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FT Data Visual vocabulary. Datalegreya. Datalegreya est une police de caractères capable d’entremêler texte et courbes de données.

Datalegreya

Elle a été conçue par Figs, sur la base de la fonte open source Alegreya Sans SC Thin dessinée par Juan Pablo Del Perla. Datalegreya peut être utilisée dans tous contextes où l’on dispose de peu de place pour représenter des données de manière synthétique : objets connectés, écrans embarqués, rapports annuels, bulletin météo, cours de la bourse, etc. Son utilisation ne nécessite pas de programme spécialisé : il suffit d’installer la police dans le système et de lancer n’importe quel programme permettant d’afficher des polices OpenType (Word, TextEdit, suite Adobe…). Son respect des standards permet également de l’employer sur le web ou de l’embarquer dans des logiciels.

Chaque caractère de Datalegreya existe en plusieurs variantes, permettant de représenter différentes valeurs au sein de la lettre. Debate: “VIS TALK. Towards a new culture of criticism in info vis” On my way back from this year’s Malofiej conference, my head full of new impressions and inspirations, I looked through the crisp new conference book Malofiej 22, which was just out of the press and again contains loads of interesting material (also my article on the work of Minard).

Debate: “VIS TALK. Towards a new culture of criticism in info vis”

I was really delighted to come across the chapter by Fernanda Viégas and Martin Wattenberg “Design and Re-Design in Data Visualization”, in which they argue for a more nuanced and professional culture of criticism within the field of data visualization. Design and Redesign – Medium. The technique of “critique by redesign” in some ways works uniquely well in data visualization.

Design and Redesign – Medium

A movie critic can’t remake a movie. An art critic can’t ask the subject of a portrait to sit for a second time. Blindfolded Cartography. Andy Woodruff, Axis Maps OpenVis Conference, April 2015 @awoodruff | axismaps.github.io/blindfolded-cartography Hello, my name is Andy.

Blindfolded Cartography

I make maps. I live on the other (better, duh) side of the river and sometimes make maps of Boston. Maps of the Year. Cartographie numérique : précis de discrétisation pour les nuls « m0le'o'blog. Cartographie numérique : précis de discrétisation pour les nuls « m0le'o'blog. The Art and Science of Data Visualisation. Andy Kirk is a UK-based freelance data visualisation specialist, and editor of visualisingdata.com.

The Art and Science of Data Visualisation

After graduating from Lancaster University in 1999 with a B.Sc (hons) in Operational Research, he held a number of business analysis and information management positions at some of the largest organisations in the UK, including West Yorkshire Police and the University of Leeds. In December 2009 Andy completed a Masters by Research M.A (Distinction) programme (University of Leeds), studying the subject of data visualisation and soon after launchedvisualisingdata.com. This has grown to become a popular source of information about the increasingly popular data visualisation field, providing readers with information about contemporary techniques, resources, latest developments and key insights. Design and Redesign – Medium.

The technique of “critique by redesign” in some ways works uniquely well in data visualization.

Design and Redesign – Medium

A movie critic can’t remake a movie. An art critic can’t ask the subject of a portrait to sit for a second time. A book critic may be able to rewrite a sentence, but not a whole book. But with data visualization, if there’s access to the underlying data set, and the data is not too complicated, it’s feasible to create at least a rough redesign. Debate: “VIS TALK. Towards a new culture of criticism in info vis” Data Visualization : Articles.

Painting by the Numbers: Data Visualization. Charles Hornbaker, a first-year student at the Harvard Business School, admits historical corn yields are not the most interesting subject in the world.

Painting by the Numbers: Data Visualization

But as part of a design team with fellow graduate students J. Benjamin Cook, Conor L. Myhrvold, and Ryan King at the Harvard School of Engineering and Applied Sciences, he's helped to realize the improbable: making corn interesting through a colorful and captivating web-based visualization called "Century of Corn.” In the design, green shapes spill across a map of the United States, covering the Midwest in a verdant spectrum, their hues deepening with time.

Learning to See Data. “The problem today is that biological data are often abstracted into the digital domain,” Dr.

Learning to See Data

Greally added, “and we need some way to capture the gestalt, to develop an instinct for what’s important.” And so it is in many fields, whether predicting climate, flagging potential terrorists or making economic forecasts. The information is all there, great expanding mountain ranges of it. What’s lacking is the tracker’s instinct for picking up a trail, the human gut feeling for where to start looking to find patterns and meaning.

How to Find the Right Chart Type for your Numeric Data. 22 Feb 2016.

How to Find the Right Chart Type for your Numeric Data

Talk: How to Visualize Data. Last week, I gave one of the visualization primer talks at BioVis in Dublin.

Talk: How to Visualize Data

My goal was to show people some examples, but also criticize the rather poor visualization culture in bioinformatics and challenge people to do better. Here is a write-up of that talk. Seán O’Donoghue introduced me by calling me “infamous” for speaking my mind and criticizing things, which was the perfect setup for my talk. Video: The Danger of Glitziness. Information is Beautiful Awards. Amit Kapoor - Visualising Multi Dimensional Data.

"The Future of Data Visualization" - Jeffrey Heer (Strata + Hadoop 2015) Sociograms - Who Shall Survive: A New Approach to the Problem of Human Interrelations : J. L. Moreno. THE PROJECT — Dear Data. Painting by the Numbers: Data Visualization. One Chart, Twelve Tools · Lisa Charlotte Rost. 17 May 2016 Which tool or charting framework do you use to visualize data? Everybody I’ve met so far has personal preferences (“I got introduced to data vis with that tool!” , “My hero uses that tool and she makes the best charts!”). Often we keep working with the first not-entirely-bad tool or language that we encountered. I think it can’t hurt to have a wider view of the options out there: To maybe discover better tools than the ones we use; but also to reassure us that the tools we use ARE really the best (so far).

If they are important tools I missed, or if I missed some features in a tool or a better way to get to the bubble chart, or if I’m wrong about a thing or two, or if you completely disagree with my opinion about these tools (which, I’m sure, will happen): Let me know on Twitter or via email (lisacharlotterost@gmail.com)!

The Data & the Visualization Form. Visualising Networks Part 1: A Critique. This is the first post of a series on network visualisation. Thanks to the facilitated access to network analysis tools and the growing interest in many disciplines towards studying the relations structuring datasets, networks have become ubiquitous objects in science, in newspapers, on tech book covers, all over the Web, and to illustrate anything big data-related (hand in hand with word clouds.). KeyVis. Keynote Session: Dr. Edward Tufte - The Future of Data Analysis.

The Office for Creative Research. Terrapattern. GitHub - fabian-beck/survis: Visual Literature Database. A Sankey diagram says more than 1000 pie charts. Data Visualization Checklist. Static and dynamic network visualization with R. [June 2016 update] This tutorial has been updated and extended. The new version includes additional information (more on interactive JS networks, geographic data, etc). If you want to see the old version, it is still available here. You can also get the new tutorial PDF and code here or on GithHub.

AR / VR

Sans titre. Pedagogy of Data Visualization Workshop. List of Physical Visualizations. This list currently has 254 entries. Recent additions: While data sculptures date back from the 1990s, the very first sculptures were Venus figurines: A Venus figurine is any Upper Paleolithic statuette portraying a woman with exaggerated physical features. The oldest ones are about 35,000 years old. Right image: modern versions. Also see V.S. The earliest data visualizations were likely physical: built by arranging stones or pebbles, and later, clay tokens. Engineering Intelligence Through Data Visualization at Uber - Uber Engineering Blog. In early 2015 we started an official data visualization team at Uber.

The idea behind it: deliver intelligence through crafting visual exploratory data analysis tools for Uber’s datasets. Every day, Uber manages billions of GPS locations. Every minute, our platform handles millions of mobile events. Every time we don’t use technology to analyze and interpret this information is an opportunity missed to better understand our business. 39 studies about human perception in 30 minutes. Bars and pies for proportions Much is said about the relative merits of bars and circles for showing proportions. All five of these studies legitimize the use of pie charts when conveying proportions and some even show their superiority over bar charts. I did not encounter any studies that said we should not use pie charts for showing proportions in all cases.

Eells (7) was among the first to publish a paper on this topic in 1926. In his time, pie charts were ridiculed much as they are today for their assumed perceptual inadequacies. The Design of Everyday Visualizations. I’ve been educated and inspired recently by the best selling design classic “The Design of Everyday Things” by UX guru Don Norman. You really have to read the entire book, which applies to all types of objects that people design – from chairs to doors to software to organizational structures. A Super-Quick Introduction to BioFabric.

Digital Humanities

Data visualisation: what’s next? — Signal Noise. Www.datavizmyths.com. Science. Introducing Vega-Lite — HCI & Design at UW. Provide sensible defaults, but allow customization. Hive Plots - Linear Layout for Network Visualization - Visually Interpreting Network Structure and Content Made Possible. Little boxes. Stephen Few: When Are 100% Stacked Bar Graphs Useful? I’ll begin this blog article by answering the question that appears in the title. I’ve found that 100% bar graphs, designed in the conventional way, are only useful for a limited set of circumstances. Unlike normal stacked bars, the lengths of 100% stacked bars never vary, for they always add up to 100%. Consequently, when multiple 100% stacked bars appear in a graph, they only provide information about the parts of some whole, never about the wholes and how they differ. Therefore, they would never be appropriate when information about totals and the parts of which they are made are both of interest, though normal stacked bars often work well in this scenario.

Winners: Kantar Information is Beautiful Awards 2015.

Datajournalism / storytelling

Event Drops Demo. IEEE VIS Personal Visualization Workshop. Design Methodology for Visualisation. SI Design Space. Responsible Data Forum — A series of collaborative events, convened to develop useful tools and strategies for dealing with the ethical, security and privacy challenges facing data-driven advocacy. Parsing complex social study data. NPR’s #15girls project looks at the lives of 15 year old girls around the world.

The reporting team was interested in using data from the World Values Survey (WVS) to help inform the reporting and produce a “by-the-numbers” piece. How Data Visualization Can Perpetuate Inequality. Sometimes, whether we know it or not, the choices we make when we visualize data can reinforce and even perpetuate racial disparity and it’s time that we talk about it. The lull of the computer monitor and the belief we are just working with numbers can make us lose sight of the fact that there are people behind this data who have given of themselves, sometimes unwillingly, and that we have a responsibility to them when we visualize.

Examples

Data analysis. Macro Connections. Plate-forme d'expérimentation en humanités numériques, réseaux sociaux, Twitter, influence sur le web et visualisation de données. Information is Beautiful Awards. Data visualization and computational art history / Lev Manovich. Up and Down the Ladder of Abstraction. Atelier de cartographie - Atelier de cartographie de Sciences Po. Tools. The Surest Path to Visual Discovery by Stephen Few. InfoVis:Wiki. Treemaps for space-constrained visualization of hierarchies. Newsmap. 45 Ways to Communicate Two Quantities - ScribbleLive. Word Tree. Blogs. Catalogues.

History

Courses. How to Become a Data Visualization Expert: A Recipe. Handbook of Graph Drawing and Visualization. People. ImagePlot visualization software: explore patterns in large image collections.