# kb

id: archivist-20260505-3
file: data/en.wikipedia.org/wiki/Archivist-3.md
category: reference
title: Archivist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Archivist
date_saved: 2026-05-05T10:09:39.518023+00:00
instance: kb-cron
parent: archivist-20260505-0
summary:
== History of the profession == The first predecessors of archival science in the West are Jacob von Rammingen's manuals of 1571. and Baldassarre Bonifacio's De Archivis libris singularis of 1632. In 1883, French archivist Gabriel Richou published the first Western text on archival theory, entitled Traité théorique et pratique des archives publiques (Treaty of Theory and Practice of the Public Archives), in which he systematized the archival theory of the respect des fonds, first published by Natalis de Wailly in 1841. In 1898, three Dutch archivists, Samuel Muller, Johan Feith, and Robert Fruin, published the Handleiding voor het ordenen en beschrijven van archieven (Manual for the Arrangement and Description of Archives). Produced for the Dutch Association of Archivists, it set out one hundred rules for archivists to base their work around. Notably, within these rules, the principle of preserving provenance and original order was first argued for as an essential trait of archival arrangement and description. Many of these principles were subsequently adopted and developed by the British archivist Hilary Jenkinson in his Manual of Archive Administration, first published in 1922, with a revised edition appearing in 1937. In 1956, T. R. Schellenberg, known as the "Father of American


id: economist-20260505-0
file: data/en.wikipedia.org/wiki/Economist-0.md
category: reference
title: Economist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economist
date_saved: 2026-05-05T10:09:41.732649+00:00
instance: kb-cron
parent: economist-20260505-0
summary:
An economist is a professional and practitioner in the social science discipline of economics. The individual may also study, develop, and apply theories and concepts from economics and write about economic policy. Within this field there are many sub-fields, ranging from the broad philosophical theories to the focused study of minutiae within specific markets, macroeconomic analysis, microeconomic analysis or financial statement analysis, involving analytical methods and tools such as econometrics, statistics, economics computational models, financial economics, regulatory impact analysis and mathematical economics. == History of the profession == Although economists are now recognized as specialists in a distinct profession, the earliest contributors to economic thought were polymath philosophers. Some of the earliest economic writings date to antiquity, notably Aristotle’s Oeconomica and Xenophon’s Oeconomicus. These works addressed, in rudimentary form, themes such as the division of labour. In the seventeenth century, as early modern economic thought began to take shape, its leading writers were typically broad-ranging scholars who wrote on economics alongside philosophy and law. This was true of Richard Cantillon, who described what later came to be known as the Cantillon effect, and of David Hume, who anticipated the quantity theory of money. Adam Smith, in An Inquiry into the


id: economist-20260505-1
file: data/en.wikipedia.org/wiki/Economist-1.md
category: reference
title: Economist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economist
date_saved: 2026-05-05T10:09:41.732649+00:00
instance: kb-cron
parent: economist-20260505-0
summary:
==== By country ==== Economics graduates are employable in varying degrees depending on the regional economic scenario and labour market conditions at the time for a given country. Apart from the specific understanding of the subject, employers value the skills of numeracy and analysis, the ability to communicate and the capacity to grasp broad issues which the graduates acquire at the university or college. Whilst only a few economics graduates may be expected to become professional economists, many find it a base for entry into a career in finance – including accounting, insurance, tax and banking, or management. A number of economics graduates from around the world have been successful in obtaining employment in a variety of major national and international firms in the financial and commercial sectors, and in manufacturing, retailing and IT, as well as in the public sector – for example, in the health and education sectors, or in government and politics. Some graduates go on to undertake postgraduate studies, either in economics, research, teacher training or further qualifications in specialist areas. ===== Brazil ===== Unlike most nations, the economist profession in Brazil is regulated by law; specifically, Law No. 1,411, of August 13, 1951. The professional


id: economist-20260505-2
file: data/en.wikipedia.org/wiki/Economist-2.md
category: reference
title: Economist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economist
date_saved: 2026-05-05T10:09:41.732649+00:00
instance: kb-cron
parent: economist-20260505-0
summary:
==== Finance and professional orientation ==== The growing centrality of finance goes hand in hand with the rise of business schools, which employ an increasingly large share of economists and occupy a growing place in top journals, while authors affiliated with public institutions become less common. Symbolically, this shifts the intellectual and political centre of gravity of the profession towards the world of business, with greater support for the private sector, deregulation, and limits on public intervention, and it fuels the idea of a partial convergence between the interests of the profession and those of finance, which may help explain changes in economists’ political stances. In this context, scholars also describe a “fix-it” culture among economists: a strong confidence in their ability to propose solutions and correct malfunctions, relying on tools they regard as effective and scientific. This suggests that this influence can reinforce the higher status often accorded to economists, while also leaving them particularly exposed in the public arena: in times of crisis, they are among the first to be questioned and blamed. ==== Influence in the state ==== Some authors report a paradox in this diagnosis of the “superiority” of economists. From the outside, as mentioned, economists


id: economist-20260505-3
file: data/en.wikipedia.org/wiki/Economist-3.md
category: reference
title: Economist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Economist
date_saved: 2026-05-05T10:09:41.732649+00:00
instance: kb-cron
parent: economist-20260505-0
summary:
=== Social composition, values and public image === Economics as a discipline is heavily male-dominated, compared with many other social sciences, and its practitioners also tend to come from relatively higher social strata. Economists form a professional group with a distinctive profile, whether in their behaviour, their political views, their social position, or their relationship to power. Like much of the academic world, they tend to place themselves somewhat to the left of centre, but on average they are less interventionist than their colleagues and appear to have a particular relationship to individual self-interest. It is difficult, however, to determine whether this relationship is produced by economic training itself, or whether individuals already more oriented towards self-interest are simply more likely to choose economics in the first place. Their views are often out of step with those of the general public, for example in their support for market-based mechanisms to address social issues (such as paying organ donors, or using carbon taxes). Their relative material comfort tends to widen the social distance that separates them from other groups, which raises questions given that economists occupy important positions at the heart of decision-making structures, and that their discipline is closely linked


id: geographer-20260505-0
file: data/en.wikipedia.org/wiki/Geographer-0.md
category: reference
title: Geographer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Geographer
date_saved: 2026-05-05T10:09:42.948366+00:00
instance: kb-cron
parent: geographer-20260505-0
summary:
A geographer is a physical scientist, social scientist or humanist whose area of study is geography, the study of Earth's natural environment and human society, including how society and nature interact. The Greek prefix "geo" means "earth" and the Greek suffix, "graphy", meaning "description", so a geographer is someone who studies the earth. The word "geography" is a Middle French word that is believed to have been first used in 1540. Although geographers are historically known as people who make maps, map making is actually the field of study of cartography, a subset of geography. Geographers do not study only the details of the natural environment or human society, but they also study the reciprocal relationship between these two. For example, they study how the natural environment contributes to human society and how human society affects the natural environment. In particular, physical geographers study the natural environment while human geographers study human society and culture. Some geographers are practitioners of GIS (geographic information system) and are often employed by local, state, and federal government agencies as well as in the private sector by environmental and engineering firms. The paintings by Johannes Vermeer titled The Geographer and The Astronomer are both


id: psychologist-20260505-0
file: data/en.wikipedia.org/wiki/Psychologist-0.md
category: reference
title: Psychologist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychologist
date_saved: 2026-05-05T10:09:44.175778+00:00
instance: kb-cron
parent: psychologist-20260505-0
summary:
A psychologist is a professional who practices psychology and studies mental states, perceptual, cognitive, emotional, and social processes and behavior. Their work often involves the experimentation, observation, and interpretation of how individuals relate to each other and to their environments. Psychologists usually acquire a bachelor's degree in psychology, followed by a master's degree or doctorate in psychology. Unlike psychiatrists and psychiatric nurse-practitioners, psychologists usually cannot prescribe medication, but depending on the jurisdiction, some psychologists with additional training can be licensed to prescribe medications; qualification requirements may be different from a bachelor's degree and master's degree. Psychologists receive extensive training in psychological testing, communication techniques, scoring, interpretation, and reporting, while psychiatrists are not usually trained in psychological testing. Psychologists are also trained in, and often specialize in, one or more psychotherapies to improve symptoms of many mental disorders, including but not limited to treatment for anxiety, depression, post-traumatic stress disorder, schizophrenia, bipolar disorder, personality disorders and eating disorders. Treatment from psychologists can be individual or in groups. Cognitive behavioral therapy is a commonly used, well studied and high efficacy psychotherapy practiced by psychologists. Psychologists can work with a range of institutions and people, such as schools, prisons, in a private clinic,


id: psychologist-20260505-1
file: data/en.wikipedia.org/wiki/Psychologist-1.md
category: reference
title: Psychologist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychologist
date_saved: 2026-05-05T10:09:44.175778+00:00
instance: kb-cron
parent: psychologist-20260505-0
summary:
== Germany == In Germany, the use of the title Diplom-Psychologe (Dipl.-Psych.) is restricted by law, and a practitioner is legally required to hold the corresponding academic title, which is comparable to a M.Sc. degree and requires at least five years of training at a university. Originally, a diploma degree in psychology awarded in Germany included the subject of clinical psychology. With the Bologna-reform, this degree was replaced by a master's degree. The academic degree of Diplom-Psychologe or M.Sc. (Psychologie) does not include a psychotherapeutic qualification, which requires three to five years of additional training. The psychotherapeutic training combines in-depth theoretical knowledge with supervised patient care and self-reflection units. After having completed the training requirements, psychologists take a state-run exam, which, upon successful completion (Approbation), confers the official title of "psychological psychotherapist" (Psychologischer Psychotherapeut). After many years of inter-professional political controversy, non-physician psychotherapy was given an adequate legal foundation through the creation of two new academic healthcare professions. == Greece == Since 1979, the title "psychologist" has been protected by law in Greece. It can only be used by people who hold a relevant license or certificate, which is issued by the Greek authorities, to practice as a psychologist. The


id: psychologist-20260505-2
file: data/en.wikipedia.org/wiki/Psychologist-2.md
category: reference
title: Psychologist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychologist
date_saved: 2026-05-05T10:09:44.175778+00:00
instance: kb-cron
parent: psychologist-20260505-0
summary:
== United Kingdom == In the UK, "registered psychologist" and "practitioner psychologist" are protected titles. The title of "neuropsychologist" is not protected. In addition, the following specialist titles are also protected by law: "clinical psychologist", "counselling psychologist", "educational psychologist", "forensic psychologist", "health psychologist", "occupational psychologist" and "sport and exercise psychologist". The Health and Care Professions Council (HCPC) is the statutory regulator for practitioner psychologists in the UK. In the UK, the use of the title "chartered psychologist" is also protected by statutory regulation, but that title simply means that the psychologist is a chartered member of the British Psychological Society, but is not necessarily registered with the HCPC. However, it is illegal for someone who is not in the appropriate section of the HCPC register to provide psychological services. The requirement to register as a clinical, counselling, or educational psychologist is a professional doctorate (and in the case of the latter two the British Psychological Society's Professional Qualification, which meets the standards of a professional doctorate). The title of "psychologist", by itself, is not protected. The British Psychological Society is working with the HCPC to ensure that the title of "neuropsychologist" is regulated as a specialist title for practitioner psychologists.


id: psychologist-20260505-3
file: data/en.wikipedia.org/wiki/Psychologist-3.md
category: reference
title: Psychologist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychologist
date_saved: 2026-05-05T10:09:44.175778+00:00
instance: kb-cron
parent: psychologist-20260505-0
summary:
Bases of behavior—biological, cognitive-affective, and cultural-social Individual differences—personality, lifespan development, psychopathology History and systems—development of psychological theories, practices and scientific knowledge Clinical practice—diagnostics, psychological assessment, psychotherapeutic interventions, psychopharmacology, ethical and legal issues Coursework in statistics and research design Clinical experience Practicum—usually three or four years of working with clients under supervision in a clinical setting. Most practicum placements begin in either the first or second year of doctoral training. Doctoral internship—usually an intensive one or two-year placement in a clinical setting Dissertation—PhD programs usually require original quantitative empirical research, while PsyD dissertations involve original quantitative or qualitative research, theoretical scholarship, program evaluation or development, critical literature analysis or clinical application and analysis. The dissertation typically takes 2–3 years to complete. Specialized electives—many programs offer sets of elective courses for specializations, such as health, child/adolescent, family, community, or neuropsychology. Personal psychotherapy—many programs require students to undertake a certain number of hours of personal psychotherapy (with a non-faculty therapist) although in recent years this requirement has become less frequent. Comprehensive exams or master's thesis: a thesis can involve original data collection and is distinct from a dissertation. Psychologists can be seen as practicing within two general categories of psychology: health service psychology,


id: psychologist-20260505-4
file: data/en.wikipedia.org/wiki/Psychologist-4.md
category: reference
title: Psychologist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Psychologist
date_saved: 2026-05-05T10:09:44.175778+00:00
instance: kb-cron
parent: psychologist-20260505-0
summary:
==== Ambiguity of title ==== To practice with the title of "psychologist", in almost all cases a doctoral degree is required (PhD, PsyD, or EdD in the U.S.). Normally, after the degree, the practitioner must fulfill a certain number of supervised postdoctoral hours ranging from 1,500 to 3,000 (usually taking one to two years), and pass the EPPP and any other state or provincial exams. By and large, a professional in the U.S. must hold a doctoral degree in psychology (PsyD, EdD, or PhD), and/or have a state license to use the title psychologist. However, regulations vary from state to state. For example, in the states of Michigan, West Virginia, and Vermont, there are psychologists licensed at the master's level. ==== Differences with psychiatrists ==== Although clinical psychologists and psychiatrists share the same fundamental aim—the alleviation of mental distress—their training, outlook, and methodologies are often different. Perhaps the most significant difference is that psychiatrists are licensed physicians, and, as such, psychiatrists are apt to use the medical model to assess mental health problems and to also employ psychotropic medications as a method of addressing mental health problems. Psychologists generally do not prescribe medication, although in some jurisdictions they do have


id: peter-paul-biro-20260505-0
file: data/en.wikipedia.org/wiki/Peter_Paul_Biro-0.md
category: reference
title: Peter Paul Biro
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peter_Paul_Biro
date_saved: 2026-05-05T10:09:49.356644+00:00
instance: kb-cron
parent: peter-paul-biro-20260505-0
summary:
Peter Paul Biró (1954–2024) was a forensic art analyst, known for his work in authenticating paintings using fingerprint analysis. He was involved in several high-profile cases, including the authentication of works attributed to artists Jackson Pollock, Leonardo da Vinci, and Goodridge Roberts. Biro's techniques and conclusions were subjects of debate and scrutiny. In 2009, The New Yorker published an article that critically examined Biro's methods. Subsequently, Biro filed a defamation lawsuit against the magazine, the article's author, and others. The courts later dismissed the lawsuit. == Early life and background == Peter Paul Biro was born in 1954 in Budapest and emigrated to Montreal with his family as a teenager. He left college early to join his father and brother in the art restoration business. Through this business, Biro helped to authenticate a painting by J. M. W. Turner. While examining the Turner painting, he developed the idea to compare a fingerprint found on it to other paintings by Turner. His idea to use fingerprint analysis added a scientific approach to art authentication, a field that had been traditionally subjective. Biro built his career on the analysis of fingerprints. He operated on the premise that artists may leave their fingerprints


id: coroner-20260505-0
file: data/en.wikipedia.org/wiki/Coroner-0.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
A coroner is a government or judicial official who is empowered to conduct or order an inquest into the manner or cause of death. The official may also investigate or confirm the identity of an unknown person who has been found dead within the coroner's jurisdiction. In Medieval England, English coroners were Crown officials who held financial powers and conducted some judicial investigations in order to counterbalance the power of sheriffs or bailiffs. Depending on the jurisdiction, the coroner may adjudge the cause of death personally, or may act as the presiding officer of a special court (a "coroner's jury"). The term coroner derives from the same source as the word crown. == Duties and functions == Responsibilities of the coroner may include overseeing the investigation and certification of deaths related to mass disasters that occur within the coroner's jurisdiction. A coroner's office typically maintains death records of those who have died within the coroner's jurisdiction. The additional roles that a coroner may oversee in judicial investigations may be subject to the attainment of suitable legal and medical qualifications. The qualifications required of a coroner vary significantly between jurisdictions and are described below under the entry for each jurisdiction. Coroners,


id: coroner-20260505-1
file: data/en.wikipedia.org/wiki/Coroner-1.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
Death investigation is the responsibility of each individual Canadian province and territory—there is no overarching federal authority. As a result, each province and territory has developed its own system and legislation to fulfill the mandate of investigating deaths that are unexpected, unexplained, or as a result of injuries or drugs. Two different death investigation systems have developed in Canada: the Coroner's system and the Medical Examiner's system. The Coroner's system is used in the majority of provinces and territories. It is a system that is centuries old and originated in Great Britain. It is found throughout the world in countries that were former British colonies, including Canada. The Medical Examiner's system (used in Alberta, Manitoba, Nova Scotia, and Newfoundland and Labrador) is just over one century old and originated in the United States. Although there are some differences between the two systems, the ultimate goal of each is the same—to investigate certain deaths defined in their legislation and establish the identity of the deceased together with the cause of death and the manner of death. In 21st-century Canada the officer responsible for investigating all unnatural and natural unexpected, unexplained, or unattended deaths goes under the title "coroner" or "medical examiner"


id: coroner-20260505-2
file: data/en.wikipedia.org/wiki/Coroner-2.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
=== United Kingdom === There are separate coroner services for England and Wales and for Northern Ireland. There are no longer coroners in Scotland. Coroners existed in Scotland between about 1400 and 1800 when they ceased to be used. Deaths in Scotland requiring judicial examination are now reported to The Crown Office and Procurator Fiscal Service, who investigates deaths on behalf of the Lord Advocate. Different teams investigating deaths include the Scottish Fatalities Investigation Unit, the National Homicide Team, the Health and Safety Investigation Unit, the Road Traffic Fatalities Investigation Unit and The Custody Deaths Unit. In the rest of the United Kingdom a coroner is a specialist judge. Whilst coroners are appointed and paid by local authorities, they are not employees of those local authorities but rather independent judicial office holders who can be removed from office only by the Lord Chief Justice and the Lord Chancellor. The Ministry of Justice, which is headed by the Lord Chancellor and Secretary of State for Justice, is responsible for coronial law and policy. However, it has no operational responsibility for the running of coroners' courts. A coroner's jurisdiction is limited to determining who the deceased was and how, when and where


id: coroner-20260505-3
file: data/en.wikipedia.org/wiki/Coroner-3.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
The coroner service in England and Wales is supervised by the Chief Coroner, a judge appointed by the Lord Chief Justice after consulting the Lord Chancellor. The Chief Coroner provides advice, guidance and training to coroners and aims to secure uniformity of practice throughout England and Wales. The post is currently part-time. The present Chief Coroner is Alexia Durran. England and Wales are divided into coroner districts by the Lord Chancellor, each district consisting of the area or areas of one or more local authorities. The relevant local authority, with the consent of the Chief Coroner and the Lord Chancellor, must appoint a senior coroner for the district. It must also appoint area coroners (in effect deputies to the senior coroner) and assistant coroners, to the number that the Lord Chancellor considers necessary in view of the physical character and population of the district. The cost of the coroner service for the district falls upon the local authority or authorities concerned, and thus ultimately upon the local inhabitants. There are 98 coroners in England and Wales, covering 109 local authority areas. To become a coroner in England and Wales the applicant must be a qualified solicitor, barrister, or a Fellow


id: coroner-20260505-4
file: data/en.wikipedia.org/wiki/Coroner-4.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
=== United States === As of 2004, of the 2,342 death investigation offices in the United States, 1,590 were coroners' offices, 82 of which served jurisdictions of more than 250,000 people. Qualifications for coroners are set by individual states and counties in the U.S. and vary widely. In many jurisdictions, little or no training is required, even though a coroner may overrule a forensic pathologist in naming a cause of death. Some coroners are elected with others appointed. Some coroners hold office by virtue of holding another office. For example, in Nebraska, a county's district attorney is also the county's coroner. Similarly, in many counties in Texas, the justice of the peace may be in charge of death investigation. In yet other places, the sheriff may be the lawful coroner. In different jurisdictions the terms "coroner" and "medical examiner" are defined differently. In some places, stringent rules require that the medical examiner be a forensic pathologist. In others, the medical examiner must be a physician, though not necessarily a pathologist nor further specialized forensic pathologist; physicians with no experience in forensic medicine have become medical examiners. In other jurisdictions, such as Wisconsin, each county sets standards, and in some, the


id: coroner-20260505-5
file: data/en.wikipedia.org/wiki/Coroner-5.md
category: reference
title: Coroner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coroner
date_saved: 2026-05-05T10:09:50.552754+00:00
instance: kb-cron
parent: coroner-20260505-0
summary:
=== Literature === M. R. Hall is a British crime novelist, who writes a series of best-selling novels featuring Bristol-based coroner, Jenny Cooper. Novelist Bernard Knight, a former Home Office pathologist and a professor of forensic pathology at the University of Wales College of Medicine, is well known for his Crowner John Mysteries series set in 12th-century Devon, England. ("Crowner" is an archaic word for "coroner" and is based on the origins of the word. See the History section above.) === Television === Although coroners are often depicted in police dramas as a source of information for detectives, there are a number of fictional coroners who have taken particular focus on television. Dr. Camille Saroyan is a federal coroner and the head of the Forensic Division at Jeffersonian Institute in the TV series Bones. British television drama series The Coroner has as its main character a coroner based in a fictional Devon town. Crossing Jordan features Jill Hennessy as Jordan Cavanaugh, M.D., a crime-solving forensic pathologist employed in the Massachusetts Office of the Chief Medical Examiner. The coroners are significant characters and main cast members on CSI: Crime Scene Investigation and its spin-offs CSI: Miami and CSI: NY. The television


id: crime-scene-cleanup-20260505-0
file: data/en.wikipedia.org/wiki/Crime_scene_cleanup-0.md
category: reference
title: Crime scene cleanup
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Crime_scene_cleanup
date_saved: 2026-05-05T10:09:51.716934+00:00
instance: kb-cron
parent: crime-scene-cleanup-20260505-0
summary:
Crime scene cleanup is a term applied to cleanup of blood, bodily fluids, and other potentially infectious materials (OPIM). It is also referred to as biohazard remediation, and forensic cleanup, because crime scenes are only a portion of the situations in which biohazard cleaning is needed. Incidents which may require this type of cleanup include accidents, suicide (or attempted suicide), homicides, and decomposition after unattended death, as well as mass trauma, industrial accidents, infectious disease contamination, animal biohazard contamination (e.g. feces or blood) or regulated waste transport, treatment, and disposal. == Usage == Television productions like CSI: Crime Scene Investigation have added to the popularity of the term "crime scene cleanup". Australia, Canada and England have added it to their professional cleaning terminology. As a profession, it is growing in popularity because of media exposure and the growth of training programs worldwide. The generic terms for crime scene cleanup include trauma cleaning, crime and trauma scene decontamination ("CTS Decon"), biohazard remediation, biohazard removal, and blood cleanup. The state of California refers to individuals who practice this profession as Valid Trauma Scene Waste Management Practitioners. == Types of cleanups == Crime scene cleanup includes blood spills following an assault, homicide or


id: crime-scene-cleanup-20260505-1
file: data/en.wikipedia.org/wiki/Crime_scene_cleanup-1.md
category: reference
title: Crime scene cleanup
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Crime_scene_cleanup
date_saved: 2026-05-05T10:09:51.716934+00:00
instance: kb-cron
parent: crime-scene-cleanup-20260505-0
summary:
The American Bio Recovery Association (ABRA) is the 3rd party credentialing body and membership organization for companies that specialize in this niche industry. Bio Recovery covers a bit more than the clean up of body fluids. Bio Recovery is the act of assessing risk, mitigating threats and remediating conditions resulting from the release of biological hazards. This may include crime and trauma mitigation (bloodborne and body fluids), suicide cleanup, outbreak response, zoonotic diseases, foodborne diseases, public health threats, illicit drugs and clandestine drug labs. ABRAs certification credentialing platform requires applicants to obtain prerequisite requirements prior to sitting for an independent exam. Questions on the exams are derived from the information in the required prerequisite and demonstrate retention of industry knowledge providing a high level of credibility. Member Companies of ABRA are held to the highest international standards for ethics and professionalism. Three levels of Credentialing are available for industry career advancement from the American Bio Recovery Association include the Certified Bio Recovery Technician (CBRT), Certified Bio Recovery Supervisor (CBRS), and the Certified Bio Recovery Master (CBRM). The Institute for Inspection Cleaning and Certification (IICRC) is the Standards development organization for the cleaning industry. It created the ANSI/IICRC S540 Standard for


id: medical-examiner-20260505-0
file: data/en.wikipedia.org/wiki/Medical_examiner-0.md
category: reference
title: Medical examiner
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Medical_examiner
date_saved: 2026-05-05T10:09:52.883401+00:00
instance: kb-cron
parent: medical-examiner-20260505-0
summary:
The medical examiner is an official who is responsible for investigating the cause of a death and overseeing the death certification process. A medical examiner is usually trained in pathology. In the United States they may perform post-mortem examinations, and in some jurisdictions have the power to initiate inquests. In England and Wales, the medical examiner is mostly responsible for scrutinising the cause of a death where an inquest is not held by a coroner. In England and Wales (and formerly in Scotland) coroners are judicial officers who have held a number of responsibilities since their inception, but today they are mostly responsible for holding inquests where the cause of a death may not be clear or suspicious. This system was adopted throughout the British Empire, including in the United States. In most of these countries, including parts of the United States, the coroner has retained this role, however parts of the United States may also have a medical examiner, which evolved from coroners during the latter half of the 19th century. The exact responsibilities of a medical examiner varies across jurisdictions, with over 2,000 separate jurisdictions for investigating unnatural deaths. == History == === United Kingdom === Medical examiners


id: regius-professor-of-forensic-medicine-glasgow-20260505-0
file: data/en.wikipedia.org/wiki/Regius_Professor_of_Forensic_Medicine_(Glasgow)-0.md
category: reference
title: Regius Professor of Forensic Medicine (Glasgow)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Regius_Professor_of_Forensic_Medicine_(Glasgow)
date_saved: 2026-05-05T10:09:54.056596+00:00
instance: kb-cron
parent: regius-professor-of-forensic-medicine-glasgow-20260505-0
summary:
The Regius Chair of Forensic Medicine at the University of Glasgow was founded in 1839 by Queen Victoria. The chair was occupied for over one sixty years by the same family, when John Glaister Jnr. succeeded his father as Regius Professor. The chair is currently vacant. == Regius Professors of Forensic Medicine == 1839 - Robert Cowan 1841 - Harry Rainy 1872 - Pierce Adolphus Simpson 1898 - John Glaister 1931 - John Glaister Jnr 1964 - Gilbert Forbes 1974 - William Arthur Harland 1985 - Alan Albert Watson 1993 - Peter Vanezis == See also == List of Professorships at the University of Glasgow University of Glasgow Medical School


id: scenes-of-crime-officer-20260505-0
file: data/en.wikipedia.org/wiki/Scenes_of_crime_officer-0.md
category: reference
title: Scenes of crime officer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Scenes_of_crime_officer
date_saved: 2026-05-05T10:09:55.217860+00:00
instance: kb-cron
parent: scenes-of-crime-officer-20260505-0
summary:
A scenes of crime officer (SOCO) is an officer who gathers forensic evidence for the British police. They are also referred to by some forces as forensic scene investigators (FSIs), crime scene investigators (CSIs) (although their job differs from that depicted in the CBS TV series), or crime scene examiners (CSEs). SOCOs are usually not police officers, but are employed by the police forces. Evidence collected is passed to the detectives of the Criminal Investigation Department and to the forensic laboratories. The SOCOs do not investigate crimes or analyse evidence themselves. To be a SOCO, at least 5 GCSEs at Grade level 9 - 4 are required. == History == SOCOs were first introduced into the Metropolitan Police in 1968, replacing the CID officers who had previously carried out these functions. Some other forces had introduced them before that time, however. Other police forces around the United Kingdom continued this trend of replacing the CID officers with civilians. This allowed the creation of dedicated departments within the police forces. Police officers often moved post after only a few years; the introduction of civilian scenes of crime officers allowed departments to build a resource based on experience. == Training == Today,


id: pearl-tytell-20260505-0
file: data/en.wikipedia.org/wiki/Pearl_Tytell-0.md
category: reference
title: Pearl Tytell
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Pearl_Tytell
date_saved: 2026-05-05T10:09:56.357159+00:00
instance: kb-cron
parent: pearl-tytell-20260505-0
summary:
Pearl L. Tytell (August 29, 1917 – September 26, 2021) was a forensic questioned document examiner and handwriting expert. With her husband, Martin Tytell, she was involved in the solving of numerous criminal cases for over half a century. == Early life == Tytell was born on August 29, 1917, in Manhattan, and named Pearl Lily Kessler. She met Martin Tytell in 1938; they married in 1943. She graduated from New York University in the 1960s with a Bachelor of Science and a Master of Arts degree. == Career == The Tytells had a typewriter rental and repair business in Manhattan from 1933 until 2001 which was devoted to creating keyboards in 142 languages. The husband and wife team were often asked to identify the specific typewriter used to type anonymous letters or to modify typed documents. In 1951 Pearl Tytell created the Tytell Questioned Document Laboratory which specialized in dating paper and ink as well as the forensic study of signatures on checks, contracts and wills to determine whether they were genuine. For fifty years she worked, testified and lectured on handwriting and typewriter identification as well as the detection of forgeries. She was a lecturer at New York


id: actuarial-credentialing-and-exams-20260505-0
file: data/en.wikipedia.org/wiki/Actuarial_credentialing_and_exams-0.md
category: reference
title: Actuarial credentialing and exams
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_credentialing_and_exams
date_saved: 2026-05-05T10:09:57.851270+00:00
instance: kb-cron
parent: actuarial-credentialing-and-exams-20260505-0
summary:
To become a qualified actuary, the actuarial credentialing and exam process usually requires passing a series of professional examinations over a period of several years. In some countries, such as Denmark, most study takes place in a university setting. In others, such as the U.S., most study takes place during employment through a series of examinations. In the UK, and countries based on its process, there is a hybrid university-exam structure. == Australia == The education system in Australia is divided into three components: an exam-based curriculum; a professionalism course; and work experience. The system is governed by the Institute of Actuaries of Australia. The exam-based curriculum is in three parts. Part I relies on exemptions from an accredited under-graduate degree from either Bond University, Monash University, Macquarie University, University of New South Wales, University of Melbourne, Australian National University or Curtin University. The courses cover subjects including finance, financial mathematics, economics, contingencies, demography, models, probability and statistics. Students may also gain exemptions by passing the exams of the Institute of Actuaries in London. Part II is the Actuarial control cycle and is also offered by each of the universities above. Part III consists of four half-year courses of which


id: actuarial-credentialing-and-exams-20260505-1
file: data/en.wikipedia.org/wiki/Actuarial_credentialing_and_exams-1.md
category: reference
title: Actuarial credentialing and exams
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_credentialing_and_exams
date_saved: 2026-05-05T10:09:57.851270+00:00
instance: kb-cron
parent: actuarial-credentialing-and-exams-20260505-0
summary:
== South Africa == Actuaries in South Africa are served by the Actuarial Society of South Africa (ASSA). Until 2010, the requirement to qualify as an actuary in South Africa was to pass the exams hosted by the UK bodies. Starting in 2010, a South African actuarial qualification hosted by ASSA has replaced this arrangement. Key changes include exam syllabuses based on South African-specific content. The UK actuarial professional bodies, however, still support qualification through the UK. Students may receive exemption from part of the examinations for qualification from approved universities. The South African qualification does have mutual recognition with many of the international actuarial bodies as well as approval of the syllabus from the International Actuarial Association. One may obtain the Chartered Enterprise Risk Actuary (CERA) designation through the ASSA. South Africa pioneered the actuarial banking route, with fellowship exams (equivalent to the UK's SA exams) in banking beginning in the late 2010s. == Sweden == Actuarial training in Sweden takes place at Stockholm University. The five-year master's program (for those with no previous university-level knowledge in mathematics, or without a bachelor's degree in mathematics) covers the subjects mathematics, mathematical statistics, insurance mathematics, financial mathematics, insurance law and insurance


id: actuarial-credentialing-and-exams-20260505-2
file: data/en.wikipedia.org/wiki/Actuarial_credentialing_and_exams-2.md
category: reference
title: Actuarial credentialing and exams
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuarial_credentialing_and_exams
date_saved: 2026-05-05T10:09:57.851270+00:00
instance: kb-cron
parent: actuarial-credentialing-and-exams-20260505-0
summary:
=== Education and exams === The Society of Actuaries' requirements for Associateship (ASA) include passing 6 preliminary examinations (probability, financial mathematics, fundamentals of actuarial mathematics, statistics for risk modeling, predictive analytics, and one from either advanced long-term actuarial mathematics or advanced short-term actuarial mathematics), validating educational experience (VEE) in 3 fields (economics, accounting and finance, and mathematical statistics), completing self-learning series and passing their assessments (advanced topics in predictive analytics, five-module fundamentals of actuarial practice), and taking a course on professionalism. For Fellowship (FSA), three other modules, three or four exams depending on specialty track, and a special fellowship admission course is added. The Casualty Actuarial Society requires the successful completion of seven examinations, two modules, and economics and corporate finance VEEs for Associateship and three additional exams for Fellowship. In addition to these requirements, casualty actuarial candidates must also complete professionalism education and be recommended for membership by existing members. Depending on which society a student chooses to pursue, there are six or seven preliminary exams. Most of the exams are multiple choice and administered on computers at Prometric testing centers. Candidates are allowed to use a calculator from an approved list. The exams are timed and last between


id: actuary-20260505-0
file: data/en.wikipedia.org/wiki/Actuary-0.md
category: reference
title: Actuary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuary
date_saved: 2026-05-05T10:09:59.049101+00:00
instance: kb-cron
parent: actuary-20260505-0
summary:
An actuary is a professional with advanced mathematical skills who deals with the measurement and management of risk and uncertainty. These risks can affect both sides of the balance sheet and require asset management, liability management, and valuation skills. Actuaries provide assessments of financial security systems, with a focus on their complexity, their mathematics, and their mechanisms. The name of the corresponding academic discipline is actuarial science. While the concept of insurance dates to antiquity, the concepts needed to scientifically measure and mitigate risks have their origins in 17th-century studies of probability and annuities. Actuaries in the 21st century require analytical skills, business knowledge, and an understanding of human behavior and information systems; actuaries use this knowledge to design programs that manage risk, by determining if the implementation of strategies proposed for mitigating potential risks does not exceed the expected cost of those risks actualized. The steps needed to become an actuary, including education and licensing, are specific to a given country, with various additional requirements applied by regional administrative units; however, almost all processes impart universal principles of risk assessment, statistical analysis, and risk mitigation, involving rigorously structured training and examination schedules, taking many years to complete. The profession


id: actuary-20260505-1
file: data/en.wikipedia.org/wiki/Actuary-1.md
category: reference
title: Actuary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuary
date_saved: 2026-05-05T10:09:59.049101+00:00
instance: kb-cron
parent: actuary-20260505-0
summary:
=== Non-traditional employment === As an outgrowth of more traditional roles, actuaries also work in the fields of risk management and enterprise risk management for both financial and non-financial corporations. Actuaries in traditional roles study and use tools and data previously in the domain of finance. Two accords—the Basel II accord for financial institutions (2004), and the analogous Solvency II accord for insurance companies (in force since 2016)—require institutions to account for operational risk separately, in addition to credit, reserve, asset, and insolvency risk. Actuarial skills are well suited to this context, because actuaries are trained in analyzing various forms of risk, and judging the potential for gain and loss associated with these forms of risk. Actuaries also participate in investment advising and asset management, and can be general business managers and chief financial officers or chief investment officers. They analyze business prospects using financial skills in valuing or discounting risky future cash flows; they also apply pricing expertise in insurance to other lines of business. For example, insurance securitization requires both actuarial and finance skills. Actuaries also act as expert witnesses in court trials, by using analysis to estimate the economic value of losses such as lost profits or


id: actuary-20260505-2
file: data/en.wikipedia.org/wiki/Actuary-2.md
category: reference
title: Actuary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Actuary
date_saved: 2026-05-05T10:09:59.049101+00:00
instance: kb-cron
parent: actuary-20260505-0
summary:
=== Development of the modern profession === In the 18th and 19th centuries, computational complexity was limited to manual calculations. The calculations required to compute fair insurance premiums can be burdensome. The actuaries of that time developed methods to construct easily used tables, using arithmetical short-cuts called commutation functions, to facilitate timely, accurate, manual calculations of premiums. In the mid-19th century, professional bodies were founded to support and further both actuaries and actuarial science, and to protect the public interest by ensuring competency and ethical standards. Since calculations were cumbersome, actuarial shortcuts were commonplace. Non-life actuaries followed in the footsteps of their life compatriots in the early 20th century. In the United States, the 1920 revision to workers' compensation rates took over two months of around-the-clock work by day and night teams of actuaries. In the 1930s and 1940s, rigorous mathematical foundations for stochastic processes were developed. Actuaries began to forecast losses using models of random events instead of deterministic methods. Computers further revolutionized the actuarial profession. From pencil-and-paper to punchcards to microcomputers, the modeling and forecasting ability of the actuary has grown vastly. Another modern development is the convergence of modern finance theory with actuarial science. In the early


id: enrolled-actuary-20260505-0
file: data/en.wikipedia.org/wiki/Enrolled_actuary-0.md
category: reference
title: Enrolled actuary
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Enrolled_actuary
date_saved: 2026-05-05T10:10:00.205458+00:00
instance: kb-cron
parent: enrolled-actuary-20260505-0
summary:
An enrolled actuary is an actuary enrolled by the Joint Board for the Enrollment of Actuaries under the Employee Retirement Income Security Act of 1974 (ERISA). Enrolled actuaries, under regulations of the Department of the Treasury and the Department of Labor, perform a variety of tasks with respect to pension plans in the United States under ERISA. As of August, 2024, there were approximately 3,400 enrolled actuaries. == Qualifications == The Joint Board for the Enrollment of Actuaries administers two examinations to prospective enrolled actuaries. Once the two examinations have been passed, and an individual has also obtained sufficient relevant professional experience, that individual becomes an enrolled actuary. The first exam (EA-1) tests basic knowledge of the mathematics of compound interest, the mathematics of life contingencies, and practical demographic analysis. The second (EA-2) examination consists of two segments, which are offered during separate exam sittings in either the fall or the spring. Segment F covers the selection of actuarial assumptions, actuarial cost methods, and the calculation of minimum (required) and maximum (tax-deductible) contributions to pension plans. Segment L tests knowledge of relevant federal pension laws (in particular, the provisions of ERISA) as they affect pension actuarial practice. == Employers ==


id: computational-scientist-20260505-0
file: data/en.wikipedia.org/wiki/Computational_scientist-0.md
category: reference
title: Computational scientist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computational_scientist
date_saved: 2026-05-05T10:10:03.648081+00:00
instance: kb-cron
parent: computational-scientist-20260505-0
summary:
A computational scientist is a person skilled in scientific computing. This person is usually a scientist, a statistician, an applied mathematician, or an engineer who applies high-performance computing and sometimes cloud computing in different ways to advance the state-of-the-art in their respective applied discipline; physics, chemistry, social sciences and so forth. Thus scientific computing has increasingly influenced many areas such as economics, biology, law, and medicine to name a few. Because a computational scientist's work is generally applied to science and other disciplines, they are not necessarily trained in computer science specifically, though concepts of computer science are often used. Computational scientists are typically researchers at academic universities, national labs, or tech companies. One of the tasks of a computational scientist is to analyze large amounts of data, often from astrophysics or related fields, as these can often generate huge amounts of data. Computational scientists often have to clean up and calibrate the data to a usable form for an effective analysis. Computational scientists are also tasked with creating artificial data through computer models and simulations. == References ==


id: computer-scientist-20260505-0
file: data/en.wikipedia.org/wiki/Computer_scientist-0.md
category: reference
title: Computer scientist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Computer_scientist
date_saved: 2026-05-05T10:10:04.789721+00:00
instance: kb-cron
parent: computer-scientist-20260505-0
summary:
A computer scientist is a scientist who specializes in the academic study of computer science and technology. Computer scientists typically work on the theoretical side of computation. Although computer scientists can also focus their work and research on specific areas (such as algorithm and data structure development and design, software engineering, information theory, database theory, theoretical computer science, numerical analysis, programming language theory, compiler, computer graphics, computer vision, robotics, computer architecture, operating system), their foundation is the theoretical study of computing from which these other fields derive. A primary goal of computer scientists is to develop or validate models, often mathematical, to describe the properties of computational systems (processors, programs, computers interacting with people, computers interacting with other computers, etc.) with an overall objective of discovering designs that yield useful benefits (faster, smaller, cheaper, more precise, etc.). == Education == Most computer scientists possess a PhD, M.S., or Bachelor's degree in computer science, in other similar fields like Information and Computer Science (CIS), or closely related disciplines such as mathematics or physics. === Areas of specialization === Theoretical computer science – including data structures and algorithms, theory of computation, information theory and coding theory, programming language theory, and formal methods


id: methodological-advisor-20260505-0
file: data/en.wikipedia.org/wiki/Methodological_advisor-0.md
category: reference
title: Methodological advisor
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Methodological_advisor
date_saved: 2026-05-05T10:10:12.583537+00:00
instance: kb-cron
parent: methodological-advisor-20260505-0
summary:
A methodological advisor or statistical consultant provides methodological and statistical advice and guidance to clients or colleagues interested in making decisions regarding the design of studies, the collection and analysis of data, and the presentation and dissemination of research findings. Trained in both methods and statistics, and communication skills, advisors may work in academia, industry, or the public sector. == Education and employment == Methodological advisors generally have post-graduate training in statistics and relevant practical experience. Advisors may also have significant education and experience in the particular field they work in. Some universities offer specific graduate programmes in fields such as biostatistics, psychological methods, or methodology and statistics for the medical, behavioural, and social sciences. Methodological consultants primarily find work in academia and industry. In the private sector, consultants may be part of an organisation, employed by a consultancy firm, or self-employed. Many universities offer in-house methodological advice for researchers, as well as, in some cases, services for outside clients. The advisors may also be researchers of their own right and be involved with particular projects. Project statisticians, in particular, are embedded with research groups and often developed a deep understanding not just of statistics, but also of the research


id: physicist-20260505-0
file: data/en.wikipedia.org/wiki/Physicist-0.md
category: reference
title: Physicist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Physicist
date_saved: 2026-05-05T10:10:13.846614+00:00
instance: kb-cron
parent: physicist-20260505-0
summary:
A physicist is a scientist who specializes in the field of physics, which encompasses the interactions of matter and energy at all length and time scales in the physical universe. Physicists generally are interested in the root or ultimate causes of phenomena, and usually frame their understanding in mathematical terms. They work across a wide range of research fields, spanning all length scales: from sub-atomic and particle physics, through biological physics, to cosmological length scales encompassing the universe as a whole. The field generally includes two types of physicists: experimental physicists who specialize in the observation of natural phenomena and the development and analysis of experiments, and theoretical physicists who specialize in mathematical modeling of physical systems to rationalize, explain and predict natural phenomena. Physicists can apply their knowledge towards solving practical problems or to developing new technologies (also known as applied physics or engineering physics). == History == The study and practice of physics is based on an intellectual ladder of discoveries and insights from ancient times to the present. Many mathematical and physical ideas used today found their earliest expression in the work of ancient civilizations, such as the Babylonian astronomers and Egyptian engineers, the Greek philosophers of


id: physicist-20260505-1
file: data/en.wikipedia.org/wiki/Physicist-1.md
category: reference
title: Physicist
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Physicist
date_saved: 2026-05-05T10:10:13.846614+00:00
instance: kb-cron
parent: physicist-20260505-0
summary:
=== United Kingdom === Chartered Physicist (CPhys) is a chartered status and a professional qualification awarded by the Institute of Physics. It is denoted by the postnominals "CPhys". Achieving chartered status in any profession denotes to the wider community a high level of specialised subject knowledge and professional competence. According to the Institute of Physics, holders of the award of the Chartered Physicist (CPhys) demonstrate the "highest standards of professionalism, up-to-date expertise, quality and safety" along with "the capacity to undertake independent practice and exercise leadership" as well as "commitment to keep pace with advancing knowledge and with the increasing expectations and requirements for which any profession must take responsibility". Chartered Physicist is considered to be equal in status to Chartered Engineer, which the IoP also awards as a member of the Engineering Council UK, and other chartered statuses in the UK. It is also considered a "regulated profession" under the European professional qualification directives. === Canada === The Canadian Association of Physicists can appoint an official designation called Professional Physicist (P. Phys.), similar to the designation of Professional Engineer (P. Eng.). This designation was unveiled at the CAP congress in 1999 and already more than 200 people carry this


id: statistician-20260505-0
file: data/en.wikipedia.org/wiki/Statistician-0.md
category: reference
title: Statistician
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Statistician
date_saved: 2026-05-05T10:10:16.081961+00:00
instance: kb-cron
parent: statistician-20260505-0
summary:
A statistician is a person who works with theoretical or applied statistics. The profession exists in both the private and public sectors. It is common to combine statistical knowledge with expertise in other subjects, and statisticians may work as employees or as statistical consultants. == Overview == According to the United States Bureau of Labor Statistics, as of 2014, 26,970 jobs were classified as statistician in the United States. Of these people, approximately 30 percent worked for governments (federal, state, or local). As of October 2021, the median pay for statisticians in the United States was $92,270. Additionally, there is a substantial number of people who use statistics and data analysis in their work but have job titles other than statistician, such as actuaries, applied mathematicians, economists, data scientists, data analysts (predictive analytics), financial analysts, psychometricians, sociologists, epidemiologists, and quantitative psychologists. Statisticians are included with the professions in various national and international occupational classifications. In many countries, including the United States, employment in the field requires either a master's degree in statistics or a related field or a PhD. According to one industry professional, "Typical work includes collaborating with scientists, providing mathematical modeling, simulations, designing randomized experiments and randomized sampling


id: 3d-slicer-20260505-0
file: data/en.wikipedia.org/wiki/3D_Slicer-0.md
category: reference
title: 3D Slicer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/3D_Slicer
date_saved: 2026-05-05T10:10:20.011628+00:00
instance: kb-cron
parent: 3d-slicer-20260505-0
summary:
3D Slicer (Slicer) is a free and open source software package for image analysis and scientific visualization. Slicer is used in a variety of medical applications, including autism, multiple sclerosis, systemic lupus erythematosus, prostate cancer, lung cancer, breast cancer, schizophrenia, orthopedic biomechanics, COPD, cardiovascular disease and neurosurgery. == About == 3D Slicer is a free open source software (BSD-style license) that is a flexible, modular platform for image analysis and visualization. 3D Slicer is extended to enable development of both interactive and batch processing tools for a variety of applications. 3D Slicer provides image registration, processing of DTI (diffusion tractography), an interface to external devices for image guidance support, and GPU-enabled volume rendering, among other capabilities. 3D Slicer has a modular organization that allows the addition of new functionality and provides a number of generic features. The interactive visualization capabilities of 3D Slicer include the ability to display arbitrarily oriented image slices, build surface models from image labels, and hardware accelerated volume rendering. 3D Slicer also supports a rich set of annotation features (fiducials and measurement widgets, customized color maps). Slicer's capabilities include: Handling DICOM images and reading/writing a variety of other formats Interactive visualization of volumetric Voxel images, polygonal


id: adept-c-library-20260505-0
file: data/en.wikipedia.org/wiki/Adept_(C++_library)-0.md
category: reference
title: Adept (C++ library)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Adept_(C++_library)
date_saved: 2026-05-05T10:10:21.196568+00:00
instance: kb-cron
parent: adept-c-library-20260505-0
summary:
Adept is a combined automatic differentiation and array software library for the C++ programming language. The automatic differentiation capability facilitates the development of applications involving mathematical optimization. Adept is notable for having applied the template metaprogramming technique of expression templates to speed-up the differentiation of mathematical statements. Along with the efficient way that it stores the differential information, this makes it significantly faster than most other C++ tools that provide similar functionality (e.g. ADOL-C, CppAD and FADBAD), although comparable performance has been reported for Stan and in some cases Sacado. Differentiation may be in forward mode, reverse mode (for use with a Quasi-Newton minimization scheme), or the full Jacobian matrix may be computed (for use with the Levenberg-Marquardt or Gauss-Newton minimization schemes). Applications of Adept have included computer functionality in the financial field, computational fluid dynamics, physical chemistry, parameter estimation and meteorology. Adept is free software distributed under the Apache License. == Example == Adept implements automatic differentiation using an operator overloading approach, in which scalars to be differentiated are written as adouble, indicating an "active" version of the normal double, and vectors to be differentiated are written as aVector. The following simple example uses these types to differentiate a


id: advanced-simulation-library-20260505-0
file: data/en.wikipedia.org/wiki/Advanced_Simulation_Library-0.md
category: reference
title: Advanced Simulation Library
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Advanced_Simulation_Library
date_saved: 2026-05-05T10:10:22.399192+00:00
instance: kb-cron
parent: advanced-simulation-library-20260505-0
summary:
Advanced Simulation Library (ASL) is a free and open-source hardware-accelerated multiphysics simulation platform. It enables users to write customized numerical solvers in C++ and deploy them on a variety of massively parallel architectures, ranging from inexpensive FPGAs, DSPs and GPUs up to heterogeneous clusters and supercomputers. Its internal computational engine is written in OpenCL and utilizes matrix-free solution techniques. ASL implements variety of modern numerical methods, i.a. level-set method, lattice Boltzmann, immersed boundary. The mesh-free, immersed boundary approach allows users to move from CAD directly to simulation, reducing pre-processing efforts and number of potential errors. ASL can be used to model various coupled physical and chemical phenomena, especially in the field of computational fluid dynamics. It is distributed under the free GNU Affero General Public License with an optional commercial license (which is based on the permissive MIT License). == History == Advanced Simulation Library is being developed by Avtech Scientific, an Israeli company. Its source code was released to the community on 14 May 2015, whose members packaged it for scientific sections of all major Linux distributions shortly thereafter. Subsequently, Khronos Group acknowledged the significance of ASL and listed it on its website among OpenCL-based resources. == Application areas


id: anuga-hydro-20260505-0
file: data/en.wikipedia.org/wiki/ANUGA_Hydro-0.md
category: reference
title: ANUGA Hydro
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ANUGA_Hydro
date_saved: 2026-05-05T10:10:23.608024+00:00
instance: kb-cron
parent: anuga-hydro-20260505-0
summary:
ANUGA Hydro is a free and open source software tool for hydrodynamic modelling, suitable for predicting the consequences of hydrological disasters such as riverine flooding, storm surges and tsunamis. For example, ANUGA can be used to create predicted inundation maps based on hypothetical tsunami or flood scenarios. The ANUGA name without qualification is used informally to mean the ANUGA Hydro tool. == ANUGA == === Background === Modelling the effects on the built environment of natural hazards such as riverine flooding, storm surges and tsunami is critical for understanding their economic and social impact on our urban communities. ANUGA has its genesis as a MatLab program developed for ACTEW (an Australian Capital Territory owned utility providing electricity, water and wastewater treatment to the residents of the Australian Capital Territory). Its development was instigated by Dr Christopher Zoppou, a senior engineer in the Hydrographics Section of ACTEW in 1998 and a former student of Professor Stephen Roberts from the Australian National University (ANU). Stephen Roberts and Christopher Zoppou embarked on the development of a two-dimensional hydrodynamic shallow water wave equation solver. Written by Stephen Roberts, the MatLab code was used by Christopher Zoppou to simulate the impact of the catastrophic collapse


id: anuga-hydro-20260505-1
file: data/en.wikipedia.org/wiki/ANUGA_Hydro-1.md
category: reference
title: ANUGA Hydro
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ANUGA_Hydro
date_saved: 2026-05-05T10:10:23.608024+00:00
instance: kb-cron
parent: anuga-hydro-20260505-0
summary:
Additional viewing capability is available via several other options: – Using Commercial Software such as WaterRide (Ref to WebSite) – Using Free tools Such as Mirone (Grid viewing software) which has a specific tool called Aquamoto – Using SWW2DEM in combination with any GIS platform – Using Crayfish viewer as plugin in QGIS – Possibly using tools such as VisIt (Ref to Web Site) === Validation studies === ANUGA has been extensively validated against wave tank experiments and field studies where available, and ships with a validation test suite with about 30 analytical solutions, wave tank and field tests. Examples include validation against the wave tank experiment for the Okushiri 1995 tsunami, wave tank runup experiments at University of Queensland, the 2004 Indian Ocean tsunami impact at Patong Beach, comparison to other models, ANUGA was a late entry in the UK 2D model Benchmarking project in 2010 using version 1.1beta_7501. As a result, not all tests were completed. However of the basic tests ANUGA was well within the comparable range of results of other models. Since late 2013 the standard test suite for the model also includes a full catchment model (Towradgi Creek Catchment) which has been validated against the


id: anuga-hydro-20260505-2
file: data/en.wikipedia.org/wiki/ANUGA_Hydro-2.md
category: reference
title: ANUGA Hydro
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/ANUGA_Hydro
date_saved: 2026-05-05T10:10:23.608024+00:00
instance: kb-cron
parent: anuga-hydro-20260505-0
summary:
The mathematical model is the 2D shallow water wave equation. As such it cannot resolve vertical convection and consequently not breaking waves or 3D turbulence (e.g. vorticity). All spatial coordinates are assumed to be UTM (meters). As such, ANUGA is unsuitable for modelling flows in areas larger than one and half UTM zones (9 degrees wide). Fluid is assumed to be inviscid – although kinematic viscosity can be used modelled using a kinematic viscosity operator. The finite volume is a very robust and flexible numerical technique, especially when implemented on an unstructured triangular mesh, but it is not the fastest method around, and over sufficiently simple geometries alternative algorithms may be able to solve the problem faster than ANUGA. Frictional resistance is implemented using Manning's formula. == Users == Geoscience Australia Australian National University Fire and Emergency Services of Western Australia Franzius-Institut, Leibniz University Hannover Australia-Indonesia Facility for Disaster Reduction Wollongong City Council Balance Research & Development Institute Teknologi Bandung, Indonesia Universitas Sanata Dharma, Yogyakarta, Indonesia DMInnovation PT Inteligensi Risiko, Jakarta PT Reasuransi MAIPARK Indonesia, Jakarta Hydrata NASA JPL == Use history == ANUGA was trialed as a conventional hydrodynamic 2D flood model on both a complex urban system and


id: armadillo-c-library-20260505-0
file: data/en.wikipedia.org/wiki/Armadillo_(C++_library)-0.md
category: reference
title: Armadillo (C++ library)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Armadillo_(C++_library)
date_saved: 2026-05-05T10:10:40.886820+00:00
instance: kb-cron
parent: armadillo-c-library-20260505-0
summary:
Armadillo is a linear algebra software library for the C++ programming language. It aims to provide efficient and streamlined base calculations, while at the same time having a straightforward and easy-to-use interface. Its intended target users are scientists and engineers. It supports integer, floating point (single and double precision), complex numbers, and a subset of trigonometric and statistics functions. Dense and sparse matrices are supported. Various matrix decompositions are provided through optional integration with Linear Algebra PACKage (LAPACK), Automatically Tuned Linear Algebra Software (ATLAS), and ARPACK. High-performance BLAS/LAPACK replacement libraries such as OpenBLAS and Intel MKL can also be used. The library employs a delayed-evaluation approach (during compile time) to combine several operations into one and reduce (or eliminate) the need for temporaries. Where applicable, the order of operations is optimised. Delayed evaluation and optimisation are achieved through template metaprogramming. Armadillo is related to the Boost Basic Linear Algebra Subprograms (uBLAS) library, which also uses template metaprogramming. However, Armadillo builds upon ATLAS and LAPACK libraries, thereby providing machine-dependent optimisations and functions not present in uBLAS. It is open-source software distributed under the permissive Apache License, making it applicable for the development of both open source and proprietary software. The project


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: 2026-05-05T10:10:42.105702+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: ascalaph-designer-20260505-0
file: data/en.wikipedia.org/wiki/Ascalaph_Designer-0.md
category: reference
title: Ascalaph Designer
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Ascalaph_Designer
date_saved: 2026-05-05T10:10:43.266788+00:00
instance: kb-cron
parent: ascalaph-designer-20260505-0
summary:
Ascalaph Designer is a computer program for general purpose molecular modelling for molecular design and simulations. It provides a graphical environment for the common programs of quantum and classical molecular modelling ORCA, NWChem, Firefly, CP2K and MDynaMix . The molecular mechanics calculations cover model building, energy optimizations and molecular dynamics. Firefly (formerly named PC GAMESS) covers a wide range of quantum chemistry methods. Ascalaph Designer is free and open-source software, released under the GNU General Public License, version 2 (GPLv2). == Key features == == Uses == == See also == List of software for molecular mechanics modeling Molecular design software Molecule editor Abalone == References == == External links == Official website SourceForge Twitter


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: 2026-05-05T10:10:44.486200+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: bigdl-20260505-0
file: data/en.wikipedia.org/wiki/BigDL-0.md
category: reference
title: BigDL
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/BigDL
date_saved: 2026-05-05T10:10:45.702369+00:00
instance: kb-cron
parent: bigdl-20260505-0
summary:
BigDL is a distributed deep learning framework for Apache Spark, created by Jason Dai at Intel. BigDL has its source code hosted on GitHub. == Features == == Applications == == See also == Comparison of deep learning software == References ==


id: biskit-20260505-0
file: data/en.wikipedia.org/wiki/Biskit-0.md
category: reference
title: Biskit
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Biskit
date_saved: 2026-05-05T10:10:46.850468+00:00
instance: kb-cron
parent: biskit-20260505-0
summary:
Biskit is an open source software package that facilitates research in structural bioinformatics and molecular modelling. Written in Python, it consists of: An object-oriented programming library for manipulating and analyzing macromolecular structures, protein complexes and molecular dynamics trajectories A set of programs for solving specific tasks, such as automatic prediction of protein structures by homology modeling, and possible prediction of protein complex structures through flexible protein-protein docking The library delegates many calculations to more specialized third-party software. It currently utilizes 15 external applications, including X-PLOR, Hex, T-Coffee, DSSP and MODELLER. The latest Biskit version, 2.4.0, was released on 4 Mar 2012. It was originally developed at the Pasteur Institute. The name "Biskit" refers to the research group's name, Unité de BioInformatique Structurale. == References == == External links == Official website


id: brian-software-20260505-0
file: data/en.wikipedia.org/wiki/Brian_(software)-0.md
category: reference
title: Brian (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Brian_(software)
date_saved: 2026-05-05T10:10:48.484045+00:00
instance: kb-cron
parent: brian-software-20260505-0
summary:
Brian is an open source Python package for developing simulations of networks of spiking neurons. == Details == Brian is aimed at researchers developing models based on networks of spiking neurons. The general design is aimed at maximising flexibility, simplicity and users' development speed. Users specify neuron models by giving their differential equations in standard mathematical form as strings, create groups of neurons and connect them via synapses. This is in contrast to the approach taken by many neural simulators in which users select from a predefined set of neuron models. Brian is written in Python. Computationally, it is based around the concept of code generation: users specify the model in Python but behind the scenes Brian generates, compiles and runs code in one of several languages (including Python, Cython and C++). In addition there is a "standalone" mode in which Brian generates an entire C++ source code tree with no dependency on Brian, allowing models to be run on platforms where Python is not available. == Example == The following code defines, runs and plots a randomly connected network of leaky integrate and fire neurons with exponential inhibitory and excitatory currents. == Comparison to other simulators == Brian is


id: caffe-software-20260505-0
file: data/en.wikipedia.org/wiki/Caffe_(software)-0.md
category: reference
title: Caffe (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Caffe_(software)
date_saved: 2026-05-05T10:10:49.666525+00:00
instance: kb-cron
parent: caffe-software-20260505-0
summary:
Caffe (Convolutional Architecture for Fast Feature Embedding) is a deep learning framework, originally developed at University of California, Berkeley. It is open source, under a BSD license. It is written in C++, with a Python interface. == History == Yangqing Jia created the Caffe project during his PhD at UC Berkeley, while working the lab of Trevor Darrell. The first version, called "DeCAF", made its first appearance in Spring 2013 when it was used for the ILSVRC challenge (later called ImageNet). The library was named Caffe and released to the public in December 2013. It reached end-of-support in 2018. It is hosted on GitHub. == Features == Caffe supports many different types of deep learning architectures geared towards image classification and image segmentation. It supports CNN, RCNN, LSTM and fully-connected neural network designs. Caffe supports GPU- and CPU-based acceleration computational kernel libraries such as Nvidia cuDNN and Intel MKL. == Applications == Caffe is being used in academic research projects, startup prototypes, and even large-scale industrial applications in vision, speech, and multimedia. Yahoo! has also integrated Caffe with Apache Spark to create CaffeOnSpark, a distributed deep learning framework. == Caffe2 == In April 2017, Facebook announced Caffe2, which included new


id: cgal-20260505-0
file: data/en.wikipedia.org/wiki/CGAL-0.md
category: reference
title: CGAL
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/CGAL
date_saved: 2026-05-05T10:10:50.777246+00:00
instance: kb-cron
parent: cgal-20260505-0
summary:
The Computational Geometry Algorithms Library (CGAL) is an open source software library of computational geometry algorithms. While primarily written in C++, Scilab bindings and bindings generated with SWIG (supporting Python and Java for now) are also available. The software is available under dual licensing scheme. When used for other open source software, it is available under open source licenses (LGPL or GPL depending on the component). In other cases commercial license may be purchased, under different options for academic/research and industrial customers. == History == The CGAL project was founded in 1996, as a consortium of eight research institutions in Europe and Israel: Utrecht University, ETH Zurich, Free University of Berlin, INRIA Sophia Antipolis, Martin-Luther-University Halle-Wittenberg, Max Planck Institute for Informatics Saarbrücken, Johannes Kepler University Linz, and Tel-Aviv University. The original funding for the project came from the ESPRIT project of the European Union. Originally, its licensing terms allowed its software to be used freely for academic purposes, with commercial licenses available for other uses. CGAL Releases 3.x were distributed under the QPL license. Starting with CGAL 4.0, released in 2012, CGAL is distributed under the GPL version 3. As of 2013 it is managed by a thirteen-member editorial board,


id: citcom-20260505-0
file: data/en.wikipedia.org/wiki/Citcom-0.md
category: reference
title: Citcom
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Citcom
date_saved: 2026-05-05T10:10:51.934088+00:00
instance: kb-cron
parent: citcom-20260505-0
summary:
CitCom (for California Institute of Technology Convection in the Mantle) is a finite element code designed to solve thermal convection problems relevant to Earth's mantle released under the GNU General Public License. Written in C, the code's latest version, CitComS, runs on a variety of parallel processing computers, including shared and distributed memory platforms. == History == CitCom was originally written in the early 1990s by Louis Moresi (Monash U.). Although the code for three-dimensional problems was incorporated from its inception, early versions of the software only solved for time-dependent convection problems within two-dimensional Cartesian domains. Moresi's original code turned out to be incredibly modular and easily extensible. Consequently, the fundamental finite element infrastructure which Louis wrote is still in place and forms the basis for much of the code contained in the present release. In the mid-1990s Moresi wrote versions of the code that solved the equations within three-dimensional Cartesian domains. Then Shijie Zhong (U. of Colorado, Boulder) successfully parallelized CitCom using message passing routines on a limited release Intel supercomputer. Zhong then created a spherical version of the code which he named CitComS. Lijie Han (Planetary Science Institute) then created a regional version of CitComS as well as


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: 2026-05-05T10:10:53.152563+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: cn3d-20260505-0
file: data/en.wikipedia.org/wiki/Cn3D-0.md
category: reference
title: Cn3D
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Cn3D
date_saved: 2026-05-05T10:10:54.478349+00:00
instance: kb-cron
parent: cn3d-20260505-0
summary:
Cn3D is a Windows, Macintosh and Unix-based software from the United States National Library of Medicine that acts as a helper application for web browsers to view three-dimensional structures from The National Center for Biotechnology Information's Entrez retrieval service. It "simultaneously displays structure, sequence, and alignment, and now has powerful annotation and alignment editing features", according to its official site. Cn3D is in public domain with source code available. The latest version of the software 4.3.1 was released 06 Dec 2013. This version has the ability to view superpositions of 3D structures with similar biological units and an enhanced version of the Vector Alignment Search Tool (VAST). == See also == List of molecular graphics systems Molecular graphics List of software for molecular mechanics modeling == References == == External links == Cn3D Home Page source code tarball of NCBI C++ toolkit which includes Cn3D


id: code-saturne-20260505-0
file: data/en.wikipedia.org/wiki/Code_Saturne-0.md
category: reference
title: Code Saturne
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Code_Saturne
date_saved: 2026-05-05T10:10:55.676043+00:00
instance: kb-cron
parent: code-saturne-20260505-0
summary:
code_saturne is a free and open-source computational fluid dynamics (CFD) solver developed by the research and development division of Électricité de France (EDF). Released under the GNU General Public License since 2007, it is based on a co-located finite volume method and can simulate incompressible or low-Mach number dilatable flows, with or without turbulence and heat transfer. The software is integrated into the SALOME platform through the salome_cfd distribution and can be coupled with the solid thermal code SYRTHES and the structural mechanics code code_aster, both also developed by EDF under free software licences. Its two-phase extension, Neptune_CFD, co-developed by EDF and the CEA, handles two-phase flows such as water-steam and water-air mixtures. == History == Development of code_saturne began in 1997 within EDF's R&D division as an internal tool for nuclear safety studies and thermohydraulics of nuclear power plants. In March 2007, EDF released the code under the GNU General Public License, adopting an open development model. This release encouraged adoption by industrial and academic partners in France and internationally. The software has since been selected as a benchmark application within the European PRACE (Partnership for Advanced Computing in Europe) project for evaluating supercomputer performance. == Features == ===


id: combine-20260505-0
file: data/en.wikipedia.org/wiki/COMBINE-0.md
category: reference
title: COMBINE
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/COMBINE
date_saved: 2026-05-05T10:10:56.875009+00:00
instance: kb-cron
parent: combine-20260505-0
summary:
COMBINE, the COmputational Modeling in BIology NEtwork, is an initiative to coordinate the development of the various community standards and formats for computational models, initially in systems biology and related fields. == History == The COMBINE initiative was started in 2010 in an attempt to start a broader series of scientific meetings in order to replace several smaller and more focused meetings and hackathons, notably the Systems Biology Graphical Notation (SBGN) and Systems Biology Markup Language (SBML) meetings. The first COMBINE meeting was organised by Igor Goryanin and held at the University of Edinburgh School of Informatics in October 2010. The final session of the meeting was followed by an event marking the 10th anniversary of SBML. COMBINE meetings have been held annually since; COMBINE 2014 was organised by the University of Southern California and COMBINE 2015 will be organised by the group of Chris Myers at the University of Utah. == Representation formats == The COMBINE initiative aims to coordinate the development of community standards and formats for computational modelling, particularly in systems biology. In doing so, it is expected a set of complementary but non-overlapping standards will be developed, covering all aspects of computational modelling in all areas


id: coot-software-20260505-0
file: data/en.wikipedia.org/wiki/Coot_(software)-0.md
category: reference
title: Coot (software)
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Coot_(software)
date_saved: 2026-05-05T10:10:58.073831+00:00
instance: kb-cron
parent: coot-software-20260505-0
summary:
The program Coot (Crystallographic Object-Oriented Toolkit) is used to display and manipulate atomic models of macromolecules, typically of proteins or nucleic acids, using 3D computer graphics. It is primarily focused on building and validation of atomic models into three-dimensional electron density maps obtained by X-ray crystallography methods, although it has also been applied to data from electron microscopy. == Overview == Coot displays electron density maps and atomic models and allows model manipulations such as idealization, real space refinement, manual rotation/translation, rigid-body fitting, ligand search, solvation, mutations, rotamers, and Ramachandran idealization. The software is designed to be easy-to-learn for novice users, achieved by ensuring that tools for common tasks are 'discoverable' through familiar user interface elements (menus and toolbars), or by intuitive behaviour (mouse controls). Recent developments have enhanced the usability of the software for expert users, with customisable key bindings, extensions, and an extensive scripting interface. Coot is free software, distributed under the GNU GPL. It is available from the Coot web site originally at the University of York, and now at the MRC Laboratory of Molecular Biology. Pre-compiled binaries are also available for Linux and Windows from the web page and CCP4, and for Mac OS X through


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: 2026-05-05T10:10:59.313086+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: cp2k-20260505-0
file: data/en.wikipedia.org/wiki/CP2K-0.md
category: reference
title: CP2K
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/CP2K
date_saved: 2026-05-05T10:11:00.486339+00:00
instance: kb-cron
parent: cp2k-20260505-0
summary:
CP2K is a freely available (GPL) quantum chemistry and solid state physics program package, written in Fortran 2008, to perform atomistic simulations of solid state, liquid, molecular, periodic, material, crystal, and biological systems. It provides a general framework for different methods: density functional theory (DFT) using a mixed Gaussian and plane waves approach (GPW) via LDA, GGA, MP2, or RPA levels of theory, classical pair and many-body potentials, semi-empirical (AM1, PM3, MNDO, MNDOd, PM6) and tight-binding Hamiltonians, as well as Quantum Mechanics/Molecular Mechanics (QM/MM) hybrid schemes relying on the Gaussian Expansion of the Electrostatic Potential (GEEP). The Gaussian and Augmented Plane Waves method (GAPW) as an extension of the GPW method allows for all-electron calculations. CP2K can do simulations of molecular dynamics, metadynamics, Monte Carlo, Ehrenfest dynamics, vibrational analysis, core level spectroscopy, energy minimization, and transition state optimization using NEB or dimer method. CP2K provides editor plugins for Vim and Emacs syntax highlighting, along with other tools for input generation and output processing. == See also == Car–Parrinello molecular dynamics Computational chemistry Molecular dynamics Monte Carlo algorithm Energy minimization Quantum chemistry Quantum chemistry computer programs Ab initio quantum chemistry methods Møller–Plesset perturbation theory Hartree–Fock method Random phase approximation Density functional


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


id: modflow-20260505-0
file: data/en.wikipedia.org/wiki/MODFLOW-0.md
category: reference
title: MODFLOW
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MODFLOW
date_saved: 2026-05-05T10:12:39.169012+00:00
instance: kb-cron
parent: modflow-20260505-0
summary:
MODFLOW is the U.S. Geological Survey modular finite-difference flow model, which is a computer code that solves the groundwater flow equation. The program is used by hydrogeologists to simulate the flow of groundwater through aquifers. The source code is free public domain software, written primarily in Fortran, and can compile and run on Microsoft Windows or Unix-like operating systems. Since its original development in the early 1980s, the USGS has made six major releases, and is now considered to be the de facto standard code for aquifer simulation. There are several actively developed commercial and non-commercial graphical user interfaces for MODFLOW. MODFLOW was constructed in what was in 1980's called a modular design. This means it has many of the attributes of what came to be called object-oriented programming. For example, capabilities (called "packages") that simulate subsidence or lakes or streams, can easily be turned on and off and the execution time and storage requirements of those packages go away entirely. If a programmer wants to change something in MODFLOW, the clean organization makes it easy. Indeed, this kind of innovation is exactly what was anticipated when MODFLOW was designed. Importantly, the modularity of MODFLOW makes it possible for different


id: modflow-20260505-1
file: data/en.wikipedia.org/wiki/MODFLOW-1.md
category: reference
title: MODFLOW
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MODFLOW
date_saved: 2026-05-05T10:12:39.169012+00:00
instance: kb-cron
parent: modflow-20260505-0
summary:
C V i , j , k − 1 2 h i , j , k − 1 m + C C i − 1 2 , j , k h i − 1 , j , k m + C R i , j − 1 2 , k h i , j − 1 , k m + ( − C V i , j , k − 1 2 − C C i − 1 2 , j , k − C R i , j − 1 2 , k − C R i , j + 1 2 , k − C C i + 1 2 , j , k − C V i , j , k + 1 2 + H C O F i , j , k ) h i , j , k m + C R i , j + 1 2 , k h i , j + 1 , k m + C C i + 1 2 , j , k h i + 1 , j , k m + C V i , j , k + 1 2 h i , j


id: modflow-20260505-2
file: data/en.wikipedia.org/wiki/MODFLOW-2.md
category: reference
title: MODFLOW
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MODFLOW
date_saved: 2026-05-05T10:12:39.169012+00:00
instance: kb-cron
parent: modflow-20260505-0
summary:
MF2K-GWM or GWM-2000 (version 1.1.4, May 31, 2011, branched from mf2k 1.17.2), with groundwater management capability using optimization MF2K-FMP (version 1.00, May 19, 2006, based on mf2k 1.15.03), with Farm Process MF2K-GWT (version 1.9.8, October 28, 2008, based on MF2K 1.17.02), groundwater flow and solute-transport model SEAWAT (version 4.00.05, October 19, 2012), variable-density flow and transport processes VSF (version 1.01, July 5, 2006), variably saturated flow === MODFLOW-2005 === MODFLOW-2005 differs from MODFLOW-2000 in that the sensitivity analysis, parameter estimation, and uncertainty evaluation capabilities are removed. Thus, the support for these capabilities now falls to "clip on" codes that are supported externally to the MODFLOW support effort. In addition, the code was reorganized to support multiple models within one MODFLOW run, as needed for the LGR (Local Grid Refinement) capability. MODFLOW-2005 is written primarily in Fortran 90 and C, with C being used for one solver. The current version of MODFLOW-2005 is version 1.12.00, released on February 3, 2017. Related or branched codes include: MODFLOW-CFP (version 1.8.00, February 23, 2011), conduit flow process to simulate turbulent or laminar groundwater flow conditions MODFLOW-LGR (version 2.0, September 19, 2013), local grid refinement GWM-2005 (version 1.4.2, March 25, 2013), groundwater management capability using


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


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


