# kb

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. ==


id: open-peer-review-20260505-0
file: data/en.wikipedia.org/wiki/Open_peer_review-0.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:49:36.870107+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Open peer review is the various possible modifications of the traditional scholarly peer review process. The three most common modifications to which the term is applied are: Open identities: Authors and reviewers are aware of each other's identity. Open reports: Review reports are published alongside the relevant article (rather than being kept confidential). Open participation: The wider community (and not just invited reviewers) are able to contribute to the review process. These modifications are supposed to address various perceived shortcomings of the traditional scholarly peer review process, in particular its lack of transparency, lack of incentives, wastefulness, bullying and harassment. == Definitions == Open identities Open peer review may be defined as "any scholarly review mechanism providing disclosure of author and referee identities to one another at any point during the peer review or publication process". Then reviewer's identities may or may not be disclosed to the public. This is in contrast to the traditional peer review process where reviewers remain anonymous to anyone but the journal's editors. Authors' names are disclosed during the process in a single-blind organisation of reviews. In the double-blind process, authors' names and reviewers' names all remain anonymous except to the editor. Open reports Open


id: open-peer-review-20260505-1
file: data/en.wikipedia.org/wiki/Open_peer_review-1.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:49:36.870107+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
== History == In 1997, the open participation journal Electronic Transactions on Artificial Intelligence was launched. Articles were first "published" (meaning the day when the article is first made public), then "reviewed" (meaning a open process of public discussion), and then "refereed" (meaning the confidential pass/fail decision of whether to accept the article or not). In 1999, the open access journal Journal of Medical Internet Research was launched, which from its inception decided to publish the names of the reviewers at the bottom of each published article. Also in 1999, the British Medical Journal moved to an open peer review system, revealing reviewers' identities to the authors but not the readers, and in 2000, the medical journals in the open access BMC series published by BioMed Central, launched using open peer review. As with the BMJ, the reviewers' names are included on the peer review reports. In addition, if the article is published the reports are made available online as part of the "pre-publication history"'. Several other journals published by the BMJ Group allow optional open peer review, as does PLoS Medicine, published by the Public Library of Science. The BMJ's Rapid Responses allows ongoing debate and criticism following publication.


id: open-peer-review-20260505-2
file: data/en.wikipedia.org/wiki/Open_peer_review-2.md
category: reference
title: Open peer review
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_peer_review
date_saved: 2026-05-05T03:49:36.870107+00:00
instance: kb-cron
parent: open-peer-review-20260505-0
summary:
Publishers: BioMed Central BMJ Group Copernicus Publications European Molecular Biology Organization (EMBO) Frontiers MDPI (authors have the option to publish peer review reports, etc.) Nature Research Open Research Europe (the European Union publishing platform for Horizon Europe and Horizon 2020 projects) Journals: eLife GigaScience Nature PeerJ PLOS ReScience C Semantic Web journal by IOS Press WikiJournal SciPost Peer review at The BMJ, BioMed Central, EMBO, eLife, ReScience C, and the Semantic Web journal involves posting the entire pre-publication history of the article online, including not only signed reviews of the article, but also its previous versions and in some cases names of handling editors and author responses to the reviewers. Furthermore, the Semantic Web journal publishes reviews of all submissions, including rejected ones, on its website, while eLife plans to publish the reviews not only for published articles, but also for rejected articles. The European Geosciences Union operates public discussions where open peer review is conducted before suitable articles are accepted for publication in the journal. Sci, an open access journal which covers all research fields, adapted a post publication public peer-review (P4R) in which it promised authors immediate visibility of their manuscripts on the journal's online platform after a


id: open-research-20260505-0
file: data/en.wikipedia.org/wiki/Open_research-0.md
category: reference
title: Open research
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_research
date_saved: 2026-05-05T03:49:38.077952+00:00
instance: kb-cron
parent: open-research-20260505-0
summary:
Open research is research that is openly accessible by others. Those who publish research in this way are often concerned with making research more transparent, more collaborative, more wide-reaching, and more efficient. Open research aims to make both research methods and the resulting data freely available, often via the internet, in order to support reproducibility and, potentially, massively distributed research collaboration. In this regard, it is related to both open source software and citizen science. Especially for research that is scientific in nature, open research may be referred to as open science. However, the term can also implicate research done in fields as varied as the social sciences, the humanities, mathematics, engineering and medicine. == Types of open projects == Important distinctions exist between different types of open projects. Projects that provide open data but don't offer open collaboration are referred to as "open access" rather than open research. Providing open data is a necessary but not sufficient condition for open research, because although the data may be used by anyone, there is no requirement for subsequent research to take place openly. For example, though there have been many calls for more open collaborative research in drug discovery and the


id: open-science-award-for-open-source-research-software-20260505-0
file: data/en.wikipedia.org/wiki/Open_Science_Award_for_Open_Source_Research_Software-0.md
category: reference
title: Open Science Award for Open Source Research Software
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Award_for_Open_Source_Research_Software
date_saved: 2026-05-05T03:49:39.274982+00:00
instance: kb-cron
parent: open-science-award-for-open-source-research-software-20260505-0
summary:
The Open Science Award for Open Source Research Software (French: Prix science ouverte du logiciel libre de la recherche) is a French scientific award given since 2022. The prize is part of the second National Plan for open science and rewards projects, teams and young researchers engaged in exemplary practices of management, dissemination and reuse of research data. It is awarded by the French Ministry of Higher Education and Research and Space. The prize has four categories: The "Scientific and Technical" (Scientifique et technique) category rewards software that stands out for its scientific quality, methodological rigour and technical excellence. The "Community" (Communauté) category highlights projects that have successfully built and nurtured an active community of users and contributors. The "Documentation" category recognises software that offers exemplary documentation facilitating use, adoption and contribution to the project. The "Jury's Favourite" (Coup de cœur du jury) category rewards an exemplary project combining several of these dimensions. In each category, the jury awards a main prize as well as a "rising star" (espoir) award for promising projects, typically started less than five years ago. == Winners == == Notes and references == == See also == Open science Open Science Award for Research Data


id: open-science-infrastructure-20260505-0
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-0.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
Open Science Infrastructure (or open scholarly infrastructure) is information infrastructure that supports the open sharing of scientific productions such as publications, datasets, metadata or code. In November 2021 the Unesco recommendation on Open Science describes it as "shared research infrastructures that are needed to support open science and serve the needs of different communities". Open science infrastructures are a form of scientific infrastructure (also called cyberinfrastructure, e-Science or e-infrastructure) that support the production of open knowledge. Beyond the management of common resources, they are frequently structured as community-led initiatives with a set collective norms and governance regulations, which makes them also a form of knowledge commons. The definition of open science infrastructures usually exclude privately owned scientific infrastructures run by leading commercial publishers. Conversely it may include actors not always characterized as scientific infrastructures that play a critical role in the ecosystem of open science, such as publishing platforms in open access (open scholarly communication service). Computing infrastructures and online services have played a key role in the production and diffusion of scientific knowledge since the 1960s. While these early scientific infrastructure were initially envisioned as community initiatives, they could not be openly used due to the lack of interconnectivity


id: open-science-infrastructure-20260505-1
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-1.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
=== Openness and the commons === Open science infrastructures are open, which differentiate them with other scientific and knowledge infrastructure and, more specifically, with subscription-based commercial infrastructures. Openness is both a core value and a directing principle that affect the aims, the governance and the management of the infrastructure. Open science infrastructure face similar issues met by other open institutions such as open data repositories or large scale collaborative project such as Wikipedia: "When we study contemporary knowledge infrastructures we find values of openness often embedded there, but translating the values of openness into the design of infrastructures and the practices of infrastructuring is a complex and contingent process". The conceptual definition of open science infrastructures has been largely influenced by the analysis of Elinor Ostrom on the commons and more specifically on the knowledge commons. In accordance with Ostrom, Cameron Neylon understates that open infrastructures are not only characterized by the management of a pool of common resources but also by the elaboration of common governance and norms. The economic theory of the commons make it possible to expand beyond the scope of limited scope of scholar associations toward large scale community-led initiatives: "Ostrom's work (…) provides a template


id: open-science-infrastructure-20260505-2
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-2.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
Scientific projects have been among the earliest use case for digital infrastructure. The theorization of scientific knowledge infrastructure even predates the development of computing technologies. The knowledge network envisioned by Paul Otlet or Vannevar Bush already incorporated numerous features of online scientific infrastructures. After the Second World War, the United States faced a "periodical crisis": existing journals could not keep up with the rapidly increasing scientific output. The issue became politically relevant after the successful launch of Sputnik: "The Sputnik crisis turned the librarians' problem of bibliographic control into a national information crisis." The emerging computing technologies were immediately considered as a potential solution to make a larger amount of scientific output readable and searchable. Access to foreign language publication was also a key issue that was expected to be solved by machine translation: in the 1950s, a significant amount of scientific publications were not available in English, especially the one coming from the Soviet bloc. Influent members of the National Science Foundation like Joshua Ledeberg advocated for the creation of a "centralized information system", SCITEL that would at first coexist with printed journals and gradually replace them altogether on account of its efficiency. In the plan laid out by


id: open-science-infrastructure-20260505-3
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-3.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
=== The Web Revolution (1990–1995) === The World Wide Web was originally framed as an open scientific infrastructure. The project was inspired by ENQUIRE, an information management software commissioned to Tim Berners-Lee by the CERN for the specific needs of high energy physics. The structure of ENQUIRE was closer to an internal web of data: it connected "nodes" that "could refer to a person, a software module, etc. and that could be interlined with various relations such as made, include, describes and so forth". While it "facilitated some random linkage between information" Enquire was not able to "facilitate the collaboration that was desired for in the international high-energy physics research community". Like any significant computing scientific infrastructure before the 1990s, the development of ENQUIRE was ultimately impeded by the lack of interoperability and the complexity of managing network communications: "although Enquire provided a way to link documents and databases, and hypertext provided a common format in which to display them, there was still the problem of getting different computers with different operating systems to communicate with each other". Sharing of data and data documentation was a major focus in the initial communication of the World Wide Web when the project


id: open-science-infrastructure-20260505-4
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-4.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
The development of the World-Wide Web had rendered numerous pre-existing scientific infrastructure obsolete. It also lifted numerous restrictions and obstacles to online contribution and network management that made it possible to attempt more ambitious project. By the end of the 1990s, the creation of public scientific computing infrastructure became a major policy issue. The first wave of web-based scientific projects in the 1990s and the early 2000s revealed critical issues of sustainability. As funding was allocated on a specific time period, critical databases, online tools or publishing platforms could hardly be maintained; and project managers were faced with a valley of death "between grant funding and ongoing operational funding". Several competing terms appeared to fill this need. In the United States, the cyber-infrastructure was used in a scientific context by a US National Science Foundation (NSF) blue-ribbon committee in 2003: "The newer term cyberinfrastructure refers to infrastructure based upon distributed computer, information and communication technology. If infrastructure is required for an industrial economy, then we could say that cyberinfrastructure is required for a knowledge economy." E-infrastructure or e-science were used in a similar meaning in the United Kingdom and European countries. Thanks to "sizable investments", major national and international infrastructures


id: open-science-infrastructure-20260505-5
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-5.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
=== Toward open science infrastructures (2015-…) === The consolidation and expansion of commercial scientific infrastructure had entailed renewed calls to secure "community-controlled infrastructure". The acquisition of the open repositories Digital Commons and SSRN by Elsevier has highlighted the lack of reliability of critical scientific infrastructure for open science. The SPARC report on European Infrastructures underlines that "a number of important infrastructures at risk and as a consequence, the products and services that comprise open infrastructure are increasingly being tempted by buyout offers from large commercial enterprises. This threat affects both not-for-profit open infrastructure as well as closed, and is evidenced by the buyout in recent years of commonly relied on tools and platforms such as SSRN, bepress, Mendeley, and Github." In contrast with the consolidation of privately owned infrastructure, the open science movement "has tended to overlook the importance of social structures and systemic constraints in the design of new forms of knowledge infrastructures". It remained mostly focused to the content of scientific research, with little integration of technical tools and few large community initiatives. "Common pool of resources is not governed or managed by the current scholarly commons initiative. There is no dedicated hard infrastructure and though there may


id: open-science-infrastructure-20260505-6
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-6.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
=== Type and roles === Open Access repositories are the most frequent form of Open Science Infrastructure with 5,791 repositories in existence in December 2021 according to OpenDOAR Yet, there is a significant diversification of the roles and the activities of open science infrastructure, at least among the largest infrastructures. In the survey of European infrastructure conducted by SPARC Europe, 95% of the respondents mention that they provide services in at least three different stages of research production out of six (Creation, Evaluation, Publishing, Hosting, Discovering and Archiving). Aggregation, hosting and indexing are especially central activities, common to most Open Science Infrastructures regardless of their focus. Specialization does happen at a higher level. A network analysis identifies "two main clusters of activities": Publishing-focused infrastructures which are associated with the "publishing and hosting traditional text formats". Among them, "paper submission (41 out of 70) and review (30) were the most commonly reported activities". Creation-focused infrastructures which deal preferably with the "processing and storing research outputs, particularly data". Theses actors provide specific services in the field of "data gathering (47 out of 71), and data analysis (40)". Besides, "computation and machine learning (18) and Experimentation (15) were roughly half as common". ===


id: open-science-infrastructure-20260505-7
file: data/en.wikipedia.org/wiki/Open_Science_Infrastructure-7.md
category: reference
title: Open Science Infrastructure
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_Science_Infrastructure
date_saved: 2026-05-05T03:49:40.430585+00:00
instance: kb-cron
parent: open-science-infrastructure-20260505-0
summary:
=== Economics === Many Open Science Infrastructure run "at a relatively low cost" as small infrastructures are an important part of the open science ecosystem. In 2020, 21 out of 53 surveyed European infrastructures "report spending less than €50,000". Consequently, more than 75% of surveyed European infrastructures are run by small teams of 5 FTEs or less. The size of the infrastructure and the extent of its funding is far from always proportional to the critical service it offers: "some of the most heavily used services make ends meet with a tiny core team of two to five people." Volunteer contributions are significant as well with is both "a strength and weakness to an OSI's sustainability". The landscape of open science infrastructures is therefore rather close to the ideals of a "decentralised network of small projects" envisioned by theoricians of the scholarly commons. A very large majority of open science infrastructure are non-commercial and collaborations or financial support from the private sector remain very limited. Overall, European infrastructures were financially sustainable in 2020 which contrasts with the situation ten years prior: in 2010, European infrastructures had much less visibility: they usually lacked "a long-term perspective" and struggled "with securing the


id: open-science-monitor-20260505-0
file: data/en.wikipedia.org/wiki/Open_science_monitor-0.md
category: reference
title: Open science monitor
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science_monitor
date_saved: 2026-05-05T03:49:41.653595+00:00
instance: kb-cron
parent: open-science-monitor-20260505-0
summary:
An open science monitor or open access monitor is a scientific framework that aims to assess the spread of open practices in a scientific context. Open science monitors or dashboards are built at different scales: from institutional to national or international. They require an accurate assessment of the total scientific output and a further breakdown between open and closed content. They rely on a variety of data sources and methodologies to achieve this end. Consequently, open science monitors have also become relevant tools for bibliometric analysis. While initially conceived to track publications in academic journals, open science monitor have diversify their scopes and indicators. A recent trend has been to map other major outputs of open science research such as datasets, software or clinical trials. == Definition == Open science monitor are a scientific infrastructure that provide a "good knowledge of the state" of scientific outputs and their "open access rate". They are also a policy tool that aims to better assess the discrepancy between actual practices and long-term objectives: they "can inform future strategies at institutional and national levels, provides guidance for policy development and review, helps to assess the effects of funding mechanisms and is crucial to negotiate


id: open-science-monitor-20260505-1
file: data/en.wikipedia.org/wiki/Open_science_monitor-1.md
category: reference
title: Open science monitor
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science_monitor
date_saved: 2026-05-05T03:49:41.653595+00:00
instance: kb-cron
parent: open-science-monitor-20260505-0
summary:
The first open science monitors were created in the 2000s and the early 2010s. They were usually conceived as a natural outgrowth of new national and international policy in favor of open access and open science. The Berlin Declaration from 2003 especially introduced the concept of a global "transition of scientific publishing toward an open access system" which require "information on publication output and on subscription and publication fees." Additionally the diversification of open science publishing into various publication venues (journals, repositories, overlay journals...) and formats (articles, conferences, datasets...) created unprecedented challenges. One of the earliest form of open science monitor was the Dutch project NARCIS ("National Academic Research and Collaborations Information System") that started operating in December 2005. NARCIS is primarily a national scientific portal that aims to integrate "all kinds of types of information from scientific institutes in the Netherlands." Yet it also has a special focus on "academic OAI repositories" and publishes global statistics on the rate of open restricted and embargoed scientific works since 2000. By 2013, Finland pioneered the influential Jyväskylä Model through its national portal JUULI. First experimented at the Open Science Centre of the University of Jyväskyl this approach aims "to centralize all


id: open-science-monitor-20260505-2
file: data/en.wikipedia.org/wiki/Open_science_monitor-2.md
category: reference
title: Open science monitor
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science_monitor
date_saved: 2026-05-05T03:49:41.653595+00:00
instance: kb-cron
parent: open-science-monitor-20260505-0
summary:
=== Local infrastructures and repositories === Local infrastructures include Current Research Information Systems directly managed by scientific institutions and universities that "help manage, understand, and evaluate research activities". At the institutional level they can bring the most extensive coverage of scientific output, especially taking into account locally published journals that would not necessarily be indexed in global scientific infrastructures. Due to their direct connections with scientific communities, local infrastructures can incentivize researchers to "enter their publications into those systems" and implement a more various range of indicators than what is commonly available in international databases. Local infrastructures are managed in a decentralized way, with varying levels of coverage and information depending on the institutions. In some cases, local repository are "fed solely by the large commercial databases" and will not have any added value. The integration of diverse local sources of data into a common and standardized schemes is a major challenge for open science monitors. The preexistence of ambitious funding policy considerably ease this process, as institutions will be already encouraged to adopt specific norms and metadata requirements. While local infrastructures are generally thoughts as providers of data for an open science monitor, the relationship can go both way.


id: open-science-monitor-20260505-3
file: data/en.wikipedia.org/wiki/Open_science_monitor-3.md
category: reference
title: Open science monitor
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_science_monitor
date_saved: 2026-05-05T03:49:41.653595+00:00
instance: kb-cron
parent: open-science-monitor-20260505-0
summary:
=== Tracking open science adoption === The French Open Science Monitor was conceived from the start to capture "open access dynamic". This has significant implication in terms of design and data flow as the "OA status of a publication evolves over time" due to embargo policies as well retrospective opening of past content. Despite significant differences in regards to methodologies or to data source, Pierre Mounier underlined in 2022 that "we observe the same dynamic" in the open access monitors of "three different European countries": the French, German and Dutch monitor all convege to show that slightly more than 60% of research is published in open access. === Economic analysis === Open science monitors aim to facilitate the estimation of scientific publishing costs. Without any aggregation of publication data, "information on expenditure for open access publication fees and especially for non-open access publication fees is often not available centrally" The monitor can also contribute to better assess the economic impact of open science across the entire academic ecosystem. While it is generally assumed that the conversion to open access publishing should not be costlier than the existing system, there can still be significant variations, especially with an APC-based model: institutions


id: open-scientific-data-20260505-0
file: data/en.wikipedia.org/wiki/Open_scientific_data-0.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
Open scientific data or open research data is a type of open data focused on publishing observations and results of scientific activities available for anyone to analyze and reuse. A major purpose of the drive for open data is to allow the verification of scientific claims, by allowing others to look at the reproducibility of results, and to allow data from many sources to be integrated to give new knowledge. The modern concept of scientific data emerged in the second half of the 20th century, with the development of large knowledge infrastructure to compute scientific information and observation. The sharing and distribution of data has been early identified as an important stake but was impeded by the technical limitations of the infrastructure and the lack of common standards for data communication. The World Wide Web was immediately conceived as a universal protocol for the sharing of scientific data, especially coming from high-energy physics. == Definition == === Scientific data === The concept of open scientific data has developed in parallel with the concept of scientific data. Scientific data was not formally defined until the late 20th century. Before the generalization of computational analysis, data has been mostly an informal terms,


id: open-scientific-data-20260505-1
file: data/en.wikipedia.org/wiki/Open_scientific_data-1.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
The emergence of scientific data is associated with a semantic shift in the way core scientific concepts like data, information and knowledge are commonly understood. Following the development of computing technologies, data and information are increasingly described as "things": "Like computation, data always have a material aspect. Data are things. They are not just numbers but also numerals, with dimensionality, weight, and texture". After the Second World War large scientific projects have increasingly relied on knowledge infrastructure to collect, process and analyze important amount of data. Punch-cards system were first used experimentally on climate data in the 1920s and were applied on a large scale in the following decade: "In one of the first Depression-era government make-work projects, Civil Works Administration workers punched some 2 million ship log observations for the period 1880–1933." By 1960, the meteorological data collections of the US National Weather Records Center has expanded to 400 millions cards and had a global reach. The physically of scientific data was by then fully apparent and threatened the stability of entire buildings: "By 1966 the cards occupied so much space that the Center began to fill its main entrance hall with card storage cabinets (figure 5.4). Officials became


id: open-scientific-data-20260505-2
file: data/en.wikipedia.org/wiki/Open_scientific_data-2.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
=== Sharing scientific data on the web (1990-1995) === The World Wide Web was originally conceived as an infrastructure for open scientific data. Sharing of data and data documentation was a major focus in the initial communication of the World Wide Web when the project was first unveiled in August 1991 : "The WWW project was started to allow high energy physicists to share data, news, and documentation. We are very interested in spreading the web to other areas, and having gateway servers for other data". The project stemmed from a close knowledge infrastructure, ENQUIRE. It was an information management software commissioned to Tim Berners-Lee by the CERN for the specific needs of high energy physics. The structure of ENQUIRE was closer to an internal web of data: it connected "nodes" that "could refer to a person, a software module, etc. and that could be interlined with various relations such as made, include, describes and so forth". While it "facilitated some random linkage between information" Enquire was not able to "facilitate the collaboration that was desired for in the international high-energy physics research community". Like any significant computing scientific infrastructure before the 1990s, the development of ENQUIRE was ultimately impeded


id: open-scientific-data-20260505-3
file: data/en.wikipedia.org/wiki/Open_scientific_data-3.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
=== Policy implementations (2010-…) === After 2010, national and supra-national institutions took a more interventionist stance. New policies have been implemented not only to ensure and incentivize the opening of scientific data, usually in continuation to existing open data program. In Europe, the "European Union Commissioner for Research, Science, and Innovation, Carlos Moedas made open research data one of the EU's priorities in 2015." First published in 2016, the FAIR Guiding Principles have become an influential framework for opening scientific data. The principles have been originally designed two years earlier during a policy ad research workshop at Lorentz, Jointly Designing a Data FAIRport. During the deliberations of the workshop, "the notion emerged that, through the definition of, and widespread support for, a minimal set of community-agreed guiding principles and practice" The principles do not attempt to define scientific data, which remains a relatively plastic concept, but strive to describe "what constitutes 'good data management'". They cover four foundational principles, "that serve to guide data producer": Findability, Accessibility, Interoperability, and Reusability. and also aim to provide a step toward machine-actionability by expliciting the underlying semantics of data. As it fully acknowledge the complexity of data management, the principles do not claim


id: open-scientific-data-20260505-4
file: data/en.wikipedia.org/wiki/Open_scientific_data-4.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
=== Citation and indexation === The first digital databases of the 1950s and the 1960s have immediately raised issues of citability and bibliographic descriptions. The mutability of computer memory was especially challenging: in contrast with printed publications, digital data could not be expected to remain stable on the long run. In 1965, Ralph Bisco underlined that this uncertainty affected all the associated documents like code notebooks, which may become increasingly out of date. Data management have to find a middle ground between continuous enhancements and some form of generic stability: "the concept of a fluid, changeable, continually improving data archive means that study cleaning and other processing must be carried to such a point that changes will not significantly affect prior analyses" Structured bibliographic metadata for database has been a debated topic since the 1960s. In 1977, the American Standard for Bibliographic Reference adopted a definition of "data file" with a strong focus on the materiability and the mutability of the dataset: neither dates nor authors were indicated but the medium or "Packaging Method" had to be specified. Two years later, Sue Dodd introduced an alternative convention, that brought the citation of data closer to the standard of references of


id: open-scientific-data-20260505-5
file: data/en.wikipedia.org/wiki/Open_scientific_data-5.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
Research reproducibility: lack of reproducibility is frequently attributed to deficiencies in research transparency and data analysis process. Consequently, as "a rationale for sharing research data, [research reproducibility] is powerful yet problematic". Reproducibility only applies to "certain kinds of research", mostly in regards to experimental sciences. Public accessibility: this rationale that "products of public funding should be available to the public" is "found in arguments for open government". While directly inspired by similar arguments made in favor of open access to publications, its range is more limited as scientific open data "has direct benefits to far fewer people, and those benefits vary by stakeholder" Research valorization: open scientific data may bring a substantial value to the private sector. This argument is especially used to support "the need for more repositories that can accept and curate research data, for better tools and services to exploit data, and for other investments in knowledge infrastructure". Increased research and innovation: open scientific data may significantly enhanced the quality of private and public research. This argument aims for "investing in knowledge infrastructure to sustain research data, curated to high standards of professional practices" Yet collaboration between the different actors and stakeholders of the data lifecycle is


id: open-scientific-data-20260505-6
file: data/en.wikipedia.org/wiki/Open_scientific_data-6.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
== Legal status == The opening of scientific data has raised a variety of legal issues in regards to ownership rights, copyrights, privacy and ethics. While it is commonly considered that researchers "own the data they collect in the course of their research", this "view is incorrect": the creation of dataset involves potentially the rights of numerous additional actors such as institutions (research agencies, funders, public bodies), associated data producers, personal data on private citizens. The legal situation of digital data has been consequently described as a "bundle of rights" due to the fact that the "legal category of "property" (...) is not a suitable model for dealing with the complexity of data governance problems" === Copyright === Copyright has been the primary focus of the legal literature of open scientific data until the 2010s. The legality of data sharing was early on identified a crucial issue. In contrast with the sharing of scientific publication, the main impediment was not copyright but uncertainty: "the concept of 'data' [was] a new concept, created in the computer age, while copyright law emerged at the time of printed publications." In theory, copyright and author rights provisions do not apply to simple collections of


id: open-scientific-data-20260505-7
file: data/en.wikipedia.org/wiki/Open_scientific_data-7.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
=== Ownership === Copyright issues with scientific datasets have been further complicated by uncertainties regarding ownership. Research is largely a collaborative activity that involves a wide range of contributions. Initiatives like CRediT (Contributor Roles Taxonomy) have identified 14 different roles, of which 4 are explicitly related to data management (Formal Analysis, Investigation, Data curation and Visualization). In the United States, ownership of research data is usually "determined by the employer of the researcher", with the principal investigator acting as the caretaker of the data rather than the owner. Until the development of research open data, US institutions have been usually more reluctant to waive copyrights on data than on publications, as they are considered strategic assets. In the European Union, there is no largely agreed framework on the ownership of data. The additional rights of external stakeholders has also been raised, especially in the context of medical research. Since the 1970s, patients have claimed some form of ownership of the data produced in the context of clinical trials, notably with important controversies concerning 'whether research subjects and patients actually own their own tissue or DNA." === Privacy === Numerous scientific projects rely on data collection of persons, notably in medical


id: open-scientific-data-20260505-8
file: data/en.wikipedia.org/wiki/Open_scientific_data-8.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
=== Integration to the research workflow === In contrast with the broad incitations for data sharing included in the early policies in favor of open scientific data, the complexity and the underlying costs and requirements of scientific data management are increasingly acknowledged: "Data sharing is difficult to do and to justify by the return on investment." Open data is not simply a supplementary task but has to be envisioned throughout the entire research process as it "requires changes in methods and practices of research." The opening of research data creates a new settlement of costs and benefits. Public data sharing introduces a new communication setting that primarily contrasts with private data exchange with research collaborators or partners. The collection, the purpose, and the limitation of data have to be explicit as it is impossible to rely on pre-existing informal knowledge: "the documentation and representations are the only means of communicating between data creator and user." Lack of proper documentation means that the burden of recontextualization falls on the potential users and may render the dataset useless. Publication requires further verification regarding the ownership of the data and the potential legal liability if the data is misused. This clarification phase becomes


id: open-scientific-data-20260505-9
file: data/en.wikipedia.org/wiki/Open_scientific_data-9.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
Research data management can be laid out in a data management plan or DMP. Data management plans were incepted in 1966 for the specific needs of aeronautic and engineering research, which already faced increasingly complex data frictions. These first examples were focused on material issues associated with the access, transfer, and storage of the data: "Until the early 2000s, DMPs were utilised in this manner: in limited fields, for projects of great technical complexity, and for limited mid-study data collection and processing purposes" After 2000, the implementation of extensive research infrastructure and the development of open science changed the scope and the purpose of data management plans. Policy-makers, rather than scientists, have been instrumental in this development: "The first publications to provide general advice and guidance to researchers around the creation of DMPs were published from 2009 following the publications from JISC and the OECD (…) DMP use, we infer, has been imposed onto the research community through external forces" Empirical studies of data practices in research have "highlighted the need for organizations to offer more formal training and assistance in data management to scientists" In a 2017-2018 international survey of 1372 scientist, most requests for help and formalization were


id: open-scientific-data-20260505-10
file: data/en.wikipedia.org/wiki/Open_scientific_data-10.md
category: reference
title: Open scientific data
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_scientific_data
date_saved: 2026-05-05T03:49:42.862927+00:00
instance: kb-cron
parent: open-scientific-data-20260505-0
summary:
The Unesco recommendation of Open Science approved in November 2021 defines open science infrastructures as "shared research infrastructures that are needed to support open science and serve the needs of different communities" Open science infrastructures have been recognized as a major factor in the implementation and the development of data sharing policies. Leading infrastructures for open scientific data include data repositories, data analysis platforms, indexes, digitized libraries, or digitized archives. Infrastructures ensure that individual researchers and institutions do not entirely support the costs of publishing, maintaining, and indexing datasets. They are also critical stakeholders in the definition and adoption of open data standards, especially regarding licensing or documentation. By the end of the 1990s, the creation of public scientific computing infrastructure became a major policy issue: "The lack of infrastructure to support release and reuse was acknowledged in some of the earliest policy reports on data sharing." The first wave of web-based scientific projects in the 1990s and the early 2000s revealed critical issues of sustainability. As funding was allocated on a specific period, critical databases, online tools or publishing platforms could hardly be maintained. Project managers were faced with a valley of death "between grant funding and ongoing operational


id: open-source-20260505-0
file: data/en.wikipedia.org/wiki/Open_source-0.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
Open source is software that is made freely available for possible modification and redistribution, also in form of source code. The licensing conditions include permission to use and view the source code, design documents, or content of the product. The open source model is a decentralized software development model that encourages open collaboration. A main principle of open source software development is peer production, with products such as source code, blueprints, and documentation freely available to the public. The open source movement in software began as a response to the limitations of proprietary code. The model is used for projects such as in open source eCommerce, open source appropriate technology, and open source drug discovery. Open source promotes universal access via an open-source or free license to a product's design or blueprint, and universal redistribution of that design or blueprint. Before the phrase open source became widely adopted, developers and producers used a variety of other terms, such as free software, shareware, and public domain software. The term Open source was introduced in 1998 and gained hold with the rise of the Internet. The open-source software movement arose to clarify copyright, licensing, domain, and consumer issues. Generally, open source refers


id: open-source-20260505-1
file: data/en.wikipedia.org/wiki/Open_source-1.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
Some economists agree that open-source is an information good or "knowledge good" with original work involving a significant amount of time, money, and effort. The cost of reproducing the work is low enough that additional users may be added at zero or near zero cost – this is referred to as the marginal cost of a product. Copyright creates a monopoly so that the price charged to consumers can be significantly higher than the marginal cost of production. This allows the author to recoup the cost of making the original work. Copyright thus creates access costs for consumers who value the work more than the marginal cost but less than the initial production cost. Access costs also pose problems for authors who wish to create a derivative work—such as a copy of a software program modified to fix a bug or add a feature, or a remix of a song—but are unable or unwilling to pay the copyright holder for the right to do so. Open source eliminates some of the access costs of consumers and creators of derivative works by reducing the restrictions of copyright. Basic economic theory predicts that lower costs would lead to higher consumption and also


id: open-source-20260505-2
file: data/en.wikipedia.org/wiki/Open_source-2.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
The open-source model is a decentralized software development model that encourages open collaboration, meaning "any system of innovation or production that relies on goal-oriented yet loosely coordinated participants who interact to create a product (or service) of economic value, which they make available to contributors and noncontributors alike." A main principle of open-source software development is peer production, with products such as source code, blueprints, and documentation freely available to the public. The open-source movement in software began as a response to the limitations of proprietary code. The model is used for projects such as in open-source appropriate technology, and open-source drug discovery. The open-source model for software development inspired the use of the term to refer to other forms of open collaboration, such as in Internet forums, mailing lists and online communities. Open collaboration is also thought to be the operating principle underlining a gamut of diverse ventures, including TEDx and Wikipedia. Open collaboration is the principle underlying peer production, mass collaboration, and wikinomics. It was observed initially in open-source software, but can also be found in many other instances, such as in Internet forums, mailing lists, Internet communities, and many instances of open content, such as Creative Commons.


id: open-source-20260505-3
file: data/en.wikipedia.org/wiki/Open_source-3.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
Linux (that much of world's server parks are running) MediaWiki (that Wikipedia is based upon) Many more: List of free and open-source software packages List of formerly proprietary software The Google Summer of Code, often abbreviated to GSoC, is an international annual program in which Google awards stipends to contributors who successfully complete a free and open-source software coding project during the summer. GSoC is a large scale project with 202 participating organizations in 2021. There are similar smaller scale projects such as the Talawa Project run by the Palisadoes Foundation (a non profit based in California, originally to promote the use of information technology in Jamaica, but now also supporting underprivileged communities in the US) === Electronics === Open-source hardware is hardware which initial specification, usually in a software format, is published and made available to the public, enabling anyone to copy, modify and redistribute the hardware and source code without paying royalties or fees. Open-source hardware evolves through community cooperation. These communities are composed of individual hardware/software developers, hobbyists, as well as very large companies. Examples of open-source hardware initiatives are: Openmoko: a family of open-source mobile phones, including the hardware specification and the operating system. OpenRISC: an


id: open-source-20260505-4
file: data/en.wikipedia.org/wiki/Open_source-4.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
Open-source principles can be applied to technical areas such as digital communication protocols and data storage formats. Open-design – which involves applying open-source methodologies to the design of artifacts and systems in the physical world. It is very nascent but has huge potential. Open-source appropriate technology (OSAT) refers to technologies that are designed in the same fashion as free and open-source software. These technologies must be "appropriate technology" (AT) – meaning technology that is designed with special consideration to the environmental, ethical, cultural, social, political, and economic aspects of the community it is intended for. An example of this application is the use of open-source 3D printers like the RepRap to manufacture appropriate technology. Teaching – which involves applying the concepts of open source to instruction using a shared web space as a platform to improve upon learning, organizational, and management challenges. An example of an Open-source courseware is the Java Education & Development Initiative (JEDI). Other examples include Khan Academy and wikiversity. At the university level, the use of open-source-appropriate technology classroom projects has been shown to be successful in forging the connection between science/engineering and social benefit: This approach has the potential to use university students' access to


id: open-source-20260505-5
file: data/en.wikipedia.org/wiki/Open_source-5.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
== Agriculture, economy, manufacturing and production == Open-source appropriate technology (OSAT), is designed for environmental, ethical, cultural, social, political, economic, and community aspects Open-design movement, development of physical products, machines and systems via publicly shared design information, including free and open-source software and open-source hardware, among many others: Open Architecture Network, improving global living conditions through innovative sustainable design OpenCores, a community developing digital electronic open-source hardware Open Design Alliance, develops Teigha, a software development platform to create engineering applications including CAD software Open Hardware and Design Alliance (OHANDA), sharing open hardware and designs via free online services Open Source Ecology (OSE), a network of farmers, engineers, architects and supporters striving to manufacture the Global Village Construction Set (GVCS) OpenStructures (OSP), a modular construction model where everyone designs on the basis of one shared geometrical OS grid Open manufacturing or "Open Production" or "Design Global, Manufacture Local", a new socioeconomic production model to openly and collaboratively produce and distribute physical objects Open-source architecture (OSArc), emerging procedures in imagination and formation of virtual and real spaces within an inclusive universal infrastructure Open-source cola, cola soft drinks made to open-sourced recipes Open-source hardware, or open hardware, computer hardware, such as microprocessors, that


id: open-source-20260505-6
file: data/en.wikipedia.org/wiki/Open_source-6.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
== Society == The rise of open-source culture in the 20th century resulted from a growing tension between creative practices that involve require access to content that is often copyrighted, and restrictive intellectual property laws and policies governing access to copyrighted content. The two main ways in which intellectual property laws became more restrictive in the 20th century were extensions to the term of copyright (particularly in the United States) and penalties, such as those articulated in the Digital Millennium Copyright Act (DMCA), placed on attempts to circumvent anti-piracy technologies. Although artistic appropriation is often permitted under fair-use doctrines, the complexity and ambiguity of these doctrines create an atmosphere of uncertainty among cultural practitioners. Also, the protective actions of copyright owners create what some call a "chilling effect" among cultural practitioners. The idea of an "open-source" culture runs parallel to "Free Culture", but is substantively different. Free culture is a term derived from the free software movement, and in contrast to that vision of culture, proponents of open-source culture (OSC) maintain that some intellectual property law needs to exist to protect cultural producers. Yet they propose a more nuanced position than corporations have traditionally sought. Instead of seeing intellectual property


id: open-source-20260505-7
file: data/en.wikipedia.org/wiki/Open_source-7.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
=== Media === Open-source journalism formerly referred to the standard journalistic techniques of news gathering and fact checking, reflecting open-source intelligence, a similar term used in military intelligence circles. Now, open-source journalism commonly refers to forms of innovative publishing of online journalism, rather than the sourcing of news stories by a professional journalist. In the 25 December 2006 issue of TIME magazine this is referred to as user created content and listed alongside more traditional open-source projects such as OpenSolaris and Linux. Weblogs, or blogs, are another significant platform for open-source culture. Blogs consist of periodic, reverse chronologically ordered posts, using a technology that makes webpages easily updatable with no understanding of design, code, or file transfer required. While corporations, political campaigns and other formal institutions have begun using these tools to distribute information, many blogs are used by individuals for personal expression, political organizing, and socializing. Some, such as LiveJournal or WordPress, use open-source software that is open to the public and can be modified by users to fit their own tastes. Whether the code is open or not, this format represents a nimble tool for people to borrow and re-present culture; whereas traditional websites made the illegal reproduction


id: open-source-20260505-8
file: data/en.wikipedia.org/wiki/Open_source-8.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
Within the academic community, there is discussion about expanding what could be called the "intellectual commons" (analogous to the Creative Commons). Proponents of this view have hailed the Connexions Project at Rice University, OpenCourseWare project at MIT, Eugene Thacker's article on "open-source DNA", the "Open Source Cultural Database", Salman Khan's Khan Academy and Wikipedia as examples of applying open source outside the realm of computer software. Open-source curricula are instructional resources whose digital source can be freely used, distributed and modified. Another strand to the academic community is in the area of research. Many funded research projects produce software as part of their work. Due to the benefits of sharing software openly in scientific endeavours, there is an increasing interest in making the outputs of research projects available under an open-source license. In the UK the Joint Information Systems Committee (JISC) has developed a policy on open-source software. JISC also funds a development service called OSS Watch which acts as an advisory service for higher and further education institutions wishing to use, contribute to and develop open-source software. On 30 March 2010, President Barack Obama signed the Health Care and Education Reconciliation Act, which included $2 billion over four years


id: open-source-20260505-9
file: data/en.wikipedia.org/wiki/Open_source-9.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
=== Ideologically-related movements === The open-access movement is a movement that is similar in ideology to the open source movement. Members of this movement maintain that academic material should be readily available to provide help with "future research, assist in teaching and aid in academic purposes." The open-access movement aims to eliminate subscription fees and licensing restrictions of academic materials. The free-culture movement is a movement that seeks to achieve a culture that engages in collective freedom via freedom of expression, free public access to knowledge and information, full demonstration of creativity and innovation in various arenas, and promotion of citizen liberties. Creative Commons is an organization that "develops, supports, and stewards legal and technical infrastructure that maximizes digital creativity, sharing, and innovation." It encourages the use of protected properties online for research, education, and creative purposes in pursuit of a universal access. Creative Commons provides an infrastructure through a set of copyright licenses and tools that creates a better balance within the realm of "all rights reserved" properties. The Creative Commons license offers a slightly more lenient alternative to "all rights reserved" copyrights for those who do not wish to exclude the use of their material. The Zeitgeist Movement


id: open-source-20260505-10
file: data/en.wikipedia.org/wiki/Open_source-10.md
category: reference
title: Open source
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_source
date_saved: 2026-05-05T03:49:44.361173+00:00
instance: kb-cron
parent: open-source-20260505-0
summary:
=== Literature on legal and economic aspects === Abramson, Bruce (2005). Digital Phoenix; Why the Information Economy Collapsed and How it Will Rise Again. MIT Press. ISBN 978-0-262-51196-4. Benkler, Y. (December 2002). "Coase's Penguin, or, Linux and The Nature of the Firm" (PDF). Yale Law Journal. 112 (3): 369–446. arXiv:cs/0109077. doi:10.2307/1562247. hdl:10535/2974. ISSN 0044-0094. JSTOR 1562247. S2CID 16684329. Berry, D.M.; Moss, G. (2008). "Libre Culture: Meditations on Free Culture" (PDF). Canada: Pygmalion Books. Bitzer, J.; Schröder, P.J.H. (2005). "The Impact of Entry and Competition by Open Source Software on Innovation Activity" (PDF). Industrial Organization. EconWPA. v. Engelhardt, S. (2008). "The Economic Properties of Software" (PDF). Jena Economic Research Papers. 2: 2008–045. v. Engelhardt, S. (2008): "Intellectual Property Rights and Ex-Post Transaction Costs: the Case of Open and Closed Source Software", Jena Economic Research Papers 2008-047. (PDF) v. Engelhardt, S.; Swaminathan, S. (2008). "Open Source Software, Closed Source Software or Both: Impacts on Industry Growth and the Role of Intellectual Property Rights" (PDF). Discussion Papers of Diw Berlin. European Commission. (2006). Economic impact of open source software on innovation and the competitiveness of the Information and Communication Technologies sector in the EU. Brussels. v. Hippel, E.; v. Krogh, G. (2003). "Open


id: open-synthetic-biology-20260505-0
file: data/en.wikipedia.org/wiki/Open_synthetic_biology-0.md
category: reference
title: Open synthetic biology
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open_synthetic_biology
date_saved: 2026-05-05T03:49:45.513393+00:00
instance: kb-cron
parent: open-synthetic-biology-20260505-0
summary:
Open synthetic biology is the idea that scientific knowledge and data should be openly accessible through common rights licensing to enable the rapid development of safe, effective and commercially viable synthetic biology applications. == Concepts == Its foundational concepts are open science and the Bermuda Principles. Open science is the idea that scientific research should be openly shared to enable massive collaboration (e.g., the Polymath Project). The Bermuda Principles is a private accord declaring that all DNA sequence data should be released in publicly accessible databases within 24 hours after generation. Open synthetic biology is a theoretical framework supporting a global ecosystem of responsible and capable research scientists working collaboratively on synthetic biology application development projects to reduce cost, time, and risks of developing new synthetic biology applications (including open synthetic biology therapeutics) from the inception of primary science to applications reaching market readiness and commercial viability. Its general principle is that participating research scientists agree to share research, data, findings and results with the open synthetic biology community and the public generally. The Open SynBio community will set standards and expectations of the participants and their "science to market" process and the community will work collaboratively with downstream stakeholders


id: open-notebook-science-20260505-0
file: data/en.wikipedia.org/wiki/Open-notebook_science-0.md
category: reference
title: Open-notebook science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-notebook_science
date_saved: 2026-05-05T03:49:46.721658+00:00
instance: kb-cron
parent: open-notebook-science-20260505-0
summary:
Open-notebook science is the practice of making the entire primary record of a research project publicly available online as it is recorded. This involves placing the personal, or laboratory, notebook of the researcher online along with all raw and processed data, and any associated material, as this material is generated. The approach may be summed up by the slogan 'no insider information'. It is the logical extreme of transparent approaches to research and explicitly includes the making available of failed, less significant, and otherwise unpublished experiments; so called 'dark data'. The practice of open notebook science, although not the norm in the academic community, has gained significant recent attention in the research and general media as part of a general trend towards more open approaches in research practice and publishing. Open notebook science can therefore be described as part of a wider open science movement that includes the advocacy and adoption of open access publication, open data, crowdsourcing data, and citizen science. It is inspired in part by the success of open-source software and draws on many of its ideas. == History == The term "open-notebook science" was first used in 2006 in a blog post by Jean-Claude Bradley, an


id: open-notebook-science-20260505-1
file: data/en.wikipedia.org/wiki/Open-notebook_science-1.md
category: reference
title: Open-notebook science
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/Open-notebook_science
date_saved: 2026-05-05T03:49:46.721658+00:00
instance: kb-cron
parent: open-notebook-science-20260505-0
summary:
== Drawbacks == The arguments against adopting open notebook science fall mainly into three categories which have differing importance in different fields of science. The primary concern, expressed particularly by biological and medical scientists is that of 'data theft' or 'being scooped'. While the degree to which research groups steal or adapt the results of others remains a subject of debate it is certainly the case that the fear of not being first to publish drives much behavior, particularly in some fields. This is related to the focus in these fields on the published peer-reviewed paper as being the main metric of career success. The second argument advanced against open notebook science is that it constitutes prior publication, thus making it impossible to patent and difficult to publish the results in the traditional peer reviewed literature. With respect to patents, publication on the web is clearly classified as disclosure. Therefore, while there may be arguments over the value of patents, and approaches that get around this problem, it is clear that open notebook science is not appropriate for research for which patent protection is an expected and desired outcome. With respect to publication in the peer-reviewed literature the case is


id: openbci-20260505-0
file: data/en.wikipedia.org/wiki/OpenBCI-0.md
category: reference
title: OpenBCI
tags: science, encyclopedia
source_url: https://en.wikipedia.org/wiki/OpenBCI
date_saved: 2026-05-05T03:49:47.934225+00:00
instance: kb-cron
parent: openbci-20260505-0
summary:
OpenBCI is an open-source brain–computer interface platform created by Joel Murphy and Conor Russomanno, after a successful Kickstarter campaign in late 2013. The company's headquarters is based in Brooklyn, NY. OpenBCI boards are low-cost biometric amplifiers used to measure and record electrical activity produced by the brain (EEG), muscles (EMG), and heart (EKG). The boards are compatible with standard EEG electrodes. They can be used with the open-source OpenBCI GUI software, or they can be integrated with other open-source EEG signal processing tools. OpenBCI boards have been scientifically validated in numerous research studies. == Hardware == The OpenBCI 32-bit board uses the ADS1299, an IC developed by Texas Instruments for biopotential measurements. The OpenBCI uses a microcontroller for on-board processing — the 8bit version (now deprecated) uses an Arduino-compatible ATmega328P IC, while the 32bit board uses a PIC microcontroller — and can write the EEG data to an SD card, or transmit it to software on a computer over a bluetooth link. In 2015, OpenBCI announced the Ganglion board with a 2nd Kickstarter campaign. It has 4 input channels for measuring EEG, EMG, and EKG, and is also Bluetooth enabled. Initially it was offered on Kickstarter at $99, before later


