# kb

id: valdemar-poulsen-gold-medal-20260505-0
file: data/en.wikipedia.org/wiki/Valdemar_Poulsen_Gold_Medal-0.md
category: reference
title: Valdemar Poulsen Gold Medal
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Valdemar_Poulsen_Gold_Medal
date_saved: 2026-05-05T04:11:20.468780+00:00
instance: kb-cron
parent: valdemar-poulsen-gold-medal-20260505-0
summary:
The Valdemar Poulsen Gold Medal, named after radio pioneer Valdemar Poulsen, was awarded each year for outstanding research in the field of radio techniques and related fields by the Danish Academy of Technical Sciences. The award was presented on November 23, the anniversary of Poulsen's birth. The award was discontinued in 1993. == Recipients == 1939 Valdemar Poulsen 1946 Robert Watson-Watt 1947 Ernst Alexanderson 1948 Edward Victor Appleton 1953 Balthasar van der Pol 1956 Harald T. Friis 1958 Hidetsugu Yagi 1960 Charles P. Ginsburg 1963 John R. Pierce 1969 Jay Wright Forrester 1973 J. B. Gunn 1976 Andrew Bobeck == References ==


id: alan-t-waterman-award-20260505-0
file: data/en.wikipedia.org/wiki/Alan_T._Waterman_Award-0.md
category: reference
title: Alan T. Waterman Award
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Alan_T._Waterman_Award
date_saved: 2026-05-05T04:11:21.610466+00:00
instance: kb-cron
parent: alan-t-waterman-award-20260505-0
summary:
The Alan T. Waterman Award, named after Alan Tower Waterman, is the United States's highest honorary award for scientists no older than 40, or no more than 10 years past receipt of their Ph.D. It is awarded on a yearly basis by the National Science Foundation. In addition to the medal, the awardee receives a grant of $1,000,000 to be used at the institution of their choice over a period of five years for advanced scientific research. == History of the Award == The United States Congress established the annual award in August 1975 to mark the 25th Anniversary of the National Science Foundation and to honor its first director, Alan T. Waterman. The annual award recognizes an outstanding young researcher in any field of science or engineering supported by the National Science Foundation. == Eligibility and nomination process == Candidates must be U.S. citizens or permanent residents. Prior to the 2018 competition, candidates must have been 35 years of age or younger or not more than 7 years beyond receipt of the Ph.D. degree by December 31 of the year in which they are nominated. As of the 2018 competition, these requirements were changed to 40 years of age


id: alan-t-waterman-award-20260505-1
file: data/en.wikipedia.org/wiki/Alan_T._Waterman_Award-1.md
category: reference
title: Alan T. Waterman Award
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Alan_T._Waterman_Award
date_saved: 2026-05-05T04:11:21.610466+00:00
instance: kb-cron
parent: alan-t-waterman-award-20260505-0
summary:
== List of recipients == 2024 Muyinatu A. Lediju Bell "For pioneering innovations in ultrasound and photoacoustic imaging, particularly coherence-based beamforming, photoacoustic-guided surgery, and deep learning. These innovations cross interdisciplinary boundaries to improve medical image quality in patients, reduce patient deaths during surgery, inspire new surgical designs, and provide more equitable healthcare." Katrina G. Claw "For her contributions to pharmacogenomics and for fostering cultural and bioethical research participation within Indigenous communities." Rebecca Kramer-Bottiglio "For creating robots that adapt and evolve to changing conditions." 2023 Natalie S. King "For groundbreaking scholarship in science, technology, engineering and mathematics education that transcends disciplinary boundaries and directly impacts local and global communities, and for demonstrating exceptional research achievements with tremendous impact on the advancement of Black girls in science, the use of research-practice partnerships to drive K-12 instruction, and the increase of STEM teacher diversity." Asegun Henry "For significant contributions in new energy technologies and advanced fundamental understanding of heat transfer addressing a broad range of problems that span from the atomic scale (the physics of heat conduction) to the gigawatt scale (grid-level energy storage)." William Anderegg "For outstanding contributions to climate change science, particularly in advancing the understanding of the sensitivity, vulnerability, and


id: alan-t-waterman-award-20260505-2
file: data/en.wikipedia.org/wiki/Alan_T._Waterman_Award-2.md
category: reference
title: Alan T. Waterman Award
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Alan_T._Waterman_Award
date_saved: 2026-05-05T04:11:21.610466+00:00
instance: kb-cron
parent: alan-t-waterman-award-20260505-0
summary:
2004 Kristi Anseth For her research at the interface of biology and engineering, resulting in the design of innovative biomaterials that significantly facilitate tissue engineering and regeneration. 2003 Angelika Amon For her seminal contributions to understanding how cells orchestrate the segregation of their chromosomes during cell division, the key process of life 2002 Erich Jarvis For his use of gene expression as a tool to map brain functional systems and to identify parts of the brain involved in perceiving, learning and producing vocal communication. 2001 Vahid Tarokh For the invention of space-time coding techniques that produce dramatic gains in the spectral efficiency of wireless digital communication systems. 2000 Jennifer A. Doudna For innovative research that led to the development of a technique that facilitates crystallization of large RNA molecules; for determining the crystal structures of catalytic RNA molecules and an RNA molecule that forms the ribonucleo-protein core of the signal recognition particle; and for deciphering structural features of those molecules that permit a greater understanding of the mechanistic basis of RNA function in both catalysis and protein synthesis. 1999 Chaitan Khosla For his outstanding work in elucidating the mechanisms of enzyme biocatalysis of polyketides, thereby opening an exciting potential route


id: alan-t-waterman-award-20260505-3
file: data/en.wikipedia.org/wiki/Alan_T._Waterman_Award-3.md
category: reference
title: Alan T. Waterman Award
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Alan_T._Waterman_Award
date_saved: 2026-05-05T04:11:21.610466+00:00
instance: kb-cron
parent: alan-t-waterman-award-20260505-0
summary:
1976 Charles Fefferman For his research in Fourier analysis, partial differential equations and several complex variables which have brought fresh insight and renewed vigor to classical areas of mathematics and contributed signally to the advancement of modern mathematical analysis. == References == == External links == Alan T. Waterman Award NSF Page Alan T. Waterman Award 2021 Change of Eligibility Criteria


id: wrigley-trophy-20260505-0
file: data/en.wikipedia.org/wiki/Wrigley_Trophy-0.md
category: reference
title: Wrigley Trophy
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Wrigley_Trophy
date_saved: 2026-05-05T04:11:22.729521+00:00
instance: kb-cron
parent: wrigley-trophy-20260505-0
summary:
The Wrigley Trophy is an award given for motorboats. It was awarded as early as 1912 with a $1,500 cash prize. In 1912 the award was disputed when James A. Pugh contested the win by J. Stuart Blackton. He argued that Baby Reliance II was allowed a late entry and had already missed two rounds of competition. == Winners == Cassandra (raceboat); George Griffith (1960) Baby Reliance II, J. Stuart Blackton (1912) == References ==


id: abc-home-20260505-0
file: data/en.wikipedia.org/wiki/ABC@Home-0.md
category: reference
title: ABC@Home
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ABC@Home
date_saved: 2026-05-05T04:11:24.347254+00:00
instance: kb-cron
parent: abc-home-20260505-0
summary:
ABC@Home was an educational and non-profit network computing project finding abc-triples related to the abc conjecture in number theory using the Berkeley Open Infrastructure for Network Computing (BOINC) volunteer computing platform. In March 2011, there were more than 7,300 active participants from 114 countries with a total BOINC credit of more than 2.9 billion, reporting about 10 teraflops (10 trillion operations per second) of processing power. In 2011, the project met its goal of finding all abc-triples of at most 18 digits. By 2015, the project had found 23.8 million triples in total, and ceased operations soon after. == See also == List of volunteer computing projects == References == == External links == The Mathematical Institute of Leiden University


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 ==


