1,121 Matching Annotations
  1. Last 7 days
  2. Nov 2019
    1. Google has confirmed that it partnered with health heavyweight Ascension, a Catholic health care system based in St. Louis that operates across 21 states and the District of Columbia.

      What happened to 'thou shalt not steal'?

    1. Speaking with MIT Technology Review, Rohit Prasad, Alexa’s head scientist, has now revealed further details about where Alexa is headed next. The crux of the plan is for the voice assistant to move from passive to proactive interactions. Rather than wait for and respond to requests, Alexa will anticipate what the user might want. The idea is to turn Alexa into an omnipresent companion that actively shapes and orchestrates your life. This will require Alexa to get to know you better than ever before.

      This is some next-level onslaught.

  3. codeactsineducation.wordpress.com codeactsineducation.wordpress.com
    1. SEL measurement is being done in myriad ways, involving multiple different conceptualizations of SEL, different political positions, and different sectoral interests.

      Here I am reminded of the book Counting What Counts

    1. This booklet itells you how to use the R statistical software to carry out some simple analyses that are common in analysing time series data.

      what is time series?

    1. "While we hope that Google will lift these unwarranted sanctions for AdNauseam, it highlights a much more serious problem for Chrome users," the AdNauseam team adds. "It is frightening to think that at any moment Google can quietly make your extensions and data disappear, without so much as a warning."
  4. Oct 2019
    1. "Element" SelectorsEach component has a data-reach-* attribute on the underlying DOM element that you can think of as the "element" for the component.
    1. I frequently talk with people who are not that concerned about surveillance, or who feel that the positives outweigh the risks. Here, I want to share some important truths about surveillance: Surveillance can facilitate human rights abuses and even genocide Data is often used for different purposes than why it was collected Data often contains errors Surveillance typically operates with no accountability Surveillance changes our behavior Surveillance disproportionately impacts the marginalized Data privacy is a public good We don’t have to accept invasive surveillance




    1. Terminar los proyectos que empezamos en 2019, con prioridad en Documentatón, ya que no es un cover, sino que es nuestro propio libro.

      Para mí el tema de acabarlo son recursos (tiempo y dinero, etc). Podemos ir avanzando de a trozos un capítulo a la vez, haciéndolo de encuentro en encuentro, pero esto daría un ritmo muy lento. La experiencia previa muestra que esto no es sostenible y que si queremos un libro terminado, más que un esfuerzo colectivo, se requerirá un alto esfuerzo individual. Como muestra la gráfica de reportes de la documentatón, una persona puede hacer más que la suma de las restantes (hablando de no temerle a la soledad):

      Sin embargo, si esto es lo que está pasando, reflejando las métricas de muchos proyectos de software libre, dependemos fuertemente de los tiempos de esos individuos. En mi caso, no puedo continuar con la documentatón hasta no resolver el tema de los artículos de mi graduación, que se volvió una verdadera telenovela (eso merece su entrada de blog aparte) y preferí asuntos como los Data Haiku, precisamente porque son actividades más puntuales que todo un libro, que luego pueden convertirse en capítulos de uno (por ejemplo el de Datactivismos) pero transmitiendo ese aire de lo ágil y de lo terminado, que precisamente quisiéramos comunicar.

      Creo que tenemos que reconocer en las dinámicas comunitarias, qué podemos hacer en ellas y en cuáles ritmos, y entender que lo otro requerirá de recursos extra (económicos, personales, etc) que tendremos que proveer como personas naturales o jurídicas, con nuestro propio esfuerzo o el de nuestras empresas/fundaciones.

    2. Selección comunitaria de temas para Data Weeks o Data Rodas. Apoyo de proyectos de los participantes de la comunidad. Reuniones periódicas de la Comunidad, algo así como Data Roda el primer viernes de cada mes, así sea para saludarnos síncronamente y ver en qué andamos y hacer un encera - brilla de bacanes.

      Estos tres puntos se podrían juntar con la idea de que los participantes propongan sus propios proyectos y se apropien de la planeación y ejecución de las Data Rodas o Data Weeks venideros.

      Sólo quitaría el carácter periódico, pues creo que una de las potencias de nuestra comunidad es responder flexiblemente a lo eventual. Por ejemplo, ahora tenemos un periodo electoral en Colombia. De allí surgió mi preocupación por visualizar financiación de campañas, pero los eventos de la semana pasada derivaron en blikis, con soporte de comentarios. Una reacción ágil a la contingencia y no el seguimiento riguroso de algo pre-planeado (a mi me gustaría retomar lo de financiación de campañas, pero será luego).

      De nuevo la sugerencia, como dije en mi entrada de respuesta a esta, y en otras ocasiones es sustituir la planeación por la coordinación. Mi propuesta de coordinación es la siguiente:

      • Los miembros que quieren ver otras temáticas las proponen en los canales comunitarios y se apersonan de su preparación y ejecución.
      • Los otros miembros respondemos a esas iniciativas autónomas, en solidaridad, acompañando esas sesiones y aportándoles.
      • Al final de cada evento, miramos hacia dónde podemos llevar los otros.
    3. Si puedo aportar una herramienta más a Grafoscopio, quiero que sea la querendura.

      Me parece muy potente la querendura como metodología. Sin embargo, por lo pronto siento que es un listado amplio de ideas sueltas en el enlace que nos presentas y me gustaría indagar por las prácticas concretas que la hacen posible.

  5. Sep 2019
    1. if n is very small (for example n = 3), rather than showing error bars and statistics, it is better to simply plot the individual data points.
    1. Keep the ergonomics of stable reference and directly mutable objects. In other words; be able to have a variable pointing to an object, and make subsequent reads or writes to it. Without needing to fear that you’re working with old data. While, in the background,..State is stored in an immutable, structurally shared tree.
    1. With MobX you don't need to normalize your data.

      flip side: https://codeburst.io/the-curious-case-of-mobx-state-tree-7b4e22d461f:

      MobX cannot guarantee your data is JSON serializable,

    1. Estimated economic benefit of data linkage

      the potential value from linking Census data to administrative data sets is only beginning to be realised and holds immense potential.(In other work for the Population Health Research Network, Lateral Economics concluded that data linkage generated over $16 for every dollar invested).

    2. Cost reduction suggestion

      there may be ways to reduce costs associated with the development of Census-equivalent statistics, including relying less on the general public to answer questions every five years

    1. “But then again,” a person who used information in this way might say, “it’s not like I would be deliberately discriminating against anyone. It’s just an unfortunate proxy variable for lack of privilege and proximity to state violence.

      In the current universe, Twitter also makes a number of predictions about users that could be used as proxy variables for economic and cultural characteristics. It can display things like your audience's net worth as well as indicators commonly linked to political orientation. Triangulating some of this data could allow for other forms of intended or unintended discrimination.

      I've already been able to view a wide range (possibly spurious) information about my own reading audience through these analytics. On September 9th, 2019, I started a Twitter account for my 19th Century Open Pedagogy project and began serializing installments of critical edition, The Woman in White: Grangerized. The @OPP19c Twitter account has 62 followers as of September 17th.

      Having followers means I have access to an audience analytics toolbar. Some of the account's followers are nineteenth-century studies or pedagogy organizations rather than individuals. Twitter tracks each account as an individual, however, and I was surprised to see some of the demographics Twitter broke them down into. (If you're one of these followers: thank you and sorry. I find this data a bit uncomfortable.)

      Within this dashboard, I have a "Consumer Buying Styles" display that identifies categories such as "quick and easy" "ethnic explorers" "value conscious" and "weight conscious." These categories strike me as equal parts confusing and problematic: (Link to image expansion)

      I have a "Marital Status" toolbar alleging that 52% of my audience is married and 49% single.

      I also have a "Home Ownership" chart. (I'm presuming that the Elizabeth Gaskell House Museum's Twitter is counted as an owner...)

      ....and more

    1. More conspicuously, since Trump’s election, the RNC — at his campaign’s direction — has excluded critical “voter scores” on the president from the analytics it routinely provides to GOP candidates and committees nationwide, with the aim of electing down-ballot Republicans. Republican consultants say the Trump information is being withheld for two reasons: to discourage candidates from distancing themselves from the president, and to avoid embarrassing him with poor results that might leak. But they say its concealment harms other Republicans, forcing them to campaign without it or pay to get the information elsewhere.
    1. Methodology To determine the link between heat and income in U.S. cities, NPR used NASA satellite imagery and U.S. Census American Community Survey data. An open-source computer program developed by NPR downloaded median household income data for census tracts in the 100 most populated American cities, as well as geographic boundaries for census tracts. NPR combined these data with TIGER/Line shapefiles of the cities.

      This is an excellent example of data journalism.

    1. On the other hand, a resource may be generic in that as a concept it is well specified but not so specifically specified that it can only be represented by a single bit stream. In this case, other URIs may exist which identify a resource more specifically. These other URIs identify resources too, and there is a relationship of genericity between the generic and the relatively specific resource.

      I was not aware of this page when the Web Annotations WG was working through its specifications. The word "Specific Resource" used in the Web Annotations Data Model Specification always seemed adequate, but now I see that it was actually quite a good fit.

  6. Aug 2019
    1. Material Design Material System Introduction Material studies About our Material studies Basil Crane Fortnightly Owl Rally Reply Shrine Material Foundation Foundation overview Environment Surfaces Elevation Light and shadows Layout Understanding layout Pixel density Responsive layout grid Spacing methods Component behavior Applying density Navigation Understanding navigation Navigation transitions Search Color The color system Applying color to UI Color usage Text legibility Dark theme Typography The type system Understanding typography Language support Sound About sound Applying sound to UI Sound attributes Sound choreography Sound resources Iconography Product icons System icons Animated icons Shape About shape Shape and hierarchy Shape as expression Shape and motion Applying shape to UI Motion Understanding motion Speed Choreography Customization Interaction Gestures Selection States Material Guidelines Communication Confirmation & acknowledgement Data formats Data visualization Principles Types Selecting charts Style Behavior Dashboards Empty states Help & feedback Imagery Launch screen Onboarding Offline states Writing Guidelines overview Material Theming Overview Implementing your theme Components App bars: bottom App bars: top Backdrop Banners Bottom navigation Buttons Buttons: floating action button Cards Chips Data tables Dialogs Dividers Image lists Lists Menus Navigation drawer Pickers Progress indicators Selection controls Sheets: bottom Sheets: side Sliders Snackbars Tabs Text fields Tooltips Usability Accessibility Bidirectionality Platform guidance Android bars Android fingerprint Android haptics Android icons Android navigating between apps Android notifications Android permissions Android settings Android slices Android split-screen Android swipe to refresh Android text selection toolbar Android widget Cross-platform adaptation Data visualization Data visualization depicts information in graphical form. Contents Principles Types Selecting charts Style Behavior Dashboards Principles Data visualization is a form of communication that portrays dense and complex information in graphical form. The resulting visuals are designed to make it easy to compare data and use it to tell a story – both of which can help users in decision making. Data visualization can express data of varying types and sizes: from a few data points to large multivariate datasets. AccuratePrioritize data accuracy, clarity, and integrity, presenting information in a way that doesn’t distort it. HelpfulHelp users navigate data with context and affordances that emphasize exploration and comparison. ScalableAdapt visualizations for different device sizes, while anticipating user needs on data depth, complexity, and modality. Types Data visualization can be expressed in different forms. Charts are a common way of expressing data, as they depict different data varieties and allow data comparison.The type of chart you use depends primarily on two things: the data you want to communicate, and what you want to convey about that data. These guidelines provide descriptions of various different types of charts and their use cases.Types of chartsChange over time charts show data over a period of time, such as trends or comparisons across multiple categories. Common use cases include: Category comparison...Read MoreChange over timeChange over time charts show data over a period of time, such as trends or comparisons across multiple categories.Common use cases include: Stock price performanceHealth statisticsChronologies Change over time charts include:1. Line charts 2. Bar charts 3. Stacked bar charts 4. Candlestick charts 5. Area charts 6. Timelines 7. Horizon charts 8. Waterfall charts Category comparisonCategory comparison charts compare data between multiple distinct categories. Use cases include: Income across different countriesPopular venue timesTeam allocations Category comparison charts include: 1. Bar charts 2. Grouped bar charts 3. Bubble charts 4. Multi-line charts 5. Parallel coordinate charts 6. Bullet charts RankingRanking charts show an item’s position in an ordered list.Use cases include: Election resultsPerformance statistics Ranking charts include: 1. Ordered bar charts 2. Ordered column charts 3. Parallel coordinate charts Part-to-wholePart-to-whole charts show how partial elements add up to a total.Use cases include: Consolidated revenue of product categoriesBudgets Part-to-whole charts include: 1. Stacked bar charts 2. Pie charts 3. Donut charts 4. Stacked area charts 5. Treemap charts 6. Sunburst charts CorrelationCorrelation charts show correlation between two or more variables.Use cases include: Income and life expectancy Correlation charts include: 1. Scatterplot charts 2. Bubble charts 3. Column and line charts 4. Heatmap charts DistributionDistribution charts show how often each values occur in a dataset. Use cases include: Population distributionIncome distribution Distribution charts include: 1. Histogram charts 2. Box plot charts 3. Violin charts 4. Density charts FlowFlow charts show movement of data between multiple states.Use cases include: Fund transfersVote counts and election results Flow charts include: 1. Sankey charts 2. Gantt charts 3. Chord charts 4. Network charts RelationshipRelationship charts show how multiple items relate to one other.Use cases includeSocial networksWord charts Relationship charts include: 1. Network charts 2. Venn diagrams 3. Chord charts 4. Sunburst charts Selecting charts Multiple types of charts can be suitable for depicting data. The guidelines below provide insight into how to choose one chart over another. Showing change over timeChange over time can be expressed using a time series chart, which is a chart that represents data points in chronological order. Charts that express...Read MoreChange over time can be expressed using a time series chart, which is a chart that represents data points in chronological order. Charts that express change over time include: line charts, bar charts, and area charts.Type of chartUsageBaseline value * Quantity of time seriesData typeLine chartTo express minor variations in dataAny valueAny time series (works well for charts with 8 or more time series)ContinuousBar chartTo express larger variations in data, how individual data points relate to a whole, comparisons, and rankingZero4 or fewerDiscrete or categoricalArea chartTo summarize relationships between datasets, how individual data points relate to a wholeZero (when there’s more than one series)8 or fewerContinuous* The baseline value is the starting value on the y-axis.Bar and pie chartsBoth bar charts and pie charts can be used to show proportion, which expresses a partial value in comparison to a total value. Bar charts,...Read MoreBoth bar charts and pie charts can be used to show proportion, which expresses a partial value in comparison to a total value. Bar charts express quantities through a bar’s length, using a common baselinePie charts express portions of a whole, using arcs or angles within a circleBar charts, line charts, and stacked area charts are more effective at showing change over time than pie charts. Because all three of these charts share the same baseline of possible values, it’s easier to compare value differences based on bar length. Do.Use bar charts to show changes over time or differences between categories. Don’t.Don’t use multiple pie charts to show changes over time. It’s difficult to compare the difference in size across each slice of the pie. Area chartsArea charts come in several varieties, including stacked area charts and overlapped area charts: Overlapping area charts are not recommended with more than two time...Read MoreArea charts come in several varieties, including stacked area charts and overlapped area charts:Stacked area charts show multiple time series (over the same time period) stacked on top of one another Overlapped area charts show multiple time series (over the same time period) overlapping one anotherOverlapping area charts are not recommended with more than two time series, as doing so can obscure the data. Instead, use a stacked area chart to compare multiple values over a time interval (with time represented on the horizontal axis). Do.Use a stacked area chart to represent multiple time series and maintain a good level of legibility. Don’t.Don’t use overlapped area charts as it obscures data values and reduces readability. Style Data visualizations use custom styles and shapes to make data easier to understand at a glance, in ways that suit the user’s needs and context.Charts can benefit from customizing the following: Graphical elementsTypographyIconographyAxes and labelsLegends and annotationsStyling different types of dataVisual encoding is the process of translating data into visual form. Unique graphical attributes can be applied to both quantitative data (such as temperature, price,...Read MoreVisual encoding is the process of translating data into visual form. Unique graphical attributes can be applied to both quantitative data (such as temperature, price, or speed) and qualitative data (such as categories, flavors, or expressions). These attributes include:ShapeColorSizeAreaVolumeLengthAnglePosition DirectionDensityExpressing multiple attributesMultiple visual treatments can be applied to more than one aspect of a data point. For example, a bar color can represent a category, while a bar’s length can express a value (like population size). Shape can be used to represent qualitative data. In this chart, each category is represented by a specific shape (circles, squares, and triangles), which makes it easy to compare data both within a specific range or against other categories. ShapeCharts can use shapes to display data in a range of ways. A shape can be styled as playful and curvilinear, or precise and high-fidelity,...Read MoreCharts can use shapes to display data in a range of ways. A shape can be styled as playful and curvilinear, or precise and high-fidelity, among other ways in between. Level of shape detailCharts can represent data at varying levels of precision. Data intended for close exploration should be represented by shapes that are suitable for interaction (in terms of touch target size and related
  7. Jul 2019
    1. Every time your child opens the email, that person knows generally where they are (or specifically, if they have other info to triangulate against).
    1. In contrast to such pseudonymous social networking, Facebook is notable for its longstanding emphasis on real identities and social connections.

      Lack of anonymity also increases Facebook's ability to properly link shadow profiles purchased from other data brokers.

    1. our sum of squares is 41.187941.187941.1879

      Just considering the Y, and not the X. Calculating the residuals from the average/mean Y.

    1. in clustering analyses, standardization may be especially crucial in order to compare similarities between features based on certain distance measures. Another prominent example is the Principal Component Analysis, where we usually prefer standardization over Min-Max scaling, since we are interested in the components that maximize the variance

      Use standardization, not min-max scaling, for clustering and PCA.

    2. As a rule of thumb I’d say: When in doubt, just standardize the data, it shouldn’t hurt.
    1. driven by data—where schools use data to identify a problem, select a strategy to address the problem, set a target for improvement, and iterate to make the approach more effective and improve student achievement.

      Gates data model.

    1. many elements used in the objective function of a learning algorithm (such as the RBF kernel of Support Vector Machines or the l1 and l2 regularizers of linear models) assume that all features are centered around zero and have variance in the same order. If a feature has a variance that is orders of magnitude larger than others, it might dominate the objective function and make the estimator unable to learn from other features correctly as expected.
  8. Jun 2019
  9. varsellcm.r-forge.r-project.org varsellcm.r-forge.r-project.org
    1. missing values are managed, without any pre-processing, by the model used to cluster with the assumption that values are missing completely at random.

      VarSelLCM package

    1. Success ina data science project comes not from access to any one exotic tool, but from having quantifiablegoals, good methodology, crossdiscipline interactions, and a repeatable workflow.



    1. Academicsarealsoatfaulthere:arecentanalysisof29millionpapersinover15,000peer-reviewedtitlespublishedaroundthetimeoftheZikaandEbolaepidemicsfoundthatlessthan1%exploredthegenderedimpactoftheoutbreaks

      How do we prevent this pattern here at Georgia Tech? There is a very obvious gender gap, especially in STEM where bad data in medicine and engineering are collected? What are some mini steps we can take to encourage pursuing data for different backgrounds? Education is always first, starting with class similar to this one informing people about how gender plays a role. Perhaps then we can create projects exploring the issue in data related to each person's major.



    1. However, this doesn’t mean that Min-Max scaling is not useful at all! A popular application is image processing, where pixel intensities have to be normalized to fit within a certain range (i.e., 0 to 255 for the RGB color range). Also, typical neural network algorithm require data that on a 0-1 scale.

      Use min-max scaling for image processing & neural networks.

    2. The result of standardization (or Z-score normalization) is that the features will be rescaled so that they’ll have the properties of a standard normal distribution with μ=0μ=0\mu = 0 and σ=1σ=1\sigma = 1 where μμ\mu is the mean (average) and σσ\sigma is the standard deviation from the mean
  10. May 2019

      This is an interesting fact, usually when I think of visualization and data I go to the classic default charts and data. I'll have to keep this iin mind.


      I really like this because I don't see it often and it actually does draw my eye to the data and capture my interest.

    1. Virtually all BPMs have utilities for creating simple, data-gathering forms. And in many types of workflows, these simple forms may be adequate. However, in any workflow that includes complex document assembly (such as loan origination workflows), BPM forms are not likely to get the job done. Automating the assembly of complex documents requires ultra-sophisticated data-gathering forms, which can only be designed and created after the documents themselves have been automated. Put another way, you won't know which questions need to be asked to generate the document(s) until you've merged variables and business logic into the documents themselves. The variables you merge into the document serve as question fields in the data gathering forms. And here's the key point - since you have to use the document assembly platform to create interviews that are sophisticated enough to gather data for your complex documents, you might as well use the document assembly platform to generate all data-gathering forms in all of your workflows.
    1. El ritmo de las actividades de diseño e instalación de redes comunitarias en veredas del municipio de Fusagasugá se ve acrecentado por las convocatorias internas de investigación de la Universidad de Cundinamarca que a lo largo del tiempo de vida de Red FusaLibrehan sido un músculo financiero que les permite acelerar los proc

      Interesante vínculo entre comunidad y universidad. En nuestro caso, no hemos logrado un vínculo permanente y si bien algunos dineros de convocatorias de investigación universitaria y convocatorias internacionales permitieron pagar parte de los Data Weeks, junto con una contribución menor de algunos asistentes, en general ha sido un proyecto financiado con recursos propios y préstamos familiares.

    1. Developing economies’ copper demand has steadily grown over the last decades, fueling economic and social improvement. By 2011, China already represented 40% of the demand.

      Why does China need so much.

    2. Codelco is a state-owned Chilean mining company and the world’s largest copper producer. Based on their annual report and USGS statistics, they produced ~10% of the world’s copper in 2015 and own 8% of global reserves. They are also a large producer of greenhouse gas emissions. Last year, Codelco produced 3,2 t CO2e/millions tmf from both indirect and direct effects, and in 2011 it consumed 12% of the total national electricity supply.

      Goddamn they should start recylcling

    1. Methodology The classic OSINT methodology you will find everywhere is strait-forward: Define requirements: What are you looking for? Retrieve data Analyze the information gathered Pivoting & Reporting: Either define new requirements by pivoting on data just gathered or end the investigation and write the report.

      Etienne's blog! Amazing resource for OSINT; particularly focused on technical attacks.

  11. Apr 2019
    1. Powered by Data wrote 4 of the resources on this page. "Measuring Outcomes" is about admin data. "Understanding the Philanthropic Landscape" is about open data - sp. open grants data. "Effective Giving" is an intro. And "Emerging Data Practices" is a tech backgrounder from June 2015.