AI Content Producer

AI Content Producer — independent reviews, comparisons, pricing and step-by-step guides on Aizhi.

Key frame

In animation and filmmaking, a key frame (or keyframe) is a drawing or shot that defines the starting and ending points of a smooth transition. These are called frames because their position in time is measured in frames on a strip of film or on a digital video editing timeline. A sequence of key frames defines which movement the viewer will see, whereas the position of the key frames on the film, video, or animation defines the timing of the movement. Because only two or three key frames over the span of a second do not create the illusion of movement, the remaining frames are filled with "inbetweens". == Use of key frames as a means to change parameters == In software packages that support animation, especially 3D graphics, there are many parameters that can be changed for any one object. One example of such an object is a light. In 3D graphics, lights function similarly to real-world lights. They cause illumination, cast shadows, and create specular highlights. Lights have many parameters, including light intensity, beam size, light color, and the texture cast by the light. Supposing that an animator wants the beam size to change smoothly from one value to another within a predefined period of time, that could be achieved by using key frames. At the start of the animation, a beam size value is set. Another value is set for the end of the animation. Thus, the software program automatically interpolates the two values, creating a smooth transition. == Video editing == In non-linear digital video editing, as well as in video compositing software, a key frame is a frame used to indicate the beginning or end of a change made to a parameter. For example, a key frame could be set to indicate the point at which audio will have faded up or down to a certain level. == Video compression == In video compression, a key frame, also known as an intra-frame, is a frame in which a complete image is stored in the data stream. In video compression, only changes that occur from one frame to the next are stored in the data stream, in order to greatly reduce the amount of information that must be stored. This technique capitalizes on the fact that most video sources (such as a typical movie) have only small changes in the image from one frame to the next. Whenever a drastic change to the image occurs, such as when switching from one camera shot to another or at a scene change, a key frame must be created. The entire image for the frame must be output when the visual difference between the two frames is so great that representing the new image incrementally from the previous frame would require more data than recreating the whole image. Because video compression only stores incremental changes between frames (except for key frames), it is not possible to fast-forward or rewind to any arbitrary spot in the video stream. That is because the data for a given frame only represents how that frame was different from the preceding one. For that reason, it is beneficial to include key frames at arbitrary intervals while encoding video. For example, a key frame may be output once for each 10 seconds of video, even though the video image does not change enough visually to warrant the automatic creation of the key frame. That would allow seeking within the video stream at a minimum of 10-second intervals. The downside is that the resulting video stream will be larger in disk size because many key frames are added when they are not necessary for the frame's visual representation. This drawback, however, does not produce significant compression loss when the bitrate is already set at a high value for better quality (as in the DVD MPEG-2 format).
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Intent-based network

Intent-Based Networking (IBN) is an approach to network management that shifts the focus from manually configuring individual devices to specifying desired outcomes or business objectives, referred to as "intents". == Description == Rather than relying on low-level commands to configure the network, administrators define these high-level intents, and the network dynamically adjusts itself to meet these requirements. IBN simplifies the management of complex networks by ensuring that the network infrastructure aligns with the desired operational goals. For example, an implementer can explicitly state a network purpose with a policy such as "Allow hosts A and B to communicate with X bandwidth capacity" without the need to understand the detailed mechanisms of the underlying devices (e.g. switches), topology or routing configurations. == Architecture == Advances in Natural Language Understanding (NLU) systems, along with neural network-based algorithms like BERT, RoBERTa, GLUE, and ERNIE, have enabled the conversion of user queries into structured representations that can be processed by automated services. This capability is crucial for managing the increasing complexity of network services. Intent-Based Networking (IBN) leverages these advancements to simplify network management by abstracting network services, reducing operational complexity, and lowering costs. A proposed three-layered architecture integrates intent-based automation into network management systems. In the business layer, intents are based on Key Performance Indicators (KPIs) and Service Level Agreements (SLAs), reflecting business objectives. The intent layer evaluates and re-plans actions dynamically, where a Knowledge module abstracts and reasons about intents, while an Agent interfaces with network objects to execute actions. The data layer observes network objects, updates topology information, and interacts with the Knowledge and Agent modules to ensure accurate and timely responses to network changes. At the bottom, the network layer contains the physical infrastructure, transforming network data into a usable format for the intent layer to act upon.
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Azuqua

Azuqua is an American cloud-based integration and automation company headquartered in Seattle, Washington. As such, they integrate SaaS applications and create automations that are designed to eliminate manual work. Azuqua's platform has the ability to set up workflows between multiple applications so disparate teams can stay in the loop. Azuqua's customers include companies such as Charles Schwab, General Electric, General Motors, HubSpot, and Airbnb. == History == Nikhil Hasija and Craig Unger founded Azuqua in 2011. In 2013, the team participated in Techstars Microsoft's Windows Azure Accelerator, a Seattle-based incubator that helps entrepreneurs gain traction through deep mentor engagement and rapid iteration cycles. Azuqua announced in 2014 that they have received their Series A funding from Ignition Partners which amounted to $5 million. 2017 included a 65% growth in new customers, a doubling of new SaaS connectors, and a 50% growth in overall employee headcount. Azuqua also received their Series B funding which totaled to $10.8 million. This funding was led by Insight Ventures Partners, with DFJ and Ignition Partners also joining the round In March 2018, Azuqua hired Todd Owens as CEO. Owens was previously CEO of Appuri, a customer data platform. Hasija has transitioned to the role of Chief Product Officer. Azuqua also hired on Dan Kogan who has taken on the role of Chief Marketing Officer. Kogan previously worked at Tableau, a BI and analytics company, as a Senior Director of Product Marketing. Okta acquired Azuqua in 2019. == Product Description/Features == Logic Library: Logic functions that can be used for data processing, branching logic, and business rules Drag and Drop Visual Designer: No-code visual designer Use of API's for each cloud service a business is using to allow the various apps to communicate and share data API Publishing: Integrations and automations can be made available as secure endpoints, webhooks, or open services Connector Builder: Build a connector to an application Connector Library: Pre-built connectors to SaaS applications Error Handling: Automations that execute when an error is detected
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Caste census

Caste census is a proposed census to be conducted in India by the Central Government of India. The proposed census was decided under the leadership of Prime Minister Narendra Modi by the cabinet committee of political affairs (CCPA) on 30 April 2025. It has been decided that a caste enumeration should be included with the forthcoming census. The exact time has not been declared yet. It is unclear that when the next census will be held. The decision of the cabinet was announced by the Central Railway Minister Ashwini Vaishnaw. It has been seen as a step that would help in drafting "equitable and targeted" policies by the present Central Government of India led by the Bhartiya Janta Party in India. The Central Home Minister Amit Shah has described the decision as a "historic decision". He has also described that the historic decision as “committed to social justice”. The leader of opposition Rahul Gandhi has welcomed the decision. He said "We have shown we can pressure govt" He has demanded a clear timeline for its completion. He has called it "The first step towards deep social reform". == Description == The caste census is a systematic recording of individuals’ caste identities during the nationwide census in the country. The Central minister Ashwini Vaishnaw expressed his view on the proposed census and said that it would "strengthen the social and economic structure of our society while the nation continues to progress”. The Caste census will happen for the first time in 100 years by the Central Government of India. It will be the part of the upcoming census in India. == History == According to Peabody, the first systematic caste-wise enumeration of households in the Indian subcontinent was conducted between 1658 and 1664 across seven districts of the then Marwar Kingdom, including Jodhpur city which was its capital. It was conducted by the then home minister Munhata Nainsi of the kingdom for the purpose of tax documentation. It was not to for classification of society or creation of social hierarchies but solving a tax related problem. During the period of the British rule in India, caste census was included in the decadal censuses to categorise the population by caste, religion and occupation. In 1871–72, the first detailed caste census was conducted by the government of British Raj in India. It was practiced between the period 1881 to 1931. The last caste census was conducted in the year 1931 in which 4,147 castes were recorded. The largest population in the whole of British India (including Pakistan and Bangladesh) was of Brahmins. The population of Brahmins was recorded more than 1.5 crores. After Brahmin community, the second place was of Jatav (Chamar)community. The population of Jatav was a little more than 1.23 crores. On the third place were Rajputs. The population of Rajputs was 81 lakhs. The Rajput caste was followed by the Kunbi caste of Maharashtra. The population of Kunbi caste was 64 lakhs and 34 thousands. The Kunbi caste was followed by Yadav (Ahir) caste. The population of Yadav (Ahir) community was 56 lakhs and 82 thousands. The Yadav (Ahir) caste was followed by Teli community. The population of Teli community was 42 lakhs and 58 thousands. The Teli community was followed by Gwala community. The population of the Gwala community was 40 lakhs. After the independence of India, the caste enumeration was stopped by the newly independent Government of India led by the prime minister Pandit Jawahar Lal Nehru in 1951. The caste enumeration was stopped to avoid reinforcing social divisions in the Indian society. But, there was an exception made for the enumeration of the Scheduled Castes (SCs) and Scheduled Tribes (STs) in the decadal censuses. Therefore, the enumeration of the Scheduled Castes and the Scheduled Tribes is being conducted in every census since 1951. In 1961, the Government of India permitted states for conducting their own surveys to compile OBC lists, but national caste census was not conducted.
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Environmental impact of AI

The environmental impact of the design, training, deployment and use of artificial intelligence includes the greenhouse gas emissions from generating electricity for data centres and computing hardware, operational and upstream water use, and material impacts from hardware manufacturing, mining and electronic waste. Estimating AI's environmental effects can be difficult because results depend on how impacts are measured, including whether accounting includes only model computation or also data-centre overhead, idle capacity, hardware manufacture, and local electricity supply. As these issues have received greater attention, governments and regulators have increasingly considered data-centre reporting requirements, energy-efficiency standards, and broader transparency measures for AI-related resource use. == Carbon footprint and energy use == AI-related energy use arises at multiple stages, including model training, fine-tuning, inference, storage, networking, and supporting infrastructure such as cooling and power conversion. === Individual level === Published estimates of energy use per AI request vary widely across models, tasks and measurement methods. A benchmark study presented at the 2024 ACM Conference on Fairness, Accountability, and Transparency found substantial differences between task types, with lower energy use for some text tasks and much higher energy use for image generation in the study's test conditions. In that benchmark, simple classification tasks consumed about 0.002–0.007 Wh per prompt on average (about 9% of a smartphone charge for 1,000 prompts), while text generation and text summarisation each used about 0.05 Wh per prompt; image generation averaged 2.91 Wh per prompt, and the least efficient image model in the study used 11.49 Wh per image (roughly equivalent to half a smartphone charge). First-party measurements in production environments have also been published. A 2025 Google study on Gemini assistant serving reported median per-prompt energy, emissions, and water-use estimates under the authors' accounting framework, while noting that different system boundaries can produce substantially different results. The study reported a median text-prompt estimate of about 0.24 Wh, which is roughly as much energy as watching nine seconds of television. The study also stated that software and infrastructure improvements reduced energy use by a factor of 33 and carbon emissions by a factor of 44 for a typical prompt over one year within the authors' framework. Researchers at the University of Michigan measured the energy consumption of various Meta Llama 3.1 models released in 2024 and found that smaller language models (8 billion parameters) use about 114 joules (0.03167 Wh) per response, while larger models (405 billion parameters) require up to 6,700 joules (1.861 Wh) per response. This corresponds to the energy needed to run a microwave oven for roughly one-tenth of a second and eight seconds, respectively. Comparisons between AI systems and human labour for specific tasks have produced mixed results and remain sensitive to assumptions about output quality, workload and system boundaries. A 2024 study in Scientific Reports reported 130 to 2900 times lower estimated carbon emissions for selected AI systems than for human writers and illustrators under its assumptions. A later Scientific Reports paper reported a counterexample for programming tasks under its assumptions, finding 5 to 19 times higher estimated emissions for the evaluated AI system than for human programmers on the benchmark used in that study. === System level === ==== Energy use and efficiency ==== AI electricity intensity depends not only on model architecture but also on hardware and facility efficiency. Data-centre operators commonly report Power usage effectiveness (PUE), which measures the ratio of total facility energy to IT equipment energy; a lower PUE indicates less overhead energy for cooling and other supporting infrastructure. Operators may also publish metrics and case studies on hardware efficiency, cooling systems and power sourcing. In its 2024 environmental report, Google stated that its 2023 total greenhouse gas emissions increased 13% year over year, primarily because of increased data-centre energy consumption and supply-chain emissions, while also reporting lower PUE than industry averages for its own facilities. The International Energy Agency has also reported that data centres remain a relatively small share of global electricity use overall, but that their local effects can be much more pronounced because demand is geographically concentrated. ==== Carbon footprint ==== At system level, AI contributes to rising electricity demand in data centres and related infrastructure. The International Energy Agency estimated that data centres used about 415 TWh of electricity in 2024, or around 1.5% of global electricity consumption, and projected that data-centre electricity use could rise to about 945 TWh by 2030, with AI identified as the main driver of that growth alongside other digital services. The carbon footprint of AI systems depends strongly on electricity sources, hardware efficiency, utilisation rates, and what stages are included in the accounting. Training large models can require substantial electricity, while total lifecycle impacts also depend on deployment scale and the amount of inference performed after training. Early analyses of frontier-model development reported rapid historical growth in training compute for selected systems, although later trends have depended on changes in model design, hardware and efficiency gains. Accounting methods that include upstream or embodied impacts, such as hardware manufacture and facilities construction, can materially affect estimates of AI-related emissions. === Decisions and strategies by individual companies === Large technology companies have reported that the expansion of AI and cloud infrastructure affects their sustainability targets, electricity demand, and resource use. Google, for example, attributed part of its emissions growth in 2023 to increased data-centre energy consumption and supply-chain emissions in its 2024 environmental report. Cloud and AI companies have also announced measures intended to reduce environmental impacts, including investment in more efficient hardware, low-carbon electricity procurement, alternative cooling systems, and water stewardship programmes. The extent, comparability, and third-party verification of such disclosures vary between firms and jurisdictions. == Water usage == Data centres can use water directly for cooling and indirectly through the water used in electricity generation, depending on the local energy mix. Public reporting on data-centre water use has often been inconsistent, making comparisons between operators and regions difficult. To standardise operational reporting, The Green Grid proposed the metric water usage effectiveness (WUE), defined as annual site water use divided by IT equipment energy use. WUE does not by itself measure local water stress, source sustainability, or all upstream water impacts. Studies of AI water use also distinguish between water withdrawal and water consumption. Research on AI-specific water use has argued that the water footprint of AI systems can be difficult to observe and may vary substantially by location, cooling design, and electricity source. A 2025 Communications of the ACM article summarised methods for estimating AI water footprints and emphasised the distinction between water withdrawal and water consumption. Li and colleagues estimated that global AI water withdrawal could reach 4.2–6.6 billion cubic metres in 2027 under the scenarios examined in their article. Using GPT-3, released by OpenAI in 2020, as an example, they estimated that training the model in Microsoft's U.S. data centres could consume about 700,000 litres of onsite water and about 5.4 million litres in total when offsite electricity-related water use was included; they also estimated that 10–50 medium-length GPT-3 responses could consume about 500 mL of water, depending on when and where the model was deployed. Published prompt-level estimates have also varied by system and accounting framework: the 2025 Google study on Gemini assistant serving reported a median text-prompt estimate of about 0.26 mL under its framework. Location can materially affect the significance of data-centre water use. Research on U.S. data centres found that one-fifth of servers' direct water footprint came from moderately to highly water-stressed watersheds, while nearly half of servers were fully or partially powered by plants located in water-stressed regions. A 2025 Reuters report, citing data from Verisk Maplecroft and NatureFinance, said that an average mid-sized data centre uses about 1.4 million litres of water per day for cooling and that Phoenix would experience a 32% increase in annual water stress if currently pl
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Social media and identity

Social media can have both positive and negative impacts on a user's identity. Scholars within the fields of psychology and communication study the relationship between social media and identity in order to understand individual behavior, psychological impacts, and social patterns. Communication within political or social groups online can result in practice application, real-world implementation of a concept, of those found identities or the adoption of them as a whole. Young people, defined as emerging adults in or entering college, are especially found to have their identities shaped through social media. Sometimes it seems as though social media is taking over and changing us for the worse. Social media is always changing and can be hard to keep up with. Platforms come and go trends change everyday. What was cool yesterday is lame today. The biggest change from recent years that users are still adjusting to is the name change of Twitter now called X. Since Elon Musk purchased the platform he changed the name but nothing else about the app. Users now feel the need to explain when talking about X. Now it is often referred to as ‘X(Twitter)’ to clarify. == Social Media Usage and Demographics == We know what social media is and how it is used but who uses it? The Pew Research center conducted a 10 year study from 2005-2015 about the demographics of social media usage. While this article is 10 years old the statistics in it are from a very formative time in social media. This is when most people joined and were consistently using social media. Age: While it is no surprise that 90% of young adults use social media they are the main demographic of users. Older adults (65 and older) really hit a boom on social media. In 2005 only 2% of older adults used any form of social media. By 2015 35% of older adults used social media. We can infer that that percentage has grown even more since 2015. Gender: It is known that women tend to use social media more than men. In 2015 it was noted that 65% of women used social media. Men were not far behind, 62% of men were reported to use social media. There are no notable differences of users from various races and ethnicities. The research also shows that more suburban and urban residents use social media over those who live in rural areas. == Young adults == Young adults are especially influenced by social media, where they find social groups to belong to. Research shows that nearly half of teens believe social media platforms has a negative impact on people their age. Psychologists believe that at a time when young adults are coming into adolescence, they are more likely to be influenced by what they see on sites like Instagram or Twitter. Most young adults will widely share, with varying degrees of accuracy, honesty, and openness, information that in the past would have been private or reserved for select individuals. Key questions include whether they accurately portray their identities online and whether the use of social media might impact young adults' identity development. Media Imagery, in particular, is said to be a major influence on the minds of young men and women. Studies have shown that it is even more relevant when it comes to the issue of body image. Social media, in part, has been created to host a safe haven for those who do not claim a solid identity in the material world, but past identities are not easy to escape from since the Internet preserves much of the information that was shared. Social media is an essential part of the social lives of young adults. They rely on it to maintain relationships, create new relationships, and stay up to date with the world around them. Adolescents find social media to be extremely helpful when changing environments, like moving off to university for example. Social media provides students, especially first year students, the opportunity to create the identity they want the world to see. However, it has been seen that these students create online personas that may not reflect their true selves bringing up the issues of impression management. Social media provides young adults with the opportunity to present themselves as something other than their authentic self. Social media providers can help build relationships and community on their platforms. This is something that will create a more positive impact from social media. When young adults interact with each other using social media they are creating something called a social self-identity. Social self identity is what individuals create when they assimilate to being in a group. Social media has gained the reputation of being isolating. If these platforms encourage community then they can help grow users' social self-identity. == Media literacy == The definition of media literacy has evolved over time to encompass a range of experiences that can occur in social media or other digital spaces. The definition of media literacy is also broad and wide ranging in its context. Currently, media literacy is the idea that one is able to analyze, evaluate, and interact with media content in a meaningful way. Educators teach media literacy skills because of the vulnerable relationship that young adults can have with social media. Some examples of media literacy practices, particularly on Twitter, include using hashtags, live tweeting, and sharing information. One of the overall goals of media literacy within the context of social media is to keep young adults aware of potentially violent, graphic, or dangerous content that they may come across on the internet, and how to determine if the content is credible while engaging responsibly with it. In order to be considered media-literate, a person must be able to take in media from online and social platforms and have the correct competencies and context to be able to organize the information. In order to be considered media-literate, the digital information must be given to the user in a way that it can be put into the correct perspective and analyzed, deducted and synthesized.Teenagers and young adults can be vulnerable to specific content online outside of their age-range. Media literacy campaigns and education research shows that targeting those who fall into this age category would be the best way to understand and target their needs as young online users. There are multiple individual studies investigating social media identity relating to media literacy online, however there is a need for much more conclusive information that analyzes multiple studies at a time. Social media literacy is still considered an under-researched topic. Many scholars in media literacy research emphasize the impact of training young adults to consume media in a safe way is the major solution for furthering internet education in children and young adults. The more information the young adults are given on media literacy, the better prepared they are to enter the digital world confidently. One scientific model that has been proposed, known as The Social Media Literacy (SMILE) model is a framework that hypothesizes that at the core of this model it is helping young adults truly know the meaning and display the actions of media literacy online. SMILE is also meant to inspire more research on the subject of media literacy as it relates to social media effects and young adult learning abilities. The model was applied through the lens of a social media positivity bias among adolescents and puts forth five different assumptions about social media and media literacy; Social media literacy as a moderator (what is seen on social media) Social media literacy as a predictor (what is seen for specific individuals on social media) Media literacy within social media is a reciprocal process The development of social media literacy depends on a conditional process of variables affecting other variables Media literacy within social media is a differential learning process, and who teaches it is highly affective of the outcome This model also stresses that human beings learn media literacy (and social media literacy) naturally as they go through life. Research suggests that having young adults taught media literacy from an educator may make them less interested (and therefore less careful) of threats on social media. == Self Presentation == People create images of themselves to present to the public, a process called self presentation. Depending on the demographic, presenting oneself as authentic can result in identity clarity. Methods of self presentation can also be influenced by geography. The framework for this relationship between a user's location and their social media presentation is called the spatial self. Users depict their spatial self in order to include their physical space as a part of their self presentation to an audience. According to a 2018 research paper, patients of plastic surgeons have gone in and asked for specific snapchat "filter" features. This led to a theory of Snap
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Data grid

A data grid is an architecture or set of services that allows users to access, modify and transfer extremely large amounts of geographically distributed data for research purposes. Data grids make this possible through a host of middleware applications and services that pull together data and resources from multiple administrative domains and then present it to users upon request. The data in a data grid can be located at a single site or multiple sites where each site can be its own administrative domain governed by a set of security restrictions as to who may access the data. Likewise, multiple replicas of the data may be distributed throughout the grid outside their original administrative domain and the security restrictions placed on the original data for who may access it must be equally applied to the replicas. Specifically developed data grid middleware is what handles the integration between users and the data they request by controlling access while making it available as efficiently as possible. == Middleware == Middleware provides all the services and applications necessary for efficient management of datasets and files within the data grid while providing users quick access to the datasets and files. There is a number of concepts and tools that must be available to make a data grid operationally viable. However, at the same time not all data grids require the same capabilities and services because of differences in access requirements, security and location of resources in comparison to users. In any case, most data grids will have similar middleware services that provide for a universal name space, data transport service, data access service, data replication and resource management service. When taken together, they are key to the data grids functional capabilities. === Universal namespace === Since sources of data within the data grid will consist of data from multiple separate systems and networks using different file naming conventions, it would be difficult for a user to locate data within the data grid and know they retrieved what they needed based solely on existing physical file names (PFNs). A universal or unified name space makes it possible to create logical file names (LFNs) that can be referenced within the data grid that map to PFNs. When an LFN is requested or queried, all matching PFNs are returned to include possible replicas of the requested data. The end user can then choose from the returned results the most appropriate replica to use. This service is usually provided as part of a management system known as a Storage Resource Broker (SRB). Information about the locations of files and mappings between the LFNs and PFNs may be stored in a metadata or replica catalogue. The replica catalogue would contain information about LFNs that map to multiple replica PFNs. === Data transport service === Another middleware service is that of providing for data transport or data transfer. Data transport will encompass multiple functions that are not just limited to the transfer of bits, to include such items as fault tolerance and data access. Fault tolerance can be achieved in a data grid by providing mechanisms that ensures data transfer will resume after each interruption until all requested data is received. There are multiple possible methods that might be used to include starting the entire transmission over from the beginning of the data to resuming from where the transfer was interrupted. As an example, GridFTP provides for fault tolerance by sending data from the last acknowledged byte without starting the entire transfer from the beginning. The data transport service also provides for the low-level access and connections between hosts for file transfer. The data transport service may use any number of modes to implement the transfer to include parallel data transfer where two or more data streams are used over the same channel or striped data transfer where two or more steams access different blocks of the file for simultaneous transfer to also using the underlying built-in capabilities of the network hardware or specifically developed protocols to support faster transfer speeds. The data transport service might optionally include a network overlay function to facilitate the routing and transfer of data as well as file I/O functions that allow users to see remote files as if they were local to their system. The data transport service hides the complexity of access and transfer between the different systems to the user so it appears as one unified data source. === Data access service === Data access services work hand in hand with the data transfer service to provide security, access controls and management of any data transfers within the data grid. Security services provide mechanisms for authentication of users to ensure they are properly identified. Common forms of security for authentication can include the use of passwords or Kerberos (protocol). Authorization services are the mechanisms that control what the user is able to access after being identified through authentication. Common forms of authorization mechanisms can be as simple as file permissions. However, need for more stringent controlled access to data is done using Access Control Lists (ACLs), Role-Based Access Control (RBAC) and Tasked-Based Authorization Controls (TBAC). These types of controls can be used to provide granular access to files to include limits on access times, duration of access to granular controls that determine which files can be read or written to. The final data access service that might be present to protect the confidentiality of the data transport is encryption. The most common form of encryption for this task has been the use of SSL while in transport. While all of these access services operate within the data grid, access services within the various administrative domains that host the datasets will still stay in place to enforce access rules. The data grid access services must be in step with the administrative domains access services for this to work. === Data replication service === To meet the needs for scalability, fast access and user collaboration, most data grids support replication of datasets to points within the distributed storage architecture. The use of replicas allows multiple users faster access to datasets and the preservation of bandwidth since replicas can often be placed strategically close to or within sites where users need them. However, replication of datasets and creation of replicas is bound by the availability of storage within sites and bandwidth between sites. The replication and creation of replica datasets is controlled by a replica management system. The replica management system determines user needs for replicas based on input requests and creates them based on availability of storage and bandwidth. All replicas are then cataloged or added to a directory based on the data grid as to their location for query by users. In order to perform the tasks undertaken by the replica management system, it needs to be able to manage the underlying storage infrastructure. The data management system will also ensure the timely updates of changes to replicas are propagated to all nodes. ==== Replication update strategy ==== There are a number of ways the replication management system can handle the updates of replicas. The updates may be designed around a centralized model where a single master replica updates all others, or a decentralized model, where all peers update each other. The topology of node placement may also influence the updates of replicas. If a hierarchy topology is used then updates would flow in a tree like structure through specific paths. In a flat topology it is entirely a matter of the peer relationships between nodes as to how updates take place. In a hybrid topology consisting of both flat and hierarchy topologies updates may take place through specific paths and between peers. ==== Replication placement strategy ==== There are a number of ways the replication management system can handle the creation and placement of replicas to best serve the user community. If the storage architecture supports replica placement with sufficient site storage, then it becomes a matter of the needs of the users who access the datasets and a strategy for placement of replicas. There have been numerous strategies proposed and tested on how to best manage replica placement of datasets within the data grid to meet user requirements. There is not one universal strategy that fits every requirement the best. It is a matter of the type of data grid and user community requirements for access that will determine the best strategy to use. Replicas can even be created where the files are encrypted for confidentiality that would be useful in a research project dealing with medical files. The following section contains several strategies for replica placement. ===== Dynamic replication ===== Dynam
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Business intelligence

Business intelligence (BI) consists of strategies, methodologies, and technologies used by enterprises for data analysis and management of business information to inform business strategies and business operations. Common functions of BI technologies include reporting, online analytical processing, analytics, dashboard development, data mining, process mining, complex event processing, business performance management, benchmarking, text mining, predictive analytics, and prescriptive analytics. BI tools can handle large amounts of structured and sometimes unstructured data to help organizations identify, develop, and otherwise create new strategic business opportunities. They aim to allow for the easy interpretation of these big data. Identifying new opportunities and implementing an effective strategy based on insights is assumed to potentially provide businesses with a competitive market advantage and long-term stability, and help them take strategic decisions. Business intelligence can be used by enterprises to support a wide range of business decisions ranging from operational to strategic. Basic operating decisions include product positioning or pricing. Strategic business decisions involve priorities, goals, and directions at the broadest level. In all cases, business intelligence is considered most effective when it combines data from the market in which a company operates (external data) with data from internal company sources, such as financial and operational information. When integrated, external and internal data provide a comprehensive view that creates ‘intelligence’ not possible from any single data source alone. Among their many uses, business intelligence tools empower organizations to gain insight into new markets, to assess demand and suitability of products and services for different market segments, and to gauge the impact of marketing efforts. BI applications use data gathered from a data warehouse (DW) or from a data mart, and the concepts of BI and DW combine as "BI/DW" or as "BIDW". A data warehouse contains a copy of analytical data that facilitates decision support. == History == The earliest known use of the term business intelligence is in Richard Millar Devens' Cyclopædia of Commercial and Business Anecdotes (1865). Devens used the term to describe how the banker Sir Henry Furnese gained profit by receiving and acting upon information about his environment, prior to his competitors: Throughout Holland, Flanders, France, and Germany, he maintained a complete and perfect train of business intelligence. The news of the many battles fought was thus received first by him, and the fall of Namur added to his profits, owing to his early receipt of the news. The ability to collect and react accordingly based on the information retrieved, Devens says, is central to business intelligence. When Hans Peter Luhn, a researcher at IBM, used the term business intelligence in an article published in 1958, he employed the Webster's Dictionary definition of intelligence: "the ability to apprehend the interrelationships of presented facts in such a way as to guide action towards a desired goal." In 1989, Howard Dresner (later a Gartner analyst) proposed business intelligence as an umbrella term to describe "concepts and methods to improve business decision making by using fact-based support systems." It was not until the late 1990s that this usage was widespread. == Definition == According to Solomon Negash and Paul Gray, business intelligence (BI) can be defined as systems that combine: Data gathering Data storage Knowledge management with analysis to evaluate complex corporate and competitive information for presentation to planners and decision makers, with the objective of improving the timeliness and the quality of the input to the decision process." According to Forrester Research, business intelligence is "a set of methodologies, processes, architectures, and technologies that transform raw data into meaningful and useful information used to enable more effective strategic, tactical, and operational insights and decision-making." Under this definition, business intelligence encompasses information management (data integration, data quality, data warehousing, master-data management, text- and content-analytics, et al.). Therefore, Forrester refers to data preparation and data usage as two separate but closely linked segments of the business-intelligence architectural stack. Some elements of business intelligence are: Multidimensional aggregation and allocation Denormalization, tagging, and standardization Realtime reporting with analytical alert A method of interfacing with unstructured data sources Group consolidation, budgeting, and rolling forecasts Statistical inference and probabilistic simulation Key performance indicators optimization Version control and process management Open item management Forrester distinguishes this from the business-intelligence market, which is "just the top layers of the BI architectural stack, such as reporting, analytics, and dashboards." === Compared with competitive intelligence === Though the term business intelligence is sometimes a synonym for competitive intelligence (because they both support decision making), BI uses technologies, processes, and applications to analyze mostly internal, structured data and business processes while competitive intelligence gathers, analyzes, and disseminates information with a topical focus on company competitors. If understood broadly, competitive intelligence can be considered as a subset of business intelligence. === Compared with business analytics === Business intelligence and business analytics are sometimes used interchangeably, but there are alternate definitions. Thomas Davenport, professor of information technology and management at Babson College argues that business intelligence should be divided into querying, reporting, Online analytical processing (OLAP), an "alerts" tool, and business analytics. In this definition, business analytics is the subset of BI focusing on statistics, prediction, and optimization, rather than the reporting functionality. == Unstructured data == Business operations can generate a very large amount of data in the form of emails, memos, notes from call centers, news, user groups, chats, reports, web pages, presentations, image files, video files, and marketing material. According to Merrill Lynch, more than 85% of all business information exists in these forms; a company might only use such a document a single time. Because of the way it is produced and stored, this information is either unstructured or semi-structured. The management of semi-structured data is an unsolved problem in the information technology industry. According to projections from Gartner (2003), white-collar workers spend 30–40% of their time searching, finding, and assessing unstructured data. BI uses both structured and unstructured data. The former is easy to search, and the latter contains a large quantity of the information needed for analysis and decision-making. Because of the difficulty of properly searching, finding, and assessing unstructured or semi-structured data, organizations may not draw upon these vast reservoirs of information, which could influence a particular decision, task, or project. This can ultimately lead to poorly informed decision-making. Therefore, when designing a business intelligence/DW solution, the specific problems associated with semi-structured and unstructured data must be accommodated, as well as those associated with structured data. === Limitations of semi-structured and unstructured data === There are several challenges to developing BI with semi-structured data. According to Inmon & Nesavich, some of those are: Physically accessing unstructured textual data – unstructured data is stored in a huge variety of formats. Terminology – Among researchers and analysts, there is a need to develop standardized terminology. Volume of data – As stated earlier, up to 85% of all data exists as semi-structured data. Couple that with the need for word-to-word and semantic analysis. Searchability of unstructured textual data – A simple search on some data, e.g. apple, results in links where there is a reference to that precise search term. (Inmon & Nesavich, 2008) gives an example: "a search is made on the term felony. In a simple search, the term felony is used, and everywhere there is a reference to felony, a hit to an unstructured document is made. But a simple search is crude. It does not find references to crime, arson, murder, embezzlement, vehicular homicide, and such, even though these crimes are types of felonies". === Metadata === To solve problems with searchability and assessment of data, it is necessary to know something about the content. This can be done by adding context through the use of metadata. Many systems already capture some metadata (e.g. filename, author, size, etc.), but more usef
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List of Fortran software and tools

This is a list of Fortran software and tools, including IDEs, compilers, libraries, debugging tools, numerical and scientific computing tools, and related projects. == Fortran compilers == Absoft Pro Fortran — Absoft Pro Fortran is discontinued and ran on Linux and macOS AOCC — from AMD Classic Flang — part of the LLVM Project LLVM Flang — part of the LLVM Project Fortran 77 — Fortran 77 was developed by Digital Equipment Corporation, it is discontinued. G95 – portable open-source Fortran 95 compiler GCC (GNU Fortran) PGI compilers – NVIDIA developed compilers after acquiring The Portland Group IBM XL Fortran — IBM XL Fortran is current and runs on Linux (Power/AIX) and integrates with Eclipse Intel Fortran Compiler – part of Intel OneAPI HPC toolkit LFortran — LFortran is current, cross-platform, and has IDE support. MinGW – cross compiler and forked into Mingw-w64 nAG Fortran Compiler - from nAG Open64 — Open64 is an open-source compiler that has been terminated and ran on Linux Open Watcom — Open Watcom is current, runs on MS-DOS and OS/2, and has IDE support. Oracle Fortran — Oracle Fortran is discontinued, ran on Linux and Solaris. ROSE — source-to-source compiler framework developed at Lawrence Livermore National Laboratory Silverfrost FTN95 — FTN95 from Silverfrost is current, runs on Windows, and has IDE support. == Integrated development environments (IDEs) and editors == Code::Blocks — supports Fortran with plugins Eclipse IDE — with Fortran support via Photran Emacs — extensible text editor with built-in Fortran modes and support for modern tooling via language servers Geany — lightweight cross-platform IDE based on GTK IntelliJ IDEA — cross-platform IDE by JetBrains with Fortran pluggin KDevelop — KDE-based IDE NetBeans — Apache software foundation IDE with Fortran configuration OpenWatcom — IDE and compiler suite for C, C++, and Fortran Simply Fortran — standalone Fortran IDE for Windows, Linux, and macOS Vim — modal text editor with native Fortran syntax support and extensive plugin-based development features Visual Studio — with Intel Fortran integration Visual Studio Code — supports Fortran via extensions == Mathematical libraries == == Scientific libraries == ABINIT — software suite to calculate optical, mechanical, vibrational, and other observable properties of materials Cantera — chemical kinetics, thermodynamics, and transport tool suite CERN Program Library — collection of Fortran libraries for physics applications from CERN CP2K — quantum chemistry and solid-state physics software package for atomistic simulations Dalton — molecular electronic structure program FFTPACK — subroutines for the fast Fourier transform Kinetic PreProcessor – open-source software tool used in atmospheric chemistry MESA — Modules for Experiments in Stellar Astrophysics Nek5000 — MPI parallel higher-order spectral element CFD solver NWChem — open-source high-performance computational chemistry software Octopus — real-space Time-Dependent Density Functional Theory code MODTRAN – model atmospheric propagation of electromagnetic radiation MOLCAS — quantum chemistry software package for multiconfigurational electronic structure calculations NOVAS – software library for astrometry-related numerical computations Physics Analysis Workstation – data analysis and graphical presentation in high-energy physics Quantum ESPRESSO — integrated suite for electronic-structure calculations and materials modeling SIESTA — first-principles materials simulation code using density functional theory Tinker — software tools for molecular design == Debugging and performance tools == GDB — GNU Debugger with Fortran support Valgrind — memory debugging and profiling tool VTune Profiler — performance analysis tool Allinea Forge — debugger and profiler for HPC applications == Build and package management == Autotools — build system supporting Fortran projects CMake — cross-platform build system supporting Fortran Make — build automation tool Spack — package manager for HPC software including Fortran libraries == Machine learning and AI libraries == Athena Fiats (Functional Inference And Training for Surrogates) FNN (Fortran Neural Network) FortNN Fortran-TF-lib (Fortran interface to TensorFlow) FTorch (Fortran interface to PyTorch) MlFortran RoseNNa == Parallel and high-performance computing tools == MPI Fortran bindings — standard interface for distributed-memory parallelism OpenMP — shared-memory parallel programming support through compiler directives Coarray Fortran — parallel programming model introduced in Fortran 2008 ScaLAPACK — parallel linear algebra package built on top of LAPACK == Testing frameworks == FUnit — open-source unit testing framework developed at NASA’s Langley Research Center, for Fortran 90, 95, and 2003. pFUnit — unit testing framework for Fortran, modeled after JUnit == Documentation and code analysis tools == FORD — automatic documentation generator for modern Fortran projects SQuORE — software quality and management platform with code analysis support Understand — static analysis and code comprehension tool for large Fortran projects
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Data thinking

Data Thinking is a framework that integrates data science with the design process. It combines computational thinking, statistical thinking, and domain-specific knowledge to guide the development of data-driven solutions in product development. The framework is used to explore, design, develop, and validate solutions, with a focus on user experience and data analytics, including data collection and interpretation The framework aims to apply data literacy and inform decision-making through data-driven insights. == Major components == According to "Computational thinking in the era of data science": Data thinking involves understanding that solutions require both data-driven and domain-knowledge-driven rules. Data thinking evaluates whether data accurately represents real-life scenarios and improves data collection where necessary. The framework highlights the importance of preserving domain-specific meaning during data analysis. Data thinking incorporates statistical and logical analysis to identify patterns and irregularities. Data thinking involves testing solutions in real-life contexts and iteratively improving models based on new data. The process requires evaluating problems from multiple abstraction levels and understanding the potential for biases in generalizations. == Major phases == === Strategic context and risk analysis === Analyzing the broader digital strategy and assessing risks and opportunities is a common step before beginning a project. Techniques like coolhunting, trend analysis, and scenario planning can be used to assist with this. === Ideation and exploration === In this phase, focus areas are identified, and use cases are developed by integrating organizational goals, user needs, and data requirements. Design thinking methods, such as personas and customer journey mapping, are applied. === Prototyping === A proof of concept is created to test feasibility and refine solutions through iterative evaluation to optimize for effective performance. === Implementation and monitoring === Solutions are tested and monitored for performance and continual improvement. == Implementing Data Thinking == The following resources explain more about data thinking and its applications: "Data Thinking: Framework for data-based solutions" by StackFuel "What is Data Thinking? A modern approach to designing a data strategy" by Mantel Group "Data Science Thinking" by SpringerLink These sources provide detailed insights into the methodology, phases, and benefits of adopting Data Thinking in organizational processes.
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Electronic lab notebook

An electronic lab notebook or electronic laboratory notebook (ELN) is a computer program designed to replace paper laboratory notebooks. Lab notebooks in general are used by scientists, engineers, and technicians to document research, experiments, and procedures performed in a laboratory. A lab notebook is often maintained to be a legal document and may be used in a court of law as evidence. Similar to an inventor's notebook, the lab notebook is also often referred to in patent prosecution and intellectual property litigation. Electronic lab notebooks offer many benefits to the user as well as organizations; they are easier to search upon, simplify data copying and backups, and support collaboration amongst many users. ELNs can have fine-grained access controls, and can be more secure than their paper counterparts. They also allow the direct incorporation of data from instruments, replacing the practice of printing out data to be stapled into a paper notebook. == Types == ELNs can be divided into two categories: "Specific ELNs" contain features designed to work with specific applications, scientific instrumentation or data types. "Cross-disciplinary ELNs" or "Generic ELNs" are designed to support access to all data and information that needs to be recorded in a lab notebook. Lab Platforms that combine an ELN, LIMS, and scientific data management together, all-in-one configurable software environment. Solutions range from specialized programs designed from the ground up for use as an ELN, to modifications or direct use of more general programs. Examples of using more general software as an ELN include using OpenWetWare, a MediaWiki install (running the same software that Wikipedia uses), WordPress, or the use of general note taking software such as OneNote as an ELN. ELN's come in many different forms. They can be standalone programs, use a client-server model, or be entirely web-based. Some use a lab-notebook approach, others resemble a blog. ELNs are embracing artificial intelligence and LLM technology to provide scientific AI chat assistants. A good many variations on the "ELN" acronym have appeared. Differences between systems with different names are often subtle, with considerable functional overlap between them. Examples include "ERN" (Electronic Research Notebook), "ERMS" (Electronic Resource (or Research or Records) Management System (or Software) and SDMS (Scientific Data (or Document) Management System (or Software). Ultimately, these types of systems all strive to do the same thing: Capture, record, centralize and protect scientific data in a way that is highly searchable, historically accurate, and legally stringent, and which also promotes secure collaboration, greater efficiency, reduced mistakes and lowered total research costs. == Objectives == A good electronic laboratory notebook should offer a secure environment to protect the integrity of both data and process, whilst also affording the flexibility to adopt new processes or changes to existing processes without recourse to further software development. The package architecture should be a modular design, so as to offer the benefit of minimizing validation costs of any subsequent changes that you may wish to make in the future as your needs change. A good electronic laboratory notebook should be an "out of the box" solution that, as standard, has fully configurable forms to comply with the requirements of regulated analytical groups through to a sophisticated ELN for inclusion of structures, spectra, chromatograms, pictures, text, etc. where a preconfigured form is less appropriate. All data within the system may be stored in a database (e.g. MySQL, MS-SQL, Oracle) and be fully searchable. The system should enable data to be collected, stored and retrieved through any combination of forms or ELN that best meets the requirements of the user. The application should enable secure forms to be generated that accept laboratory data input via PCs and/or laptops / palmtops, and should be directly linked to electronic devices such as laboratory balances, pH meters, etc. Networked or wireless communications should be accommodated for by the package which will allow data to be interrogated, tabulated, checked, approved, stored and archived to comply with the latest regulatory guidance and legislation. A system should also include a scheduling option for routine procedures such as equipment qualification and study related timelines. It should include configurable qualification requirements to automatically verify that instruments have been cleaned and calibrated within a specified time period, that reagents have been quality-checked and have not expired, and that workers are trained and authorized to use the equipment and perform the procedures. == Regulatory and legal aspects == The laboratory accreditation criteria found in the ISO 17025 standard needs to be considered for the protection and computer backup of electronic records. These criteria can be found specifically in clause 4.13.1.4 of the standard. Electronic lab notebooks used for development or research in regulated industries, such as medical devices or pharmaceuticals, are expected to comply with FDA regulations related to software validation. The purpose of the regulations is to ensure the integrity of the entries in terms of time, authorship, and content. Unlike ELNs for patent protection, FDA is not concerned with patent interference proceedings, but is concerned with avoidance of falsification. Typical provisions related to software validation are included in the medical device regulations at 21 CFR 820 (et seq.) and Title 21 CFR Part 11. Essentially, the requirements are that the software has been designed and implemented to be suitable for its intended purposes. Evidence to show that this is the case is often provided by a Software Requirements Specification (SRS) setting forth the intended uses and the needs that the ELN will meet; one or more testing protocols that, when followed, demonstrate that the ELN meets the requirements of the specification and that the requirements are satisfied under worst-case conditions. Security, audit trails, prevention of unauthorized changes without substantial collusion of otherwise independent personnel (i.e., those having no interest in the content of the ELN such as independent quality unit personnel) and similar tests are fundamental. Finally, one or more reports demonstrating the results of the testing in accordance with the predefined protocols are required prior to release of the ELN software for use. If the reports show that the software failed to satisfy any of the SRS requirements, then corrective and preventive action ("CAPA") must be undertaken and documented. Such CAPA may extend to minor software revisions, or changes in architecture or major revisions. CAPA activities need to be documented as well. Aside from the requirements to follow such steps for regulated industry, such an approach is generally a good practice in terms of development and release of any software to assure its quality and fitness for use. There are standards related to software development and testing that can be applied (see ref.).
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Strategic Air Command Digital Information Network

The Strategic Air Command DIgital Network (SACDIN) was a United States military computer network that provided computerized record communications, replacing the Data Transmission Subsystem and part of the Data Display Subsystem of the SAC Automated Command and Control System. SACDIN enabled a rapid flow of communications from headquarters SAC to its fielded forces, such as B-52 bases and ICBM Launch Control Centers. == Logistics == Major portions of SACDIN were developed, engineered and installed by the International Telephone and Telegraph (ITT) company, under contract to the Electronic Systems Center. == Chronology == 1969 - Headquarters SAC submits a request to the Joint Chiefs of Staff to study an expanded communications system, known as the SAC Total Information Network (SATIN). It would interconnect Air Force Satellite Communications (AFSATCOM), Advanced Airborne Command Post (AABNCP), Airborne Command Post (ABNCP), high frequency/single sideband radio HF/SSB radio, SAC Automated Command and Control System (SACCS), Automatic Digital Information Network (AUTODIN), Survivable Low Frequency Communications System (SLFCS) and Command Data Buffer (CDB) 1977 1 November - SATIN IV was effectively terminated by Congress. The restructured program was renamed SAC Digital Network (SACDIN), and was formulated to meet SAC's minimum essential data communications requirements, but also had the capability to grow in a modular fashion. 1986 ?? ??? - SACDIN replaces much of the SAC Automated Command and Control System (SACCS) and the SAC Automated Total Information Network (SATIN)
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INaturalist

iNaturalist is an American 501(c)(3) nonprofit social network of naturalists, citizen scientists, and biologists built on the concept of mapping and sharing observations of biodiversity across the globe. iNaturalist may be accessed via its website or from its mobile applications. iNaturalist includes an automated species identification tool, and users further assist each other in identifying organisms from photographs and sound recordings. As of 5 August 2025, iNaturalist users had contributed nearly 300 million observations of plants, animals, fungi, and other organisms worldwide, and 400,000 users were active in the previous 30 days. iNaturalist serves as an important resource of open data for biodiversity research, conservation, and education, describing itself as "an online social network of people sharing biodiversity information to help each other learn about nature." It is the primary application for crowd-sourced biodiversity data in places such as Mexico, southern Africa, and Australia, and the project has been called "a standard-bearer for natural history mobile applications." Most of iNaturalist's software is open source. It has contributed to over 4,000 research papers and is widely used by scientists, land managers, and conservationists worldwide. The platform has also been active in the discovery of new species and rediscovery of species previously assumed to be extinct. == History == iNaturalist began in 2008 as a UC Berkeley School of Information Master's final project of Nate Agrin, Jessica Kline, and Ken-ichi Ueda. Agrin and Ueda continued work on the site with Sean McGregor, a web developer. In 2011, Ueda began collaboration with Scott Loarie, a research fellow at Stanford University and lecturer at UC Berkeley. Ueda and Loarie are the current co-directors of iNaturalist.org. The organization merged with the California Academy of Sciences on 24 April 2014. In 2017, iNaturalist became a joint initiative between the California Academy of Sciences and the National Geographic Society. With these collaborations and growing popularity of the site since 2012, the number of participants and observations has roughly doubled each year. In 2014, iNaturalist reached 1 million observations. Later, as of October 2023, there were 181 million observations (163 million verifiable). On 11 July 2023 iNaturalist announced its status as a newly independent 501(c)(3) nonprofit organization. === Google AI controversy === On 9 June 2025 Google announced that iNaturalist would be part of its "Generative AI Accelerator". This announcement, paired with the initial lack of information on the iNaturalist site, led to outcry from many iNaturalist users in the blog comments and forum, worrying about the consequences for the environment, volunteer engagement, reliability and raised questions about the decision making within iNaturalist, while some saw the backlash as a sign that people want to resist 'corrosive technologies'. PZ Myers, a biology professor who uses iNaturalist in his teaching, published an article on his website Pharyngula stating that "any decision that drives people away and replaces them with a hallucinating bot is a bad decision". == Platforms == Users can interact with iNaturalist in the following ways: through the iNaturalist.org website, through two mobile apps: iNaturalist (iOS/Android) and Seek by iNaturalist (iOS/Android), or through partner organizations such as the Global Biodiversity Information Facility (GBIF) website. On the iNaturalist.org website, visitors can search the public dataset and interact with other people adding observations and identifications. The website provides tools for registered users to add, identify, and discuss observations, write journal posts, explore information about species, create project pages to recruit participation, and coordinate work on their topics of interest. On the iNaturalist mobile app, users can create and share nature observations to the online dataset, explore observations both nearby and around the world, and learn about different species. Seek by iNaturalist, a separate app marketed to families, requires no online account registration and all observations may remain private. Seek incorporates features of gamification, such as providing a list of nearby organisms to find and encouraging the collection of badges and participation in challenges. Seek was initially released in the spring of 2018. == Observations == The iNaturalist platform is based on crowdsourcing of observations and identifications. An iNaturalist observation records a person's encounter with an individual organism at a particular time and place. An iNaturalist observation may also record evidence of an organism, such as animal tracks, nests, or scat. The scope of iNaturalist excludes natural but inert subjects such as geologic or hydrologic features. Users typically upload photos as evidence of their findings, though audio recordings are also accepted, and such evidence is not a strict requirement. Users may share observation locations publicly, "obscure" them to display a less precise location or make the locations completely private. iNaturalist users can add identifications to each other's observations in order to confirm or improve the identification of the observation. Observations are classified as "Casual", "Needs ID" (needs identification), or "Research Grade" based on the quality of the data provided and the community identification process. Any quality of data can be downloaded from iNaturalist and "Research Grade" observations are often incorporated into other online databases such as the Global Biodiversity Information Facility and the Atlas of Living Australia. === Automated species identification === In addition to observations being identified by others in the community, iNaturalist includes an automated species identification tool, first released in 2017. Images can be identified via a computer vision model which has been trained on the large database of the observations on iNaturalist. Multiple species suggestions are typically provided with the suggestion that the software guesses to be most likely is at the top of the list. A broader taxon such as a genus or family is commonly provided if the model is unsure of the species. It is trained once or twice a year, and the threshold for species included in the training set has changed over time. It can be difficult for the model to guess correctly if the species in question is infrequently observed or hard to identify from images alone, or if the image submitted has poor lighting, is blurry, or contains multiple subjects. In February 2023, iNaturalist released v2.1 of its computer vision model, which was trained on a new source model which performed significantly better than the previous models trained using a different source model. In April 2025 iNaturalist released an updated app for iOS, changing the original version to "iNaturalist Classic." == Projects == Users have created and contributed to tens of thousands of different projects on iNaturalist. The platform is commonly used to record observations during bioblitzes, which are biological surveying events that attempt to record all the species that occur within a designated area, and a specific project type on iNaturalist. Other project types include collections of observations by location or taxon or documenting specific types of observations such as animal tracks and signs, the spread of invasive species, roadkill, fishing catches, or discovering new species. In 2011, iNaturalist was used as a platform to power the Global Amphibian and Global Reptile BioBlitzes, in which observations were used to help monitor the occurrence and distribution of the world's reptiles and amphibian species. The US National Park Service partnered with iNaturalist to record observations from the 2016 National Parks BioBlitz. That project exceeded 100,000 observations in August 2016. In 2017, the United Nations Environment Programme teamed up with iNaturalist to celebrate World Environment Day.. In 2022, Reef Ecologic teamed up with iNaturalist to celebrate World Oceans Day. === City Nature Challenge === In 2016, Lila Higgins from the Natural History Museum of Los Angeles County and Alison Young from the California Academy of Sciences co-founded the City Nature Challenge (CNC). In the first City Nature Challenge, naturalists in Los Angeles and the San Francisco Bay Area documented over 20,000 observations with the iNaturalist platform. In 2017, the CNC expanded to 16 cities across the United States and collected over 125,000 observations of wildlife in 5 days. The CNC expanded to a global audience in 2018, with 68 cities participating from 19 countries, with some cities using community science platforms other than iNaturalist to participate. In 4 days, over 17,000 people cataloged over 440,000 nature observations in urban regions around the world. In 2019, the CNC once again expanded, with 35,000 parti
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Social media use by businesses

Social media use by businesses includes a range of applications. Although social media accessed via desktop computers offer an online shopping variety of opportunities for companies in a wide range of business sectors, mobile social media, which users can access when they are "on the go" via tablet computers or smartphones, benefit companies because of the location- and time-sensitive awareness of their users. Mobile social media tools can be used for marketing research, communication, sales promotions/discounts, informal employee learning/organizational development, relationship development/loyalty programs, and e-commerce. Marketing research: Mobile social media applications provide companies data about offline consumer movements at a level of detail that was previously accessible to online companies only. These applications allow any business to know the exact time a customer who uses social media entered one of its locations, as well as know the social media comments made during the visit. Communication: Mobile social media communication takes two forms: company-to-consumer (in which a company may establish a connection to a consumer based on its location and provide reviews about locations nearby) and user-generated content. For example, McDonald's offered $5 and $10 gift-cards to 100 users randomly selected among those checking in at one of its restaurants. This promotion increased check-ins by 33% (from 2,146 to 2,865), resulted in over 50 articles and blog posts, and prompted several hundred thousand news feeds and Twitter messages. Sales promotions and discounts: Although customers have had to use printed coupons in the past, mobile social media allows companies to tailor promotions to specific users at specific times. For example, when launching its California-Cancun service, Virgin America offered users who checked in through Loopt at one of three designated taco trucks in San Francisco or Los Angeles between 11 a.m. and 3 p.m. on 31 August 2010, two tacos for $1 and two flights to Cancun or Cabo for the price of one. This special promotion was only available to people who were at a certain location at a certain time. Relationship development and loyalty programs: In order to increase long-term relationships with customers, companies can develop loyalty programs that allow customers who check-in via social media regularly at a location to earn discounts or perks. For example, American Eagle Outfitters remunerates such customers with a tiered 10%, 15%, or 20% discount on their total purchase. Informal employee learning/organizational development is facilitated by social media. Technologies such as blogs, wiki pages, web forums, social networks and other social media act as technology enhanced learning (TEL) tools, and their users perceive change in organizational structure, culture and knowledge management. The prerequisite for the successful use of social media are motivated employees who want to use the new technologies. It is central for companies to understand the factors that determine the willingness to use social media. Customer service and support: A company can gain cost savings and increase revenue and customer satisfaction by using social media platforms in customer service and support. By using social media tools, company's have easy and widescale contact to its customers and simultaneously increase their brand knowledge. E-commerce: Social media sites are increasingly implementing marketing-friendly strategies, creating platforms that are mutually beneficial for users, businesses, and the networks themselves in the popularity and accessibility of e-commerce, or online purchases. The user who posts their comments about a company's product or service benefits because they are able to share their views with their online friends and acquaintances. The company benefits because it obtains insight (positive or negative) about how their product or service is viewed by consumers. Mobile social media applications such as Amazon.com and Pinterest have started to influence an upward trend in the popularity and accessibility of e-commerce. E-commerce businesses may refer to social media as consumer-generated media (CGM). A common thread running through all definitions of social media is a blending of technology and social interaction for the co-creation of value for the business or organization that is using it. People obtain valuable information, education, news, and other data from electronic and print media. Social media are distinct from industrial and traditional media such as newspapers, magazines, television, and film as they are comparatively inexpensive marketing tools and are highly accessible. They enable anyone, including private individuals, to publish or access information easily. Industrial media generally require significant resources to publish information, and in most cases the articles go through many revisions before being published. This process adds to the cost and the resulting market price. Originally social media was only used by individuals, but now it is used by both businesses and nonprofit organizations and also in government and politics. One characteristic shared by both social and industrial media is the capability to reach small or large audiences; for example, either a blog post or a television show may reach no people or millions of people. Some of the properties that help describe the differences between social and industrial media are: Quality: In industrial (traditional) publishing—mediated by a publisher—the typical range of quality is substantially narrower (skewing to the high quality side) than in niche, unmediated markets like user-generated social media posts. The main challenge posed by the content in social media sites is the fact that the distribution of quality has high variance: from very high-quality items to low-quality, sometimes even abusive or inappropriate content. Reach: Both industrial and social media technologies provide scale and are capable of reaching a global audience. Industrial media, however, typically use a centralized framework for organization, production, and dissemination, whereas social media are by their very nature more decentralized, less hierarchical, and distinguished by multiple points of production and utility. Frequency: The number of times users access a type of media per day. Heavy social media users, such as young people, check their social media account numerous times throughout the day. Accessibility: The means of production for industrial media are typically government or corporate (privately owned); social media tools are generally available to the public at little or no cost, or they are supported by advertising revenue. While social media tools are available to anyone with access to Internet and a computer or mobile device, due to the digital divide, the poorest segment of the population lacks access to the Internet and computer. Low-income people may have more access to traditional media (TV, radio, etc.), as an inexpensive TV and aerial or radio costs much less than an inexpensive computer or mobile device. Moreover, in many regions, TV or radio owners can tune into free over the air programming; computer or mobile device owners need Internet access to go to social media sites. Usability: Industrial media production typically requires specialized skills and training. For example, in the 1970s, to record a pop song, an aspiring singer would have to rent time in an expensive professional recording studio and hire an audio engineer. Conversely, most social media activities, such as posting a video of oneself singing a song require only modest reinterpretation of existing skills (assuming a person understands Web 2.0 technologies); in theory, anyone with access to the Internet can operate the means of social media production, and post digital pictures, videos or text online. Immediacy: The time lag between communications produced by industrial media can be long (days, weeks, or even months, by the time the content has been reviewed by various editors and fact checkers) compared to social media (which can be capable of virtually instantaneous responses). The immediacy of social media can be seen as a strength, in that it enables regular people to instantly communicate their opinions and information. At the same time, the immediacy of social media can also be seen as a weakness, as the lack of fact checking and editorial "gatekeepers" facilitates the circulation of hoaxes and fake news. Permanence: Industrial media, once created, cannot be altered (e.g., once a magazine article or paper book is printed and distributed, changes cannot be made to that same article in that print run) whereas social media posts can be altered almost instantaneously, when the user decides to edit their post or due to comments from other readers. Community media constitute a hybrid of industrial and social media. Though community-owned, some community radio,
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Pamphlet war

A pamphlet war is a protracted argument or discussion through printed media, especially between the time the printing press became common, and when state intervention like copyright laws made such public discourse more difficult. The purpose was to defend or attack a certain perspective or idea. Pamphlet wars have occurred multiple times throughout history, as both social and political platforms. Pamphlet wars became viable platforms for this protracted discussion with the advent and spread of the printing press. Cheap printing presses, and increased literacy made the late 17th century a key stepping stone for the development of pamphlet wars, a period of prolific use of this type of debate. Over 2200 pamphlets were published between 1600–1715 alone. Pamphlet wars are generally credited for powering many key social changes of the era, including the Reformation and the Revolution Controversy, the English philosophical debate set off by the French Revolution. == History of the pamphlet in England == Throughout Europe in the 16th century, printed tracts were used to argue religious doctrine and foment support for religious causes. In England, Henry VIII used print literature to justify his break from the Catholic Church. During the subsequent reigns of Edward and Mary, print polemics escalated into propaganda warfare, as print media gained enormous potential to sway common opinion. By the 1560s, print was widely used to convey news. In 1562, the first pamphlets appeared, which discussed the English forces sent to aid the Protestant French Huguenots. In 1569, pamphlets reported the revolt of the Northern Earls and the subsequent Rebellion of the same year. In the 1580s, pamphlets began to replace broadsheet ballads as the means to convey information to the general public. Over the next century, the pamphlet became the principal means of garnering support for a cause or an idea, and was particularly influential during the English Civil Wars (1642-1651) and the Glorious Revolution of 1688. Through the ensuing decades, the pamphlet lost some popularity due to the emergence of newspapers and journals, but continued to be an important medium of public debate, as illustrated by the Revolution Controversy a full century later in the 1790s. == Pamphlet printing == Coming from a Latin word, "pamphlet" literally means "small book." In the early days of printing, the format of the book or pamphlet depended on the size of the paper used and the number of times it was folded. If a page was only folded once, it was called a folio. If it was folded twice, it was known as a quarto. An octave was a paper folded three times. A pamphlet was usually 1-12 sheets of paper folded in quarto, or 8-96 pages. It was sold for one or two pennies apiece. The printing of a pamphlet involved many people: the author, the printer, suppliers, print-makers, compositor, correctors, pressmen, binders, and distributors. Once the pamphleteer had written the pamphlet, it was sent to the printing house to be corrected, set into type, and printed. The papers were then given to the printer's warehouse-keeper, who bundled the copies and sent them to the bookseller, who was probably the one financing the printing. He was responsible to bind the pamphlets, usually by sewing them, and then sold them wholesale to individual bookselling vendors. The booksellers then sold them from a stall in the marketplace. == Pamphlet subjects == Pamphlets began as the means of conveyance for religious debates, and therefore religious topics were one of the main subjects they dealt with. The definition of a pamphlet came to mean a short work dealing with social, political, or religious issues. Typical topics included the Civil war, Church of England doctrines, Acts of Parliament, the Popish Plot (see below), the Stuart Era, and Cromwell propaganda. In addition, pamphlets were also used for romantic fiction, autobiography, scurrilous personal abuse, and social criticism. They contained much of the propaganda of the 17th century in the midst of the religious and political turmoil. They were also used for debates between the Puritans and the Anglican. During the Glorious Revolution, pamphlets were political weapons. == Authors == There were many authors of pamphlets. However some of the more popular authors include Daniel Defoe, Thomas Hobbes, Jonathan Swift, John Milton, and Samuel Pepys. Also included in the midst are Thomas Nashe, Joseph Addison, Richard Steele, and Matthew Prior. In 1591–1592, Robert Greene released a series of pamphlets which later inspired many other authors including Thomas Middleton and Thomas Dekker. == Critics == Pamphlets, along with their vast popularity, received criticism. There were many in the time period who believed that pamphlets were full of foolishness. They thought the pamphlets were not good enough literature and that they would turn people from "good" writing. They believed that pamphlets would be the end of the great volumes of literature and that great writing would be forgotten. == News reporting == Pamphlets made a great difference in the way news was reported to the general public. With the publication of pamphlets, it was no longer difficult for people to hear of events taking place far away. The closer the occurrence was to London, the easier and faster people heard of it. For example, the Battle of Edgehill took place on 23 October 1642. The first pamphlet reporting the incident was printed on 25 October 24 hours after some of the orders reported had been given. While not entirely accurate, and hurriedly made, the pamphlet nonetheless was able to tell the general public what had happened in the battle. A more accurate, specific, and readable account was available in a pamphlet printed on 26 October, and the "authorized" version was available only five days after the battle took place. == Marprelate pamphlets == In 1588, a series of pamphlets marked a turning point for the Puritans, dividing them from other Protestants in the country. The authors wrote under the pseudonym of Martin Marprelate and his two sons of the same name. The true identities of the authors were never discovered. The pamphlets aimed to provoke authorities to take action against censorship. The series was among the first to ask questions directly of its readers. == Early pamphlet wars == === Elizabethan pamphlet wars === As a means of forming or swaying public opinion, pamphlets like these had a part in influencing society, even as the content was itself influenced by society. During the 16th century and continuing for a short while in the early 17th century in England there was rise in the use of pamphlet wars to discuss a myriad of issues spanning from the civil war, to religious freedoms and the roles of women in society. The Queen herself participated in these discussions, making sure that she was widely read and understood by her people in order to gain favour and establish herself as the monarch despite being a woman. Examples of her use of this medium appear in To the Troops at Tilbury written in 1588, On Mary's Execution written in 1586, and many more. Another famous writer of this period to take advantage of the pamphlet was Emilia Lanier, famous for her arguments about the role of women. A common idea promoted by many literary works and the general attitude towards women, Lanier's work "Eve's Apology in Defence of Women" refuted the belief that Eve is responsible for the fall of man. A very uncommon and unpopular stance to take, Lanier accomplishes her defence through structuring it as an apology, one of the earliest subversive feminist texts. Similarly, Francis Bacon wrote his Essays to promote his idea of morality and other complicated social issues. For example, his work, "Of Love" examines the various understandings of the concept of love, particularly as it was perceived during the Elizabethan era. === Eikon Series === From 1649 until 1651, some five pamphlets were published in a debate about the execution of King Charles I of England (1600-1649). Prior to his execution, King Charles wrote the first pamphlet in the discussion, Eikon Basilike’’ (from the Greek “eikon” for image and “basileus” for king). The subtitle of this work - Portraiture of His Sacred Majesty in His Solitudes and Sufferings - indicates that Charles sought to portray himself as a martyr to the cause of regal prerogative. In the following months, several response pamphlets were published (collectively known as the "Eikon" series), including: Eikon Alethine, Eikon e Pistes, Eikonoklastes, and Eikon Aklastos,” alternately attacking or defending the king, his regicide, and his self-portrait in “Eikon Basilike.” == Popish Plot and Elizabeth Cellier == In the 1680s, after being acquitted of the "Meal-Tub Plot" for which she was accused, Elizabeth Cellier wrote Malice Defeated, which, along with The Matchless Picaro, sparked a pamphlet war surrounding debate of the ascension of a Catholic king to the thro
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