# kb

id: aled-edwards-20260505-0
file: data/en.wikipedia.org/wiki/Aled_Edwards-0.md
category: reference
title: Aled Edwards
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Aled_Edwards
date_saved: 2026-05-05T03:49:06.519735+00:00
instance: kb-cron
parent: aled-edwards-20260505-0
summary:
Aled Morgan Edwards (born June 1, 1962) is the founder and Chief Executive of the Structural Genomics Consortium, a charitable public-private partnership. He is Professor of Medical Genetics and Medical Biophysics at the University of Toronto, Visiting Professor of Chemical Biology at the University of Oxford, and adjunct professor at McGill University. == Early life == Born in Holyhead, Wales, Edwards moved to Canada in 1965 with his parents Undeg and Iwan Edwards, a choral conductor who was awarded the Order of Canada in 1995 for his contributions to Canadian music. == Education == Edwards earned his bachelor's degree (1983) and his Ph.D. (1988) in biochemistry from McGill University supervised by Peter Braun. He carried out post-doctoral studies at Stanford University in the laboratory of Roger Kornberg, where he first crystallized RNA polymerase II, a structure for which Kornberg was awarded the Nobel Prize in Chemistry in 2006. == Research contributions == From 1992 to 1997, while a professor at McMaster University, Edwards became interested in developing structural biology methods and was among the first to use mass spectrometry to identify regions of proteins prone to crystallization. He used this technique to facilitate the crystallography of key proteins involved in


id: european-open-science-cloud-20260505-0
file: data/en.wikipedia.org/wiki/European_Open_Science_Cloud-0.md
category: reference
title: European Open Science Cloud
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/European_Open_Science_Cloud
date_saved: 2026-05-05T03:49:07.702753+00:00
instance: kb-cron
parent: european-open-science-cloud-20260505-0
summary:
The European Open Science Cloud (EOSC) is a European Commission initiative aiming at developing an infrastructure providing its users with services promoting open science practices. Besides being open science oriented, the envisaged infrastructure is built by aggregating services provided by several providers following a System of systems approach. Its goal is to establish a federation of infrastructures facilitating effortless access to interoperable research assets and enhanced services spanning geographical boundaries and diverse academic fields. The initiative started in 2015 with the plan that its organizers finish it by 2020. A European Union committee on research endorsed a plan for the cloud's development in May 2018. The European Open Science Cloud officially launched in November 2018, starting to provide access to services via their EOSC Portal. Public meetings about the project have emphasized the ideological motivations for promoting open science. == EOSC Governance == The development of EOSC is governed by three bodies: An executive board tasked to ensure implementation and accountability; a Governance board, working as an institutional group gathering representatives from the Member States and Associated Countries and from the Commission to ensure effective supervision of the EOSC implementation; and a Stakeholder Forum working as the community actively contributing


id: expression-atlas-20260505-0
file: data/en.wikipedia.org/wiki/Expression_Atlas-0.md
category: reference
title: Expression Atlas
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Expression_Atlas
date_saved: 2026-05-05T03:49:08.798435+00:00
instance: kb-cron
parent: expression-atlas-20260505-0
summary:
The Expression Atlas is a database maintained by the European Bioinformatics Institute that provides information on gene expression patterns from RNA-Seq and Microarray studies, and protein expression from Proteomics studies. The Expression Atlas allows searches by gene, splice variant, protein attribute, disease, treatment or organism part (cell types/tissues). Individual genes or gene sets can be searched for. All datasets in Expression Atlas have its metadata manually curated and its data analysed through standardised analysis pipelines. There are two components to the Expression Atlas, the Baseline Atlas and the Differential Atlas: == Baseline Atlas == The Baseline Atlas provides information about which gene products are present (and at what abundance) under "normal" conditions. This component of the Expression Atlas consists of RNA-seq experiments from ArrayExpress repositories. It aims to answer questions such as: Which genes are specifically expressed in kidney? What is the expression pattern for gene SAA4 in normal tissues? == Differential Atlas == The Differential Atlas allows users to identify genes that are up- or down-regulated in different experimental conditions. == See also == Human Protein Atlas The Cancer Genome Atlas == References == == Further reading == == External links == "About Expression Atlas". European Molecular Biology Laboratory


id: fair-data-20260505-0
file: data/en.wikipedia.org/wiki/FAIR_data-0.md
category: reference
title: FAIR data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/FAIR_data
date_saved: 2026-05-05T03:49:10.096945+00:00
instance: kb-cron
parent: fair-data-20260505-0
summary:
FAIR data is data which meets the 2016 FAIR principles of findability, accessibility, interoperability, and reusability (FAIR). The FAIR principles emphasize machine-actionability (i.e., the capacity of computational systems to find, access, interoperate, and reuse data with none or minimal human intervention) because humans increasingly rely on computational support to deal with data as a result of the increase in the volume, complexity, and rate of production of data. The abbreviation FAIR/O data is sometimes used to indicate that the dataset or database in question complies with the FAIR principles and also carries an explicit data‑capable open license. == FAIR principles published by GO FAIR == Findable The first step in (re)using data is to find them. Metadata and data should be easy to find for both humans and computers. Machine-readable metadata are essential for automatic discovery of datasets and services, so this is an essential component of the FAIRification process. F1. (Meta)data are assigned a globally unique and persistent identifier F2. Data are described with rich metadata (defined by R1 below) F3. Metadata clearly and explicitly include the identifier of the data they describe F4. (Meta)data are registered or indexed in a searchable resource Accessible Once the user finds the


id: figshare-20260505-0
file: data/en.wikipedia.org/wiki/Figshare-0.md
category: reference
title: Figshare
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Figshare
date_saved: 2026-05-05T03:49:11.518948+00:00
instance: kb-cron
parent: figshare-20260505-0
summary:
Figshare is an online open access repository where researchers can preserve and share their research outputs, including figures, datasets, images, and videos. It is free to upload content and free to access, in adherence to the principle of open data. Figshare is one of a number of portfolio businesses supported by Digital Science, a subsidiary of Springer Nature. == History == Figshare was launched in January 2011 by Mark Hahnel and has been supported by Digital Science since a January 2012 relaunch. Hahnel first developed the platform as a personal custom solution for the organization and publication of diverse research products generated in support of his PhD in stem cell biology. In January 2013, Figshare announced a partnership with PLOS to integrate Figshare data hosting, access, and visualization with their associated PLOS articles. In September 2013, the service launched a hosted institutional repository service. In December 2013, they announced integration with ImpactStory to support the collection of altmetrics. Figshare also hosts the Reproducibility Collection as a founding member of The Reproducibility Initiative, which acts as an independent and blinded validator for replication of submitted data. Figshare releases 'The State of Open Data' each year to assess the changing academic landscape


id: galaxy-zoo-20260505-0
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-0.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
Galaxy Zoo is a crowdsourced astronomy project which invites people to assist in the morphological classification of large numbers of galaxies. It is an example of citizen science as it enlists the help of members of the public to help in scientific research. There have been 15 versions as of July 2017. Galaxy Zoo is part of the Zooniverse, a group of citizen science projects. An outcome of the project is to better determine the different aspects of objects and to separate them into classifications. == Origins == A key factor leading to the creation of the project was the problem of what has been referred to as data deluge, where research produces vast sets of information to the extent that research teams are not able to analyse and process much of it. Kevin Schawinski, previously an astrophysicist at Oxford University and co-founder of Galaxy Zoo, described the problem that led to Galaxy Zoo's creation when he was set the task of classifying the morphology of more than 900,000 galaxies by eye that had been imaged by the Sloan Digital Sky Survey at the Apache Point Observatory in New Mexico, USA. "I classified 50,000 galaxies myself in a week, it


id: galaxy-zoo-20260505-1
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-1.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
== Volunteers == Galaxy Zoo recruited volunteers to help with the largest galaxy census ever carried out. Opening the project to the general public saved the professional astronomers the task of studying all the galaxies themselves, resulting in classification of a large number of galaxies undertaken in a shorter time than what smaller research teams would be able to do, classifying 900,000 galaxies in months rather than years if done by smaller research teams. Computer programs had been unable to reliably classify galaxies: several groups had attempted to develop image-analysis programs. Kevin Schawinski stated: "The human brain is actually much better than a computer at these pattern recognition tasks." However, volunteers astonished the project's organizers by classifying the entire catalog years ahead of schedule. An online forum was later set up two weeks after the initial start, partially due to a large volume of emails being sent around, to the point that it was troublesome for those receiving them to process and respond to them. This led volunteers to point out anomalies that on closer inspection have turned out to be new astronomical objects such as 'Hanny's Voorwerp' and 'the Green Pea galaxies'. "I'm incredibly impressed by what they've managed


id: galaxy-zoo-20260505-2
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-2.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
=== Galaxy Zoo 2 === This consisted of some 250,000 of the brightest galaxies from the Sloan Digital Sky Survey. Galaxy Zoo 2 allowed for a much more detailed classification, by shape and by the intensity or dimness of the galactic core, and with a special section for oddities like mergers or ring galaxies. The sample also contained fewer optical oddities. The project which collected classifications from February 2009 - April 2010 and closed with some 60 million classifications. === Galaxy Zoo mergers === This studied the role of interacting galaxies. Interacting galaxies are galaxies that exhibit a gravitational influence on one another. This influence is exhibited over the course of millions or even billions of years as two or more galaxies pass nearby one another. The near passage of two massive structures can cause the galaxies to be distorted and possibly merge. The Galaxy Zoo Mergers aimed to provide a set of tools that allowed users to randomly sample various sets of simulation parameters in rapid succession by showing 8 simulation outputs at a time. This started in November 2009 and was retired in June 2012. === Galaxy Zoo supernovae === Galaxy Zoo used images partner from the Palomar


id: galaxy-zoo-20260505-3
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-3.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
=== Related === In June 2019, citizen scientists through Galaxy Zoo reported that the usual Hubble classification, particularly concerning spiral galaxies, may not be supported, and may need updating. == Rotation of galaxies == One of the original aims for Galaxy Zoo was to explore which way galaxies rotated. Cosmologist Kate Land stated: "Some people have argued that galaxies are rotating all in agreement with each other, not randomly as we'd expect. We want people to classify the galaxies according to which way they're rotating and I'll be able to go and see if there's anything bizarre going on. If there are any patterns that we're not expecting, it could really turn up some surprises." In Galaxy Zoo 1, volunteers were asked to judge from the SDSS images whether the galaxies were elliptical or spiral and, if spiral, whether they were rotating in a clockwise (Z-wise) or anti-clockwise (S-wise) direction. The rotation, also called the chirality, of galaxies has been examined in several Galaxy Zoo related papers. Among the results a psychological bias was demonstrated. Galaxy Zoo scientists wanted to determine whether spiral galaxies were evenly distributed, or whether an intrinsic property of the universe caused them to rotate one


id: galaxy-zoo-20260505-4
file: data/en.wikipedia.org/wiki/Galaxy_Zoo-4.md
category: reference
title: Galaxy Zoo
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Galaxy_Zoo
date_saved: 2026-05-05T03:49:12.688307+00:00
instance: kb-cron
parent: galaxy-zoo-20260505-0
summary:
The properties of Galactic Dust have been examined in several Galaxy Zoo papers. The interstellar medium of spiral galaxies is filled by gas and small solid particles called dust grains. Despite constituting only a minor fraction of the galactic mass (between 0.1% and 0.01% for the Milky Way), dust grains have a major role in shaping the appearance of a galaxy. Because of their dimension (typically smaller than a few tenths of a micron), they are very effective in absorbing and scattering the radiation emitted by stars in the ultraviolet, optical and near-infrared. Although the interstellar regions are more devoid of matter than any vacuum artificially created on earth, there is matter in space. These regions have very low densities and consist mainly of gas (99%) and dust. In total, approximately 15% of the visible matter in the Milky Way is composed of interstellar gas and dust. The study of dust in galaxies is interesting for many reasons. For example, the dimming effects of dust need to be corrected for to estimate the total mass of a galaxy from measurements of its light. Standard candles used to measure the expansion history of the Universe also need to be corrected for


id: global-urban-evolution-project-20260505-0
file: data/en.wikipedia.org/wiki/Global_Urban_Evolution_Project-0.md
category: reference
title: Global Urban Evolution Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Global_Urban_Evolution_Project
date_saved: 2026-05-05T03:49:13.929625+00:00
instance: kb-cron
parent: global-urban-evolution-project-20260505-0
summary:
The Global Urban Evolution Project is an international collaborative project which was started by Marc T. J. Johnson at the Centre for Urban Environments of the University of Toronto Mississauga (UTM). It includes partners from at least 5 continents, 26 countries, and 160 cities. As a field study of evolution, and as a global study of the effects of urbanization on evolution, its scale is unprecedented. It has been described as "the best replicated test of parallel evolution, on the largest scale ever attempted". The project uses white clover as a model organism for studying global urbanization and urban evolution. White clover was chosen because it already grew in most cities worldwide. It examines the plant's production of hydrogen cyanide (HCN) in urban and more rural environments ("urban-rural clines"). Hydrogen cyanide deters herbivores and increases clover's tolerance for water stress. The project has demonstrated that urban environments are altering the ways in which plants evolve locally, and that similar changes are occurring globally, a demonstration of parallel evolution. It enables researchers to better understand the nature of urban environments, the adaptive capacity of species, and their ability to deal with rapid global environmental changes. == Project history == In 2018,


id: harking-20260505-0
file: data/en.wikipedia.org/wiki/HARKing-0.md
category: reference
title: HARKing
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/HARKing
date_saved: 2026-05-05T03:49:15.371909+00:00
instance: kb-cron
parent: harking-20260505-0
summary:
HARKing (hypothesizing after the results are known) is an acronym coined by social psychologist Norbert Kerr that refers to the questionable research practice of "presenting a post hoc hypothesis in the introduction of a research report as if it were an a priori hypothesis". Hence, a key characteristic of HARKing is that post hoc hypothesizing is falsely portrayed as a priori hypothesizing. HARKing may occur when a researcher tests an a priori hypothesis but then omits that hypothesis from their research report after they find out the results of their test. Post hoc analysis or post hoc theorizing then may lead to a post hoc hypothesis. == Types == Several types of HARKing have been distinguished, including: THARKing Transparently hypothesizing after the results are known, rather than the secretive, undisclosed, HARKing that was first proposed by Kerr. In this case, researchers openly declare that they developed their hypotheses after they observed their research results. CHARKing (or Pure HARKing) CHARKing or "pure HARKing" refers to the practice of constructing new hypotheses after the results are known and presenting them as a priori hypotheses. CHARKing is often regarded as the prototypical form of HARKing. RHARKing RHARKing refers to retrieving old hypotheses from


id: initiative-for-open-citations-20260505-0
file: data/en.wikipedia.org/wiki/Initiative_for_Open_Citations-0.md
category: reference
title: Initiative for Open Citations
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Initiative_for_Open_Citations
date_saved: 2026-05-05T03:49:20.289810+00:00
instance: kb-cron
parent: initiative-for-open-citations-20260505-0
summary:
The Initiative for Open Citations (I4OC) is a project launched publicly in April 2017, that describes itself as: "a collaboration between scholarly publishers, researchers, and other interested parties to promote the unrestricted availability of scholarly citation data and to make these data available." It is intended to facilitate improved citation analysis. == Methodology == The citations are stored in Crossref and are made available through the Crossref REST API. They are also available from the OpenCitations Corpus, a database that harvests citation data from Crossref and other sources. The data are considered by those involved in the Initiative to be in the public domain, and so a CC0 licence is used. The stated benefits of this approach are: 1. discoverability of published content; 2. the building of new services, and 3. creation of a public citation graph to explore connections between knowledge fields. The Royal Society of Chemistry earmarked the I4OC as a feature of excellence in publishing, and IOP Publishing participates to implement their commitment to the San Francisco Declaration on Research Assessment. == Launch == The initiative was established in response to a paper on citations in Wikidata, Citations needed for the sum of all human knowledge: Wikidata


id: international-hapmap-project-20260505-0
file: data/en.wikipedia.org/wiki/International_HapMap_Project-0.md
category: reference
title: International HapMap Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/International_HapMap_Project
date_saved: 2026-05-05T03:49:21.458141+00:00
instance: kb-cron
parent: international-hapmap-project-20260505-0
summary:
The International HapMap Project is an organization that aimed to develop a haplotype map (HapMap) of the human genome, to describe the common patterns of human genetic variation. HapMap is used to find genetic variants affecting health, disease and responses to drugs and environmental factors. The information produced by the project is made freely available for research. The International HapMap Project is a collaboration among researchers at academic centers, non-profit biomedical research groups and private companies in Canada, China (including Hong Kong), Japan, Nigeria, the United Kingdom, and the United States. It officially started with a meeting on October 27 to 29, 2002, and was expected to take about three years. It comprises three phases; the complete data obtained in Phase I were published on 27 October 2005. The analysis of the Phase II dataset was published in October 2007. The Phase III dataset was released in spring 2009 and the publication presenting the final results published in September 2010. == Background == Unlike with the rarer Mendelian diseases, combinations of different genes and the environment play a role in the development and progression of common diseases (such as diabetes, cancer, heart disease, stroke, depression, and asthma), or in the


id: international-hapmap-project-20260505-1
file: data/en.wikipedia.org/wiki/International_HapMap_Project-1.md
category: reference
title: International HapMap Project
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/International_HapMap_Project
date_saved: 2026-05-05T03:49:21.458141+00:00
instance: kb-cron
parent: international-hapmap-project-20260505-0
summary:
== Scientific strategy == It was expensive in the 1990s to sequence patients' whole genomes. So the National Institutes of Health embraced the idea for a "shortcut", which was to look just at sites on the genome where many people have a variant DNA unit. The theory behind the shortcut was that, since the major diseases are common, so too would be the genetic variants that caused them. Natural selection keeps the human genome free of variants that damage health before children are grown, the theory held, but fails against variants that strike later in life, allowing them to become quite common (In 2002 the National Institutes of Health started a $138 million project called the HapMap to catalog the common variants in European, East Asian and African genomes). For the Phase I, one common SNP was genotyped every 5,000 bases. Overall, more than one million SNPs were genotyped. The genotyping was carried out by 10 centres using five different genotyping technologies. Genotyping quality was assessed by using duplicate or related samples and by having periodic quality checks where centres had to genotype common sets of SNPs. The Canadian team was led by Thomas J. Hudson at McGill University in


id: journal-article-tag-suite-20260505-0
file: data/en.wikipedia.org/wiki/Journal_Article_Tag_Suite-0.md
category: reference
title: Journal Article Tag Suite
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Journal_Article_Tag_Suite
date_saved: 2026-05-05T03:49:22.745801+00:00
instance: kb-cron
parent: journal-article-tag-suite-20260505-0
summary:
The Journal Article Tag Suite (JATS) is format used to describe scientific literature published online. It is a technical standard developed by the National Information Standards Organization (NISO) and approved by the American National Standards Institute with the code Z39.96-2012. The NISO project was a continuation of the work done by NLM/NCBI, and popularized by the NLM's PubMed Central as a de facto standard for archiving and interchange of scientific open-access journals and its contents with XML. With the NISO standardization the NLM initiative has gained a wider reach, and several other repositories, such as SciELO and Redalyc, adopted the XML formatting for scientific articles. The JATS provides a set of XML elements and attributes for describing the textual and graphical content of journal articles as well as some non-article material such as letters, editorials, and book and product reviews. JATS allows for descriptions of the full article content or just the article header metadata; and allows other kinds of contents, including research and non-research articles, letters, editorials, and book and product reviews. == History == Since its introduction, NCBI's NLM Archiving and Interchange DTD suite has become the de facto standard for journal article markup in scholarly publishing. With


id: journal-article-tag-suite-20260505-1
file: data/en.wikipedia.org/wiki/Journal_Article_Tag_Suite-1.md
category: reference
title: Journal Article Tag Suite
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Journal_Article_Tag_Suite
date_saved: 2026-05-05T03:49:22.745801+00:00
instance: kb-cron
parent: journal-article-tag-suite-20260505-0
summary:
The DOCTYPE header is optional, a legacy from SGML and DTD-oriented validators. The dtd-version attribute can be used even without a DTD header. The root element article is common for any version of JATS or "JATS family", as NLM DTDs. The rules for front, body and back tags validation, depends on the JATS version, but all versions have similar structure, with good compatibility in a range of years. The evolution of the schema preserves an overall stability. Less common, "only front", "only front and back" variations are also used for other finalities than full-content representation. The general article composition (as an DTD-content expression) is (front, body?, back?, floats-group?, (sub-article* | response*)) == Tools == There are a variety of tools for create, edit, convert and transform JATS. They range from simple forms to complete conversion automation: === Conversion to JATS === Take as input a scientific document, and, with some human support, produce a JATS output. OpenOffice (LibreOffice) and MS Word documents to JATS: Typeset: provides automated set of converters for MS-Word to JATS XML. OxGarage: can convert documents from various formats into "National Library of Medicine (NLM) DTD 3.0". meTypeset: meTypeset "is a fork of the OxGarage stack" "to


id: metanetx-20260505-0
file: data/en.wikipedia.org/wiki/MetaNetX-0.md
category: reference
title: MetaNetX
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/MetaNetX
date_saved: 2026-05-05T03:49:24.009125+00:00
instance: kb-cron
parent: metanetx-20260505-0
summary:
MetaNetX is a database maintained by the SIB Swiss Institute of Bioinformatics for the automated model construction, and the genome annotation for large-scale metabolic networks. MetaNetX provides a number of tools to access, analyse and manipulate metabolic networks. MetaNetX provides a bunch of pre-mapped metabolic models. To ease model comparison, MetaNetX has developed a resource to unify metabolites and biochemical reactions in the context of metabolic models. This unified namespace is called MetaNetX/MNXref. MNXref reconciles chemical compounds by structural similarity and biochemical reaction context. Then reconciles biochemical reactions on the basis of the chemical compound reconciliation in an iterative way. Each reconciled group of chemical compounds, biochemical reactions and cellular compartments is a bag of similar items. MNXref sets a referent for each group. MetaNetX allows search in MNXref by chemical compounds, biochemical reactions and cellular compartments. Currently, MetaNetX/MNXref reconciles those resources: BiGG ChEBI enviPath HMDB GO KEGG LipidMaps MetaCyc Reactome Rhea SABIO-RK SwissLipids The SEED VMH == References ==


id: mixed-signals-preprocessing-psychophysiological-data-in-brain-vision-analyzer-20260505-0
category: reference
title: Mixed Signals: Preprocessing Psychophysiological Data in Brain Vision Analyzer
tags: science, encyclopedia
date_saved: 2026-05-05T03:49:25.200239+00:00
instance: kb-cron
parent: mixed-signals-preprocessing-psychophysiological-data-in-brain-vision-analyzer-20260505-0
file:
data/en.wikipedia.org/wiki/Mixed_Signals:_Preprocessing_Psychophysiological_Data_in_Brain_Vision_Analyzer-0.md

summary:
Mixed Signals: Preprocessing Psychophysiological Data in Brain Vision Analyzer is a psychophysiology manual for experimental psychologists. Originally compiled by Paul Pfaff at the Experimental Psychopathology and Clinical Psychology lab at Utrecht University, the updated edition of the manual was printed in 2025. The manual covers the preprocessing of a wide range of psychophysiological data types common in experimental psychology. Specific sections are dedicated to skin conductance response, facial electromyography (EMG), startle EMG, smiling/frowning EMG, and postural sway. Preprocessing steps outlined include baseline correction, filtering, artifact rejection, and signal standardization. Recommendations are provided based on reviews of the latest literature in the field. The publication is part of ongoing open science initiatives in psychology to standardize research procedures in the aftermath of the replication crisis and has received positive reviews. == Further reading == Cacioppo, J. T., Tassinary, L. G., & Berntson, G. (Eds.). (2007). Handbook of psychophysiology. Cambridge University Press. Brain Vision Analyzer User Manual Version 1.05 accessible at: https://www.linguistik.hu-berlin.de/de/institut/professuren/psycholinguistik/psyling/archiv/files/ekp/Analyzer.pdf/@@download/file/Analyzer.pdf == References ==

source_url:
https://en.wikipedia.org/wiki/Mixed_Signals:_Preprocessing_Psychophysiological_Data_in_Brain_Vision_Analyzer


id: peter-murray-rust-20260505-0
file: data/en.wikipedia.org/wiki/Peter_Murray-Rust-0.md
category: reference
title: Peter Murray-Rust
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Peter_Murray-Rust
date_saved: 2026-05-05T03:49:26.337246+00:00
instance: kb-cron
parent: peter-murray-rust-20260505-0
summary:
Peter Murray-Rust is a chemist currently working at the University of Cambridge. As well as his work in chemistry, Murray-Rust is also known for his support of open access and open data. == Education == He was educated at Bootham School, a private school in York, and at Balliol College, Oxford. After obtaining a Doctor of Philosophy with a thesis entitled A structural investigation of some compounds showing charge-transfer properties, he became lecturer in chemistry at the (new) University of Stirling and was first warden of Andrew Stewart Hall of Residence. In 1982, he moved to Glaxo Group Research at Greenford to head Molecular Graphics, Computational Chemistry and later protein structure determination. He was Professor of Pharmacy in the University of Nottingham from 1996 to 2000, setting up the Virtual School of Molecular Sciences. He is now Reader Emeritus in Molecular Informatics at the University of Cambridge and Senior Research Fellow Emeritus at Churchill College, Cambridge. == Research == His research interests have involved the automated analysis of data in scientific publications, creation of virtual communities, e.g. The Virtual School of Natural Sciences in the Globewide Network Academy, and the Semantic Web. With Henry Rzepa, he has extended this to


id: open-energy-modelling-initiative-20260505-0
file: data/en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative-0.md
category: reference
title: Open Energy Modelling Initiative
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative
date_saved: 2026-05-05T03:49:27.553982+00:00
instance: kb-cron
parent: open-energy-modelling-initiative-20260505-0
summary:
The Open Energy Modelling Initiative (openmod) is a grassroots community of energy system modellers from universities and research institutes across Europe and elsewhere. The initiative promotes the use of open-source software and open data in energy modelling for research and policy advice. The Open Energy Modelling Initiative documents a variety of open-source energy models and addresses practical and conceptual issues regarding their development and application. The initiative runs an email list, an internet forum, and a wiki and hosts occasional academic workshops. A statement of aims is available. == Context == The application of open-source development to energy modelling dates back to around 2003. This section provides some background for the growing interest in open methods. === Growth in open energy modelling === Just two active open energy modelling projects were cited in a 2011 paper: OSeMOSYS and TEMOA. Balmorel was also public at that time, having been made available on a website in 2001. As of October 2021, the Open Energy Platform lists 17 open energy frameworks and about 50 open energy models. === Academic literature === This 2012 paper presents the case for using "open, publicly accessible software and data as well as crowdsourcing techniques to develop robust


id: open-energy-modelling-initiative-20260505-1
file: data/en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative-1.md
category: reference
title: Open Energy Modelling Initiative
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Energy_Modelling_Initiative
date_saved: 2026-05-05T03:49:27.553982+00:00
instance: kb-cron
parent: open-energy-modelling-initiative-20260505-0
summary:
=== Public policy support === In May 2016, the European Union announced that "all scientific articles in Europe must be freely accessible as of 2020". This is a step in the right direction, but the new policy makes no mention of open software and its importance to the scientific process. In August 2016, the United States government announced a new federal source code policy which mandates that at least 20% of custom source code developed by or for any agency of the federal government be released as open-source software (OSS). The US Department of Energy (DOE) is participating in the program. The project is hosted on a dedicated website and subject to a three-year pilot. Open-source campaigners are using the initiative to advocate that European governments adopt similar practices. In 2017 the Free Software Foundation Europe (FSFE) issued a position paper calling for free software and open standards to be central to European science funding, including the flagship EU program Horizon 2020. The position paper focuses on open data and open data processing and the question of open modelling is not traversed per se. === Adoption by regulators and industry generally === A trend evident by 2023 is the adoption


id: open-energy-system-databases-20260505-0
file: data/en.wikipedia.org/wiki/Open_energy_system_databases-0.md
category: reference
title: Open energy system databases
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_databases
date_saved: 2026-05-05T03:49:28.737080+00:00
instance: kb-cron
parent: open-energy-system-databases-20260505-0
summary:
Open energy system database projects employ open data methods to collect, clean, and republish energy-related datasets for open use. The resulting information is then available, given a suitable open license, for statistical analysis and for building numerical energy system models, including open energy system models. Permissive licenses like Creative Commons CC0 and CC BY are preferred, but some projects will house data made public under market transparency regulations and carrying unqualified copyright. The databases themselves may furnish information on national power plant fleets, renewable generation assets, transmission networks, time series for electricity loads, dispatch, spot prices, and cross-border trades, weather information, and similar. They may also offer other energy statistics including fossil fuel imports and exports, gas, oil, and coal prices, emissions certificate prices, and information on energy efficiency costs and benefits. Much of the data is sourced from official or semi-official agencies, including national statistics offices, transmission system operators, and electricity market operators. Data is also crowdsourced using public wikis and public upload facilities. Projects usually also maintain a strict record of the provenance and version histories of the datasets they hold. Some projects, as part of their mandate, also try to persuade primary data providers to release their


id: open-energy-system-databases-20260505-1
file: data/en.wikipedia.org/wiki/Open_energy_system_databases-1.md
category: reference
title: Open energy system databases
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_databases
date_saved: 2026-05-05T03:49:28.737080+00:00
instance: kb-cron
parent: open-energy-system-databases-20260505-0
summary:
== Open energy system database projects == Energy system models are data intensive and normally require detailed information from a number of sources. Dedicated projects to collect, collate, document, and republish energy system datasets have arisen to service this need. Most database projects prefer open data, issued under free licenses, but some will accept datasets with proprietary licenses in the absence of other options. The OpenStreetMap project, which uses the Open Database License (ODbL), contains geographic information about energy system components, including transmission lines. Wikimedia projects such as Wikidata and Wikipedia have a growing set of information related to national energy systems, such as descriptions of individual power stations. The following table summarizes projects that specifically publish open energy system data. Some are general repositories while others (for instance, oedb) are designed to interact with open energy system models in real-time. Three of the projects listed work with linked open data (LOD), a method of publishing structured data on the web so that it can be networked and subject to semantic queries. The overarching concept is termed the semantic web. Technically, such projects support RESTful APIs, RDF, and the SPARQL query language. A 2012 paper reviews the use of LOD


id: open-energy-system-databases-20260505-2
file: data/en.wikipedia.org/wiki/Open_energy_system_databases-2.md
category: reference
title: Open energy system databases
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_databases
date_saved: 2026-05-05T03:49:28.737080+00:00
instance: kb-cron
parent: open-energy-system-databases-20260505-0
summary:
The Open Data Energy Networks (Open Data Réseaux Énergies or ODRÉ) portal is run by eight partners, led by the French national transmission system operator (TSO) Réseau de Transport d'Électricité (RTE). The portal was previously known as Open Data RTE. The site offers electricity system datasets under a Creative Commons CC BY 2.0 compatible license, with metadata, an RSS feed for notifying updates, and an interface for submitting questions. Re-users of information obtained from the site can also register third-party URLs (be they publications or webpages) against specific datasets. The portal uses the French Government Licence Ouverte license and this is explicitly compatible with the United Kingdom Open Government Licence (OGL), the Creative Commons CC BY 2.0 license (and thereby later versions), and the Open Data Commons ODC-BY license. The site hosts electricity, gas, and weather information related to France. === UK Power Networks Open Data Portal === The Open Data Portal is run by UK Power Networks, a GB Distribution Network Operator (DNO), hosted on the OpenDataSoft platform. The Portal offers electricity network datasets under a Creative Commons CC BY 4.0 compatible license, with metadata, a newsfeed, and a data request form. Re-users of information obtained from the site


id: open-energy-system-databases-20260505-3
file: data/en.wikipedia.org/wiki/Open_energy_system_databases-3.md
category: reference
title: Open energy system databases
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_databases
date_saved: 2026-05-05T03:49:28.737080+00:00
instance: kb-cron
parent: open-energy-system-databases-20260505-0
summary:
Open Energy Information (OpenEI) is a collaborative website, run by the US government, providing open energy data to software developers, analysts, users, consumers, and policymakers. The platform is sponsored by the United States Department of Energy (DOE) and is being developed by the National Renewable Energy Laboratory (NREL). OpenEI launched on 9 December 2009. While much of its data is from US government sources, the platform is intended to be open and global in scope. OpenEI provides two mechanisms for contributing structured information: a semantic wiki (using MediaWiki and the Semantic MediaWiki extension) for collaboratively-managed resources and a dataset upload facility for contributor-controlled resources. US government data is distributed under a CC0 public domain dedication, whereas other contributors are free to select an open data license of their choice. Users can rate data using a five-star system, based on accessibility, adaptability, usefulness, and general quality. Individual datasets can be manually downloaded in an appropriate format, often as CSV files. Scripts for processing data can also be shared through the site. In order to build a community around the platform, a number of forums are offered covering energy system data and related topics. Most of the data on OpenEI is exposed


id: open-energy-system-databases-20260505-4
file: data/en.wikipedia.org/wiki/Open_energy_system_databases-4.md
category: reference
title: Open energy system databases
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_databases
date_saved: 2026-05-05T03:49:28.737080+00:00
instance: kb-cron
parent: open-energy-system-databases-20260505-0
summary:
reegle is a clean energy information portal covering renewable energy, energy efficiency, and climate compatible development topics. reegle was launched in 2006 by REEEP and REN21 with funding from the Dutch (VROM), German (BMU), and UK (Defra) environment ministries. Originally released as a specialized internet search engine, reegle was relaunched in 2011 as an information portal. reegle offers and utilizes linked open data (LOD) (described elsewhere on this page). Sources of data include UN and World Bank databases, as well as dedicated partners around the world. reegle maintains a comprehensive structured glossary (driven by an LOD-compliant thesaurus) of energy and climate compatible development terms to assist with the tagging of datasets. The glossary also facilitates intelligent web searches. reegle offers country profiles which collate and display energy data on a per-country basis for most of the world. These profiles are kept current automatically using LOD techniques. As of 2021, the portal is no longer active. === Renewables.ninja === Renewables.ninja is a website that can calculate the hourly power output from solar photovoltaic installations and wind farms located anywhere in the world. The website is a joint project between the Department of Environmental Systems Science, ETH Zurich, Zürich, Switzerland and the


id: open-energy-system-models-20260505-0
file: data/en.wikipedia.org/wiki/Open_energy_system_models-0.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
Open energy-system models are energy-system models that are open source. However, some of them may use third-party proprietary software as part of their workflows to input, process, or output data. Preferably, these models use open data, which facilitates open science. Energy-system models are used to explore future energy systems and are often applied to questions involving energy and climate policy. The models themselves vary widely in terms of their type, design, programming, application, scope, level of detail, sophistication, and shortcomings. For many models, some form of mathematical optimization is used to inform the solution process. Energy regulators and system operators in Europe and North America began adopting open energy-system models for planning purposes in the early‑2020s. Open models and open data are increasingly being used by government agencies to guide the develop of net‑zero public policy as well. Companies and engineering consultancies are likewise adopting open models for analysis. == General considerations == === Organization === The open energy modeling projects listed here fall exclusively within the bottom-up paradigm, in which a model is a relatively literal representation of the underlying system. Several drivers favor the development of open models and open data. There is an increasing interest in making


id: open-energy-system-models-20260505-1
file: data/en.wikipedia.org/wiki/Open_energy_system_models-1.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
=== Surveys === A survey completed in 2021 investigated the degree to which open energy-system modeling frameworks support flexibility options, broken down by supply, demand, storage, sector coupled, and network response. Of the frameworks surveyed, none supported all types, which suggests that the soft coupling of complementary frameworks could provide more holistic assessments of flexibility. Even so, most candidates opt for perfect foresight and do not natively admit probabilistic actions or explicit behavioral responses. == Electricity sector models == Open electricity sector models are confined to just the electricity sector. These models invariably have a temporal resolution of one hour or less. Some models concentrate on the engineering characteristics of the system, including a good representation of high-voltage transmission networks and AC power flow. Others models depict electricity spot markets and are known as dispatch models. While other models embed autonomous agents to capture, for instance, bidding decisions using techniques from bounded rationality. The ability to handle variable renewable energy, transmission systems, and grid storage are becoming important considerations. === AMIRIS === AMIRIS is the open Agent-based Market model for the Investigation of Renewable and Integrated energy Systems. The AMIRIS simulation framework was first developed by the German Aerospace Center


id: open-energy-system-models-20260505-2
file: data/en.wikipedia.org/wiki/Open_energy_system_models-2.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
Under development at the European Commission's Joint Research Centre (JRC), Petten, the Netherlands, Dispa-SET is a unit commitment and dispatch model intended primarily for Europe. It is written in Python (with Pyomo) and GAMS and uses Python for data processing. A valid GAMS license is required. The model is formulated as a mixed integer problem and JRC uses the proprietary CPLEX sover although open source libraries may also be deployed. Technical descriptions are available for versions 2.0 and 2.1. Dispa-SET is hosted on GitHub, together with a trial dataset, and third-party contributions are encouraged. The codebase has been tested on Windows, macOS, and Linux. Online documentation is available. The SET in the project name refers to the European Strategic Energy Technology Plan (SET-Plan), which seeks to make Europe a leader in energy technologies that can fulfill future (2020 and 2050) energy and climate targets. Energy system modeling, in various forms, is central to this European Commission initiative. The model power system is managed by a single operator with full knowledge of the economic and technical characteristics of the generation units, the loads at each node, and the heavily simplified transmission network. Demand is deemed fully inelastic. The system is subject


id: open-energy-system-models-20260505-3
file: data/en.wikipedia.org/wiki/Open_energy_system_models-3.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
EMMA is the European Electricity Market Model. It is a techno-economic model covering the integrated Northwestern European power system. EMMA is being developed by the energy economics consultancy Neon Neue Energieökonomik, Berlin, Germany. The source code and datasets can be downloaded from the project website. A manual is available. EMMA is written in GAMS and uses the CPLEX commercial solver. EMMA models electricity dispatch and investment, minimizing the total cost with respect to investment, generation, and trades between market areas. In economic terms, EMMA classifies as a partial equilibrium model of the wholesale electricity market with a focus on the supply-side. EMMA identifies short-term or long-term optima (or equilibria) and estimates the corresponding capacity mix, hourly prices, dispatch, and cross-border trading. Technically, EMMA is a pure linear program (no integer variables) with about two million non-zero variables. As of 2016, the model covers Belgium, France, Germany, the Netherlands, and Poland and supports conventional generation, renewable generation, and cogeneration. EMMA has been used to study the economic effects of the increasing penetration of variable renewable energy (VRE), specifically solar power and wind power, in the Northwestern European power system. A 2013 study finds that increasing VRE shares will depress prices and,


id: open-energy-system-models-20260505-4
file: data/en.wikipedia.org/wiki/Open_energy_system_models-4.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
NEMO, the National Electricity Market Optimiser, is a chronological dispatch model for testing and optimizing different portfolios of conventional and renewable electricity generation technologies. It applies solely to the Australian National Electricity Market (NEM), which, despite its name, is limited to east and south Australia. NEMO has been in development at the Centre for Energy and Environmental Markets (CEEM), University of New South Wales (UNSW), Sydney, Australia since 2011. The project maintains a small website and runs an email list. NEMO is written in Python. NEMO itself is described in two publications. The data sources are also noted. Optimizations are carried out using a single-objective evaluation function, with penalties. The solution space of generator capacities is searched using the CMA-ES (covariance matrix adaptation evolution strategy) algorithm. The timestep is arbitrary but one hour is normally employed. NEMO has been used to explore generation options for the year 2030 under a variety of renewable energy (RE) and abated fossil fuel technology scenarios. A 2012 study investigates the feasibility of a fully renewable system using concentrated solar power (CSP) with thermal storage, windfarms, photovoltaics, existing hydroelectricity, and biofuelled gas turbines. A number of potential systems, which also meet NEM reliability criteria, are


id: open-energy-system-models-20260505-5
file: data/en.wikipedia.org/wiki/Open_energy_system_models-5.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
pandapower is a power system analysis and optimization program being jointly developed by the Energy Management and Power System Operation research group, University of Kassel and the Department for Distribution System Operation, Fraunhofer Institute for Energy Economics and Energy System Technology (IEE), both of Kassel, Germany. The codebase is hosted on GitHub and is also available as a package. The project maintains a website, an emailing list, and online documentation. pandapower is written in Python. It uses the pandas library for data manipulation and analysis and the PYPOWER library to solve for power flow. Unlike some open source power system tools, pandapower does not depend on proprietary platforms like MATLAB. pandapower supports the automated analysis and optimization of distribution and transmission networks. This allows a large of number of scenarios to be explored, based on different future grid configurations and technologies. pandapower offers a collection of power system elements, including: lines, 2-winding transformers, 3-winding transformers, and ward-equivalents. It also contains a switch model that allows the modeling of ideal bus-bus switches as well as bus-line/bus-trafo switches. The software supports topological searching. The network itself can be plotted, with or without geographical information, using the matplotlib and plotly libraries. A 2016


id: open-energy-system-models-20260505-6
file: data/en.wikipedia.org/wiki/Open_energy_system_models-6.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
renpass is an acronym for Renewable Energy Pathways Simulation System. renpass is a simulation electricity model with high regional and temporal resolution, designed to capture existing systems and future systems with up to 100% renewable generation. The software is being developed by the Centre for Sustainable Energy Systems (CSES or ZNES), University of Flensburg, Germany. The project runs a website, from where the codebase can be download. renpass is written in R and links to a MySQL database. A PDF manual is available. renpass is also described in a PhD thesis. As of 2015, renpass is being extended as renpassG!S, based on oemof. renpass is an electricity dispatch model which minimizes system costs for each time step (optimization) within the limits of a given infrastructure (simulation). Time steps are optionally 15 minutes or one hour. The method assumes perfect foresight. renpass supports the electricity systems found in Austria, Belgium, the Czech Republic, Denmark, Estonia, France, Finland, Germany, Latvia, Lithuania, Luxembourg, the Netherlands, Norway, Poland, Sweden, and Switzerland. The optimization problem for each time step is to minimize the electricity supply cost using the existing power plant fleet for all regions. After this regional dispatch, the exchange between the regions is


id: open-energy-system-models-20260505-7
file: data/en.wikipedia.org/wiki/Open_energy_system_models-7.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
SWITCH is a loose acronym for solar, wind, conventional and hydroelectric generation, and transmission. SWITCH is an optimal planning model for power systems with large shares of renewable energy. SWITCH is being developed by the Department of Electrical Engineering, University of Hawaiʻi at Mānoa, Hawaii, USA. The project runs a small website and hosts its codebase and datasets on GitHub. SWITCH is written in Pyomo, an optimization components library programmed in Python. It can use either the open source GLPK solver or the commercial CPLEX solver. SWITCH is a power system model, focused on renewables integration. It can identify which generator and transmission projects to build in order to satisfy electricity demand at the lowest cost over a several-year period while also reducing CO2 emissions. SWITCH utilizes multi-stage stochastic linear optimization with the objective of minimizing the present value of the cost of power plants, transmission capacity, fuel usage, and an arbitrary per-tonne CO2 charge (to represent either a carbon tax or a certificate price), over the course of a multi-year investment period. It has two major sets of decision variables. First, at the start of each investment period, SWITCH selects how much generation capacity to build in each of


id: open-energy-system-models-20260505-8
file: data/en.wikipedia.org/wiki/Open_energy_system_models-8.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
AnyMOD.jl is a framework for planning macro‑energy systems at a high level of spatio-temporal detail. The framework covers the expansion and operation of short-term and seasonal storage, fossil and renewable generation, transmission infrastructure, and sector coupling technologies. It can be used to plan long‑term pathways under perfect foresight. AnyMOD.jl is implemented in Julia and relies on the JuMP library for optimization and DataFrames.jl for data management. Models are formulated as linear optimization problems and can be solved with open-source libraries like HiGHS or commercial solvers like CPLEX. To increase accessibility and enable version-controlled development, specific models are fully defined using CSV files. Compared to similar tools, AnyMOD.jl puts an emphasis on innovative methods to achieve high detail and capture intermittent renewables, while maintaining a comprehensive scope in terms of regions and sectors. These methods include varying the spatio-temporal resolution by energy carrier within the same model and a scaling algorithm to improve the properties of the underlying optimization problem. Methods from stochastic programming are now being implemented to better address the uncertainties associated with renewable generation. As of 2022, most studies deploying the tool have focused on the German energy system in a European context, for instance investigating the trade‑offs


id: open-energy-system-models-20260505-9
file: data/en.wikipedia.org/wiki/Open_energy_system_models-9.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
Calliope is an energy system modeling framework, with a focus on flexibility, high spatial and temporal resolution, and the ability to execute different runs using the same base-case dataset. The project is being developed at the Department of Environmental Systems Science, ETH Zurich, Zürich, Switzerland. The project maintains a website, hosts the codebase at GitHub, operates an issues tracker, and runs two email lists. Calliope is written in Python and uses the Pyomo library. It can link to the open source GLPK solver and the commercial CPLEX solver. PDF documentation is available. And a two‑page software review is available. A Calliope model consists of a collection of structured text files, in YAML and CSV formats, that define the technologies, locations, and resource potentials. Calliope takes these files, constructs a pure linear optimization (no integer variables) problem, solves it, and reports the results in the form of pandas data structures for analysis. The framework contains five abstract base technologies – supply, demand, conversion, storage, transmission – from which new concrete technologies can be derived. The design of Calliope enforces the clear separation of framework (code) and model (data). A 2015 study uses Calliope to compare the future roles of nuclear power


id: open-energy-system-models-20260505-10
file: data/en.wikipedia.org/wiki/Open_energy_system_models-10.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
EnergyPATHWAYS is a bottom-up energy sector model used to explore the near-term implications of long-term deep decarbonization. The lead developer is energy and climate protection consultancy, Evolved Energy Research, San Francisco, USA. The code is hosted on GitHub. EnergyPATHWAYS is written in Python and links to the open source Cbc solver. Alternatively, the GLPK, or CPLEX solvers can be employed. EnergyPATHWAYS utilizes the PostgreSQL object-relational database management system (ORDBMS) to manage its data. EnergyPATHWAYS is a comprehensive accounting framework used to construct economy-wide energy infrastructure scenarios. While portions of the model do use linear programming techniques, for instance, for electricity dispatch, the EnergyPATHWAYS model is not fundamentally an optimization model and embeds few decision dynamics. EnergyPATHWAYS offers detailed energy, cost, and emissions accounting for the energy flows from primary supply to final demand. The energy system representation is flexible, allowing for differing levels of detail and the nesting of cities, states, and countries. The model uses hourly least-cost electricity dispatch and supports power-to-gas, short-duration energy storage, long-duration energy storage, and demand response. Scenarios typically run to 2050. A predecessor of the EnergyPATHWAYS software, named simply PATHWAYS, has been used to construct policy models. The California PATHWAYS model was used to


id: open-energy-system-models-20260505-11
file: data/en.wikipedia.org/wiki/Open_energy_system_models-11.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
GENeSYS‑MOD couples the demand sectors covering electricity, buildings, industry, and transport and finds the cost-optimal investment into conventional and renewable energy generation, storage, and infrastructure. The research focus is on long-term system development and pathway analysis. The model was first used to analyze decarbonization scenarios at the global level, broken down into ten regions. However, the framework is highly flexible, allowing for calculations at various levels of detail, from individual households to global aggregations, depending on the desired research question and availability of input data. A 2019 study examined the low‑carbon transition of the European energy system and specifically the problem of stranded assets under a range of scenarios. It found that up to €200 billion in fossil-fueled capacities could be stranded by 2035 unless stronger policy signals are able to address short‑term planning biases. Another 2019 study evaluates China's energy system transformation, highlighting the need to reduce coal consumption by 60% by 2050 to meet global climate targets. Renewable energies, and in particular photovoltaics and onshore wind, emerge as cost-effective solutions, but overcoming local resistance and increasing stakeholder engagement remain crucial for success. A 2021 study investigates the European Green Deal goal of achieving 100% greenhouse gas reductions by


id: open-energy-system-models-20260505-12
file: data/en.wikipedia.org/wiki/Open_energy_system_models-12.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
A key paper describing OSeMOSYS is available. A 2011 study uses OSeMOSYS to investigate the role of household investment decisions. A 2012 study extends OSeMOSYS to capture the salient features of a smart grid. The paper explains how to model variability in generation, flexible demand, and grid storage and how these impact on the stability of the grid. OSeMOSYS has been applied to village systems. A 2015 paper compares the merits of stand-alone, mini-grid, and grid electrification for rural areas in Timor-Leste under differing levels of access. In a 2016 study, OSeMOSYS is modified to take into account realistic consumer behavior. Another 2016 study uses OSeMOSYS to build a local multi-regional energy system model of the Lombardy region in Italy. One of the aims of the exercise was to encourage citizens to participate in the energy planning process. Preliminary results indicate that this was successful and that open modeling is needed to properly include both the technological dynamics and the non-technological issues. A 2017 paper covering Alberta, Canada factors in the risk of overrunning specified emissions targets because of technological uncertainty. Among other results, the paper finds that solar and wind technologies are built out seven and five years earlier


id: open-energy-system-models-20260505-13
file: data/en.wikipedia.org/wiki/Open_energy_system_models-13.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
The basic functionality of PyPSA is described in a 2018 paper. PyPSA bridges traditional steady-state power flow analysis software and full multi-period energy system models. It can be invoked using either non-linear power flow equations for system simulation or linearized approximations to enable the joint optimization of operations and investment across multiple periods. Generator ramping and multi-period up and down-times can be specified, DSM is supported, but demand remains price inelastic. A 2018 study examines potential synergies between sector coupling and transmission reinforcement in a future European energy system constrained to reduce carbon emissions by 95%. The PyPSA-Eur-Sec-30 model captures the demand-side management potential of battery electric vehicles (BEV) as well as the role that power-to-gas, long-term thermal energy storage, and related technologies can play. Results indicate that BEVs can smooth the daily variations in solar power while the remaining technologies smooth the synoptic and seasonal variations in both demand and renewable supply. Substantial buildout of the electricity grid is required for a least-cost configuration. More generally, such a system is both feasible and affordable. The underlying datasets are available from Zenodo. As of January 2018, PyPSA is used by more than a dozen research institutes and companies worldwide. Some


id: open-energy-system-models-20260505-14
file: data/en.wikipedia.org/wiki/Open_energy_system_models-14.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
In practical terms, the framework allows for modeling competition between technologies that can serve the same purpose, such as power generation, while also providing insights into when and where a specific technology is required. Additionally, it can be applied to transportation problems, where the optimal exchange of a commodity between at least two distinct regions needs to be determined. Furthermore, it addresses storage problems, where the optimal balance between production and consumption at different points in time is calculated. REMix offers several key features that make it a robust tool for energy system modeling. It is designed to handle large-scale models with high spatial and technological resolutions, making it suitable for complex analyses. The framework also incorporates path optimization, allowing for multi-year analyses and strategic planning over extended periods. Ongoing work deals with very large instances involving path optimization using the parallel solver PIPS-IPM++. A notable feature is its custom accounting capability, provided through the indicator module, which enables flexible definitions of what contributes to the objective functions. Additionally, REMix supports flexible modeling, offering multiple approaches to integrate and model technologies, allowing users to tailor the framework to their specific needs. Finally, it supports multi-criteria optimization, where, beyond cost minimization,


id: open-energy-system-models-20260505-15
file: data/en.wikipedia.org/wiki/Open_energy_system_models-15.md
category: reference
title: Open energy system models
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_energy_system_models
date_saved: 2026-05-05T03:49:30.157219+00:00
instance: kb-cron
parent: open-energy-system-models-20260505-0
summary:
The GAMS language requires a proprietary environment and its significant cost effectively limits participation to those who can access an institutional copy. == Programming components == Programming components, in this context, are coherent blocks of code or compiled libraries that can be relatively easily imported or linked to by higher‑level modeling frameworks in order to obtain some well‑defined functionality. === Technology modules === A number of technical component models are now also open source. While these component models do not constitute systems models aimed at public policy development (the focus of this page), they nonetheless warrant a mention. Technology modules can be linked or otherwise adapted into these broader initiatives. Sandia photovoltaic array performance model pvlib photovoltaics facility library hplib heat pump facility library windpowerlib wind turbine library hydropowerlib hydroelectricity library === Auction models === A number of electricity auction models have been written in GAMS, AMPL, MathProg, and other languages. These include: the EPOC nodal pricing model Australian National Electricity Market examples using MathProg can be found at b:GLPK/Electricity markets === Open solvers === Many projects rely on a pure linear or mixed integer solver to perform classical optimization, constraint satisfaction, or some mix of the two. While there


id: open-notebook-science-challenge-20260505-0
file: data/en.wikipedia.org/wiki/Open_Notebook_Science_Challenge-0.md
category: reference
title: Open Notebook Science Challenge
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Notebook_Science_Challenge
date_saved: 2026-05-05T03:49:31.605154+00:00
instance: kb-cron
parent: open-notebook-science-challenge-20260505-0
summary:
The Open Notebook Science Challenge is a crowdsourcing research project which collects measurements of the non-aqueous solubility of organic compounds and publishes these as open data; findings are reported in an open notebook science manner. Although anyone may contribute research data, the competition is only open to post-secondary students in the US and UK. The challenge in turn forms part of the UsefulChem project, an ongoing open notebook science effort to synthesize and screen potential new anti-malarial drugs. Data from the Solubility Challenge will be used to build predictive computational models of solubility for use in optimising syntheses. The challenge began on September 28, 2008 and, as of February 2014, involves researchers and their students from at least 4 different institutions and has resulted in the acquisition of over 7672 solubility measurements. == Prizes == To encourage participation, each month an award is given to the student who does, in the opinion of the judges, the best work. In order to participate, students have to be a US or UK resident. The award is a US$500 cash prize. The first three winners also received a year's subscription to the journal Nature. The awards are sponsored by Submeta and Nature. ==


