article processing charge (APC), also known as a publication fee, is a fee which is sometimes charged to authors. Most commonly, it is involved in making an academic work available as open access (OA), in either a full OA journal or in a hybrid journal. This fee may be paid by the author, the author's institution, or their research funder. Sometimes, publication fees are also involved in traditional journals or for paywalled content. Some publishers waive the fee in cases of hardship or geographic location, but this is not a widespread practice. An article processing charge does not guarantee that the author retains copyright to the work, or that it will be made available under a Creative Commons license. == Background == Journals use a variety of ways to generate the income required to cover publishing costs (including editorial costs, any costs of administering the peer review system), such as subsidies from institutions and subscriptions. A majority of open access journals do not charge article processing charges, but a significant and growing number of them do. They are the most common funding method for professionally published open access articles. APC fees applied to academic research are usually expensive, effectively


id: article-processing-charge-20260505-1
file: data/en.wikipedia.org/wiki/Article_processing_charge-1.md
category: reference
title: Article processing charge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Article_processing_charge
date_saved: 2026-05-05T10:14:46.180705+00:00
instance: kb-cron
parent: article-processing-charge-20260505-0
summary:
==== Cost to scientists and funding bodies ==== Article processing charges shift the burden of payment from readers to authors (or their funders), which creates a new set of concerns. One concern is that if a publisher makes a profit from accepting papers, it has an incentive to accept anything submitted, rather than selecting and rejecting articles based on quality. This could be remedied, however, by charging for the peer-review rather than acceptance. Another concern is that institutional budgets may need to be adjusted in order to provide funding for the article processing charges required to publish in many open access journals (e.g. those published by BioMed Central). It has been argued that this may reduce the ability to publish research results due to lack of sufficient funds, leading to some research not becoming a part of the public record. Another concern is the redirection of money by major funding agencies such as the National Institutes of Health and the Wellcome Trust from the direct support of research to the support of open access publication. Robert Terry, Senior Policy Advisor at the Wellcome Trust, has said that he feels that 1–2% of their research budget will change from the creation


id: base-search-engine-20260505-0
file: data/en.wikipedia.org/wiki/BASE_(search_engine)-0.md
category: reference
title: BASE (search engine)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BASE_(search_engine)
date_saved: 2026-05-05T10:14:47.317292+00:00
instance: kb-cron
parent: base-search-engine-20260505-0
summary:
BASE (Bielefeld Academic Search Engine) is a multi-disciplinary search engine to scholarly internet resources, created by Bielefeld University Library in Bielefeld, Germany. It is based on free and open-source software such as Apache Solr and VuFind. It harvests OAI metadata from institutional repositories and other academic digital libraries that implement the Open Archives Initiative Protocol for Metadata Harvesting (OAI-PMH), and then normalizes and indexes the data for searching. In addition to OAI metadata, the library indexes selected web sites and local data collections, all of which can be searched via a single search interface. == History == BASE was developed at the German university of Bielefeld beginning in 2002. The project's initial goal was to develop a search engine that would provide users access to the university's research resources. Yet as the initiative advanced, the creators came to see the need for a more thorough search engine that might provide users access to academic resources outside of the university. The initial iteration of BASE was released as a prototype in 2004 and made accessible to the general public for testing. The search engine was created to index and offer access to scholarly materials such journals, institutional repositories, and digital collections


id: beall-s-list-20260505-0
file: data/en.wikipedia.org/wiki/Beall's_List-0.md
category: reference
title: Beall's List
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Beall's_List
date_saved: 2026-05-05T10:14:49.561109+00:00
instance: kb-cron
parent: beall-s-list-20260505-0
summary:
Beall's List was a list of predatory open-access publishers that was maintained by University of Colorado Denver librarian Jeffrey Beall on his blog Scholarly Open Access. The list aimed to document open-access publishers who did not perform real peer review, effectively publishing any article as long as the authors pay the article processing charge. Originally started as a personal endeavor in 2008, Beall's List became a widely followed piece of work by the mid-2010s. The list was used by scientists to identify exploitative publishers and detect publisher spam. The influence of Beall's List led some publishers on the list to threaten defamation lawsuits against Beall, as well as to lodge official complaints against Beall's work to the University of Colorado. In January 2017, Beall removed the list from his blog, scholarlyoa.com. Six months later, he published an article in the journal Biochemia Medica claiming that pressure from his employer led to the blog shutdown, although the university's official statement and a response by Beall's direct supervisor both disputed this account. The closure of Beall's List was cited by some as a loss of an important resource, and successors have set out to continue Beall's work. == Early history == Beall


id: beall-s-list-20260505-1
file: data/en.wikipedia.org/wiki/Beall's_List-1.md
category: reference
title: Beall's List
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Beall's_List
date_saved: 2026-05-05T10:14:49.561109+00:00
instance: kb-cron
parent: beall-s-list-20260505-0
summary:
==== "Dr Fraud" experiment ==== In 2015, four researchers created a fictitious sub-par scientist named Anna O. Szust (oszust is Polish for "fraud"), and applied on her behalf for an editor position to 360 scholarly journals. Szust's qualifications were dismal for the role of an editor; she had never published a single article and had no editorial experience. The books and book chapters listed on her CV were made-up, as were the publishing houses that allegedly published the books. One-third of the journals to which Szust applied were sampled from Beall's List. Forty of these predatory journals accepted Szust as editor without any background vetting and often within days or even hours. By comparison, she received minimal to no positive response from the "control" journals which "must meet certain standards of quality, including ethical publishing practices." Among journals sampled from the Directory of Open Access Journals (DOAJ), 8 of 120 accepted Szust. The DOAJ has since removed some of the affected journals in a 2016 purge. None of the 120 sampled journals listed in Journal Citation Reports (JCR) offered Szust the position. The results of the experiment were published in Nature in March 2017, and widely presented in the press.


id: beall-s-list-20260505-2
file: data/en.wikipedia.org/wiki/Beall's_List-2.md
category: reference
title: Beall's List
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Beall's_List
date_saved: 2026-05-05T10:14:49.561109+00:00
instance: kb-cron
parent: beall-s-list-20260505-0
summary:
Since Beall's List closed, similar lists have been started by others, including CSIR-Structural Engineering Research Centre, and an anonymous group at Stop Predatory Journals. Cabell's International, a company that offers scholarly publishing analytics and other scholarly services, has also offered both a black list and a white list for subscription on their website. Since 2021, the Norwegian Scientific Index includes the category "level X" that includes journals suspected of being predatory; its establishment was linked to expressions of concern regarding the publisher MDPI. A site entitled Beall's List of Potential Predatory Journals and Publishers states that it includes the original list as at 15 January 2017, with updates listed separately, maintained by an anonymous European postdoctoral researcher. == See also == Journalology == References == == Further reading == Buschman, John (2020). "A Political Sociology of the Beall's List Affair". The Library Quarterly. 90 (3): 298–313. doi:10.1086/708959. S2CID 224809316. == External links == Beall, Jeffrey. "List of Publishers: Potential, possible, or probable predatory scholarly open-access publishers" (last archived ed.). Archived from the original on January 12, 2017. Beall, Jeffrey. "List of Standalone Journals: Potential, possible, or probable predatory scholarly open-access journals" (last archived ed.). Archived from the original on January


id: bibsam-consortium-20260505-0
file: data/en.wikipedia.org/wiki/Bibsam_Consortium-0.md
category: reference
title: Bibsam Consortium
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Bibsam_Consortium
date_saved: 2026-05-05T10:14:50.757805+00:00
instance: kb-cron
parent: bibsam-consortium-20260505-0
summary:
Bibsam Consortium is a consortium in which 85 higher education and research institutions in Sweden participate to negotiate license agreements for electronic information resources. The consortium is headed by the National Library of Sweden and negotiates as well as administrates license agreements for e-resource packages. The participating institutions sign a power of attorney which allows the National Librarian to sign contracts with the e-resource providers. == History and scope == The Bibsam Consortium was formed in 1996 in order to negotiate license agreements for electronic resources on behalf of Swedish Universities, research institutes and government agencies. The total turnover of the agreements in 2015 was € 33 million and € 35 million in 2017 with 73% of the turnover being generated by the ten largest universities in Sweden. There are six members in National Library of Sweden to negotiate and administer the 100 license agreements for approximately 40 e-resource packages. == Negotiation with Elsevier == In 2018, the Bibsam Consortium terminated its agreement with Elsevier publishers in order to stop rising prices of publishing and to support open access publishing. The termination went into effect on 1 July 2018, as a result of which Swedish Universities and colleges had no


id: centre-pour-l-dition-lectronique-ouverte-20260505-0
file: data/en.wikipedia.org/wiki/Centre_pour_l'Édition_Électronique_Ouverte-0.md
category: reference
title: Centre pour l'Édition Électronique Ouverte
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Centre_pour_l'Édition_Électronique_Ouverte
date_saved: 2026-05-05T10:14:53.040562+00:00
instance: kb-cron
parent: centre-pour-l-dition-lectronique-ouverte-20260505-0
summary:
The Centre pour l'Édition Électronique Ouverte (CLEO, Cléo; transl. Centre for Open Electronic Publishing), based in Marseille, France, is overseen by Aix-Marseille University, the Centre National de la Recherche Scientifique, School for Advanced Studies in the Social Sciences, and University of Avignon and the Vaucluse. It produces the open access academic publishing portal OpenEdition.org, which includes platforms Calenda, Hypotheses, OpenEdition Books, and OpenEdition Journals. OpenEdition focuses on publications in the academic fields of humanities and social sciences. The centre also issues a blog about open access. == OpenEdition Books == include: Bak-Geller, Sarah (28 July 2022). "Patrimonio alimentario y ciudadanía indígena. El caso coca de Mezcala, Jalisco (México)". In Rebaï, Nasser; Bilhaut, Anne-Gaël; de Suremain, Charles-Édouard; Katz, Esther; Paredes, Myriam (eds.). Patrimonios alimentarios en América Latina : Recursos locales, actores y globalización (in Spanish). IRD Éditions. pp. 191–214. ISBN 978-2-7099-2943-1. Retrieved 27 April 2023 – via Centre pour l'Édition Électronique Ouverte. Introducción == OpenEdition Journals == The following list includes some examples of titles in Journals.openedition.org (prior to December 2017 known as Revues.org): == See also == OpenEdition access via Wikipedia Library Open access journal Open access in France List of academic databases and search engines == References == ==


id: copim-20260505-0
file: data/en.wikipedia.org/wiki/COPIM-0.md
category: reference
title: COPIM
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/COPIM
date_saved: 2026-05-05T10:14:54.247395+00:00
instance: kb-cron
parent: copim-20260505-0
summary:
The Copim community is an international group of researchers, universities, librarians, open access book publishers and infrastructure providers. It is building community-owned, open systems and infrastructures to enable open-access book publishing to flourish. The collaboration is being funded by Research England and Arcadia Fund, via two consecutive projects between November 2019 and April 2026. The community's name is derived from the original project acronym of COPIM (Community-led Open Publication Infrastructures for Monographs). During its first project phase (11/2019-04/2023), the community has been involved in the foundational project of the same name. As of 05/2023, this is now followed by a second project phase under the title of Open Book Futures, through which the Copim community aims to expand and accelerate the uptake of the infrastructures developed during its initial project phase. Following the principle of 'Scaling Small', the project has developed a set of proof-of-concepts of non-profit and community-owned, open infrastructures to enable open access book publishing to prosper. Copim has been named as a Supporting Action in UKRI's 2020 Open Access Review Consultation. == Work Packages == In seven distinct Work Packages, the COPIM project explored: how to scope and build support for an integration of open access books


id: the-cost-of-knowledge-20260505-0
file: data/en.wikipedia.org/wiki/The_Cost_of_Knowledge-0.md
category: reference
title: The Cost of Knowledge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_Cost_of_Knowledge
date_saved: 2026-05-05T10:14:56.579118+00:00
instance: kb-cron
parent: the-cost-of-knowledge-20260505-0
summary:
The Cost of Knowledge is a protest by academics against the business practices of academic journal publisher Elsevier. Among the reasons for the protests were a call for lower prices for journals and to promote increased open access to information. The main work of the project was to ask researchers to sign a statement committing not to support Elsevier journals by publishing, performing peer review, or providing editorial services for these journals. == History == Before the advent of the Internet, it was difficult for scholars to distribute articles giving their research results. Historically, publishers performed services including proofreading, typesetting, copyediting, printing, and worldwide distribution. In more recent times, all researchers became expected to give the publishers digital copies of their work which needed no further processing – in other words, the modern academic is expected to do, often for free, duties traditionally assigned to the publisher, and for which, traditionally, the publisher is paid in exchange. For digital distribution, printing is unnecessary, copying is (almost) free, and worldwide distribution happens online instantly. Internet technology, and with it the aforementioned significant decrease in overhead costs, enabled the four major scientific publishers – Elsevier, Springer, Wiley, and Informa – to cut


id: the-cost-of-knowledge-20260505-1
file: data/en.wikipedia.org/wiki/The_Cost_of_Knowledge-1.md
category: reference
title: The Cost of Knowledge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/The_Cost_of_Knowledge
date_saved: 2026-05-05T10:14:56.579118+00:00
instance: kb-cron
parent: the-cost-of-knowledge-20260505-0
summary:
=== Impact and reception === In February 2012, analysts of the Exane Paribas bank reported a financial impact on Elsevier with the company's stock prices falling due to the boycott. Dennis Snower criticised the monopoly of scientific publishers, but said at the same time that he did not support the boycott even though he himself is the editor-in-chief of an open-access journal on economics. He thinks that more competition among the various journals should instead be encouraged. The Senate of the University of Kansas has been reported to consider joining the boycott of Elsevier. In allusion to the revolutions of the Arab Spring, the German Frankfurter Allgemeine Zeitung daily newspaper called the movement the "Academic Spring" (German: Akademischer Frühling). When the British Wellcome Trust made a commitment to open up science, The Guardian similarly called this the "Academic Spring". After the Wellcome Trust announcement, The Cost of Knowledge campaign was recognized by that newspaper as the start of something new. A 2016 study evaluating the boycott stated that in the past four years 38% of signatories had abandoned their "won't publish in an Elsevier outlet" commitment and that only around 5000 researchers were still clearly boycotting Elsevier by publishing elsewhere.


id: data-publishing-20260505-0
file: data/en.wikipedia.org/wiki/Data_publishing-0.md
category: reference
title: Data publishing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Data_publishing
date_saved: 2026-05-05T10:14:59.043159+00:00
instance: kb-cron
parent: data-publishing-20260505-0
summary:
Data publishing (also data publication) is the act of releasing research data in published form for use by others. It is a practice consisting in preparing certain data or data set(s) for public use thus to make them available to everyone to use as they wish. This practice is an integral part of the open science movement. There is a large and multidisciplinary consensus on the benefits resulting from this practice. The main goal is to elevate data to be first class research outputs. There are a number of initiatives underway as well as points of consensus and issues still in contention. There are several distinct ways to make research data available, including: publishing data as supplemental material associated with a research article, typically with the data files hosted by the publisher of the article hosting data on a publicly available website, with files available for download hosting data in a repository that has been developed to support data publication, e.g. figshare, Dryad, Dataverse, Zenodo. A large number of general and specialty (such as by research topic) data repositories exist. For example, the UK Data Service enables users to deposit data collections and re-share these for research purposes. publishing a


id: directory-of-open-access-journals-20260505-0
file: data/en.wikipedia.org/wiki/Directory_of_Open_Access_Journals-0.md
category: reference
title: Directory of Open Access Journals
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Directory_of_Open_Access_Journals
date_saved: 2026-05-05T10:15:03.862197+00:00
instance: kb-cron
parent: directory-of-open-access-journals-20260505-0
summary:
The Directory of Open Access Journals (DOAJ) is a website that hosts a community-curated list of open access journals, maintained by Infrastructure Services for Open Access (IS4OA). It was launched in 2003 with 300 open access journals and now has expanded to over 22,886 indexed open access journals and 12,746,462 articles. The mission of DOAJ is to "increase the visibility, accessibility, reputation, usage and impact of quality, peer-reviewed, open access scholarly research journals globally, regardless of discipline, geography or language." In 2015, DOAJ launched a reapplication process based on updated and expanded inclusion criteria. At the end of the process (December 2017), close to 5,000 journals, out of the 11,600 indexed in May 2016, had been removed from their database, in majority for failure to reapply. Notwithstanding the substantial cleanup, the number of journals included in DOAJ has continued to grow, to reach 14,299 as of 3 March 2020. As of April 2025, the independent database contains more than 21,480 open access journals and 11,045,921 articles covering all areas of science, technology, medicine, social sciences and the humanities. DOAJ provides a change log on Google Sheets that has been updated since March 2014 and identifies the journals added and the


id: disciplinary-repository-20260505-0
file: data/en.wikipedia.org/wiki/Disciplinary_repository-0.md
category: reference
title: Disciplinary repository
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Disciplinary_repository
date_saved: 2026-05-05T10:15:05.025845+00:00
instance: kb-cron
parent: disciplinary-repository-20260505-0
summary:
A disciplinary repository (or subject repository) is an online archive, often an open-access repository, containing works or data associated with these works of scholars in a particular subject area. Disciplinary repositories can accept work from scholars from any institution. A disciplinary repository shares the roles of collecting, disseminating, and archiving work with other repositories, but is focused on a particular subject area. These collections can include academic and research papers. Disciplinary repositories can acquire their content in many ways. Many rely on author or organization submissions, such as SSRN. Others such as CiteSeerX crawl the web for scholar and researcher websites and download publicly available academic papers from those sites. AgEcon, established in 1995, grew as a result of active involvement of academia and societies. A disciplinary repository generally covers one broad based discipline, with contributors from many different institutions supported by a variety of funders; the repositories themselves are likely to be funded from one or more sources within the subject community. Deposit of material in a disciplinary repository is sometimes mandated by research funders. Disciplinary repositories can also act as stores of data related to a particular subject, allowing documents along with data associated with that work to


id: economics-of-open-science-20260505-0
file: data/en.wikipedia.org/wiki/Economics_of_open_science-0.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
The economics of open science describe the economic aspects of making a wide range of scientific outputs (e.g., publications, data, software) to all levels of society. Open science involves a plurality of economic models and goods. Historically, academic journals and other academic institutions, such as learned societies, have favored a knowledge club or a toll access model: publications are managed as a community service for the selected benefit of academic readers and authors. During the second half of the 20th century, the "big 5" largest publishers (Elsevier, Springer, Wiley, Taylor & Francis, and the American Chemical Society) have partly absorbed or outcompeted non-profits structure and applied an industrial approach to scholarly publishing. The development of the web shifted the focus of scholarly communication from publication to a large variety of outputs (data, software, metrics). It also challenged the values and the organization of existing actors with the development of an international initiatives in favor of open access and open science. While initially distanced by new competitors, the main commercial publishers have started to flip to author-pay models after 2000, funded through article processing charges and the negotiation of transformative deals. Actors like Elsevier or Wiley have diversified their activities from


id: economics-of-open-science-20260505-1
file: data/en.wikipedia.org/wiki/Economics_of_open_science-1.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Open science club === The economic theory of club goods was originally developed in the 1965 by James Buchanan to complement the distinction between private and public goods. While clubs are private organizations they also manage the allocation of the resources between the individual members, in a similar manner to a public service. Membership criteria are a fundamental feature of clubs and affect their efficiency: "The central question in a theory of clubs is that of determining the membership margin, so to speak, the size of the most desirable cost and consumption sharing arrangement." ==== Definitions of knowledge club ==== Before the Second World War, academic publishing was mostly characterized by a wide range of community-driven scholarly structures with little concerns for profitability. They relied on informal community norms rather than commercial regulations. Theses structures have been described as knowledge clubs: "until the second part of the twentieth century, most journals could be assimilated to a club model". While managed by a community and publicly available, knowledge club are demic and mostly used to the benefit of their members. As a defining feature of the club model, scientific authors are not paid for their publications: "ever since the first


id: economics-of-open-science-20260505-2
file: data/en.wikipedia.org/wiki/Economics_of_open_science-2.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
==== Knowledge club and open science ==== The OA Diamond Study states that non-commercial journals in open access "maintain a secular tradition of "club" journals, set up for the uses and interests of a specific closed community of knowledge." While access to read is no longer a material condition for membership, non-commercial journals are still largely managed for the benefits of a community. They are "still strongly embedded in institutional environments (from a legal and governance perspective)." Club journals have gained a new relevance with the development of electronic publishing and played a fundamental role in the early development of online open science in the early 1990s. Pioneers of open access electronic publishings were non-commercial and community-driven initiatives that built up on a trend of grassroot publishing innovation in the social sciences and the humanities: "In the late '80s and early '90s, a host of new journal titles launched on listservs and (later) the Web. Journals such as Postmodern Cultures, Surfaces, the Bryn Mawr Classical Review and the Public-Access Computer Systems Review were all managed by scholars and library workers rather than publishing professionals." The development of the web and free editing tools for scientific publications like Open Journal System


id: economics-of-open-science-20260505-3
file: data/en.wikipedia.org/wiki/Economics_of_open_science-3.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
The small society presses, struggling to cope with growing scale, were supported and then largely supplanted by the 'Big 5' commercial presses: Elsevier (which acquired Pergamon in 1991), Wiley, Springer, Taylor & Francis and Sage. These newly-empowered players brought an industrial approach to the publication and dissemination process, for the first time realising the benefits that these specialised capital and skills could provide by operating at a scale that was unprecedented to that date. As it became a private good, the scientific journal was also transformed into an industrial product, with an increased standardization of publishing norms, peer-review process or copyrights. In contrast smaller scientific publishers participate to a more typical publishing market, with a persistent competition. Four out of the five most costly journals "are published by big five publishers". With the conversion to electronic journals, scientific publishing became a hybrid market: along with individual subscriptions, leading publishers introduced big deals or pluri-annual licenses to large bundle of journal titles. Big deals were typically negotiated with national networks of research libraries and academic institutions. Big deals proved advantageous to publishers as well as they limited the "administrative costs" of managing a large number of contracts between journals and buyers.


id: economics-of-open-science-20260505-4
file: data/en.wikipedia.org/wiki/Economics_of_open_science-4.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
==== From readers to authors: hybrid and full open access ==== The early developments of Open science and the open sharing of scientific publication on the web was at first a challenge for leading commercial publishers: the executive board of Elsevier "had failed to grasp the significance of electronic publishing altogether, and therefore the deadly danger that it posed—the danger, namely, that scientists would be able to manage without the journal". Initial experiments of scientific journals were mostly led by non-commercial initiatives. Commercially viable open access journals have appeared in the late 1990s, under the leadership of new competitors such as the Public Library of Science (PLOS), MDPI or Hindawi. They relies generally on the author-pay model: the journal provides an editorial service to the author of a publication and charge an article-processing charge to cover the editing costs. This practice is anterior to open access, as subscription-based journals held by scientific societies occasionally charged for additional services (such as photographies in color). For Peter Suber, this economic model is similar to broadcast television: "If advertisers can pay all the costs of production, then a TV studio can broadcast a show without charging viewers. In the case of scholarly research


id: economics-of-open-science-20260505-5
file: data/en.wikipedia.org/wiki/Economics_of_open_science-5.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
the largest supplier of academic journals is also in charge of evaluating and validating research quality and impact (e.g., Pure, Plum Analytics, Sci Val), identifying academic experts for potential employers (e.g., Expert Lookup5), managing the research networking platforms through which to collaborate (e.g., SSRN, Hivebench, Mendeley), managing the tools through which to find funding (e.g., Plum X, Mendeley, Sci Val), and controlling the platforms through which to analyze and store researchers' data (e.g., Hivebench, Mendeley). Since it has expanded beyond publishing, the vertical integration of privately owned infrastructures has become extensively integrated to daily research activities: "the privatised control of scholarly infrastructures is especially noticeable in the context of 'vertical integration' that publishers such as Elsevier and SpringerNature are seeking by controlling all aspects of the research lifecycle, from submission to publication and beyond". In contrast with publication, which was an outsourced business separated from institutional and community activities, the new services developed by large publishers are embedded in the infrastructure of universities and create potentially stronger dependency links: "Pure embeds Elsevier within the university workflow process through its abilities to manage research at the university level, including the provision of a dashboard to facilitate decision making by university research


id: economics-of-open-science-20260505-6
file: data/en.wikipedia.org/wiki/Economics_of_open_science-6.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
==== The emergence of digital knowledge commons ==== Until the 1990s open knowledge was not considered as a commons in economic theory but as a "classic example of a pure public good, a good available to all and where one person's use does not subtract from another's use". For Elinor Ostrom and Charlotte Hess, this framework is no longer viable as the principle of non-excludability has been significantly weakened: "new technologies can enable the capture of what were once free and open public goods (...) Knowledge, which can seem so ubiquitous in digital form, is, in reality, more vulnerable than ever before" In a scientific context, examples of new enclosures of public goods may include all the surveillance data systems put in place by Elsevier, Springer or Academic social networks that capturate activities such as social interactions, reference collections. The uncontrolled development of the early web highlighted the need for common management of knowledge resources: "People started to notice behaviors and conditions on the web — congestion, free riding, conflict, overuse, and pollution — that had long been identified with other types of commons." The open access of the 1990s and early 2000s movement aimed to ensure that science will


id: economics-of-open-science-20260505-7
file: data/en.wikipedia.org/wiki/Economics_of_open_science-7.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
==== Consolidation of the common ecosystem (2015-) ==== Since 2015, open science infrastructures, platforms and journals have converged to the creation of digital academic commons. While they were initially conceived either as a public good (a "grid" that ensure the distribution of a non-excludable resource) or a club good (with a limited range beyond a specific communities), open science infrastructures have been increasingly structured around a shared ecosystem of services and standards has emerged through the network of dependencies from one infrastructure to another. Open science infrastructures face similar issues met by other open institutions such as open data repositories or large scale collaborative project such as Wikipedia: "When we study contemporary knowledge infrastructures we find values of openness often embedded there, but translating the values of openness into the design of infrastructures and the practices of infrastructuring is a complex and contingent process". The conceptual definition of open science infrastructures has been largely influenced by the analysis of Elinor Ostrom on the commons and more specifically on the knowledge commons. In accordance with Ostrom, Cameron Neylon understates that open infrastructures are not only a public good characterized by the management of a pool of common resources but also by


id: economics-of-open-science-20260505-8
file: data/en.wikipedia.org/wiki/Economics_of_open_science-8.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Editorial work and evaluation === Editorial work and the management of peer review remain the core activity of academic journals and their most identified contribution and service rendered to scientific communities. It is the main expenses of the non-commercial journals surveyed by the OA Diamond Study: "the five main expenses/payables of the journal are editing (531), copy-editing (463), technical and software support (393), typesetting (384), and design (336)". Translation is a frequently quoted added cost, that may have been incentivicized by open science as the potential audience of local non-academic readers create incentives to maintain a multilingual website. Even before their conversion to electronic publishing, non-for-profits journals have maintained an affordable price, due to a nearly exclusive focus on editorial services: "in 2013, the mean price per article in for-profit journals was 3.2 times higher than in non-for-profit journals, and that the corresponding ratio for the median price per article was even 4.33:1". With Internet publication, costs can be significantly lower, due to additional cuts in transaction and labor costs, use of share platforms and infrastructure or reliance on voluntary work: "over 60% of journals reported annual costs in the previous year under $/€10,000, including in-kind contributions." In contrast


id: economics-of-open-science-20260505-9
file: data/en.wikipedia.org/wiki/Economics_of_open_science-9.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Commercial services and prestige === The overall price per article of commercial publishers is consistently higher than in the non-commercial sectors. This discrepancy has been partly accounted by the maintenance of several services necessary to run a business activity (pricing, transaction management, marketing...), that non-commercial structure can drop entirely with little impact. Leading commercial journals claim to be more selective in regard to article submissions, which has the effect of creating a more complex and time-consuming acceptation workflow: "The more effort a publisher invests in each paper, and the more articles a journal rejects after peer review, the more costly is each accepted article to publish [although] the key question is whether the extra effort adds useful value". Besides, "costs vary widely in this sector". Without any transformation of the editorial workflow or efficiency gain a standard well-established subscription journal could "charge about $3,700 per paper to cover costs". Yet, in a publisher like Nature, the costs would be "at £20,000–30,000 ($30,000–40,000) per paper". In the higher end of the commercial spectrum, cost per articles are more likely to embed services that are not directly related to publishing : "One possible reason for such variation between journals and publishers


id: economics-of-open-science-20260505-10
file: data/en.wikipedia.org/wiki/Economics_of_open_science-10.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Transaction costs === In a market economy, the diffusion of private goods is usually assorted with transactions costs. Theses costs cover all the services required to manage the commoditization of a product, both from the side of the producer as from the side of a consumer. In a wider sense, it encompasses all the work and time allocated all the stakeholders of the transactions to perform it, such as benchmarks, negotiations or contractualizations. Savings on transaction costs are frequently quoted as a significant advantage of non-excludable resource system (common good, public good) over private markets. Since access to the resource is weakly restrained and conditioned by unformal rules, its allocation is less costly overall: "a community could (...) produce better quality outcomes at lower economic costs, because of lower transaction costs, than alternative institutional systems involving private property" According to a 2017 OECD report, market allocation of open knowledge outputs is not sufficiently efficient: as the price of a public good converges to zero, any attempt at setting up a price will result in excessive exclusion and reduced collective benefits. The market may not be the best mechanism for the allocation of a public good, as any price above


id: economics-of-open-science-20260505-11
file: data/en.wikipedia.org/wiki/Economics_of_open_science-11.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Access costs === Access to pre-existent research outputs such as publications, datasets or code is a major condition of research. The subscription model used to rely on the assumptions that researchers only need to access a few specialized publications. In practice, research is more unpredictible and frequently rely crossing methods and observations from different fields or even different disciplines. In 2011, a survey of JISC showed that 68% of UK researchers felt they did have a sufficiently wide access to journal and conference papers. Non-academic professional audiences experience difficulties in accessing directly relevant research: "a quarter of those in industry/commerce described their current level of access as fairly/very difficult" as "the main barrier was unwillingness to pay". A survey of business use of research in Denmark highlighted a large range of strategies to avoid the high cost of pay-per-view, especially through collaboration with academics that have institutional access. The impact of open access is amplified by the high degree of internationalization of research: "83% of the economic return on cancer research is drawn from research from non-UK sources" which are less likely to be accessible in a subscription-based model. Beyond the extended coverage, Open science can enhance the efficiency


id: economics-of-open-science-20260505-12
file: data/en.wikipedia.org/wiki/Economics_of_open_science-12.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Research efficiency === Impacts of open science on research efficiency stem from the benefits of enhanced access to previous work. Due to the complexity of bibliography search, closed subscription system "can lead to high levels of duplication—that is, where separate teams work on the same thing unbeknownst to each other." The issue is not limited to academic research, but affect industrial R&D as well: "an analysis of pharmaceutical patents by 18 large companies showed that 86% of target compounds were investigated by two or more companies" Non-publication of data or intermediary results can also have cascading effects on the overall quality of research. Meta-analysis rely on the reproductibility of pre-existent observations and experiments to identify the scientific consensus on a specific topic or field of research. They can be affected by statistical errors and bias, as well as the pre-selection of statistically significant results. Extensive opening of final and intermediary data sources makes it easier to spot potential mistakes. Text and data mining projects have more recently become a major focus of studies on potential gain of research efficiency. In contrast with the standard procedures of state of the art, text mining projects process very large corpus and are


id: economics-of-open-science-20260505-13
file: data/en.wikipedia.org/wiki/Economics_of_open_science-13.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
=== Economic and social development === The potential economic impact for open science research outputs is significant. Innovation commons are a major, although overlooked, source of economic growth: "The innovation commons is the true origin of innovation. It is the source from which the subsequent markers of innovation emerge — the entrepreneurial actors, the innovating firms, the new markets, and so on." Recent developments like the growth of data analytics services across a large variety of economic sectors have created further needs for research data: "There are many other values (...) that are promoted through the longterm stewardship and open availability of research data. The rapidly expanding area of artificial intelligence (AI) relies to a great extent on saved data." In 2019, the combined data market of the 27 countries of the European Union and the United Kingdom was estimated at 400 billion euros and had a sustained growth of 7.6% per year. although no estimation was given of the specific value of research data, research institutions were identified as important stakeholders in the emerging ecosystem of "data commons". In 2011, a JISC report estimated that there was 1.8 million knowledge workers in the United Kingdom working in R&D, IT,


id: economics-of-open-science-20260505-14
file: data/en.wikipedia.org/wiki/Economics_of_open_science-14.md
category: reference
title: Economics of open science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economics_of_open_science
date_saved: 2026-05-05T10:15:06.320001+00:00
instance: kb-cron
parent: economics-of-open-science-20260505-0
summary:
== Economic regulation of open science == Economic regulation of scientific publishing has long be stuck in a "collective action dilemma", due to the lack of coordinations between all stakeholders: "To truly reduce their costs, librarians would have to build a shared online collection of scholarly resources jointly managed by the academic community as a whole, but individual academic institutions lack the private incentives necessary to invest in a shared collection." Although it was initially expected by some economists, that the academic publishing markets would be structurally disrupted by new open access competitors, change was mostly driven by scientific communities, scientific institutions and, lately, coordinations of funders. In the 2000s, forms of regulation appear at a local scale to solve obvious market failure in the management of open science outuput. Development of research data repositories has been sustained by the implementation of "government or funder mandates for open data that require the producers of data to make them openly accessible". Mandates have been less easily expanded to other scientific outputs such as publications, that could not be covered by open data programs and were already invested by large commercial structures. In the great recession, scientific institutions and libraries had to


id: embargo-academic-publishing-20260505-0
file: data/en.wikipedia.org/wiki/Embargo_(academic_publishing)-0.md
category: reference
title: Embargo (academic publishing)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Embargo_(academic_publishing)
date_saved: 2026-05-05T10:15:07.515279+00:00
instance: kb-cron
parent: embargo-academic-publishing-20260505-0
summary:
In academic publishing, an embargo is a period during which access to academic journals is not allowed to users who have not paid for access (or have access through their institution). The purpose of this is to ensure publishers have revenue to support their activities, although the impact of embargoes on publishers is hotly debated, with some studies finding no impact while publisher experience suggests otherwise. A 2012 survey of libraries by the Association of Learned, Professional, and Society Publishers on the likelihood of journal cancellations in cases where most of the content was made freely accessible after six months suggests there would be a major negative impact on subscriptions, but this result has been debated. Various types exist: A 'moving wall' is a fixed period of months or years. A fixed date is a particular time point that does not change. A current year (or other period) is setting a time point on Jan. 1 of the current year, so that all material earlier than that is available. Although fixed during the year, it will change each year. == Purpose == There are various purposes: For delayed open access journals, the embargo separates the most recent period, for which


id: eprint-20260505-0
file: data/en.wikipedia.org/wiki/Eprint-0.md
category: reference
title: Eprint
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Eprint
date_saved: 2026-05-05T10:15:08.666512+00:00
instance: kb-cron
parent: eprint-20260505-0
summary:
In academic publishing, an eprint or e-print is a digital version of a research document (usually a journal article, but could also be a thesis, conference paper, book chapter, or a book) that is accessible online, usually as green open access, whether from a local institutional or a central digital repository. When applied to journal articles, the term "eprints" covers both preprints (before peer review) and postprints (after peer review). Digital versions of materials other than research documents are not usually called e-prints, but some other name, such as e-books. == See also == Electronic article Electronic journal Electronic publishing Open access Open science == References == == External links == What is an eprint? as defined in the FAQ section of eprints.org Eprints as defined by Stevan Harnad


id: fair-access-to-science-and-technology-research-act-20260505-0
file: data/en.wikipedia.org/wiki/Fair_Access_to_Science_and_Technology_Research_Act-0.md
category: reference
title: Fair Access to Science and Technology Research Act
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fair_Access_to_Science_and_Technology_Research_Act
date_saved: 2026-05-05T10:15:09.839963+00:00
instance: kb-cron
parent: fair-access-to-science-and-technology-research-act-20260505-0
summary:
The Fair Access to Science and Technology Research Act (FASTR) is a bill in the United States that would mandate earlier public release of taxpayer-funded research. The bill has been introduced in 2013, 2015, and 2017. Sen. Ron Wyden (D-Ore.) and Sen. John Cornyn (R-Texas) introduced the Senate version, while the bill was introduced to the House by Reps. Zoe Lofgren (D-Calif.), Mike Doyle (D-Penn.) and Kevin Yoder (R-Kans.). The bill is a successor to the Federal Research Public Access Act (FRPAA), which had been introduced in 2006, 2010, and 2012. Senator Wyden advocated for the passage of the bill by arguing that "taxpayer funded research should never be hidden behind a paywall." FASTR has been described as "The Other Aaron's Law", named for open-access activist Aaron Swartz who died in a dramatic case in support of open access research in January 2013. The Senate Committee on Homeland Security and Governmental Affairs unanimously approved the bill on July 29, 2015. It was the first time that the bill or any of its predecessors had gained committee approval and been forwarded to a full house of Congress. The bill is often compared to and discussed in conjunction with the Public Access


id: federal-research-public-access-act-20260505-0
file: data/en.wikipedia.org/wiki/Federal_Research_Public_Access_Act-0.md
category: reference
title: Federal Research Public Access Act
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Federal_Research_Public_Access_Act
date_saved: 2026-05-05T10:15:11.008884+00:00
instance: kb-cron
parent: federal-research-public-access-act-20260505-0
summary:
The Federal Research Public Access Act (FRPAA) is a proposal to require open public access to research funded by eleven U.S. federal government agencies. It was originally proposed by Senators John Cornyn and Joe Lieberman in 2006 and then again in 2010, and then once more in 2012. A later version of the bill, the Fair Access to Science and Technology Research Act, was introduced in 2013, 2015 and 2017. == Provisions of bill == The FRPAA would require that those eleven agencies with research expenditures over $100 million, create online repositories of journal articles of the research completed by that agency and make them publicly available. They must be maintained and preserved by the agency, or another repository that permits free and open access. It must be available to users without charge within six months after it has been published in a peer-reviewed journal. The agencies included in this bill are: Department of Agriculture Department of Commerce Department of Defense Department of Education Department of Energy Department of Health and Human Services Department of Homeland Security Department of Transportation Environmental Protection Agency National Aeronautics and Space Administration National Science Foundation == Legislative history == == Reaction == === Support


id: force11-20260505-0
file: data/en.wikipedia.org/wiki/FORCE11-0.md
category: reference
title: FORCE11
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FORCE11
date_saved: 2026-05-05T10:15:12.231934+00:00
instance: kb-cron
parent: force11-20260505-0
summary:
FORCE11 is an international coalition of researchers, librarians, publishers and research funders working to reform or enhance the research publishing and communication system. Initiated in 2011 as a community of interest on scholarly communication, FORCE11 is a registered 501(c)(3) organization based in the United States but with members and partners around the world. Key activities include an annual conference, the Scholarly Communications Institute and a range of working groups. == History == FORCE11 grew out of the FORC Workshop held in Dagstuhl, Germany in August 2011. This meeting resulted in the collaborative creation of a white paper which summarized the problems of scholarly communication and proposed a vision to address them. == Activities == Through various working groups FORCE11 has undertaken a range of activities to improve the standards, interoperability and functionality of digital research communications and developed various statements on principles and policies for best practice. These include: FAIR Data Principles: The development of a set of principles based on making data Findable, Accessible, Interoperable, and Reusable (FAIR) Research Resource Identification Initiative (RRID): supporting new guidelines and identifiers in biomedical publications Joint Declaration of Data Citation Principles (JDDCP): intended to help achieve widespread, uniform human and machine accessibility of


id: free-journal-network-20260505-0
file: data/en.wikipedia.org/wiki/Free_Journal_Network-0.md
category: reference
title: Free Journal Network
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Free_Journal_Network
date_saved: 2026-05-05T10:15:13.437879+00:00
instance: kb-cron
parent: free-journal-network-20260505-0
summary:
The Free Journal Network is an index of open access scholarly journals, specifically for those that do not charge article processing charges. == Criteria == The network founded in early 2018 in order to promote free, open access journals, a publishing model that is sometimes called diamond or platinum open access. Such journals are typically smaller than equivalent commercial journals (often supported by academic societies). Main criteria include: adherence to the Fair Open Access Principles that are publicly supported by many renowned scientists, publication of article titles and abstracts in English, clear publication ethics and quality assurance policies. == FJN Member Journals == As of November 2024, there are 90 journals that have been accepted into the Free Journal Network. Some notable examples include: Discrete Analysis European Journal of Taxonomy Glossa Journal of Open Source Software Journal of Political Ecology Norwegian Journal of Geology SciPost Physics Volcanica == See also == Directory of Open Access Journals Open Access Scholarly Publishers Association == References == == External links == Official website


id: toby-green-publisher-20260505-0
file: data/en.wikipedia.org/wiki/Toby_Green_(publisher)-0.md
category: reference
title: Toby Green (publisher)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Toby_Green_(publisher)
date_saved: 2026-05-05T10:15:15.693892+00:00
instance: kb-cron
parent: toby-green-publisher-20260505-0
summary:
Toby Green is a scholarly publisher who has worked with Academic Press, Applied Science Publishers, Pergamon Press, Elsevier Science, and the Organisation for Economic Co-operation and Development (OECD). At OECD, Green launched important digital publishing initiatives such as SourceOECD (2001), its successor the OECD iLibrary (2010), and the OECD Better Life Index (2011). In 2017, OECD Publishing won the Academic and Professional Publisher Award from The London Book Fair. Green has chaired the Council of the Association of Learned and Professional Society Publishers (ALPSP, 2010) and served on the publishing board of the Royal Society of Chemistry and the board of Annual Reviews. In 2019, Green co-founded Coherent Digital to improve access to grey literature policy documents from IGOs, NGOs, research centers and think tanks. == Early life and education == Green was born in Broadwindsor, Dorset, England and educated at Marlborough College. He attended the University of Warwick in Coventry, receiving a BSc in Microbiology and Virology in 1980. == Career == Toby Green is a scholarly publisher who has worked with a wide range of organizations, publishing content for commercial organizations, scholarly societies, intergovernmental organizations (IGOs) and nongovernmental organizations (NGOs). Since 1982, Green has worked for Academic Press


id: grey-literature-network-service-20260505-0
file: data/en.wikipedia.org/wiki/Grey_Literature_Network_Service-0.md
category: reference
title: Grey Literature Network Service
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Grey_Literature_Network_Service
date_saved: 2026-05-05T10:15:16.890978+00:00
instance: kb-cron
parent: grey-literature-network-service-20260505-0
summary:
GreyNet International, the Grey Literature Network Service, is an independent organization founded in 1992. It is dedicated to research, publication, open access, education, and bringing public awareness to grey literature. Grey literature is often defined as "Information produced and distributed on all levels of government, academics, business and industry in electronic and print formats not controlled by commercial publishing i.e. where publishing is not the primary activity of the producing body." GreyNet is corporate author of the Proceeding issues from the International Conference Series on Grey Literature, The Grey Journal, An International Journal on Grey Literature, as well as other types of publications such as reports, program books, and newsletters. GreyNet also maintains a Listserv and a presence on a number of social media including LinkedIn, Netvibes, Twitter, and Facebook. GreyNet is a not for profit organization fostering the production and dissemination of scientific literature. It is also engaged in the open source movement and was invited to the 10th Libre Software Meeting 2009 in Nantes, France, with a communication on knowledge sharing in the field of grey literature. During the 11th International Conference on Grey Literature in December 2009, GreyNet signed a Partnership Agreement with ICSTI, International Council for


id: grey-literature-network-service-20260505-1
file: data/en.wikipedia.org/wiki/Grey_Literature_Network_Service-1.md
category: reference
title: Grey Literature Network Service
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Grey_Literature_Network_Service
date_saved: 2026-05-05T10:15:16.890978+00:00
instance: kb-cron
parent: grey-literature-network-service-20260505-0
summary:
== GreyNet and the OpenGrey Repository == For the past 20 years, GreyNet has sought to serve researchers and authors in the field of grey literature. To further this end, GreyNet has signed on to the OpenGrey repository and in so doing seeks to preserve and make openly available research results originating in the International Conference Series on Grey Literature. GreyNet together with INIST-CNRS have designed the format for a metadata record, which encompasses standardized PDF attachments of the full-text conference preprints, PowerPoint presentations, abstracts, biographical notes, and post-publication commentaries. GreyNet's collection of over 270 conference preprints is both current and comprehensive. Comment from Peter Suber, Open Access News (Thursday, January 29, 2009): GreyNet started making its conference proceedings OA through its repository in May 2008. I applaud its determination to complete the collection retroactively, even if it means buying permission from a publisher. Note to other conference organizers: This is a reason to self-archive your proceedings as you go, or at least to retain the right to self-archive them without a fee. == GreyNet and the DANS Data Archive == GreyNet’s research data is cross-linked to the corresponding conference preprint in OpenGrey via the DANS Data Archive. In this


id: grey-literature-network-service-20260505-2
file: data/en.wikipedia.org/wiki/Grey_Literature_Network_Service-2.md
category: reference
title: Grey Literature Network Service
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Grey_Literature_Network_Service
date_saved: 2026-05-05T10:15:16.890978+00:00
instance: kb-cron
parent: grey-literature-network-service-20260505-0
summary:
== Bibliography == Farace D. & Schöpfel J. (eds.) (2010). Grey Literature in Library and Information Studies. De Gruyter Saur. Schöpfel J., Stock C., Farace D.J., Frantzen J. Citation Analysis and Grey Literature: Stakeholders in the Grey Circuit. The Grey Journal 2005, vol. 1, n° 1, p. 31-40. hdl:10068/697850 Farace D., Frantzen J., Schöpfel J., Stock C. Knowledge Generation in the Field of Grey Literature: A Review of Conference-based Research Results]. GL8 Conference Proceedings. Eighth International Conference on Grey Literature: Harnessing the Power of Grey. New Orleans, 4–5 December 2006. hdl:10068/697768 Farace D.J., Frantzen J., Schöpfel J., Stock C. Grey Literature: A Pilot Course constructed and implemented via Distance Education. The Grey Journal 2008, vol. 4, n° 1, p. 41-45. hdl:10068/697878 Farace D., Frantzen J., Schöpfel J., Stock C., Henrot N. OpenSIGLE, Home to GreyNet’s Research Community and its Grey Literature Collections: Initial Results and a Project Proposal. GL10 Conference Proceedings. Tenth International Conference on Grey Literature: Designing the Grey Grid for Information Society. Amsterdam, 8–9 December 2008. Gelfand J. Interview with Dominic Farace, founder of GreyNet. International Journal on Grey Literature. 2000, vol. 1, n° 2, p. 73-76. Covers how Dominic Farace, the GreyNet director, first became involved in


id: guerilla-open-access-manifesto-20260505-0
file: data/en.wikipedia.org/wiki/Guerilla_Open_Access_Manifesto-0.md
category: reference
title: Guerilla Open Access Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Guerilla_Open_Access_Manifesto
date_saved: 2026-05-05T10:15:18.127551+00:00
instance: kb-cron
parent: guerilla-open-access-manifesto-20260505-0
summary:
The Guerilla Open Access Manifesto is a document published by (and widely attributed to) Aaron Swartz in 2008 that argues for transgressive approaches to achieving the goals of the open access movement through civil disobedience, willful violation of copyright and contracts that restrict redistribution of knowledge, and activities that exist in legal grey areas. The goal of the open access movement taken up by the manifesto include the removal of barriers and paywalls that prohibit the general public from accessing scientific research publications and other forms of data. While most of the open access movement has focused on standing up new open access publishers, working with traditional publishers to switch to open access, and organizing scholars who produce and edit articles, these focuses primarily affect the accessibility of future publications. The manifesto is largely concerned with the existing proprietary articles and data that are unlikely to be released as open access by the current copyright holders. The manifesto appears to have been written in 2008 at a meeting of librarians and was subsequently published on Swartz's personal blog. Although the authorship of the document is widely attributed to Swartz, his role in writing the manifesto and the degree to which


id: guerilla-open-access-manifesto-20260505-1
file: data/en.wikipedia.org/wiki/Guerilla_Open_Access_Manifesto-1.md
category: reference
title: Guerilla Open Access Manifesto
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Guerilla_Open_Access_Manifesto
date_saved: 2026-05-05T10:15:18.127551+00:00
instance: kb-cron
parent: guerilla-open-access-manifesto-20260505-0
summary:
== Text of the Manifesto == Information is power. But like all power, there are those who want to keep it for themselves. The world's entire scientific and cultural heritage, published over centuries in books and journals, is increasingly being digitized and locked up by a handful of private corporations. Want to read the papers featuring the most famous results of the sciences? You'll need to send enormous amounts to publishers like Reed Elsevier. There are those struggling to change this. The Open Access Movement has fought valiantly to ensure that scientists do not sign their copyrights away but instead ensure their work is published on the Internet, under terms that allow anyone to access it. But even under the best scenarios, their work will only apply to things published in the future. Everything up until now will have been lost. That is too high a price to pay. Forcing academics to pay money to read the work of their colleagues? Scanning entire libraries but only allowing the folks at Google to read them? Providing scientific articles to those at elite universities in the First World, but not to children in the Global South? It's outrageous and unacceptable. "I agree,"


