# kb

id: africa-home-20260505-0
file: data/en.wikipedia.org/wiki/Africa@home-0.md
category: reference
title: Africa@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Africa@home
date_saved: 2026-05-05T04:11:25.584781+00:00
instance: kb-cron
parent: africa-home-20260505-0
summary:
Africa@home is a website that allow users to use their home computers to contribute for humanitarian causes at Africa. This project first went public on 13 July 2006. It partners with Swiss Tropical Institute, the University of Geneva, CERN, and ICVolunteers (ICV). It is sponsored by the Geneva International Academic Network (GIAN). Africa@home together with ICVolunteers, recruited volunteers across Africa to help with the project. The Malaria Control Project (MCP) was the first and the only volunteer computing project run by Africa@home. MCP ran for 10 years and became inactive since 21 June 2016. == See also == Malaria Control Project List of volunteer computing projects Berkeley Open Infrastructure for Network Computing (BOINC) Volunteer computing Grid computing Geneva International Academic Network == References ==


id: aqua-home-20260505-0
file: data/en.wikipedia.org/wiki/AQUA@home-0.md
category: reference
title: AQUA@home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/AQUA@home
date_saved: 2026-05-05T04:11:26.742917+00:00
instance: kb-cron
parent: aqua-home-20260505-0
summary:
AQUA@home was a volunteer computing project operated by D-Wave Systems that ran on the Berkeley Open Infrastructure for Network Computing (BOINC) software platform. It ceased functioning in August 2011. Its goal was to predict the performance of superconducting adiabatic quantum computers on a variety of problems arising in fields ranging from materials science to machine learning. It designed and analyzed quantum computing algorithms, using Quantum Monte Carlo techniques. AQUA@home was the first BOINC project to provide multi-threaded applications. It was also the first project to deploy an OpenCL test application under BOINC. == References == == External links == Papers resulting from AQUA@home's computations


id: artificial-intelligence-system-20260505-0
file: data/en.wikipedia.org/wiki/Artificial_Intelligence_System-0.md
category: reference
title: Artificial Intelligence System
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Artificial_Intelligence_System
date_saved: 2026-05-05T04:11:28.011060+00:00
instance: kb-cron
parent: artificial-intelligence-system-20260505-0
summary:
Artificial Intelligence System (AIS) was a volunteer computing project undertaken by Intelligence Realm, Inc. with the long-term goal of simulating the human brain in real time, complete with artificial consciousness and artificial general intelligence. They claimed to have found, in research, the "mechanisms of knowledge representation in the brain which is equivalent to finding artificial intelligence", before moving into the developmental phase. == History == The project's initial goal was recreating the largest brain simulation to date, performed by neuroscientist Eugene M. Izhikevich of The Neurosciences Institute in San Diego, California. Izhikevich simulated 1 second of activity of 100 billion neurons (the estimated number of neurons in the human brain) in 50 days using a cluster of 27 3-gigahertz processors. He extrapolated that a real-time simulation of the brain could not be achieved before 2016. The project aimed to disprove this prediction. Artificial Intelligence System announced on Sep 5, 2007 that they will use the Berkeley Open Infrastructure for Network Computing (BOINC) software to perform intensive calculations. On July 12, 2008, the first phase of the project had been completed by reaching the 100 billion neuron mark. The project then continued to simulate neurons while they completed the development of


id: astropulse-20260505-0
file: data/en.wikipedia.org/wiki/Astropulse-0.md
category: reference
title: Astropulse
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Astropulse
date_saved: 2026-05-05T04:11:29.753725+00:00
instance: kb-cron
parent: astropulse-20260505-0
summary:
Astropulse is a volunteer computing project to search for primordial black holes, pulsars, and extraterrestrial intelligence (ETI). Volunteer resources are harnessed through the Berkeley Open Infrastructure for Network Computing (BOINC) platform. In 1999, the Space Sciences Laboratory launched SETI@home, which relied on a network of desktop computers around the world to provide massively parallel computation. SETI@home utilizes recorded data from the Arecibo radio telescope and searches for narrow-bandwidth radio signals from space, signifying the presence of extraterrestrial technology. It was soon recognized that this same data might be scoured for other signals of value to the astronomy and physics community. == Development == For about 6 years, Astropulse existed in an experimental beta testing phase not available to the general community. In July 2008, Astropulse was integrated into SETI@home, so that the massive network of SETI participants could also contribute to the search for other astronomical signals of value. Astropulse also makes contributions to the search for ET: first, project proponents believe it may identify a different type of ET signal not identified by the original SETI@Home algorithm; second, proponents believe it may create additional support for SETI by providing a second possible concrete result from the overall search project.


id: astropulse-20260505-1
file: data/en.wikipedia.org/wiki/Astropulse-1.md
category: reference
title: Astropulse
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Astropulse
date_saved: 2026-05-05T04:11:29.753725+00:00
instance: kb-cron
parent: astropulse-20260505-0
summary:
== Potential pulse finds == One goal of Astropulse is to detect postulated mini black holes that might be evaporating due to "Hawking radiation". Such mini black holes are postulated to have been created during the Big Bang, unlike currently known black holes. The Astropulse project hopes that this evaporation would produce radio waves that Astropulse can detect. The evaporation wouldn't create radio waves directly. Instead, it would create an expanding fireball of high-energy gamma rays and particles. This fireball would interact with the surrounding magnetic field, pushing it out and generating radio waves. Rotating radio transients (RRATs) are a type of neutron stars discovered in 2006 by a team led by Maura McLaughlin from the Jodrell Bank Observatory at the University of Manchester in the UK. RRATs are believed to produce radio emissions which are very difficult to locate, because of their transient nature. Early efforts have been able to detect radio emissions (sometimes called RRAT flashes) for less than one second a day, and, like with other single-burst signals, one must take great care to distinguish them from terrestrial radio interference. Distributing computing and the Astropulse algorithm may thus lend itself to further detection of RRATs. Pulses with


id: berkeley-open-infrastructure-for-network-computing-20260505-0
file: data/en.wikipedia.org/wiki/Berkeley_Open_Infrastructure_for_Network_Computing-0.md
category: reference
title: Berkeley Open Infrastructure for Network Computing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Berkeley_Open_Infrastructure_for_Network_Computing
date_saved: 2026-05-05T04:11:30.883606+00:00
instance: kb-cron
parent: berkeley-open-infrastructure-for-network-computing-20260505-0
summary:
The Berkeley Open Infrastructure for Network Computing (BOINC, pronounced –rhymes with "oink") is an open-source middleware system for volunteer computing (a type of distributed computing). Developed originally to support SETI@home, it became the platform for many other applications in areas as diverse as medicine, molecular biology, mathematics, linguistics, climatology, environmental science, and astrophysics, among others. The purpose of BOINC is to enable researchers to utilize processing resources of personal computers and other devices around the world. BOINC development began with a group based at the Space Sciences Laboratory (SSL) at the University of California, Berkeley, and led by David P. Anderson, who also led SETI@home. As a high-performance volunteer computing platform, BOINC brings together 34,236 active participants employing 136,341 active computers (hosts) worldwide, processing daily on average 20.164 PetaFLOPS as of 16 November 2021 (it would be the 21st largest processing capability in the world compared with an individual supercomputer). The National Science Foundation (NSF) funds BOINC through awards SCI/0221529, SCI/0438443 and SCI/0721124. Guinness World Records ranks BOINC as the largest computing grid in the world. BOINC code runs on various operating systems, including Microsoft Windows, macOS, Android, Linux, and FreeBSD. BOINC is free software released under the terms of


id: big-and-ugly-rendering-project-20260505-0
file: data/en.wikipedia.org/wiki/Big_and_Ugly_Rendering_Project-0.md
category: reference
title: Big and Ugly Rendering Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Big_and_Ugly_Rendering_Project
date_saved: 2026-05-05T04:11:32.009870+00:00
instance: kb-cron
parent: big-and-ugly-rendering-project-20260505-0
summary:
Big and Ugly Rendering Project (BURP) is a non-commercial volunteer computing project using the BOINC framework for the rendering of 3D graphics that has been in hibernation as of 2020. The project website currently shows the status as "extended maintenance" until 2027. BURP utilizes the volunteer and grid computing software BOINC, to distribute computing tasks to volunteer computers. BURP is free software distributed under the GNU General Public License V3. Because BURP is used to refer to both the BOINC project and BURP back-end software, some confusion can arise when talking about other services running the BURP software. == History == The main BURP website went online on 17 June 2004. At that time the only supported renderer was YafaRay (Yet Another Free Raytracer). That August it became clear that YafaRay was not the best choice, and focus was shifted towards Blender, a renderer with more features and a compact file format. By the end of October enough tests had been done to show that not only is the distributed rendering of 3D animations possible, it can achieve performance that rivals many commercial render farms. The current trend of increasing network bandwidth throughout the world will make it even more


id: boinc-client-server-technology-20260505-0
file: data/en.wikipedia.org/wiki/BOINC_client–server_technology-0.md
category: reference
title: BOINC client–server technology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BOINC_client–server_technology
date_saved: 2026-05-05T04:11:37.325441+00:00
instance: kb-cron
parent: boinc-client-server-technology-20260505-0
summary:
BOINC client–server technology refers to the model under which BOINC works. The BOINC framework consists of two layers which operate under the client–server architecture. Once the BOINC software is installed in a machine, the server starts sending tasks to the client. The operations are performed client-side and the results are uploaded to the server-side. == Design and structure of BOINC == BOINC is designed to be a free structure for anyone wishing to start a distributed computing project. BOINC consists of a server system and client software that communicate with each other to distribute, process, and return workunits. == Server structure == A major part of the BOINC system is the backend server. The server can run on one or many machines to allow BOINC to scale easily to projects of any size. BOINC servers run on Linux-based computers and use Apache, PHP, and MySQL for their web and database systems. Scientific computations run on participants' computers. After uploading from the user's client to a science investigator's database, the backend server validates and analyzes the results. The validation process involves running all tasks on multiple contributor PCs and comparing the results. BOINC servers also provide these features: homogeneous redundancy (sending


id: boinc-credit-system-20260505-0
file: data/en.wikipedia.org/wiki/BOINC_Credit_System-0.md
category: reference
title: BOINC Credit System
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BOINC_Credit_System
date_saved: 2026-05-05T04:11:38.564783+00:00
instance: kb-cron
parent: boinc-credit-system-20260505-0
summary:
Within the BOINC platform for volunteer computing, the BOINC Credit System helps volunteers keep track of how much CPU time they have donated to various projects. This ensures users are returning accurate results for both scientific and statistical reasons. == Purposes for a credit system == Online distributed computing relies heavily, if not entirely, on volunteer computers. For this reason, projects such as SETI@home and other BOINC projects depend on a complicated balance among long-term users and the cycle of new users and retiring users. === Reasons for participation === Efficiency: Using a computer's resources which would otherwise be idle. To contribute to scientific research in general. To advance a specific field. To stress test computers, as with overclocking. For competition's sake (by joining a team). Some individuals and teams run numerous computers, with some dedicated specifically to BOINC in hopes of climbing to the top of the world charts. Some teams are institutional, e.g. universities and research centers. For personal benefit and recognition. Projects such as NASA's PlanetQuest plan on allowing individuals to name those planets discovered using their computers. Projects such as BURP, and Leiden Classical allow users to submit their own operations for use in the system.


id: charity-engine-20260505-0
file: data/en.wikipedia.org/wiki/Charity_Engine-0.md
category: reference
title: Charity Engine
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Charity_Engine
date_saved: 2026-05-05T04:11:39.893406+00:00
instance: kb-cron
parent: charity-engine-20260505-0
summary:
Charity Engine is a free PC app based on Berkeley University's BOINC software, run by The Worldwide Computer Company Limited. The project works by selling spare home computing power to universities and corporations, then sharing the profits between eight partner charities and periodic cash prize draws for the users; those running the Charity Engine BOINC software on their home computers. When there are no corporations purchasing the computing power, Charity Engine donates it to existing volunteer computing projects such as Rosetta@home, Einstein@Home, and Malaria Control, and prize draws are funded by donations. The company was founded by former journalist Mark McAndrew, who was writing a science fiction novel featuring a similar organisation. He abandoned the book in favour of creating the idea in real life, with the assistance of professor David Anderson from UC Berkeley who created BOINC. The company was incorporated in 2008, but did not start trading until 2011. The company received €70,000 of EU innovation funding through the Framework Programme 7 (FP7). == Protein folding == In August 2014 the Rosetta@home project reported Charity Engine had contributed over 125,000 new PCs to its grid. In January 2017, Charity Engine was credited as a significant contributor to solving


id: clean-energy-project-20260505-0
file: data/en.wikipedia.org/wiki/Clean_Energy_Project-0.md
category: reference
title: Clean Energy Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Clean_Energy_Project
date_saved: 2026-05-05T04:11:41.061221+00:00
instance: kb-cron
parent: clean-energy-project-20260505-0
summary:
The Clean Energy Project (CEP) was a virtual high-throughput discovery and design effort for the next generation of plastic solar cell materials that has finished. It studies millions of candidate structures to identify suitable compounds for the harvesting of renewable energy from the sun and for other organic electronic applications. It ran on the BOINC platform. == Project purpose == The project searched for the most suitable organic compounds with which to make solar cells, the best polymeric membranes with which to make fuel cells, and how best to assemble the molecules for such devices. == Current project status == On June 24, 2013, the Clean Energy Project released its database to the public and the research community. The release was featured on the White House Blog and by several news organizations including the MIT Technology Review. The database contains 150 million density functional theory calculations on 2.3 million molecules. == Publications == C. Amador-Bedolla, R. Olivares-Amaya, J. Hachmann, A. Aspuru-Guzik, Towards Materials Informatics for Organic Photovoltaics, in Informatics for Materials Science and Engineering, K. Rajan, Ed., Elsevier, Amsterdam (2013). In press. R. Olivares-Amaya, C. Amador-Bedolla, J. Hachmann, S. Atahan-Evrenk, R.S. Sánchez-Carrera, L. Vogt, A. Aspuru-Guzik, Accelerated Computational Discovery of


id: climateprediction-net-20260505-0
file: data/en.wikipedia.org/wiki/Climateprediction.net-0.md
category: reference
title: Climateprediction.net
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Climateprediction.net
date_saved: 2026-05-05T04:11:42.306440+00:00
instance: kb-cron
parent: climateprediction-net-20260505-0
summary:
climateprediction.net (CPDN) is a volunteer computing project to investigate and reduce uncertainties in climate modelling. It aims to do this by running hundreds of thousands of different models (a large climate ensemble) using the donated idle time of ordinary personal computers, thereby leading to a better understanding of how models are affected by small changes in the many parameters known to influence the global climate. The project relies on the BOINC framework where voluntary participants agree to run some processes of the project at the client-side in their personal computers after receiving tasks from the server-side for treatment. CPDN, which is run primarily by Oxford University in England, has harnessed more computing power and generated more data than any other climate modelling project. It has produced over 100 million model years of data so far. As of June 2016, there are more than 12,000 active participants from 223 countries with a total BOINC credit of more than 27 billion, reporting about 55 teraflops (55 trillion operations per second) of processing power. == Aims == The aim of the climateprediction.net project is to investigate the uncertainties in various parameterizations that have to be made in state-of-the-art climate models. The model is


id: climateprediction-net-20260505-1
file: data/en.wikipedia.org/wiki/Climateprediction.net-1.md
category: reference
title: Climateprediction.net
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Climateprediction.net
date_saved: 2026-05-05T04:11:42.306440+00:00
instance: kb-cron
parent: climateprediction-net-20260505-0
summary:
Classic Slab Model - The original experiment not under BOINC. See #The original model for further details. This model remains in use solely for the OU short course. BOINC Slab Model - The same as the classic Slab Model, but released under BOINC. ThermoHaline Circulation (THC) Model - An investigation of how the climate might change in the event of a decrease in the strength of the ThermoHaline Circulation. This experiment has now been closed to new participants as they have sufficient results. It was a four phase model totaling 60 model years. The first three phases were identical to the above Slab Models. The fourth phase imposed the effects of a 50% slowdown in the Thermohaline circulation by imposing SST changes in the north Atlantic derived from other runs. Sulfur Cycle Model - An investigation of the effect of sulfate aerosols on the climate. The experiment will model sulfur in a number of compound forms including dimethyl sulfide and sulfate aerosols. This experiment started in August 2005 and was a pre-requirement for the Hindcast. It is a 5 phase model totalling 75 model years. Timesteps are around 70% longer, making the model around 2.8 times longer than the initial


id: climateprediction-net-20260505-2
file: data/en.wikipedia.org/wiki/Climateprediction.net-2.md
category: reference
title: Climateprediction.net
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Climateprediction.net
date_saved: 2026-05-05T04:11:42.306440+00:00
instance: kb-cron
parent: climateprediction-net-20260505-0
summary:
=== Explanation === Climate sensitivity is defined as the equilibrium response of global mean temperature to doubling levels of carbon dioxide. As of June 2025, current levels of carbon dioxide are 426 ppm and grew by 3.72ppm in the last year, compared with preindustrial levels of 280 ppm. Climate sensitivities of greater than 5 °C are widely accepted as being catastrophic. The possibility of such high sensitivities being plausible given observations had been reported prior to the climateprediction.net experiment but "this is the first time GCMs have produced such behaviour". Even the models with very high climate sensitivity were found to be "as realistic as other state-of-the-art climate models". The test of realism was done with a root mean square error test. This does not check on realism of seasonal changes and it is possible that more diagnostic measures may place stronger constraints on what is realistic. Improved realism tests are being developed. It is important to the experiment and the goal of obtaining a probability distribution function (pdf) of climate outcomes to get a very wide range of behaviours even if only to rule out some behaviours as unrealistic. Larger sets of simulations have more reliable pdfs. Therefore, models


id: climateprediction-net-20260505-3
file: data/en.wikipedia.org/wiki/Climateprediction.net-3.md
category: reference
title: Climateprediction.net
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Climateprediction.net
date_saved: 2026-05-05T04:11:42.306440+00:00
instance: kb-cron
parent: climateprediction-net-20260505-0
summary:
== Visualisations == Many volunteer computing projects have screensavers to visually indicate the activity of the application, but they do not usually show its results as they are being calculated. By contrast, climateprediction.net not only uses a built-in visualisation to show the climate of the world being modelled, but it is interactive which allows different aspects of climate (temperature, rainfall, etc.) to be displayed. In addition, there are other, more advanced visualisation programs that allow the user to see more of what the model is doing (usually by analysing previously generated results) and to compare different runs and models. The real-time desktop visualisation for the model launched in 2003 was developed by Jeremy Walton at NAG, enabling users to track the progress of their simulation as the cloud cover and temperature changes over the surface of the globe. Other, more advanced visualisation programs in use include CPView and IDL Advanced Visualisation. They have similar functionality. CPView was written by Martin Sykes, a participant in the experiment. The IDL Advanced Visualisation was written by Andy Heaps of the University of Reading (UK), and modified to work with the BOINC version by Tesella Support Services plc. Only CPView allows you to look


id: cosmology-home-20260505-0
file: data/en.wikipedia.org/wiki/Cosmology@Home-0.md
category: reference
title: Cosmology@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cosmology@Home
date_saved: 2026-05-05T04:11:43.586816+00:00
instance: kb-cron
parent: cosmology-home-20260505-0
summary:
Cosmology@Home was a volunteer computing project that used the BOINC platform and was formerly run at the Departments of Astronomy and Physics at the University of Illinois at Urbana-Champaign. The project has moved to the Institut Lagrange de Paris and the Institut d'Astrophysique de Paris, both of which are located in the Pierre and Marie Curie University. == Goals == The goal of Cosmology@Home is to compare theoretical models of the universe to the data measured to date and search for the model that best matches it. Other goals may include: results from Cosmology@Home can help design future cosmological observations and experiments. results from Cosmology@Home can help prepare for the analysis of future data sets, e.g. from the Planck spacecraft. == Science == The goal of Cosmology@Home is to search for the model that best describes our Universe and to find the range of models that agree with the available astronomical and particle physics data. The models generated by Cosmology@Home can be compared to measurements of the universe's expansion speed from the Hubble Space Telescope as well as fluctuations in the Cosmic microwave background as measured by the Wilkinson Microwave Anisotropy Probe. == Method == Cosmology@Home uses an innovative way


id: discovering-dengue-drugs-together-20260505-0
file: data/en.wikipedia.org/wiki/Discovering_Dengue_Drugs_–_Together-0.md
category: reference
title: Discovering Dengue Drugs – Together
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Discovering_Dengue_Drugs_–_Together
date_saved: 2026-05-05T04:11:44.894086+00:00
instance: kb-cron
parent: discovering-dengue-drugs-together-20260505-0
summary:
Discovering Dengue Drugs – Together (DDDT) was a World Community Grid project launched in 2007 sponsored by scientists at the University of Texas Medical Branch at Galveston and the University of Chicago. Its goal was to identify new antiviral drugs effective against viruses from the family Flaviviridae. The project closed in 2015. Like all World Community Grid projects, Discovering Dengue Drugs – Together used a computational grid made up of thousands of client computers belonging to independent volunteers, in conjunction with servers that distribute portions of the work to the clients. == Phase 1 == The first phase of the project launched on August 21, 2007 and used AutoDock 2007 (the same software used for FightAIDS@Home) to test how well antiviral drug candidates are predicted to bind to the target virus's proteases. The specific viruses targeted by the project were: Dengue virus Hepatitis C virus West Nile virus Yellow fever virus Compounds predicted to bind strongly (protease inhibitors) were identified as potential antiviral drug candidates. Around 2.2 million compounds were examined in Phase 1, with around 20,000 of the best scoring compounds being passed to the second phase of the project. In 2008, the project was temporarily suspended because of


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: 2026-05-05T04:11:46.073356+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: 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: 2026-05-05T04:11:47.567076+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: 2026-05-05T04:11:47.567076+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: 2026-05-05T04:11:47.567076+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: 2026-05-05T04:11:47.567076+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: 2026-05-05T04:11:47.567076+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: 2026-05-05T04:11:47.567076+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: 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: 2026-05-05T04:11:48.742363+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: 2026-05-05T04:11:54.396386+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: fightaids-home-20260505-0
file: data/en.wikipedia.org/wiki/FightAIDS@Home-0.md
category: reference
title: FightAIDS@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FightAIDS@Home
date_saved: 2026-05-05T04:11:55.590840+00:00
instance: kb-cron
parent: fightaids-home-20260505-0
summary:
FightAIDS@Home is a volunteer computing project operated by the Olson Laboratory at The Scripps Research Institute. It runs on internet-connected home computers, and since July 2013 also runs on Android smartphones and tablets. It aims to use biomedical software simulation techniques to search for ways to cure or prevent the spread of HIV/AIDS. == Methods == Olson's target is HIV protease, a key molecular machine of the virus that when blocked stops it from maturing. These blockers, known as "protease inhibitors", are thus a way of avoiding the onset of AIDS and prolonging life. The Olson Laboratory is using computational methods to identify new candidate drugs that have the right shape and chemical characteristics to block HIV protease. This general approach is called structure-based drug design, and according to the National Institutes of Health's National Institute of General Medical Sciences, it has already had a dramatic effect on the lives of people living with AIDS. FightAIDS@Home makes use of the AutoDock VINA software, which tests how well a particular molecule binds to the HIV-1 protease. In October 2015 FightAIDS@Home Phase 2 was launched, using the computationally intensive Binding Energy Distribution Analysis Method (BEDAM) to "more thoroughly evaluate the top candidates


id: fiocruz-genome-comparison-project-20260505-0
file: data/en.wikipedia.org/wiki/Fiocruz_Genome_Comparison_Project-0.md
category: reference
title: Fiocruz Genome Comparison Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Fiocruz_Genome_Comparison_Project
date_saved: 2026-05-05T04:11:56.811012+00:00
instance: kb-cron
parent: fiocruz-genome-comparison-project-20260505-0
summary:
The Fiocruz Genome Comparison Project is a collaborative effort involving Brazil's Oswaldo Cruz Institute and IBM's World Community Grid, designed to produce a database comparing the genes from many genomes with each other using SSEARCH. The program SSEARCH performs a rigorous Smith–Waterman alignment between a protein sequence and another protein sequence, a protein database, a DNA or a DNA library. The nature of the computation in the project allows it to easily take advantage of volunteer computing. This, along with the likely humanitarian benefits of the research, has led the World Community Grid (a volunteer computing grid that uses idle computer clock time) to run the Fiocruz project. All products are in the public domain by contract with WCG. == Description == The problem is that a very large information body (structural, functional, cross-references, etc.) is attached to protein database entries. Once entered the information is rarely updated or corrected. This annotation of predicted protein function is often incomplete, uses non-standard nomenclature or can be incorrect when cross referenced from previous sometimes incorrectly annotated sequences. Additionally, many proteins composed of several structural and/or functional domains are overlooked by automated systems. The comparative information today is huge when compared to the


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: 2026-05-05T04:11:58.147758+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: 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: 2026-05-05T04:11:59.346167+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: gridcoin-20260505-0
file: data/en.wikipedia.org/wiki/Gridcoin-0.md
category: reference
title: Gridcoin
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Gridcoin
date_saved: 2026-05-05T04:12:00.566746+00:00
instance: kb-cron
parent: gridcoin-20260505-0
summary:
Gridcoin (abbreviation: GRC) is a cryptocurrency which rewards volunteer computing performed on the BOINC platform. BOINC was originally developed to support SETI@home, but has also been used for research in other areas. Gridcoin was created on October 16, 2013, by Rob Halförd. Initially using the energy-intensive proof of work, as used by Bitcoin, Gridcoin migrated to a proof of stake protocol in 2014, similar to Peercoin, in an attempt to address the environmental impact of cryptocurrency mining. An exploit was demonstrated in August 2017 that revealed the email addresses of Gridcoin users and allowed the theft of other users' work. The research team had disclosed the vulnerability to the developers in September 2016, and a patch was released in March 2017 with version 3.5.8.7. However the implementation of the fix introduced other issues. The implementation Gridcoin-Research was created as a fork of Bitcoin and Peercoin and is licensed under the MIT License. It uses Qt 5 for its user interface and prebuilt executables of the wallet are distributed for Windows, macOS, and Debian. == References == == External links == Official website


id: gridrepublic-20260505-0
file: data/en.wikipedia.org/wiki/GridRepublic-0.md
category: reference
title: GridRepublic
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/GridRepublic
date_saved: 2026-05-05T04:12:01.716639+00:00
instance: kb-cron
parent: gridrepublic-20260505-0
summary:
GridRepublic is a BOINC Account Manager. It focuses on creating a clean and simple way to join and interact with BOINC. GridRepublic was started with a mission to raise public awareness and participation in volunteer computing with BOINC. GridRepublic was formed in 2004 by Matthew Blumberg as a mechanism to control the multiple projects from one place. The code for the BOINC software had to be redesigned to allow for the Account Manager system to be implemented. GridRepublic's website has won numerous awards including being named finalist at the 2007 SXSW Interactive Festival and the 2008 Stockholm Challenge. GridRepublic has also been the recipients of a Google Grant allowing for advertising through Google. == Projects == GridRepublic supports a wide range of the BOINC projects. The list of supported projects and the development status of projects are periodically updated. Some of its popular projects include: Climateprediction.net Climate change modeling on personal computers Einstein@home Pulsar stars from LIGO and GEO data Rosetta@home Protein folding research SETI@home Searching radio and light data for signs of intelligent life == Software == GridRepublic is a non-profit organisation, an online application, and software. The software is open source and a customized version of BOINC. ==


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: 2026-05-05T04:12:02.985444+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: 2026-05-05T04:12:04.139358+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: 2026-05-05T04:12:05.359382+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


