44 Matching Annotations
  1. 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
  2. Jul 2019
  3. May 2019
    1. 1RWDOOPRYLHVKDYHWREHGRFXPHQWDULHVDQGQRWDOOYLVXDOL]DWLRQKDVWREHWUDGLWLRQDOFKDUWVDQGJUDSKV

      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.

  4. Feb 2019
    1. To help us get better comprehension of the structure of an argument, we can also call forth a schematic or graphical display

      I might be getting ahead of what's to come, since I am annotating as I am reading, but this gets me thinking about some visualization approaches I saw in the 1990s by the brilliant and forgotten Roy Stringer working on what he called "Navihedra" - while they were often seen as navigational, his ideas seemed to be rooted in better representations of the kinds of structures Engelbart is telling us

      In brief, however, Navihedra are 3D models based on Platonic solids and relationships between pieces of information are articulated in terms of the spatial relationships represented by the vertices of the polyhedron. That is, units of information (of any kind, media, size or complexity) are attached to a specific vertex and bi-directionally hyperlinked to all the immediately adjacent vertices. The overall structure being determined by some perceived relevance reflected in proximity. Proximate vertices are understood to locate units of information/argument that are more closely related to one another than units of information that are not directly hyperlinked. Furthermore, this 3 dimensional arrangement can be rotated in space so that differing patterns of inter-relatedness can be viewed. Creating such an arrangement is much more difficult than it might appear and requires an author to consider the structure/presentation of even a simple argument like the one contained in this article with at least as much care as a more conventional presentation.

      Sadly these were produced in a media form hardly displayable now (Macromedia Shockwave), remnants are in the Internet Archive.

    1. Deep Learning Multidimensional Projections

      深度学习版的降维可视化!

      其中有好些是与 UMAP 和 t-sne 做的对比。

  5. Jan 2019
    1. Nyhan and Reifler also found that presenting challenging information in a chart or graph tends to reduce disconfirmation bias. The researchers concluded that the decreased ambiguity of graphical information (as opposed to text) makes it harder for test subjects to question or argue against the content of the chart.

      Amazingly important double-edged finding for discussions of data visualization!

  6. Dec 2018
    1. A Tutorial on Distance Metric Learning: Mathematical Foundations, Algorithms and Software

      很不错的 Distance Metric Learning 综述性材料,富含概念,如何设计DML算法,DML 算法的数学理论是怎样的(凸优化、矩阵分析、信息论)等等。最后开源了Python 库 pyDML 以方便研究此 paper 中的算法。

    2. How convolutional neural network see the world - A survey of convolutional neural network visualization methods

      果断收藏并且要细读下。。。Paper Summary 准备!

      这可是对 CNN 可视化方法的 review 啊!

      一篇很棒的综述,专门说 CNN 的可视化的!要好好读读了!

      Paper Summary 准备!

  7. Nov 2018
    1. Why scatter plots suggest causality, and what we can do about it

      看了半天我真是不明白,转了45度再把图捏成方形的,就可以写篇 paper 宣传了?。。。[哼]

    2. UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction

      此文提供了一个和 t-sne 非常类似的降维可视化算法。效果相当不错!也开源了算法代码。

      按照作者的说法,UMAP 比 T-SNE 算法更好的优点有二:更快!更准!

    3. Local Explanation Methods for Deep Neural Networks Lack Sensitivity to Parameter Values

      This paper shows that local explanations for DNNs with random-initialized weights are qualitatively and quantitatively similar to explanations produced by DNNs with learned weights.

      • Pros:

      The paper is clear, the problem is well stated and the method is sound.

      • Cons:

      The impact of the findings in this paper is unclear. Perhaps the most important point made in the paper is the importance of the architecture over fine-tuning of the weights for explanation tasks (and more in general).

      其实 goodfellow 这文章篇幅很短,可视化图像的效果是很棒的!

    4. Sanity Checks for Saliency Maps

      专门探讨对各种 Saliency methods (显著图方法)的。

      Goodfellow 署名的该文章内含有大量很棒的可视化效果。

    5. Using Machine Learning to Predict the Evolution of Physics Research

      内涵各种物理期刊。。。可视化挺不错。。。

  8. Oct 2018
  9. idyll-lang.org idyll-lang.org
    1. A toolkit for creating data-driven stories and explorable explanations.

      Markup language for creating data driven stories

  10. Jun 2018
  11. May 2018
    1. Thus, the digital object ossifies out of two histories, one virtual and another visual.Within computation, the object arises out of a desire to create a model of the worldwithin the computer but at the same time out of an attempt to create a whole new visualworld native to the compute

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  12. Mar 2018
  13. Oct 2017
  14. Sep 2017
    1. After we were done, pictures were taken of the group and distributed online to groups in other cities performing similar activities, contributing to the spectacle of the day.

      En los eventos locales se toman fotos durante el evento, al margen de los resultados. En el Data Week en cambio, las fotos con pocas en comparación (a veces nulas), particularmente en consideración a la privacidad. La lógica del espectáculo/impacto está más centrada en las visualizaciones mismas.

    1. That “hackers” can model beneficial process disrupts the often presumed subversive nature of hacking as much as it does easy assumptions about a Foucaultian notion of governmentality. Prototypes act as working evidence to lobby for changing government process, particularly those that improve digital infrastructure or direct communication with citizens. The capa-bility of code to act as a persuasive argument has long been noted, and modeling can produce charged debates about the very meaning of “civic.”

      [...] On a level of hackathons, prototypes can be speculative (Lodato and DiSalvo, in press) rather than an “outcome,” revealing conflicting notions of “civic tech” (Shaw, 2014).

      Nuestro enfoque ha estado centrado más en la modelación, que es requerida para la visualización, pero también en la idea de construir capacidad en la infraestructura y en la comunidad, lo cual va más allá del prototipo volátil, que se abandona después.

  15. Aug 2017
  16. Apr 2017
    1. opml2json A simple tool to convert opml files exported by Mindnode Pro to JSON consumable by D3 Javascript library.
  17. Mar 2017
    1. Prophet : Facebook에서 오픈 소스로 공개한 시계열 데이터의 예측 도구로 R과 Python으로 작성되었다.

      python statics opensource, also can use R

  18. Feb 2017
  19. Jan 2017
    1. Haiku for Clouds

      The collective noun for a plural of haiku is a 'visualization'. See below:

  20. Dec 2016
    1. sites such as Facebook and Twitter automatically and continuously refresh the page; it’s impossible to get to the bottom of the feed.

      Well is not. A scrapping web technique used for the Data Selfies project goes to the end of the scrolling page for Twitter (after almost scrolling 3k tweets), which is useful for certain valid users of scrapping (like overwatch of political discourse on twitter).

      So, can be infinite scrolling be useful, but not allowed by default on this social networks. Could we change the way information is visualized to get an overview of it instead of being focused on small details all the time in an infitite scroll tread mill.

  21. Sep 2016
    1. If efficiency incentives and tools have been effective for utilities, manufacturers, and designers, what about for end users? One concern I’ve always had is that most people have no idea where their energy goes, so any attempt to conserve is like optimizing a program without a profiler.
    2. This is aimed at people in the tech industry, and is more about what you can do with your career than at a hackathon. I’m not going to discuss policy and regulation, although they’re no less important than technological innovation. A good way to think about it, via Saul Griffith, is that it’s the role of technologists to create options for policy-makers.

      Nice to see this conversation happening between technology and broader socio-political problems so explicit in Bret's discourse.

      What we're doing in fact is enabling this conversation between technologist and policy-makers first, and we're highlighting it via hackathon/workshops, but not reducing it only to what happens there (an interesting critique to the techno-solutionism hackathon is here), using the feedback loops in social networks, but with an intention of mobilizing a setup that goes beyond. One example is our twitter data selfies (picture/link below). The necesity of addressing urgent problem that involve techno-socio-political complex entanglements is more felt in the Global South.

      ^ Up | Twitter data selfies: a strategy to increase the dialog between technologist/hackers and policy makers (click here for details).

  22. Jun 2016
    1. Also, the more complex a software project becomes, the more work you have to put into and it grows exponentially. So, keep it simple and make it fast. It's much easier to write software, throw it away and start over again quickly, than having this huge generic system that tries to do everything. It doesn't make sense. It's just too much work. You'd get this huge software system with thousand dependencies and, in the end, it's really hard to innovate, get new stuff in there, or, the worst case, to change the concept. Almost every software that we have published is not generic but is used only for one case. So, keep it simple and get a prototype in under three days.

      Agile visualization its a worthy exception to this trend. It is generic while being flexible and moldable. My first projects start with an easy prototype in a week and became full projects in a couple of months average. Then I can reuse the visual components by using abstraction and making visual builders.

      The couple of months average included the learning of the programming language and environment, the data cleaning and completion. With the builders the time has started to decrease exponentially.

    2. What type of team do you need to create these visualisations? 
OpenDataCity has a special team of really high-level nerds. Experts on hardware, servers, software development, web design, user experience and so on. I contribute the more mathematical view on the data. But usually a project is done by just one person, who is chief and developer, and the others help him or her. So, it's not like a group project. Usually, it's a single person and a lot of help. That makes it definitely faster, than having a big team and a lot of meetings.

      This strengths the idea that data visualization is a field where a personal approach is still viable, as is shown also by a lot of individuals that are highly valuated as data visualizers.

  23. Feb 2016
  24. Jan 2016
    1. UT Austin SDS 348, Computational Biology and Bioinformatics. Course materials and links: R, regression modeling, ggplot2, principal component analysis, k-means clustering, logistic regression, Python, Biopython, regular expressions.

  25. Nov 2015
    1. The effectiveness of infographics, or any other form of communication, can be measured in terms of whether people:

      • pay attention to it
      • understand it
      • remember it later

      Titles are important. Ideally, the title should concisely state the main point you want people to grasp.

      Recall of both labels and data can be improved by using redundancy -- text as well as images. For example:

      • flags in addition to country names
      • proportional bubbles in addition to numbers.
  26. Aug 2015
  27. Jun 2015
  28. Mar 2015
  29. Nov 2014
    1. This is an ongoing attempt at an algorithmically-generated, readability-adjusted scatter-plot of the musical genre-space, based on data tracked and analyzed for 1306 genres by The Echo Nest. The calibration is fuzzy, but in general down is more organic, up is more mechanical and electric; left is denser and more atmospheric, right is spikier and bouncier.