AI Content Generation Tools

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Artificial intelligence and elections

As artificial intelligence (AI) has become more mainstream, there is growing concern about how this will influence elections. Potential targets of AI include election processes, election offices, election officials and election vendors. There are also global efforts to improve elections using AI. == Tactics == Generative AI capabilities allow creation of misleading content. Examples of this include text-to-video, deepfake videos, text-to-image, AI-altered images, text-to-speech, voice cloning, and text-to-text. In the context of an election, a deepfake video of a candidate may propagate information that the candidate does not endorse. Chatbots could spread misinformation related to election locations, times or voting methods. In contrast to malicious actors in the past, these techniques require little technical skill and can spread rapidly. LLM-generated messages have the capacity to persuade humans on political issues. Researchers have begun to investigate how people rate messages that LLMs generate for how persuasive they are. When it came to policy issues, the LLM-generated messages received a 2.91 compared to a 2.80 when it came to smartness between the AI and humans. The LLM-generated messages were often more technical and analytical than human-generated messages. Generative AI has been used to micro-target people during tight political elections. The generation of targeted large language models has triggered concern that they will be used to leverage readily scale microtargeting. Rephrasing inputs have been used to generate fraudulent emails and phishing websites. Rephrasing inputs in a microtargeting does not violate the terms of OpenAI usage. There are no safeguards to prevent the use of rephrasing and creation of fraudulent emails. Political campaign managers have access to this allowing for them to create targeted content. == Usage by country == === Argentina === ==== 2023 elections ==== During the 2023 Argentine primary elections, Javier Milei's team distributed AI generated images including a fabricated image of his rival Sergio Massa and drew 3 million views. The team also created an unofficial Instagram account entitled "AI for the Homeland." Sergio Massa's team also distributed AI generated images and videos. === Bangladesh === ==== 2024 elections ==== In the run up to the 2024 Bangladeshi general election, deepfake videos of female opposition politicians appeared. Rumin Farhana was pictured in a bikini while Nipun Ray was shown in a swimming pool. === Canada === ==== 2025 elections ==== In the run up to the 2025 Canadian federal election, the use of AI tools is likely to figure prominently. India, Pakistan and Iran are all expected to make efforts to subvert the national vote using disinformation campaigns to deceive voters and sway diaspora communities. In a report by the Canadian Centre for Cyber Security called "Cyber Threats to Canada's Democratic Process: 2025 Update", it states that malicious actors including China and Russia: "are most likely to use generative AI as a means of creating and spreading disinformation, designed to sow division among Canadians and push narratives conducive to the interests of foreign states". === France === ==== 2024 elections ==== In the 2024 French legislative election, deepfake videos appeared claiming: i) That they showed the family of Marine le Pen. In the videos, young women, supposedly Le Pen's nieces, are seen skiing, dancing and at the beach "while making fun of France’s racial minorities": However, the family members don't exist. On social media there were over 2 million views. ii) In a video seen on social media, a deepfake video of a France24 broadcast appeared to report that the Ukrainian leadership had "tried to lure French president Emmanuel Macron to Ukraine to assassinate him and then blame his death on Russia". === Ghana === ==== 2024 elections ==== During the months before the December 2024 Ghanaian general election, a network of at least 171 fake accounts has been used to spam social media. Posts have been used by a group identified as "@TheTPatriots" to promote the New Patriotic Party, although it is not known whether the two are connected. All the networks' posts were "highly likely" to have been generated by ChatGPT and appear to be the "first secretly partisan network using AI to influence elections in Ghana". The opposition National Democratic Congress was also criticized with its leader John Mahama being called a drunkard. === India === ==== 2024 elections ==== In the 2024 Indian general election, politicians used deepfakes in their campaign materials. These deepfakes included politicians who had died prior to the election. Mathuvel Karunanidhi's party posted with his likeness even though he had died 2018. A video The All-India Anna Dravidian Progressive Federation party posted showed an audio clip of Jayaram Jayalalithaa even though she had died in 2016. The Deepfakes Analysis Unit (DAU) is an open source platform created in March 2024 for the public to share misleading content and assess if it had been AI-generated. AI was also used to translate political speeches in real time. This translating ability was widely used to reach more voters. === Indonesia === ==== 2024 elections ==== In the 2024 Indonesian presidential election, Prabowo Subianto made extensive use of AI-generated art in his campaign, which ranged from images of himself as an adorable child to various child portrayals in his advertisements. The Indonesian Children's Protection Commission condemned these ads, labeling them as a form of misuse. Other candidates, Anies Baswedan and Ganjar Pranowo, also incorporated AI art into their campaigns. Throughout the election period, all presidential candidates faced attacks from deepfakes, both in video and audio formats. === Ireland === ==== 2024 elections ==== In the last weeks of the 2024 Irish general election a spoof election poster appeared in Dublin featuring "an AI-generated candidate with three arms". The candidate is called Aidan Irwin, but no-one stood in the election with that name. A slogan on the poster says "put matters into artificial intelligence’s hands". The convincing election poster shows a man that "has six fingers on one hand, three arms, and a distorted thumb". === New Zealand === ==== 2023 elections ==== In May 2023, ahead of the 2023 New Zealand general election in October 2023, the New Zealand National Party published a "series of AI-generated political advertisements" on its Instagram account. After confirming that the images were faked, a party spokesperson said that it was "an innovative way to drive our social media". === Pakistan === ==== 2024 elections ==== AI has been used by the imprisoned ex-Prime Minister Imran Khan and his media team in the 2024 Pakistani general election: i) An AI generated audio of his voice was added to a video clip and was broadcast at a virtual rally. ii) An op-ed in The Economist written by Khan was later claimed by himself to have been written by AI which was later denied by his team. The article was liked and shared on social media by thousands of users. === South Africa === ==== 2024 elections ==== In the 2024 South African general election, there were several uses of AI content: i) A deepfaked video of Joe Biden emerged on social media showing him saying that "The U.S. would place sanctions on SA and declare it an enemy state if the African National Congress (ANC) won". ii) In a deepfake video, Donald Trump was shown endorsing the uMkhonto weSizwe party. It was posted to social media and was viewed more than 158,000 times. iii) Less than 3 months before the elections, a deepfake video showed U.S. rapper Eminem endorsing the Economic Freedom Fighters party while criticizing the ANC. The deepfake was viewed on social media more than 173,000 times. === South Korea === ==== 2022 elections ==== In the 2022 South Korean presidential election, a committee for one presidential candidate Yoon Suk Yeol released an AI avatar 'Al Yoon Seok-yeol' that would campaign in places the candidate could not go. The other presidential candidate Lee Jae-myung introduced a chatbot that provided information about the candidate's pledges. ==== 2024 elections ==== Deepfakes were used to spread misinformation before the 2024 South Korean legislative election with one source reporting 129 deepfake violations of election laws within a two week period. Seoul hosted the 2024 Summit for Democracy, a virtual gathering of world leaders initiated by US President Joe Biden in 2021. The focus of the summit was on digital threats to democracy including artificial intelligence and deepfakes. === Taiwan === ==== 2024 elections ==== AI-generated content was used during the 2024 Taiwanese presidential election. Among the media were: i) A deepfake video of General Secretary of the Chinese Communist Party Xi Jinping which showed him supporting the presidential elections. Created on social media, the video was "widely circulated
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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.
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CENDI

CENDI (Commerce, Energy, NASA, Defense Information Managers Group) is an interagency group of senior Scientific and Technical Information (STI) managers from 14 United States federal agencies. CENDI managers cooperate by exchanging information and ideas, collaborating to address common issues, and undertaking joint initiatives. CENDI's accomplishments range from impacting federal information policy to educating a broad spectrum of stakeholders on all aspects of federal STI systems, including its value to research and the taxpayer, and to operational improvements in agency and interagency STI operations. == History == CENDI traces its roots to the Committee on Scientific and Technical Information (COSATI) of the Federal Council on Science and Technology. COSATI was established in the early 1960s to coordinate the management of the results from the U.S. government's increasing commitment to scientific research and technology development. The scientific and technical information (STI) managers of the government's major research and development (R&D) agencies worked within COSATI to standardize guidelines for cataloging and indexing technical reports. COSATI ceased formal operations in the early 1970s. To continue the cooperation begun under COSATI, managers of agency STI programs from Commerce (National Technical Information Service), Energy (Office of Scientific and Technical Information), NASA (HQ/STI Division), and Defense (Defense Technical Information Center) began meeting periodically to discuss common topics and stimulate more effective cooperation. In 1985, a Memorandum of Understanding was signed by the four charter agencies and CENDI was established. From this small core of STI managers, CENDI has grown to its current membership, which represents the major science agencies, the national libraries, and agencies involved in the dissemination and long-term management of scientific and technical information. The vision of CENDI is to facilitate cooperative enterprise where capabilities are shared and challenges are faced together so that the sum of the accomplishments is greater than each individual agency can achieve on its own amongst federal STI agencies. The abbreviation CENDI refers to the "Commerce, Energy, NASA, Defense Information Managers Group". == Membership == New members from other federal R&D information organizations may be admitted by unanimous agreement of the members. However, it is the intent of the group that membership in CENDI should remain small and focus on organizations with STI or supporting responsibilities. Each agency provides funding to CENDI. == Members == The members of CENDI are: Defense Technical Information Center (United States Department of Defense) Office of Research and Development and Office of Environmental Information (United States Environmental Protection Agency) Government Printing Office Library of Congress NASA Scientific and Technical Information Program National Agricultural Library (United States Department of Agriculture) National Archives and Records Administration National Library of Education (United States Department of Education) National Library of Medicine (United States Department of Health and Human Services) National Science Foundation National Technical Information Service (United States Department of Commerce) National Transportation Library (United States Department of Transportation) Office of Scientific and Technical Information (United States Department of Energy) USGS/Biological Resources Discipline (United States Department of the Interior) == Mission and operation == CENDI's mission is to help improve the productivity of federal science- and technology-based programs through effective scientific, technical, and related information support systems. In fulfilling its mission, CENDI agencies play an important role in addressing science- and technology-based national priorities and strengthening U.S. competitiveness. === Goals === STI Coordination and Leadership: Provide coordination and leadership for information exchange on important STI policy issues. Improvement of STI Systems: Promote the development of improved STI systems through the productive interrelationship of content and technology. STI Understanding: Promote better understanding of STI and STI management. === Principals and Alternates === CENDI is made up of senior federal STI managers and each organization appoints a Principal representative. This person is the point of contact for that organization within CENDI. Each Principal has an Alternate. The Principals and Alternates comprise the main group that meets on a regular basis, usually every other month. === Secretariat === A Tennessee-based information management company, -- Information International Associates, Inc., currently serves as the CENDI Secretariat. The Secretariat provides day-to-day operations to CENDI. The Secretariat prepares the necessary materials for the Principals' meetings, provides support for the working group and task group meetings, assists in developing papers, and maintains the CENDI files and outreach tools. === Task Groups and Working Groups === The chair(s) of a working group is appointed by the Principals and has the overall responsibility for the group's activities. The Secretariat provides support at the request of the Working Group chair(s). The Working Groups and Task Groups that are currently operating are: Copyright and Intellectual Property Working Group Distribution Markings Task Group Digital Preservation Task Group Digitization Specifications Task Group Image Metadata Task Group Science.gov (see below) STI Policy Working Group Terminology Resources Task Group === Science.gov and Worldwidescience.org === In 2001, in response to the April 2001 workshop on "Strengthening the Public Information Infrastructure for Science", and taking into consideration a request from Firstgov (now USA.gov) to develop specialized topical portals, CENDI formed an alliance to develop an interagency website for access to STI. This website, called Science.gov, is a one-stop source of STI, including both selected, authoritative government websites and deep Web databases of technical reports, journal articles, conference proceedings, and other published materials. Through the volunteer efforts of members and involving over 100 staff, content and architecture is developed for the site. The Science.gov website is hosted by the Department of Energy (DOE) Office of Scientific and Technical Information (OSTI). The site was formally launched in December 2002. As a result of the success of Science.gov, under DOE leadership and in cooperation with the International Council of Scientific and Technical Information, a worldwide coordination across national portals called WorldWideScience was launched in 2008. === Work with non-member organizations === CENDI works with several cooperating non-member organizations on a regular basis. These agencies are in academia, federal government, legal and policy analysis, international, non-governmental, and private organizations.
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Traité de Documentation

Traité de documentation: le livre sur le livre, théorie et pratique is a landmark book by Belgian author Paul Otlet, first published in 1934. == Legacy == The book is considered a landmark in the history of information science, with concepts predicting the rise of the World Wide Web and search engines. In [Otlet's] most famous publication of 1934, Traité de Documentation, he wrote of a desk in the form of a wheel from which different projects (workspaces) could be switched as they rotated — foreshadowing the multiple desktops and tabs of contemporary computer interfaces. Inspired by the arrival of radio, phonograph, cinema, and television, Otlet also posited that there were as yet many “inventions to be discovered,” including the reading and annotation of remote documents and computer speech.
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Tandem Money

Tandem is one of the UK's original challenger banks. Tandem is a digital bank with a mobile app, and no branches. The acquisition of Harrods Bank in 2017 allowed the company to provide services using the former's banking licence. Tandem Bank Limited is authorised by the Prudential Regulation Authority and regulated by the Financial Conduct Authority. Tandem has offices across the UK in Blackpool, Cardiff, Durham and London, employing over 500 people. == History == The company was founded by Ricky Knox, Matt Cooper and Michael Kent in 2014. In December 2016, Tandem announced that it had secured a £35 million investment from The Sanpower Group, the Chinese company that also owned the department store House of Fraser; however, £29 million of this investment was later revoked by Sanpower over concerns that the Chinese Government would object to the investment following increased restrictions on outbound investment in China. This resulted in a delay in the launch of Tandem's savings products, which, at the time of the revocation, was expected imminently and, more importantly, meant that Tandem volunteered the return of their banking license but retained all other permissions. In April 2018, Tandem launched fixed-term savings accounts, offering one-, two- and three-year terms through its app. === Acquisitions === In August 2017, it was announced that Tandem would fully acquire Harrods Bank, founded in 1893, in a deal that would bring a near-£200m loan book, over £300m of deposits and nearly £80 million of capital. Prior to its sale to Tandem Money, Harrods Bank catered for high-net-worth (HNW) individuals and operated from the Harrods store in Knightsbridge, London. It offered a variety of personal and business current and savings accounts, mortgages, foreign currency and gold bullion trading services. On 7 August 2017, Tandem Money Limited announced a deal to acquire 100% of Harrods Bank Limited shares. The purchase deal closed successfully on 11 January 2018. In March 2018, Tandem agreed to acquire Pariti Technologies Limited, developers of the Pariti money management application. In August 2020 Tandem acquired green home improvement loan specialists Allium Lending Group. It was announced on 8 February 2021 that Tandem had agreed to purchase the mortgage book from private bank Bank and Clients, consisting of 300 B&C customers for an undisclosed amount. In January 2022 Tandem Bank acquired consumer lender Oplo, creating a combined business with £1.2 billion of total assets. In April 2023, it was announced that Tandem had acquired money-sharing app Loop Money. At the time of the purchase, one of Loop's founders – Paul Pester – was also chairman at Tandem. == Features == Tandem Bank offers customers savings, mortgages, personal and secured loans, green home improvement loans and motor finance. In November 2022, the bank launched its new Tandem Marketplace, providing information and resources to help promote greener living.
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User-subjective approach

The user-subjective approach is the first interaction design approach dedicated specifically to personal information management (PIM). The approach offers design principles with which PIM systems (e.g. operating systems, email applications and web browsers) can make systematic use of subjective (i.e. user-dependent) attributes. The approach evolved in three stages: (a) theoretical foundations first published in a Journal of the American Society for Information Science and Technology during 2003. The paper introduces the approach and its design principles (b) evidence and implementation was published in another JASIST paper in 2008. The paper gives empirical evidence in support of the approach as well as seven novel design schemes that derives from it. It has won the Best JASIST paper award in 2009.(c) specific design evaluation this stage has already begun with evaluation of the first user-subjective design prototype called GrayArea in a Conference on Human Factors in Computing Systems paper published in 2009. == Theoretical foundations == The user-subjective approach takes advantage of the fact that in PIM the person who retrieves the information is the same person who had previously stored it. PIM can be seen as a communication between the person and him\her self at two different times: the time of storage and the time of retrieval. The PIM system design should help facilitate that unique communication by allowing the user use subjective (user-dependent) attributes in addition to the standard objective ones. PIM systems should capture these subjective attributes when the user interacts with the information item (either automatically or by using direct manipulation interface) in order to help the user retrieve the item later on. The user-subjective approach identifies three subjective attributes – the project which the item was classified to, its degree of importance to the user, and the context in which the item was used during the interaction with it. The approach also assigns a design principle for each. The principles (discussed below) are deliberately abstract to allow for a variety of different implementations. === The subjective project classification principle === The subjective project classification principle suggests that PIM systems design should allow all information items related to a project be classified under the same category regardless of whether they are files, emails, Web Favorites or of any other format. This stands in sharp contrast with the present PIM system design where there are distinct folder hierarchies for each of these formats. The current design forces the user to store information related to a single project in separate locations depending on their format causing the project fragmentation problem. === The subjective importance principle === The subjective importance principle suggests that the subjective importance of information should affect its degree of visual salience and accessibility: important information items should be highly visible and accessible as they are more likely to be retrieved (the promotion principle) and those of lower importance should be demoted (i.e. making them less visible) so as not to distract the user (the demotion principle). While the promotion principle is not new and has been widely applied in PIM system design, the demotion principle is novel and has been applied only sporadically in these systems. Currently these systems allow only two options: keeping information (where unneeded information items could clutter folders and obscure the target item) and deleting it (where there is a risk that the item will not be there when needed). Demotion suggests a third option where the item is less visible so it doesn’t distract the user but is kept within its original context in case the user would need it after all. === The subjective context principle === The subjective context principle suggests that PIM systems should allow users retrieve their information items in the same context that they had previously used in order to bridge the time gap between these two events. By "context" the approach refers to other information items that were used at the time of interaction with the item, thoughts that the users may have regarding the item, the phase the user got to in the interaction with the item and other people the user collaborates with regarding the information item. == Evidence and implementations == === Evidence === The user-subjective approach was evaluated in a multioperational designed study which used questionnaires, screen shots and in-depth interviews (N = 84). The research tested the use of subjective attributes in current PIM systems and its dependency on design. Results show that participants used subjective attributes whenever design allowed them to. When it didn't, they either used their own alternative ways to use these attributes or avoided using subjective attributes at all. Regarding the subjective project classification principle – many of the participants' recent files, emails and web pages related to the same projects (indicating that they were working on the same project using different formats), and they had saved files of different format in the same project folders. However, as design does not suggest storing emails and web favorites with files, users avoid doing so. Regarding the subjective importance principle – users tended to retrieve their important information from highly visible and accessible locations offered by current design (e.g. by using the desktop), however since current systems offers no way to demote files of low subjective importance participants tended to use their own walk around ways for doing so (e.g. by moving them to a folder called "old" inside their original folder). Regarding the subjective context principle – participants tended to talk spontaneously about the context of their information items during the interview. These evidence imply that current PIM systems could possibly be improved if it would allow users to make more use of subjective attributes of their personal information. === Implementations === Each of the user-subjective design principles can be implemented in various ways. Moreover, as the approach is generative it offers PIM designers to use these principles in order to create their own user subjective designs. Below are design schemes that demonstrate an implementation of each of the principles. A more complete set of implementation examples can be found in the user-subjective website Archived 2011-02-01 at the Wayback Machine. The single hierarchy solution – addresses the project fragmentation problem (the current situation where the users stores and retrieve their project-related files, emails and web favorites at different hierarchies) and implements the subjective classification principle by offering the user a single folder hierarchy for all information items. At the operation system level the users would navigate to a folder and find there all project related files, emails, web favorites, tasks, contacts and notes. This would allow them to retrieve all their project-related information items from a single location regardless of their formats. When looking at these folders at their mail box the users would see only their emails and only web favorites through their browser. The single hierarchy design scheme has not been evaluated yet. GrayArea – implements the demotion principle by allowing users to move subjectively unimportant files to a gray area at the bottom end of their folders. This clears the upper part of the folder from file that are unlikely to be retrieved while allowing the users to retrieve these unimportant file in their original context in case they are needed after all. GrayArea design scheme was positively evaluated (see next section). ItemHistory – is an implementation of the subjective context principle. It allows users to reach all information items that were previously retrieved while that information item was open. This design scheme has not been evaluated to date. == Specific design evaluation == The evaluation of specific designs is the third and final step of the approach development. It had begun with the assessment of GrayArea. === GrayArea evaluation === GrayArea was evaluated by using a prototype that simulated the participants' folders but included a gray area where they could drag & drop their subjectively unimportant files. In the study 96 participants were asked to clean up their folders from unimportant files once with GrayArea and once without it. Results show that the use of GrayArea reduced the clutter in folders, that it was easier for participants to demote files than to delete them and that they would use it if provided in their next operating system. These results encourage commercial implementation of GrayArea and the development and testing of other user-subjective designs. == Chronological development == The user-subjective approach was developed by
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Ecoinformatics

Ecoinformatics, or ecological informatics, is the science of information in ecology and environmental science. It integrates environmental and information sciences to define entities and natural processes with language common to both humans and computers. However, this is a rapidly developing area in ecology and there are alternative perspectives on what constitutes ecoinformatics. A few definitions have been circulating, mostly centered on the creation of tools to access and analyze natural system data. However, the scope and aims of ecoinformatics are certainly broader than the development of metadata standards to be used in documenting datasets. Ecoinformatics aims to facilitate environmental research and management by developing ways to access, integrate databases of environmental information, and develop new algorithms enabling different environmental datasets to be combined to test ecological hypotheses. Ecoinformatics is related to the concept of ecosystem services. Ecoinformatics characterize the semantics of natural system knowledge. For this reason, much of today's ecoinformatics research relates to the branch of computer science known as knowledge representation, and active ecoinformatics projects are developing links to activities such as the Semantic Web. Current initiatives to effectively manage, share, and reuse ecological data are indicative of the increasing importance of fields like ecoinformatics to develop the foundations for effectively managing ecological information. Examples of these initiatives are National Science Foundation Datanet projects, DataONE, Data Conservancy, and Artificial Intelligence for Environment & Sustainability. == Software Development Lifecycle == Central to the concept of ecoinformatics is the Software Development Lifecycle (SDLC), a systematic framework for writing, implementing, and maintaining software products. Typically in Ecoinformatics projects, the development pipeline includes data collection, usually from several different environmental data sources, then integrating these data sources together, and then analyzing the data. Here, each step of the SDLC is described in the context of ecoinformatics, per Michener et al. It is important to note that the plan, collect, assure, describes and preserve steps refer to the data collection entity, which can be individual researchers or large data-collection networks, while the discover, integrate, and analyze steps typically refer to the individual researcher. Plan: Ecoinformatics projects require data from several databases. Each database holds different data, and therefore researchers should identify what types of environmental or ecological data they will need to answer their research question. Collect: Data is collected in several different ways. In ecoinformatics, this is usually restricted to manually entering data into a spreadsheet, and parsing data from an existing database. The growth of relational databases has made it easier for ecologists to download relevant data and integrate datasets together Assure: Data entries should be checked thoroughly to validate their accuracy and usability, such as to check for outliers and erroneous points. The same principle applies to data downloaded from datasets. This responsibility falls on both the ecologist downloading the data, and the entity that sets up the data collection system. Describe: An accurate description of the metadata of a dataset that is used in a study should include enough information to deduce the data collection and processing methodology, when the data were collected, why the data were collected, and how the data were stored. This is important for reproducibility, especially for projects that build on each other and may recycle data Preserve: After data is collected by an institutional entity, it should be archived such that it is easily accessible. Ideally, this is in databases that are maintained and not at risk of deprecation Discover: While there are good practices for discovering data to start a research project, this process is often marred by a lack of usable, published data, as researchers may collect data specific to their study, but may not publish this data for wider use. On the data collection end, this can be addressed by better data-sharing practices, such as by linking datasets when publishing papers or studies. On the data procurement end, this can be addressed by more precise data searching, such as using key words to find relevant datasets. Integrate: Synthesizing datasets together can be difficult and labor-intensive, largely due to the methodological differences in data collection. There are several approaches to this, but the best practices typically involve computational approaches, namely using R or Python, to automate the processes and prevent errors Analyze: Data analysis can take several forms, and should be tailored to the specific ecological project. However, all data analysis methods should be well-documented, including the procedure for analysis, justification for analysis methods, and any shortcomings in a specific approach. == Applications of Ecoinformatics Across Ecology == === Ecosystem Ecology === Source: Ecosystem studies, by definition, encompass interactions across the entire life sciences spectrum, from microscopic biochemical reactions to large-scale geological phenomena. As a result, big databases may not be designed specifically for any particular research question, but should be inclusive enough to support most studies. Since ecosystem-level questions require a broad perspective, data-related ecosystem projects would likely incorporate data from several databases. A common framework for incorporating data into ecosystem-level studies is the network science model, in which data collection mechanisms and resources are treated like a large, interconnected network instead of individual entities. The network may include several data collection stations within one databases, or may span across multiple databases. Currently there are several large-scale networks, but they do not generate data on the scale to consider ecology as a big data science. A current challenge for ecoinformatics in ecosystem ecology is that most funding is prioritized for generating new data rather than maintaining existing data infrastructures. Integrating data across the different spatial scales can also be difficult, since each dataset may hold different types of data. === Urban Ecology === Source: The current push for smart cities, and sensor network integration into infrastructure, has positioned as a major source of data for ecological studies. Typical urban ecology questions address the effects of urbanization on the local ecosystem, and how to drive future development to promote urban biodiversity. While sensor networks in cities typically collect environmental data to optimize city processes, they may also be used for ecological initiatives, especially with respect to understanding the complex, multi-layered relationship between cities and their local ecosystem. It can also be used to better understand the current landscape of cities, and identify avenues for rewinding of cities. For example, analyzing mobility patterns can identify areas that may lend themselves well to building parks and green spaces. Bird watching data can also be used to identify the types of bird species in a local area. === Infectious Disease === Source: Like other disciplines of ecology, emerging infectious disease and epidemiology span multiple scales, from understanding the genetics that drive disease trends to large-scale spatiotemporal analyses. As a result, infectious disease studies can incorporate everything from bioinformatics, genetic sequences, amino acid sequences, and environmental observation data. On the micro-scale, these data can then be used to predict infectivity/transmissibility, drug resistance, drug candidates, and mutation sites. On the macro-scale, it can be used to identify societal trends or environmental factors that lend themselves to spillover, locations of infection, and practices that cause disease transmission. == Databases == Source: USGS National Streamflow sensor network GBIF Neotoma Paleobiology database European Vegetation Archive USDA Forest Inventory Analysis TRY BIEN AmeriFlux TEAM iNaturalist NEON GLEON LTER CZO TERN SAEON
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Token-based replay

Token-based replay technique is a conformance checking algorithm that checks how well a process conforms with its model by replaying each trace on the model (in Petri net notation ). Using the four counters produced tokens, consumed tokens, missing tokens, and remaining tokens, it records the situations where a transition is forced to fire and the remaining tokens after the replay ends. Based on the count at each counter, we can compute the fitness value between the trace and the model. == The algorithm == Source: The token-replay technique uses four counters to keep track of a trace during the replaying: p: Produced tokens c: Consumed tokens m: Missing tokens (consumed while not there) r: Remaining tokens (produced but not consumed) Invariants: At any time: p + m ≥ c ≥ m {\displaystyle p+m\geq c\geq m} At the end: r = p + m − c {\displaystyle r=p+m-c} At the beginning, a token is produced for the source place (p = 1) and at the end, a token is consumed from the sink place (c' = c + 1). When the replay ends, the fitness value can be computed as follows: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})} == Example == Suppose there is a process model in Petri net notation as follows: === Example 1: Replay the trace (a, b, c, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity c {\displaystyle \mathbf {c} } consumes 1 token and produces 1 token ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} and c = 2 + 1 = 3 {\displaystyle c=2+1=3} ). Step 5: The activity d {\displaystyle \mathbf {d} } consumes 2 tokens and produces 1 token ( p = 5 + 1 = 6 {\displaystyle p=5+1=6} , c = 3 + 2 = 5 {\displaystyle c=3+2=5} ). Step 6: The token at the end place is consumed ( c = 5 + 1 = 6 {\displaystyle c=5+1=6} ). The trace is complete. The fitness of the trace ( a , b , c , d {\displaystyle \mathbf {a,b,c,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 0 6 ) + 1 2 ( 1 − 0 6 ) = 1 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {0}{6}})+{\frac {1}{2}}(1-{\frac {0}{6}})=1} === Example 2: Replay the trace (a, b, d) on the model M === Step 1: A token is initiated. There is one produced token ( p = 1 {\displaystyle p=1} ). Step 2: The activity a {\displaystyle \mathbf {a} } consumes 1 token to be fired and produces 2 tokens ( p = 1 + 2 = 3 {\displaystyle p=1+2=3} and c = 1 {\displaystyle c=1} ). Step 3: The activity b {\displaystyle \mathbf {b} } consumes 1 token and produces 1 token ( p = 3 + 1 = 4 {\displaystyle p=3+1=4} and c = 1 + 1 = 2 {\displaystyle c=1+1=2} ). Step 4: The activity d {\displaystyle \mathbf {d} } needs to be fired but there are not enough tokens. One artificial token was produced and the missing token counter is increased by one ( m = 1 {\displaystyle m=1} ). The artificial token and the token at place [ b , d ] {\displaystyle [\mathbf {b,d} ]} are consumed ( c = 2 + 2 = 4 {\displaystyle c=2+2=4} ) and one token is produced at place end ( p = 4 + 1 = 5 {\displaystyle p=4+1=5} ). Step 5: The token in the end place is consumed ( c = 4 + 1 = 5 {\displaystyle c=4+1=5} ). The trace is complete. There is one remaining token at place [ a , c ] {\displaystyle [\mathbf {a,c} ]} ( r = 1 {\displaystyle r=1} ). The fitness of the trace ( a , b , d {\displaystyle \mathbf {a,b,d} } ) on the model M {\displaystyle \mathbf {M} } is: 1 2 ( 1 − m c ) + 1 2 ( 1 − r p ) = 1 2 ( 1 − 1 5 ) + 1 2 ( 1 − 1 5 ) = 0.8 {\displaystyle {\frac {1}{2}}(1-{\frac {m}{c}})+{\frac {1}{2}}(1-{\frac {r}{p}})={\frac {1}{2}}(1-{\frac {1}{5}})+{\frac {1}{2}}(1-{\frac {1}{5}})=0.8}
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Quantexa

Quantexa is a UK-based software company that develops artificial intelligence-based applications for data analytics and decision-making. The company was founded in 2016 and is headquartered in London, with operations in North America, Europe, and the Asia-Pacific region. As of 2025, Quantexa reported a valuation of $2.6 billion and provides services to organizations in over 70 countries. Investors include Warburg Pincus, HSBC, and the Ontario Teachers’ Pension Plan. == History == Quantexa was founded in London in 2016 by several co-founders, including Jamie Hutton, Richard Seewald, Imam Hoque, Felix Hoddinott, and Vishal Marria, who also serves as the company's chief executive officer. The company was established to develop tools intended to address limitations in traditional data analysis methods, particularly those related to identifying hidden connections across large datasets. The name "Quantexa" is derived from the company's focus on quantitative methods and data analysis. In 2023, Quantexa acquired Dublin-based AI firm Aylien. In April 2023, the company completed a Series E funding round, raising $129 million at a valuation of approximately $1.8 billion, marking its entry into "unicorn" status. In October 2024, the company reported annual recurring revenue (ARR) exceeding $100 million. In early 2025, Quantexa participated in the World Economic Forum's Unicorn Program, which supports high-growth technology companies. In March 2025, Quantexa completed a Series F funding round of $175 million, led by Teachers' Venture Growth, the venture arm of the Ontario Teachers' Pension Plan. That August, the company was reported to be considering a 2026 IPO. The company formed a partnership with Zurich in October 2025, the first insurer to add its AI-based Decision Intelligence platform to enhance fraud detection.
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International Philosophical Bibliography

The International Philosophical Bibliography (IPB), also known in French as Répertoire bibliographique de la philosophie (RBP), is a bibliographic database covering publications on the history of philosophy and continental philosophy. The database comprises records of publications in over 30 languages. Annually, about 12,000 records are added. The indexes include, among other elements, over 84,000 names of authors, editors, translators, reviewers, and collaborators, as well as more than 3,000 commentaries on philosophical works, making it the world's most complete index in Philosophy. Since 1934, the IPB has been developed by the Higher Institute of Philosophy at the University of Louvain (UCLouvain), first in Leuven and since 1978 in Louvain-la-Neuve. The online version was launched by Peeters Publishers in 1997 and continues to be updated quarterly.
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Pointer jumping

Pointer jumping or path doubling is a design technique for parallel algorithms that operate on pointer structures, such as linked lists and directed graphs. Pointer jumping allows an algorithm to follow paths with a time complexity that is logarithmic with respect to the length of the longest path. It does this by "jumping" to the end of the path computed by neighbors. The basic operation of pointer jumping is to replace each neighbor in a pointer structure with its neighbor's neighbor. In each step of the algorithm, this replacement is done for all nodes in the data structure, which can be done independently in parallel. In the next step when a neighbor's neighbor is followed, the neighbor's path already followed in the previous step is added to the node's followed path in a single step. Thus, each step effectively doubles the distance traversed by the explored paths. Pointer jumping is best understood by looking at simple examples such as list ranking and root finding. == List ranking == One of the simpler tasks that can be solved by a pointer jumping algorithm is the list ranking problem. This problem is defined as follows: given a linked list of N nodes, find the distance (measured in the number of nodes) of each node to the end of the list. The distance d(n) is defined as follows, for nodes n that point to their successor by a pointer called next: If n.next is nil, then d(n) = 0. For any other node, d(n) = d(n.next) + 1. This problem can easily be solved in linear time on a sequential machine, but a parallel algorithm can do better: given n processors, the problem can be solved in logarithmic time, O(log N), by the following pointer jumping algorithm: The pointer jumping occurs in the last line of the algorithm, where each node's next pointer is reset to skip the node's direct successor. It is assumed, as in common in the PRAM model of computation, that memory access are performed in lock-step, so that each n.next.next memory fetch is performed before each n.next memory store; otherwise, processors may clobber each other's data, producing inconsistencies. The following diagram follows how the parallel list ranking algorithm uses pointer jumping for a linked list with 11 elements. As the algorithm describes, the first iteration starts initialized with all ranks set to 1 except those with a null pointer for next. The first iteration looks at immediate neighbors. Each subsequent iteration jumps twice as far as the previous. Analyzing the algorithm yields a logarithmic running time. The initialization loop takes constant time, because each of the N processors performs a constant amount of work, all in parallel. The inner loop of the main loop also takes constant time, as does (by assumption) the termination check for the loop, so the running time is determined by how often this inner loop is executed. Since the pointer jumping in each iteration splits the list into two parts, one consisting of the "odd" elements and one of the "even" elements, the length of the list pointed to by each processor's n is halved in each iteration, which can be done at most O(log N) time before each list has a length of at most one. == Root finding == Following a path in a graph is an inherently serial operation, but pointer jumping reduces the total amount of work by following all paths simultaneously and sharing results among dependent operations. Pointer jumping iterates and finds a successor — a vertex closer to the tree root — each time. By following successors computed for other vertices, the traversal down each path can be doubled every iteration, which means that the tree roots can be found in logarithmic time. Pointer doubling operates on an array successor with an entry for every vertex in the graph. Each successor[i] is initialized with the parent index of vertex i if that vertex is not a root or to i itself if that vertex is a root. At each iteration, each successor is updated to its successor's successor. The root is found when the successor's successor points to itself. The following pseudocode demonstrates the algorithm. algorithm Input: An array parent representing a forest of trees. parent[i] is the parent of vertex i or itself for a root Output: An array containing the root ancestor for every vertex for i ← 1 to length(parent) do in parallel successor[i] ← parent[i] while true for i ← 1 to length(successor) do in parallel successor_next[i] ← successor[successor[i]] if successor_next = successor then break for i ← 1 to length(successor) do in parallel successor[i] ← successor_next[i] return successor The following image provides an example of using pointer jumping on a small forest. On each iteration the successor points to the vertex following one more successor. After two iterations, every vertex points to its root node. == History and examples == Although the name pointer jumping would come later, JáJá attributes the first uses of the technique in early parallel graph algorithms and list ranking. The technique has been described with other names such as shortcutting, but by the 1990s textbooks on parallel algorithms consistently used the term pointer jumping. Today, pointer jumping is considered a software design pattern for operating on recursive data types in parallel. As a technique for following linked paths, graph algorithms are a natural fit for pointer jumping. Consequently, several parallel graph algorithms utilizing pointer jumping have been designed. These include algorithms for finding the roots of a forest of rooted trees, connected components, minimum spanning trees, and biconnected components. However, pointer jumping has also shown to be useful in a variety of other problems including computer vision, image compression, and Bayesian inference.
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DIKW pyramid

The DIKW pyramid (also known as the knowledge pyramid or information hierarchy) is a model describing relationships between data, information, knowledge and wisdom sometimes also stylized as a chain, refer to models of possible structural and functional relationships between a set of components—often four, data, information, knowledge, and wisdom. The concept has roots predating the 1980s. In the latter years of that decade, interest in the models grew after explicit presentations and discussions, including from Milan Zeleny, Russell Ackoff, and Robert W. Lucky. Subsequent important discussions extended along theoretical and practical lines into the coming decades. While debate continues as to actual meaning of the component terms of DIKW-type models, and the actual nature of their relationships—including occasional doubt being cast over any simple, linear, unidirectional model—even so they have become very popular visual representations in use by business, the military, and others. Among the academic and popular, not all versions of the DIKW-type models include all four components (earlier ones excluding data, later ones excluding or downplaying wisdom, and several including additional components (for instance Ackoff inserting "understanding" before and Zeleny adding "enlightenment" after the wisdom component). In addition, DIKW-type models are no longer always presented as pyramids, instead also as a chart or framework (e.g., by Zeleny), as flow diagrams (e.g., by Liew, and by Chisholm et al.), and sometimes as a continuum (e.g., by Choo et al.). == Short description == As Rowley noted in 2007, the DIKW model "is often quoted, or used implicitly, in definitions of data, information and knowledge in the information management, information systems and knowledge management literatures, but [as of that date] there ha[d] been limited direct discussion of the hierarchy". Reviews of textbooks and a survey of scholars in relevant fields indicate that there was not a consensus as to definitions used in the model as of that date, and as reviewed by Liew in that year, even less "in the description of the processes that transform components lower in the hierarchy into those above them". Zins work, published in 2007—from studies in 2003-2005 that documented "130 definitions of data, information, and knowledge formulated by 45 scholars", published in 2007—to suggest that the data–information–knowledge components of DIKW refer to a class of no less than five models, as a function of whether data, information, and knowledge are each conceived of as subjective, objective (what Zins terms, "universal" or "collective") or both. In Zins' usage, subjective and objective "are not related to arbitrariness and truthfulness, which are usually attached to the concepts of subjective knowledge and objective knowledge". Information science, Zins argues, studies data and information, but not knowledge, as knowledge is an internal (subjective) rather than an external (universal–collective) phenomenon. == Representations == === Graphical representation === DIKW is a hierarchical model often depicted as a pyramid, sometimes as a chain, with data at its base and wisdom at its apex (or chain-beginning and -end). Both Zeleny and Ackoff have been credited with originating the pyramid representation, although neither used a pyramid to present their ideas. According to Wallace, Debons and colleagues may have been the first to "present the hierarchy graphically". Many variations of the DIKW-type pyramid have been produced. One, in use by knowledge managers in the United States Department of Defense, attempts to show the DIKW progression to enable effective decisions and consequent activities supporting shared understanding throughout defense organizations, as well as supporting management of risks associated with decisions. DIKW-type hierarchical information paradigms have also been represented as two-dimensional charts, and as flow diagrams, where relationships between the components may be presented less hierarchically, with defining aspects of the relationships, feedback loops, etc. === Computational representation === Intelligent decision support systems are trying to improve decision making by introducing new technologies and methods from the domain of modeling and simulation in general, and in particular from the domain of intelligent software agents in the contexts of agent-based modeling. The following example describes a military decision support system, but the architecture and underlying conceptual idea are transferable to other application domains: The value chain starts with data quality describing the information within the underlying command and control systems. Information quality tracks the completeness, correctness, currency, consistency and precision of the data items and information statements available. Knowledge quality deals with procedural knowledge and information embedded in the command and control system such as templates for adversary forces, assumptions about entities such as ranges and weapons, and doctrinal assumptions, often coded as rules. Awareness quality measures the degree of using the information and knowledge embedded within the command and control system. Awareness is explicitly placed in the cognitive domain. By the introduction of a common operational picture, data are put into context, which leads to information instead of data. The next step, which is enabled by service-oriented web-based infrastructures (but not yet operationally used), is the use of models and simulations for decision support. Simulation systems are the prototype for procedural knowledge, which is the basis for knowledge quality. Finally, using intelligent software agents to continually observe the battle sphere, apply models and simulations to analyze what is going on, to monitor the execution of a plan, and to do all the tasks necessary to make the decision maker aware of what is going on, command and control systems could even support situational awareness, the level in the value chain traditionally limited to pure cognitive methods. == History == Danny P. Wallace, a professor of library and information science, explained that the origin of the DIKW pyramid is uncertain: The presentation of the relationships among data, information, knowledge, and sometimes wisdom in a hierarchical arrangement has been part of the language of information science for many years. Although it is uncertain when and by whom those relationships were first presented, the ubiquity of the notion of a hierarchy is embedded in the use of the acronym DIKW as a shorthand representation for the data-to-information-to-knowledge-to-wisdom transformation.Many authors think that the idea of the DIKW relationship originated from two lines in the poem "Choruses", by T. S. Eliot, that appeared in the pageant play The Rock, in 1934: === Knowledge, intelligence, and wisdom === In 1927, Clarence W. Barron addressed his employees at Dow Jones & Company on the hierarchy: "Knowledge, Intelligence and Wisdom". === Data, information, knowledge === In 1955, English-American economist and educator Kenneth Boulding presented a variation on the hierarchy consisting of "signals, messages, information, and knowledge". However, "[t]he first author to distinguish among data, information, and knowledge and to also employ the term 'knowledge management' may have been American educator Nicholas L. Henry", in a 1974 journal article. === Data, information, knowledge, wisdom === Other early versions (prior to 1982) of the hierarchy that refer to a data tier include those of Chinese-American geographer Yi-Fu Tuan and sociologist-historian Daniel Bell.. In 1980, Irish-born engineer Mike Cooley invoked the same hierarchy in his critique of automation and computerization, in his book Architect or Bee?: The Human / Technology Relationship. Thereafter, in 1987, Czechoslovakia-born educator Milan Zeleny mapped the components of the hierarchy to knowledge forms: know-nothing, know-what, know-how, and know-why. Zeleny "has frequently been credited with proposing the [representation of DIKW as a pyramid ]... although he actually made no reference to any such graphical model." The hierarchy appears again in a 1988 address to the International Society for General Systems Research, by American organizational theorist Russell Ackoff, published in 1989. Subsequent authors and textbooks cite Ackoff's as the "original articulation" of the hierarchy or otherwise credit Ackoff with its proposal. Ackoff's version of the model includes an understanding tier (as Adler had, before him), interposed between knowledge and wisdom. Although Ackoff did not present the hierarchy graphically, he has also been credited with its representation as a pyramid. In 1989, Bell Labs veteran Robert W. Lucky wrote about the four-tier "information hierarchy" in the form of a pyramid in his book Silicon Dreams. In the same year as Ackoff presented his a
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The 2028 Global Intelligence Crisis

The 2028 Global Intelligence Crisis is a report authored by James van Geelen and Alap Shah and published by Citrini Research in February 2026, on the impact of artificial intelligence on humanity's future. Written in the form of a scenario analysis, it was viewed millions of times online and reportedly caused a fall in the stock market prices of major tech and financial firms. It also received criticism among others, for its allegedly flawed economic logic. The 'thought exercise', as the authors called it, painted a gloomy picture for the near future, where outputs keep growing while consumer's ability to spend collapses. "...driven by ai agents that don’t sleep, take sick days or require health insurance”, "outputs that are shown in national accounts increases, "but never circulates through the real economy"(which the report calls 'Ghost GDP'), the authors argued. In other words, the authors predict a scenario where the owners of the AI firms will accumulate a vast fortune but there will be scant demand from consumers as AI would cause massive unemployment. The authors caution the reader that what they make is a scenario and not a prediction. In the scenario they visualise, any service whose value proposition is “I will navigate complexity that you find tedious” is getting disrupted. The reports argues that the unique ability of human beings to analyse, decide, create, persuade, and coordinate was “the thing that could not be replicated at scale,” and call the historical scarcity of this precious entity 'friction'. When this friction becomes zero, a gamut of changes occur which then triggers a cascading of changes across the economy. ”Travel booking platforms are an early casualty; Financial advice. tax prep., and routine legal work follow suit. National unemployment rate go as high 10.2% and the S&P 500 goes for a massive 38% peak-to-trough crash. In contrast to the previous technological revolutions the high-earning professionals suffers more and get forced to take up roles in the gig economy. Labour supply becomes abundant and this cuts wages all across the economy. The dent in income for the employees then affects other sectors of the economy such as the residential mortgage market. The losses for the software companies triggers loan defaults and heralds peril for the private credit sector.
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Information scientist

The term information scientist developed in the latter part of the twentieth century by Wm. Hovey Smith to describe an individual, usually with a relevant subject degree (such as one in Information and Computer Science - CIS) or high level of subject knowledge, providing focused information to scientific and technical research staff in industry. It is a role quite distinct from and complementary to that of a librarian. Developments in end-user searching, together with some convergence between the roles of librarian and information scientist, have led to a diminution in its use in this context, and the term information officer or information professional (information specialist) are also now used. The term was, and is, also used for an individual carrying out research in information science. Brian C. Vickery mentions that the Institute of Information Scientists (IIS) was established in London during 1958 and lists the criteria put forward by this institute "Criteria for Information Science" (appendix 1) as well as his own "Areas of study in information science" (appendix 2). The IIS merged with the Library Association in 2002 to form the Chartered Institute of Library and Information Professionals (CILIP). == Notable Information Scientists == See also Award of Merit - Association for Information Science and Technology Marcia Bates David Blair (information technologist) Samuel C. Bradford Michael Buckland John M. Carroll Blaise Cronin Emilia Currás Brenda Dervin Eugene Garfield Paul B. Kantor Frederick Wilfrid Lancaster Calvin Mooers Tefko Saracevic Linda C. Smith Robert Saxton Taylor Brian Campbell Vickery Thomas D. Wilson == Additional reading == Ellis, David and Merete Haugan. (1997) "Modelling the information seeking patterns of engineers and research scientists in an industrial environment" (Journal of Documentation, Volume 53(4): pp. 384–403) Poole, Alex H. (2024). "'There's a big difference between going through life with the wind at your back, and going through life leaning into the wind': Feminism in Post-World War II Information Science". Proceedings of the Association for Information Science and Technology. 61: 300–313. doi:10.1002/pra2.1029. Vickery, Brian Campbell (1988) "Essays presented to B. C. Vickery" (Journal of Documentation, Volume 44, pp. 199–283). Vickery, B. & Vickery, A. (1987) Information Science in theory and practice (London: Bowker-Saur, pp. 361–369)
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BioCreative

BioCreAtIvE (A critical assessment of text mining methods in molecular biology) consists in a community-wide effort for evaluating information extraction and text mining developments in the biological domain. It was preceded by the Knowledge Discovery and Data Mining (KDD) Challenge Cup for detection of gene mentions. == Community Challenges == === First edition (2004-2005) === Three main tasks were posed at the first BioCreAtIvE challenge: the entity extraction task, the gene name normalization task, and the functional annotation of gene products task. The data sets produced by this contest serve as a Gold Standard training and test set to evaluate and train Bio-NER tools and annotation extraction tools. === Second edition (2006-2007) === The second BioCreAtIvE challenge (2006-2007) had also 3 tasks: detection of gene mentions, extraction of unique idenfiers for genes and extraction information related to physical protein-protein interactions. It counted with participation of 44 teams from 13 countries. === Third edition (2011-2012) === The third edition of BioCreative included for the first time the InterActive Task (IAT), designed to evaluate the practical usability of text mining tools in real-world biocuration tasks. === Fifth edition (2016) === BioCreative V had 5 different tracks, including an interactive task (IAT) for usability of text mining systems and a track using the BioC format for curating information for BioGRID.
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