Chartability is a set of heuristics (testable questions) for ensuring that data visualizations, systems, and interfaces are accessible. Chartability is organized into principles with testable criteria and focused on creating an outcome that is an inclusive data experience for people with disabilities.
Chart Design Principles | Hands-On Data Visualization
Tell your story and show it with data, using free and easy-to-learn tools on the web. This introductory book teaches you how to design interactive charts and customized maps for your website, beginning with easy drag-and-drop tools, such as Google Sheets, Datawrapper, and Tableau Public. You will also gradually learn how to edit open-source code templates built with Chart.js, Highcharts, and Leaflet on GitHub. Follow along with the step-by-step tutorials, real-world examples, and online resources. This book is ideal for students, non-profit organizations, small business owners, local governments, journalists, academics, or anyone who wants to tell their story and show the data. No coding experience is required.
A checklist for designing and improving the visualization of scientific data
Creating clear and engaging scientific figures is crucial to communicate complex data. In this Comment, I condense principles from design, visual perception and data visualization research in a checklist that can help researchers to improve their data visualization, by focusing on clarity, accessibility and design best practices.
Datatype — variable font that turns text into charts
An OpenType variable font that turns simple text expressions into inline charts. No JavaScript, no images — just type.
Meet Datatype, my new font that turns text into charts. Available on Google Fonts and of course in Workspace. Simple syntax like {b:10,25,7,95} turns into sparklines, pie charts, and bar charts that look great in your docs, slides, and sheets. https://t.co/0cgEkOcFfM https://t.co/wViF3fPtMr
Meet Datatype, my new font that turns text into charts. Available on Google Fonts and of course in Workspace.Simple syntax like {b:10,25,7,95} turns into sparklines, pie charts, and bar charts that look great in your docs, slides, and sheets.https://t.co/0cgEkOcFfM pic.twitter.com/wViF3fPtMr— Frank Tisellano (@franktisellano) March 12, 2026
An AI Agent Published a Hit Piece on Me
Scott Shambaugh helps maintain the excellent and venerable matplotlib Python charting library, including taking on the thankless task of triaging and reviewing incoming pull requests. A GitHub account called @crabby-rathbun …
10 Usability Heuristics for User Interface Design
Jakob Nielsen's 10 general principles for interaction design. They are called "heuristics" because they are broad rules of thumb and not specific usability guidelines.
On power, aesthetics, materiality and change in data visualization
Notes from the Information+ Conference at MIT/Northeastern University, Boston.
Observable Canvases | Explore and visualize data together
Observable’s collaborative data canvas helps you explore data, perform analysis, and build expressive charts and dashboards.
Read Noise in DNs versus ISO Setting
Click on the camera model in list to toggle visibility. Zooming is enabled. Use the resize box to resize the chart and list. To share a chart consider getting a link with the link button rather than or in addition to simply using a screenshot. Note that _12 indicates 12-bit data and _14 indicates 14-bit data.
An Award-winning Data Visualization Designer
Visual Cinnamon | Data Visualization Design & Data Art | Data made insightful, effective & beautiful through data visualization and data art
ggsql: A grammar of graphics for SQL
Introducing ggsql, a grammar of graphics for SQL that lets you describe visualizations directly inside SQL queries.
Sikuli: using GUI screenshots for search and automation
Usability is one of the factors that determines the success of a software system. Aiming to improve it’s usability, it is necessary to evaluate the interfaces using scientific evaluation methods, like questionnaires. Often the results of these ...
DR Group
For high-dimensional data, dimensionality reduction (DR) methods have been invaluable tools for human understanding, as they can map datasets into two or three dimensions for visualization, providing an intuitive way to understand the data. In recent years, tons of DR methods for data visualization have been proposed, and some are much better than others. In this blog post, we explore several DR methods, discuss how to determine which methods are effective, and explain why some are better than others.
Really cool idea 🤯 - Install this web font - Just add plain text to the DOM - It renders a chart using font ligatures No JS, no hydration FOUC franktisellano.github.io/datatype/
