AI Headshot Make

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  Fluency Voice Technology

  Fluency Voice Technology was a company that developed and sold packaged speech recognition solutions for use in call centers. Fluency's Speech Recognition solutions are used by call centers worldwide to improve customer service and significantly reduce costs and are available on-premises and hosted. == History == 1998 – Fluency was created as a spin-off from the Voice Research & Development team of a company called netdecisions. This R&D operation was established in Cambridge UK. The focus of the development was speech recognition systems based on the VXML standard. 2001 – Fluency became a separate entity in May 2001. Fluency began the creation of a software development platform specifically aimed at automating call center activities. This platform became Fluency's VoiceRunner. 2002 to 2004 – Fluency establishes accomplishes many successful deployments in customer sites such as National Express and Barclaycard. 2003 – Fluency expanded into the USA. Fluency also acquires Vocalis of Cambridge, UK in August 2003. 2004 – Fluency receives £6 million investment from leading European Venture Capitalists and establishes a global OEM partnership with Avaya, and the acquisition of SRC Telecom. 2008 – Fluency is acquired by Syntellect Ltd == Customers == Call Centers around the world use Fluency to improve service and reduce costs. They include Travelodge, Standard Life Bank, Sutton and East Surrey Water, Pizza Hut, CWT, Barclays, Powergen, First Choice, OutRight, J D Williams, Capital Blue Cross, Chelsea Building Society, EDF, bss, TV Licensing and Capita Software Services.

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  Enumeration algorithm

  In computer science, an enumeration algorithm is an algorithm that enumerates the answers to a computational problem. Formally, such an algorithm applies to problems that take an input and produce a list of solutions, similarly to function problems. For each input, the enumeration algorithm must produce the list of all solutions, without duplicates, and then halt. The performance of an enumeration algorithm is measured in terms of the time required to produce the solutions, either in terms of the total time required to produce all solutions, or in terms of the maximal delay between two consecutive solutions and in terms of a preprocessing time, counted as the time before outputting the first solution. This complexity can be expressed in terms of the size of the input, the size of each individual output, or the total size of the set of all outputs, similarly to what is done with output-sensitive algorithms. == Formal definitions == An enumeration problem P {\displaystyle P} is defined as a relation R {\displaystyle R} over strings of an arbitrary alphabet Σ {\displaystyle \Sigma } : R ⊆ Σ ∗ × Σ ∗ {\displaystyle R\subseteq \Sigma ^{}\times \Sigma ^{}} An algorithm solves P {\displaystyle P} if for every input x {\displaystyle x} the algorithm produces the (possibly infinite) sequence y {\displaystyle y} such that y {\displaystyle y} has no duplicate and z ∈ y {\displaystyle z\in y} if and only if ( x , z ) ∈ R {\displaystyle (x,z)\in R} . The algorithm should halt if the sequence y {\displaystyle y} is finite. == Common complexity classes == Enumeration problems have been studied in the context of computational complexity theory, and several complexity classes have been introduced for such problems. A very general such class is EnumP, the class of problems for which the correctness of a possible output can be checked in polynomial time in the input and output. Formally, for such a problem, there must exist an algorithm A which takes as input the problem input x, the candidate output y, and solves the decision problem of whether y is a correct output for the input x, in polynomial time in x and y. For instance, this class contains all problems that amount to enumerating the witnesses of a problem in the class NP. Other classes that have been defined include the following. In the case of problems that are also in EnumP, these problems are ordered from least to most specific: Output polynomial, the class of problems whose complete output can be computed in polynomial time. Incremental polynomial time, the class of problems where, for all i, the i-th output can be produced in polynomial time in the input size and in the number i. Polynomial delay, the class of problems where the delay between two consecutive outputs is polynomial in the input (and independent from the output). Strongly polynomial delay, the class of problems where the delay before each output is polynomial in the size of this specific output (and independent from the input or from the other outputs). The preprocessing is generally assumed to be polynomial. Constant delay, the class of problems where the delay before each output is constant, i.e., independent from the input and output. The preprocessing phase is generally assumed to be polynomial in the input. == Common techniques == Backtracking: The simplest way to enumerate all solutions is by systematically exploring the space of possible results (partitioning it at each successive step). However, performing this may not give good guarantees on the delay, i.e., a backtracking algorithm may spend a long time exploring parts of the space of possible results that do not give rise to a full solution. Flashlight search: This technique improves on backtracking by exploring the space of all possible solutions but solving at each step the problem of whether the current partial solution can be extended to a partial solution. If the answer is no, then the algorithm can immediately backtrack and avoid wasting time, which makes it easier to show guarantees on the delay between any two complete solutions. In particular, this technique applies well to self-reducible problems. Closure under set operations: If we wish to enumerate the disjoint union of two sets, then we can solve the problem by enumerating the first set and then the second set. If the union is non disjoint but the sets can be enumerated in sorted order, then the enumeration can be performed in parallel on both sets while eliminating duplicates on the fly. If the union is not disjoint and both sets are not sorted then duplicates can be eliminated at the expense of a higher memory usage, e.g., using a hash table. Likewise, the cartesian product of two sets can be enumerated efficiently by enumerating one set and joining each result with all results obtained when enumerating the second step. == Examples of enumeration problems == The vertex enumeration problem, where we are given a polytope described as a system of linear inequalities and we must enumerate the vertices of the polytope. Enumerating the minimal transversals of a hypergraph. This problem is related to monotone dualization and is connected to many applications in database theory and graph theory. Enumerating the answers to a database query, for instance a conjunctive query or a query expressed in monadic second-order. There have been characterizations in database theory of which conjunctive queries could be enumerated with linear preprocessing and constant delay. The problem of enumerating maximal cliques in an input graph, e.g., with the Bron–Kerbosch algorithm Listing all elements of structures such as matroids and greedoids Several problems on graphs, e.g., enumerating independent sets, paths, cuts, etc. Enumerating the satisfying assignments of representations of Boolean functions, e.g., a Boolean formula written in conjunctive normal form or disjunctive normal form, a binary decision diagram such as an OBDD, or a Boolean circuit in restricted classes studied in knowledge compilation, e.g., NNF. == Connection to computability theory == The notion of enumeration algorithms is also used in the field of computability theory to define some high complexity classes such as RE, the class of all recursively enumerable problems. This is the class of sets for which there exist an enumeration algorithm that will produce all elements of the set: the algorithm may run forever if the set is infinite, but each solution must be produced by the algorithm after a finite time.

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  Information flow

  In discourse-based grammatical theory, information flow is any tracking of referential information by speakers. Information may be new, i.e., just introduced into the conversation; given, i.e., already active in the speakers' consciousness; or old, i.e., no longer active. The various types of activation, and how these are defined, are model-dependent. Information flow affects grammatical structures such as: Word order (topic, focus, and afterthought constructions). Active, passive, or middle voice. Choice of deixis, such as articles; "medial" deictics such as Spanish ese and Japanese sore are generally determined by the familiarity of a referent rather than by physical distance. Overtness of information, such as whether an argument of a verb is indicated by a lexical noun phrase, a pronoun, or not mentioned at all. Clefting: Splitting a single clause into two clauses, each with its own verb, e.g. ‘The chicken turtles tasted like chicken.’ becomes ‘It was the chicken turtle | that tasted like chicken.’ In this case, clefting is used to shift the focus of the sentence to the subject, the chicken turtle. Front focus: Placing at the start (front) of a sentence information that would normally occur later in the sentence, to give it extra prominence. For example, in pop culture, Yoda's speech often utilizes such syntactic construction, such as when he says 'much to learn you still have' to Luke Skywalker. End focus (or end weight): Given or familiar information followed by new information. This gives prominence to the final part of the sentences and can enable suspense to build, e.g. ‘Through the door came a gigantic wolf’.(Umer Prince)

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  MarkLogic Server

  MarkLogic Server is a document-oriented database developed by MarkLogic. It is a NoSQL multi-model database that evolved from an XML database to natively store JSON documents and RDF triples, the data model for semantics. MarkLogic is designed to be a data hub for operational and analytical data. == History == MarkLogic Server was built to address shortcomings with existing search and data products. The product first focused on using XML as the document markup standard and XQuery as the query standard for accessing collections of documents up to hundreds of terabytes in size. Currently the MarkLogic platform is widely used in publishing, government, finance and other sectors. MarkLogic's customers are mostly Global 2000 companies. == Technology == MarkLogic uses documents without upfront schemas to maintain a flexible data model. In addition to having a flexible data model, MarkLogic uses a distributed, scale-out architecture that can handle hundreds of billions of documents and hundreds of terabytes of data. It has received Common Criteria certification, and has high availability and disaster recovery. MarkLogic is designed to run on-premises and within public or private cloud environments like Amazon Web Services. == Features == Indexing MarkLogic indexes the content and structure of documents including words, phrases, relationships, and values in over 200 languages with tokenization, collation, and stemming for core languages. Functionality includes the ability to toggle range indexes, geospatial indexes, the RDF triple index, and reverse indexes on or off based on your data, the kinds of queries that you will run, and your desired performance. Full-text search MarkLogic supports search across its data and metadata using a word or phrase and incorporates Boolean logic, stemming, wildcards, case sensitivity, punctuation sensitivity, diacritic sensitivity, and search term weighting. Data can be searched using JavaScript, XQuery, SPARQL, and SQL. Semantics MarkLogic uses RDF triples to provide semantics for ease of storing metadata and querying. ACID Unlike other NoSQL databases, MarkLogic maintains ACID consistency for transactions. Replication MarkLogic provides high availability with replica sets. Scalability MarkLogic scales horizontally using sharding. MarkLogic can run over multiple servers, balancing the load or replicating data to keep the system up and running in the event of hardware failure. Security MarkLogic has built in security features such as element-level permissions and data redaction. Optic API for Relational Operations An API that lets developers view their data as documents, graphs or rows. Security MarkLogic provides redaction, encryption, and element-level security (allowing for control on read and write rights on parts of a document). == Applications == Banking Big Data Fraud prevention Insurance Claims Management and Underwriting Master data management Recommendation engines == Licensing == MarkLogic is available under various licensing and delivery models, namely a free Developer or an Essential Enterprise license.[3] Licenses are available from MarkLogic or directly from cloud marketplaces such as Amazon Web Services and Microsoft Azure. == Releases == 2001 – Cerisent XQE 1: ACID transactions, Full-text search, XML Storage, XQuery, Role-based security 2004 – Cerisent XQE 2: Scale-out architecture, Enhanced search (stemming, thesaurus, wildcard), Backup and restore 2005 – MarkLogic Server 3: Continuing search improvements, Content Processing Framework (including PDF, Word, Excel, PPT), Failover 2008 – MarkLogic Server 4: Geospatial search, entity extraction, advanced XQuery, performance, scalability enhancements, auditing 2011 – MarkLogic Server 5: Flexible replication / DDIL, real-time indexing, advanced search, improved analytics, concurrency enhancements 2012 – MarkLogic Server 6: REST and Java APIs, App Builder, enhanced UI, improved search 2013 – MarkLogic Server 7: Semantic graph, bitemporal data, tiered storage, improved search, better management 2015 – MarkLogic Server 8: A Native JSON storage, Server-side JavaScript, Bitemporal, Node.js client API, Incremental backup, Flexible replication[16] 2017 – MarkLogic Server 9: Data integration across Relational and Non-Relational data, Advanced Encryption, Element Level Security, Redaction 2019 – MarkLogic Server 10: Enhanced Data Hub, improved SQL, security, analytics performance, cloud support 2022 – MarkLogic Server 11: MarkLogic Ops Director (Monitoring and Administration Improvements), expanded PKI 2025 – MarkLogic Server 12: Generative AI and Native Vector Search, Graph Algorithm Support, Virtual TDEs (relational views on the fly)

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  Integrated writing environment

  An integrated writing environment (IWE) is software that provides comprehensive writing and knowledge management functionality for writers and information workers. IWEs enable writers and information workers to perform a variety of tasks related to the document in the IWE in a single environment. This provides a distraction-free workspace and streamlined writing experience. IWEs provide similar efficiency and functionality benefits to writers and information professionals that integrated development environments (IDEs) provide to software developers. == Overview == IWEs are designed to maximize productivity and help improve the quality of written work by integrating together tools that allow users to work effectively in a single application. The IWE features may include integrated content search, reversion management, outlining, note management, and reference management, as may be suitable for the target field of use. == List of IWEs == Celtx This IWE is intended for screenplay writers and has screenplay writing and management tools. Celtex provides tools for the pre-production work phase, story development, storyboarding, script breakdowns, production scheduling, and reports. Scrivener This IWE targets novel, research paper, and script writing. Scrivener provides tools to organize notes and research documents for easy access and referencing. After completing the writing, Scrivener allows the user to export the document to formats supported by common word processors, such as Microsoft Word. TeXstudio This IWE targets LaTeX documents and provides interactive spelling checker, code folding, and syntax highlighting.

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  Time Warp Edit Distance

  In the data analysis of time series, Time Warp Edit Distance (TWED) is a measure of similarity (or dissimilarity) between pairs of discrete time series, controlling the relative distortion of the time units of the two series using the physical notion of elasticity. In comparison to other distance measures, (e.g. DTW (dynamic time warping) or LCS (longest common subsequence problem)), TWED is a metric. Its computational time complexity is O ( n 2 ) {\displaystyle O(n^{2})} , but can be drastically reduced in some specific situations by using a corridor to reduce the search space. Its memory space complexity can be reduced to O ( n ) {\displaystyle O(n)} . It was first proposed in 2009 by P.-F. Marteau. == Definition == δ λ , ν ( A 1 p , B 1 q ) = M i n { δ λ , ν ( A 1 p − 1 , B 1 q ) + Γ ( a p ′ → Λ ) d e l e t e i n A δ λ , ν ( A 1 p − 1 , B 1 q − 1 ) + Γ ( a p ′ → b q ′ ) m a t c h o r s u b s t i t u t i o n δ λ , ν ( A 1 p , B 1 q − 1 ) + Γ ( Λ → b q ′ ) d e l e t e i n B {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{p},B_{1}^{q})=Min{\begin{cases}\delta _{\lambda ,\nu }(A_{1}^{p-1},B_{1}^{q})+\Gamma (a_{p}^{'}\to \Lambda )&{\rm {delete\ in\ A}}\\\delta _{\lambda ,\nu }(A_{1}^{p-1},B_{1}^{q-1})+\Gamma (a_{p}^{'}\to b_{q}^{'})&{\rm {match\ or\ substitution}}\\\delta _{\lambda ,\nu }(A_{1}^{p},B_{1}^{q-1})+\Gamma (\Lambda \to b_{q}^{'})&{\rm {delete\ in\ B}}\end{cases}}} whereas Γ ( α p ′ → Λ ) = d L P ( a p ′ , a p − 1 ′ ) + ν ⋅ ( t a p − t a p − 1 ) + λ {\displaystyle \Gamma (\alpha _{p}^{'}\to \Lambda )=d_{LP}(a_{p}^{'},a_{p-1}^{'})+\nu \cdot (t_{a_{p}}-t_{a_{p-1}})+\lambda } Γ ( α p ′ → b q ′ ) = d L P ( a p ′ , b q ′ ) + d L P ( a p − 1 ′ , b q − 1 ′ ) + ν ⋅ ( | t a p − t b q | + | t a p − 1 − t b q − 1 | ) {\displaystyle \Gamma (\alpha _{p}^{'}\to b_{q}^{'})=d_{LP}(a_{p}^{'},b_{q}^{'})+d_{LP}(a_{p-1}^{'},b_{q-1}^{'})+\nu \cdot (|t_{a_{p}}-t_{b_{q}}|+|t_{a_{p-1}}-t_{b_{q-1}}|)} Γ ( Λ → b q ′ ) = d L P ( b p ′ , b p − 1 ′ ) + ν ⋅ ( t b q − t b q − 1 ) + λ {\displaystyle \Gamma (\Lambda \to b_{q}^{'})=d_{LP}(b_{p}^{'},b_{p-1}^{'})+\nu \cdot (t_{b_{q}}-t_{b_{q-1}})+\lambda } Whereas the recursion δ λ , ν {\displaystyle \delta _{\lambda ,\nu }} is initialized as: δ λ , ν ( A 1 0 , B 1 0 ) = 0 , {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{0},B_{1}^{0})=0,} δ λ , ν ( A 1 0 , B 1 j ) = ∞ f o r j ≥ 1 {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{0},B_{1}^{j})=\infty \ {\rm {{for\ }j\geq 1}}} δ λ , ν ( A 1 i , B 1 0 ) = ∞ f o r i ≥ 1 {\displaystyle \delta _{\lambda ,\nu }(A_{1}^{i},B_{1}^{0})=\infty \ {\rm {{for\ }i\geq 1}}} with a 0 ′ = b 0 ′ = 0 {\displaystyle a'_{0}=b'_{0}=0} === Implementations === An implementation of the TWED algorithm in C with a Python wrapper is available at TWED is also implemented into the Time Series Subsequence Search Python package (TSSEARCH for short) available at [1]. An R implementation of TWED has been integrated into the TraMineR, a R package for mining, describing and visualizing sequences of states or events, and more generally discrete sequence data. Additionally, cuTWED is a CUDA- accelerated implementation of TWED which uses an improved algorithm due to G. Wright (2020). This method is linear in memory and massively parallelized. cuTWED is written in CUDA C/C++, comes with Python bindings, and also includes Python bindings for Marteau's reference C implementation. ==== Python ==== Backtracking, to find the most cost-efficient path: ==== MATLAB ==== Backtracking, to find the most cost-efficient path:

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  Single customer view

  A single customer view is an aggregated, consistent and holistic representation of the data held by an organisation about its customers that can be viewed in one place, such as a single page. The advantage to an organisation of attaining this unified view comes from the ability it gives to analyse past behaviour in order to better target and personalise future customer interactions. A single customer view is also considered especially relevant where organisations engage with customers through multichannel marketing, since customers expect those interactions to reflect a consistent understanding of their history and preferences. However, some commentators have challenged the idea that a single view of customers across an entire organisation is either natural or meaningful, proposing that the priority should instead be consistency between the multiple views that arise in different contexts. Where representations of a customer are held in more than one data set, achieving a single customer view can be difficult: firstly because customer identity must be traceable between the records held in those systems, and secondly because anomalies or discrepancies in the customer data must be data cleansed for data quality. As such, the acquisition by an organisation of a single customer view is one potential outcome of successful master data management. Since 31 December, 2010, maintaining a single customer view, and submitting it within 72 hours, has become mandatory for financial institutions in the United Kingdom due to new rules introduced by the Financial Services Compensation Scheme.

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  Lancichinetti–Fortunato–Radicchi benchmark

  Lancichinetti–Fortunato–Radicchi benchmark is an algorithm that generates benchmark networks (artificial networks that resemble real-world networks). They have a priori known communities and are used to compare different community detection methods. The advantage of the benchmark over other methods is that it accounts for the heterogeneity in the distributions of node degrees and of community sizes. == The algorithm == The node degrees and the community sizes are distributed according to a power law, with different exponents. The benchmark assumes that both the degree and the community size have power law distributions with different exponents, γ {\displaystyle \gamma } and β {\displaystyle \beta } , respectively. N {\displaystyle N} is the number of nodes and the average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . There is a mixing parameter μ {\displaystyle \mu } , which is the average fraction of neighboring nodes of a node that do not belong to any community that the benchmark node belongs to. This parameter controls the fraction of edges that are between communities. Thus, it reflects the amount of noise in the network. At the extremes, when μ = 0 {\displaystyle \mu =0} all links are within community links, if μ = 1 {\displaystyle \mu =1} all links are between nodes belonging to different communities. One can generate the benchmark network using the following steps. Step 1: Generate a network with nodes following a power law distribution with exponent γ {\displaystyle \gamma } and choose extremes of the distribution k min {\displaystyle k_{\min }} and k max {\displaystyle k_{\max }} to get desired average degree is ⟨ k ⟩ {\displaystyle \langle k\rangle } . Step 2: ( 1 − μ ) {\displaystyle (1-\mu )} fraction of links of every node is with nodes of the same community, while fraction μ {\displaystyle \mu } is with the other nodes. Step 3: Generate community sizes from a power law distribution with exponent β {\displaystyle \beta } . The sum of all sizes must be equal to N {\displaystyle N} . The minimal and maximal community sizes s min {\displaystyle s_{\min }} and s max {\displaystyle s_{\max }} must satisfy the definition of community so that every non-isolated node is in at least in one community: s min > k min {\displaystyle s_{\min }>k_{\min }} s max > k max {\displaystyle s_{\max }>k_{\max }} Step 4: Initially, no nodes are assigned to communities. Then, each node is randomly assigned to a community. As long as the number of neighboring nodes within the community does not exceed the community size a new node is added to the community, otherwise stays out. In the following iterations the “homeless” node is randomly assigned to some community. If that community is complete, i.e. the size is exhausted, a randomly selected node of that community must be unlinked. Stop the iteration when all the communities are complete and all the nodes belong to at least one community. Step 5: Implement rewiring of nodes keeping the same node degrees but only affecting the fraction of internal and external links such that the number of links outside the community for each node is approximately equal to the mixing parameter μ {\displaystyle \mu } . == Testing == Consider a partition into communities that do not overlap. The communities of randomly chosen nodes in each iteration follow a p ( C ) {\displaystyle p(C)} distribution that represents the probability that a randomly picked node is from the community C {\displaystyle C} . Consider a partition of the same network that was predicted by some community finding algorithm and has p ( C 2 ) {\displaystyle p(C_{2})} distribution. The benchmark partition has p ( C 1 ) {\displaystyle p(C_{1})} distribution. The joint distribution is p ( C 1 , C 2 ) {\displaystyle p(C_{1},C_{2})} . The similarity of these two partitions is captured by the normalized mutual information. I n = ∑ C 1 , C 2 p ( C 1 , C 2 ) log 2 ⁡ p ( C 1 , C 2 ) p ( C 1 ) p ( C 2 ) 1 2 H ( { p ( C 1 ) } ) + 1 2 H ( { p ( C 2 ) } ) {\displaystyle I_{n}={\frac {\sum _{C_{1},C_{2}}p(C_{1},C_{2})\log _{2}{\frac {p(C_{1},C_{2})}{p(C_{1})p(C_{2})}}}{{\frac {1}{2}}H(\{p(C_{1})\})+{\frac {1}{2}}H(\{p(C_{2})\})}}} If I n = 1 {\displaystyle I_{n}=1} the benchmark and the detected partitions are identical, and if I n = 0 {\displaystyle I_{n}=0} then they are independent of each other.

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  AdBlock

  AdBlock is an ad-blocking browser extension for Google Chrome, Apple Safari (desktop and mobile), Firefox, Samsung Internet, Microsoft Edge and Opera. AdBlock allows users to prevent page elements, such as advertisements, from being displayed. It is free to download and use, and it includes optional donations to the developers. The AdBlock extension was created on December 8, 2009, which is the day that supports for extensions was added to Google Chrome. It was one of the first Google Chrome extensions that was made. Since 2016, AdBlock has been based on the Adblock Plus source code. In July 2018, AdBlock acquired uBlock, a commercial ad-blocker owned by uBlock LLC and based on uBlock Origin. In April 2021, eyeo GmbH (developer of Adblock Plus) announced its purchase of AdBlock, Inc (formerly BetaFish, Inc). == Crowdfunding == Gundlach launched a crowdfunding campaign on Crowdtilt in August 2013 in order to fund an ad campaign to raise awareness of ad-blocking and to rent a billboard at Times Square. After the one-month campaign, it raised $55,000. == Sales and acceptable ads == AdBlock was sold to an anonymous buyer in 2015 and on October 15, 2015, Gundlach's name was taken down from the site. In the terms of the deal, the original developer Michael Gundlach left operations to Adblock's continuing director, Gabriel Cubbage, and as of October 2, 2015, AdBlock began participating in the Acceptable Ads program. Acceptable Ads identifies "non-annoying" ads, which AdBlock shows by default. The intent is to allow non-invasive advertising, to either maintain support for websites that rely on advertising as a main source of revenue or for websites that have an agreement with the program. == Filters == AdBlock uses EasyList, the same filter syntax as Adblock Plus for Firefox, and natively supports the use of a number of filter lists. == Partnership with Amnesty International == On March 12, 2016, in support of World Day Against Cyber Censorship, and in partnership with Amnesty International, instead of blocking ads, AdBlock replaced ads with banners linked to articles on Amnesty's website, written by prominent free speech advocates such as Edward Snowden, to raise awareness of government-imposed online censorship and digital privacy issues around the world. The campaign was met with both praise and criticism, with AdBlock's CEO, Gabriel Cubbage, defending the decision in an essay on AdBlock's website, saying "We’re showing you Amnesty banners, just for today, because we believe users should be part of the conversation about online privacy. Tomorrow, those spaces will be vacant again. But take a moment to consider that in an increasingly information-driven world, when your right to digital privacy is threatened, so is your right to free expression." Meanwhile, Simon Sharwood of The Register characterized Cubbage's position as "'You should control your computer except when we feel political', says AdBlock CEO". == AdBlock for Firefox == On September 13, 2014, the AdBlock team released a version for Firefox users, ported from the code for Google Chrome, released under the same free software license as the original Adblock. The extension was removed on April 2, 2015, by an administrator on Mozilla Add-ons. On December 7, 2015, the official AdBlock site's knowledge base article stated that with version 44 or higher of Firefox desktop and Firefox Mobile, AdBlock will not be supported. The last version of Adblock for those platforms will work on older versions of Firefox. AdBlock was released again on Mozilla Add-ons on November 17, 2016. On April 1, 2012, Adblock developer Michael Gundlach tweaked the code to display LOLcats instead of simply blocking ads. Initially developed as a short-lived April Fools joke, the response was so positive that CatBlock was continued to be offered as an optional add-on supported by a monthly subscription. On October 23, 2014, the developer decided to end official support for CatBlock, and made it open-source, under GPLv3 licensing, as the original extension.

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  Predictor–corrector method

  In numerical analysis, predictor–corrector methods belong to a class of algorithms designed to integrate ordinary differential equations – to find an unknown function that satisfies a given differential equation. All such algorithms proceed in two steps: The initial, "prediction" step, starts from a function fitted to the function-values and derivative-values at a preceding set of points to extrapolate ("anticipate") this function's value at a subsequent, new point. The next, "corrector" step refines the initial approximation by using the predicted value of the function and another method to interpolate that unknown function's value at the same subsequent point. == Predictor–corrector methods for solving ODEs == When considering the numerical solution of ordinary differential equations (ODEs), a predictor–corrector method typically uses an explicit method for the predictor step and an implicit method for the corrector step. === Example: Euler method with the trapezoidal rule === A simple predictor–corrector method (known as Heun's method) can be constructed from the Euler method (an explicit method) and the trapezoidal rule (an implicit method). Consider the differential equation y ′ = f ( t , y ) , y ( t 0 ) = y 0 , {\displaystyle y'=f(t,y),\quad y(t_{0})=y_{0},} and denote the step size by h {\displaystyle h} . First, the predictor step: starting from the current value y i {\displaystyle y_{i}} , calculate an initial guess value y ~ i + 1 {\displaystyle {\tilde {y}}_{i+1}} via the Euler method, y ~ i + 1 = y i + h f ( t i , y i ) . {\displaystyle {\tilde {y}}_{i+1}=y_{i}+hf(t_{i},y_{i}).} Next, the corrector step: improve the initial guess using trapezoidal rule, y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle y_{i+1}=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.} That value is used as the next step. === PEC mode and PECE mode === There are different variants of a predictor–corrector method, depending on how often the corrector method is applied. The Predict–Evaluate–Correct–Evaluate (PECE) mode refers to the variant in the above example: y ~ i + 1 = y i + h f ( t i , y i ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} It is also possible to evaluate the function f only once per step by using the method in Predict–Evaluate–Correct (PEC) mode: y ~ i + 1 = y i + h f ( t i , y ~ i ) , y i + 1 = y i + 1 2 h ( f ( t i , y ~ i ) + f ( t i + 1 , y ~ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},{\tilde {y}}_{i}),\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},{\tilde {y}}_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )}.\end{aligned}}} Additionally, the corrector step can be repeated in the hope that this achieves an even better approximation to the true solution. If the corrector method is run twice, this yields the PECECE mode: y ~ i + 1 = y i + h f ( t i , y i ) , y ^ i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ~ i + 1 ) ) , y i + 1 = y i + 1 2 h ( f ( t i , y i ) + f ( t i + 1 , y ^ i + 1 ) ) . {\displaystyle {\begin{aligned}{\tilde {y}}_{i+1}&=y_{i}+hf(t_{i},y_{i}),\\{\hat {y}}_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\tilde {y}}_{i+1}){\bigr )},\\y_{i+1}&=y_{i}+{\tfrac {1}{2}}h{\bigl (}f(t_{i},y_{i})+f(t_{i+1},{\hat {y}}_{i+1}){\bigr )}.\end{aligned}}} The PECEC mode has one fewer function evaluation than PECECE mode. More generally, if the corrector is run k times, the method is in P(EC)k or P(EC)kE mode. If the corrector method is iterated until it converges, this could be called PE(CE)∞.

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  Enterprise Objects Framework

  The Enterprise Objects Framework, or simply EOF, was introduced by NeXT in 1994 as a pioneering object-relational mapping product for its NeXTSTEP and OpenStep development platforms. EOF abstracts the process of interacting with a relational database by mapping database rows to Java or Objective-C objects. This largely relieves developers from writing low-level SQL code. EOF enjoyed some niche success in the mid-1990s among financial institutions who were attracted to the rapid application development advantages of NeXT's object-oriented platform. Since Apple Inc's merger with NeXT in 1996, EOF has evolved into a fully integrated part of WebObjects, an application server also originally from NeXT. Many of the core concepts of EOF re-emerged as part of Core Data, which further abstracts the underlying data formats to allow it to be based on non-SQL stores. == History == In the early 1990s NeXT Computer recognized that connecting to databases was essential to most businesses and yet also potentially complex. Every data source has a different data-access language (or API), driving up the costs to learn and use each vendor's product. The NeXT engineers wanted to apply the advantages of object-oriented programming, by getting objects to "talk" to relational databases. As the two technologies are very different, the solution was to create an abstraction layer, insulating developers from writing the low-level procedural code (SQL) specific to each data source. The first attempt came in 1992 with the release of Database Kit (DBKit), which wrapped an object-oriented framework around any database. Unfortunately, NEXTSTEP at the time was not powerful enough and DBKit had serious design flaws. NeXT's second attempt came in 1994 with the Enterprise Objects Framework (EOF) version 1, a complete rewrite that was far more modular and OpenStep compatible. EOF 1.0 was the first product released by NeXT using the Foundation Kit and introduced autoreleased objects to the developer community. The development team at the time was only four people: Jack Greenfield, Rich Williamson, Linus Upson and Dan Willhite. EOF 2.0, released in late 1995, further refined the architecture, introducing the editing context. At that point, the development team consisted of Dan Willhite, Craig Federighi, Eric Noyau and Charly Kleissner. EOF achieved a modest level of popularity in the financial programming community in the mid-1990s, but it would come into its own with the emergence of the World Wide Web and the concept of web applications. It was clear that EOF could help companies plug their legacy databases into the Web without any rewriting of that data. With the addition of frameworks to do state management, load balancing and dynamic HTML generation, NeXT was able to launch the first object-oriented Web application server, WebObjects, in 1996, with EOF at its core. In 2000, Apple Inc. (which had merged with NeXT) officially dropped EOF as a standalone product, meaning that developers would be unable to use it to create desktop applications for the forthcoming Mac OS X. It would, however, continue to be an integral part of a major new release of WebObjects. WebObjects 5, released in 2001, was significant for the fact that its frameworks had been ported from their native Objective-C programming language to the Java language. Critics of this change argue that most of the power of EOF was a side effect of its Objective-C roots, and that EOF lost the beauty or simplicity it once had. Third-party tools, such as EOGenerator, help fill the deficiencies introduced by Java (mainly due to the loss of categories). The Objective-C code base was re-introduced with some modifications to desktop application developers as Core Data, part of Apple's Cocoa API, with the release of Mac OS X Tiger in April 2005. == How EOF works == Enterprise Objects provides tools and frameworks for object-relational mapping. The technology specializes in providing mechanisms to retrieve data from various data sources, such as relational databases via JDBC and JNDI directories, and mechanisms to commit data back to those data sources. These mechanisms are designed in a layered, abstract approach that allows developers to think about data retrieval and commitment at a higher level than a specific data source or data source vendor. Central to this mapping is a model file (an "EOModel") that you build with a visual tool — either EOModeler, or the EOModeler plug-in to Xcode. The mapping works as follows: Database tables are mapped to classes. Database columns are mapped to class attributes. Database rows are mapped to objects (or class instances). You can build data models based on existing data sources or you can build data models from scratch, which you then use to create data structures (tables, columns, joins) in a data source. The result is that database records can be transposed into Java objects. The advantage of using data models is that applications are isolated from the idiosyncrasies of the data sources they access. This separation of an application's business logic from database logic allows developers to change the database an application accesses without needing to change the application. EOF provides a level of database transparency not seen in other tools and allows the same model to be used to access different vendor databases and even allows relationships across different vendor databases without changing source code. Its power comes from exposing the underlying data sources as managed graphs of persistent objects. In simple terms, this means that it organizes the application's model layer into a set of defined in-memory data objects. It then tracks changes to these objects and can reverse those changes on demand, such as when a user performs an undo command. Then, when it is time to save changes to the application's data, it archives the objects to the underlying data sources. === Using Inheritance === In designing Enterprise Objects developers can leverage the object-oriented feature known as inheritance. A Customer object and an Employee object, for example, might both inherit certain characteristics from a more generic Person object, such as name, address, and phone number. While this kind of thinking is inherent in object-oriented design, relational databases have no explicit support for inheritance. However, using Enterprise Objects, you can build data models that reflect object hierarchies. That is, you can design database tables to support inheritance by also designing enterprise objects that map to multiple tables or particular views of a database table. == Enterprise Objects (EOs) == An Enterprise Object is analogous to what is often known in object-oriented programming as a business object — a class which models a physical or conceptual object in the business domain (e.g. a customer, an order, an item, etc.). What makes an EO different from other objects is that its instance data maps to a data store. Typically, an enterprise object contains key-value pairs that represent a row in a relational database. The key is basically the column name, and the value is what was in that row in the database. So it can be said that an EO's properties persist beyond the life of any particular running application. More precisely, an Enterprise Object is an instance of a class that implements the com.webobjects.eocontrol.EOEnterpriseObject interface. An Enterprise Object has a corresponding model (called an EOModel) that defines the mapping between the class's object model and the database schema. However, an enterprise object doesn't explicitly know about its model. This level of abstraction means that database vendors can be switched without it affecting the developer's code. This gives Enterprise Objects a high degree of reusability. == EOF and Core Data == Despite their common origins, the two technologies diverged, with each technology retaining a subset of the features of the original Objective-C code base, while adding some new features. === Features Supported Only by EOF === EOF supports custom SQL; shared editing contexts; nested editing contexts; and pre-fetching and batch faulting of relationships, all features of the original Objective-C implementation not supported by Core Data. Core Data also does not provide the equivalent of an EOModelGroup—the NSManagedObjectModel class provides methods for merging models from existing models, and for retrieving merged models from bundles. === Features Supported Only by Core Data === Core Data supports fetched properties; multiple configurations within a managed object model; local stores; and store aggregation (the data for a given entity may be spread across multiple stores); customization and localization of property names and validation warnings; and the use of predicates for property validation. These features of the original Objective-C implementation are not supported by the Java implementation.

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  Discoverability

  Discoverability is the degree to which something, especially a piece of content or information, can be found in a search of a file, database, or other information system. Discoverability is a concern in library and information science, many aspects of digital media, software and web development, and in marketing, since products and services cannot be used if people cannot find it or do not understand what it can be used for. In human-computer interaction the term is further used to describe the discoverability of interactions, features and interactive systems overall . Metadata, or "information about information", such as a book's title, a product's description, or a website's keywords, affects how discoverable something is on a database or online. Adding metadata to a product that is available online can make it easier for end users to find the product. For example, if a song file is made available online, making the title, band name, genre, year of release, and other pertinent information available in connection with this song means the file can be retrieved more easily. The organization of information through the implementation of alphabetical structures or the integration of content into search engines exemplifies strategies employed to enhance the discoverability of information. The concept of discoverability, while related to but distinct from accessibility and usability, which are other qualities that affect the usefulness of a piece of information, is a critical aspect of information retrieval. == Etymology == The concept of "discoverability" in an information science and online context is a loose borrowing from the concept of the similar name in the legal profession. In law, "discovery" is a pre-trial procedure in a lawsuit in which each party, through the law of civil procedure, can obtain evidence from the other party or parties by means of discovery devices such as a request for answers to interrogatories, request for production of documents, request for admissions and depositions. Discovery can be obtained from non-parties using subpoenas. When a discovery request is objected to, the requesting party may seek the assistance of the court by filing a motion to compel discovery. == Purpose == The usability of any piece of information directly relates to how discoverable it is, either in a "walled garden" database or on the open Internet. The quality of information available on this database or on the Internet depends upon the quality of the meta-information about each item, product, or service. In the case of a service, because of the emphasis placed on service reusability, opportunities should exist for reuse of this service. However, reuse is only possible if information is discoverable in the first place. To make items, products, and services discoverable, the process is as follows: Document the information about the item, product or service (the metadata) in a consistent manner. Store the documented information (metadata) in a searchable repository. while technically a human-searchable repository, such as a printed paper list would qualify, "searchable repository" is usually taken to mean a computer-searchable repository, such as a database that a human user can search using some type of search engine or "find" feature. Enable search for the documented information in an efficient manner. supports number 2, because while reading through a printed paper list by hand might be feasible in a theoretical sense, it is not time and cost-efficient in comparison with computer-based searching. Apart from increasing the reuse potential of the services, discoverability is also required to avoid development of solution logic that is already contained in an existing service. To design services that are not only discoverable but also provide interpretable information about their capabilities, the service discoverability principle provides guidelines that could be applied during the service-oriented analysis phase of the service delivery process. === Specific to digital media === In relation to audiovisual content, according to the meaning given by the Canadian Radio-television and Telecommunications Commission (CRTC) for the purpose of its 2016 Discoverability Summit, discoverability can be summed up to the intrinsic ability of given content to "stand out of the lot", or to position itself so as to be easily found and discovered. A piece of audiovisual content can be a movie, a TV series, music, a book (eBook), an audio book or podcast. When audiovisual content such as a digital file for a TV show, movie, or song, is made available online, if the content is "tagged" with identifying information such as the names of the key artists (e.g., actors, directors and screenwriters for TV shows and movies; singers, musicians and record producers for songs) and the genres (for movies genres, music genres, etc.). When users interact with online content, algorithms typically determine what types of content the user is interested in, and then a computer program suggests "more like this", which is other content that the user may be interested in. Different websites and systems have different algorithms, but one approach, used by Amazon (company) for its online store, is to indicate to a user: "customers who bought x also bought y" (affinity analysis, collaborative filtering). This example is oriented around online purchasing behaviour, but an algorithm could also be programmed to provide suggestions based on other factors (e.g., searching, viewing, etc.). Discoverability is typically referred to in connection with search engines. A highly "discoverable" piece of content would appear at the top, or near the top of a user's search results. A related concept is the role of "recommendation engines", which give a user recommendations based on his/her previous online activity. Discoverability applies to computers and devices that can access the Internet, including various console video game systems and mobile devices such as tablets and smartphones. When producers make an effort to promote content (e.g., a TV show, film, song, or video game), they can use traditional marketing (billboards, TV ads, radio ads) and digital ads (pop-up ads, pre-roll ads, etc.), or a mix of traditional and digital marketing. Even before the user's intervention by searching for a certain content or type of content, discoverability is the prime factor which contributes to whether a piece of audiovisual content will be likely to be found in the various digital modes of content consumption. As of 2017, modes of searching include looking on Netflix for movies, Spotify for music, Audible for audio books, etc., although the concept can also more generally be applied to content found on Twitter, Tumblr, Instagram, and other websites. It involves more than a content's mere presence on a given platform; it can involve associating this content with "keywords" (tags), search algorithms, positioning within different categories, metadata, etc. Thus, discoverability enables as much as it promotes. For audiovisual content broadcast or streamed on digital media using the Internet, discoverability includes the underlying concepts of information science and programming architecture, which are at the very foundation of the search for a specific product, information or content. === Human-Computer Interaction === In human–computer interaction (HCI), discoverability refers to the ability of users to perceive and comprehend a system, function, or input method upon encountering it, despite a lack of prior awareness or knowledge, whether through intentional effort or serendipitously . The concept was popularised by Don Norman, who framed it around whether users can determine what actions are possible and how to perform them . Discoverability is considered a precondition for learnability, though the two concepts are frequently conflated in the literature . == Applications == === Within a webpage === Within a specific webpage or software application ("app"), the discoverability of a feature, content or link depends on a range of factors, including the size, colour, highlighting features, and position within the page. When colour is used to communicate the importance of a feature or link, designers typically use other elements as well, such as shadows or bolding, for individuals, who cannot see certain colours. Just as traditional paper printing created other physical locations that stood out, such as being "above the fold" of a newspaper versus "below the fold", a web page or app's screenview may have certain locations that give features additional visibility to users, such as being right at the bottom of the web page or screen. The positional advantages or disadvantages of various locations depend on different cultures and languages (e.g., left to right vs. right to left). Some locations have become established, such as having toolbars at the top of a screen or webpage. Some designers have argued t

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  Once (dating platform)

  Once is an online dating platform founded in 2015. The platform offers users one selected match per day for more meaningful connections. == History == Once was established in 2015, the founders included dating industry entrepreneur Jean Meyer, who became a CEO of the company, as well as Guillaume Sempe and Guilhem Duche. It focused on providing a single daily match to its users. On its early stages Once secured a $3.5 million seed round from Partech Ventures and some private investors. The same year, it opened offices in Paris, and London. By 2016, it reached 1 million users. In 2020, the company was acquired by Dating Group for $18 million. Following the acquisition, Once underwent rebranding. Alexandra Beaumont took over leadership of the brand in 2021, driving growth, rebranding, and innovation. == Overview == Once provides an online dating service with a focus on thoughtful connections. Users receive one selected match per day, which encourages meaningful interactions. The platform operates primarily in the United States, the United Kingdom, Canada, France, and Spain. The platform is supported by Android, iOS, and Apple Watch OS.

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  Archetype (information science)

  In the field of informatics, an archetype is a formal re-usable model of a domain concept. Traditionally, the term archetype is used in psychology to mean an idealized model of a person, personality or behaviour (see Archetype). The usage of the term in informatics is derived from this traditional meaning, but applied to domain modelling instead. An archetype is defined by the OpenEHR Foundation (for health informatics) as follows: An archetype is a computable expression of a domain content model in the form of structured constraint statements, based on some reference model. openEHR archetypes are based on the openEHR reference model. Archetypes are all expressed in the same formalism. In general, they are defined for wide re-use, however, they can be specialized to include local particularities. They can accommodate any number of natural languages and terminologies. == Formal specifications == The modern archetype formalism is specified and maintained by the openEHR Foundation, and although originally developed for the health IT domain, is completely domain-independent, and has been used in geospatial modelling, telecommunications, and defence. The archetype formalism consists of a number of specifications including: 'ADL 1.4': original release of Archetype Definition Language (ADL) and Archetype Object Model (AOM); widely implemented in health IT domain; 'ADL 2': modern release of Archetype Definition Language (ADL), Archetype Object Model (AOM), Archetype Identification specification and Archetype Technology Overview. The Archetype Technology Overview provides a short technical overview of the archetype formalism useful for new users. The ADL/AOM 1.4 specifications were provided to ISO TC 215 in 2008 by the openEHR Foundation and became the ISO 13606-2 standard, extant until 2019. ISO TC 215 accepted the AOM 2 specification as the basis for a revision of this standard, which was issued in 2019. In late 2015, the Object Management Group (OMG) accepted an RfP entitled 'Archetype Modeling Language (AML)' as a new candidate standard. This specification is a form of ADL re-engineered as a UML profile so as to enable archetype modelling to be supported within UML tools. == Tools == A number of tools area available for working with archetypes. Most are listed on the openEHR modelling tools page. They include: ADL Designer, a modern AOM2-based web editing application Archetype Editor, an original desktop clinical modelling tool Template Designer, an original desktop clinical templating tool LinkEHR, an archetype and data integration tool ADL Workbench, reference compiler and visualiser tool == Example ==

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  Data management plan

  A data management plan or DMP is a formal document that outlines how data are to be handled both during a research project, and after the project is completed. The goal of a data management plan is to consider the many aspects of data management, metadata generation, data preservation, and analysis before the project begins; this may lead to data being well-managed in the present, and prepared for preservation in the future. DMPs were originally used in 1966 to manage aeronautical and engineering projects' data collection and analysis, and expanded across engineering and scientific disciplines in the 1970s and 1980s. Up until the early 2000s, DMPs were used "for projects of great technical complexity, and for limited mid-study data collection and processing purposes". In the 2000s and later, E-research and economic policies drove the development and uptake of DMPs. == Importance == Preparing a data management plan before data are collected is claimed to ensure that data are in the correct format, organized well, and better annotated. This could arguably save time in the long term because there is no need to re-organize, re-format, or try to remember details about data. It is also claimed to increase research efficiency since both the data collector and other researchers might be able to understand and use well-annotated data in the future. One component of a data management plan is data archiving and preservation. By deciding on an archive ahead of time, the data collector can format data during collection to make its future submission to a database easier. If data are preserved, they are more relevant since they can be re-used by other researchers. It also allows the data collector to direct requests for data to the database, rather than address requests individually. A frequent argument in favor of preservation is that data that are preserved have the potential to lead to new, unanticipated discoveries, and they prevent duplication of scientific studies that have already been conducted. Data archiving also provides insurance against loss by the data collector. In the 2010s, funding agencies increasingly required data management plans as part of the proposal and evaluation process, despite little or no evidence of their efficacy. == Major components == "There is no general and definitive list of topics that should be covered in a DMP for a research project", and researchers are often left to their own devices as to how to fill out a DMP. === Information about data and data format === A description of data to be produced by the project. This might include (but is not limited to) data that are: Experimental Observational Raw or derived Physical collections Models Simulations Curriculum materials Software Images How will the data be acquired? When and where will they be acquired? After collection, how will the data be processed? Include information about Software used Algorithms Scientific workflows File formats that will be used, justify those formats, and describe the naming conventions used. Quality assurance & quality control measures that will be taken during sample collection, analysis, and processing. If existing data are used, what are their origins? How will the data collected be combined with existing data? What is the relationship between the data collected and existing data? How will the data be managed in the short-term? Consider the following: Version control for files Backing up data and data products Security & protection of data and data products Who will be responsible for management === Metadata content and format === Metadata are the contextual details, including any information important for using data. This may include descriptions of temporal and spatial details, instruments, parameters, units, files, etc. Metadata is commonly referred to as "data about data". Issues to be considered include: How detailed has the metadata to be in order to make the data meaningful? How will the metadata be created and/or captured? Examples include lab notebooks, GPS hand-held units, Auto-saved files on instruments, etc. What format will be used for the metadata? What are the metadata standards commonly used in the respective scientific discipline? There should be justification for the format chosen. === Policies for access, sharing, and re-use === Describe any obligations that exist for sharing data collected. These may include obligations from funding agencies, institutions, other professional organizations, and legal requirements. Include information about how data will be shared, including when the data will be accessible, how long the data will be available, how access can be gained, and any rights that the data collector reserves for using data. Address any ethical or privacy issues with data sharing Address intellectual property & copyright issues. Who owns the copyright? What are the institutional, publisher, and/or funding agency policies associated with intellectual property? Are there embargoes for political, commercial, or patent reasons? Describe the intended future uses/users for the data Indicate how the data should be cited by others. How will the issue of persistent citation be addressed? For example, if the data will be deposited in a public archive, will the dataset have a persistent identifier (e.g., ARK, DOI, Handle, PURL, URN) assigned to it? === Long-term storage and data management === Researchers should identify an appropriate archive for the long-term preservation of their data. By identifying the archive early in the project, the data can be formatted, transformed, and documented appropriately to meet the requirements of the archive. Researchers should consult colleagues and professional societies in their discipline to determine the most appropriate database, and include a backup archive in their data management plan in case their first choice goes out of existence. Early in the project, the primary researcher should identify what data will be preserved in an archive. Usually, preserving the data in its most raw form is desirable, although data derivatives and products can also be preserved. An individual should be identified as the primary contact person for archived data, and ensure contact information is always kept up-to-date in case there are requests for data or information about data. === Budget === Data management and preservation costs may be considerable, depending on the nature of the project. By anticipating costs ahead of time, researchers ensure that the data will be properly managed and archived. Potential expenses that should be considered are Human resources and staff as they handle data preparation, management, documentation, and preservation Hardware and/or software needed for data management, backing up, security, documentation, and preservation Costs associated with submitting the data to an archive The data management plan should include how these costs will be paid. == NSF Data Management Plan == All grant proposals submitted to National Science Foundation (NSF) must include a Data Management Plan that is no more than two pages. This is a supplement (not part of the 15-page proposal) and should describe how the proposal will conform to the Award and Administration Guide policy (see below). It may include the following: The types of data The standards to be used for data and metadata format and content Policies for access and sharing Policies and provisions for re-use Plans for archiving data Policy summarized from the NSF Award and Administration Guide, Section 4 (Dissemination and Sharing of Research Results): Promptly publish with appropriate authorship Share data, samples, physical collections, and supporting materials with others, within a reasonable time frame Share software and inventions Investigators can keep their legal rights over their intellectual property, but they still have to make their results, data, and collections available to others Policies will be implemented via Proposal review Award negotiations and conditions Support/incentives == ESRC Data Management Plan == Since 1995, the UK's Economic and Social Research Council (ESRC) have had a research data policy in place. The current ESRC Research Data Policy states that research data created as a result of ESRC-funded research should be openly available to the scientific community to the maximum extent possible, through long-term preservation and high-quality data management. ESRC requires a data management plan for all research award applications where new data are being created. Such plans are designed to promote a structured approach to data management throughout the data lifecycle, resulting in better quality data that is ready to archive for sharing and re-use. The UK Data Service, the ESRC's flagship data service, provides practical guidance on research data management planning suitable for social science researchers in the UK and around the world. ESRC has a longstanding arrangement with the UK Data A

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