A dynamic concept graph consisting of nodes, each representing an idea, and edges showing the hierarchical structure among them.LLMs generates the hierarchical structure automatically but the structure is editable through our gestures as we see fitattract and repulse in force between nodes reflect the proximity of the ideas they containnodes can be merged, split, grouped to generate new ideasA data landscape where we can navigate on various scales (micro- and macro views).each data entry turns into a landform or structure, with its physical properties (size, color, elevation, .etc) mirroring its attributesapply sort, group, filter on data entries to reshape the landscape and look for patterns

A cool concept of displaying your life in story: Life in weeksI am reminded of this site, where I first encountered the ‘life in weeks’ idea.Other apps/sites that help you visualize or track your life:https://zrxj5vvjvl.codesandbox.io/https://jhornitzky.github.io/yolograph/demo/ - shows you what percentage of years you lived based on an average lifespan of 70 yearshttp://pewu.github.io/life-in-weeks/ - customizablehttps://lifecal.me/ - an apphttps://entire.life/ - a webapp?

An exploratory plot is all about you getting to know the data. An explanatory graphic, on the other hand, is about telling a story using that data to a specific audience.

At The Economist, we take data visualisation seriously. Every week we publish around 40 charts across print, the website and our apps. With every single one, we try our best to visualise the numbers accurately and in a way that best supports the story. But sometimes we get it wrong. We can do better in future if we learn from our mistakes — and other people may be able to learn from them, too.

Thomassen's girth-3 hypohamiltonian graph

What Tools/Libraries Do You Use To Visualize Genomic Feature Data?

Spectral Python (SPy) is a pure Python module for processing hyperspectral image data. It has functions for reading, displaying, manipulating, and classifying hyperspectral imagery. It can be used interactively from the Python command prompt or via Python scripts

Visualizing Distributions

From Bits to Narratives: The Rapid Evolution of Data Visualization Engines