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D4Science

D4Science is a Data Infrastructure offering services by community-driven virtual research environments. In particular, it supports communities of practice willing to implement open science practices, thus it is an Open Science Infrastructure. The infrastructure follows the system of systems approach, where the constituent systems (Service providers) offer "resources" (namely services and by them data, computing, storage) assembled together to implement the overall set of D4Science services. In particular, D4Science aggregates "domain agnostic" service providers as well as community-specific ones to build a unifying space where the aggregated resources can be exploited via Virtual research Environments and their services. It is spread across several sites, the primary one is hosted by the Istituto di Scienza e Tecnologie dell'Informazione of National Research Council (Italy). At the earth of this infrastructure there is an Open Source Software named gCube system. == Services == D4Science offers: Virtual Research Environment as a Service providing any community of practice with a dedicated working environment supporting any knowledge production process in a collaborative way, in fact every VRE enables computer-supported cooperative work by design. D4Science-based VREs are web-based, community-oriented, collaborative, user-friendly, open-science-enabler working environments for scientists and practitioners willing to work together to perform a set of (research) task. From the end-user perspective, each VRE manifests in a unifying web application (and a set of application programming interfaces (APIs)): (a) comprising several applications organised in specific menu items and (b) running in a plain web browser. Every application is providing VRE users with facilities implemented by relying on one or more services provisioned by diverse providers. Among the basic services every VRE is equipped with there are a Social Networking area enabling collaborative and open discussions on any topic and disseminating information of interest for the community, for example, the availability of a research outcome; a Workspace for storing, organizing and sharing any version of a research artifact, including dataset and model implementation; a User Management dashboard for managing membership and roles; a Catalogue Service recording the assets worth being published thus to make it possible for others to be informed and make use of these assets. Science Gateway as a Service providing a community of practice with a dedicated science gateway hosting a selected set of virtual research environments. Data Analytics at scale for data analytics including: a proprietary data analytics platform (DataMiner) to execute analytics tasks either by relying on methods provided by the user or by others. It is endowed with importing and sharing facilities for analytics methods implemented in heterogeneous forms including R, Java, Python, and KNIME. The platform enacts tasks execution by a distributed and hybrid computing infrastructure. Moreover, one of the worth highlighting feature of this platform is its open science-friendliness. All the analytics methods integrated in it are exposed by a standard protocol (the OGC WPS protocol) clients can use to get informed on available methods as well as to start processes, monitor their execution and access results. Every analytics task performed by the platform automatically produces a provenance record catering for the reproducibility of the task; an RStudio-based development environment for R enabling to perform statistical computing tasks in the cloud. This RStudio environment is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides seamless access to the VRE Workspace enabling sharing of resources with other members of the same working environment. a Jupyter-based notebook environment for developing and executing interactive computing by JupyterLab instances. Each JupyterLab is (i) preconfigured with libraries and packages to ease the execution of common data analytics tasks, and (ii) provides access to the VRE Workspace enabling sharing of resources with other members of the same working environment. == Community == The D4Science Infrastructure serves more than 24,000 registered users (August 2024) through 177 active VREs offered via 20 Science gateways. This extensive infrastructure not only supports a diverse range of scientific communities but also fosters significant engagement and collaboration among researchers worldwide. Engagement within the D4Science community is robust, with users benefiting from user-friendly application environments tailored to their specific needs. The platform allows users to securely preserve, access, and share their data from anywhere, fostering a collaborative and inclusive research environment. Additionally, groups of users can create their own virtual environments and customise them with the applications they need, further enhancing the platform's flexibility and usability. Supported communities and cases range from Agri-food to Social Data Science, Earth Science and Marine Science. These diverse applications demonstrate the versatility and broad applicability of the D4Science Infrastructure, making it an invaluable resource for researchers across various scientific domains. == History == The D4Science development has been supported by several European-funded projects. DILIGENT (2004-2007) in the Sixth Framework Programme for Research and Technological Development was the forerunner where a testbed infrastructure built by integrating digital library and grid computing technologies and resources was conceived and developed to serve the needs of communities of practice involved in knowledge development. In the context of the Seventh Framework Programme for research, technological development and demonstration the development of the D4Science initiative. In this period the infrastructure was established and developed to serve communities of practices from domains ranging from Earth Science to Marine Science with worldwide scope In the context of the H2020 research and innovation programme the maturity level of the D4Science infrastructure was high enough to allow a large and very diverse set of communities of practice to benefit from it and its services and further contribute to its development. Moreover, the services offered by the infrastructure have been developed to support open science practices. The operation and improvement of the D4Science infrastructure facilities are still ongoing while its exploitation is progressively growing.
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Data exhaust

Data exhaust (also exhaust data) is the trail of data generated as a by-product of users' online activity, behaviour, and transactions, rather than data they deliberately create or submit. It forms part of a broader category of unconventional data that also includes geospatial, network, and time-series data, and may be useful for predictive analytics. Data exhaust can take the form of cookies, temporary files, log files, clickstream records and stored preferences. Actions such as visiting a web page, following a link, or dwelling on an element may all generate exhaust data that is recorded without the user's active awareness. Unlike primary content — which the user intentionally creates — exhaust data is a passive side effect of interaction. A bank, for example, might treat the amounts and parties involved in a transaction as primary data, while secondary data could include whether the transaction was carried out at a cash machine rather than a branch. == Uses == Data exhaust collected by companies is often information that is not immediately useful in isolation, but can be aggregated and analysed to improve products, personalise content, identify trends, and support quality control. Companies may also store exhaust data for future analysis or sell it to third parties. Shoshana Zuboff has described this practice as a core mechanism of what she terms surveillance capitalism, in which behavioural data generated by users is converted into predictive products. Kosciejew notes that large quantities of often raw data are collected in this way, much of which is never analysed. == Medical exhaust data == Many medical devices — including pacemakers, dialysis machines and surgical cameras — generate exhaust data as a by-product of their operation. The majority of this data is never captured or analysed, and is typically discarded once a procedure ends or a device completes its routine monitoring cycle. The potential use of data generated by implanted devices such as pacemakers raises additional legal and ethical questions around ownership and consent. Using electronic health records for research also creates challenges because of the volume of data involved, creating a need for automated algorithms to process it. == Privacy and regulation == The collection and distribution of data exhaust is not in itself illegal in most jurisdictions, but its use raises questions of privacy and informed consent. Steps commonly taken to address these concerns include data anonymisation, offering users an opt-out from the sale of their data, and publishing explicit privacy policies that disclose what data is collected and how it is used.
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Social media and psychology

Social media began in the form of generalized online communities. These online communities formed on websites like Geocities.com in 1994, Theglobe.com in 1995, and Tripod.com in 1995. Many of these early communities focused on social interaction by bringing people together through the use of chat rooms. The chat rooms encouraged users to share personal information, ideas, or even personal web pages. Later the social networking community Classmates took a different approach by simply having people link to each other by using their personal email addresses. By the late 1990s, social networking websites began to develop more advanced features to help users find and manage friends. These newer generation of social networking websites began to flourish with the emergence of SixDegrees.com in 1997, Makeoutclub in 2000, Hub Culture in 2002, and Friendster in 2002. However, the first profitable mass social networking website was the South Korean service, Cyworld. Cyworld initially launched as a blog-based website in 1999 and social networking features were added to the website in 2001. Other social networking websites emerged like Myspace in 2002, LinkedIn in 2003, and Bebo in 2005. In 2009, the social networking website Facebook (launched in 2004) became the largest social networking website in the world. Both Instagram and Kik were launched in October 2010. Active users of Facebook increased from just a million in 2004 to over 750 million by the year 2011. Making internet-based social networking both a cultural and financial phenomenon. In September 2011, Snapchat was launched and reported over 300 million users in 2021. == Psychology of social networking == A social network is a social structure made up of individuals or organizations who communicate and interact with each other. Social networking sites – such as Facebook, Twitter, Instagram, Pinterest and LinkedIn – are defined as technology-enabled tools that assist users with creating and maintaining their relationships. A study found that middle schoolers reported using social media to see what their friends are doing, to post pictures, and to connect with friends. Human behavior related to social networking is influenced by major individual differences, meaning that people differ quite systematically in the quantity and quality of their social relationships. Two of the main personality traits that are responsible for this variability are the traits of extraversion and introversion. Extraversion refers to the tendency to be socially dominant, exert leadership, and influence on others. In contrast, introversion reflects a tendency towards shyness, social phobia, or even avoid social situations altogether, which could potentially reduce the number of social contacts a person may have. These individual differences may result in different social networking outcomes. Other psychology factors related to social media and Media psychology are depression, anxiety, attachment, self-identity, well-being, and the need to belong. === Neuroscience === The three domains that neural systems rely on to be strengthened to support social media use are social cognition, self-referential cognition, and social rewarding. When someone posts something on social media, they think of how their audience will react, while the audience thinks of the motivations behind posting the information. Both parties are analyzing the other's thoughts and feelings, which coherently rely on multiple network systems of the brain including the dorsomedial prefrontal cortex, bilateral temporoparietal junction, anterior temporal lobes, inferior frontal gyri, and posterior cingulate cortex. All of these systems work to help us process social behaviors and thoughts drawn out on social media. Social media requires a great deal of self-referential thought. People use social media as a platform to express their opinions and show off their past and present selves. In other words, as Bailey Parnell said in her Ted Talk, we're showing off our "highlight reel" (4). When one receives feedback from others, the individual obtains more reflected self-appraisal which leads to comparisons of their social behaviors or "highlights" to other users. Self-referential thought involves activity in the medial prefrontal cortex and the posterior cingulate cortex. The brain uses these systems when thinking of oneself. A 2021 umbrella review found that most associations between adolescent social media use and mental health were characterized as weak or inconsistent, though certain studies identified 'substantial' negative impacts, particularly linked to passive consumption and problematic use. Social media also provides a constant supply of rewards that keeps users coming back for more. Whenever users receive a like or a new follower, it activates the brain's social reward system which includes the ventromedial prefrontal cortex, ventral striatum, and ventral tegmental area. This system has been found to activate in response to positive feedback from peers, suggesting that users experience online acceptance in a similar manner to other material rewards or positive experiences, further acting as a potential reward. While these areas of the brain become strengthened, other parts of the brain start to weaken. Technology is encouraging multi-tasking, especially because of how easy it is to switch from one task to another by opening another tab or using two devices at once. The brain's hippocampus is mainly associated with long-term memory. In a study done by Russell Poldark, a professor at UCLA, they found that "for the task learned without distraction, the hippocampus was involved. However, for the task learned with the distraction of the beeps, the hippocampus was not involved; but the striatum was, which is the brain system that underlies our ability to learn new skills." The study concludes that multitasking can cause reliance on the striatum more than the hippocampus, which can change the way we learn. The striatum is known to be connected to mainly the brain's reward system. The brain will strengthen the neurons to the striatum while it weakens the neurons to the hippocampus to make the brain more efficient. Because our brain starts to rely on the striatum more than the hippocampus, it becomes harder for us to process new information. Nicholas Carr, author of The Shallows: How The Internet Is Changing Our Brains, agrees: "What psychologists and brain scientists tell us about interruptions is that they have a fairly profound effect on the way we think. It becomes much harder to sustain attention, to think about one thing for a long period of time, and to think deeply when new stimuli are pouring at you all day long. I argue that the price we pay for being constantly inundated with information is a loss of our ability to be contemplative and to engage in the kind of deep thinking that requires you to concentrate on one thing." === Well-Being === How does well-being relate to social media? In an article titled Social Impact of Psychological Research on Well-Being Shared in Social Media, Pulido et al. found a 15.7% social impact in their results. These new results were compared to a previous study conducted by Pulido et al., which had a high of 4.98% compared to 27.5% in the new study. These results show the ESISM, which is evidence of social impact present. In a two-year span, the difference between social impact rose 22.52% according to these studies. When taking into consideration that an increasingly large number of teens report either being active on, or having used, some form of social media, ranging from apps such as Facebook to TikTok, researching the effects of social media on the well-being of teens and young adults has become more of a topic of focus in recent years. === Depression === Especially in today's society, social media has gained a new perspective on younger generations. It is what younger generations are born into and are growing up to use, particularly what is running today's society. Social Media has its downfalls regarding depression and mental health. Many users often compare their lives regarding what they see on these platforms. In an article Does Social Media Cause Depression? by the Child Mind Institute, Miller states that "several studies, teenage and young adult users who spend the most time on Instagram, Facebook and other platforms for have shown to have substantially (from 13 to 66 percent) higher rates of reported depression than those who spent the least time", what the study shows how Facebook and Instagram, platforms showcasing daily lives and or lifestyles, or less fulfilling or less satisfied or more flaunting base or superficial. Instead of social community, there has become a perception of individuals striving for a life that is not real, whether that is editing photos or making life seem perfect when it is not. This causes a sense of depression by the weight of a comparing game. In "How Social Media Affects Y
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Weird SoundCloud

Weird SoundCloud, or SoundClown, is a mashup parody music scene taking place on the online distribution platform SoundCloud. The scene has been described by its producers and music journalists to be a satirical take on electronic dance music, and useless, throwaway internet content. One critic, Audra Schroeder, categorized it as an in-joke that is "deconstructing and reshaping memes and popular music, recontextualizing the sacred texts of millennial chat rooms." == Origins == In a January 2014 interview, DJ Kevin Wang suggested that the Weird SoundCloud has "been around in the last one to two years", but started to gain much more popularity the previous year through electronic dance music internet blogs. Weird SoundCloud producer Ideaot suggested that some in the phenomenon came from the YouTube poop scene. Another producer in the community, DJ @@ (AT-AT), reasoned that producers joining the scene "want to express their musicality, see it as a more mature form of YouTube Poop," or are "just looking for recognition on social media sites." AT-AT said that it was "a fun thing to do, and after I stopped making proper music I felt I needed a bit of an outlet for my creativity. The fact that people enjoyed it and/or treated it as a travesty (Direct quote from one of my tracks) spurs me on." == Characteristics == Weird SoundCloud is a mash-up and parody music genre labeled by journalist Audra Schroeder as an in-joke that is "deconstructing and reshaping memes and popular music, recontextualizing the sacred texts of millennial chat rooms." Most tracks range from around 30 seconds to one minute in length. The people who make weird SoundCloud are known as SoundClowns, a term coined by producer Dicksoak. Ideaot described the weird SoundCloud community as "largely just people who are friends with each other." Noisey critic Ryan Bassil spotlight the variety of music coming out of the weird SoundCloud landscape: "One minute you could be listening to the Seinfeld theme reimagined as an aneurysm inducing dubstep corker, the next, you're recovering from hearing a version of Tenacious D's "Tribute" that's akin to having a stroke." Bassil analyzes that the tracks "often take the past and repurpose it into something that, although not altogether useful, sounds fresh and reflective of the abstract, confusing panoramic that encapsulates the modern internet." Bassil compared the lexicon of SoundClown's track titles to that of Reddit and Twitter users. According to Dicksoak, most works of the style are critiques of EDM or "are just uploaded because they sound funny." However, Bassil disagreed, writing that there are also many tracks that keep repurposing a certain meme, such as "mom's spaghetti" or the re-use of vocals from recordings by hip hop group Death Grips. He describe the scene's re-use of memes as a satirical take on pointless online content that is only on the internet to "do nothing other than fill the void": They're changing the format of the original work's intended message or audience - a technique often employed by top-tier digital media companies - and in doing so they're sarcastically, ironically, taking the piss out of what Web 2.0's turned into - an open arena where the most ridiculous, unashamed, often pointless piggy-back content can rack up thousands and thousands of clicks. == Notable examples == There are mash-ups that "disrupt the flow of popular music", in the words of writer Schroeder, such as a "flutedrop" remix of the Miley Cyrus song "Wrecking Ball" and Shaliek's mashup of music by Bruno Mars and Korn. In November 2013, Wang released a set of mp3 files on SoundCloud named Best Drops Ever, which included tracks like "A Drop So Epic a Bunch of NYU Bros Already Bought a 3-Day Weekend Pass for It" and "A Drop So Crazy You'll Kill Your Family". All of the tracks start as normal electronic dance music build-ups, before they drop into a "bait and switch" audio or film clip such as Filet-O-Fish commercials, the Whitney Houston song "I Will Always Love You" and the film Bambi (1942) that ruins the anticipation. The collection is a parody of the over-importance and over-focus of the drop and lack of care of the overall quality of a song common in the modern electronic dance music scene. Wang has released more than 45 tracks in the weird SoundCloud, some of them receiving around a million plays. Subgenres of Weird SoundCloud include Macklecore, mash-ups and remixes that include the works of American hip-hop recording artist Macklemore, and Biggiewave, which include samples of songs from the album Ready to Die (1994) by The Notorious B.I.G. Common audio and meme sources used include Skrillex, the Martin Garrix track "Animals", Thomas the Tank Engine, Shrek, Macklemore, "Gangnam Style", the Bruno Mars track "Uptown Funk", the Disturbed track "Down with the Sickness", Space Jam, the Childish Gambino track "Bonfire", the Death Grips track "Takyon" and air horn sound effects. == Reception == Bassil praised the SoundClown scene as "loveable and strangely honest", reasoning that it "just reminds me that we're all humans on the internet, all searching for #content that means something, something to connect with, but usually only dredging up bastardised versions of things we've already read, seen, or watched before." Bassil also described the weird SoundCloud as a more successful version of a similar scene known as weird YouTube; the reason for the success of SoundClowns is due to SoundCloud's discovery algorithm: "Small collectives and trends are able to form, and there's an abundance of tracks from artists who are almost forging careers out of it, as opposed to uploading one viral hit." Publications have made lists of weird SoundCloud works, such as BuzzFeed's "23 Of The Weirdest Songs On Soundcloud", Obsev's "Weird SoundCloud Mashups That Must've Been Made While Drunk", and Thump's "9 of the Best and Most Upsetting Soundclowns we Could Find", where writer Isabelle Hellyer called it the "most influential genre of music in human history." A Your EDM writer called it "oddly addicting."
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Anderson's rule (computer science)

In the field of computer security, Anderson's rule refers to a principle formulated by Ross J. Anderson: systems that handle sensitive personal information involve a trilemma of security, functionality, and scale, of which you can choose any two. A system that has information on many data subjects and to which many people require access is hard to secure unless its functionality is severely restricted. If it has rich functionality, you may have to restrict the number of people with access, or accept that some information will leak.
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Data Reference Model

The Data Reference Model (DRM) is one of the five reference models of the Federal Enterprise Architecture. == Overview == The DRM is a framework whose primary purpose is to enable information sharing and reuse across the United States federal government via the standard description and discovery of common data and the promotion of uniform data management practices. The DRM describes artifacts which can be generated from the data architectures of federal government agencies. The DRM provides a flexible and standards-based approach to accomplish its purpose. The scope of the DRM is broad, as it may be applied within a single agency, within a community of interest, or cross-community of interest. == Data Reference Model topics == === DRM structure === The DRM provides a standard means by which data may be described, categorized, and shared. These are reflected within each of the DRM's three standardization areas: Data Description: Provides a means to uniformly describe data, thereby supporting its discovery and sharing. Data Context: Facilitates discovery of data through an approach to the categorization of data according to taxonomies. Additionally, enables the definition of authoritative data assets within a community of interest. Data Sharing: Supports the access and exchange of data where access consists of ad hoc requests (such as a query of a data asset), and exchange consists of fixed, re-occurring transactions between parties. Enabled by capabilities provided by both the Data Context and Data Description standardization areas. === DRM Version 2 === The Data Reference Model version 2 released in November 2005 is a 114-page document with detailed architectural diagrams and an extensive glossary of terms. The DRM also make many references to ISO standards specifically the ISO/IEC 11179 metadata registry standard. === DRM usage === The DRM is not technically a published technical interoperability standard such as web services, it is an excellent starting point for data architects within federal and state agencies. Any federal or state agencies that are involved with exchanging information with other agencies or that are involved in data warehousing efforts should use this document as a guide.
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PitchYaGame

PitchYaGame or #PitchYaGame (sometimes abbreviated to PYG) is a volunteer movement hosted on the social media platform Twitter to showcase, and present awards for, independent video games from around the world. == Description == PitchYaGame is hosted on the social media platform Twitter to showcase independent video games from around the world. Video pitches are presented by developers in June and November each year, and use the hashtag #PitchYaGame to identify and reference news about the showcase and the individual pitches, and the presentation of awards. The showcase was founded in May 2020 by Liam Twose, with the mission of recognising independent video games, and "focused on empowering indie game developers to strengthen their position in the industry." Twose has made clear that PitchYaGame is a showcase and not a hardcore competition, with "[j]ust enough of a push to make sure people put their best pitch forward." The team now comprises Twose (@LiamTwose at Twitter), operations manager "Indie Game Lover" (@IndieGameLover), and host Sarah Clancy (@ImSarahNow). The pitches were originally made monthly, with entries split into a number of categories, but this proved unmanageable. PitchYaGame collaborator, Sarah Clancy reported that judging the many entries on a monthly basis was "difficult and unwieldy." Therefore, pitches were later switched to six monthly, "feature creep" was reduced, and awards streamlined into gold, silver, bronze, runners-up, and most viral. == Sponsorship == In June 2021, PitchYaGame prizes were sponsored by Xsolla, and in November 2021 by Aurora Punks and Cold Pixel. No cash prizes were available in 2022, as the organisers moved PitchYaGame into a less-competitive, "more showcase centric format". == Reception == In October 2020, Elijah Beahm at The Escapist wrote that "One of the greatest challenges for any game is landing a solid pitch. You have to sell people, maybe even a publisher, to take your idea seriously. Most of the time, it's an obfuscated process that leaves the average developer scratching their heads, but Liam Twose and his team behind #PitchYaGame, 'PYG' for short, are looking to change all that with some clever social engineering." In March 2021, Cameron Koch at GameSpot wrote that "Using the #PitchYaGame, thousands of indie developers tweeted out pitches for their games on November 2 as part of a social media contest, and the results are astounding." He went on to say that "There is no arguing with the results. According to Twose, around 1100-1300 games were shared with the hashtag, and some real gems look to have shined through." In November 2021, Stafano "Stef" Castelli at IGN Italia wrote that "I myself enjoyed 'browsing through' the competitors, discovering a handful of intriguing video games in development." (translated from Italian). In November 2022, Eric Bartelson at Premortem Games wrote that "It's a great way to get games noticed by fellow developers, but also publishers, investors and press." In June 2023, Mark Plunkett in Kotaku wrote about the impossibility of keeping up with all the video game releases, and described PitchYaGame, which has attracted over 10,000 pitches since 2020, as an "astoundingly simple idea" that has "become an increasingly useful spot to catch up on some excellent-looking games that we may have otherwise completely slept on."
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Social media coverage of the Olympics

Over the years, television broadcast rights have distinguished what Olympic-related content can be accessed by fans online. By doing so, mobile-friendly social platforms began to integrate into the Olympics. Athletes and fans use these platforms to share live updates, special moments, and behind-the-scenes specials. Various social media platforms have been used for Olympic content, including Twitter and Facebook. Some marketers credit social media for prompting the official U.S. broadcasters, NBC, to live stream events, including early rounds. == Background == The Olympics is able to advertise to its viewers and its host country with the use of data it collects through Social media marketing. Prominent social media platforms include: Twitter, Facebook, Instagram, Tumblr, YouTube, Google, MSN, Yahoo and many more. Campaign Initiatives and Artificial Intelligence technologies have been used to analyze the social media content of users. Information from consumers such as their preferences, demographics, age and locality are all analyzed to gain consumer insight. Campaign initiatives and AI technologies were used for such purposes in the 2010 Vancouver Winter Olympics and are in use currently. Social media marketing of the Olympics is a new phenomena, beginning prior to the 2008 Beijing Olympics == Variations == There are two classifications of social media marketing recognized by the IOC: Officially sanctioned content from rights holders and sponsors that maximizes the use of Olympic content (imagery, hashtag) Unofficial content that is generated by brands that leverage the excitement of the Olympics == 2008 Beijing Summer Olympics == Social media marketing emerged as a phenomenon during the 2008 Beijing Olympics, which progressed as a marketing and an advertising tactic ever since. The Beijing Olympics became the test subject for social media marketing initiatives started by advertising agencies. In 2008, social media marketing began the transition from one-sided communication to mass communication of the Olympic Games. Although social media marketing of the Olympic Games began in 2008, the audience to the Olympics was still primarily reached through television–reaching an audience of 4.3 billion viewers. At the time, the viewers of the Olympic Games through Internet website platforms made up an audience of approximately 390 million individuals. What was the beginning of Olympic social media marketing, was also the beginning of a more globalized experience of the Olympic Games via social media. Twitter, now a prominent social media platform, began in 2006 and grew to three million active users by the beginning of the 2008 Beijing Olympics. Members of Facebook, another prominent social media platform, tracking the Olympic Games grew from approximately one million during the Olympic Games of Athens 2004 to 90 million during the 2008 Beijing Olympics. Social media use, in general, increased by 24 percent between 2007 and 2008–from 63 percent of U.S. adults to 87 percent of U.S. adults. == 2010 Vancouver Winter Olympics == The International Olympic Committee (IOC) deemed The Vancouver Winter Olympics as "the first social media games” based on its fan base through social media platforms. The IOC launched their Facebook page a month before the games began, attracting 1.5 million fans. Shifting to online viewing attracted a younger audience than past Olympic games with over 60 percent of Facebook fans being under 24 years of age. Athletes like Lindsey Vonn and Shaun White reached fans on social media as the platform posted behind-the-scenes coverage on their experiences. The IOC used social media to create competitions between athletes and fans streamed online. Its YouTube channel hosted a “Best of Us” challenge in which the public could compete in games with their favorite athletes, acquiring three million viewers. Photos spread across social media platforms, such as Flickr, which had 11,000 photos posted by 600 photographers, bringing a new perspective to the games. Twitter contributed constant live updates of the competitions. The IOC's Twitter following doubled to 12,000 followers during the Vancouver Olympics, creating a larger viewer population for the games. The IOC created social media guidelines as more athletes and fans got online to interact with the Olympics. Social media was still relatively new as a marketing platform, so these guidelines confused many individuals. == 2012 London Summer Olympics == The London 2012 Olympic Games succeeded in broadcasting, participation and marketing. For the first time, the IOC broadcast the Olympic Games live and on-demand through YouTube, allowing fans to access the Games anytime, anywhere through live streaming. The combination of conventional broadcasting and mobile platforms reached a global audience of 4.8 billion people. Social media soared with Facebook, Twitter and Google+, attracting 4.7 million followers. Athletes shared photographs, interacted online with fans and updated daily, either in person or via an agent. Instagram was established by 2012, making itself a premier photo-sharing platform perfect for athletes to capture their emotions. Lewis Wiltshire, head of sport for Twitter UK said, "Never before have fans had such direct access to their sporting heroes." Social media created conversation on fan opinions regarding athletes, including 962,756 total mentions of Usain Bolt, “Fastest Man in History,” who defended the 100 meter and 200 meter gold medals. Michael Phelps followed with 828,081 total mentions. Olympic sponsors were active on social media; created several campaigns to promote their brands; and inspired viewers with mass participation and personalized events. The Adidas “Take the Stage” Campaign recognized talent around the world, installing a photo booth and inviting the 550 Olympics athletes to take the stage. (IOC Marketing Report 2012). David Beckham surprised fans at the photo booth in Westfield shopping centre, gaining popularity in UK media. Coca-Cola, Acer Inc., McDonald's, Visa Inc. and several others used similar tactics of participation to attract viewers. == 2014 Sochi Winter Olympics == === Channels === The 2014 Winter Olympic Games were held in Sochi, a city in Krasnodar Krai, Russia, establishing the first “social media Olympics” for Russia. The most popular Russian social media and networking service, VK, created an Olympic page, similar to Facebook's. The Olympic VK page has 2.8 million fans and—the most popular official community on the platform. Throughout the games, VK had 54 million Olympic mentions, an average of 1.5 million per day. Numbers grew on other social media pages: more than 2 million fans joined the Olympic Facebook page, 168,101 followed the Olympic Twitter, 150,000 followed the Olympic Instagram and three million visited the Olympic website in February 2014. There were 90,000 total updates on social media by Sochi 2014 Olympians and teams. The United States was the most active country during the games logging 22,598 posts across Facebook, Twitter, and Instagram. === Engagement === With social media there is also hashtags. The most popular hashtag was #sochi2014 with almost 11,000 uses. The next top five hashtags were #wearewinter, #teamusa, #olympics, #goaus and #wirfuerD. Another popular hashtag was #Sochiproblems, depicting local struggles. Photos of the poor state of Sochi on all platforms made the games the number one trending topic one week before the opening ceremony. #SochiFail and #SochiProblems gave multiple reports of the poor living arrangements, incomplete construction, broken elevators, and polluted waters. This was one way that social media provided awareness to its users. === Media Perceptions === Media perceptions varied during the games; the Olympics was viewed as a confrontation between Eastern and Western Civilizations. The LGBT community took a stand against the games. Sponsors for the games including Coca-Cola, Mcdonald's, and P&G protested against Russian authorities and Russian anti-LGBT laws. Many protests took a stand against Russian laws, which created a discussion between human rights advocates. Advocates believed organizations should not promote certain values in western markets while supporting an anti-human rights government in another market. == 2016 Rio Summer Olympics == Social media marketing was an influential tool in the promotion and analysis of the 2016 Rio Olympics. Thomas Bach, President of the International Olympic Committee said that the power of sport demonstrates that diversity and interconnectedness can enlighten us all. With over 25,000+ sources of accredited media covering the games, the 2016 games were the most consumed Olympic games to date. Marketing for the Rio Olympics began in 2013 and ultimately lasted 3 years. There were 26 million visits to Olympic.org, the official website of the Olympic games, and over 7 billion views of official Olympic content on social media. There were o
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Security switch

A security switch is a hardware device designed to protect computers, laptops, smartphones and similar devices from unauthorized access or operation, distinct from a virtual security switch which offers software protection. Security switches should be operated by an authorized user only; for this reason, it should be isolated from other devices, in order to prevent unauthorized access, and it should not be possible to bypass it, in order to prevent malicious manipulation. The primary purpose of a security switch is to provide protection against surveillance, eavesdropping, malware, spyware, and theft of digital devices. Unlike other protections or techniques, a security switch can provide protection even if security has already been breached, since it does not have any access from other components and is not accessible by software. It can additionally disconnect or block peripheral devices, and perform "man in the middle" operations. A security switch can be used for human presence detection since it can only be initiated by a human operator. It can also be used as a firewall. == Types == === Hardware kill switch === A hardware kill switch (HKS) is a physical switch that cuts the signal or power line to the device or disable the chip running them. == Examples == A cellphone is compromised by malicious software, and the device initiates video and audio recording. When the user activates the “prevent capture of audio/video” mode of the security switch, that either physically disconnects or cut the power to the microphone and the camera, which stops the recording. A laptop that has an embedded security switch is stolen. The security switch detects a lack of communication from a specific external source for 12 hours, and responds by disconnecting the screen, keyboard and other key components, rendering the laptop useless, with no possibility of recovery, even with a full format. A user wishes to prevent tracking of their location. The user then activates geolocation protection and the security switch disables all GPS communication, eliminating the possibility of tracking the device's location. A user desires to eliminate the possibility of their PIN being copied from their smartphone. They can activate the secure input function, causing the security switch to disconnect the touch screen from the operating system, so input signals are not available to any devices except the switch. A security switch performs scheduled monitoring and finds that a program is attempting to download malicious content from the internet. It then activates internet security function and disables internet access, interrupting the download. If laptop software is compromised by air-gap malware, the user may activate the security switch and disconnect the speaker and microphone, so it can not establish communication with the device. == History == Google started to work on a hardware kill switch for AI in 2016. In 2019, Apple, and Google, along with a handful of smaller players, are designing “kill switches” that cut the power to the microphones or cameras in their devices. Googles first product that implemented this is Nest Hub Max. Hardware kill switches are already available and widely tested on the PinePhone, Librem, Shiftphone, to cut power to the input peripherals (microphone, camera) but also the network connectivity modules (wifi, cellular network).
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Social media use in African politics

Since the Egyptian Revolution in 2011 and the Tunisian Revolution, social media, especially Facebook, Twitter, and YouTube, began to gain traction as a political tool in Africa. Various political actors have used social media to pursue a wide range of political objectives. State actors can use social media to encourage political discourse, campaign, or implement censorship and surveillance. Non-state actors, such as civil society organizations and opposition movements, can use social media to address political concerns and to organize widespread uprisings, such as the 2014 Burkinabé uprising. Meanwhile, extremist organizations can use social media to further their propaganda and recruitment. However, social media has been criticized for its limited accessibility and for facilitating the spread of misinformation, causing some skepticism about its effectiveness. Due to low entry barriers and user-generated content, social media provides a platform where people from different social classes can engage and interact with one another. Under traditional media, the public had limited opportunities to voice their political opinions. Social media enables people to both create and consume content. The public has become increasingly comfortable and confident in expressing political opinions online, often away from government scrutiny. Scholars argue that social media use has democratizing effects in African countries. == State actors == === Promoting political discourse === Through social media, the government and its citizens can discuss policy ideas, policy implementation, and political actions. Regardless of geographical location and distance, people are able to voice their opinions to the government. Social media includes citizens who were previously not able to express their discontent or share their ideas to the government. As state actors keep the public informed, social media can increase civic engagement. With more civic engagement, policies can be discussed without politicization. Before the commonplace use of social media, African countries faced weak feedback mechanisms that effectively excluded the average African citizen from policy discourse. In South Africa, the government uses social media to connect with constituencies. The South African president runs an official Twitter, Facebook, YouTube, and Flickr accounts to engage with the public. === Campaigning === Political parties also use social media for political campaigns during election periods. In South Africa, the ANC (African National Congress) and DA (Democratic Alliance) use social media for political purposes. These parties specifically use Facebook as a tool for campaigning and engaging with the public to improve their relationship with citizens. Nigerian President Goodluck Jonathan employed social media to campaign for the presidential election in 2011, which he won. When President Goodluck Jonathan announced his bid for the presidency on social media in 2010, it reached about 217,000 people. As his campaign progressed, President Goodluck Jonathan was able to increase his followers to half a million by early 2011. === Censorship & Surveillance === While state actors can use social media to encourage their party or discourse, social media can be used to censor and surveil citizens. For example, the ANC and DA use Facebook to monitor South Africans. The government is able to track down people who have spoken against the government and translate this information into physical action to stop any possibility of a revolution. Social media platforms can be shut down to manipulate the flow of information. In Chad, citizens cannot access information through online platforms. This censorship blocked "Facebook, Twitter, WhatsApp and Viber". In the Democratic Republic of Congo, the government shut down the internet before contested elections. In Zimbabwe, the government shut down the internet to hide civilian protests against fuel price increases. == Non-state actors == === Civil society organizations (CSOs) === Civil society organizations have also used social media networks in an effort to recruit supporters and communicate with the public. CSOs can use social media to mobilize people to support their cause, such as the Ghanaian Committee for Joint Action (CJA). In 2005 and 2006, the CJA gathered support to protest against the 50% fuel price increase. CSOs can play the role of a counterforce against state actors and state propaganda during times of crises, such as protests and military clashes. In some cases, CSOs release their own videos and photos on social media which challenges traditional forms of media. CSOs have also served to monitor elections to reduce corruption and violence during election day. For instance, the Zambian Bantu Watch started the #bantuwatch social media campaign to monitor the 2011 presidential election. Zambians used Facebook and Twitter to report polling station results to mitigate election fraud and election violence. In South Africa, CSOs created 'amandla.mobi' to campaign for public policies by creating petitions. Through 'amandla.mobi', CSOs are able to circulate petitions on social media to collect signatures. South African CSOs reported how social media helped their organizations to gain support and share ideas. However, CSOs struggle to attract media attention and often have to pay for media coverage. === Opposition forces against the government === Social media is also used by the public or opposition forces against the government. Through horizontal social media, organizing can lead to street protests and revolutions, some of which are successful. For instance, during the Egyptian revolution of 2011, "The Day of the Revolution Against Torture, Poverty, Corruption, and Unemployment" and "We Are All Khaled Said" gathered support against President Hosni Mubarak. In particular, "We Are All Khaled Said" had Egyptian citizens gather around the death of Khaled Said who was brutally tortured and killed by the Egyptian government because Said wanted to uncover government corruption. As unrest erupted into public demonstrations, President Hosni Mubarak was forced to resign. Witnessing the success of social media during the Egyptian revolution, the Tunisian Revolution, or the Jasmine Revolution, mobilized through Facebook and Twitter. Likewise, in South Africa, Malawi, and Mozambique, these countries have used social media as "new protest drums." Due to social media's low entry barrier, opposition forces against the government can facilitate political discourse that can lead to accountability. Whistleblowers and opposition forces are able to expose corruption through social media, where they face less repression while reaching a larger audience. For example, the youth of Zimbabwe and South Africa use Facebook to discuss politics without judgment. Specifically, in Zimbabwe, political youth used Facebook to avoid state surveillance. Social media is used as a supplemental tool for activism. In 2015, South African student activists started the hashtag #RhodesMustFall to push the issue of colonialism and racism at the forefront of the public. === Extremist organizations === Social media is easily accessible and created by user-based content. Therefore, marginalized groups are able to use social media to spread extremist ideas. For instance, Boko Haram created the Media Office of West Africa Province and perpetuated propaganda through Twitter and YouTube. Boko Haram's online propaganda campaign targets and persuades young dissuaded Nigerians to join their cause. It is important to note that social media has also been used against Boko Haram. In April 2014, Boko Haram kidnapped 276 schoolgirls and an international campaign fought for their return through #BringBackOurGirls. Another extremist group, Al-Shabaab, has created an online presence through Twitter and YouTube. Through these social media networks, Al-Shabaab recruits new members to their extremist group through their propaganda which emphasizes the group's successes. Albeit their efforts, Al-Shabaab has not been very successful in coordinating their members but they are successful in financing their group. Furthermore, the Islamic State of Iraq and the Levant (ISIL) use social media to target and recruit individuals to their cause. ISIL's social media usage is more diverse compared to Boko Haram and Al-Shabaab; ISIL uses "Facebook, Twitter, YouTube, WhatsApp, Telegram, JustPaste.it, Kik and Ask.fm." Since ISIL's Twitter accounts kept getting shut down, ISIL uses Telegram and WhatsApp chat rooms to privately conduct meetings. Due to the spread of extremist ideology, Zhuravskaya et al. acknowledge social media's potential to be misused. == Challenges == Although social media can be used as a political tool, it faces challenges in Africa. Due to low literacy rates in Africa, social media networks exclude many of the population members. In addition, lack of access to electricity and the internet can fur
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Pinoy baiting

Pinoy baiting is a phrase that has been used to refer to acts by non-Filipino individuals, usually celebrities or YouTubers, of posting content online purportedly with the intention of getting the attention of Filipinos, by being surprised about the Philippines or its people. Pinoy baiters are defined as giving superficial and allegedly insincere praises and similar reactions that give recognition to the Philippines or its people. Subsequent responses by Filipinos to what have been referred to as acts of Pinoy baiting have been criticized as a form of cultural cringe. This criticism would subsequently give the advice that Filipinos should not constantly require validation from non-Filipinos about themselves or their country. == Pinoy baiting mediums == === Reaction videos === On social media such as YouTube, channels with specific focus on showing their reaction towards and opinions about certain videos or topics are called reaction channels. Reaction videos are very popular and require minimal effort to create, and thus made it easy for alleged Pinoy baiting to thrive within this video-making genre. === Travel vlogs === Vlogging, short for video blogging, grew in popularity in the 2020s. Most of the popular alleged Pinoy-baiting channels tend to be vlog channels, normally following the same script under such titles as "The Philippines changed us/me", "First impression of the Philippines", "Is this really Manila?" and "Filipinos are such Kind/Good People!", and made while travelling to touristy areas such as Boracay or Bonifacio Global City and taste-testing the fast food chain Jollibee, among others. == Criticism of the phrase == Philippines-based Korean vlogger Jessica Lee had been accused by some YouTube viewers of engaging in Pinoy baiting. In a response vlog, Lee acknowledged that there may be individuals engaging in this "business strategy" of gaining views and subscribers from one of the largest communities online. However, she questioned the objectivity of some use of the phrase, citing any vlogging subject as fair game for a negative impression of being a "baiting" tool for the vlogger treating of that subject. She also invoked vloggers' freedom to choose whatever subject they want to talk about in a deep or shallow manner, while enjoining citizens to exercise their free-market right to unfollow vloggers they hate and follow those vloggers that "make them happy". She also gave her critics an explanation why she ended up vlogging about Philippine and Filipino subjects.
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Brain Imaging Data Structure

The Brain Imaging Data Structure (BIDS) is a standard for organizing, annotating, and describing data collected during neuroimaging experiments. It is based on a formalized file and directory structure and metadata files (based on JSON and TSV) with controlled vocabulary. This standard has been adopted by a multitude of labs around the world as well as databases such as OpenNeuro, SchizConnect, Developing Human Connectome Project, and FCP-INDI, and is seeing uptake in an increasing number of studies. While originally specified for MRI data, BIDS has been extended to several other imaging modalities such as MEG, EEG, and intracranial EEG (see also BIDS Extension Proposals). == History == The project is a community-driven effort. BIDS, originally OBIDS (Open Brain Imaging Data Structure), was initiated during an INCF sponsored data sharing working group meeting (January 2015) at Stanford University. It was subsequently spearheaded and maintained by Chris Gorgolewski. Since October 2019, the project is headed by a Steering Group and maintained by a separate team of maintainers, the Maintainers Group, according to a governance document that was approved of by the BIDS community in a vote. BIDS has advanced under the direction and effort of contributors, the community of researchers that appreciate the value of standardizing neuroimaging data to facilitate sharing and analysis. == BIDS Extension Proposals == BIDS can be extended in a backwards compatible way and is evolving over time. This is accomplished through BIDS Extension Proposals (BEPs), which are community-driven processes following agreed-upon guidelines. A full list of finalized BEPs and BEPs in progress can be found on the BIDS website
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Immuni

Immuni was an open-source COVID-19 contact tracing app used for digital contact tracing in Italy, dismissed on 31 December 2022, after a long and debated criticism for having been a failure due to the lack of trust placed by citizens. Immuni COVID-19 contact-tracing app had in fact been downloaded only by 12% of Italians between 14 and 75 years old (the government had previously stated that, in order for the app to work properly, it should have been downloaded by at least 60% of Italians). It makes use of the Apple/Google Exposure Notification system. == Development == It was developed by Bending Spoons and released by the Italian Ministry of Health on 1 June 2020. After a testing phase in 4 Italian regions (Abruzzo, Apulia, Liguria, Marche), the app started being active in the whole country on 15 June 2020. The app was initially released on App Store and Google Play, and since 1 February 2021 it is available on the Huawei AppGallery as well. === Source code === The source code was published on GitHub on the 25 May. The app only works in Italy, but compatibility with other European contact tracing apps was a goal. Since 19 October 2020 the app supports key-exchanges with the EU Interoperability Gateway and is therefore able to communicate with contact tracing apps of other EU countries. == Shutdown == As of 16 December 2020, the app was downloaded more than 10 million times, a number which increased to 21.882.502 downloads the day before the app's shutdown. On 27 December 2022 the Italian Ministry of Health announced that the app and its infrastructures will be dismissed on the 31 December of the same year.
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Cover-coding

Cover-coding is a technique for obscuring the data that is transmitted over an insecure link, to reduce the risks of snooping. An example of cover-coding would be for the sender to perform a bitwise XOR (exclusive OR) of the original data with a password or random number which is known to both sender and receiver. The resulting cover-coded data is then transmitted from sender to the receiver, who uncovers the original data by performing a further bitwise XOR (exclusive OR) operation on the received data using the same password or random number. ISO 18000-6C (EPC Class 1 Generation 2) RFID tags protect some operations with a cover code. The reader requests a random number from the tag, and the tag responds with a new random number. The reader then encrypts future communications with this number, using bitwise XOR, to the data it sends. Cover coding is secure if the tag signal can't be intercepted and the random number is not re-used. Compared to the loud transmissions from the reader, tag backscatter is much weaker and difficult -- but not impossible -- to intercept.
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Data analysis

Data analysis is the process of inspecting, cleansing, transforming, and modeling data with the goal of discovering useful information, informing conclusions, and supporting decision-making. Data analysis has multiple facets and approaches, encompassing diverse techniques under a variety of names, and is used in different business, science, and social science domains. In today's business world, data analysis plays an important role in making decisions more scientific and helping businesses operate more effectively. It is widely used in fields such as business analytics, healthcare, and artificial intelligence to extract meaningful insights from data. Data mining is a particular data analysis technique that focuses on statistical modeling and knowledge discovery for predictive rather than purely descriptive purposes, while business intelligence covers data analysis that relies heavily on aggregation, focusing mainly on business information. In statistical applications, data analysis can be divided into descriptive statistics, exploratory data analysis (EDA), and confirmatory data analysis (CDA). EDA focuses on discovering new features in the data, while CDA focuses on confirming or falsifying existing hypotheses. Predictive analytics focuses on the application of statistical models for predictive forecasting or classification, while text analytics applies statistical, linguistic, and structural techniques to extract and classify information from textual sources, a variety of unstructured data. All of the above are varieties of data analysis. == Data analysis process == Data analysis is a process for obtaining raw data, and subsequently converting it into information useful for decision-making by users. Statistician John Tukey, defined data analysis in 1961, as:"Procedures for analyzing data, techniques for interpreting the results of such procedures, ways of planning the gathering of data to make its analysis easier, more precise or more accurate, and all the machinery and results of (mathematical) statistics which apply to analyzing data." There are several phases, and they are iterative, in that feedback from later phases may result in additional work in earlier phases. === Data requirements === The data is necessary as inputs to the analysis, which is specified based upon the requirements of those directing the analytics (or customers, who will use the finished product of the analysis). The general type of entity upon which the data will be collected is referred to as an experimental unit (e.g., a person or population of people). Specific variables regarding a population (e.g., age and income) may be specified and obtained. Data may be numerical or categorical (i.e., a text label for numbers). === Data collection === Data may be collected from a variety of sources. A list of data sources are available for study & research. The requirements may be communicated by analysts to custodians of the data; such as, Information Technology personnel within an organization. Data collection or data gathering is the process of gathering and measuring information on targeted variables in an established system, which then enables one to answer relevant questions and evaluate outcomes. The data may also be collected from sensors in the environment, including traffic cameras, satellites, recording devices, etc. It may also be obtained through interviews, downloads from online sources, or reading documentation. === Data processing === Data integration is a precursor to data analysis: Data, when initially obtained, must be processed or organized for analysis. For instance, this may involve placing data into rows and columns in a table format (known as structured data) for further analysis, often through the use of spreadsheet (e.g. Excel) or statistical software. === Data cleaning === Once processed and organized, the data may be incomplete, contain duplicates, or contain errors. The need for data cleaning will arise from problems in the way that the data is entered and stored. Data cleaning is the process of preventing and correcting these errors. Common tasks include record matching, identifying inaccuracy of data, overall quality of existing data, deduplication, and column segmentation. Such data problems can also be identified through a variety of analytical techniques. For example; with financial information, the totals for particular variables may be compared against separately published numbers that are believed to be reliable. Unusual amounts, above or below predetermined thresholds, may also be reviewed. There are several types of data cleaning that are dependent upon the type of data in the set; this could be phone numbers, email addresses, employers, or other values. Quantitative data methods for outlier detection can be used to get rid of data that appears to have a higher likelihood of being input incorrectly. Text data spell checkers can be used to lessen the amount of mistyped words. However, it is harder to tell if the words are contextually (i.e., semantically and idiomatically) correct. === Exploratory data analysis === Once the datasets are cleaned, they can then begin to be analyzed using exploratory data analysis. The process of data exploration may result in additional data cleaning or additional requests for data; thus, the initialization of the iterative phases mentioned above. Descriptive statistics, such as the average, median, and standard deviation, are often used to broadly characterize the data. Data visualization is also used, in which the analyst is able to examine the data in a graphical format in order to obtain additional insights about messages within the data. === Modeling and algorithms === Mathematical formulas or mathematical models (supported by algorithms) may be applied to the data in order to identify relationships among the variables; for example, checking for correlation and by determining whether or not there is the presence of causality. In general terms, models may be developed to evaluate a specific variable based on other variable(s) contained within the dataset, with some residual error depending on the implemented model's accuracy (e.g., Data = Model + Error). Inferential statistics utilizes techniques that measure the relationships between particular variables. For example, regression analysis may be used to model whether a change in advertising (independent variable X), provides an explanation for the variation in sales (dependent variable Y), i.e. is Y a function of X? This can be described as (Y = aX + b + error), where the model is designed such that (a) and (b) minimize the error when the model predicts Y for a given range of values of X. === Data product === A data product is a computer application that takes data inputs and generates outputs, feeding them back into the environment. It may be based on a model or algorithm. For instance, an application that analyzes data about customer purchase history, and uses the results to recommend other purchases the customer might enjoy. === Communication === Once data is analyzed, it may be presented in many formats to the users of the analysis to support their requirements. The users may have feedback, which results in additional analysis. When determining how to communicate the results, the analyst may consider implementing a variety of data visualization techniques to help communicate the message more clearly and efficiently to the audience. Data visualization uses information displays (graphics such as, tables and charts) to help communicate key messages contained in the data. Tables are a valuable tool by enabling the ability of a user to query and focus on specific numbers; while charts (e.g., bar charts or line charts), may help explain the quantitative messages contained in the data. == Quantitative messages == Stephen Few described eight types of quantitative messages that users may attempt to communicate from a set of data, including the associated graphs. Time-series: A single variable is captured over a period of time, such as the unemployment rate over a 10-year period. A line chart may be used to demonstrate the trend. Ranking: Categorical subdivisions are ranked in ascending or descending order, such as a ranking of sales performance (the measure) by salespersons (the category, with each salesperson a categorical subdivision) during a single period. A bar chart may be used to show the comparison across the salespersons. Part-to-whole: Categorical subdivisions are measured as a ratio to the whole (i.e., a percentage out of 100%). A pie chart or bar chart can show the comparison of ratios, such as the market share represented by competitors in a market. Deviation: Categorical subdivisions are compared against a reference, such as a comparison of actual vs. budget expenses for several departments of a business for a given time period. A bar chart can show the comparison of the actual versus the reference amount. Frequency distribution:
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