Ember Charts A charting library built with the Ember.js and d3.js frameworks. It includes time series, bar, pie, and scatter charts which are easy to extend and modify. The out-of-the-box behavior these chart components represents our thoughts on best practices in chart interactivity and presentation. Features Highly customizable and extensible. Chartkick Simplify your admin dashboard - create new charts in seconds! Works with Rails, Sinatra and most browsers (including IE 6) A perfect companion to groupdate, hightop, and active_median Get handcrafted updates for new features
Pizza Pie Charts Pizza is an easy to use plugin. It's built with our responsive framework Foundation so you can quickly install it into your current Foundation project. Water BuffaloBisonSheepGoatShetland Pony PepperoniSausageCheeseMushroomChickenOther Cube Time Series Data Collection & Analysis Cube is a system for collecting timestamped events and deriving metrics. By collecting events rather than metrics, Cube lets you compute aggregate statistics post hoc. It also enables richer analysis, such as quantiles and histograms of arbitrary event sets. Cube is built on MongoDB and available under the Apache License on GitHub. Collecting Data
Automatically Preparing Edge/Node Data for Gephi Okay, I've done some work with Gephi lately, but I find myself with a problem I can't quite solve. I work on reprinting networks, and thus far have generated network graphs from spreadsheets of reprinting with the original newspaper in one column (source) and reprinting newspaper in the second (target). Import edge table-->Gephi creates a pretty graph. 30 Simple Tools For Data Visualization There have never been more technologies available to collect, examine, and render data. Here are 30 different notable pieces of data visualization software good for any designer's repertoire. They're not just powerful; they're easy to use. In fact, most of these tools feature simple, point-and-click interfaces, and don’t require that you possess any particular coding knowledge or invest in any significant training. Let the software do the hard work for you. Your client will never know.
Get Your Data into Gephi: A Quick and Basic Tutorial If you’re interested in learning more about information visualization by trying out some yourself, here’s a quick tutorial on how to get a very basic dataset showing character relationships from a piece of literature into Gephi! You might also check out my posts on the Bloomsday Ulysses visualization project (this year’s more in-depth analysis, last year’s smaller project), Gephi tutorials (how I used Gephi for my “View DHQ” DH knowledge networks project, Gephi terminology and ideas for exploration), and ACH Microgrant visualization work. Basic Gephi Dataset Creation In the Bloomsday project, we recorded data about what character interacted with which other characters, and used a scale of 1-7 to indicate the perceived intimacy of those interactions (e.g. from one person thinking of another person, to an involved conversation between two people). Here’s an example of these two columns in the Bloomsday visualization dataset:
30 Simple Tools For Data Visualization There have never been more technologies available to collect, examine, and render data. Here are 30 different notable pieces of data visualization software good for any designer's repertoire. They're not just powerful; they're easy to use. In fact, most of these tools feature simple, point-and-click interfaces, and don’t require that you possess any particular coding knowledge or invest in any significant training. Let the software do the hard work for you. Your client will never know.
View topic - how to prepare data set /create clusters Hi there, I am new to GEPHI and exploring the possibilities of this software to visualize my network analysis.I am trying to map a network of actors consisting of researchers and policy makers and their interactions. The actors are the nodes and the edges are their interactions in several fora, for example a government working group or a research team. I would like to visualize these arena's as clusters. The point is, that I want to show that some actors are crucial in this network as they have a central position in several clusters (f.e. not only in the ministry, but also in the private sector), or function as intermediaries or brokers. I work with my own data set which is constructed from qualitative sources (laws, regulations, newspaper articles, scientific articles etc.)