Digital curation is the selection, preservation, maintenance, collection, and archiving of digital assets. It is a process that establishes, maintains, and adds value to repositories of digital data for present and future use. The implementation of digital curation is often carried out by archivists, librarians, scientists, historians, and scholars to ensure users have access to reliable, high-quality resources. Enterprises are also starting to adopt digital curation as a means to improve the quality of information and data within their operational and strategic processes. A successful digital curation initiative will help to mitigate digital obsolescence, keeping the information accessible to users indefinitely. Digital curation includes various aspects, including digital asset management, data curation, digital preservation, and electronic records management. == Word History == Much like the word archive has layered meanings and uses, the word curation is both a noun and a verb, used originally in the field of museology to represent a wide range of activities, most often associated with collection care, long-term preservation, and exhibition design. Curation can be a reference to physical repositories that store cultural heritage or natural resource collections (e.g., a curatorial repository) or a representation of varied policies and processes involved with the long-term care and management of heritage collections, digital archives, and research data (e.g, curatorial/collections management plans, curation life-cycle, and data curation). Yet curation is also associated with short-term objectives and processes of selection and interpretation for the purposes of presentation, such as for gallery exhibitions and websites, which contribute to knowledge creation. It has also been applied to interaction with social media including compiling digital images, web links, and movie files. The term curation entered the legal framework through federal historic preservation laws, starting with the National Historic Preservation Act of 1966, and was further defined and coded into federal regulations through 36 CFR Part 79: Curation of Federally-owned and Administered Archaeological Collections. Curation has since permeated into an array of disciplines but remains closely tied to heritage and information management. == Core Principles and Activities == The term "digital curation" was first used in the e-science and biological science fields as a means of differentiating the additional suite of activities ordinarily employed by library and museum curators to add value to their collections and enable its reuse from the smaller subtask of simply preserving the data, a significantly more concise archival task. Additionally, the historical understanding of the term "curator" demands more than simple care of the collection. A curator is expected to command academic mastery of the subject matter as a requisite part of appraisal and selection of assets and any subsequent adding of value to the collection through application of metadata. === Principles === There are five commonly accepted principles that govern the occupation of digital curation: Manage the complete birth-to-retirement life cycle of the digital asset. Evaluate and cull assets for inclusion in the collection. Apply preservation methods to strengthen the asset’s integrity and reusability for future users. Act proactively throughout the asset life cycle to add value to both the digital asset and the collection. Facilitate the appropriate degree of access to users. === Methodology === The Digital Curation Center offers the following step-by-step life cycle procedures for putting the above principles into practice: Sequential Actions: Conceptualize: Consider what digital material you will be creating and develop storage options. Take into account websites, publications, email, among other types of digital output. Create: Produce digital material and attach all relevant metadata, typically the more metadata the more accessible the information. Appraise and select: Consult the mission statement of the institution or private collection and determine what digital data is relevant. There may also be legal guidelines in place that will guide the decision process for a particular collection. Ingest: Send digital material to the predetermined storage solution. This may be an archive, repository or other facility. Preservation action: Employ measures to maintain the integrity of the digital material. Store: Secure data within the predetermined storage facility. Access, use, and reuse: Determine the level of accessibility for the range of digital material created. Some material may be accessible only by password and other material may be freely accessible to the public. Routinely check that material is still accessible for the intended audience and that the material has not been compromised through multiple uses. Transform: If desirable or necessary the material may be transferred into a different digital format. Occasional Actions: Dispose: Discard any digital material that is not deemed necessary to the institution. Reappraise: Reevaluate material to ensure that is it still relevant and is true to its original form. Migrate: Migrate data to another format in order to protect data for using better in the future. == Related terms == The term "digital curation" is sometimes used interchangeably with terms such as "digital preservation" and "digital archiving." While digital preservation does focus a significant degree of energy on optimizing reusability, preservation remains a subtask to the concept of digital archiving, which is in turn a subtask of digital curation. For example, archiving is a part of curation, but so are subsequent tasks such as themed collection-building, which is not considered an archival task. Similarly, preservation is a part of archiving, as are the tasks of selection and appraisal that are not necessarily part of preservation. Data curation is another term that is often used interchangeably with digital curation, however common usage of the two terms differs. While "data" is a more all-encompassing term that can be used generally to indicate anything recorded in binary form, the term "data curation" is most common in scientific parlance and usually refers to accumulating and managing information relative to the process of research. Data-driven research of education request the role of information professional gradually develop tradition of digital service to data curation particularly at the management of digital research data. So, while documents and other discrete digital assets are technically a subset of the broader concept of data, in the context of scientific vernacular digital curation represents a broader purview of responsibilities than data curation due to its interest in preserving and adding value to digital assets of any kind. == Challenges == === Rate of creation of new data and data sets === The ever lowering cost and increasing prevalence of entirely new categories of technology has led to a quickly growing flow of new data sets. These come from well established sources such as business and government, but the trend is also driven by new styles of sensors becoming embedded in more areas of modern life. This is particularly true of consumers, whose production of digital assets is no longer relegated strictly to work. Consumers now create wider ranges of digital assets, including videos, photos, location data, purchases, and fitness tracking data, just to name a few, and share them in wider ranges of social platforms. Additionally, the advance of technology has introduced new ways of working with data. Some examples of this are international partnerships that leverage astronomical data to create "virtual observatories," and similar partnerships have also leveraged data resulting from research at the Large Hadron Collider at CERN and the database of protein structures at the Protein Data Bank. === Storage format evolution and obsolescence === By comparison, archiving of analog assets is notably passive in nature, often limited to simply ensuring a suitable storage environment. Digital preservation requires a more proactive approach. Today’s artifacts of cultural significance are notably transient in nature and prone to obsolescence when social trends or dependent technologies change. This rapid progression of technology occasionally makes it necessary to migrate digital asset holdings from one file format to another in order to mitigate the dangers of hardware and software obsolescence which would render the asset unusable. === Underestimation of human labor costs === Modern tools for program planning often underestimate the amount of human labor costs required for adequate digital curation of large collections. As a result cost-benefit assessments often paint an inaccurate picture of both the amount of work involved and the true cost to the institution for bot
STIT logic
STIT logic (from seeing to it that) is a family of modal and branching-time logics for reasoning about agency and choice. A typical STIT operator has the form [ i s t i t : φ ] {\displaystyle [i\ {\mathsf {stit}}:\varphi ]} , usually read as "agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } ", and is interpreted in models where agents choose between alternative possible futures. STIT logics are used in action theory, deontic logic, epistemic logic, and the theory of intelligent agents to formalise notions such as "could have done otherwise", responsibility, joint action, and strategic ability in an indeterministic world. == Etymology == The acronym STIT comes from the English phrase "seeing to it that", introduced in influential work by Nuel Belnap and Michael Perloff on the logical analysis of agentive expressions. In this tradition, "to see to it that φ {\displaystyle \varphi } " is treated as a primitive agency operator, rather than being reduced to ordinary modal necessity. == History == Modern STIT logic arose in the 1980s in the context of branching-time semantics and formal theories of agency. Belnap and Perloff's article "Seeing to it that: A canonical form for agentives" introduced the idea of treating expressions of the form "agent i sees to it that φ" as a primitive modal operator, and analysed such sentences using a branching tree of moments and histories. This approach was further developed in a series of papers on indeterminism and agency and provided the conceptual core for later STIT formalisms. In the 1990s the basic formal systems of STIT logic were worked out. Horty and Belnap's influential paper on the deliberative STIT operator distinguished between a "Chellas" STIT that merely records the result of an agent's present choice and a "deliberative" STIT that requires the agent's choice to make a difference, and connected STIT with issues of action, omission, ability and obligation. Around the same time, Ming Xu proved completeness and decidability results for basic STIT systems, including a single-agent logic with Kripke-style semantics and axiomatizations for multi-agent deliberative STIT, thereby establishing STIT as a well-behaved normal modal framework. This early work was systematised in Belnap, Perloff and Xu's monograph Facing the Future: Agents and Choices in Our Indeterminist World, which presents a general branching-time semantics for individual and group STIT operators, discusses independence-of-agents conditions and articulates the metaphysical picture of an indeterministic "tree" of moments. At roughly the same time, Horty's book Agency and Deontic Logic developed deontic STIT logics in which obligations are tied to agents' available choices rather than to static states of affairs, and used the resulting systems to analyse "ought implies can", contrary-to-duty obligations and deontic paradoxes. These works helped to position STIT at the intersection of action theory, temporal logic and deontic logic. From the late 1990s and 2000s onward, STIT logics were combined with epistemic, temporal and strategic modalities. Broersen introduced complete STIT logics for knowledge and action and deontic-epistemic STIT systems that distinguish different modes of mens rea, with applications to responsibility and the specification of multi-agent systems. Work on group and coalitional agency investigated axiomatisations and complexity results for group STIT logics, and related STIT-based analyses of agency to coalition logic and alternating-time temporal logic (ATL) by exhibiting formal embeddings between the frameworks. Explicit temporal operators were added to STIT in so-called temporal STIT logics. Lorini proposed a temporal STIT with "next" and "until" operators along histories and showed how it can be applied to normative reasoning about ongoing behaviour and commitments. Ciuni and Lorini compared different semantics for temporal STIT, clarifying the relationships between branching-time, game-based and epistemic approaches, while Boudou and Lorini gave a semantics for temporal STIT based on concurrent game structures, thus strengthening links with standard models of multi-agent interaction used for ATL and strategy logic. In parallel, complexity-theoretic work by Balbiani, Herzig and Troquard and by Schwarzentruber and co-authors investigated the satisfiability and model-checking problems for various STIT fragments, showing for instance that many expressive group STIT logics are undecidable or of high computational complexity. In the 2010s, STIT ideas were combined with justification logic, imagination operators and refined deontic notions. Justification STIT logics, developed by Olkhovikov and others, merge explicit justifications with STIT-style agency so that producing a proof can itself be treated as an action that brings about knowledge, and they come with completeness and decidability results. Olkhovikov and Wansing introduced STIT imagination logics, together with axiomatic systems and tableau calculi, to model acts of voluntary imagining and their role in doxastic control. Other authors have proposed STIT-based logics of responsibility, blameworthiness and intentionality for use in philosophical and AI settings. Xu's survey article "Combinations of STIT with Ought and Know" (2015) reviews many of these developments and emphasises the interplay between deontic and epistemic STIT logics. Current research on STIT focuses on proof theory, automated reasoning and richer expressive resources. Lyon and van Berkel, building on earlier work on labelled calculi for STIT, have developed cut-free sequent systems and proof-search algorithms that yield syntactic decision procedures for a range of deontic and non-deontic multi-agent STIT logics and support applications such as duty checking and compliance checking in autonomous systems. Sawasaki has proposed first-order cstit-based STIT logics that can distinguish de re and de dicto readings of agency statements and has proved strong completeness results for Hilbert systems over finite models, moving the STIT programme beyond the purely propositional level. Further work investigates interpreted-system and computationally grounded semantics for STIT and its extensions in order to model the behaviour of autonomous agents in multi-agent settings, and proposes STIT-based semantics for epistemic notions based on patterns of information disclosure in interactive systems. == Branching-time semantics == STIT logics are usually interpreted over branching-time models. A standard STIT frame consists of: a non-empty set of moments T {\displaystyle T} , partially ordered by < {\displaystyle <} so that ( T , < ) {\displaystyle (T,<)} forms a tree (every pair of moments with a common predecessor has a greatest lower bound); a set of histories, each history being a maximal linearly ordered subset of T {\displaystyle T} ; a non-empty set of agents A g {\displaystyle Ag} ; for each agent i ∈ A g {\displaystyle i\in Ag} and moment m {\displaystyle m} , a choice function c h o i c e i m {\displaystyle {\mathsf {choice}}_{i}^{m}} that partitions the set of histories passing through m {\displaystyle m} into choice cells. The idea is that a moment represents a time at which choices are made, and histories represent complete possible future courses of events. At each moment, each agent's choice corresponds to selecting one of the available cells of histories determined by their choice function. Formulas are evaluated at pairs ( m , h ) {\displaystyle (m,h)} of a moment and a history through that moment (sometimes written m / h {\displaystyle m/h} ). A valuation assigns truth-values to atomic propositions at such indices; Boolean connectives are interpreted pointwise as in Kripke-style modal logic. == Chellas and deliberative STIT operators == Several STIT operators have been distinguished in the literature. A common approach uses two closely related operators, often called Chellas STIT and deliberative STIT. Let H m {\displaystyle H_{m}} be the set of histories passing through a moment m {\displaystyle m} , and write H m {\displaystyle H_{m}} ⟦ φ ⟧ m = { h ∈ H m ∣ M , m / h ⊨ φ } {\displaystyle {\text{⟦}}\varphi {\text{⟧}}_{m}=\{h\in H_{m}\mid M,m/h\models \varphi \}} for the set of histories at m {\displaystyle m} where φ {\displaystyle \varphi } holds. The Chellas STIT operator, often written [ i c s t i t : φ ] {\displaystyle [i\ {\mathsf {cstit}}:\varphi ]} , is given by M , m / h ⊨ [ i c s t i t : φ ] iff c h o i c e i m ( h ) ⊆ ⟦ φ ⟧ m . {\displaystyle M,m/h\models [i\ {\mathsf {cstit}}:\varphi ]\quad {\text{iff}}\quad {\mathsf {choice}}_{i}^{m}(h)\subseteq {\text{⟦}}\varphi {\text{⟧}}_{m}.} Intuitively, agent i {\displaystyle i} sees to it that φ {\displaystyle \varphi } if φ {\displaystyle \varphi } holds at all histories compatible with their present choice. The deliberative STIT operator, [ i d s t i t : φ ] {\displaystyle [i\ {\mathsf {dstit}}:\varphi ]} , adds
Artificial Intelligence Applications Institute
The Artificial Intelligence Applications Institute (AIAI) at the School of Informatics at the University of Edinburgh is a non-profit technology transfer organisation that promoted research in the field of artificial intelligence. == History == The Artificial Intelligence Applications Institute (AIAI) was founded in 1983 at the University of Edinburgh as a specialist research and technology-transfer unit focusing on the practical uses of artificial intelligence (AI). The institute was established by Professor Jim Howe and colleagues from the Science and Engineering Research Council (SERC) Special Interest Group in AI in the Department of Artificial Intelligence, with a mission to apply AI techniques to solve real-world industrial and governmental problems. Under the directorship of Austin Tate, who served from 1985 to 2019, AIAI became one of the leading UK research centres devoted to AI programming systems, intelligent planning systems, decision support, and knowledge-based engineering. It collaborated with both academic partners and international organisations such as the European Space Agency and the UK Ministry of Defence. In 2001, AIAI joined the newly created Centre for Intelligent Systems and their Applications (CISA) within the University's School of Informatics. In December 2019, the institute was renamed the Artificial Intelligence and its Applications Institute to reflect a broader integration of fundamental and applied AI research. == Research programmes == AIAI’s research spans multiple areas of artificial intelligence, including: AI programming Systems - Edinburgh Prolog, Edinburgh Common Lisp, Logo; Knowledge representation and reasoning – development of ontologies, rule-based inference, and semantic modelling; Automated planning and scheduling – intelligent task management systems used in aerospace, manufacturing, and emergency response; Natural language processing and intelligent agents – interaction frameworks for human–computer collaboration; AI ethics and decision-making – research into responsible deployment and evaluation of autonomous systems. The institute also contributes to interdisciplinary fields such as computational creativity, explainable AI, and human–AI interaction. AIAI maintains close collaboration with the Bayes Centre and the Alan Turing Institute through joint research programmes and doctoral training initiatives. == Technology transfer and impact == From its inception, AIAI has combined academic research with technology-transfer activity, offering professional training, industrial consultancy, and bespoke software systems. It pioneered one of the earliest knowledge-based project-management systems, O-Plan, later evolved into the I-Plan framework used for autonomous planning and workflow management.
Visual Peer Review
== Development and history == Visual Peer Review was first described in a 2017 classroom study by Friedman and Rosen, which examined how students evaluate peer-produced data visualizations using structured rubrics. Developed within the broader fields of data visualization, information visualization, and educational technology, the system emphasized clear labeling, visual integrity, and reduction of chartjunk. Students assigned rubric scores and provided written explanations, aligning the activity with established principles of peer review. Follow-up research expanded both the methodological and analytic dimensions of the framework. Friedman and colleagues applied natural language processing (NLP) to peer-review text to analyze part-of-speech patterns, sentence complexity, and comment length. These analyses offered insight into how students expressed critique and engaged with core design principles. Later studies incorporated advanced statistical modeling to evaluate system-level behavior, including peer review networks and reviewer typologies. Between 2021 and 2024, the framework underwent iterative refinement through a series of studies that explored interface design, behavioral nudges, reviewer engagement, and social network dynamics. The system was influenced by earlier work in computer-supported peer review—particularly My Reviewers, a rubric-based writing assessment platform developed by Joe Moxley at the University of South Florida. While Moxley's platform focused on text-based feedback, Visual Peer Review adapted its core structure to support critique of DataVis and visual analytics. To guide structured analysis and feedback, Friedman and Rosen also drew on the “what, why, and how” framework introduced by Liu and Stasko (2010), which emphasizes understanding a visualization's purpose, task alignment, and encoding strategy. == Framework and components == Visual Peer Review is designed to support critique, reflection, and learning in courses focusing on data visualization, visual analytics, and related fields in educational technology. The system consists of interconnected component. Core components include: Visual Artifacts: Students generate original visualizations using software such as R (e.g., ggplot2), Tableau, Python, or Adobe Illustrator. These artifacts may include statistical graphics, dashboards, or design-oriented infographics. Rubric-Based Assessment: Peer reviewers evaluate submitted visualizations using structured rubrics grounded in visualization theory and design heuristics. Rubric dimensions typically include: Use of labeling and axis scales Minimalization of chartjunk and clutter (following Tufte's principles) Optimization of the data–ink ratio Preservation of visual integrity through accurate representation (lie factor) Written Peer Comments: In addition to scoring, reviewers provide narrative feedback explaining their reasoning. These comments aim to improve design literacy, strengthen visual reasoning, and support the learning process common to peer review across educational contexts. Instructor Analytics Dashboard: Instructors access an analytics dashboard that displays peer-review activity across the course. Metrics include comment length, rubric coverage, participation patterns, and potential indicators of disengagement. These features position the framework within the domain of learning analytics, where visualized data helps instructors monitor student progress and identify support needs. == Ongoing development == Current work focuses on enhancing rubric structure, integrating principles from human–computer interaction, DataVis and expanding learning-analytics capabilities. Ongoing studies investigate how interface design, reviewer behavior, and classroom context influence the quality of feedback and overall engagement. Continuing development positions Visual Peer Review at the intersection of data visualization education, peer assessment, and educational technology.
List of information schools
This list of information schools, sometimes abbreviated to iSchools, includes members of the iSchools organization. The iSchools organization reflects a consortium of over 130 information schools across the globe. == History == The first iSchools Caucus was formed in 1988 by Syracuse, Pittsburgh, and Drexel and was called the Gang of Three (sometimes gang of four with Rutgers). Syracuse renamed the School of Library Science as the School of Information Studies in 1974, and is considered as the first “iSchool” in history. The group was formally named "the iSchools Caucus" or more casually, the iCaucus. By 2003, the group expanded to include the Universities of Michigan, Washington, Illinois, UNC, Florida State, Indiana, and Texas, and was called the Gang of Ten. The current iSchools Caucus organization was formalized by 2005, with additions of UC Berkeley, UC Irvine, UCLA, Penn State, Georgia Tech, Maryland, Toronto, Carnegie Mellon and Singapore Management University. == iSchools organization == The iSchools promote an interdisciplinary approach to understanding the opportunities and challenges of information management, with a core commitment to concepts like universal access and user-centered organization of information. The field is concerned broadly with questions of design and preservation across information spaces, from digital and virtual spaces such as online communities, social networking, the World Wide Web, and databases to physical spaces such as libraries, museums, collections, and other repositories. "School of Information", "Department of Information Studies", or "Information Department" are often the names of the participating organizations. Degree programs at iSchools include course offerings in areas such as information architecture, design, policy, and economics; knowledge management, user experience design, and usability; preservation and conservation; librarianship and library administration; the sociology of information; and human-computer interaction and computer science. === Leadership === The executive committee of the iSchools is made up of the current chair (Ina Fourie, University of Pretoria, South Africa), past chair (Gillian Oliver, Monash University, Australia) and the chair elect (Javed Mostafa, University of Toronto Canada), plus representatives from the three regions (North America, Europe, and Asia-Pacific). The current executive director is Slava Sterzer. == Member institutions == Between 2010 and 2026, the organization expanded globally beyond North America, growing to 133 member schools as of March 2026. For an updated and complete list of member schools, please visit the member database of the iSchools. == iConferences == Members of the iSchools organize a regular academic conference, known as the iConference, hosted by a different member institution each year. September 2005: Pennsylvania State University October 2006: University of Michigan February 2008: University of California, Los Angeles February 2009: University of North Carolina February 2010: University of Illinois at Urbana-Champaign February 2011: University of Washington, Seattle February 2012: University of Toronto February 2013: University of North Texas March 2014: Humboldt-Universität zu Berlin March 2015: University of California, Irvine March 2016: Drexel University March 2017: Wuhan University March 2018: University of Sheffield and Northumbria University March 2019: University of Maryland March 2020: University of Borås (virtual only) March 2021: Renmin University of China (virtual only) February/March 2022: University of Texas at Austin, University College Dublin & Kyushu University (virtual only) March 2023: Universitat Oberta de Catalunya March 2024: Jilin University March 2025: Indiana University March/April 2026: Edinburgh Napier University 2027: Victoria University of Wellington == Other schools of information == Other information schools and programs include: Documentation Research and Training Centre, Indian Statistical Institute, Bangalore San Jose State University, School of Information University of Southern California Library Science Degree Ankara University, Department of Information and Records Management, Ankara/Turkey Marmara University, Department of Information and Records Management, Istanbul/Turkey University of Kelaniya, Department of Library and Information Science, Kelaniya/Sri Lanka University of Colombo, National Institute of Library and Information Science (NILIS), Colombo/Sri Lanka Chicago State University, Department of Information Studies
Microsoft Sway
Microsoft Sway is a presentation program and is part of the Microsoft 365 family of products. Sway was offered for general release by Microsoft in August 2015. It allows users who have a Microsoft account to combine text and media to create a presentable website. Users can pull content locally from the device in use, or from internet sources such as Bing, Facebook, OneDrive, and YouTube. Sway is distinguished from Microsoft FrontPage and Microsoft Expression Web – unrelated web design programs previously developed by Microsoft – in that Sway includes a method for hosting sites. Sway sites are stored on Microsoft's servers and are tied to the user's Microsoft account. They can be viewed and edited from any web browser through Office on the web. There is no offline editing or viewing function, but sites can be accessed using the app for Windows, and formerly iOS. == History == Sway was developed internally by Microsoft. In late 2014, the company announced an invite-only preview version of Sway and announced that Sway would not require an Office 365 subscription. An iOS app was released as a preview on 31 October 2014, but was discontinued on 17 December 2018 due to low usage. As of July 17, 2021, the Sway iOS app's discontinuance in 2018 was the last piece of news posted in the Sway tech blog. The Sway feature blog has not received an update since April 2017. The Microsoft Office Roadmap did not include any items related to Sway ever since. The iOS application is no longer under active development, and is not available for download. Since 2023, Microsoft has been consolidating the domains of its Microsoft 365 apps and services under cloud.microsoft. By 2025, the vast majority of services, including Sway, have already migrated to the cloud.microsoft domain. == Features == Users are able to add content from various sources into their Sway presentations. Some of the integrated services are owned by Microsoft, including OneNote, Bing, and other Sway sites. The program also provides native integration with other services, including YouTube, Facebook, Twitter, Mixcloud, and Infogram.
Document capture software
Document capture software refers to applications that provide the ability and feature set to automate the process of scanning paper documents or importing electronic documents, often for the purposes of feeding advanced document classification and data collection processes. Most scanning hardware, both scanners and copiers, provides the basic ability to scan to any number of image file formats, including: PDF, TIFF, JPG, BMP, etc. This basic functionality is augmented by document capture software, which can add efficiency and standardization to the process. == Typical features == Typical features of Document Capture Software include: Barcode recognition Patch Code recognition Separation Optical Character Recognition (OCR) Optical Mark Recognition (OMR) Quality Assurance Indexing Migration === Goal for implementation of a document capture solution === The goal for implementing a document capture solution is to reduce the amount of time spent scanning, separating, enhancing, organizing, classifying, normalizing, and collecting information from document collections, and to produce metadata along with an image/PDF file, and/or OCR text. This information is then migrated to a file share, FTP site, database, Document Management or Enterprise Content Management system. These systems often provide a search function, allowing search of the assets based on the produced metadata, and then viewed using document imaging software. == General document capture system solutions == === Integration with document management system === ECM (Enterprise Content management) and their DMS component (Document Management System) are being adopted by many organizations as a corporate document management system for all types of electronic files, e.g. MS word, PDF ... However, much of the information held by organisations is on paper and this needs to be integrated within the same document repository. By converting paper documents into digital format through scanning, organizations convert paper into image formats such as TIF, JPG, and PDF, and also extract valuable index information or business data from the document using OCR technology. Digital documents and associated metadata can easily be stored in the ECM in a variety of formats. The most popular of these formats is PDF which not only provides an accurate representation of the document but also allows all the OCR text in the document to be stored behind the PDF image. This format is known as PDF with hidden text or text-searchable PDF. This allows users to search for documents by using keywords in the metadata fields or by searching the content of PDF files across the repository. ==== Advantages of scanning documents into a ECM/DMS ==== Information held on paper is usually just as valuable to organisations as the electronic documents that are generated internally. Often this information represents a large proportion of the day to day correspondence with suppliers and customers. Having the ability to manage and share this information internally through a document management system such as SharePoint or a CMIS-compatible repository improves collaboration between departments or employees and also eliminates the risk of losing this information through disasters such as floods or fire. Organisations adopting an ECM/DMS often implement electronic workflow which allows the information held on paper to be included as part of an electronic business process and incorporated into a customer record file along with other associated office documents and emails. For business critical documents, such as purchase orders and supplier invoices, digitising documents helps speed up business transactions as well as reduce manual effort involved in keying data into business systems, such as CRM, ERP and Accounting. Scanned invoices can also be routed to managers for payment approval via email or an electronic workflow. == Electronic document capture == In the earlier implementations of Document Capture Software, the technology focused solely on the digitization and capture of information from paper documents. Document images were acquired from document scanners via TWAIN/ISIS drivers. Only image-based file formats like TIF, JPG, and BMP were typically compatible with these solutions. But in recent years, as the volume of electronically-created documents and the number of proprietary file formats continues to increase at exponential rates, the need for handling documents existing in electronic formats has grown. The relevant document capture products have adapted to function with non-image file formats with the end-goal of creating a unified processing workflow capable of handling all incoming documents The ability to import files from a variety of sources is one example of such adaptation. Importing documents from ECM/DMS software solutions, email servers, FTP, and EDI is now as much of a requirement of document capture software as is paper capture. The normalization of output files to text-based PDF format is now another critical factor in long-term archival of proprietary electronic file formats. Normalization expands access and usage of files to users throughout the enterprise, rather than only those that created the original electronic file.