Visualization. The Dangers of Faith In Data. Here are the notes from my Warm Gun SF keynote, based on one of the stories from The Year Without Pants (An Amazon.com best book of the year). Thanks to folks that were there for being a great crowd. “Faith in data grows in relation to your distance from the collection of it” The Data Paradox. No matter how much data you have, you will still depend on intuition to decide how to interpret, explain and use the data (See: Amygdala).
Intuition is also used to pick how the data is collected, what samples to use, and to define what the outliers are. In A/B testing, you use intuition to decide what B is. Underneath all of our rational intellect is intuition, which influences our “rational” behavior far more than we admit. Also see: Data Death Spiral, How To Call BS On A Guru. You can watch the actual keynote presentation below: Visualizations That Really Work. Executive Summary Not long ago, the ability to create smart data visualizations (or dataviz) was a nice-to-have skill for design- and data-minded managers. But now it’s a must-have skill for all managers, because it’s often the only way to make sense of the work they do. Decision making increasingly relies on data, which arrives with such overwhelming velocity, and in such volume, that some level of abstraction is crucial. Thanks to the internet and a growing number of affordable tools, visualization is accessible for everyone—but that convenience can lead to charts that are merely adequate or even ineffective.
By answering just two questions, Berinato writes, you can set yourself up to succeed: Is the information conceptual or data-driven? And Am I declaring something or exploring something? This article is adapted from the author’s just-published book, Good Charts: The HBR Guide to Making Smarter, More Persuasive Data Visualizations. Data Visualization - Research Data Services - Subject Guides at Syracuse University Libraries. Researchers may find the following tools useful in their work. Emphasis is given to free (or at least having free components) and online tools or services. Data Management Planning: DMPTool - The DMPTool provides a step-by-step interface for creating a Data Management Plan for NSF, NIH and many other funding agencies.
Electronic Lab Notebooks: Open Science Framework - Interdisciplinary research project management and collaboration platform. Data Analysis/Visualization: TableauPublic - Free version of their desktop and online data visualization platform. Directory of digital research tools: TowardsOpenResearch.pd. 50 Great Examples of Data Visualization.
Wrapping your brain around data online can be challenging, especially when dealing with huge volumes of information. And trying to find related content can also be difficult, depending on what data you’re looking for. But data visualizations can make all of that much easier, allowing you to see the concepts that you’re learning about in a more interesting, and often more useful manner. Below are 50 of the best data visualizations and tools for creating your own visualizations out there, covering everything from Digg activity to network connectivity to what’s currently happening on Twitter.
Music, Movies and Other Media Narratives 2.0 visualizes music. Different music tracks are segmented into single channels that are then shown in a fan-like structure. Liveplasma is a music and movie visualization app that aims to help you discover other musicians or movies you might enjoy. Tuneglue is another music visualization service. Digg, Twitter, Delicious, and Flickr Internet Visualizations. 6 lessons academic research tells us about making data visualizations – Poynter.
As more media outlets embrace data, and data visualizations become integral to storytelling, it's increasingly important to understand what makes an effective graphical presentation. While much has been written about data visualization tools and ways to teach data visualization, much less has been written about the academic research on effective visualization. Journalists may not be aware of the body of scholarly work on data visualization other than a passing awareness of Edward Tufte's popular books.
But examining the academic literature on data visualization can have real implications for the practical, working journalist. Here are 6 lessons that we can learn from academia: 1. Researchers from Cornell University found that merely including a graph in an article significantly increases reader persuasion. 2. 1. position (dot plots, scatter plots) 2. length (bar and column charts) 3. angle (pie charts) 4. area (bubble charts) 5. color (choropleth maps) 3. 4. 5. 6. Information aesthetics - Data Visualization & Information Design.
Fun data visualization with the Gooey effect - Visual Cinnamon. Just over a year ago I posted a blog about the Gooey effect that makes it seem as if things (SVGs) start sticking together once they come close to each other. As if they are water droplets merging together. For my preparation for the “SVGs beyond mere shapes” talk I returned to the gooey effect and in this blog, I’d like to teach you a few more techniques to take full use of the power of the gooey. This blog is part of the SVGs beyond mere shapes blog series. It’s based on the similar named talk. My goal with the talk was to inspire people to experiment with the norm, to create new ways of making a visual more effective or fun.
Even for a subject as narrow as SVG filters and gradients, there are more things possible than you might think. In the original blog, I talked about the gooey filter code where in the last step you place the original sharp SVG shape back on top of the blurred version. You might have cases where it is not important that the circles remain at their exact sizes. FlowingData. (the teeming void) Mitchell Whitelaw.