164 Matching Annotations
  1. Mar 2024
  2. www.monarchmoney.com www.monarchmoney.com
    1. Our diagrams and charts make it easy to see where every dollar of your hard-earned money is flowing, so you can track your spending patterns at a glance.
  3. Jan 2024
  4. Dec 2023
  5. Mar 2023
    1. There are two main reasons to use logarithmic scales in charts and graphs.
      • respond to skewness towards large values / outliers by spreading out the data.
      • show multiplicative factors rather than additive (ex: b is twice that of a).

        The data values are spread out better with the logarithmic scale. This is what I mean by responding to skewness of large values.

      In Figure 2 the difference is multiplicative. Since 27 = 26 times 2, we see that the revenues for Ford Motor are about double those for Boeing. This is what I mean by saying that we use logarithmic scales to show multiplicative factors

    2. One reason for choosing a dot plot rather than a bar chart is that it is less cluttered. We will be learning other benefits of dot plots in this and future posts.
      • Length of bar/line has no meaning in a log-scale

        A dot plot is judged by its position along an axis; in this case, the horizontal or x axis. A bar chart is judged by the length of the bar. I don’t like using lengths with logarithmic scales. That is a second reason that I prefer dot plots over bar charts for these data.

  6. Jan 2023
    1. Data Viz with Python and RLearn to Make Plots in Python and R

      data viz with python and R

  7. Nov 2022
    1. An independent initiative made by Owen Cornec who has also made many other beautiful data visualizations. Wikiverse vividly captures the fact that Wikipedia is a an awe-inspiring universe to explore.

  8. Jun 2022
    1. 80% of data analysis is spent on the process of cleaning and preparing the data

      Imagine having unnecessary and wrong data in your document, you would most likely have to experience the concept of time demarcation -- the reluctance in going through every single row and column to eliminate these "garbage data". Clearly, owning all kinds of data without organizing them feels like stuffing your closet with clothes that you should have donated 5 years ago. It is a time-consuming and soul-destroying process for us. Luckily, in R, we have something in R called "tidyverse" package, which I believe the author talks about in the next paragraph, to make life easier for everyone. I personally use dplyr and ggplot2 when I deal with data cleaning, and they are extremely helpful. WIthout these packages' existence, I have no idea when I will be able to reach the final step of data visualization.

  9. May 2022
    1. Recognizing that the CEC hyperthreat operates at micro and macro scales across most forms of human activity and that a whole-of-society approach is required to combat it, the approach to the CEC hyperthreat partly relies on a philosophical pivot. The idea here is that a powerful understanding of the CEC hyperthreat (how it feels, moves, and operates), as well as the larger philosophical and survival-based reasons for hyper-reconfiguration, enables all actors and groups to design their own bespoke solutions. Consequently, the narrative and threat description act as a type of orchestration tool across many agencies. This is like the “shared consciousness” idea in retired U.S. Army general Stanley A. McChrystal’s “team of teams” approach to complexity.7       Such an approach is heavily dependent on exceptional communication of both the CEC hyperthreat and hyper-response pathways, as well as providing an enabling environment in terms of capacity to make decisions, access information and resources. This idea informs Operation Visibility and Knowability (OP VAK), which will be described later.  

      Such an effort will require a supporting worldwide digital ecosystem. In the recent past, major evolutionary transitions (MET) (Robin et al, 2021) of our species have been triggered by radical new information systems such as spoken language, and then inscribed language. Something akin to a Major Competitive Transitions (MCT) may be required to accompany a radical transition to a good anthropocene. (See annotation: https://hyp.is/go?url=https%3A%2F%2Fwww.frontiersin.org%2Farticles%2F10.3389%2Ffevo.2021.711556%2Ffull&group=world)

      If large data is ingested into a public Indyweb, because Indyweb is naturally a graph database, a salience landscape can be constructed of the hyperthreat and data visualized in its multiple dimensions and scales.

      Metaphorically, it can manifest as a hydra with multiple tentacles reach out to multiple scales and dimensions. VR and AR technology can be used to expose the hyperobject and its progression.

      The proper hyperthreat is not climate change alone, although that is the most time sensitive dimension of it, but rather the totality of all blowbacks of human progress...the aggregate of all progress traps that have been allowed to grow, through a myopic prioritization of profit over global wellbeing due to the invisibility of the hyperobject, from molehills into mountains.

  10. Apr 2022
    1. Trevor Bedford. (2022, January 10). Given ~680k cases per day, this would in turn suggest 0.8% or 1% of the US being infected with SARS-CoV-2 every day. This would translate to perhaps 5% or 10% of individuals currently infected with SARS-CoV-2 in the US. 15/15 [Tweet]. @trvrb. https://twitter.com/trvrb/status/1480610448563060738

  11. Mar 2022
  12. Feb 2022
  13. Jan 2022
    1. Zimmerman, M. I., Porter, J. R., Ward, M. D., Singh, S., Vithani, N., Meller, A., Mallimadugula, U. L., Kuhn, C. E., Borowsky, J. H., Wiewiora, R. P., Hurley, M. F. D., Harbison, A. M., Fogarty, C. A., Coffland, J. E., Fadda, E., Voelz, V. A., Chodera, J. D., & Bowman, G. R. (2021). SARS-CoV-2 simulations go exascale to predict dramatic spike opening and cryptic pockets across the proteome. Nature Chemistry, 13(7), 651–659. https://doi.org/10.1038/s41557-021-00707-0

    1. Dr. Cecília Tomori. (2021, December 27). Maryland—Just awful to watch what’s unfolding. Now at 1714 hospitalizations ⬆️ 130 in 24 hrs. 16.5% test positivity. Some counties have acted but no statewide 😷 policy! No measures to slow the spread. Https://coronavirus.maryland.gov https://t.co/C03cSRO2AX [Tweet]. @DrTomori. https://twitter.com/DrTomori/status/1475503877977948166

    1. Ariel Karlinsky. (2022, January 2). Russia at 1.04 MILLION excess deaths since March 2020, which is about 240% higher than their reported COVID-19 deaths. This is 1st place worldwide (for countries with data) in absolute excess mortality, 2nd place on per capita terms and 9th on p-score. #poptwitter #epitwitter https://t.co/aLBRRht3z2 [Tweet]. @ArielKarlinsky. https://twitter.com/ArielKarlinsky/status/1477531141510946818

  14. Dec 2021
    1. Trisha Greenhalgh. (2021, December 27). This is nothing short of scandalous. Unless and until those leading the public health response acknowledge the AIRBORNE nature of the virus and give transmission mitigation advice commensurate with how airborne viruses spread, we will be yo-yoing from wave to wave ad infinitum. [Tweet]. @trishgreenhalgh. https://twitter.com/trishgreenhalgh/status/1475502337594646528

    1. Colin Davis. (2021, December 20). Update for 20th Dec. The trend line still reflects 1.8 day doubling (it’s 1.7 days if we look at just the last week). Today’s number is down, but I wouldn’t read too much into that at this point. Https://t.co/kOCjxhRbop [Tweet]. @ProfColinDavis. https://twitter.com/ProfColinDavis/status/1472969632705392640

    1. Tom Moultrie. (2021, December 17). A 1-figure Gauteng update, bringing in data through Wednesday 15/12 (PCR only; by date of collection). The turn continues. On similar metrics (not shown) ALL northern provinces (NW, GT, MP, LP) seem to have now turned. Https://t.co/6Bh3kZsooK [Tweet]. @tomtom_m. https://twitter.com/tomtom_m/status/1471723711287996416

    1. Dave Keating. (2021, December 8). Boris Johnson’s continued pretence that UK is one of the most vaccinated countries in the world, repeated again in press conference just now announcing new restrictions, is getting tiresome. That has not been the case for many many months, despite 🇬🇧🇺🇸 vaccine hoarding early on. Https://t.co/tQt6aXGtNI [Tweet]. @DaveKeating. https://twitter.com/DaveKeating/status/1468655107436802052

    1. Art Poon. (2021, November 28). Our first https://filogeneti.ca/CoVizu update with B.1.1.529. As expected, number of mutations is well over molecular clock prediction (~13 diffs). Relatively low numbers of identical genomes implies large number of unsampled infections. We update every two days from GISAID. https://t.co/m8w2CjL1c0 [Tweet]. @art_poon. https://twitter.com/art_poon/status/1465001066194481162

  15. Nov 2021
    1. Prof. Christina Pagel. (2021, November 24). Meanwhile AY.4.2 (Delta grandchild) continues its very slow path to English dominance. Makes life a bit harder by being a bit more transmissible but luckily doesn’t seem any worse than Delta in any other respect. Https://t.co/kB0V0Z66GT [Tweet]. @chrischirp. https://twitter.com/chrischirp/status/1463508172941967365

    1. COVID-19 Living Evidence. (2021, November 12). As of 12.11.2021, we have indexed 257,633 publications: 18,674 pre-prints 238,959 peer-reviewed publications Pre-prints: BioRxiv, MedRxiv Peer-reviewed: PubMed, EMBASE, PsycINFO https://t.co/ytOhLG90Pi [Tweet]. @evidencelive. https://twitter.com/evidencelive/status/1459163720450519042

    1. Jeffrey Barrett. (2021, October 19). Proportion of AY.4.2 (now on http://covid19.sanger.ac.uk) has been steadily increasing in England, which is a pattern that is quite different from other AY lineages. Several of them rose when there was still Alpha to displace, but none has had a consistent advantage vs other Delta. Https://t.co/mD5gQzKxgV [Tweet]. @jcbarret. https://twitter.com/jcbarret/status/1450408485829718039

  16. Oct 2021
    1. Gil Feldman. (2021, October 26). @EricTopol Updated data from Israel. The booster works, without any doubt! Red (empty battery): Un-vax Light green (half battery): 2nd dose without the booster Green (full battery): With the booster https://t.co/HbZBvDMQs6 [Tweet]. @feldman_gil. https://twitter.com/feldman_gil/status/1452845319251767299

    1. John Roberts on Twitter: “154k booster 💉reported today in 🏴󠁧󠁢󠁥󠁮󠁧󠁿, bringing the total to 1.58m, out of 4.56m. So that’s another 3m eligible for a jab as soon as they can be scheduled in. 1/ https://t.co/tw1JmrOiUo” / Twitter. (n.d.). Retrieved October 15, 2021, from https://twitter.com/john_actuary/status/1445785517774176262

  17. Sep 2021
    1. Sam Wang on Twitter: “These are risk levels that you pose to other people. They’re compared with you as—A nonsmoker—A sober driver—A vaccinated person. Unvaccinated? 5x as likely to get sick, for 3x as long. Total risk to others? 15x a vaccinated person Details:https://t.co/ckTWaivK8n https://t.co/PhpLvX2dsm” / Twitter. (n.d.). Retrieved September 19, 2021, from https://twitter.com/SamWangPhD/status/1438361144759132167

  18. Aug 2021
    1. Prof. Christina Pagel on Twitter: “THREAD latest on B.1.617.2 variant in England: B.1.617.2 (1st discovered in India) is now dominant in England. Here is a thread summarising latest PHE report and Sanger local data. TLDR: it is NOT good news. 1/7” / Twitter. (n.d.). Retrieved August 24, 2021, from https://twitter.com/chrischirp/status/1399333330286415876

  19. Jul 2021
  20. Jun 2021
    1. Helen McArdle on Twitter: “The good news: An astonishing 98.2% of over-60s in Scotland are now fully vaccinated. That’s an amazing uptake. It doesn’t mean they are 100% protected of course (and especially not when case rates are high) but their risk of hospitalisation/death is cut by over 90% https://t.co/DzAxkpLvcR” / Twitter. (n.d.). Retrieved June 30, 2021, from https://twitter.com/HMcArdleHT/status/1409821893557768195

  21. May 2021
    1. ReconfigBehSci on Twitter: ‘the SciBeh initiative is about bringing knowledge to policy makers and the general public, but I have to say this advert I just came across worries me: Where are the preceding data integrity and data analysis classes? Https://t.co/5LwkC1SVyF’ / Twitter. (n.d.). Retrieved 18 February 2021, from https://twitter.com/SciBeh/status/1362344945697308674

  22. Apr 2021
  23. Mar 2021
    1. Ashish K. Jha, MD, MPH. (2020, December 12). Michigan vs. Ohio State Football today postponed due to COVID But a comparison of MI vs OH on COVID is useful Why? While vaccines are coming, we have 6-8 hard weeks ahead And the big question is—Can we do anything to save lives? Lets look at MI, OH for insights Thread [Tweet]. @ashishkjha. https://twitter.com/ashishkjha/status/1337786831065264128

  24. Feb 2021
  25. Dec 2020
    1. Treemaps are a visualization method for hierarchies based on enclosure rather than connection [JS91]. Treemaps make it easy to spot outliers (for example, the few large files that are using up most of the space on a disk) as opposed to parent-child structure.

      Treemaps visualize enclosure rather than connection. This makes them good visualizations to spot outliers (e.g. large files on a disk) but not for understanding parent-child relationships.

  26. Oct 2020
  27. Sep 2020
    1. "The Data Visualisation Catalogue is a project developed by Severino Ribecca to create a library of different information visualisation types." I like the explanations of when one might use a particular type of data visualization to highlight - or obscure! - what the data is saying.

  28. Aug 2020
  29. Jul 2020
  30. Jun 2020
    1. Starr, T. N., Greaney, A. J., Hilton, S. K., Crawford, K. H., Navarro, M. J., Bowen, J. E., Tortorici, M. A., Walls, A. C., Veesler, D., & Bloom, J. D. (2020). Deep mutational scanning of SARS-CoV-2 receptor binding domain reveals constraints on folding and ACE2 binding [Preprint]. Microbiology. https://doi.org/10.1101/2020.06.17.157982

  31. May 2020
  32. Apr 2020
  33. Feb 2020
  34. Jan 2020
  35. Dec 2019
  36. plaintext-productivity.net plaintext-productivity.net
    1. Avoiding complicated outlining or mind-mapping software saves a bunch of mouse clicks or dreaming up complicated visualizations (it helps if you are a linear thinker).

      Hmm. I'm not sure I agree with this thought/sentiment (though it's hard to tell since it's an incomplete sentence). I think visualizations and mind-mapping software might be an even better way to go, in terms of efficiency of editing (since they are specialized for the task), enjoyment of use, etc.

      The main thing text files have going for them is flexibility, portability, client-neutrality, the ability to get started right now without researching and evaluating a zillion competing GUI app alternatives.

  37. Nov 2019
  38. 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
  39. Jul 2019
  40. 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.

  41. 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!

  42. Dec 2018
  43. Jun 2018
  44. 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

    Tags

    Annotators

  45. Oct 2017
  46. 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.

  47. Aug 2017
  48. Mar 2017
    1. Prophet : Facebook에서 오픈 소스로 공개한 시계열 데이터의 예측 도구로 R과 Python으로 작성되었다.

      python statics opensource, also can use R

  49. 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.

  50. 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).

  51. Jun 2016
    1. 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.

  52. Feb 2016
  53. 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.

  54. 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.
  55. Mar 2015