AI Dungeon is a single-player/multiplayer text adventure game which uses artificial intelligence (AI) to generate content and allows players to create and share adventures and custom prompts. The game's first version was made available in May 2019, and its second version (initially called AI Dungeon 2) was released on Google Colaboratory in December 2019. It was later ported that same month to its current cross-platform web application. The AI model was then reformed in July 2020. == Gameplay == AI Dungeon is a text adventure game that uses artificial intelligence to generate random storylines in response to player-submitted stimuli. In the game, players are prompted to choose a setting for their adventure (e.g. fantasy, mystery, apocalyptic, cyberpunk, zombies), followed by other options relevant to the setting (such as character class for fantasy settings). After beginning an adventure, four main interaction methods can be chosen for the player's text input: Do: Must be followed by a verb, allowing the player to perform an action. Say: Must be followed by dialogue sentences, allowing players to communicate with other characters. Story: Can be followed by sentences describing something that happens to progress the story, or that players want the AI to know for future events. See: Must be followed by a description, allowing the player to perceive events, objects, or characters. Using this command creates an AI generated image, and does not affect gameplay. The game adapts and responds to most actions the player enters. Providing blank inputs can be used to prompt the AI to generate further content, and the game also provides players with options to undo or redo or modify recent events to improve the game's narrative. Players can also tell the AI what elements to "remember" for reference in future parts of their playthrough. === User-generated content === In addition to AI Dungeon's pre-configured settings, players can create custom "adventures" from scratch by describing the setting in text format, which the AI will then generate a setting from. These custom adventures can be published for others to play, with an interface for browsing published adventures and leaving comments under them. === Multiplayer === AI Dungeon includes a multiplayer mode in which different players each have their own character and take turns interacting with the AI within the same game session. Multiplayer supports both online play across multiple devices or local play using a shared device. The game's hosts are able to supervise the AI and modify its output. Unlike the single-player game, in which actions and stories use second person narration, multiplayer game stories are presented using third-person narration. === Worlds === AI Dungeon allows players to set their adventures within specific "Worlds" that give context to the broader environment where the adventure takes place. This feature was first released with two different worlds available for selection: Xaxas, a "world of peace and prosperity"; and Kedar, a "world of dragons, demons, and monsters". == Development == === AI Dungeon Classic (Early GPT-2) === The first version of AI Dungeon (sometimes referred to as AI Dungeon Classic) was designed and created by Nick Walton of Brigham Young University's "Perception, Control, and Cognition" deep learning laboratory in March 2019 during a hackathon. Before this, Walton had been working as an intern for several companies in the field of autonomous vehicles. This creation used an early version of the GPT-2 natural-language-generating neural network, created by OpenAI, allowing it to generate its original adventure narratives. During his first interactions with GPT-2, Walton was partly inspired by the tabletop game Dungeons & Dragons (D&D), which he had played for the first time with his family a few months earlier: I realized that there were no games available that gave you the same freedom to do anything that I found in [Dungeons & Dragons] ... You can be so creative compared to other games. This led him to wonder if an AI could function as a dungeon master. Unlike later versions of AI Dungeon, the original did not allow players to specify any action they wanted. Instead, it generated a finite list of possible actions to choose from. This first version of the game was released to the public in May 2019. It is not to be confused with another GPT-2-based adventure game, GPT Adventure, created by Northwestern University neuroscience postgraduate student Nathan Whitmore, also released on Google Colab several months after the public release of AI Dungeon. === AI Dungeon 2 (Full GPT-2) === In November 2019, a new, "full" version of GPT-2 was released by OpenAI. This new model included support for 1.5 billion parameters (which determine the accuracy with which a machine learning model can perform a task), compared with the 126 million parameter version used in the earliest stages of AI Dungeon's development. The game was recreated by Walton, leveraging this new version of the model, and temporarily rebranded as AI Dungeon 2. AI Dungeon 2's AI was given more focused training compared to its predecessor, using genre-specific text. This training material included approximately 30 megabytes of content web-scraped from chooseyourstory.com (an online community website of content inspired by interactive gamebooks, written by contributors of multiple skill levels, using logic of differing complexity) and multiple D&D rulebooks and adventures. The new version was released in December 2019 as open-source software available on GitHub. It was accessible via Google Colab, an online tool for data scientists and AI researchers that allows for free execution of code on Google-hosted machines. It could also be run locally on a PC, but in both cases, it required players to download the full model, around 5 gigabytes of data. Within days of the initial release, this mandatory download resulted in bandwidth charges of over $20,000, forcing the temporary shut-down of the game until a peer-to-peer alternative solution was established. Due to the game's sudden and explosive growth that same month, however, it became closed-source, proprietary software and was relaunched by Walton's start-up development team, Latitude (with Walton taking on the role of CTO). This relaunch constituted mobile apps for iOS and Android (built by app developer Braydon Batungbacal) on December 17. Other members of this team included Thorsten Kreutz for the game's long-term strategy and the creator's brother, Alan Walton, for hosting infrastructure. At this time, Nick Walton also established a Patreon campaign to support the game's further growth (such as the addition of multiplayer and voice support, along with longer-term plans to include music and image content) and turn the game into a commercial endeavor, which Walton felt was necessary to cover the costs of delivering a higher-quality version of the game. AI Dungeon was one of the only known commercial applications to be based upon GPT-2. Following its first announcement in December 2019, a multiplayer mode was added to the game in April 2020. Hosting a game in this mode was originally restricted to premium subscribers, although any players could join a hosted game. === Dragon model release (GPT-3) === In July 2020, the developers introduced a premium-exclusive version of the AI model, named Dragon, which uses OpenAI's API for leveraging the GPT-3 model without maintaining a local copy (released on June 11, 2020). GPT-3 was trained with 570 gigabytes of text content (approximately one trillion words, with a $12 million development cost) and can support 175 billion parameters, compared to the 40 gigabytes of training content and 1.5 billion parameters of GPT-2. The free model was also upgraded to a less-advanced version of GPT-3 and was named Griffin. Speaking shortly after this release, on the differences between GPT-2 and GPT-3, Walton stated: [GPT-3 is] one of the most powerful AI models in the world... It's just much more coherent in terms of understanding who the characters are, what they're saying, what's going on in the story and just being able to write an interesting and believable story. In the latter half of 2020, the "Worlds" feature was added to AI Dungeon, providing players with a selection of overarching worlds in which their adventures can take place. In February 2021, it was announced that AI Dungeon's developers, Latitude, had raised $3.3 million in seed funding (led by NFX, with participation from Album VC and Griffin Gaming Partners) to "build games with 'infinite' story possibilities." This funding intended to move AI content creation beyond the purely text-based nature of AI Dungeon as it existed at the time. After its announcement on August 20, a new "See" interaction mode was made available for all players and added to the game on August 30, 2022. AI Dungeon was retired from Steam on March 12, 2024. == Reception == Approximate
Elements (toolchain)
RemObjects Elements is a toolchain for software development, comprising six programming languages: C#, Swift, Go, Java, Oxygene (a form of modern Object Pascal), and Visual Basic .NET. All languages interoperate, meaning a single project can use any combination of languages, and they can all be compiled to .NET, the JVM, native, or WebAssembly. Elements supports Microsoft Windows, all Apple Inc. platforms (including iOS, visionOS and watchOS), Android, and Linux. Elements also supports language conversion, allowing source code in one language to be rewritten in another. Elements is supported in Visual Studio, but RemObjects also makes their own IDEs, Fire (on MacOS) and Water (on Windows.) == Background == RemObjects began in 2002, creating software for Delphi, but in 2005 in response to the growth of .NET and that Delphi was targeting only native Windows, they released Oxygene (known as Chrome at the time) as a new version of Object Pascal, with more modern syntax as well as being .NET-native. Since then, five other languages have been added to the suite, as well as compiling for the web via WebAssembly and to native architectures (eg Intel 32/64 or ARM64). Elements is primarily intended for developers who want to pull together libraries and codebases written in multiple languages, including legacy codebases in older languages while modernizing either with newer syntax and features or by adding in the use of newer or more popular languages. Because of the Oxygene flavour of Object Pascal, supporting Delphi apps is a primary focus, including allowing Pascal to be compiled for other architectures or providing language features that match other prominent languages. == Approach == New versions of the Elements come out approximately every week. RemObjects names its programming languages after chemical elements, sometimes with poetic or musical spelling, rather than referring to them directly. They are: C#: Hydrogene Object Pascal: Oxygene Java: Iodine Visual Basic: Mercury Go: Gold Swift: Silver == History == The Elements compiler was first introduced with version 1.0 in 2005 under the name "Chrome", with support for only the Oxygene language on the .NET platform, primarily as a response to the then-new and not well-received Delphi .NET compiler from Embarcadero. Chrome saw updates to version 1.5 'Floorshow' and Chrome 2.0 'Joyride' over the next few years, moving in parallel with major advancements on the .NET platform for .NET 2.0 (Generics) and .NET 3.x (LINQ), respectively. With the release of version 3.0 (code-named Oxygène after the Jean-Michel Jarre album of the same name) Chrome was rebranded to Oxygene in 2008, and also shipped co-branded by Embarcadero as Delphi Prism (later just Prism) as part of RAD Studio, replacing Embarcadero's own and now-defunct Delphi.NET compiler. 2010 saw the release of Oxygene 4 ("Echoes"), the last version to focus on just a single language and platform. With Oxygene 5 in 2011 and Oxygene 6 in 2013, RemObjects introduced new platform support for Java/Android (code-name "Cooper") and then Cocoa, the Apple development platform (code-name "Toffee"). Elements 7.0 was released at the beginning of 2014, adding the second programming language, C# to the compiler, and delegating Oxygene from the product name to merely branding the Object Pascal-based language. Over the subsequent years, Elements gained support for additional languages, with Apple Swift in 2015, Java in 2017, and subsequently Google's Go and Mercury, a revitalized Visual Basic.NET. Elements also gained support for its fourth target platform, "Island", for CPU-native compilation for Windows, Linux, and WebAssembly. In addition to the chemical elements-based names for the different languages, the "Elements" concept was carried on with the introduction of dedicated development environments alchemically named Fire (for the Mac, in 2015) and Water (for Windows, in 2018). == Fire and Water (IDEs) == Fire and Water are integrated development environments developed by RemObjects Software. They are designed specifically for use with the Elements Compiler. Fire is the version developed for macOS, while Water is intended for Microsoft Windows. Both IDEs are designed to work closely with the Elements compiler and are primarily intended for developers using the RemObjects language ecosystem. They support software development across multiple platforms, including .NET, Android, iOS, macOS, Windows, Linux, and WebAssembly. The IDEs include standard development tools such as syntax highlighting, code completion, debugging, and project navigation. Build operations are managed using a custom system known as EBuild, which is part of the broader Elements toolchain. The IDEs are distributed as part of the RemObjects Elements package and are updated in coordination with the compiler itself. == In media == Oxygene has been mentioned several times by Verity Stob in their Chronicles of Delphi series, currently living at The Register.
OpenSMILE
openSMILE is source-available software for automatic extraction of features from audio signals and for classification of speech and music signals. "SMILE" stands for "Speech & Music Interpretation by Large-space Extraction". The software is mainly applied in the area of automatic emotion recognition and is widely used in the affective computing research community. The openSMILE project exists since 2008 and is maintained by the German company audEERING GmbH since 2013. openSMILE is provided free of charge for research purposes and personal use under a source-available license. For commercial use of the tool, the company audEERING offers custom license options. == Application Areas == openSMILE is used for academic research as well as for commercial applications in order to automatically analyze speech and music signals in real-time. In contrast to automatic speech recognition which extracts the spoken content out of a speech signal, openSMILE is capable of recognizing the characteristics of a given speech or music segment. Examples for such characteristics encoded in human speech are a speaker's emotion, age, gender, and personality, as well as speaker states like depression, intoxication, or vocal pathological disorders. The software further includes music classification technology for automatic music mood detection and recognition of chorus segments, key, chords, tempo, meter, dance-style, and genre. The openSMILE toolkit serves as benchmark in manifold research competitions such as Interspeech ComParE, AVEC, MediaEval, and EmotiW. == History == The openSMILE project was started in 2008 by Florian Eyben, Martin Wöllmer, and Björn Schuller at the Technical University of Munich within the European Union research project SEMAINE. The goal of the SEMAINE project was to develop a virtual agent with emotional and social intelligence. In this system, openSMILE was applied for real-time analysis of speech and emotion. The final SEMAINE software release is based on openSMILE version 1.0.1. In 2009, the emotion recognition toolkit (openEAR) was published based on openSMILE. "EAR" stands for "Emotion and Affect Recognition". In 2010, openSMILE version 1.0.1 was published and was introduced and awarded at the ACM Multimedia Open-Source Software Challenge. Between 2011 and 2013, the technology of openSMILE was extended and improved by Florian Eyben and Felix Weninger in the context of their doctoral thesis at the Technical University of Munich. The software was also applied for the project ASC-Inclusion, which was funded by the European Union. For this project, the software was extended by Erik Marchi in order to teach emotional expression to autistic children, based on automatic emotion recognition and visualization. In 2013, the company audEERING acquired the rights to the code-base from the Technical University of Munich and version 2.0 was published under a source-available research license. Until 2016, openSMILE was downloaded more than 50,000 times worldwide and has established itself as a standard toolkit for emotion recognition. == Awards == openSMILE was awarded in 2010 in the context of the ACM Multimedia Open Source Competition. The software tool is applied in numerous scientific publications on automatic emotion recognition. openSMILE and its extension openEAR have been cited in more than 1000 scientific publications until today.
Non-personal data
Non-Personal Data (NPD) is electronic data that does not contain any information that can be used to identify a natural person. Thus, it can either be data that has no personal information to begin with (such as weather data, stock prices, data from anonymous IoT sensors); or it is data that had personal data that was subsequently pseudoanonymized (for example, identifiable strings substituted with random strings) or anonymized (such as by irreversibly removing all personal data). NPD is part of the overall Data Governance Strategy of a region or country. While personal data are covered by Data Protection Legislation such as GDPR, other kinds of data would fall under the scope of NPD Regulation. == Importance of non-personal data == It has been pointed out that the future is data-driven. What this means is that much of the present innovation taking place in domains such as Machine Learning and Artificial Intelligence is fueled by data, which is needed for calibrating the complex models (comprising neural network-based as well as other kinds). The larger the volume, diversity and quality of the data, the higher is the quality of the model, leading to better predictions and explanations. However, there is a flip-side to data availability. The newly-emerging awareness of privacy and the consequent need for powerful Data Protection Regulations (such as GDPR) makes it increasingly difficult or impossible to obtain data in the quantities required. This is a contradiction, and the only way out would be to remove all personal data from data sets (either by Data anonymization or Pseudonymization coupled with noise injection, at which point it becomes NPD. Therefore, many innovation-friendly countries are coming out with regulatory regimes that would ensure that personal data is protected, while, at the same time, non-personal data can be extracted from personal data so that innovation is fostered. In other words, NPD 'unlocks' value that was locked away in data sets that have personally-identifiable information. It is expected that multiple NPD data sets will begin to be available on free or commercial basis from different providers once the regulations are in place. == Emerging regulatory frameworks == Non-Personal Data has significant uses that may be economic, social, political or security-related. Several countries and regions are in the process of regulating the use of NPD. In May 2019, the European Union operationalized its Regulation of the Free Flow of NPD. India announced a nine-member expert committee to make recommendations on the regulation of NPD in 2019, which published its first report in mid-2020. The report was opened for public comments, after which it was revised and published in December 2020. == Proposed NPD regulatory framework in India == The following were the objectives of the proposed Indian regulation as per the revised report: Sovereignty: India has rights over the data of India, its people and organisations. Benefit India: Benefits of data must accrue to India and its people. Benefits the world: Innovation, new models and algorithms for the world. Privacy: Misuse, reidentification and harms must be prevented. Simplicity: The regulations should be simple, digital and unambiguous. Innovation and entrepreneurship: The data should be freely available for innovation and entrepreneurship in India. == Concerns == The major concern in the use of NPD is if there are techniques (statistical or AI-based) by which multiple data sets can be used to extract personally-identifiable data.
Archetype (information science)
In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==
ReRites
ReRites (also known as RERITES, ReadingRites, Big Data Poetry) is a literary work of "Human + A.I. poetry" by David Jhave Johnston that used neural network models trained to generate poetry which the author then edited. ReRites won the Robert Coover Award for a Work of Electronic Literature in 2022. == About the project == The ReRites project began as a daily rite of writing with a neural network, expanded into a series of performances from which video documentation has been published online, and concluded with a set of 12 books and an accompanying book of essays published by Anteism Books in 2019. In Electronic Literature, Scott Rettberg describes the early phases of the project in 2016, when it bore the preliminary name Big Data Poetry. Jhave (the artist name that David Jhave Johnston goes by) describes the process of writing ReRites as a rite: "Every morning for 2 hours (normally 6:30–8:30am) I get up and edit the poetic output of a neural net. Deleting, weaving, conjugating, lineating, cohering. Re-writing. Re-wiring authorship: hybrid augmented enhanced evolutionary". There is video documentation of the writing process. The human editing of the neural network's output is fundamental to this project, and Jhave gives examples of both unedited text extracts and his edited versions in publications about the project. Kyle Booten describes ReRites as "simultaneously dusty and outrageously verdant, monotonously sublime and speckled with beautiful and rare specimens". === Performances === ReRites was first shared with an audience through a series of performances where audience members and poets would participate in reading the automatically generated texts, which appeared on screen so fast that human readers could barely keep up. This has been described as allowing participants to "re-discover[..] the peculiar pleasures of being embodied", or, in Jhave's own words, as a space where human participants were "playing their wits and voices against an evocative infinite deep-learning muse". The first performance was at Brown University's Interrupt Festival in 2019. It has been performed many times since, including at the Barbican Centre in London and Anteism Books. === Print publications === For a single year Jhave published one book of poetry from the ReRites project each month. These twelve volumes are accompanied by a book of essays, all published by Anteism Books. The accompanying essays provide critical responses to the project from poets and scholars including Allison Parrish, Johanna Drucker, Kyle Booten, Stephanie Strickland, John Cayley, Lai-Tze Fan, Nick Montfort, Mairéad Byrne, and Chris Funkhouser. Allison Parrish notes elsewhere that these paratexts to ReRites serve a legitimising function for a genre of poetry that is not yet institutionally acknowledged. === Technical details === Starting in 2016 under the name Big Data Poetry, Jhave generated poems using, in his own words, "neural network code (..) adapted from three corporate github-hosted machine-learning libraries: TensorFlow (Google), PyTorch (Facebook), and AWD-LSTM (SalesForce)". He explains that the "models were trained on a customised corpus of 600,000 lines of poetry ranging from the romantic epoch to the 20th century avant garde". Jhave maintains a GitHub repository with some of the code supporting ReRites. == Reception == ReRites is described by John Cayley as "one of the most thorough and beautiful" poetic responses to machine learning. The work's influence on the field of electronic literature was acknowledged in 2022, when the work won the Electronic Literature Organization's Robert Coover Award for a Work of Electronic Literature. The jury described ReRites as particularly poignant in the time of the pandemic, as it was "a documentation of the performance of the private ritual of writing and the obsessive-compulsive need for writers to communicate — even when no one else is reading". The question of authorship and voice in ReRites has been raised by several critics. Although generated poetry is an established genre in electronic literature, Cayley notes that unlike the combinatory poems created by authors like Nick Montfort, where the author explicitly defines which words and phrases will be recombined, ReRites has "not been directed by literary preconceptions inscribed in the program itself, but only by patterns and rhythms pre-existing in the corpora". In an essay for the Australian journal TEXT, David Thomas Henry Wright asks how to understand authorship and authority in ReRites: "Who or what is the authority of the work? The original data fed into the machine, that is not currently retrievable or discernible from the final works? The code that was taken and adapted for his purposes? Or Jhave, the human editor?" Wright concludes that Jhave is the only actor with any intentionality and therefore the authority of the work. The centrality of the human editor is also emphasised by other scholars. In a chapter analysing ReRites Malthe Stavning Erslev argues that the machine learning misrepresents the dataset it is trained on. While ReRites uses 21st century neural networks, it has been compared to earlier literary traditions. Poet Victoria Stanton, who read at one of the ReRites performances, has compared ReRites to found poetry, while David Thomas Henry Wright compares it to the Oulipo movement and Mark Amerika to the cut-up technique. Scholars also position ReRites firmly within the long tradition of generative poetry both in electronic literature and print, stretching from the I Ching, Queneau's Cent Mille Milliards de Poemes and Nabokov's Pale Fire to computer-generated poems like Christopher Strachey's Love Letter Generator (1952) and more contemporary examples. Jhave describes the process of working with the output from the neural network as "carving". In his book My Life as an Artificial Creative Intelligence, Mark Amerika writes that the "method of carving the digital outputs provided by the language model as part of a collaborative remix jam session with GPT-2, where the language artist and the language model play off each other’s unexpected outputs as if caught in a live postproduction set, is one I share with electronic literature composer David Jhave Johnston, whose AI poetry experiments precede my own investigations."
Ontology for Biomedical Investigations
The Ontology for Biomedical Investigations (OBI) is an open-access, integrated ontology for the description of biological and clinical investigations. OBI provides a model for the design of an investigation, the protocols and instrumentation used, the materials used, the data generated and the type of analysis performed on it. The project is being developed as part of the OBO Foundry and as such adheres to all the principles therein such as orthogonal coverage (i.e. clear delineation from other foundry member ontologies) and the use of a common formal language. In OBI the common formal language used is the Web Ontology Language (OWL). As of March 2008, a pre-release version of the ontology was made available at the project's SVN repository. == Scope == The Ontology for Biomedical Investigations (OBI) addresses the need for controlled vocabularies to support integration and joint ("cross-omics") analysis of experimental data, a need originally identified in the transcriptomics domain by the FGED Society, which developed the MGED Ontology as an annotation resource for microarray data.Smith B, Ashburner M, Rosse C, Bard J, Bug W, Ceusters W, et al. (November 2007). "The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration". Nature Biotechnology. 25 (11): 1251–5. doi:10.1038/nbt1346. PMC 2814061. PMID 17989687. OBI uses the basic formal ontology upper-level ontology as a means of describing general entities that do not belong to a specific problem domain. As such, all OBI classes are a subclass of some BFO class. The ontology has the scope of modeling all biomedical investigations and as such contains ontology terms for aspects such as: biological material – for example blood plasma instrument (and parts of an instrument therein) – for example DNA microarray, centrifuge information content – such as an image or a digital information entity such as an electronic medical record design and execution of an investigation (and individual experiments therein) – for example study design, electrophoresis material separation data transformation (incorporating aspects such as data normalization and data analysis) – for example principal components analysis dimensionality reduction, mean calculation Less 'concrete' aspects such as the role a given entity may play in a particular scenario (for example the role of a chemical compound in an experiment) and the function of an entity (for example the digestive function of the stomach to nutriate the body) are also covered in the ontology. == OBI consortium == The MGED Ontology was originally identified in the transcriptomics domain by the FGED Society and was developed to address the needs of data integration. Following a mutual decision to collaborate, this effort later became a wider collaboration between groups such as FGED, PSI and MSI in response to the needs of areas such as transcriptomics, proteomics and metabolomics and the FuGO (Functional Genomics Investigation Ontology) was created. This later became the OBI covering the wider scope of all biomedical investigations. As an international, cross-domain initiative, the OBI consortium draws upon a pool of experts from a variety of fields, not limited to biology. The current list of OBI consortium members is available at the OBI consortium website. The consortium is made up of a coordinating committee which is a combination of two subgroups, the Community Representative (those representing a particular biomedical community) and the Core Developers (ontology developers who may or may not be members of any single community). Separate to the coordinating committee is the Developers Working Group which consists of developers within the communities collaborating in the development of OBI at the discretion of current OBI Consortium members. == Papers on OBI ==