Altair is a declarative statistical visualization library for Python, based on Vega-Lite.

With Altair, you can spend more time understanding your data and its meaning. Altair’s API is simple, friendly and consistent and built on top of the powerful Vega-Lite visualization grammar. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code.

t-SNE is the very popular algorithm to extremely reduce the dimensionality of your data in order to visually present it. It is capable of mapping hundreds of dimensions to just 2 while preserving important data relationships, that is, when closer samples in the original space are closer in the reduced space. t-SNE works quite well for small and moderately sized real-world datasets and does not require much tuning of its hyperparameters. In other words, if you’ve got less than 100,000 points, you will apply that magic black box thing and get a beautiful scatter plot in return.

Matter.js is a 2D JavaScript physics library that supports rigid body collisions and constraints.

Visdom is a flexible tool for creating, organizing, and sharing visualizations of live, rich data created by Facebook Research. Visdom aims to facilitate visualization of (remote) data with an emphasis on supporting scientific experimentation and collaboration. Visdom was inspired by tools like display and relies on Plotly as a plotting front-end. Supports Torch and Numpy.

Seeing Theory is a project designed and created by Daniel Kunin with support from Brown University's Royce Fellowship Program and National Science Foundation group STATS4STEM. The goal of the project is to make statistics more accessible to a wider range of students through interactive visualizations.

Statistics, is quickly becoming the most important and multi-disciplinary field of mathematics. According to the American Statistical Association, statistician is one of the top ten fastest-growing occupations and statistics is one of the fastest-growing bachelor degrees. Statistical literacy is essential to our data driven society. Yet, for all the increased importance and demand for statistical competence, the pedagogical approaches in statistics have barely changed. Using Mike Bostock’s data visualization software, D3.js, Seeing Theory visualizes the fundamental concepts covered in an introductory college statistics or Advanced Placement statistics class. Students are encouraged to use Seeing Theory as an additional resource to their textbook, professor and peers.

The Palm Generator is a Three.js module to generate palm trees. It follows a model that describes the arrangement of leaves on a plant stem, called phyllotaxis. You can find information about the usage and the license on GitHub. You can create your palm using the PalmGenerator Online Editor.

GPlates is desktop software for the interactive visualisation of plate-tectonics.

GPlates offers a novel combination of interactive plate-tectonic reconstructions, geographic information system (GIS) functionality and raster data visualisation. GPlates enables both the visualisation and the manipulation of plate-tectonic reconstructions and associated data through geological time. GPlates runs on Windows, Linux and MacOS X. GPlates has an online user manual.

Rumint (room-int) is an open source network and security data visualization tool. You can use it to capture and visualize live network traffic.

Shadertoy is the first application to allow developers all over the globe to push pixels from code to screen using WebGL since 2009.

This website is the natural evolution of that original idea. On one hand, it has been rebuilt in order to provide the computer graphics developers and hobbyists with a great platform to prototype, experiment, teach, learn, inspire and share their creations with the community. On the other, the expressiveness of the shaders has arisen by allowing different types of inputs such as video, webcam or sound.

The Visualization and Graphics Lab is an informal group of faculty and students that meet regularly to discuss and work on research in the data visualization and graphics fields. If you are interested in joining our group, please contact us or stop by one of our regular meetings.

A gallery comparing colorschemes from stylesheets defined in Python Matplotlib with source code available on github..

A long compilation of d3 tutorials for data visualization are hosted on github, from core concepts to specialized techniques and applications. Also, links to several books, blogs, slides, courses, and lecture videos are listed.

Bl.ocks (pronounced “Blocks”) is a simple viewer for sharing data visualization code examples hosted on GitHub Gist created and run by Mike Bostock. The main source for your example is in index.html. This file can contain relative links to other files in your Gist, such as images, scripts or stylesheets. And of course you can use absolute links, such as CDN-hosted D3, jQuery or Leaflet. To explain your example, add a README.md written in Markdown. (You can omit the index.html if you just want to write, too.)

The Geoparser is a software tool that can process information from any type of file, extract geographic coordinates, and visualize locations on a map. Users who are interested in seeing a geographical representation of information or data can choose to search for locations using the Geoparser, through a search index or by uploading files from their computer. The Geoparser will parse the files and visualizes cities or latitude-longitude points on the map. After the information is parsed and points are plotted on the map, users are able to filter their results by density, or by searching a key word and applying a "facet" to the parsed information. On the map, users can click on location points to reveal more information about the location and how it is related to their search.

An interactive visualization of earthquake seismic waves traveling through the Earth's interior and radiating outward on the surface and simultaneously being recorded on seismograms. Choose from 10 major earthquakes. Hosted by the Incorporated Research Institutions for Seismology (IRIS).

An article and complementary website on how to visualize event sequences as timelines by Matthew Brehmer and others from the InfoVis Group at the UBC Department of computer science.

A blog on data visualization, data science, and biology by Thomas Lin Pedersen.

I recently published a bit of self-promotion in the form of an animation based on the new geom_voronoi_tile() and geom_voronoi_segment() functions in ggforce. This prompted someone to ask for the source code and I off-handedly said I would make a blog post about. So here we are, but I’m not going to show you how I made that animation (sorry). Instead I’m going to show you how to make your very own animated Christmas card using ggplot2, ggforce, and tweenr (as well as mgcv and deldir for some computations).

A Udacity course to learn the fundamentals of data visualization and practice communicating with data. This course covers how to apply design principles, human perception, color theory, and effective storytelling to data visualization. If you present data to others, aspire to be an analyst or data scientist, or if you’d like to become more technical with visualization tools, then you can grow your skills with this course.

The course does not cover exploratory approaches to discover insights about data. Instead, the course focuses on how to visually encode and present data to an audience once an insight has been found.

My name is Adi Khen and I’m a PhD student at the Scripps Institution of Oceanography at UC San Diego. I LOVE sea hares (Aplysia californica), drawing, and benthic ecology image analysis.